Philadelphia Phillies star Bryce Harper thinks Major League Baseball is "dumb" for its policy that makes the Olympics off-limits for baseball's best players.

Hannele Yki-Järvinen
Dec 1, 2014; 37:3235-3243
Emerging Technologies and Therapeutics

Matthew C. Riddle
Oct 1, 2014; 37:2755-2762
Emerging Technologies and Therapeutics

Raytheon Missile Systems was awarded a $19 million contract modification to exercise options for Navy Rolling Airframe Missile and Guided Missile Launching Systems, according to the Department of Defense.

John B. Buse
Jul 1, 2002; 20:
Feature Articles

Breslau : E. Morgenstern, 1882.

Tubingen : H. Laupp, Jr, 1889.

London (3 Haymarket) : Published ... by W.B. Tiffin, June 21st 1830.

[London] (Haymarket) : Published ... for the proprietors, by T McLean, Septr. 1. 1836 ([London?] : Printed by Lahee & Co.)

[London] (Historic Gallery, Pall Mall) : Published by R. Bowyer, June 1804

Halle (Saale) : Deutsche Akademie der Naturforscher Leopoldina - Nationale Akademie der Wissenschaften ; Stuttgart : Wissenschaftliche Verlagsgesellschaft, 2019.

Data-space inversion (DSI) and related procedures represent a family of methods applicable for data assimilation in subsurface flow settings. These methods differ from model-based techniques in that they provide only posterior predictions for quantities (time series) of interest, not posterior models with calibrated parameters. DSI methods require a large number of flow simulations to first be performed on prior geological realizations. Given observed data, posterior predictions can then be generated directly. DSI operates in a Bayesian setting and provides posterior samples of the data vector. In this work we develop and evaluate a new approach for data parameterization in DSI. Parameterization reduces the number of variables to determine in the inversion, and it maintains the physical character of the data variables. The new parameterization uses a recurrent autoencoder (RAE) for dimension reduction, and a long-short-term memory (LSTM) network to represent flow-rate time series. The RAE-based parameterization is combined with an ensemble smoother with multiple data assimilation (ESMDA) for posterior generation. Results are presented for two- and three-phase flow in a 2D channelized system and a 3D multi-Gaussian model. The RAE procedure, along with existing DSI treatments, are assessed through comparison to reference rejection sampling (RS) results. The new DSI methodology is shown to consistently outperform existing approaches, in terms of statistical agreement with RS results. The method is also shown to accurately capture derived quantities, which are computed from variables considered directly in DSI. This requires correlation and covariance between variables to be properly captured, and accuracy in these relationships is demonstrated. The RAE-based parameterization developed here is clearly useful in DSI, and it may also find application in other subsurface flow problems.

Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. For a given input sample, they assign a relevance score to each individual input variable, such as the pixels of an image. In this work we adapt the information bottleneck concept for attribution. By adding noise to intermediate feature maps we restrict the flow of information and can quantify (in bits) how much information image regions provide. We compare our method against ten baselines using three different metrics on VGG-16 and ResNet-50, and find that our methods outperform all baselines in five out of six settings. The method's information-theoretic foundation provides an absolute frame of reference for attribution values (bits) and a guarantee that regions scored close to zero are not necessary for the network's decision. For reviews: https://openreview.net/forum?id=S1xWh1rYwB For code: https://github.com/BioroboticsLab/IBA

This paper develops a general framework for analyzing asymptotics of $V$-statistics. Previous literature on limiting distribution mainly focuses on the cases when $n o infty$ with fixed kernel size $k$. Under some regularity conditions, we demonstrate asymptotic normality when $k$ grows with $n$ by utilizing existing results for $U$-statistics. The key in our approach lies in a mathematical reduction to $U$-statistics by designing an equivalent kernel for $V$-statistics. We also provide a unified treatment on variance estimation for both $U$- and $V$-statistics by observing connections to existing methods and proposing an empirically more accurate estimator. Ensemble methods such as random forests, where multiple base learners are trained and aggregated for prediction purposes, serve as a running example throughout the paper because they are a natural and flexible application of $V$-statistics.

Given a multivariate data set, sparse principal component analysis (SPCA) aims to extract several linear combinations of the variables that together explain the variance in the data as much as possible, while controlling the number of nonzero loadings in these combinations. In this paper we consider 8 different optimization formulations for computing a single sparse loading vector; these are obtained by combining the following factors: we employ two norms for measuring variance (L2, L1) and two sparsity-inducing norms (L0, L1), which are used in two different ways (constraint, penalty). Three of our formulations, notably the one with L0 constraint and L1 variance, have not been considered in the literature. We give a unifying reformulation which we propose to solve via a natural alternating maximization (AM) method. We show the the AM method is nontrivially equivalent to GPower (Journ'{e}e et al; JMLR 11:517--553, 2010) for all our formulations. Besides this, we provide 24 efficient parallel SPCA implementations: 3 codes (multi-core, GPU and cluster) for each of the 8 problems. Parallelism in the methods is aimed at i) speeding up computations (our GPU code can be 100 times faster than an efficient serial code written in C++), ii) obtaining solutions explaining more variance and iii) dealing with big data problems (our cluster code is able to solve a 357 GB problem in about a minute).

In the variational relevance vector machine, the gamma distribution is representative as a hyperprior over the noise precision of automatic relevance determination prior. Instead of the gamma hyperprior, we propose to use the inverse gamma hyperprior with a shape parameter close to zero and a scale parameter not necessary close to zero. This hyperprior is associated with the concept of a weakly informative prior. The effect of this hyperprior is investigated through regression to non-homogeneous data. Because it is difficult to capture the structure of such data with a single kernel function, we apply the multiple kernel method, in which multiple kernel functions with different widths are arranged for input data. We confirm that the degrees of freedom in a model is controlled by adjusting the scale parameter and keeping the shape parameter close to zero. A candidate for selecting the scale parameter is the predictive information criterion. However the estimated model using this criterion seems to cause over-fitting. This is because the multiple kernel method makes the model a situation where the dimension of the model is larger than the data size. To select an appropriate scale parameter even in such a situation, we also propose an extended prediction information criterion. It is confirmed that a multiple kernel relevance vector regression model with good predictive accuracy can be obtained by selecting the scale parameter minimizing extended prediction information criterion.

Machine learning models with both good predictability and high interpretability are crucial for decision support systems. Linear regression is one of the most interpretable prediction models. However, the linearity in a simple linear regression worsens its predictability. In this work, we introduce a locally adaptive interpretable regression (LoAIR). In LoAIR, a metamodel parameterized by neural networks predicts percentile of a Gaussian distribution for the regression coefficients for a rapid adaptation. Our experimental results on public benchmark datasets show that our model not only achieves comparable or better predictive performance than the other state-of-the-art baselines but also discovers some interesting relationships between input and target variables such as a parabolic relationship between CO2 emissions and Gross National Product (GNP). Therefore, LoAIR is a step towards bridging the gap between econometrics, statistics, and machine learning by improving the predictive ability of linear regression without depreciating its interpretability.

Uncertainties in a structure is inevitable, which generally lead to variation in dynamic response predictions. For a complex structure, brute force Monte Carlo simulation for response variation analysis is infeasible since one single run may already be computationally costly. Data driven meta-modeling approaches have thus been explored to facilitate efficient emulation and statistical inference. The performance of a meta-model hinges upon both the quality and quantity of training dataset. In actual practice, however, high-fidelity data acquired from high-dimensional finite element simulation or experiment are generally scarce, which poses significant challenge to meta-model establishment. In this research, we take advantage of the multi-level response prediction opportunity in structural dynamic analysis, i.e., acquiring rapidly a large amount of low-fidelity data from reduced-order modeling, and acquiring accurately a small amount of high-fidelity data from full-scale finite element analysis. Specifically, we formulate a composite neural network fusion approach that can fully utilize the multi-level, heterogeneous datasets obtained. It implicitly identifies the correlation of the low- and high-fidelity datasets, which yields improved accuracy when compared with the state-of-the-art. Comprehensive investigations using frequency response variation characterization as case example are carried out to demonstrate the performance.

Model Stealing (MS) attacks allow an adversary with black-box access to a Machine Learning model to replicate its functionality, compromising the confidentiality of the model. Such attacks train a clone model by using the predictions of the target model for different inputs. The effectiveness of such attacks relies heavily on the availability of data necessary to query the target model. Existing attacks either assume partial access to the dataset of the target model or availability of an alternate dataset with semantic similarities.

This paper proposes MAZE -- a data-free model stealing attack using zeroth-order gradient estimation. In contrast to prior works, MAZE does not require any data and instead creates synthetic data using a generative model. Inspired by recent works in data-free Knowledge Distillation (KD), we train the generative model using a disagreement objective to produce inputs that maximize disagreement between the clone and the target model. However, unlike the white-box setting of KD, where the gradient information is available, training a generator for model stealing requires performing black-box optimization, as it involves accessing the target model under attack. MAZE relies on zeroth-order gradient estimation to perform this optimization and enables a highly accurate MS attack.

Our evaluation with four datasets shows that MAZE provides a normalized clone accuracy in the range of 0.91x to 0.99x, and outperforms even the recent attacks that rely on partial data (JBDA, clone accuracy 0.13x to 0.69x) and surrogate data (KnockoffNets, clone accuracy 0.52x to 0.97x). We also study an extension of MAZE in the partial-data setting and develop MAZE-PD, which generates synthetic data closer to the target distribution. MAZE-PD further improves the clone accuracy (0.97x to 1.0x) and reduces the query required for the attack by 2x-24x.

François Bachoc, David Preinerstorfer, Lukas Steinberger.

Source: The Annals of Statistics, Volume 48, Number 1, 440--463.

Abstract:
We suggest general methods to construct asymptotically uniformly valid confidence intervals post-model-selection. The constructions are based on principles recently proposed by Berk et al. ( Ann. Statist. 41 (2013) 802–837). In particular, the candidate models used can be misspecified, the target of inference is model-specific, and coverage is guaranteed for any data-driven model selection procedure. After developing a general theory, we apply our methods to practically important situations where the candidate set of models, from which a working model is selected, consists of fixed design homoskedastic or heteroskedastic linear models, or of binary regression models with general link functions. In an extensive simulation study, we find that the proposed confidence intervals perform remarkably well, even when compared to existing methods that are tailored only for specific model selection procedures.

Yuxin Chen, Jianqing Fan, Cong Ma, Kaizheng Wang.

Source: The Annals of Statistics, Volume 47, Number 4, 2204--2235.

Abstract:
This paper is concerned with the problem of top-$K$ ranking from pairwise comparisons. Given a collection of $n$ items and a few pairwise comparisons across them, one wishes to identify the set of $K$ items that receive the highest ranks. To tackle this problem, we adopt the logistic parametric model—the Bradley–Terry–Luce model, where each item is assigned a latent preference score, and where the outcome of each pairwise comparison depends solely on the relative scores of the two items involved. Recent works have made significant progress toward characterizing the performance (e.g., the mean square error for estimating the scores) of several classical methods, including the spectral method and the maximum likelihood estimator (MLE). However, where they stand regarding top-$K$ ranking remains unsettled. We demonstrate that under a natural random sampling model, the spectral method alone, or the regularized MLE alone, is minimax optimal in terms of the sample complexity—the number of paired comparisons needed to ensure exact top-$K$ identification, for the fixed dynamic range regime. This is accomplished via optimal control of the entrywise error of the score estimates. We complement our theoretical studies by numerical experiments, confirming that both methods yield low entrywise errors for estimating the underlying scores. Our theory is established via a novel leave-one-out trick, which proves effective for analyzing both iterative and noniterative procedures. Along the way, we derive an elementary eigenvector perturbation bound for probability transition matrices, which parallels the Davis–Kahan $mathop{mathrm{sin}} olimits Theta $ theorem for symmetric matrices. This also allows us to close the gap between the $ell_{2}$ error upper bound for the spectral method and the minimax lower limit.

Major League Baseball could hold a rescheduled opening day between mid-June and July 4 and play at least 80 games this season, The Athletic's Ken Rosenthal reported Tuesday.

Major League Baseball owners will discuss a length for the amateur draft next week and are likely to start the selections on the original date of June 10, a person familiar with the deliberations told The Associated Press.

Tampa Bay Rays' Blake Snell claimed champion status after defeating Lucas Giolito of the Chicago White Sox in the inaugural MLB The Show player league on Sunday.

Major League Soccer is easing its training restrictions, allowing clubs to use their practice fields but only for individual workouts and under strict rules.

The NFL campaign and an abbreviated baseball season may be possible if games are played without fans and players are kept in lockdown, U.S. President Donald Trump’s leading infectious disease adviser said Wednesday.

Interior Health officials are asking people who went to the Save-On-Foods pharmacy in Columbia Place Shopping Centre in Kamloops, March 10, 13 and 14 along with March 16 to 21 to self-isolate following a positive case of COVID-19 at the store.

A man in Kamloops, B.C., has died and two others are in hospital with life-threatening injuries after a stabbing at a party at an apartment complex Saturday night. 

The B.C. Wildfire Service and the Adams Lake Fire Department are responding to a wildfire burning east of Kamloops in B.C.'s southern Interior.

Most of the animals at the B.C. Wildlife Park in Kamloops are unfazed by the absence of paying visitors since it closed in March over COVID-19 concerns. But Blue the turkey appears "bummed," staff say.

Ontario Premier Doug Ford says the Toronto Maple Leafs' parent company has been in contact with the province about the possibility of Canada's biggest city serving as a so-called "hockey pod" for teams should the NHL resume its season.

Framed prints of Steven Camley's cartoons are available by calling 0141 302 6210.”

Framed prints of Steven Camley's cartoons are available by calling 0141 302 6210

Framed prints of Steven Camley's cartoons are available by calling 0141 302 6210.”

Framed prints of Steven Camley's cartoons are available by calling 0141 302 6210

Framed prints of Steven Camley's cartoons are available by calling 0141 302 6210.

Monday 13 January: Royals’ family meeting

This week, our resident cartoonist turned his pen on the Coronavirus outbreak, Bernie Sanders rising popularity in the United States and the battle for an SNP seat at Holyrood.

This week, our resident cartoonist turned his pen on the Coronavirus outbreak, bullying claims surrounding the home secretary and Scotland making in to the world list of top beaches.

Framed prints of Steven Camley's cartoons are available by calling 0141 302 6210

Malta. Report on the individual review of the annual submission of Malta submitted in 2017. Note by the expert review team.

More than 120,000 acres of Delaware farmland are now permanently preserved for future generations, state officials announced today, marking the 20th anniversary of the state's preservation program. With 17 farms on 2,245 acres entering the latest round of the program, the new milestone means that 24 percent of First State farmland is now preserved for perpetuity.

“I am proud to announce the largest round of Delaware farmland permanently preserved through the Delaware Agricultural Lands Preservation Program in the last several years. This is a result of federal funding from the Natural Resources Conservation Service and funding from both New Castle County and Kent County,” said Delaware Secretary of Agriculture Michael Scuse. More than 124,000 acres of Delaware farmland are now permanently preserved for future generations, with 3,039 acres of easements selected into the state’s preservation program.

More than 127,000 acres of Delaware farmland are now permanently preserved for future generations, with the purchase of the development rights of 41 farms totaling 3,534 acres. This is the 22nd consecutive year of easement selections by the Delaware Agricultural Lands Preservation Foundation.

The Delaware Agricultural Lands Preservation Foundation voted to extended district enrollment until December 31, 2018 to any agricultural landowners who want to preserve their farms and still have the opportunity to submit an application for the upcoming round. Farms must be enrolled in a preservation district before the landowner can sell an easement.

This blog post could be subtitled "To Catch a Thief" or maybe "Go ahead. Steal this blog. I dare you."* That's because I've used this technique several times to catch and report other web sites who lift the blog content from blogs.sas.com and present it as their own. Syndicating blog [...]

The post Read RSS feeds with SAS using XML or JSON appeared first on The SAS Dummy.

The restrictive immigration policies of the UK appear to be over as it has decided to offer a work visa for two years after graduation from a British university to the overseas students. UK Jobs get a boostPresently, the duration for graduates having…