Job control is what happens when you press ^Z to suspend a program, or when you start a program in the background using &

Currently, Pine Island Glacier together with its neighbouring Thwaites glacier are responsible for about 10% of the ongoing increase in global sea level.

Typically, a process associated with a controlling terminal is foreground process and its process group is called foreground process group. When you start a process from the command line, it's a foreground process:

The shell process itself is in yet another process group all of its own and so doesn't receive the signal when one of those process groups is in the foreground. It's that simple.

Switching "jobs" between foreground and background is (some details aside) a matter of the shell telling the terminal which process group is now the foreground one.

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Create Extra Credit using Weighted Assignment Groups

EBF was much more potent than Pax5 in inducing B celldevelopment, as its expression in MPPs yielded at least 100-foldmore B lineage progeny than did expression of Pax5 (Fig. 3band data not shown). These data suggest that promotion of B cellgeneration from MPPs by EBF is not mediated solely throughactivation of Pax5 expression.

We do not utilize a formulaic or standard, formalized benchmarking level or element in setting our executive compensation relative to that of other companies. 

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It happened in 2000, when Gore had more popular votes than Bush yet fewer electoral votes, but that was the first time since 1888.

Subgroups of the computer underground with different attitudes and motives use different terms to demarcate themselves from each other. These classifications are also used to exclude specific groups with whom they do not agree.

The default groups, that we talked about before, like domain users and domain admins are security groups. They're used to grant or deny access to IT resources.

A distribution group, is only designed to group accounts and contacts for email communication. You can't use distribution groups for assigning permission to resources.

unbiased

Friends and foes alike neither admire nor fear President Trump’s leadership

The challenges before us—the worst public health crisis in a century, the worst economic downturn since the Great Depression, the worst period of global upheaval in a generation, the urgent global crisis posed by climate change, the intolerable racial injustice that still stains the fabric of our nation—will test America’s character like never before.

a more perfect union

So how should we think about federalism in the ageof coronavirus? The answer is to emphasize theimportance of building social solidarity — the beliefin a shared fate for all Americans that transcendsstate or regional identities

institutional antagonism willprevent the concentration of power, encouragesindividualist mentalities that lead to self-interestedactions and erode national unity.

“We’re changing federalism from the idea of shared expertise in different policy areas into partisan stakes in the ground that are meant to obstruct opponents,” Robertson says.

“The coronavirus response is actually sort of a perfect measuring stick of our transition to our contemporary, very polarized model of federalism.”

He has threatened to withhold federal funds from school districts that don’t open for in-person instruction.

He has threatened to withhold federal funds from school districts that don’t open for in-person instruction.

It could create incentives for action by conditioning a portion of funds going to states in any future relief packages on states’ adherence to the measures

Lacking strong federal leadership to guide a uniform response, the United States quickly fulfilled the World Health Organization’s prediction that it would become the new epicenter of Covid-19.

subject to constitutionally protected individual rights such as due process, equal protection, and freedom of travel and association

Strong, decisive national action is therefore imperative.

El matrimonio igualitario Argentina, Uruguay, México, Brasil y Colombia son los únicos países de América Latina que han legalizado el matrimonio de parejas homosexuales, con todas las condiciones legales que tienen los matrimonios convencionales. De igual manera, Holanda, Bélgica, Suecia, Alemania, Sudáfrica, España, Inglaterra, Portugal y otras 12 naciones más han hecho lo propio.   Entretanto, sólo 11 países permiten la unión civil entre parejas del mismo sexo (no incluye todos los derechos legítimos maritales) entre estos Ecuador, Chile, Austria, Italia y Grecia. Cambio de identidad El proceso de cambio de género o sexo es un proceso quirúrgico y psicológico que contempla el cambio físico. En la actualidad, naciones como Venezuela, Colombia, Uruguay, Bolivia y Perú permiten que la transformación también sea legal, con el cambio del nombre y género en los documentos de identidad de las personas LGBTI. Sin embargo, en Brasil y Argentina, este tipo de decretos han sido desaprobados o son procesos difíciles por lo riguroso de los exámenes y requisitos que truncan el derecho al trámite. Igualdad en derechos patrimoniales Las garantías, protecciones legales en los matrimonios tras el fallecimiento y divorcio también son logros alcanzados en menos de 20 años por la comunidad LGBTI. Pero estos aún están condicionados dependiendo del país, por la diferencia entre permitir la unión civil (algunos derechos de cónyuges) o el matrimonio igualitario (derechos legítimos).   Adopción homoparental Uruguay, México, Canadá (el primer país en permitirlo en 1999) y algunos estados de EE.UU. han legalizado la adopción para personas unidas del mismo sexo. Asimismo, España, Holanda, Francia y otros permiten que las parejas no heterosexuales puedan tener hijos por medio de la adopción legal. Protección a víctimas de discriminación Hasta 1948 la homosexualidad era considerada una enfermedad mental, pero esto cambió lo que permitió la apertura de sistemas de protección a víctimas de discriminación y agresión. En la actualidad, existen programas de atención social y judicial para el seguimiento de los casos de hostigamiento y asesinatos contra los homosexuales. Además hay coberturas de salud gratuitas para tratar enfermedades como el sida y la programas de integración laboral en distintas áreas. Tags Día contra la Homofobia LGBTI Derechos Discriminación BBC - La Vanguardia - El Excelsior - La Semana Por: teleSUR- db - SB - JCM Noticias Relacionadas OLP exhorta a Oriente Medio a tomar medidas contra Guatemala Bolivia afirma que Venezuela frena dominación de EE.UU. 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all the subcolletions must have the same name, for instance tags

The world’s largest exhibitions organizer, London-based Informa plc, outlined on Thursday morning a series of emergency actions it’s taking to alleviate the impact of the COVID-19 pandemic on its events business, which drives nearly two-thirds of the company’s overall revenues. Noting that the effects have been “significantly deeper, more volatile and wide-reaching,” than was initially anticipated, the company says it’s temporarily suspending dividends, cutting executive pay and issuing new shares worth about 20% of its total existing capital in an effort to strengthen its balance sheet and reduce its approximately £2.4 billion ($2.9 billion) in debt to £1.4 billion ($1.7 billion). Further, Informa says it’s engaged in “constructive discussions” with its U.S.-based debt holders over a covenant waiver agreement.

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intellectual disability (ID)

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Suppose the algorithm chooses a tree that splits on education but not on age. Conditional on this tree, the estimated coefficients are consistent. But that does not imply that treatment effects do not also vary by age, as education may well covary with age; on other draws of the data, in fact, the same procedure could have chosen a tree that split on age instead

hese heterogenous treatment effects can be used to assign treatments; Misra and Dubé (2016) illustrate this on the problem of price targeting, applying Bayesian regularized methods to a large-scale experiment where prices were randomly assigned

Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016) take care of high-dimensional controls in treatment effect estimation by solving two simultaneous prediction problems, one in the outcome and one in the treatment equation.

These same techniques applied here result in split-sample instrumental variables (Angrist and Krueger 1995) and “jackknife” instrumental variables

Understood this way, the finite-sample biases in instrumental variables are a consequence of overfitting.

Even when we are interested in a parameter β ˆ, the tool we use to recover that parameter may contain (often implicitly) a prediction component. Take the case of linear instrumental variables understood as a two-stage procedure: first regress x = γ′z + δ on the instrument z, then regress y = β′x + ε on the fitted values x ˆ. The first stage is typically handled as an estimation step. But this is effectively a prediction task: only the predictions x ˆ enter the second stage; the coefficients in the first stage are merely a means to these fitted values.

Prediction in the Service of Estimation

New Data

Zhao and Yu (2006) who establish asymptotic model-selection consistency for the LASSO. Besides assuming that the true model is “sparse”—only a few variables are relevant—they also require the “irrepresentable condition” between observables: loosely put, none of the irrelevant covariates can be even moderately related to the set of relevant ones.

First, it encourages the choice of less complex, but wrong models. Even if the best model uses interactions of number of bathrooms with number of rooms, regularization may lead to a choice of a simpler (but worse) model that uses only number of fireplaces. Second, it can bring with it a cousin of omitted variable bias, where we are typically concerned with correlations between observed variables and unobserved ones. Here, when regular-ization excludes some variables, even a correlation between observed variables and other observed (but excluded) ones can create bias in the estimated coefficients.

97the variables are correlated with each other (say the number of rooms of a house and its square-footage), then such variables are substitutes in predicting house prices. Similar predictions can be produced using very different variables. Which variables are actually chosen depends on the specific finite sample.

Through its regularizer, LASSO produces a sparse prediction function, so that many coefficients are zero and are “not used”—in this example, we find that more than half the variables are unused in each run

One obvious problem that arises in making such inferences is the lack of stan-dard errors on the coefficients. Even when machine-learning predictors produce familiar output like linear functions, forming these standard errors can be more complicated than seems at first glance as they would have to account for the model selection itself. In fact, Leeb and Pötscher (2006, 2008) develop conditions under which it is impossible to obtain (uniformly) consistent estimates of the distribution of model parameters after data-driven selection.

First, econometrics can guide design choices, such as the number of folds or the function class.

These choices about how to represent the features will interact with the regularizer and function class: A linear model can reproduce the log base area per room from log base area and log room number easily, while a regression tree would require many splits to do so.

Ta b l e 2Some Machine Learning Algorithms

Picking the prediction func-tion then involves two steps: The first step is, conditional on a level of complexity, to pick the best in-sample loss-minimizing function.8 The second step is to estimate the optimal level of complexity using empirical tuning (as we saw in cross-validating the depth of the tree).

egularization combines with the observability of predic-tion quality to allow us to fit flexible functional forms and still find generalizable structure.

This procedure works because prediction quality is observable: both predic-tions y ˆ and outcomes y are observed. Contrast this with parameter estimation, where typically we must rely on assumptions about the data-generating process to ensure consistency.

In empirical tuning, we create an out-of-sample experiment inside the original sample.

Performance of Different Algorithms in Predicting House Values

We consider 10,000 randomly selected owner-occupied units from the 2011 metropolitan sample of the American Housing Survey. In addition to the values of each unit, we also include 150 variables that contain information about the unit and its location, such as the number of rooms, the base area, and the census region within the United States. To compare different prediction tech-niques, we evaluate how well each approach predicts (log) unit value on a separate hold-out set of 41,808 units from the same sample. All details on the sample and our empirical exercise can be found in an online appendix available with this paper athttp://e-jep.org

Making sense of complex data such as images and text often involves a prediction pre-processing step.

The fundamental insight behind these breakthroughs is as much statis-tical as computational. Machine intelligence became possible once researchers stopped approaching intelligence tasks procedurally and began tackling them empirically.

In another category of applications, the key object of interest is actually a parameter β, but the inference procedures (often implicitly) contain a prediction task. For example, the first stage of a linear instrumental variables regres-sion is effectively prediction. The same is true when estimating heterogeneous treatment effects, testing for effects on multiple outcomes in experiments, and flexibly controlling for observed confounders.

Machine Learning: An Applied Econometric Approach

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Abnormal femoral head epiphysis

Irregular vertebrae

Hypoplastic vertebrae

INR

RALF

Hepatomegaly

Prothrombin time

Total bilirubin

AST

ALT

Glucose