on 05-Sep-2019 (Thu)

La necesidad del estudio geodésico surge por el hecho de que la Tierra no es plana, y cuando el territorio que pretendemos estudiar es lo suficientemente extenso, la curvatura de la Tierra no puede ser ignorada. Este es el caso que vamos a encontrar cuando trabajemos con un SIG, y es por ello que los SIG implementan los elementos necesarios para poder efectuar un manejo de la información geográfica riguroso y acorde con los conceptos de la geodesia.

If you are using initrd preseeding, you only have to make sure a file named preseed.cfg is included in the root directory of the initrd. The installer will automatically check if this file is present and load it.

For syslinux this means setting the timeout to 1 in syslinux.cfg.

The easiest way to create a preconfiguration file is to use the example file linked in Section B.4, “Contents of the preconfiguration file (for bionic)” as basis and work from there.

An alternative method is to do a manual installation and then, after rebooting, use the debconf-get-selections from the debconf-utils package to dump both the debconf database and the installer's cdebconf database to a single file:

$ debconf-get-selections --installer > file
$ debconf-get-selections >> file

To check if the format of your preconfiguration file is valid before performing an install, you can use the command debconf-set-selections -c preseed.cfg .

View templates.dat for the raw templates and questions.dat for the current values and for the values assigned to variables.

Recommended password hashing algorithms are SHA-256 and SHA512.

The following command (available from the whois package) can be used to generate a SHA-512 based crypt(3) hash for a password:

mkpasswd -m sha-512

For detailed information see the files partman-auto-recipe.txt and partman-auto-raid-recipe.txt included in the debian-installer package. Both files are also available from the debian-installer source repository. Note that the supported functionality may change between releases.

create file systems on the partitions

Initialize and activate swap

Mount one partition as /mnt/ubuntu (the installation point, to be the root (/) filesystem on your new system).

official way to install an Ubuntu base system, is debootstrap . It uses wget and ar , but otherwise depends only on /bin/sh and basic Unix/Linux tools^[24]. Install wget and ar if they aren't already on your current system, then download and install debootstrap .

Make a work folder for extracting the .deb into

Download the debootstrap .deb from the pool, copy the package to the work folder, and extract the files from it.

debootstrap can download the needed files directly from the archive when you run it. You can substitute any Ubuntu archive mirror for archive.ubuntu.com/ubuntu in the command example below, preferably a mirror close to you network-wise. Mirrors are listed at http://wiki.ubuntu.com/Archive.

If you have an Ubuntu bionic CD mounted at /cdrom, you could substitute a file URL instead of the http URL: file:/cdrom/ubuntu/

Substitute one of the following for ARCH in the debootstrap command: amd64, arm64, armhf, i386, powerpc, ppc64el, or s390x.

# /usr/sbin/debootstrap --arch ARCH bionic /mnt/ubuntu

Now you've got a real Ubuntu system, though rather lean, on disk. chroot into it:

# LANG=C.UTF-8 chroot /mnt/ubuntu /bin/bash

terminal definition to be compatible with the Ubuntu base system

Debootstrap will have created a very basic /etc/apt/sources.list

install some additionally required packages, like makedev (needed for the next section): apt install makedev

create a default set of static device files

Paget’s disease: pale cells with adenocarcinoma staining features.

Carcinoembryonic antigen (CEA), EMA, CK-7, CK-8, usually positive.

Mucin in cytoplasm is often positive with mucicarmine, Alcian blue, colloidal iron, and PAS with and without diastase.

Basal cells are often compressed and uninvolved. No dyskeratosis

To place a Kickstart file on a CD, you would need to remaster the ISO image to include your Kickstart file, and add ks=cdrom:/path/to/ks.cfg to the kernel boot parameters. See the manual page for mkisofs for details.

Bowen’s disease (squamous cell carcinoma in situ, 18.10): pale keratinocytes may be present which contain glycogen (PAS positive, diastase labile) with frequent dyskeratosis (1.27). Full-thickness atypia often involves basal cells also. High molecular weight keratin positive. Mucin stains, CEA, EMA, CK-7, CK-8 and low molecular weight keratin negative. However, cases have been published with exceptions, such as positive staining for CK-7 and EMA. Usually no pagetoid cells in the stratum corneum, which sometimes occurs with Paget’s disease and melanoma

A new preseed command has been added to provide a convenient way to preseed additional items in the debconf database that are not directly accessible using the ordinary Kickstart syntax:

preseed --owner gdm shared/default-x-display-manager select gdm

Note that if the value contains any special characters, then the value must be quoted, as follows:

preseed preseed/late_command string "sed -i 's/foo/bar/g' /target/etc/hosts"

Borst–Jadassohn phenomenon: discrete clones of basaloid, squamatized, or pale keratinocytes in the epidermis that appear different than their neighbors. This can be benign or malignant. It is mainly seen with irritated seborrheic keratosis (18.2) or Bowen’s disease (18.10), and rarely with hidroacanthoma simplex (a form of eccrine poroma limited to the epidermis, 23.10).

To generate a Kickstart file, install the system-config-kickstart package and run system-config-kickstart.

Melanoma (20.11): S-100 (very sensitive, but not specific), HMB-45 and MART-1 (both very specific, but not sensitive) stains positive. Fontana melanin stain is also positive, but is less useful because keratinocytes may contain melanin transferred from melanocytes. Melanosomes by electron microscopy

Mycosis fungoides (24.1): malignant T-lymphocytes (sometimes cerebriform) in spaces called Pautrier microabscesses. Stains such as CD45 (leukocyte common antigen, used for screening), CD4 (T-helper cells), and CD3 (pan-T cells) usually positive

Lymphomatoid papulosis (24.5): CD30 positive large atypical lymphocytes, and most smaller ones in the epidermis stain with T-cell markers

Langerhans cell histiocytosis (24.18): malignant Langerhans cells, often kidney-shaped nuclei, CDla or S-100 positive

Build rich hierarchies of interconnections and integrative questions. Don't put in orphan questions.

I make it a rule to never put in one question. Rather, I try to put at least two questions in, preferably three or more.

One simple example strategy is to use multiple variants of the “same” question.

What is incremental learning?

This article describes the fastest avenue towards rock-solid lifetime knowledge: Incremental Learning.

Incremental learning is the fastest and the most comprehensive way of learning available to students at the moment of writing (2013).

Incremental learning is a consolidation of computer-based techniques that accelerate and optimize the process of learning from all conceivable material available in electronic form, and not only.

Currently, SuperMemo is the only software that implements incremental learning. In SuperMemo, the student feeds the program with all forms of learning material and/or data (texts, pictures, videos, sounds, etc.). Those learning materials are then gradually converted into durable knowledge that can last a lifetime.

Incremental learning helps the student convert all forms of learning material into durable and lasting memories.

In incremental learning, the student usually remembers 95% of his or her top priority material. That knowledge is relatively stable and lasts in student's memory as long as the process continues, and well beyond.

Incremental learning easily ensures 95% recall of top-priority learning material for lifetime (as long as the student ensures a regular review along the prescription provided by the program).

The cost of high knowledge retention is very small when compared with various traditional learning methods. For example, in learning a language, the vocabulary of an educated native speaker can be retained in SuperMemo at the cost of 20 minutes per day in the first years of the process, and mere minutes in later years (assuming the original set is acquired in portions spread over 4 years in 30-50 min. sessions).

Incremental learning ensures high recall at a fraction of the cost in time (as compared to textbook learning).

The incremental learning derives its name from the incremental nature of the learning process. In incremental learning, all facets of knowledge receive a regular treatment, and there is a regular inflow of new knowledge that builds upon the past knowledge. In incremental learning, the student sits in the driving seat and determines which knowledge should be mastered. He or she determines when this happens, with what degree of detail, at what priority, and at what desired degree of recall/retention. For example, in a single session, the student may learn a few facts of geography, discover a few rules of healthy lifestyle, figure out a few statistical formulas, read a couple of paragraphs from a friend's blog, process a few minutes of his home video collection, annotate a few family pictures, watch a few pieces from his YouTube video collection, and read a few articles in subjects related to a forthcoming exam. In other words, all areas of knowledge keep growing in parallel in proportion to interests and importance.

Typical learning at school puts an emphasis on a few areas of knowledge and neglects all the remaining areas. A medical student may spend a few months mastering anatomy, while gradually forgetting his biochemistry material in the meantime (or the other way round). At the same time, he or she will not find time to study important issues of the day that will always depend on a given person in a given context. With blinkers imposed by the heavy load of school material, the student may never find time, for example, to figure out what incremental learning is. Narrow horizons and narrow perspectives only make it harder to further rationalize the selection of the learning material.

Incremental learning is the opposite of the irrational school system learning in which a heavy focus is put on just a

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1.1 Prognosis and Prediction in Medicine Prognosis is central to medicine. All diagnostic and therapeutic actions aim to improve prognosis: • Screening : If we screen for early signs of disease, we may, for example, find cancers early in their course of disease, and treat them better than when they were detected later. But whether screening is useful depends on the improve- ment in prognosis that is achieved compared to a no screening strategy. Some cancers may not have caused any impact on life expectancy, while side-effects of treatment may be substantial. • Diagnosis : If we do a diagnostic test, we may detect an underlying disease. But some diseases are not treatable, or the natural course might be very similar to what is achieved with treatment. • Therapy : New treatments become available nearly every day, but their impact on prognosis is often rather limited, despite high hopes at early stages. “Magic bul- lets” are rare. Treatment effects are often small relative to the effects of determi- nants of the natural history of a disease, such as the patient’s age. The individual benefits need to exceed any side effects, harms and economic costs.

Statistical modelling to make predictions encounters various challenges, including deal- ing with model uncertainty and limited sample size. Model uncertainty arises from the fact that we usually do not fully pre-specify a model before we fit it to a data set. 69, 101 An iterative process is often followed with model checking and model modification. On the other hand, standard statistical methods assume that a model was pre-specified. In that case, parameter estimates such as regression coefficients, their corresponding standard errors, 95% confidence intervals, and p -values are largely unbiased. When the structure of a model was at least partly based on findings in the data, bias may occur, and we underestimate the uncertainty of conclusions drawn from the model.

Fortunately, some statistical tools have become available which help to study model uncertainty. Especially, a statistical re-sampling procedure named “boot- strapping” is helpful for many aspects of model development and validation. 108 The bootstrap hence is an important tool in prediction research

For example, when we study complications of a procedure that occur with an inci- dence of 0.1%, a study with 10,000 patients will contain only 10 events. The number 10 determines the effective sample size in such a study. In small samples, model uncertainty may be large, and we may not be able to derive reliable predic- tions from a model.

In practice, we often have relatively small sam- ples available. For example, a review of 31 prognostic models in traumatic brain injury showed that 22 were based on samples with less than 500 patients. 307 The main challenges are hence with the development of a good prediction model with a relatively small study sample.

Third, with small sample size we have to be prepared to make stronger model- ling assumptions. For example, Altman illustrates the use of a parametric test (ANOVA) to compare 3 groups with 8, 9, and 5 patients in his seminal text “Practical statistics for medical research”. 8 With larger samples, we would more readily switch to a non-parametric test such as a Kruskal–Wallis test. With small sample size, we may have to assume linearity of a continuous predictor (Chap. 9) and no interaction between predictors (Chap. 13). We will subsequently have lim- ited power to test deviations from these model assumptions. It hence becomes more important what our starting point of the analysis is. From a Bayesian viewpoint, we could say that our prior information becomes more important, since the information contributed by our study is limited

Fourth, we have to match our ambitions in research questions with the effective sample size that is available. When the sample size is very small, we should only ask relatively simple questions, while more complex questions can be addressed with larger sample sizes. A question such as: “What are the most important predic- tors in this prediction problem” is actually more complex than a question such as “What are the predictions of the outcome given this set of predictors” (Chap. 11). Table 1.1 lists questions on predictors (known or determined from the data?), func- tional form (known or determined from the data?), and regression coefficients (known or determined from the data?) and the consequence for the required sample size in a study

Fomentar los modos de transporte no motorizados, dándoles relevancia en la movilidad urbana e incrementando las oportunidades para el peatón y la bicicleta como modos de transporte alternativo con consecuencias muy positivas sobre la salud pública, la economía doméstica y el medio ambiente.

Se propone la definición de itinerarios preferentes para mercancías en los principales ejes de transporte terrestre.

Por criterios económicos y ambientales se impone la necesidad de aprovechar las infraestructuras existentes y sobre éstas implantar una nueva gestión con criterios de sostenibilidad. Por lo tanto, adquieren gran relevancia, entre otras, medidas como la conversión de carriles convencionales a carriles bus y/o carriles bici, fomento de las líneas de ferrocarril para el transporte de mercancías.

6.1.6 RED FERROVIARIA DE ALTAS PRESTACIONES Modernizar y extender la red ferroviaria que sirve de soporte a los servicios de viajeros de media y larga distancia, para promover el uso del ferrocarril como medio de transporte eficiente y sostenible

6.1.10 AJUSTAR LOS SISTEMAS DE TRANSPORTE A LAS NECESIDADES Y DEMANDAS DE ZONAS RURALES PERIFÉRICAS , CON BAJA DENSIDAD DE POBLACIÓN O TERRITORIOS INSULARES Garantizar la vertebración territorial de las zonas rurales periféricas, con baja densidad de población ., o territorios insulares. En algunos casos se pueden implantar sistemas de transporte a la demanda.

omentar actuaciones en medio urbano, para lograr una movilidad alternativa al vehículo privado, mediante infraestructuras reservadas, específicamente dedicadas al transporte colectivo de viajeros como los carriles Bus y BUS/VAO, con el fin de aumentar la capacidad del tráfico general en las principales vías de acceso a las ciudades de mayor tamaño.

Implantar progresivamente carriles de alta ocupación en los principales corredores metropolitanos de la red viaria de titularidad Estatal -en coordinación con las instituciones afectadas en función de las competencias de las distintas Administraciones-, atendiendo a las circunstancias particulares de cada situación concreta y preferentemente a los siguientes factores: volumen de tráfico y problemas de congestión; demanda de viajes en autobús; existencia o previsión de realización de intercambiadores de transporte; y viabilidad física y disponibilidad de espacio para la inserción de la plataforma.

La EEMS contempla:

Planes de movilidad urbana sostenible en todas aquellas ciudades que tengan transporte público (sin perjuicio de que lo hagan pequeñas ciudades)

Planes de movilidad para empresas y polígonos industriales

Planes de movilidad en centros educativos, comerciales y de ocio.

Apoyar y colaborar en la definición y desarrollo a nivel supranacional de una arquitectura global multimodal de sistemas inteligentes de transporte, cuyos elementos claves serían: (i) las redes de comunicación; (ii) los sensores de posicionamiento; (iii) las bases de datos y de información geográfica y ambientalesy (v) los servicios de información integral para el usuario de los sistemas de transporte.

ADECUACIÓN DE LA VELOCIDAD EN LAS VÍAS DE ACCESO A LAS GRANDES CIUDADES Gestión dinámica de la velocidad, reduciendo el límite de velocidad en las vías de acceso y circunvalaciones de las grandes ciudades para gestionar el tráfico en función de parámetros de congestión, medioambientales y conducción eficiente”

La primera medida que la EEMS nombra en el apartado de Cambio Climático y reducción de la dependencia energética es priorizar el transporte público. (EEMS pg. 27)

otras herramientas de gestión como la regulación semafórica favorable al transporte publico

Medidas de la EEMS para la calidad del aire y el ruido -> ÁREAS CON LIMITACIÓN DE VELOCIDAD PARA VEHÍCULOS Potenciar las denominadas zonas 30 que permiten reducir la intensidad y velocidad de los vehículos, para una clara mejora de la salud y bienestar, preferentemente en áreas residenciales

MEJORA DE LA ACCESIBILIDAD PARA LAS PERSONAS CON MOVILIDAD REDUCIDA Para ello se han de acometer actuaciones 9 de adecuación de barreras arquitectónicas, pasarelas, pasos peatonales, accesibilidad al transporte público, etc. para grupos sociales con movilidad reducida. Realizar actuaciones en las paradas y vehículos de transporte público de forma que se disponga de una flota e infraestructuras de transporte público que permitan la accesibilidad de grupos sociales con movilidad reducida.

5 DIRECTRICES GENERALES

In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters. A key challenge is to handle the increased amount of data and extended training time. We have developed a new distributed agent IMPALA (Importance Weighted Actor-Learner Architecture) that not only uses resources more efficiently in single-machine training but also scales to thousands of machines without sacrificing data efficiency or resource utilisation. We achieve stable learning at high throughput by combining decoupled acting and learning with a novel off-policy correction method called V-trace. We demonstrate the effectiveness of IMPALA for multi-task reinforcement learning on DMLab-30 (a set of 30 tasks from the DeepMind Lab environment (Beattie et al., 2016)) and Atari-57 (all available Atari games in Arcade Learning Environment (Bellemare et al., 2013a)). Our results show that IMPALA is able to achieve better performance than previous agents with less data, and crucially exhibits positive transfer between tasks as a result of its multi-task approach.

grep -r "mydomain.com" /etc/apache2/

When the sample size is very small, we should only ask relatively simple questions, while more complex questions can be addressed with larger sample sizes.