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Flashcard 1425555721484

Tags

#cfa-level #economics #microeconomics #reading-13-demand-and-supply-analysis-introduction #study-session-4

Question

Reading 13 explains the concepts and tools of demand and supply analysis—the study of [...] to determine transaction prices and quantities.

Answer

how buyers and sellers interact

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Reading 13 explains the concepts and tools of demand and supply analysis—the study of how buyers and sellers interact to determine transaction prices and quantities.

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Study Session 4
This study session focuses on the microeconomic principles used to describe the marketplace behavior of consumers and firms. Reading 13 explains the concepts and tools of demand and supply analysis—the study of how buyers and sellers interact to determine transaction prices and quantities. Reading 14 covers the theory of the consumer, which addresses the demand for goods and services by individuals who make decisions to maximize the satisfaction they receive fr

Flashcard 1429089422604

Tags

#sister-miriam-joseph #trivium

Question

A learner must use [...] to join the facts together to form a significant whole. This makes learning easier, more interesting, and much more valuable.

Answer

mental hooks and eyes

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A learner must use mental hooks and eyes to join the facts together to form a significant whole. This makes learning easier, more interesting, and much more valuable.

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Flashcard 1429120093452

Tags

#sister-miriam-joseph #trivium

Question

[...] is right reason about something to be done

Answer

Prudence

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Prudence is right reason about something to be done

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Flashcard 1430795193612

Tags

#cfa #cfa-level-1 #economics #microeconomics #reading-14-demand-and-supply-analysis-consumer-demand #section-3-utility-theory #study-session-4

Question

A distinctly different consumption bundle would contain all of the same goods but [...], again allowing for the possibility of a [...] of one or more of the goods.

Answer

in different quantities

zero quantity

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A distinctly different consumption bundle would contain all of the same goods but in different quantities, again allowing for the possibility of a zero quantity of one or more of the goods.

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3. UTILITY THEORY: MODELING PREFERENCES AND TASTES
mer might enjoy consuming. Each of those goods and services can be represented in a given basket by a non-negative quantity, respectively, of all the possible goods and services. Any given basket could have zero of one or more of those goods. A distinctly different consumption bundle would contain all of the same goods but in different quantities, again allowing for the possibility of a zero quantity of one or more of the goods. For example, bundle Amight have the same amount of all but one of the goods and services as bundle B but a different amount of that one. Bundles A and B would be considered two distinct

Flashcard 1432405019916

Tags

#conjunction #italian #italian-grammar

Question

i giudici dicono che bisogna cambiare la legge

Answer

the judges say that the law should be changed

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uses within a sentence: Marco e Davide ‘Marco and Davide’; con amore ma con disciplina ‘with love but with discipline’; sono andata a letto perché ero stanca ‘I went to bed because I was tired’; i giudici dicono che bisogna cambiare la legge ‘the judges say that the law should be changed’. Conjunctions can either be coordinating, linking two phrases or clauses of equal weight, or subordinating, linking a main clause and subordinate clause.<body><html>

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Flashcard 1432441195788

Tags

#sister-miriam-joseph #trivium

Question

Humans are [...] and therefore have someone to say things to.

Answer

social

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humans alone among animals have the power to think. Consequently, they alone have language in the proper sense of the word. 1 This follows from their nature, for they are rational and therefore have something to say, social and therefore have someone to say it to, and animal and therefore require a physical mode of communicating ideas from one mind, which

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Flashcard 1432851451148

Tags

#i-q #types-of-inteligence

Question

the [...] is the only area of the brain where an increase in blood flow takes place when volunteers tackle complicated puzzles.

Answer

lateral prefrontal cortex

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the lateral prefrontal cortex istheonlyarea of the brain where an increase in blood flow takes place when volunteers tackle complicated puzzles.

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Flashcard 1442133183756

Tags

#build-in-sleep #six-tips-for-working-with-the-brain

Question

This blended approach [...] allows me to create three retrievals spaced with sleep, and it also starts to build the habits of the behaviors I am trying to cultivate.

Answer

(Pre-learn, teaching -or getting tought- and TED talks and stuff afterwards)

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This blended approach (Pre-learn, teaching -or getting tought- and TED talks and stuff afterwards) allows me to create three retrievals spaced with sleep, and it also starts to build the habits of the behaviors I am trying to cultivate.

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benefit seems to be better at three, so I focus on that number of retrievals in my own learning design. You can certainly build three retrievals into one learning event, but retention will be even more powerful if you add sleep to the mix. Tip #5: Build in sleep between learning It turns out that the sleeping brain plays a large role in how long-term memories are formed. While we sleep, the brain pushes information that we learned that day from our short-term memory into our long-term memory. It's when we sleep that our brain adds the day's learning onto existing schemas, and physically builds and strengthens neural pathways. It also does a little housecleaning. Every day, we take in thousands of bits of information and it is during sleep that our brain chooses which of those bits is worthy of being retained. It even revisits items already in long-term memory and deletes the information that has not been activated in a while. The animated movie Inside Out does a great job of depicting this process. While Riley is sleeping, the minion-like workers in her brain decide to vacuum out most of the names of the U.S. presidents. So how can we use sleep to enhance our learning events? Flip the classroom and use blended learning. I now have learners do some pre-learning a few days prior, then we take a deeper dive in the classroom through hands-on application. I extend their learning with post-event opportunities and resources. For example, when I design leadership training, learners are asked to watch a corresponding online course at lynda.com. They can do this at their own pace and it frees me up from teaching some of that content so that I can use our in-person time for more focused work. When we come together, we do in-depth hands-on practice of the skills I want them to use. And after the event, I provide them with additional learning materials such as links to TED Talks, articles, and assignments to further hone their skills. This blended approach allows me to create three retrievals spaced with sleep, and it also starts to build the habits of the behaviors I am trying to cultivate. Tip #6: Be a habit designer Ultimately, the goal of most learning activities is behavior change. No matter the topic, we are trying to elicit new and better behaviors in the learner

Flashcard 1450669116684

Tags

#cfa #cfa-level-1 #economics #reading-15-demand-and-supply-analysis-the-firm #section-3-analysis-of-revenue-costs-and-profit

Question

the minimum point on the AVC does not correspond to the [...] for [...]

Answer

least-cost quantity

average total cost.

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the minimum point on the AVC does not correspond to the least-cost quantity for average total cost.

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Average variable cost (AVC) is derived by dividing total variable cost by quantity. For example, average variable cost at 5 units is (300 ÷ 5) or 60. Over an initial range of production, average variable cost declines and then reaches a minimum point. Thereafter, cost increases as the firm utilizes more of its production capacity. This higher cost results primarily from production constraints imposed by the fixed assets at higher volume levels. The minimum point on the AVC coincides with the lowest average variable cost. However, the minimum point on the AVC does not correspond to the least-cost quantity for average total cost. In Exhibit 13, average variable cost is minimized at 2 units, whereas average total cost is the lowest at 3 units. Average total cost (ATC) is calculated by dividing total costs by quantity or by summing average fixed cost and average variable cost. For instance, in Exhibit 13, at 8 un

Flashcard 1464622255372

Tags

#art-of-memory #linking-methods

Question

Base object : Besides it being an "object", it may also be

[...] If it is an item from your Peg System, then we call the base it peg.

In method of loci we call it locus (base has a position with respect to other objects).

Answer

a person, or something general like room, wall, ground, etc.

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Base object : Besides it being an "object", it may also be a person, or something general like room, wall, ground, etc. If it is an item from your Peg System, then we call t he base it peg. In method of loci we call it locus (base has a position with respect to other objects

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r30's mnemonics
on? 3.5 3.5 Some exceptions 3.6 Inside vs next-to placement 4 4. Interaction Method 5 5. Link Systems 5.1 5.1 Mnemonic Link System 5.1.1 5.1.1 IM- or TM-based chain? 5.2 5.2 Pinned Link System 6 6. When to use which linking technique? 1. Used terminology „Base object“ or just „base“ – real object (or just image) that we already know. In linking techniques it is the first object in your link.Besides it being an "object", it may also be a person, or something general like room, wall, ground, etc. If it is an item from your Peg System, then we call it peg. In method of loci we call it locus (base has a position with respect to other objects). „Data object“ or just „dobe“– object that we want to memorize. It may be visual or nonvisual, e.g an abstract concept. If it is nonvisual then it is useful to first create a mnemonic image for the dobe (see How to Create Mnemonic Images). In MLS (and PLS) a random middle dobe of the chain is also base (you use it to recall the next dobe). Dobe is linked to base (an association made between them). Later you can use that base to recall the dobe. I took the liberty to classify different types of mnemonic linking techniques. The following two methods cover all the ways (that I can think of) to link dobe to base : Transformation m

Flashcard 1477234527500

Tags

#matlab #programming

Question

The expression y>0returns a logical vector with 1s where sin(x) is positive, and 0s otherwise. Element-by-element multiplication by y with [operator] then picks out the positive elements of y

Answer

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The expression y>0returns a logical vector with 1s where sin(x) is positive, and 0s otherwise. Element-by-element multiplication by y with .* then picks out the positive elements of y

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Flashcard 1479147916556

Tags

#daniel-goleman #emotional-brain #emotional-iq #how-the-brain-grew #what-are-emotions-for #when-passions-overwhelm-reasons

Question

The most ancient root of our emotional life is in the sense [...]

Answer

of smell

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The most ancient root of our emotional life is in the sense of smell

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Flashcard 1480551435532

Tags

#bayes #programming #r #statistics

Question

A value represents the central tendency of the distribution if [close to where?]

Answer

the value is close to the highly probable values of the distribution

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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

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Flashcard 1484186586380

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

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Flashcard 1484269686028

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

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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

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Flashcard 1484322114828

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

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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

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Flashcard 1484326046988

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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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

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Flashcard 1484540742924

Tags

#biochem #biology #cell

Question

These principles are dramatically illustrated in the protein coat or capsid of many simple viruses, which takes the form of a [...]

Answer

hollow sphere based on an icosahedron

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These principles are dramatically illustrated in the protein coat or capsid of many simple viruses, which takes the form of a hollow sphere based on an icosahedron

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Flashcard 1484542315788

Tags

#biochem #biology #cell

Question

The first large macromolecular aggregate shown to be capable of self-as- sembly from its component parts was [...]

Answer

tobacco mosaic virus (TMV ).

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The first large macromolecular aggregate shown to be capable of self-as- sembly from its component parts was tobacco mosaic virus (TMV ).

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Flashcard 1484543888652

Tags

#biochem #biology #cell

Question

the simplest case, a long core protein or other macromolecule provides a scaffold that determines the extent of the final assembly. This is the mechanism that deter- mines the length of the [...] particle, where the RNA chain provides the core. Similarly, a core protein interacting with actin is thought to determine the length of the thin filaments in muscle.

Answer

TMV

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the simplest case, a long core protein or other macromolecule provides a scaffold that determines the extent of the final assembly. This is the mechanism that deter- mines the length of the TMV particle, where the RNA chain provides the core. Similarly, a core protein interacting with actin is thought to determine the length of the thin filaments in muscle.</bo

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Flashcard 1484545461516

Tags

#biochem #biology #cell

Question

the simplest case, a long core protein or other macromolecule provides a scaffold that determines the extent of the final assembly. This is the mechanism that deter- mines the length of the TMV particle, where the RNA chain provides the core. Similarly, a core protein interacting with [...] is thought to determine the length of the thin filaments in muscle.

Answer

actin

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er macromolecule provides a scaffold that determines the extent of the final assembly. This is the mechanism that deter- mines the length of the TMV particle, where the RNA chain provides the core. Similarly, a core protein interacting with actin is thought to determine the length of the thin filaments in muscle.<body><html>

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Flashcard 1484547034380

Tags

#biochem #biology #cell

Question

Typically, hundreds of monomers will aggregate to form an unbranched fibrous structure that is several micrometers long and [...] in width

Answer

5 to 15 nm

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Typically, hundreds of monomers will aggregate to form an unbranched fibrous structure that is several micrometers long and 5 to 15 nm in width

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Flashcard 1484548607244

Tags

#biochem #biology #cell

Question

Typically, hundreds of monomers will aggregate to form an unbranched fibrous structure that is [...] long and 5 to 15 nm in width

Answer

several micrometers

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Typically, hundreds of monomers will aggregate to form an unbranched fibrous structure that is several micrometers long and 5 to 15 nm in width

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Flashcard 1484550180108

Tags

#biochem #biology #cell

Question

A surprisingly large fraction of pro- teins have the potential to form [...] structures, because the short segment of the polypeptide chain that forms the spine of the fibril can have a variety of different sequences and follow one of several different paths (Figure 3–32). However, very few proteins will actually form this structure inside cells

Answer

amyloid fibril

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A surprisingly large fraction of pro- teins have the potential to form such structures, because the short segment of the polypeptide chain that forms the spine of the fibril can have a variety of different sequences and follow one of several different paths (

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Flashcard 1484551752972

Tags

#biochem #biology #cell

Question

A surprisingly large fraction of pro- teins have the potential to form Amyloid fibril structures, because [...] (Figure 3–32). However, very few proteins will actually form this structure inside cells

Answer

the short segment of the polypeptide chain that forms the spine of the fibril can have a variety of different sequences and follow one of several different paths

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Flashcard 1484554112268

Tags

#biochem #biology #cell

Question

A set of closely related diseases—[...] in sheep, Creutzfeldt–Jakob disease (CJD) in humans, Kuru in humans, and bovine spongiform encephalopathy (BSE) in cattle—are caused by a misfolded, aggregated form of a particular protein called PrP

Answer

scra- pie

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A set of closely related diseases—scra- pie in sheep, Creutzfeldt–Jakob disease (CJD) in humans, Kuru in humans, and bovine spongiform encephalopathy (BSE) in cattle—are caused by a misfolded, aggregated form of a particular pr

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Flashcard 1484555685132

Tags

#biochem #biology #cell

Question

A set of closely related diseases—scra- pie in sheep, [...] disease (CJD) in humans, Kuru in humans, and bovine spongiform encephalopathy (BSE) in cattle—are caused by a misfolded, aggregated form of a particular protein called PrP

Answer

Creutzfeldt–Jakob

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Flashcard 1484557257996

Tags

#biochem #biology #cell

Question

A set of closely related diseases—scra- pie in sheep, Creutzfeldt–Jakob disease (CJD) in humans, [...] in humans, and bovine spongiform encephalopathy (BSE) in cattle—are caused by a misfolded, aggregated form of a particular protein called PrP

Answer

Kuru

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Flashcard 1484558830860

Tags

#biochem #biology #cell

Question

A set of closely related diseases—scra- pie in sheep, Creutzfeldt–Jakob disease (CJD) in humans, Kuru in humans, and [...] (BSE) in cattle—are caused by a misfolded, aggregated form of a particular protein called PrP

Answer

bovine spongiform encephalopathy

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Flashcard 1484560403724

Tags

#biochem #biology #cell

Question

A set of closely related diseases—scra- pie in sheep, Creutzfeldt–Jakob disease (CJD) in humans, Kuru in humans, and bovine spongiform encephalopathy (BSE) in cattle—are caused by a [...]

Answer

misfolded, aggregated form of a particular protein called PrP

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Flashcard 1484561976588

Tags

#biochem #biology #cell

Question

Eukaryotic cells, for example, store many different peptide and protein hormones that they will secrete in specialized “secretory granules,” which package a high concentra- tion of their cargo in dense cores with a regular structure (see Figure 13–65). We now know that these structured cores consist of [...], which in this case have a structure that causes them to dissolve to release soluble cargo after being secreted by exocytosis to the cell exterior

Answer

amyloid fibrils

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protein hormones that they will secrete in specialized “secretory granules,” which package a high concentra- tion of their cargo in dense cores with a regular structure (see Figure 13–65). We now know that these structured cores consist of amyloid fibrils, which in this case have a structure that causes them to dissolve to release soluble cargo after being secreted by exocytosis to the cell exterior<body><html>

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Flashcard 1484563549452

Tags

#biochem #biology #cell

Question

Eukaryotic cells, for example, store many different peptide and protein hormones that they will secrete in specialized “secretory granules,” which package a high concentra- tion of their cargo in dense cores with a regular structure (see Figure 13–65). We now know that these structured cores consist of amyloid fibrils, which in this case have a structure that causes them [...]

Answer

to dissolve to release soluble cargo after being secreted by exocytosis to the cell exterior

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nules,” which package a high concentra- tion of their cargo in dense cores with a regular structure (see Figure 13–65). We now know that these structured cores consist of amyloid fibrils, which in this case have a structure that causes them to dissolve to release soluble cargo after being secreted by exocytosis to the cell exterior<body><html>

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When A has [...] then solving a linear equation using the pseudoin v erse provides one of the man y p ossible solutions. Speciﬁcally , it pro vides the solution x = A + y with minimal Euclidean norm || || x 2 among all p ossible solutions

Answer

more columns than rows,

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When A has more columns than rows, then solving a linear equation using the pseudoin v erse provides one of the man y p ossible solutions. Speciﬁcally , it pro vides the solution x = A + y with minimal Euclidean norm ||

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When A has more columns than rows, then solving a linear equation using the [...] provides one of the man y p ossible solutions. Speciﬁcally , it pro vides the solution x = A + y with minimal Euclidean norm || || x 2 among all p ossible solutions

Answer

pseudoin v erse

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When A has more columns than rows, then solving a linear equation using the pseudoin v erse provides one of the man y p ossible solutions. Speciﬁcally , it pro vides [...]

Answer

the solution x = A + y with minimal Euclidean norm ||x||₂ among all p ossible solutions

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the [...] provides an alternativ e w a y of writing the F rob enius norm of a matrix: || || A F = T r( AA )

Answer

trace op erator

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the trace op erator provides an alternativ e w a y of writing the F rob enius norm of a matrix: || || A F = T r( AA )

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the trace op erator provides an alternativ e w a y of writing the [...] of a matrix: || || A F = T r( AA )

Answer

F rob enius norm

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the trace op erator provides an alternativ e w a y of writing the F rob enius norm of a matrix: || || A F = T r( AA )

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the trace op erator provides an alternativ e w a y of writing the F rob enius norm of a matrix: ||A||_F = [...]

Answer

Tr( AA^T )

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the trace op erator provides an alternativ e w a y of writing the F rob enius norm of a matrix: ||A|| F = Tr( AA T )

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the trace op erator is in v arian t to the [...]: T r( ) = T r( A A )

Answer

transp ose op erator

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the trace op erator is in v arian t to the transp ose op erator: T r( ) = T r( A A )

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the [...] is in v arian t to the transp ose op erator: T r( ) = T r( A A )

Answer

trace op erator

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the trace op erator is in v arian t to the transp ose op erator: T r( ) = T r( A A )

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the trace op erator is in v arian t to the transp ose op erator: T r(A) = [...]

Answer

T r(A^T )

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the trace op erator is in v arian t to the transp ose op erator: T r(A) = T r(A T )

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This inv ariance to cyclic p erm utation holds even if [...] F or example, for A ∈ R m n × and B ∈ R n m × , w e ha v e T r(AB ) = T r( BA)

Answer

the resulting pro duct has a diﬀeren t shap e.

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This inv ariance to cyclic p erm utation holds even if the resulting pro duct has a diﬀeren t shap e. F or example, for A ∈ R m n × and B ∈ R n m × , w e ha v e T r(AB ) = T r( BA)

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One simple mac hine learning algorithm, [...] can b e deriv ed using only knowledge of basic linear algebra

Answer

principal components analysis or PCA

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One simple mac hine learning algorithm, principal components analysis or PCA can b e deriv ed using only knowledge of basic linear algebra

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Lossy compression means [...]

Answer

storing the p oints in a wa y that requires less memory but ma y lose some precision

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Lossy compression means storing the p oints in a wa y that requires less memory but ma y lose some precision

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[...] means storing the p oints in a wa y that requires less memory but ma y lose some precision

Answer

Lossy compression

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Lossy compression means storing the p oints in a wa y that requires less memory but ma y lose some precision

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T o k eep the enco ding problem easy , PCA [...]

Answer

constrains the colum ns of D to b e orthogonal to eac h other.

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T o k eep the enco ding problem easy , PCA constrains the colum ns of D to b e orthogonal to eac h other.

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There are three p ossible sources of uncertain t y: [...] 2. Incomplete observ ability . Ev en deterministic systems can app ear sto chastic when w e cannot observ e all of the v ariables that drive the b ehavior of the system. F or example, in the Mont y Hall problem, a game sho w con testan t is ask ed to choose b etw een three do ors and wins a prize held b ehind the c hosen do or. T w o do ors lead to a goat while a third leads to a car. The outcome giv en the contestan t’s c hoice is deterministic, but from the con testan t’s p oin t of view, the outcome is uncertain. 3. Incomplete mo deling. When we use a mo del that must discard some of the information we hav e observ ed, the discarded information results in uncertain t y in the mo del’s predictions.

Answer

1. Inheren t stochasticit y in the system b eing mo deled. F or example, most in terpretations of quantum mechanics describ e the dynamics of subatomic particles as b eing probabilistic. W e can also create theoretical scenarios that w e p ostulate to ha v e random dynamics, such as a hypothetical card game where w e assume that the cards are truly sh uﬄed in to a random order.

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There are three p ossible sources of uncertain t y: 1. Inheren t stochasticit y in the system b eing mo deled. F or example, most in terpretations of quantum mechanics describ e the dynamics of subatomic particles as b eing probabilistic. W e can also create theoretical scenarios that w e p ostulate to ha v e random dynamics, such as a hypothetical card game where w e assume that the cards are truly sh uﬄed in to a random order. 2. Incomplete observ ability . Ev en deterministic systems can app ear sto chastic when w e cannot observ e all of the v ariables that drive the b ehavior of the system. F or example, i

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2. Incomplete observ ability . Ev en deterministic systems can app ear sto chastic when w e cannot observ e all of the v ariables that drive the b ehavior of the system. F or example, in the Mont y Hall problem, a game sho w con testan t is ask ed to choose b etw een three do ors and wins a prize held b ehind the c hosen do or. T w o do ors lead to a goat while a third leads to a car. The outcome giv en the contestan t’s c hoice is deterministic, but from the con testan t’s p oin t of view, the outcome is uncertain.

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of subatomic particles as b eing probabilistic. W e can also create theoretical scenarios that w e p ostulate to ha v e random dynamics, such as a hypothetical card game where w e assume that the cards are truly sh uﬄed in to a random order. 2. Incomplete observ ability . Ev en deterministic systems can app ear sto chastic when w e cannot observ e all of the v ariables that drive the b ehavior of the system. F or example, in the Mont y Hall problem, a game sho w con testan t is ask ed to choose b etw een three do ors and wins a prize held b ehind the c hosen do or. T w o do ors lead to a goat while a third leads to a car. The outcome giv en the contestan t’s c hoice is deterministic, but from the con testan t’s p oin t of view, the outcome is uncertain. 3. Incomplete mo deling. When we use a mo del that must discard some of the information we hav e observ ed, the discarded information results in uncertain t y in the mo del’s prediction

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There are three p ossible sources of uncertain t y: 1. Inheren t stochasticit y in the system b eing mo deled. F or example, most in terpretations of quantum mechanics describ e the dynamics of subatomic particles as b eing probabilistic. W e can also create theoretical scenarios that w e p ostulate to ha v e random dynamics, such as a hypothetical card game where w e assume that the cards are truly sh uﬄed in to a random order. 2. Incomplete observ ability . Ev en deterministic systems can app ear sto chastic when w e cannot observ e all of the v ariables that drive the b ehavior of the system. F or example, in the Mont y Hall problem, a game sho w con testan t is ask ed to choose b etw een three do ors and wins a prize held b ehind the c hosen do or. T w o do ors lead to a goat while a third leads to a car. The outcome giv en the contestan t’s c hoice is deterministic, but from the con testan t’s p oin t of view, the outcome is uncertain. [...]

Answer

3. Incomplete mo deling. When we use a mo del that must discard some of the information we hav e observ ed, the discarded information results in uncertain t y in the mo del’s predictions.

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s and wins a prize held b ehind the c hosen do or. T w o do ors lead to a goat while a third leads to a car. The outcome giv en the contestan t’s c hoice is deterministic, but from the con testan t’s p oin t of view, the outcome is uncertain. 3. Incomplete mo deling. When we use a mo del that must discard some of the information we hav e observ ed, the discarded information results in uncertain t y in the mo del’s predictions.<body><html>

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A random v ariable is [...]

Answer

a v ariable that can take on diﬀerent v alues randomly

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A random v ariable is a v ariable that can take on diﬀerent v alues randomly

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On its o wn, a random v ariable is just [...] it m ust b e coupled with a probability distribution that sp eciﬁes how likely each of these states are.

Answer

a description of the states that are p ossible;

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On its o wn, a random v ariable is just a description of the states that are p ossible; it m ust b e coupled with a probability distribution that sp eciﬁes how likely each of these states are.

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On its o wn, a random v ariable is just a description of the states that are p ossible; it m ust b e [...]

Answer

coupled with a probability distribution that sp eciﬁes how likely each of these states are.

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T o b e a probability mass function on a random v ariable x , a function P m ust satisfy the follo wing prop erties:
• The domain of P [...]
• ∀x ∈ x , 0 ≤ P ( x ) ≤ 1 . An imp ossible ev en t has probabilit y and no state can 0 b e less probable than that. Likewise, an ev en t that is guaran teed to happ en has probabilit y , and no state can ha v e a greater c hance of o ccurring. 1
• \(\sum_{x \in x}P(x) = 1\) . W e refer to this prop erty as b eing normalized . Without this prop ert y , we could obtain probabilities greater than one by computing the probabilit y of one of man y ev en ts o ccurring.

Answer

must b e the set of all p ossible states of x.

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T o b e a probability mass function on a random v ariable x , a function P m ust satisfy the follo wing prop erties:
• The domain of P must b e the set of all p ossible states of x.
• ∀x ∈ x , [...]
• \(\sum_{x \in x}P(x) = 1\) . W e refer to this prop erty as b eing normalized . Without this prop ert y , we could obtain probabilities greater than one by computing the probabilit y of one of man y ev en ts o ccurring.

Answer

0 ≤ P ( x ) ≤ 1 . An imp ossible ev en t has probabilit y and no state can 0 b e less probable than that. Likewise, an ev en t that is guaran teed to happ en has probabilit y , and no state can ha v e a greater c hance of o ccurring. 1

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T o b e a probability mass function on a random v ariable x , a function P m ust satisfy the follo wing prop erties: • The domain of P must b e the set of all p ossible states of x. • ∀x ∈ x , 0 ≤ P ( x ) ≤ 1 . An imp ossible ev en t has probabilit y and no state can 0 b e less probable than that. Likewise, an ev en t that is guaran teed to happ en has probabilit y , and no state can ha v e a greater c hance of o ccurring. 1 • ∑x∈xP(x)=1∑x∈xP(x)=1 . W e refer to this prop erty as b eing normalized . Without this prop ert y , we could obtain probabilities greater than one by computing the probabilit y o

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T o b e a probability mass function on a random v ariable x , a function P m ust satisfy the follo wing prop erties:
• The domain of P must b e the set of all p ossible states of x.
• ∀x ∈ x , 0 ≤ P ( x ) ≤ 1 . An imp ossible ev en t has probabilit y and no state can 0 b e less probable than that. Likewise, an ev en t that is guaran teed to happ en has probabilit y , and no state can ha v e a greater c hance of o ccurring. 1
• \(\sum_{x \in x}P(x) = 1\) . W e refer to this prop erty as b eing [...] . Without this prop ert y , we could obtain probabilities greater than one by computing the probabilit y of one of man y ev en ts o ccurring.

Answer

normalized

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no state can 0 b e less probable than that. Likewise, an ev en t that is guaran teed to happ en has probabilit y , and no state can ha v e a greater c hance of o ccurring. 1 • ∑x∈xP(x)=1∑x∈xP(x)=1 . W e refer to this prop erty as b eing normalized . Without this prop ert y , we could obtain probabilities greater than one by computing the probabilit y of one of man y ev en ts o ccurring.<body><html>

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obtain probabilities greater than one by computing the probabilit y of one of man y ev en ts o ccurring.

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ewise, an ev en t that is guaran teed to happ en has probabilit y , and no state can ha v e a greater c hance of o ccurring. 1 • ∑x∈xP(x)=1∑x∈xP(x)=1 . W e refer to this prop erty as b eing normalized . Without this prop ert y , we could obtain probabilities greater than one by computing the probabilit y of one of man y ev en ts o ccurring.<body><html>

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W e can place a uniform distribution on x —that is, make each of its states equally lik ely—b y setting its probabilit y mass function to [...]

Answer

P (x = x_i ) = 1/k

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W e can place a uniform distribution on x —that is, make each of its states equally lik ely—b y setting its probabilit y mass function to P (x = x i ) = 1/k

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The probability distribution o v er the subset is kno wn as the [...]

Answer

marginal probability distribution.

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The probability distribution o v er the subset is kno wn as the marginal probability distribution.

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The name “[...]” comes from the pro cess of computing marginal probabilities on pap er. When the v alues of P ( x y , ) are written in a grid with diﬀeren t v alues of x in rows and diﬀerent v alues of y in columns, it is natural to sum across a row of the grid, then write P ( x ) in the margin of the pap er just to the righ t of the ro w

Answer

marginal probabilit y

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The name “marginal probabilit y” comes from the pro cess of computing marginal probabilities on pap er. When the v alues of P ( x y , ) are written in a grid with diﬀeren t v alues of x in rows and diﬀerent v alues of

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The name “marginal probabilit y” comes from [...]. When the v alues of P ( x y , ) are written in a grid with diﬀeren t v alues of x in rows and diﬀerent v alues of y in columns, it is natural to sum across a row of the grid, then write P ( x ) in the margin of the pap er just to the righ t of the ro w

Answer

the pro cess of computing marginal probabilities on pap er

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The name “marginal probabilit y” comes from the pro cess of computing marginal probabilities on pap er. When the v alues of P ( x y , ) are written in a grid with diﬀeren t v alues of x in rows and diﬀerent v alues of y in columns, it is natural to [...], then write P ( x ) in the margin of the pap er just to the righ t of the ro w

Answer

sum across a row of the grid

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me “marginal probabilit y” comes from the pro cess of computing marginal probabilities on pap er. When the v alues of P ( x y , ) are written in a grid with diﬀeren t v alues of x in rows and diﬀerent v alues of y in columns, it is natural to sum across a row of the grid, then write P ( x ) in the margin of the pap er just to the righ t of the ro w<body><html>

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The exp ectation or exp ected v alue of some function f ( x ) with resp ect to a probabilit y distribution P ( x ) is the [...] that f tak es on when x is dra wn from . F or discrete v ariables this can be computed with a summation: P E x ∼ P [ ( )] = f x x P x f x , ( ) ( )

Answer

a v erage or mean v alue

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The exp ectation or exp ected v alue of some function f ( x ) with resp ect to a probabilit y distribution P ( x ) is the a v erage or mean v alue that f tak es on when x is dra wn from . F or discrete v ariables this can be computed with a summation: P E x ∼ P [ ( )] = f x x P x f x , ( ) ( )

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a summation: E_x~P[f(x)]=\(\sum_{x} P(x)f(x)\)∑xP(x)f(x)

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pan>The exp ectation or exp ected v alue of some function f ( x ) with resp ect to a probabilit y distribution P ( x ) is the a v erage or mean v alue that f tak es on when x is dra wn from . F or discrete v ariables this can be computed with a summation: E x~P[f(x)] =∑xP(x)f(x)∑xP(x)f(x)<body><html>

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scheduled repetition interval		last repetition or drill

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Flashcard 1485461654796

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#has-images

Answer

answer here

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scheduled repetition interval		last repetition or drill

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Flashcard 1485822364940

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#cement #has-images #ies

Answer

status	not learned	measured difficulty	37% [default]	last interval [days]
repetition number in this series	0	memorised on		scheduled repetition
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Flashcard 1485828656396

Question

Definizione di Sistema Distribuito.
Un sistema distribuito è:

Answer

Una collezione di computer indipendenti che appaiono agli utenti come un sistema singolo coerente .

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Annotation 1485833899276

Trasparenza rispetto all'Accesso: Nasconde le differenze nella rappresentazione dei dati e su come una risorsa è acceduta.

Se usiamo dropbox noi non sappiamo le caratteristiche dell'infrastruttura, sappiamo solo cosa fare per accedere (login)

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Annotation 1485836258572

Trasparenza rispetto alla Locazione: Nasconde dove una risorsa si trovi.
Se accediamo diverse volte a Gmail, probabilmente accediamo a server fisici diversi... Ma questo è trasparente all'utente.

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Annotation 1485838617868

Trasparenza alla Migrazione: Nasconde che una risorsa si possa spostare in una differente locazione.
Sposta le risorse mentre noi le accediamo... Senza che ce ne possiamo accorgerne.

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Annotation 1485840977164

Trasparenza alla Rilocazione: Nasconde che una risorsa si possa spostare in una differente locazione mentre viene usata.

Ci sono cloud che spostano la macchina virtuale su un'altra macchina fisica nel frattempo che io la sto usando.

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Annotation 1485843336460

Concorrenza Nasconde che una risorsa possa essere condivisa da più utenti concorrenti

Non fa a vedere all'utente che la risorsa la stanno accedendo anche altri.. Facile in lettura ma non se qualcuno accede in scrittura.

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Flashcard 1485845695756

Question

Trasparenza alla Persistenza

Answer

Nasconde se una risorsa (software) è in memoria o nel disco.

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Flashcard 1485848317196

Question

Definizione di Lamport di sistema distribuito.

Answer

Tu sai di averne (usarne) uno quando il guasto di un computer di cui non hai mai sentito parlare non ti permette di fare il tuo lavoro.

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Annotation 1485850152204

Servizi Centralizzati Un solo server per tutti gli utenti. Certamente non è scalabile... All'aumentare degli utenti il sistema non scala e si siede.

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Annotation 1485852511500

Dati Centralizzati Un singolo elenco telefonico on line

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Annotation 1485854084364

Algoritmi Centralizzati Effettuare il routing basato su informazioni complete

Algoritmo che svolge la maggior parte sua computazione in una macchina (in un metodo, in un singolo pezzo di codice) .

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Annotation 1485856443660

Algoritmi Decentralizzati
Un algoritmo non viene eseguito da una sola macchina ma è diviso in tanti pezzi che sono eseguiti da computer diversi.

1.  Nessuna macchina ha l informazione completa sullo stato del sistema
Esempio: Non esiste un computer che conosce tutto internet... Il concetto è che siccome in un sistema distribuito non si può conoscere da un punto tutto lo stato della rete, allora gli algoritmi fanno l'assunzione che non si può avere da un nodo l'informazione completa sullo stato del sistema..

2.  Le macchine prendono decisioni sulla base di informazioni locali.

3.  Il fallimento di una macchina non fa fallire l algoritmo.
Esempio: Abbiamo preso una matrice e l'abbiamo divisa in righe. E abbiamo dato ad ogni nodo una riga. Se un nodo fallisce abbiamo perso la somma di una riga.
Se invece, abbiamo un nodo che fa la somma di tutte le righe, se fallisce questo fallisce tutto l'algoritmo.

4.  Non vi è una assunzione di un clock globale.
Le scelte dell'algoritmo non devono dipendere dal clock del computer... Perché da macchina a macchina cambia.. Quindi porterebbe ad inconsistenza.
Non posso assumere che vi sia un clock globale.

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Annotation 1485858802956

Più grande è il sistema, maggiore è l'incertezza. "Caspita, allora siamo nei guai !!! "

Nonostanze c'è l'incertezza, i sistemi distribuiti funzionano bene gestendo l'incertezza.

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Annotation 1485861948684

La differenza tra chi tra: a)  un server o b)  un client controlla le forme che sono state riempite.

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Flashcard 1485868502284

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Flashcard 1485871910156

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Answer

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scheduled repetition interval		last repetition or drill

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Flashcard 1485875318028

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Answer

A-3,B-1,C-4,D-2

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Flashcard 1485878725900

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Flashcard 1485882133772

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Flashcard 1485885541644

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Answer

D

50 kg------------>0.035m³
1429 kg------------>1m^{3

1 tonne=1000kg}
1000kg------------>0.699m³
No. of bags=0.699/0.035 =20 bags

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Flashcard 1485888949516

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Answer

C

the normal consistency for OPC ranges from 26 to 33%.

Take 400 g of cement and place it in the enameled tray.

Mix about 25% water by weight of dry cement thoroughly to get a cement paste. Total time taken to obtain thoroughly mixed water cement paste i.e. “Gauging time” should not be more than 3 to 5 minutes.

the depth of penetration becomes 33 to 35 mm.

Where,

W=Quantity of water added

C=Quantity of cement used

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Annotation 1485904153868

In computer architecture, a bus^[1] (a contraction of the Latin omnibus) is a communication system that transfers data between components inside a computer, or between computers. This expression covers all related hardware components (wire, optical fiber, etc.) and software, including communication protocols.^[2]

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Bus (computing) - Wikipedia
ed. (February 2013) (Learn how and when to remove this template message) [imagelink] 4 PCI Express bus card slots (from top to 2nd bottom: x4, x16, x1 and x16), compared to a 32-bit conventional PCI bus card slot (very bottom) In computer architecture, a bus [1] (a contraction of the Latin omnibus) is a communication system that transfers data between components inside a computer, or between computers. This expression covers all related hardware components (wire, optical fiber, etc.) and software, including communication protocols. [2] Early computer buses were parallel electrical wires with multiple connections, but the term is now used for any physical arrangement that provides the same logical function as a paral

Annotation 1485909396748

In computer architecture, a bus is a communication system that transfers data between components inside a computer, or between computers.

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Open it
In computer architecture, a bus [1] (a contraction of the Latin omnibus) is a communication system that transfers data between components inside a computer, or between computers. This expression covers all related hardware components (wire, optical fiber, etc.) and software, including communication protocols. [2]

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status	not learned	measured difficulty	37% [default]	last interval [days]
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Edited, memorised or added to reading queue

on 06-Mar-2017 (Mon)

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