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#deeplearning #neuralnetworks

The transp ose of a matrix pro duct has a simple form: \((AB)^{T} = B^{T}A^{T}\) . (2.9) This allo ws us to demonstrate equation , b y exploiting the fact that the v alue 2.8 of suc h a pro duct is a scalar and therefore equal to its o wn transp ose:\( x^Ty = (x^Ty)^T = y^T x\)

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#deeplearning #neuralnetworks

W e can now solve equation Ax = b by the following steps:

1. A^{-1}Ax = A^{−1}b

2. I_{n}x = A^{−1}b

1. A

2. I

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#bayes #programming #r #statistics

The “evidence” for the model, p(D), is the overall probability of the data according to the model, determined by averaging across all possible parameter values weighted by the strength of belief in those parameter value

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#matlab #programming

The residual r=A*x-b is now r= -0.3333 -0.3333 0.3333 What happens in this case is that MATLAB produces the least squares best fit. This is the value of x which makes the magnitude of r, i.e., r(1) ^{2} + r(2) ^{2} + r(3)^{2} , as small as possible.

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#bayes #programming #r #statistics

The space of possibilities is the joint parameter space involving all combinations of parameter values. If we represent each parameter with a comb of, say, 1,000 values, then for P parameters there are 1,000 ^{P} combinations of parameter values.

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#deeplearning #neuralnetworks

The L 1 norm is commonly used in machine learning when the diﬀerence b etw een zero and nonzero elements is v ery imp ortan t. Every time an element of x mo v es a w a y from 0 by \(\epsilon\), the L 1 norm increases b y \(\epsilon\).

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#deeplearning #neuralnetworks

T o compute diag(v)x , we only need to scale each element x_{i} b y v_{i} . In other w ords, diag(v)x = \(x\cdot y\)

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#deeplearning #neuralnetworks

A unit vector is a vector with unit norm ||x||_{2} = 1

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#deeplearning #neuralnetworks

A vector x and a vector y are orthogonal to each other if x^{T}y = 0 .

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#deeplearning #neuralnetworks

An eigenvector of a square matrix A is a non-zero vector v suc h that m ulti- plication b y A alters only the scale of v: Av = λv

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#deeplearning #neuralnetworks

Av= λv

The scalar λ is kno wn as the eigen v alue corresp onding to this eigenv ector. (One can also ﬁnd a left eigen v ector suc h that v A = λ v , but we are usually concerned with righ t eigenv ectors

The scalar λ is kno wn as the eigen v alue corresp onding to this eigenv ector. (One can also ﬁnd a left eigen v ector suc h that v A = λ v , but we are usually concerned with righ t eigenv ectors

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#deeplearning #neuralnetworks

SVD of A = UVD^{T}

The elemen ts along the diagonal of D are kno wn as the singular v alues of the matrix A . The columns of U are known as the left-singular v ectors . The columns of V are kno wn as as the right-singular v ectors

The elemen ts along the diagonal of D are kno wn as the singular v alues of the matrix A . The columns of U are known as the left-singular v ectors . The columns of V are kno wn as as the right-singular v ectors

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#deeplearning #neuralnetworks

W e can actually interpret the singular v alue decomp osition of A in terms of the eigendecomposition of functions of A . The left-singular v ectors of A are the eigen v ectors of AA^{T} . The righ t-singular v ectors of A are the eigen vectors of A^{T}A . The non-zero singular v alues of A are the square ro ots of the eigen v alues of A^{T}A . The same is true for AA^{T}

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#bayes #programming #r #statistics

In other words, the normalizer for the beta distribution is the beta function \(B(a,b) = \int d\theta \space \theta^{a-1}(1-\theta)^{b-1}\)

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#bayes #programming #r #statistics

In R, beta(θ|a, b) is dbeta(θ,a,b),and B(a, b) is beta(a,b)

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#bayes #programming #r #statistics

The standard deviation of the beta distribution is \(\sqrt{μ(1 − μ)/(a + b +1)}\). Notice that the standard deviation gets smaller when the concentration κ = a + b gets larger.

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s. Unsourced material may be challenged and removed. (May 2011) (Learn how and when to remove this template message) [imagelink] Waste management in Kathmandu, Nepal [imagelink] Waste management in Stockholm, Sweden <span>Waste management or Waste disposal is all the activities and actions required to manage waste from its inception to its final disposal. [1] This includes amongst other things, collection, transport, treatment and disposal of waste together with monitoring and regulation. It also encompasses the legal and regulatory framework that relates to waste management encompassing guidance on recycling etc. The term normally relates to all kinds of waste, whether generated during the extraction of raw materials, the processing of raw materials into intermediate and final products, the co

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#deeplearning #neuralnetworks

Question

An [...] is the com bination of an enco der function that con v erts the input data into a diﬀeren t representation, and a deco der function that conv erts the new representation back into the original format

Answer

autoenco der

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An autoenco der is the com bination of an enco der function that con v erts the input data into a diﬀeren t representation, and a deco der function that conv erts the new representation back into the o

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#deeplearning #neuralnetworks

Question

An autoenco der is the com bination of an [...], and a deco der function that conv erts the new representation back into the original format

Answer

enco der function that con v erts the input data into a diﬀeren t representation

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An autoenco der is the com bination of an enco der function that con v erts the input data into a diﬀeren t representation, and a deco der function that conv erts the new representation back into the original format

Tags

#deeplearning #neuralnetworks

Question

An autoenco der is the com bination of an enco der function that con v erts the input data into a diﬀeren t representation, and a [...]

Answer

deco der function that conv erts the new representation back into the original format

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An autoenco der is the com bination of an enco der function that con v erts the input data into a diﬀeren t representation, and a deco der function that conv erts the new representation back into the original format

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#matlab #programming

Question

[...] determinant.

Answer

det

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det determinant.

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#matlab #programming

Question

det [...]

Answer

determinant.

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det determinant.

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#matlab #programming

Question

[...] eigenvalue decomposition

Answer

eig

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eig eigenvalue decomposition

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#matlab #programming

Question

eig [...]

Answer

eigenvalue decomposition

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eig eigenvalue decomposition

Tags

#matlab #programming

Question

[...] singular value decomposition

Answer

svd

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svd singular value decomposition

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#matlab #programming

Question

svd [...]

Answer

singular value decomposition

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svd singular value decomposition

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#biochem #biology #cell

Question

The piecing together of the complete [...] pathway in the 1930s was a major triumph of biochemistry

Answer

glycolytic

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The piecing together of the complete glycolytic pathway in the 1930s was a major triumph of biochemistry

Tags

#biochem #biology #cell

Question

The piecing together of the complete glycolytic pathway in the [...] was a major triumph of biochemistry

Answer

1930s

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The piecing together of the complete glycolytic pathway in the 1930s was a major triumph of biochemistry

Tags

#biochem #biology #cell

Question

Two central reactions in glycolysis (steps 6 and 7) convert the [...] into 3-phosphoglycerate (a carboxylic acid; see Panel 2–8, pp. 104–105), thus oxidizing an aldehyde group to a carboxylic acid group.

Answer

three-carbon sugar inter- mediate glyceraldehyde 3-phosphate (an aldehyde)

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Two central reactions in glycolysis (steps 6 and 7) convert the three-carbon sugar inter- mediate glyceraldehyde 3-phosphate (an aldehyde) into 3-phosphoglycerate (a carboxylic acid; see Panel 2–8, pp. 104–105), thus oxidizing an aldehyde group to a carboxylic acid group.

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#biochem #biology #cell

Question

Two central reactions in glycolysis (steps 6 and 7) convert the three-carbon sugar inter- mediate glyceraldehyde 3-phosphate (an aldehyde) into [...]; see Panel 2–8, pp. 104–105), thus oxidizing an aldehyde group to a carboxylic acid group.

Answer

3-phosphoglycerate (a carboxylic acid

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Two central reactions in glycolysis (steps 6 and 7) convert the three-carbon sugar inter- mediate glyceraldehyde 3-phosphate (an aldehyde) into 3-phosphoglycerate (a carboxylic acid; see Panel 2–8, pp. 104–105), thus oxidizing an aldehyde group to a carboxylic acid group.

Tags

#biochem #biology #cell

Question

In step 6, the enzyme [...] couples the energetically favorable oxidation of an aldehyde to the energetically unfavorable formation of a high-energy phosphate bond. At the same time, it enables energy to be stored in NADH. The formation of the high-energy phosphate bond is driven by the oxidation reaction, and the enzyme thereby acts like the “paddle wheel” coupler

Answer

glyceraldehyde 3-phosphate dehydrogenase

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In step 6, the enzyme glyceraldehyde 3-phosphate dehydrogenase couples the energetically favorable oxidation of an aldehyde to the energetically unfavorable formation of a high-energy phosphate bond. At the same time, it enables energy to be s

Tags

#biochem #biology #cell

Question

In step 6, the enzyme glyceraldehyde 3-phosphate dehydrogenase couples [...]. At the same time, it enables energy to be stored in NADH. The formation of the high-energy phosphate bond is driven by the oxidation reaction, and the enzyme thereby acts like the “paddle wheel” coupler

Answer

the energetically favorable oxidation of an aldehyde to the energetically unfavorable formation of a high-energy phosphate bond

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In step 6, the enzyme glyceraldehyde 3-phosphate dehydrogenase couples the energetically favorable oxidation of an aldehyde to the energetically unfavorable formation of a high-energy phosphate bond. At the same time, it enables energy to be stored in NADH. The formation of the high-energy phosphate bond is driven by the oxidation reaction, and the enzyme thereby acts like the

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#deeplearning #neuralnetworks

Question

W e use the [...] sign to index the complement of a set.

Answer

−

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W e use the − sign to index the complement of a set. F or example x − 1 is the v ector con taining all elemen ts of x except for x 1 , and x − S is the v ector con taining all of the elements of exce

#deeplearning #neuralnetworks

W e can identi fy all of the n um b ers with v ertical co ordinate i b y writing a “ ” for the horizontal

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W e can identi fy all of the n um b ers with v ertical co ordinate i b y writing a “ ” for the horizontal : co ordinate. F or example, A i, : denotes the horizon tal cross section of A with v ertical co ordinate i

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#deeplearning #neuralnetworks

Question

The transp ose of a matrix is [...]

Answer

the mirror image of the matrix across a diagonal line, called the main diagonal , running do wn and to the righ t, starting from its upp er left corner.

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The transp ose of a matrix is the mirror image of the matrix across a diagonal line, called the main diagonal , running do wn and to the righ t, starting from its upp er left corner.

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#deeplearning #neuralnetworks

Question

Note that the standard pro duct of tw o matrices is just a matrix con taining not the pro duct of the individual elements. Suc h an op eration exists and is called the [...] , and is denoted as . A

Answer

elemen t-wise pro duct Hadamard pro duct or

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Note that the standard pro duct of tw o matrices is just a matrix con taining not the pro duct of the individual elements. Suc h an op eration exists and is called the elemen t-wise pro duct Hadamard pro duct or , and is denoted as . A

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#deeplearning #neuralnetworks

Question

Is the dot pro duct b etw een tw o v ectors comm utativ e?

Answer

Yes

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the dot pro duct b etw een tw o v ectors is comm utativ e: x y y = x

Tags

#deeplearning #neuralnetworks

Question

The transp ose of a matrix pro duct has a simple form [...] (2.9) This allo ws us to demonstrate equation , b y exploiting the fact that the v alue 2.8 of suc h a pro duct is a scalar and therefore equal to its o wn transp ose:\( x^Ty = (x^Ty)^T = y^T x\)

Answer

simple form: \((AB)^{T} = B^{T}A^{T}\) .

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The transp ose of a matrix pro duct has a simple form: \((AB)^{T} = B^{T}A^{T}\) . (2.9) This allo ws us to demonstrate equation , b y exploiting the fact that the v alue 2.8 of suc h a pro duct is a scalar and therefore equal to its o wn transp ose:\( x^Ty = (x^Ty)^T

Tags

#deeplearning #neuralnetworks

Question

W e can now solve equation Ax = b by the following steps:

^{[...]}

Answer

1. A^{-1}Ax = A^{−1}b

2. I_{n}x = A^{−1}b

2. I

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W e can now solve equation Ax = b by the following steps: 1. A -1 Ax = A −1 b 2. I n x = A −1 b

Tags

#biochem #biology #cell

Question

fatty acids enter the bloodstream, where they bind to the abundant blood protein, [...]

Answer

serum albumin.

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fatty acids enter the bloodstream, where they bind to the abundant blood protein, serum albumin.

Tags

#biochem #biology #cell

Question

In addition to three molecules of NADH, each turn of the cycle also produces one molecule of [...] from FAD (see Figure 2–39), and one mol- ecule of the ribonucleoside triphosphate GTP from GDP

Answer

FADH 2 (reduced flavin adenine dinucleotide)

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In addition to three molecules of NADH, each turn of the cycle also produces one molecule of FADH 2 (reduced flavin adenine dinucleotide) from FAD (see Figure 2–39), and one mol- ecule of the ribonucleoside triphosphate GTP from GDP

Tags

#biochem #biology #cell

Question

In addition to three molecules of NADH, each turn of the cycle also produces one molecule of FADH 2 (reduced flavin adenine dinucleotide) from FAD (see Figure 2–39), and one mol- ecule of the [...]

Answer

ribonucleoside triphosphate GTP from GDP

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In addition to three molecules of NADH, each turn of the cycle also produces one molecule of FADH 2 (reduced flavin adenine dinucleotide) from FAD (see Figure 2–39), and one mol- ecule of the ribonucleoside triphosphate GTP from GDP

Tags

#biochem #biology #cell

Question

n total, the complete oxidation of a molecule of glucose to H 2 O and CO 2 is used by the cell to produce about [...] molecules of ATP.

Answer

30

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n total, the complete oxidation of a molecule of glucose to H 2 O and CO 2 is used by the cell to produce about 30 molecules of ATP.

Tags

#biochem #biology #cell

Question

All of the nitrogens in the purine and pyrimidine bases (as well as some of the carbons) are derived from the plentiful amino acids glutamine, aspartic acid, and glycine, whereas the ribose and deoxyribose sugars are derived from [...]

Answer

glucose.

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pan>All of the nitrogens in the purine and pyrimidine bases (as well as some of the carbons) are derived from the plentiful amino acids glutamine, aspartic acid, and glycine, whereas the ribose and deoxyribose sugars are derived from glucose.<span><body><html>

Tags

#biochem #biology #cell

Question

All of the nitrogens in the purine and pyrimidine bases (as well as some of the carbons) are derived from the plentiful amino [...], whereas the ribose and deoxyribose sugars are derived from glucose.

Answer

acids glutamine, aspartic acid, and glycine

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All of the nitrogens in the purine and pyrimidine bases (as well as some of the carbons) are derived from the plentiful amino acids glutamine, aspartic acid, and glycine, whereas the ribose and deoxyribose sugars are derived from glucose.

Tags

#biochem #biology #cell

Question

All of the nitrogens in the purine and pyrimidine bases (as well as some of the carbons) are derived from the plentiful amino acids glutamine, aspartic acid, and glycine, whereas the [...] are derived from glucose.

Answer

ribose and deoxyribose sugars

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All of the nitrogens in the purine and pyrimidine bases (as well as some of the carbons) are derived from the plentiful amino acids glutamine, aspartic acid, and glycine, whereas the ribose and deoxyribose sugars are derived from glucose.

Tags

#biochem #biology #cell

Question

All of the [...] (as well as some of the carbons) are derived from the plentiful amino acids glutamine, aspartic acid, and glycine, whereas the ribose and deoxyribose sugars are derived from glucose.

Answer

nitrogens in the purine and pyrimidine bases

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All of the nitrogens in the purine and pyrimidine bases (as well as some of the carbons) are derived from the plentiful amino acids glutamine, aspartic acid, and glycine, whereas the ribose and deoxyribose sugars are derived from glucose.<

Tags

#biochem #biology #cell

Question

Sulfur is abundant on Earth in its most oxidized form, [...].

Answer

sulfate (SO 4 2– )

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Sulfur is abundant on Earth in its most oxidized form, sulfate (SO 4 2– ). To be useful for life, sulfate must be reduced to sulfide (S 2– ), the oxidation state of sulfur required for the synthesis of essential biological molecules, including the amino ac

#biochem #biology #cell

To be useful for life, sulfate must be reduced to sulfide (S 2– )

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Sulfur is abundant on Earth in its most oxidized form, sulfate (SO 4 2– ). To be useful for life, sulfate must be reduced to sulfide (S 2– ), the oxidation state of sulfur required for the synthesis of essential biological molecules, including the amino acids methionine and cysteine, coenzyme A (see Figure 2–39), and the i

Tags

#biochem #biology #cell

Question

Sulfur is abundant on Earth in its most oxidized form, sulfate (SO 4 2– ). To be useful for life, sulfate must be reduced to sulfide (S 2– ), the oxidation state of sulfur required for the synthesis of essential biological molecules, including the [...] (see Figure 14–16)

Answer

amino acids methionine and cysteine, coenzyme A (see Figure 2–39), and the iron-sulfur centers essential for electron transport

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is abundant on Earth in its most oxidized form, sulfate (SO 4 2– ). To be useful for life, sulfate must be reduced to sulfide (S 2– ), the oxidation state of sulfur required for the synthesis of essential biological molecules, including the <span>amino acids methionine and cysteine, coenzyme A (see Figure 2–39), and the iron-sulfur centers essential for electron transport (see Figure 14–16)<span><body><html>

Question

[default - edit me]

Answer

The pressure in the pipe is nearest (A) 843 Pa (B) 981 Pa (C) 1270 P

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#biochem #biology #cell

Question

Humans and other animals cannot [...] and must therefore acquire the sulfur they need for their metabolism in the food that they eat

Answer

reduce sulfate

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Humans and other animals cannot reduce sulfate and must therefore acquire the sulfur they need for their metabolism in the food that they eat

Tags

#biochem #biology #cell

Question

Humans and other animals cannot reduce sulfate and must therefore [...]

Answer

acquire the sulfur they need for their metabolism in the food that they eat

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Humans and other animals cannot reduce sulfate and must therefore acquire the sulfur they need for their metabolism in the food that they eat

Tags

#biochem #biology #cell

Question

The characteristic “size” for each atom is specified by [...]. The contact distance between any two noncovalently bonded atoms is the sum of their van der Waals radii

Answer

a unique van der Waals radius

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The characteristic “size” for each atom is specified by a unique van der Waals radius. The contact distance between any two noncovalently bonded atoms is the sum of their van der Waals radii

Tags

#biochem #biology #cell

Question

The characteristic “size” for each atom is specified by a unique van der Waals radius. The contact distance between any two noncovalently bonded atoms is the [...]

Answer

sum of their van der Waals radii

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The characteristic “size” for each atom is specified by a unique van der Waals radius. The contact distance between any two noncovalently bonded atoms is the sum of their van der Waals radii

Tags

#biochem #biology #cell

Question

The characteristic “size” for each atom is specified by a unique van der Waals radius. The [...] is the sum of their van der Waals radii

Answer

contact distance between any two noncovalently bonded atoms

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The characteristic “size” for each atom is specified by a unique van der Waals radius. The contact distance between any two noncovalently bonded atoms is the sum of their van der Waals radii

Tags

#matlab #programming

Question

[...] can also be done with the reshape function

Answer

Reshaping

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Reshaping can also be done with the reshape function

Tags

#matlab #programming

Question

Reshaping can also be done with the [...] function

Answer

reshape

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Reshaping can also be done with the reshape function

Tags

#matlab #programming

Question

On the right-hand side of an assignment, [...] gives all the elements of a strung out by columns in one long column vector

Answer

a(:)

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

On the right-hand side of an assignment, a(:) gives all the elements of a strung out by columns in one long column vector

Tags

#matlab #programming

Question

On the right-hand side of an assignment, a(:) gives [...]

Answer

all the elements of a strung out by columns in one long column vector

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

On the right-hand side of an assignment, a(:) gives all the elements of a strung out by columns in one long column vector

Tags

#matlab #programming

Question

As a special case, [...] may be used to replace all the elements of a matrix with a scalar, e.g., a(:) = -1

Answer

a single colon subscript

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

As a special case, a single colon subscript may be used to replace all the elements of a matrix with a scalar, e.g., a(:) = -1

Tags

#matlab #programming

Question

As a special case, a single colon subscript may be used to [...], e.g., a(:) = -1

Answer

replace all the elements of a matrix with a scalar

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

As a special case, a single colon subscript may be used to replace all the elements of a matrix with a scalar, e.g., a(:) = -1

Tags

#matlab #programming

Question

You can’t delete a single element from a matrix while keeping it a matrix, so a statement like [...] results in an error

Answer

a(1,2) = [ ]

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

You can’t delete a single element from a matrix while keeping it a matrix, so a statement like a(1,2) = [ ] results in an error

Tags

#deeplearning #neuralnetworks

Question

T o analyze ho w man y solutions the equation has, we can think of the columns of A as sp ecifying [...] (the p oin t sp eciﬁed b y the v ector of all zeros), and determine ho w many wa ys there are of reac hing b . In this view, each element of x sp eciﬁes ho w far we should trav el in eac h of these directions, with x i sp ecifying how far to mo v e in the direction of column :

Answer

diﬀerent directions we can tra v el from the origin

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

T o analyze ho w man y solutions the equation has, we can think of the columns of A as sp ecifying diﬀerent directions we can tra v el from the origin (the p oin t sp eciﬁed b y the v ector of all zeros), and determine ho w many wa ys there are of reac hing b . In this view, each element of x sp eciﬁes ho w far we should trav el in ea

Tags

#deeplearning #neuralnetworks

Question

T o analyze ho w man y solutions the equation has, we can think of the columns of A as sp ecifying diﬀerent directions we can tra v el from the origin (the p oin t sp eciﬁed b y the v ector of all zeros), and determine ho w many wa ys there are of reac hing b . In this view, each element of x [...], with x_{ i }sp ecifying how far to mo v e in the direction of column i:

Answer

sp eciﬁes ho w far we should trav el in eac h of these directions

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

we can think of the columns of A as sp ecifying diﬀerent directions we can tra v el from the origin (the p oin t sp eciﬁed b y the v ector of all zeros), and determine ho w many wa ys there are of reac hing b . In this view, each element of x <span>sp eciﬁes ho w far we should trav el in eac h of these directions, with x i sp ecifying how far to mo v e in the direction of column :<span><body><html>

Tags

#deeplearning #neuralnetworks

Question

T o analyze ho w man y solutions the equation has, we can think of the columns of A as sp ecifying diﬀerent directions we can tra v el from the origin (the p oin t sp eciﬁed b y the v ector of all zeros), and determine ho w many wa ys there are of reac hing b . In this view, each element of x sp eciﬁes ho w far we should trav el in eac h of these directions, with x i [...]n :

Answer

sp ecifying how far to mo v e in the direction of colum

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

ra v el from the origin (the p oin t sp eciﬁed b y the v ector of all zeros), and determine ho w many wa ys there are of reac hing b . In this view, each element of x sp eciﬁes ho w far we should trav el in eac h of these directions, with x i <span>sp ecifying how far to mo v e in the direction of column :<span><body><html>

Tags

#bayes #programming #r #statistics

Question

The “evidence” for the model, p(D), is [...]

Answer

the overall probability of the data according to the model, determined by averaging across all possible parameter values weighted by the strength of belief in those parameter value

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The “evidence” for the model, p(D), is the overall probability of the data according to the model, determined by averaging across all possible parameter values weighted by the strength of belief in those parameter value

Tags

#bayes #programming #r #statistics

Question

The [...] for the model, p(D), is the overall probability of the data according to the model, determined by averaging across all possible parameter values weighted by the strength of belief in those parameter value

Answer

“evidence”

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The “evidence” for the model, p(D), is the overall probability of the data according to the model, determined by averaging across all possible parameter values weighted by the strength of belief in th

Tags

#bayes #programming #r #statistics

Question

In many models, the probability of data, p(D|θ), does not depend in any way on other data. That is, the joint probability p(D, D |θ) equals p(D|θ)·p(D |θ). In other words, in this sort of model, the data probabilities are [...]. Under this condition, then the order of updating has no effect of the final posterior

Answer

independent (recall that independence was defined in Section 4.4.2)

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

body>In many models, the probability of data, p(D|θ), does not depend in any way on other data. That is, the joint probability p(D, D |θ) equals p(D|θ)·p(D |θ). In other words, in this sort of model, the data probabilities are independent (recall that independence was defined in Section 4.4.2). Under this condition, then the order of updating has no effect of the final posterior<body><html>

Tags

#bayes #programming #r #statistics

Question

a general phenomenon in Bayesian inference: The posterior is a compromise [...].

Answer

between the prior distribution and the likelihood function

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

a general phenomenon in Bayesian inference: The posterior is a compromise between the prior distribution and the likelihood function.

Tags

#bayes #programming #r #statistics

Question

The compromise favors the prior to the extent that [...]. The compromise favors the likelihood function (i.e., the data) to the extent that the prior distribution is flat and the data are many.

Answer

the prior distribution is sharply peaked and the data are few

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The compromise favors the prior to the extent that the prior distribution is sharply peaked and the data are few. The compromise favors the likelihood function (i.e., the data) to the extent that the prior distribution is flat and the data are many.

Tags

#bayes #programming #r #statistics

Question

The compromise favors the prior to the extent that the prior distribution is sharply peaked and the data are few. The compromise favors the likelihood function (i.e., the data) to the extent that [...]

Answer

the prior distribution is flat and the data are many.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The compromise favors the prior to the extent that the prior distribution is sharply peaked and the data are few. The compromise favors the likelihood function (i.e., the data) to the extent that the prior distribution is flat and the data are many.

Tags

#bayes #programming #r #statistics

Question

The compromise favors the [...] to the extent that the prior distribution is sharply peaked and the data are few. The compromise favors the likelihood function (i.e., the data) to the extent that the prior distribution is flat and the data are many.

Answer

prior

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The compromise favors the prior to the extent that the prior distribution is sharply peaked and the data are few. The compromise favors the likelihood function (i.e., the data) to the extent that the prior distributio

Tags

#bayes #programming #r #statistics

Question

The compromise favors the prior to the extent that the prior distribution is sharply peaked and the data are few. The compromise favors the [...] to the extent that the prior distribution is flat and the data are many.

Answer

likelihood function (i.e., the data)

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The compromise favors the prior to the extent that the prior distribution is sharply peaked and the data are few. The compromise favors the likelihood function (i.e., the data) to the extent that the prior distribution is flat and the data are many.

Tags

#bayes #programming #r #statistics

Question

the mode of the posterior distribution is between [...], but the posterior mode is closer to the likelihood mode for larger sample sizes

Answer

the mode of the prior distribution and the mode of the likelihood function

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

the mode of the posterior distribution is between the mode of the prior distribution and the mode of the likelihood function, but the posterior mode is closer to the likelihood mode for larger sample sizes

Tags

#bayes #programming #r #statistics

Question

the mode of the posterior distribution is between the mode of the prior distribution and the mode of the likelihood function, but the posterior mode is closer to [...] for larger sample sizes

Answer

the likelihood mode

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

the mode of the posterior distribution is between the mode of the prior distribution and the mode of the likelihood function, but the posterior mode is closer to the likelihood mode for larger sample sizes

Tags

#matlab #programming

Question

the functions [...] generate matrices of 1s, 0s and random numbers, respectively. With a single argument n, they generate n × n (square) matrices. With two arguments n and m they generate n × m ma- trices.

Answer

zeros, ones and rand

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

the functions zeros, ones and rand generate matrices of 1s, 0s and random numbers, respectively. With a single argument n, they generate n × n (square) matrices. With two arguments n and m they generate n × m ma- trices.

Tags

#matlab #programming

Question

The function [...] generates an n × n identity matrix, i.e., a matrix with 1s on the main ‘diagonal’, and 0s everywhere else

Answer

eye(n)

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The function eye(n) generates an n × n identity matrix, i.e., a matrix with 1s on the main ‘diagonal’, and 0s everywhere else

Tags

#matlab #programming

Question

[...] extracts or creates a diagonal

Answer

diag

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

diag extracts or creates a diagonal

Tags

#matlab #programming

Question

diag [...]

Answer

extracts or creates a diagonal

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

diag extracts or creates a diagonal

Tags

#matlab #programming

Question

[...] extracts the upper triangular part

Answer

triu

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

triu extracts the upper triangular part

Tags

#matlab #programming

Question

triu [...]

Answer

extracts the upper triangular part

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

triu extracts the upper triangular part

Tags

#biochem #biology #cell

Question

although the overall conformation of each pro- tein is unique, two regular folding patterns are often found within them. Both pat- terns were discovered more than [...] ago from studies of hair and silk

Answer

60 years

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

although the overall conformation of each pro- tein is unique, two regular folding patterns are often found within them. Both pat- terns were discovered more than 60 years ago from studies of hair and silk

Tags

#biochem #biology #cell

Question

although the overall conformation of each pro- tein is unique, two regular folding patterns are often found within them. Both pat- terns were discovered more than 60 years ago from studies of [...]

Answer

hair and silk

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

although the overall conformation of each pro- tein is unique, two regular folding patterns are often found within them. Both pat- terns were discovered more than 60 years ago from studies of hair and silk

Tags

#biochem #biology #cell

Question

Within [...] of the discovery of the α helix, a second folded structure, called a β sheet, was found in the protein fibroin, the major constituent of silk. These two patterns are particularly common because they result from hydro- gen-bonding between the N–H and C=O groups in the polypeptide backbone, without involving the side chains of the amino acids

Answer

a year

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Within a year of the discovery of the α helix, a second folded structure, called a β sheet, was found in the protein fibroin, the major constituent of silk. These two patterns are particularly comm

Tags

#biochem #biology #cell

Question

Within a year of the discovery of the α helix, a second folded structure, called a [...], was found in the protein fibroin, the major constituent of silk. These two patterns are particularly common because they result from hydro- gen-bonding between the N–H and C=O groups in the polypeptide backbone, without involving the side chains of the amino acids

Answer

β sheet

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Within a year of the discovery of the α helix, a second folded structure, called a β sheet, was found in the protein fibroin, the major constituent of silk. These two patterns are particularly common because they result from hydro- gen-bonding between the N–H and C=O groups

Tags

#biochem #biology #cell

Question

Within a year of the discovery of the α helix, a second folded structure, called a β sheet, was found in the protein [...], the major constituent of silk. These two patterns are particularly common because they result from hydro- gen-bonding between the N–H and C=O groups in the polypeptide backbone, without involving the side chains of the amino acids

Answer

fibroin

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Within a year of the discovery of the α helix, a second folded structure, called a β sheet, was found in the protein fibroin, the major constituent of silk. These two patterns are particularly common because they result from hydro- gen-bonding between the N–H and C=O groups in the polypeptide backbone, with

Tags

#biochem #biology #cell

Question

Within a year of the discovery of the α helix, a second folded structure, called a β sheet, was found in the protein fibroin, the major constituent of [...]. These two patterns are particularly common because they result from hydro- gen-bonding between the N–H and C=O groups in the polypeptide backbone, without involving the side chains of the amino acids

Answer

silk

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Within a year of the discovery of the α helix, a second folded structure, called a β sheet, was found in the protein fibroin, the major constituent of silk. These two patterns are particularly common because they result from hydro- gen-bonding between the N–H and C=O groups in the polypeptide backbone, without involving the side chains o

Tags

#biochem #biology #cell

Question

Within a year of the discovery of the α helix, a second folded structure, called a β sheet, was found in the protein fibroin, the major constituent of silk. These two patterns are particularly common because [...]

Answer

they result from hydro- gen-bonding between the N–H and C=O groups in the polypeptide backbone, without involving the side chains of the amino acids

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

l>Within a year of the discovery of the α helix, a second folded structure, called a β sheet, was found in the protein fibroin, the major constituent of silk. These two patterns are particularly common because they result from hydro- gen-bonding between the N–H and C=O groups in the polypeptide backbone, without involving the side chains of the amino acids<html>

Tags

#matlab #programming

Question

[...] inverse.

Answer

inv

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

inv inverse.

Tags

#matlab #programming

Question

inv [...]

Answer

inverse.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

inv inverse.

Tags

#matlab #programming

Question

[...] orthogonal factorization

Answer

qr

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

qr orthogonal factorization

Tags

#matlab #programming

Question

qr [...]

Answer

orthogonal factorization

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

qr orthogonal factorization

Tags

#matlab #programming

Question

To see how MATLAB solves this system, first recall that the left division operator \ may be used on scalars, i.e., a\bis the same as b/aif a and b are scalars. However, it can also be used on vectors and matrices, in order to [...]

Answer

solve linear equations

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

body>To see how MATLAB solves this system, first recall that the left division operator \ may be used on scalars, i.e., a\bis the same as b/aif a and b are scalars. However, it can also be used on vectors and matrices, in order to solve linear equations<body><html>

Tags

#matlab #programming

Question

When we have more equations than unknowns, the system is called [...]

Answer

over- determined

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

When we have more equations than unknowns, the system is called over- determined

Tags

#matlab #programming

Question

When we [...], the system is called over- determined

Answer

have more equations than unknowns

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

When we have more equations than unknowns, the system is called over- determined

Tags

#matlab #programming

Question

The residual r=A*x-b is now r= -0.3333 -0.3333 0.3333 What happens in this case is that MATLAB produces the [...]. This is the value of x which makes the magnitude of r, i.e., r(1) ^{2} + r(2) ^{2} + r(3)^{2} , as small as possible.

Answer

least squares best fit

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The residual r=A*x-b is now r= -0.3333 -0.3333 0.3333 What happens in this case is that MATLAB produces the least squares best fit. This is the value of x which makes the magnitude of r, i.e., r(1) 2 + r(2) 2 + r(3) 2 , as small as possible.

Tags

#matlab #programming

Question

The residual r=A*x-b is now r= -0.3333 -0.3333 0.3333 What happens in this case is that MATLAB produces the least squares best fit. This is the value of x which ^{[...]}

Answer

makes the magnitude of r, i.e., r(1) ^{2} + r(2) ^{2} + r(3)^{2} , as small as possible.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The residual r=A*x-b is now r= -0.3333 -0.3333 0.3333 What happens in this case is that MATLAB produces the least squares best fit. This is the value of x which makes the magnitude of r, i.e., r(1) 2 + r(2) 2 + r(3) 2 , as small as possible.

Tags

#matlab #programming

Question

If there are fewer equations than unknowns, the system is called [...]. In this case there are an infinite number of solutions; MATLAB will find one which has zeros for some of the unknowns.

Answer

under- determined

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If there are fewer equations than unknowns, the system is called under- determined. In this case there are an infinite number of solutions; MATLAB will find one which has zeros for some of the unknowns.

Tags

#matlab #programming

Question

If there are fewer equations than unknowns, the system is called under- determined. In this case there are an [...] number of solutions; MATLAB will find one which has zeros for some of the unknowns.

Answer

infinite

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If there are fewer equations than unknowns, the system is called under- determined. In this case there are an infinite number of solutions; MATLAB will find one which has zeros for some of the unknowns.

Tags

#matlab #programming

Question

If there are fewer equations than unknowns, the system is called under- determined. In this case there are an infinite number of solutions; MATLAB will [...]

Answer

find one which has zeros for some of the unknowns.

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If there are fewer equations than unknowns, the system is called under- determined. In this case there are an infinite number of solutions; MATLAB will find one which has zeros for some of the unknowns.

Tags

#matlab #programming

Question

A system like this is called [...] meaning that a small change in the co- efficients leads to a large change in the solution. The MATLAB function rcond returns the condition estimator, which tests for ill conditioning

Answer

ill-conditioned,

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A system like this is called ill-conditioned, meaning that a small change in the co- efficients leads to a large change in the solution. The MATLAB function rcond returns the condition estimator, which tests for ill conditioning</s

Tags

#matlab #programming

Question

A system like this is called ill-conditioned, meaning that a [...]. The MATLAB function rcond returns the condition estimator, which tests for ill conditioning

Answer

small change in the co- efficients leads to a large change in the solution

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scheduled repetition interval | last repetition or drill |

A system like this is called ill-conditioned, meaning that a small change in the co- efficients leads to a large change in the solution. The MATLAB function rcond returns the condition estimator, which tests for ill conditioning

Tags

#matlab #programming

Question

A system like this is called ill-conditioned, meaning that a small change in the co- efficients leads to a large change in the solution. The MATLAB function [...] returns the condition estimator, which tests for ill conditioning

Answer

rcond

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A system like this is called ill-conditioned, meaning that a small change in the co- efficients leads to a large change in the solution. The MATLAB function rcond returns the condition estimator, which tests for ill conditioning

Tags

#matlab #programming

Question

A system like this is called ill-conditioned, meaning that a small change in the co- efficients leads to a large change in the solution. The MATLAB function rcond returns the [...]

Answer

condition estimator, which tests for ill conditioning

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A system like this is called ill-conditioned, meaning that a small change in the co- efficients leads to a large change in the solution. The MATLAB function rcond returns the condition estimator, which tests for ill conditioning

Tags

#matlab #programming

Question

The function [...] provides a neat visualization of sparse matrices.

Answer

spy

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The function spy provides a neat visualization of sparse matrices.

Tags

#matlab #programming

Question

The function spy provides a [...]

Answer

neat visualization of sparse matrices.

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The function spy provides a neat visualization of sparse matrices.

Tags

#bayes #programming #r #statistics

Question

The space of possibilities is the j[...]. If we represent each parameter with a comb of, say, 1,000 values, then for P parameters there are 1,000 P combinations of parameter values.

Answer

oint parameter space involving all combinations of parameter values

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The space of possibilities is the joint parameter space involving all combinations of parameter values. If we represent each parameter with a comb of, say, 1,000 values, then for P parameters there are 1,000 P combinations of parameter values.

Tags

#bayes #programming #r #statistics

Question

The space of possibilities is the joint parameter space involving all combinations of parameter values. If we represent each parameter with a comb of, say, 1,000 values, then for P parameters there are ^{[...] of parameter values.}

Answer

1,000 ^{P} combinations

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The space of possibilities is the joint parameter space involving all combinations of parameter values. If we represent each parameter with a comb of, say, 1,000 values, then for P parameters there are 1,000 P combinations of parameter values.

Tags

#biochem #biology #cell

Question

Note that all of the N–H groups point up in this diagram and that all of the C=O groups point down (toward the C-terminus); this gives a [...] to the helix, with the C-terminus having a partial negative and the N-terminus a partial positive charge (

Answer

polarity

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Note that all of the N–H groups point up in this diagram and that all of the C=O groups point down (toward the C-terminus); this gives a polarity to the helix, with the C-terminus having a partial negative and the N-terminus a partial positive charge (

Tags

#biochem #biology #cell

Question

Note that all of the N–H groups point up in this diagram and that all of the C=O groups point down (toward the C-terminus); this gives a polarity to the helix, with the C-terminus having a [...] and the N-terminus a partial positive charge (

Answer

partial negative

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Note that all of the N–H groups point up in this diagram and that all of the C=O groups point down (toward the C-terminus); this gives a polarity to the helix, with the C-terminus having a partial negative and the N-terminus a partial positive charge (

Tags

#biochem #biology #cell

Question

Note that all of the N–H groups point up in this diagram and that all of the C=O groups point down (toward the C-terminus); this gives a polarity to the helix, with the C-terminus having a partial negative and the N-terminus a [...] charge (

Answer

partial positive

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y>Note that all of the N–H groups point up in this diagram and that all of the C=O groups point down (toward the C-terminus); this gives a polarity to the helix, with the C-terminus having a partial negative and the N-terminus a partial positive charge (<body><html>

Tags

#biochem #biology #cell

Question

The β sheet is shown in (C) and (D). In this example, adjacent peptide chains run in [...] directions. Hydrogen-bonding between peptide bonds in different strands holds the individual polypeptide chains (strands) together in a β sheet, and the amino acid side chains in each strand alternately project above and below the plane of the sheet

Answer

opposite (antiparallel)

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The β sheet is shown in (C) and (D). In this example, adjacent peptide chains run in opposite (antiparallel) directions. Hydrogen-bonding between peptide bonds in different strands holds the individual polypeptide chains (strands) together in a β sheet, and the amino acid side chains in each

Tags

#biochem #biology #cell

Question

The β sheet is shown in (C) and (D). In this example, adjacent peptide chains run in opposite (antiparallel) directions. Hydrogen-bonding between peptide bonds in different strands holds the individual polypeptide chains (strands) together in a β sheet, and the amino acid side chains in each strand [...]

Answer

alternately project above and below the plane of the sheet

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nt peptide chains run in opposite (antiparallel) directions. Hydrogen-bonding between peptide bonds in different strands holds the individual polypeptide chains (strands) together in a β sheet, and the amino acid side chains in each strand <span>alternately project above and below the plane of the sheet<span><body><html>

Tags

#biochem #biology #cell

Question

The β sheet is shown in (C) and (D). In this example, adjacent peptide chains run in opposite (antiparallel) directions. [...] between peptide bonds in different strands holds the individual polypeptide chains (strands) together in a β sheet, and the amino acid side chains in each strand alternately project above and below the plane of the sheet

Answer

Hydrogen-bonding

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The β sheet is shown in (C) and (D). In this example, adjacent peptide chains run in opposite (antiparallel) directions. Hydrogen-bonding between peptide bonds in different strands holds the individual polypeptide chains (strands) together in a β sheet, and the amino acid side chains in each strand alternately project a

Tags

#biochem #biology #cell

Question

An [...] is generated when a single polypeptide chain twists around on itself to form a rigid cylinder. A hydrogen bond forms between every fourth peptide bond, linking the C=O of one peptide bond to the N–H of another (see Figure 3–7A). This gives rise to a regular helix with a complete turn every 3.6 amino acids.

Answer

α helix

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An α helix is generated when a single polypeptide chain twists around on itself to form a rigid cylinder. A hydrogen bond forms between every fourth peptide bond, linking the C=O of one peptide

Tags

#biochem #biology #cell

Question

An α helix is generated when a single polypeptide chain twists around on itself to form a rigid cylinder. A hydrogen bond forms between every [...] peptide bond, linking the C=O of one peptide bond to the N–H of another (see Figure 3–7A). This gives rise to a regular helix with a complete turn every 3.6 amino acids.

Answer

fourth

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An α helix is generated when a single polypeptide chain twists around on itself to form a rigid cylinder. A hydrogen bond forms between every fourth peptide bond, linking the C=O of one peptide bond to the N–H of another (see Figure 3–7A). This gives rise to a regular helix with a complete turn every 3.6 amino acids.

Tags

#biochem #biology #cell

Question

An α helix is generated when a single polypeptide chain twists around on itself to form a rigid cylinder. A hydrogen bond forms between every fourth peptide bond, linking the C=O of one peptide bond to the N–H of another (see Figure 3–7A). This gives rise to a regular helix with a complete turn every [...] amino acids.

Answer

3.6

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around on itself to form a rigid cylinder. A hydrogen bond forms between every fourth peptide bond, linking the C=O of one peptide bond to the N–H of another (see Figure 3–7A). This gives rise to a regular helix with a complete turn every <span>3.6 amino acids.<span><body><html>

Tags

#biochem #biology #cell

Question

α helices wrap around each other to form a particularly sta- ble structure, known as a [...] This structure can form when the two (or in some cases, three or four) α helices have most of their nonpolar (hydrophobic) side chains on one side, so that they can twist around each other with these side chains facing inward

Answer

coiled-coil.

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α helices wrap around each other to form a particularly sta- ble structure, known as a coiled-coil. This structure can form when the two (or in some cases, three or four) α helices have most of their nonpolar (hydrophobic) side chains on one side, so that they can twist around each

Tags

#biochem #biology #cell

Question

[...] wrap around each other to form a particularly sta- ble structure, known as a coiled-coil. This structure can form when the two (or in some cases, three or four) α helices have most of their nonpolar (hydrophobic) side chains on one side, so that they can twist around each other with these side chains facing inward

Answer

α helices

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α helices wrap around each other to form a particularly sta- ble structure, known as a coiled-coil. This structure can form when the two (or in some cases, three or four) α helices have most of

Tags

#biochem #biology #cell

Question

α helices wrap around each other to form a particularly sta- ble structure, known as a coiled-coil. This structure can form when [...]

Answer

the two (or in some cases, three or four) α helices have most of their nonpolar (hydrophobic) side chains on one side, so that they can twist around each other with these side chains facing inward

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

α helices wrap around each other to form a particularly sta- ble structure, known as a coiled-coil. This structure can form when the two (or in some cases, three or four) α helices have most of their nonpolar (hydrophobic) side chains on one side, so that they can twist around each other with these side chains facing inward

Tags

#biochem #biology #cell

Question

This is the protein domain, [...]

Answer

a substructure produced by any contiguous part of a polypeptide chain that can fold independently of the rest of the protein into a compact, stable structure.

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This is the protein domain, a substructure produced by any contiguous part of a polypeptide chain that can fold independently of the rest of the protein into a compact, stable structure.

Tags

#biochem #biology #cell

Question

This is the [...], a substructure produced by any contiguous part of a polypeptide chain that can fold independently of the rest of the protein into a compact, stable structure.

Answer

protein domain

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This is the protein domain, a substructure produced by any contiguous part of a polypeptide chain that can fold independently of the rest of the protein into a compact, stable structure. </

Tags

#biochem #biology #cell

Question

A domain usually contains between [...] amino acids, and it is the modular unit from which many larger proteins are constructed

Answer

40 and 350

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A domain usually contains between 40 and 350 amino acids, and it is the modular unit from which many larger proteins are constructed

Tags

#biochem #biology #cell

Question

the [...], which functions in signaling pathways inside vertebrate cells

Answer

Src protein kinase

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the Src protein kinase, which functions in signaling pathways inside vertebrate cells

Tags

#biochem #biology #cell

Question

the Src protein kinase, which functions in [...] inside vertebrate cells

Answer

signaling pathways

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the Src protein kinase, which functions in signaling pathways inside vertebrate cells

Tags

#biochem #biology #cell

Question

The smallest protein molecules contain only [...] domain, whereas larger proteins can contain several dozen domains, often connected to each other by short, relatively unstructured lengths of polypeptide chain that can act as flexible hinges between domains.

Answer

a single

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The smallest protein molecules contain only a single domain, whereas larger proteins can contain several dozen domains, often connected to each other by short, relatively unstructured lengths of polypeptide chain that can act as flexibl

Tags

#biochem #biology #cell

Question

The smallest protein molecules contain only a single domain, whereas larger proteins can contain [...] domains, often connected to each other by short, relatively unstructured lengths of polypeptide chain that can act as flexible hinges between domains.

Answer

several dozen

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The smallest protein molecules contain only a single domain, whereas larger proteins can contain several dozen domains, often connected to each other by short, relatively unstructured lengths of polypeptide chain that can act as flexible hinges between domains.

Tags

#biochem #biology #cell

Question

The smallest protein molecules contain only a single domain, whereas larger proteins can contain several dozen domains, often connected to each other by [...]

Answer

short, relatively unstructured lengths of polypeptide chain that can act as flexible hinges between domains.

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The smallest protein molecules contain only a single domain, whereas larger proteins can contain several dozen domains, often connected to each other by short, relatively unstructured lengths of polypeptide chain that can act as flexible hinges between domains.

Tags

#biochem #biology #cell

Question

Since each of the 20 amino acids is chemically distinct and each can, in princi- ple, occur at any position in a protein chain, there are [...] different possible polypeptide chains four amino acids long, or 20 n different pos- sible polypeptide chains n amino acids long. For a typical protein length of about 300 amino acids, a cell could theoretically make more than 10 390 (20 300 ) different polypeptide chains

Answer

20 × 20 × 20 × 20 = 160,000

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Since each of the 20 amino acids is chemically distinct and each can, in princi- ple, occur at any position in a protein chain, there are 20 × 20 × 20 × 20 = 160,000 different possible polypeptide chains four amino acids long, or 20 n different pos- sible polypeptide chains n amino acids long. For a typical protein length of about 300 amino acids

Tags

#biochem #biology #cell

Question

Since each of the 20 amino acids is chemically distinct and each can, in princi- ple, occur at any position in a protein chain, there are 20 × 20 × 20 × 20 = 160,000 different possible polypeptide chains four amino acids long, or 20 n different pos- sible polypeptide chains n amino acids long. For a typical protein length of about 300 amino acids, a cell could theoretically make more than [...] different polypeptide chains

Answer

10 390 (20 300 )

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20 = 160,000 different possible polypeptide chains four amino acids long, or 20 n different pos- sible polypeptide chains n amino acids long. For a typical protein length of about 300 amino acids, a cell could theoretically make more than <span>10 390 (20 300 ) different polypeptide chains<span><body><html>

Tags

#biochem #biology #cell

Question

Many similar examples show that two pro- teins with more than [...] identity in their amino acid sequences usually share the same overall structure

Answer

25%

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Many similar examples show that two pro- teins with more than 25% identity in their amino acid sequences usually share the same overall structure

Tags

#biochem #biology #cell

Question

Many similar examples show that two pro- teins with more than 25% identity in their amino acid sequences usually [...]

Answer

share the same overall structure

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Many similar examples show that two pro- teins with more than 25% identity in their amino acid sequences usually share the same overall structure

Tags

#biochem #biology #cell

Question

there are a limited number of ways in which protein domains fold up in nature—maybe as few as [...], if we consider all organisms. For most of these so-called protein folds, representative structures have been determined.

Answer

2000

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there are a limited number of ways in which protein domains fold up in nature—maybe as few as 2000, if we consider all organisms. For most of these so-called protein folds, representative structures have been determined.

Tags

#biochem #biology #cell

Question

The present database of known protein sequences contains more than [...] entries

Answer

twenty million

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The present database of known protein sequences contains more than twenty million entries

Tags

#biochem #biology #cell

Question

The encoded polypeptides range widely in size, from [...] amino acids to a gigantic pro- tein of 33,000 amino acids.

Answer

6

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The encoded polypeptides range widely in size, from 6 amino acids to a gigantic pro- tein of 33,000 amino acids.

Tags

#biochem #biology #cell

Question

The encoded polypeptides range widely in size, from 6 amino acids to a gigantic pro- tein of [...] amino acids.

Answer

33,000

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The encoded polypeptides range widely in size, from 6 amino acids to a gigantic pro- tein of 33,000 amino acids.

Tags

#biochem #biology #cell

Question

most proteins are composed of a series of protein domains, in which different regions of the polypeptide chain fold independently to form compact structures. Such multidomain proteins are believed to have originated from the [process] , cre- ating a new gene.

Answer

accidental joining of the DNA sequences that encode each domain

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dy>most proteins are composed of a series of protein domains, in which different regions of the polypeptide chain fold independently to form compact structures. Such multidomain proteins are believed to have originated from the accidental joining of the DNA sequences that encode each domain, cre- ating a new gene.<body><html>

Tags

#biochem #biology #cell

Question

In an evolutionary process called [...], many large proteins have evolved through the joining of preexisting domains in new com- binations (Figure 3–14). Novel binding surfaces have often been created at the juxtaposition of domains, and many of the functional sites where proteins bind to small molecules are found to be located there.

Answer

domain shuffling

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In an evolutionary process called domain shuffling, many large proteins have evolved through the joining of preexisting domains in new com- binations (Figure 3–14). Novel binding surfaces have often been created at the juxtaposition o

Tags

#biochem #biology #cell

Question

β-sheet-based domains may have achieved their evolutionary success because [...]

Answer

they provide a convenient framework for the generation of new binding sites for ligands, requiring only small changes to their protruding loops

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β-sheet-based domains may have achieved their evolutionary success because they provide a convenient framework for the generation of new binding sites for ligands, requiring only small changes to their protruding loops

Tags

#biochem #biology #cell

Question

Stiff extended structures composed of a series of domains are especially common in [...] and in the extracellular portions of cell-surface receptor proteins.

Answer

extracellular matrix molecules

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Stiff extended structures composed of a series of domains are especially common in extracellular matrix molecules and in the extracellular portions of cell-surface receptor proteins.

Tags

#biochem #biology #cell

Question

Stiff extended structures composed of a series of domains are especially common in extracellular matrix molecules and in the [...]

Answer

extracellular portions of cell-surface receptor proteins.

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

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Stiff extended structures composed of a series of domains are especially common in extracellular matrix molecules and in the extracellular portions of cell-surface receptor proteins.

Tags

#biochem #biology #cell

Question

[...] composed of a series of domains are especially common in extracellular matrix molecules and in the extracellular portions of cell-surface receptor proteins.

Answer

Stiff extended structures

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Stiff extended structures composed of a series of domains are especially common in extracellular matrix molecules and in the extracellular portions of cell-surface receptor proteins.

Tags

#biochem #biology #cell

Question

N- and C-terminal ends at opposite poles of the domain. When the DNA encoding such a domain undergoes [...], which is not unusual in the evolution of genomes (discussed in Chapter 4), the duplicated domains with this “in-line” arrangement can be readily linked in series to form extended structures—either with themselves or with other in-line domains

Answer

tandem duplica- tion

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

N- and C-terminal ends at opposite poles of the domain. When the DNA encoding such a domain undergoes tandem duplica- tion, which is not unusual in the evolution of genomes (discussed in Chapter 4), the duplicated domains with this “in-line” arrangement can be readily linked in series to form extended str

Tags

#biochem #biology #cell

Question

[...] of all domain families are common to archaea, bacteria, and eukaryotes, only about 5% of the two-domain combi- nations are similarly shared.

Answer

half

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

half of all domain families are common to archaea, bacteria, and eukaryotes, only about 5% of the two-domain combi- nations are similarly shared.

Tags

#biochem #biology #cell

Question

half of all domain families are common to archaea, bacteria, and eukaryotes, only about [...] of the two-domain combi- nations are similarly shared.

Answer

5%

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

half of all domain families are common to archaea, bacteria, and eukaryotes, only about 5% of the two-domain combi- nations are similarly shared.

Tags

#biochem #biology #cell

Question

most proteins containing especially useful two-domain combinations arose through [...] rel- atively late in evolution

Answer

domain shuffling

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

most proteins containing especially useful two-domain combinations arose through domain shuffling rel- atively late in evolution

Tags

#biochem #biology #cell

Question

most proteins containing especially useful two-domain combinations arose through domain shuffling rel- atively [...] in evolution

Answer

late

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

most proteins containing especially useful two-domain combinations arose through domain shuffling rel- atively late in evolution

Tags

#biochem #biology #cell

Question

we currently lack even the tiniest hint of what the function might be for more than [...] of the proteins that have thus far been identified through examining the human genome

Answer

10,000

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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scheduled repetition interval | last repetition or drill |

we currently lack even the tiniest hint of what the function might be for more than 10,000 of the proteins that have thus far been identified through examining the human genome

Tags

#biochem #biology #cell

Question

combinations of protein domains, with the result that there are nearly [...] as many combinations of domains found in human proteins as in a worm or a fly.

Answer

twice

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scheduled repetition interval | last repetition or drill |

combinations of protein domains, with the result that there are nearly twice as many combinations of domains found in human proteins as in a worm or a fly.

Tags

#deeplearning #neuralnetworks

Question

Determining whether Ax = b has a solution th us amounts to testing whether [...] . This particular span is known as the column space range or the of . A

Answer

b is in the span of the columns of A

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Determining whether Ax = b has a solution th us amounts to testing whether b is in the span of the columns of A . This particular span is known as the column space range or the of . A

Tags

#deeplearning #neuralnetworks

Question

Determining whether Ax = b has a solution th us amounts to testing whether b is in the span of the columns of A . This particular span is known as the [...]

Answer

column space range or the of . A

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Determining whether Ax = b has a solution th us amounts to testing whether b is in the span of the columns of A . This particular span is known as the column space range or the of . A

Tags

#deeplearning #neuralnetworks

Question

Ha ving n m ≥ is only a necessary condition for ev ery p oin t to ha ve a solution. It is not a suﬃcien t condition, b ecause [...]

Answer

it is p ossible for some of the columns to b e redundan t.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Ha ving n m ≥ is only a necessary condition for ev ery p oin t to ha ve a solution. It is not a suﬃcien t condition, b ecause it is p ossible for some of the columns to b e redundan t.

Tags

#deeplearning #neuralnetworks

Question

Ha ving [...] is only a necessary condition for ev ery p oin t to ha ve a solution. It is not a suﬃcien t condition, b ecause it is p ossible for some of the columns to b e redundan t.

Answer

n m ≥

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Ha ving n m ≥ is only a necessary condition for ev ery p oin t to ha ve a solution. It is not a suﬃcien t condition, b ecause it is p ossible for some of the columns to b e redundan t.<

Tags

#deeplearning #neuralnetworks

Question

Ha ving n m ≥ is only a necessary condition for [...] It is not a suﬃcien t condition, b ecause it is p ossible for some of the columns to b e redundan t.

Answer

ev ery p oin t to ha ve a solution.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Ha ving n m ≥ is only a necessary condition for ev ery p oin t to ha ve a solution. It is not a suﬃcien t condition, b ecause it is p ossible for some of the columns to b e redundan t.

Tags

#deeplearning #neuralnetworks

Question

F ormally , this kind of redundancy is kno wn as [...] . A set of v ectors is linearly indep enden t if no v ector in the set is a linear combination of the other vectors.

Answer

linear dep endence

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

F ormally , this kind of redundancy is kno wn as linear dep endence . A set of v ectors is linearly indep enden t if no v ector in the set is a linear combination of the other vectors.

Tags

#deeplearning #neuralnetworks

Question

F ormally , this kind of redundancy is kno wn as linear dep endence . A set of v ectors is linearly indep enden t if [...]

Answer

no v ector in the set is a linear combination of the other vectors.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

F ormally , this kind of redundancy is kno wn as linear dep endence . A set of v ectors is linearly indep enden t if no v ector in the set is a linear combination of the other vectors.

Tags

#deeplearning #neuralnetworks

Question

the deriv ativ es of the [...] with resp ect to each element of x eac h dep end only on the corresp onding elemen t of x , while all of the deriv ativ es of the L 2 norm dep end on the en tire vector

Answer

squared L 2 norm

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

the deriv ativ es of the squared L 2 norm with resp ect to each element of x eac h dep end only on the corresp onding elemen t of x , while all of the deriv ativ es of the L 2 norm dep end on the en tire vector</b

Tags

#deeplearning #neuralnetworks

Question

the deriv ativ es of the squared L 2 norm with resp ect to each element of x eac h dep end only on [...] , while all of the deriv ativ es of the L 2 norm dep end on the en tire vector

Answer

the corresp onding elemen t of x

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

the deriv ativ es of the squared L 2 norm with resp ect to each element of x eac h dep end only on the corresp onding elemen t of x , while all of the deriv ativ es of the L 2 norm dep end on the en tire vector

Tags

#deeplearning #neuralnetworks

Question

the deriv ativ es of the squared L 2 norm with resp ect to each element of x eac h dep end only on the corresp onding elemen t of x , while all of the deriv ativ es of the L 2 norm dep end on [...]

Answer

the en tire vector

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

the deriv ativ es of the squared L 2 norm with resp ect to each element of x eac h dep end only on the corresp onding elemen t of x , while all of the deriv ativ es of the L 2 norm dep end on the en tire vector

Tags

#deeplearning #neuralnetworks

Question

The [...] is commonly used in machine learning when the diﬀerence b etw een zero and nonzero elements is v ery imp ortan t.

Answer

L 1 norm

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The L 1 norm is commonly used in machine learning when the diﬀerence b etw een zero and nonzero elements is v ery imp ortan t. Every time an element of x mo v es a w a y from 0 b y , the L 1 norm

Tags

#deeplearning #neuralnetworks

Question

The L 1 norm is commonly used in machine learning when [...] is v ery imp ortan t. Every time an element of x mo v es a w a y from 0 b y , the L 1 norm increases b y .

Answer

the diﬀerence b etw een zero and nonzero elements

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The L 1 norm is commonly used in machine learning when the diﬀerence b etw een zero and nonzero elements is v ery imp ortan t. Every time an element of x mo v es a w a y from 0 b y , the L 1 norm increases b y .

Tags

#deeplearning #neuralnetworks

Question

Diagonal matrices are of interest in part b ecause [...]

Answer

multiplying by a diagonal matrix is very computationally eﬃcien t.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Diagonal matrices are of interest in part b ecause multiplying by a diagonal matrix is very computationally eﬃcien t.

Tags

#deeplearning #neuralnetworks

Question

[...] matrices are of interest in part b ecause multiplying by a diagonal matrix is very computationally eﬃcien t.

Answer

Diagonal

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Diagonal matrices are of interest in part b ecause multiplying by a diagonal matrix is very computationally eﬃcien t.

Tags

#deeplearning #neuralnetworks

Question

T o compute [...] , we only need to scale each element x_{i} b y v_{i} . In other w ords, diag(v)x = \(x\cdot y\)

Answer

diag(v)x

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

T o compute diag(v)x , we only need to scale each element x i b y v i . In other w ords, diag(v)x = x⋅yx⋅y

Tags

#deeplearning #neuralnetworks

Question

T o compute diag(v)x , we only need to _{[...] . In other w ords, diag(v)x = \(x\cdot y\)}

Answer

scale each element x_{i} b y v_{i}

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

T o compute diag(v)x , we only need to scale each element x i b y v i . In other w ords, diag(v)x = x⋅yx⋅y

Tags

#narm

Question

There are the 3 "A" core expressions the are used to protect the attacment relationship, and lead to the 5 different survival styles, What are these 3 core expressions? (each start with the letter A)

Answer

Anger, Aggression, Authenticitcy

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Tags

#narm

Question

- When core needs are not met by children of rel trauma, what 4 negative experiences (3 start w/D and 1 w/ i) that the adaptive stratagies are trying to help deal with.
- What are the key parts of self does NARM empahsize to help develp a healthy regulatroy system?

Answer

- Disconnection, Dysregulation, Disorganisation and Issolation.
- Organized, Coherent, and Functional

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Tags

#narm

Question

What is the goal of the Pride Based Counter Indenfication?

Answer

Pride based counter identifications reflect how we would liek to see ourselves or others see us, the paroix is the more energy a person invest in these defensive the stronger the shame based id becomes.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Tags

#narm

Question

What are the 12 steps of the NARM healing cycle?

Answer

- INCREASING AWARENESS OF ADAPTIVE SURVIVAL SYTLES
- Inquiry into identiy
- Disidentification from shame and pride based identifications
- Self hatred self rejection and jugements diminished
- Reconnection with core needs and capacities
- Resoriaton of connection and aliveness
- INCREASING CAPCITY FOR SOMATIC AWARENESS
- Somatic Mindfulness
- Discharge of shoke states
- increasing regulation and presence
- Increasing contact with the body
- Greater capacity for self regulations

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Tags

#narm

Question

What is the difference between psychodynamic and esoteric approaches to ego?

What are the pros/cons to these different approaches?

What are the pros/cons to these different approaches?

Answer

Psychodynamic work to sokidify the sense of identify and strengthen the ego, where as esoteric orientations hold that egow is an illusion and separates us fro Being and keeps us from experiencing teh spaciousness, fulidity, and fullness of our essential nature.

Both perspectives are important, Esoteric approaches address the limitations of what they call Ego, but generally do not incorarate the clinical awareness of the importance of attachement adn developmental trauma in creation of the sense of self.

Brief answer: psychodynmic focus on Strengthen ego vs Esoteric focus on release ego. Psychodynamic supports impact on ego caused by rel trauma while esoteric supports learning how to better regulate and re-wire the adaptive stratagies developed by the truama.

Both perspectives are important, Esoteric approaches address the limitations of what they call Ego, but generally do not incorarate the clinical awareness of the importance of attachement adn developmental trauma in creation of the sense of self.

Brief answer: psychodynmic focus on Strengthen ego vs Esoteric focus on release ego. Psychodynamic supports impact on ego caused by rel trauma while esoteric supports learning how to better regulate and re-wire the adaptive stratagies developed by the truama.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Tags

#narm

Question

What is the paradox of change?

Answer

The more we try to change ourselves, the more we prevent change from ocurring, on the other hand, the more we allow ourselves to fully experience who we are, the greater the possibility of change.

Brief: More we resist the more we persist.

Brief: More we resist the more we persist.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Tags

#narm

Question

Name a core NARM principle that defines emotional health.

Answer

The capacity for connection with self and others defines emotional health.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Tags

#narm

Question

Elaborate on the 5 adaptive styles in terms of Survial Adaptions (disclousre) and Stratagy Used to PRotect the Attachement Relationship.

Answer

Core Need | Survival Adaption | Stratagey USed to Protect The Attach Rel |

Connection | Foreclousing Connection, Disconnect from body and social engagement | Children gife up their very sense of existence, discounnect and attempt to become invisible. |

Attunemet | Foreclosing the awareness and expression of personal needs | Chidrent give up thier own need in order to focus on the needs of others, particularly the needs of the parents |

Trust | Forclosing trust and healthy interdependence | Children give up their quthenticity in order to be who the parents want them to be: best friend, sport star, confidante, etc. |

Autonomy | Foreclosing authentic expression, responding with what they think is expected of them | Children give up direct expression of independence in oreder not to feel abandoned or crushed |

Love-Sexuality | Foreclosing love and heart connection, foreclosing sexuality, Forclosing integration of love and sexuality | Children try to avoid rejection by perfecting themselves, hoping that they can win love through looks or performance. |

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Tags

#narm

Question

What does each adaptive style develop around to help make sense of the broad spectrum of symptoms often seen in developmental trauam?

Answer

Connection: develops around teh need for the conact and the fear of connection

Attunement: Develops around the conflict between having personal needs and the rejection of them.

Trust: develops around both the desire for and the fear of setting limits and expressing independence.

Love-Sexuality: Develops around wanting to love and be loved and the fear of vulnrability. It also develops aroudn the splitting of love and sexuality.

Attunement: Develops around the conflict between having personal needs and the rejection of them.

Trust: develops around both the desire for and the fear of setting limits and expressing independence.

Love-Sexuality: Develops around wanting to love and be loved and the fear of vulnrability. It also develops aroudn the splitting of love and sexuality.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Tags

#deeplearning #neuralnetworks

Question

[...] matrices do not hav e inv erses but it is still p ossible to multiply by them cheaply

Answer

Non-square diagonal

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Non-square diagonal matrices do not hav e inv erses but it is still p ossible to multiply by them cheaply

Tags

#deeplearning #neuralnetworks

Question

Non-square diagonal matrices do not hav e [...] but it is still p ossible to multiply by them cheaply

Answer

inv erses

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Non-square diagonal matrices do not hav e inv erses but it is still p ossible to multiply by them cheaply

Tags

#deeplearning #neuralnetworks

Question

Non-square diagonal matrices do not hav e inv erses but it is still p ossible [...]

Answer

to multiply by them cheaply

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Non-square diagonal matrices do not hav e inv erses but it is still p ossible to multiply by them cheaply

Tags

#deeplearning #neuralnetworks

Question

A [...] is a vector with unit norm ||x||_{2} = 1

Answer

unit vector

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

A unit vector is a vector with unit norm ||x|| 2 = 1

Tags

#deeplearning #neuralnetworks

Question

A unit vector is a vector with unit norm _{[...]}

Answer

||x||_{2} = 1

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

A unit vector is a vector with unit norm ||x|| 2 = 1

Tags

#deeplearning #neuralnetworks

Question

A vector x and a vector y are [...] to each other if x^{T}y = 0 .

Answer

orthogonal

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

A vector x and a vector y are orthogonal to each other if x y = 0 .

Tags

#deeplearning #neuralnetworks

Question

A vector x and a vector y are orthogonal to each other if ^{[...]}

Answer

x^{T}y = 0 .

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

A vector x and a vector y are orthogonal to each other if x T y = 0 .

Tags

#deeplearning #neuralnetworks

Question

In R n , at most [...] v ectors ma y b e mutually orthogonal with nonzero norm. If the v ectors are not only orthogonal but also ha ve unit norm, we call them orthonormal

Answer

n

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

In R n , at most n v ectors ma y b e mutually orthogonal with nonzero norm. If the v ectors are not only orthogonal but also ha ve unit norm, we call them orthonormal

Tags

#deeplearning #neuralnetworks

Question

In R n , at most n v ectors ma y b e mutually orthogonal with nonzero norm. If the v ectors are not only orthogonal but also ha ve unit norm, we call them [...]

Answer

orthonormal

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

In R n , at most n v ectors ma y b e mutually orthogonal with nonzero norm. If the v ectors are not only orthogonal but also ha ve unit norm, we call them orthonormal

Tags

#deeplearning #neuralnetworks

Question

An [...] of a square matrix A is a non-zero vector v suc h that m ulti- plication b y A alters only the scale of v : Av = vλ

Answer

eigen v ector

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

An eigen v ector of a square matrix A is a non-zero vector v suc h that m ulti- plication b y alters only the scale of : A v Av v = λ

Tags

#deeplearning #neuralnetworks

Question

An eigenvector of a square matrix A is a [...]

Answer

non-zero vector v suc h that m ulti- plication b y A alters only the scale of v: Av = λv

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

An eigenvector of a square matrix A is a non-zero vector v suc h that m ulti- plication b y A alters only the scale of v: Av = λv

Tags

#deeplearning #neuralnetworks

Question

Av = vλ . (2.39) The scalar λ is kno wn as the [...] corresp onding to this eigenv ector. (One can also ﬁnd a left eigen v ector suc h that v A = λ v , but we are usually concerned with righ t eigenv ectors

Answer

eigen v alue

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Av v = λ . (2.39) The scalar λ is kno wn as the eigen v alue corresp onding to this eigenv ector. (One can also ﬁnd a left eigen v ector suc h that v A = λ v , but we are usually concerned with righ t eigenv ectors

Tags

#deeplearning #neuralnetworks

Question

Av v = λ . (2.39) The scalar [...] is kno wn as the eigen v alue corresp onding to this eigenv ector. (One can also ﬁnd a left eigen v ector suc h that v A = λ v , but we are usually concerned with righ t eigenv ectors

Answer

λ

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Av v = λ . (2.39) The scalar λ is kno wn as the eigen v alue corresp onding to this eigenv ector. (One can also ﬁnd a left eigen v ector suc h that v A = λ v , but we are usually concerned with righ t eigenv ecto

Tags

#deeplearning #neuralnetworks

Question

[...] matrices often arise when the entries are generated by some function of t w o argumen ts that do es not dep end on the order of the arguments

Answer

Symmetric

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Symmetric matrices often arise when the entries are generated by some function of t w o argumen ts that do es not dep end on the order of the arguments

Tags

#deeplearning #neuralnetworks

Question

Symmetric matrices often arise when [...]

Answer

the entries are generated by some function of t w o argumen ts that do es not dep end on the order of the arguments

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Symmetric matrices often arise when the entries are generated by some function of t w o argumen ts that do es not dep end on the order of the arguments

Tags

#deeplearning #neuralnetworks

Question

A matrix whose eigen v alues are all p ositive or zero-v alued is called [...]

Answer

p ositiv e semideﬁ- nite

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

A matrix whose eigen v alues are all p ositive or zero-v alued is called p ositiv e semideﬁ- nite

Tags

#deeplearning #neuralnetworks

Question

A matrix whose [...] is called p ositiv e semideﬁ- nite

Answer

eigen v alues are all p ositive or zero-v alued

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

A matrix whose eigen v alues are all p ositive or zero-v alued is called p ositiv e semideﬁ- nite

Tags

#deeplearning #neuralnetworks

Question

The [...] provides another wa y to factorize a matrix, into singular vectors and singular v alues

Answer

singular v alue decomp osition (SVD)

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The singular v alue decomp osition (SVD) provides another wa y to factorize a matrix, into singular vectors and singular v alues

Tags

#deeplearning #neuralnetworks

Question

The singular v alue decomp osition (SVD) provides another wa y to [...]

Answer

factorize a matrix, into singular vectors and singular v alues

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The singular v alue decomp osition (SVD) provides another wa y to factorize a matrix, into singular vectors and singular v alues

Tags

#deeplearning #neuralnetworks

Question

if a matrix is [...], the eigendecomp osition is not deﬁned, and w e m ust use a singular v alue decomp osition instead

Answer

not square

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

if a matrix is not square, the eigendecomp osition is not deﬁned, and w e m ust use a singular v alue decomp osition instead

Tags

#deeplearning #neuralnetworks

Question

if a matrix is not square, the eigendecomp osition is not deﬁned, and w e m ust use a [...] instead

Answer

singular v alue decomp osition

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

if a matrix is not square, the eigendecomp osition is not deﬁned, and w e m ust use a singular v alue decomp osition instead

Tags

#deeplearning #neuralnetworks

Question

if a matrix is not square, the [...] is not deﬁned, and w e m ust use a singular v alue decomp osition instead

Answer

eigendecomp osition

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

if a matrix is not square, the eigendecomp osition is not deﬁned, and w e m ust use a singular v alue decomp osition instead

Tags

#deeplearning #neuralnetworks

Question

The elemen ts along the diagonal of D are kno wn as the [...] of the matrix A . The columns of U are known as the left-singular v ectors . The columns of are kno wn as as the V right-singular v ectors

Answer

singular v alues

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The elemen ts along the diagonal of D are kno wn as the singular v alues of the matrix A . The columns of U are known as the left-singular v ectors . The columns of are kno wn as as the V right-singular v ectors

Tags

#deeplearning #neuralnetworks

Question

The [...] are kno wn as the singular v alues of the matrix A . The columns of U are known as the left-singular v ectors . The columns of are kno wn as as the V right-singular v ectors

Answer

elemen ts along the diagonal of D

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The elemen ts along the diagonal of D are kno wn as the singular v alues of the matrix A . The columns of U are known as the left-singular v ectors . The columns of are kno wn as as the V right-singular v ectors</spa

Tags

#deeplearning #neuralnetworks

Question

The elemen ts along the diagonal of D are kno wn as the singular v alues of the matrix A . The columns of U are known as the [...] . The columns of are kno wn as as the V right-singular v ectors

Answer

left-singular v ectors

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The elemen ts along the diagonal of D are kno wn as the singular v alues of the matrix A . The columns of U are known as the left-singular v ectors . The columns of are kno wn as as the V right-singular v ectors

Tags

#deeplearning #neuralnetworks

Question

The elemen ts along the diagonal of D are kno wn as the singular v alues of the matrix A . The [...] are known as the left-singular v ectors . The columns of are kno wn as as the V right-singular v ectors

Answer

columns of U

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The elemen ts along the diagonal of D are kno wn as the singular v alues of the matrix A . The columns of U are known as the left-singular v ectors . The columns of are kno wn as as the V right-singular v ectors

Tags

#deeplearning #neuralnetworks

Question

SVD of A = UVD^{T}

The elemen ts along the diagonal of D are kno wn as the singular v alues of the matrix A . The columns of U are known as the left-singular v ectors . The columns of V are kno wn as as the [...]

The elemen ts along the diagonal of D are kno wn as the singular v alues of the matrix A . The columns of U are known as the left-singular v ectors . The columns of V are kno wn as as the [...]

Answer

right-singular v ectors

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

><head>SVD of A = UVD T The elemen ts along the diagonal of D are kno wn as the singular v alues of the matrix A . The columns of U are known as the left-singular v ectors . The columns of V are kno wn as as the right-singular v ectors<html>

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Question

SVD of A = UVD^{T}

The elemen ts along the diagonal of D are kno wn as the singular v alues of the matrix A . The columns of U are known as the left-singular v ectors . The [...] are kno wn as as the right-singular v ectors

The elemen ts along the diagonal of D are kno wn as the singular v alues of the matrix A . The columns of U are known as the left-singular v ectors . The [...] are kno wn as as the right-singular v ectors

Answer

columns of V

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

SVD of A = UVD T The elemen ts along the diagonal of D are kno wn as the singular v alues of the matrix A . The columns of U are known as the left-singular v ectors . The columns of V are kno wn as as the right-singular v ectors

Tags

#deeplearning #neuralnetworks

Question

W e can actually interpret the singular v alue decomp osition of A in terms of the [...] . The left-singular v ectors of A are the eigen v ectors of AA . The righ t-singular v ectors of A are the eigen vectors of A A . The non-zero singular v alues of A are the square ro ots of the eigen v alues of A A . The same is true for AA

Answer

eigendecomposition of functions of A

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

W e can actually interpret the singular v alue decomp osition of A in terms of the eigendecomposition of functions of A . The left-singular v ectors of A are the eigen v ectors of AA . The righ t-singular v ectors of A are the eigen vectors of A A . The non-zero singular v alues of A are the square r

Tags

#deeplearning #neuralnetworks

Question

W e can actually interpret the singular v alue decomp osition of A in terms of the eigendecomposition of functions of A . The left-singular v ectors of A are the ^{[...]} . The righ t-singular v ectors of A are the eigen vectors of A^{T}A . The non-zero singular v alues of A are the square ro ots of the eigen v alues of A^{T}A . The same is true for AA^{T}

Answer

eigen v ectors of AA^{T}

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

W e can actually interpret the singular v alue decomp osition of A in terms of the eigendecomposition of functions of A . The left-singular v ectors of A are the eigen v ectors of AA T . The righ t-singular v ectors of A are the eigen vectors of A T A . The non-zero singular v alues of A are the square ro ots of the eigen v alues of A T A . The same is true for AA T<

Tags

#deeplearning #neuralnetworks

Question

W e can actually interpret the singular v alue decomp osition of A in terms of the eigendecomposition of functions of A . The [...] of A are the eigen v ectors of AA^{T} . The righ t-singular v ectors of A are the eigen vectors of A^{T}A . The non-zero singular v alues of A are the square ro ots of the eigen v alues of A^{T}A . The same is true for AA^{T}

Answer

left-singular v ectors

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

W e can actually interpret the singular v alue decomp osition of A in terms of the eigendecomposition of functions of A . The left-singular v ectors of A are the eigen v ectors of AA T . The righ t-singular v ectors of A are the eigen vectors of A T A . The non-zero singular v alues of A are the square ro ots of the eigen v alues o

Tags

#deeplearning #neuralnetworks

Question

W e can actually interpret the singular v alue decomp osition of A in terms of the eigendecomposition of functions of A . The left-singular v ectors of A are the eigen v ectors of AA^{T} . The [...] of A are the eigen vectors of A^{T}A . The non-zero singular v alues of A are the square ro ots of the eigen v alues of A^{T}A . The same is true for AA^{T}

Answer

righ t-singular v ectors

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

W e can actually interpret the singular v alue decomp osition of A in terms of the eigendecomposition of functions of A . The left-singular v ectors of A are the eigen v ectors of AA T . The righ t-singular v ectors of A are the eigen vectors of A T A . The non-zero singular v alues of A are the square ro ots of the eigen v alues of A T A . The same is true for AA T

Tags

#deeplearning #neuralnetworks

Question

W e can actually interpret the singular v alue decomp osition of A in terms of the eigendecomposition of functions of A . The left-singular v ectors of A are the eigen v ectors of AA^{T} . The righ t-singular v ectors of A are the eigen vectors of ^{[...] . The non-zero singular v alues of A are the square ro ots of the eigen v alues of ATA . The same is true for AAT}

Answer

A^{T}A

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

can actually interpret the singular v alue decomp osition of A in terms of the eigendecomposition of functions of A . The left-singular v ectors of A are the eigen v ectors of AA T . The righ t-singular v ectors of A are the eigen vectors of <span>A T A . The non-zero singular v alues of A are the square ro ots of the eigen v alues of A T A . The same is true for AA T<span><body><html>

Tags

#deeplearning #neuralnetworks

Question

W e can actually interpret the singular v alue decomp osition of A in terms of the eigendecomposition of functions of A . The left-singular v ectors of A are the eigen v ectors of AA^{T} . The righ t-singular v ectors of A are the eigen vectors of A^{T}A . The [...] of A are the square ro ots of the eigen v alues of A^{T}A . The same is true for AA^{T}

Answer

non-zero singular v alues

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

interpret the singular v alue decomp osition of A in terms of the eigendecomposition of functions of A . The left-singular v ectors of A are the eigen v ectors of AA T . The righ t-singular v ectors of A are the eigen vectors of A T A . The <span>non-zero singular v alues of A are the square ro ots of the eigen v alues of A T A . The same is true for AA T<span><body><html>

Tags

#deeplearning #neuralnetworks

Question

W e can actually interpret the singular v alue decomp osition of A in terms of the eigendecomposition of functions of A . The left-singular v ectors of A are the eigen v ectors of AA^{T} . The righ t-singular v ectors of A are the eigen vectors of A^{T}A . The non-zero singular v alues of A are the ^{[...] . The same is true for AAT}

Answer

square ro ots of the eigen v alues of A^{T}A

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

sition of A in terms of the eigendecomposition of functions of A . The left-singular v ectors of A are the eigen v ectors of AA T . The righ t-singular v ectors of A are the eigen vectors of A T A . The non-zero singular v alues of A are the <span>square ro ots of the eigen v alues of A T A . The same is true for AA T<span><body><html>

Tags

#matlab #programming

Question

The function [...] returns 1 if A is global, and 0 otherwise

Answer

isglobal(A)

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The function isglobal(A) returns 1 if A is global, and 0 otherwise

Tags

#matlab #programming

Question

A variable in a function may be declared persistent. Local variables nor- mally cease to exist when a function returns. Persistent variables, however, remain in existence between function calls. A persistent variable is initial- ized to the [...]

Answer

empty array

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

n>A variable in a function may be declared persistent. Local variables nor- mally cease to exist when a function returns. Persistent variables, however, remain in existence between function calls. A persistent variable is initial- ized to the <span>empty array<span><body><html>

Tags

#matlab #programming

Question

The function [...] inside an M-file p revents the M-file from being cleared. A locked M-file is unlocked with munlock. The function mislocked indi- cates whether an M-file can be cleared or not.

Answer

mlock

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The function mlock inside an M-file p revents the M-file from being cleared. A locked M-file is unlocked with munlock. The function mislocked indi- cates whether an M-file can be cleared or not.</s

Tags

#matlab #programming

Question

The function mlock inside an M-file [...]. A locked M-file is unlocked with munlock. The function mislocked indi- cates whether an M-file can be cleared or not.

Answer

p revents the M-file from being cleared

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The function mlock inside an M-file p revents the M-file from being cleared. A locked M-file is unlocked with munlock. The function mislocked indi- cates whether an M-file can be cleared or not.

Tags

#matlab #programming

Question

The function mlock inside an M-file p revents the M-file from being cleared. A locked M-file is unlocked with [...]. The function mislocked indi- cates whether an M-file can be cleared or not.

Answer

munlock

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The function mlock inside an M-file p revents the M-file from being cleared. A locked M-file is unlocked with munlock. The function mislocked indi- cates whether an M-file can be cleared or not.

Tags

#matlab #programming

Question

The function mlock inside an M-file p revents the M-file from being cleared. A locked M-file is unlocked with munlock. The function [...] indi- cates whether an M-file can be cleared or not.

Answer

mislocked

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The function mlock inside an M-file p revents the M-file from being cleared. A locked M-file is unlocked with munlock. The function mislocked indi- cates whether an M-file can be cleared or not.

Tags

#matlab #programming

Question

ou might want to write a function that doesn’t return values (such func- tions are called [...] in languages like Pascal and Fortran, and void in C++ and Java

Answer

procedures or subroutines

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

ou might want to write a function that doesn’t return values (such func- tions are called procedures or subroutines in languages like Pascal and Fortran, and void in C++ and Java

Tags

#matlab #programming

Question

ou might want to write a function that doesn’t return values (such func- tions are called procedures or subroutines in languages like Pascal and Fortran, and [...] in C++ and Java

Answer

void

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

ou might want to write a function that doesn’t return values (such func- tions are called procedures or subroutines in languages like Pascal and Fortran, and void in C++ and Java

Tags

#matlab #programming

Question

the functions [...] can be used to determine the number of actual input and output arguments.

Answer

nargin and nargout

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

the functions nargin and nargout can be used to determine the number of actual input and output arguments.

Tags

#matlab #programming

Question

the functions nargin and nargout can be used to [...]

Answer

determine the number of actual input and output arguments.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

the functions nargin and nargout can be used to determine the number of actual input and output arguments.

Tags

#matlab #programming

Question

A function M-file may contain the code for more than one function. The first function in a file is the primary function, and is the one invoked with the M-file name. Additional functions in the file are called [...], and are visible only to the primary function and to other subfunctions.

Answer

subfunctions

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

l>A function M-file may contain the code for more than one function. The first function in a file is the primary function, and is the one invoked with the M-file name. Additional functions in the file are called subfunctions, and are visible only to the primary function and to other subfunctions.<html>

Tags

#matlab #programming

Question

A function M-file may contain the code for more than one function. The first function in a file is the primary function, and is the one invoked with the M-file name. Additional functions in the file are called subfunctions, and are visible only to [...]

Answer

the primary function and to other subfunctions.

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

ction M-file may contain the code for more than one function. The first function in a file is the primary function, and is the one invoked with the M-file name. Additional functions in the file are called subfunctions, and are visible only to <span>the primary function and to other subfunctions.<span><body><html>

Tags

#matlab #programming

Question

You can use the [...] function to save the parsed version of an M-file for use in later MATLAB sessions, or by users from whom you want to hide your algorithms.

Answer

pcode

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

You can use the pcode function to save the parsed version of an M-file for use in later MATLAB sessions, or by users from whom you want to hide your algorithms.

Tags

#matlab #programming

Question

You can use the pcode function to [...] for use in later MATLAB sessions, or by users from whom you want to hide your algorithms.

Answer

save the parsed version of an M-file

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

You can use the pcode function to save the parsed version of an M-file for use in later MATLAB sessions, or by users from whom you want to hide your algorithms.

Tags

#matlab #programming

Question

The [...] enables you to see where the bottlenecks in your pro- grams are, e.g., which functions are consuming most of the time. With this information you can often redesign programs to be more efficient. To find out more about this utility open the MATLAB Help documentation via the ‘?’ at the top of the desktop and type “Profiler” in the search space. You should be at the document titled “The Profiler Utility

Answer

MATLAB Profiler

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

The MATLAB Profiler enables you to see where the bottlenecks in your pro- grams are, e.g., which functions are consuming most of the time. With this information you can often redesign programs to be more e

Tags

#bayes #programming #r #statistics

Question

Because there are two nominal values, we refer to this sort of data as “[...],” or “nominal with two levels,” or “binomial.”

Answer

dichotomous

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

Because there are two nominal values, we refer to this sort of data as “dichotomous,” or “nominal with two levels,” or “binomial.”

Tags

#bayes #programming #r #statistics

Question

Because there are two nominal values, we refer to this sort of data as “dichotomous,” or “[...],” or “binomial.”

Answer

nominal with two levels

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Because there are two nominal values, we refer to this sort of data as “dichotomous,” or “nominal with two levels,” or “binomial.”

Tags

#bayes #programming #r #statistics

Question

Because there are two nominal values, we refer to this sort of data as “dichotomous,” or “nominal with two levels,” or “[...].”

Answer

binomial

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scheduled repetition interval | last repetition or drill |

Because there are two nominal values, we refer to this sort of data as “dichotomous,” or “nominal with two levels,” or “binomial.”

Tags

#bayes #programming #r #statistics

Question

Because [...], we refer to this sort of data as “dichotomous,” or “nominal with two levels,” or “binomial.”

Answer

there are two nominal values

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scheduled repetition interval | last repetition or drill |

Because there are two nominal values, we refer to this sort of data as “dichotomous,” or “nominal with two levels,” or “binomial.”

Tags

#bayes #programming #r #statistics

Question

The [...] function, although it specifies a probability at each value of θ,isnot a probability distribution. In particular, it does not integrate to 1

Answer

likelihood

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The likelihood function, although it specifies a probability at each value of θ,isnot a probability distribution. In particular, it does not integrate to 1

Tags

#bayes #programming #r #statistics

Question

In other words, the normalizer for the beta distribution is the [equation]

Answer

beta function \(B(a,b) = \int d\theta \space \theta^{a-1}(1-\theta)^{b-1}\)

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In other words, the normalizer for the beta distribution is the beta function \(B(a,b) = \int d\theta \space \theta^{a-1}(1-\theta)^{b-1}\)

Tags

#bayes #programming #r #statistics

Question

In R, beta(θ|a, b) is [...],and B(a, b) is beta(a,b)

Answer

dbeta(θ,a,b)

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In R, beta(θ|a, b) is dbeta(θ,a,b),and B(a, b) is beta(a,b)

Tags

#bayes #programming #r #statistics

Question

In R, beta(θ|a, b) is dbeta(θ,a,b),and B(a, b) is [...]

Answer

beta(a,b)

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In R, beta(θ|a, b) is dbeta(θ,a,b),and B(a, b) is beta(a,b)

Tags

#bayes #programming #r #statistics

Question

Our goal is to convert a prior belief expressed in terms of central tendency and sample size into [...]

Answer

equivalent values of a and b in the beta distribution

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Our goal is to convert a prior belief expressed in terms of central tendency and sample size into equivalent values of a and b in the beta distribution

Tags

#bayes #programming #r #statistics

Question

It turns out that the mean of the beta(θ|a, b) distribution is [...] and the mode is ω = (a − 1)/(a + b − 2) for a > 1and b > 1(μ is Greek letter mu and ω is Greek letter omega)

Answer

μ = a/(a + b)

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

It turns out that the mean of the beta(θ|a, b) distribution is μ = a/(a + b) and the mode is ω = (a − 1)/(a + b − 2) for a > 1and b > 1(μ is Greek letter mu and ω is Greek letter omega)

Tags

#bayes #programming #r #statistics

Question

It turns out that the mean of the beta(θ|a, b) distribution is μ = a/(a + b) and the mode is [...] for a > 1and b > 1(μ is Greek letter mu and ω is Greek letter omega)

Answer

ω = (a − 1)/(a + b − 2)

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

It turns out that the mean of the beta(θ|a, b) distribution is μ = a/(a + b) and the mode is ω = (a − 1)/(a + b − 2) for a > 1and b > 1(μ is Greek letter mu and ω is Greek letter omega)

Tags

#bayes #programming #r #statistics

Question

The spread of the beta distribution is related to the “concentration” [...]

Answer

κ = a +b (κ is Greek letter kappa)

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The spread of the beta distribution is related to the “concentration” κ = a +b (κ is Greek letter kappa)

Tags

#bayes #programming #r #statistics

Question

a beta(θ|12, 12) distribution has a standard deviation of [...]

Answer

0.1

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a beta(θ|12, 12) distribution has a standard deviation of 0.1

Tags

#bayes #programming #r #statistics

Question

a [...] distribution has a standard deviation of 0.1

Answer

beta(θ|12, 12)

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a beta(θ|12, 12) distribution has a standard deviation of 0.1

Tags

#bayes #programming #r #statistics

Question

The standard deviation of the beta distribution is √ μ(1 − μ)/(a + b +1). Notice that the standard deviation gets smaller [...]

Answer

when the concentration κ = a + b gets larger.

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The standard deviation of the beta distribution is √ μ(1 − μ)/(a + b +1). Notice that the standard deviation gets smaller when the concentration κ = a + b gets larger.

Tags

#bayes #programming #r #statistics

Question

The standard deviation of the beta distribution is **[...]** . Notice that the standard deviation gets smaller when the concentration κ = a + b gets larger.

Answer

\(\sqrt{μ(1 − μ)/(a + b +1)}\)

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The standard deviation of the beta distribution is μ(1−μ)/(a+b+1)−−−−−−−−−−−−−−−−√μ(1−μ)/(a+b+1). Notice that the standard deviation gets smaller when the concentration κ = a + b gets larger.

Tags

#bayes #programming #r #statistics

Question

If the prior distribution is beta(θ|a, b), and the data have z heads in N flips, then the posterior distribution is [...]

Answer

beta(θ|z + a, N − z + b)

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If the prior distribution is beta(θ|a, b), and the data have z heads in N flips, then the posterior distribution is beta(θ|z + a, N − z + b)

Tags

#bayes #programming #r #statistics

Question

If the initial prior is a beta distribution, then the posterior distribution is always a [...]

Answer

beta distribution

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If the initial prior is a beta distribution, then the posterior distribution is always a beta distribution

Tags

#biochem #biology #cell

Question

In the simplest case, two identical folded polypeptide chains bind to each other in a “[...]” arrangement, forming a symmetric complex of two protein subunits (a dimer) held together by interactions between two identical binding sites.

Answer

head-to-head

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In the simplest case, two identical folded polypeptide chains bind to each other in a “head-to-head” arrangement, forming a symmetric complex of two protein subunits (a dimer) held together by interactions between two identical binding sites.

Tags

#biochem #biology #cell

Question

Why is a helix such a common structure in biology? [...]

Answer

As we have seen, biological structures are often formed by linking similar subunits into long, repetitive chains. If all the subunits are identical, the neighboring subunits in the chain can often fit together in only one way, adjusting their relative positions to minimize the free energy of the con- tact between them. As a result, each subunit is positioned in exactly the same way in relation to the next, so that subunit 3 fits onto subunit 2 in the same way that subunit 2 fits onto subunit 1, and so on. Because it is very rare for subunits to join up in a straight line, this arrangement generally results in a helix

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Why is a helix such a common structure in biology? As we have seen, biological structures are often formed by linking similar subunits into long, repetitive chains. If all the subunits are identical, the neighboring subunits in the chain can often fit together in only one way, adjusting their relative positions to minimize the free energy of the con- tact between them. As a result, each subunit is positioned in exactly the same way in relation to the next, so that subunit 3 fits onto subunit 2 in the same way that subunit 2 fits onto subunit 1, and so on. Because it is very rare for subunits to join up in a straight line, this arrangement generally results in a helix

Tags

#biochem #biology #cell

Question

Handedness is [...] by turning the helix upside down, but it is reversed if the helix is reflected in the mirror

Answer

not affected

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Handedness is not affected by turning the helix upside down, but it is reversed if the helix is reflected in the mirror

Tags

#biochem #biology #cell

Question

Collagen is a [...] formed by three extended protein chains that wrap around one another (bottom). Many rodlike collagen molecules are cross-linked together in the extracellular space to form unextendable collagen fibrils (top) that have the tensile strength of steel. The striping on the collagen fibril is caused by the regular repeating arrangement of the collagen molecules within the fibril

Answer

triple helix

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Collagen is a triple helix formed by three extended protein chains that wrap around one another (bottom). Many rodlike collagen molecules are cross-linked together in the extracellular space to form unextendable

Tags

#biochem #biology #cell

Question

Collagen is a triple helix formed by three extended protein chains that wrap around one another (bottom). Many rodlike collagen molecules are cross-linked together in the extracellular space to form [...] (top) that have the tensile strength of steel. The striping on the collagen fibril is caused by the regular repeating arrangement of the collagen molecules within the fibril

Answer

unextendable collagen fibrils

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scheduled repetition interval | last repetition or drill |

Collagen is a triple helix formed by three extended protein chains that wrap around one another (bottom). Many rodlike collagen molecules are cross-linked together in the extracellular space to form unextendable collagen fibrils (top) that have the tensile strength of steel. The striping on the collagen fibril is caused by the regular repeating arrangement of the collagen molecules within the fibril</sp

Tags

#biochem #biology #cell

Question

Collagen is a triple helix formed by three extended protein chains that wrap around one another (bottom). Many rodlike collagen molecules are cross-linked together in the extracellular space to form unextendable collagen fibrils (top) that have the tensile strength of [...]. The striping on the collagen fibril is caused by the regular repeating arrangement of the collagen molecules within the fibril

Answer

steel

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ormed by three extended protein chains that wrap around one another (bottom). Many rodlike collagen molecules are cross-linked together in the extracellular space to form unextendable collagen fibrils (top) that have the tensile strength of <span>steel. The striping on the collagen fibril is caused by the regular repeating arrangement of the collagen molecules within the fibril<span><body><html>

Tags

#biochem #biology #cell

Question

Collagen is a triple helix formed by three extended protein chains that wrap around one another (bottom). Many rodlike collagen molecules are cross-linked together in the extracellular space to form unextendable collagen fibrils (top) that have the tensile strength of steel. The striping on the collagen fibril is caused by the [...]

Answer

regular repeating arrangement of the collagen molecules within the fibril

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

another (bottom). Many rodlike collagen molecules are cross-linked together in the extracellular space to form unextendable collagen fibrils (top) that have the tensile strength of steel. The striping on the collagen fibril is caused by the <span>regular repeating arrangement of the collagen molecules within the fibril<span><body><html>

Tags

#biochem #biology #cell

Question

Elastin polypeptide chains are cross-linked together in the extracellular space to form [...]. Each elastin molecule uncoils into a more extended conformation when the fiber is stretched and recoils spontaneously as soon as the stretching force is relaxed. The cross-linking in the extracellular space mentioned creates covalent linkages between lysine side chains, but the chemistry is different for collagen and elastin.

Answer

rubberlike, elastic fibers

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Elastin polypeptide chains are cross-linked together in the extracellular space to form rubberlike, elastic fibers. Each elastin molecule uncoils into a more extended conformation when the fiber is stretched and recoils spontaneously as soon as the stretching force is relaxed. The cross-linking in

Tags

#biochem #biology #cell

Question

Elastin polypeptide chains are cross-linked together in the extracellular space to form rubberlike, elastic fibers. Each elastin molecule uncoils into a more extended conformation when the fiber is stretched and recoils spontaneously as soon as the stretching force is relaxed. The cross-linking in the extracellular space mentioned creates [...], but the chemistry is different for collagen and elastin.

Answer

covalent linkages between lysine side chains

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elastic fibers. Each elastin molecule uncoils into a more extended conformation when the fiber is stretched and recoils spontaneously as soon as the stretching force is relaxed. The cross-linking in the extracellular space mentioned creates <span>covalent linkages between lysine side chains, but the chemistry is different for collagen and elastin.<span><body><html>

Tags

#biochem #biology #cell

Question

Many proteins were also known to have intrinsically disordered tails at one or the other end of a structured domain (see, for example, the histones in Figure 4–24). But the extent of such disordered structure only became clear when genomes were sequenced. This allowed bio- informatic methods to be used to analyze the amino acid sequences that genes encode, searching for disordered regions based on their unusually [...]

Answer

low hydropho- bicity and relatively high net charge.

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ent of such disordered structure only became clear when genomes were sequenced. This allowed bio- informatic methods to be used to analyze the amino acid sequences that genes encode, searching for disordered regions based on their unusually <span>low hydropho- bicity and relatively high net charge.<span><body><html>

Tags

#biochem #biology #cell

Question

Many proteins were also known to have [...] at one or the other end of a structured domain (see, for example, the histones in Figure 4–24). But the extent of such disordered structure only became clear when genomes were sequenced. This allowed bio- informatic methods to be used to analyze the amino acid sequences that genes encode, searching for disordered regions based on their unusually low hydropho- bicity and relatively high net charge.

Answer

intrinsically disordered tails

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Many proteins were also known to have intrinsically disordered tails at one or the other end of a structured domain (see, for example, the histones in Figure 4–24). But the extent of such disordered structure only became clear when genomes were sequenc