Republican legislators last month replaced the state's accountability system with a new one amid debate over the powers of the governor. The state education department says it's not ESSA-compliant.

New state mandates start next school year aimed at identifying and supporting students with dyslexia. The 2016 law also led to development of training for teachers.

Feature screening is a powerful tool in processing high-dimensional data. When the sample size N and the number of features p are both large, the implementation of classic screening methods can be numerically challenging. In this paper, we propose a distributed screening framework for big data setup. In the spirit of 'divide-and-conquer', the proposed framework expresses a correlation measure as a function of several component parameters, each of which can be distributively estimated using a natural U-statistic from data segments. With the component estimates aggregated, we obtain a final correlation estimate that can be readily used for screening features. This framework enables distributed storage and parallel computing and thus is computationally attractive. Due to the unbiased distributive estimation of the component parameters, the final aggregated estimate achieves a high accuracy that is insensitive to the number of data segments m. Under mild conditions, we show that the aggregated correlation estimator is as efficient as the centralized estimator in terms of the probability convergence bound and the mean squared error rate; the corresponding screening procedure enjoys sure screening property for a wide range of correlation measures. The promising performances of the new method are supported by extensive numerical examples.

Given a response $Y$ and a vector $X = (X^1, dots, X^d)$ of $d$ predictors, we investigate the problem of inferring direct causes of $Y$ among the vector $X$. Models for $Y$ that use all of its causal covariates as predictors enjoy the property of being invariant across different environments or interventional settings. Given data from such environments, this property has been exploited for causal discovery. Here, we extend this inference principle to situations in which some (discrete-valued) direct causes of $ Y $ are unobserved. Such cases naturally give rise to switching regression models. We provide sufficient conditions for the existence, consistency and asymptotic normality of the MLE in linear switching regression models with Gaussian noise, and construct a test for the equality of such models. These results allow us to prove that the proposed causal discovery method obtains asymptotic false discovery control under mild conditions. We provide an algorithm, make available code, and test our method on simulated data. It is robust against model violations and outperforms state-of-the-art approaches. We further apply our method to a real data set, where we show that it does not only output causal predictors, but also a process-based clustering of data points, which could be of additional interest to practitioners.

We present a probabilistic framework for studying adversarial attacks on discrete data. Based on this framework, we derive a perturbation-based method, Greedy Attack, and a scalable learning-based method, Gumbel Attack, that illustrate various tradeoffs in the design of attacks. We demonstrate the effectiveness of these methods using both quantitative metrics and human evaluation on various state-of-the-art models for text classification, including a word-based CNN, a character-based CNN and an LSTM. As an example of our results, we show that the accuracy of character-based convolutional networks drops to the level of random selection by modifying only five characters through Greedy Attack.

Ahmad Younso.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 1, 112--126.

Abstract:
We consider a new nonparametric rule of classification, inspired from the classical moving window rule, that allows for the classification of spatially dependent functional data containing some completely missing curves. We investigate the consistency of this classifier under mild conditions. The practical use of the classifier will be illustrated through simulation studies.

Sophie Achard, Jean-François Coeurjolly

Source: Statist. Surv., Volume 4, 117--147.

Abstract:
This paper gives an overview of the problem of estimating the Hurst parameter of a fractional Brownian motion when the data are observed with outliers and/or with an additive noise by using methods based on discrete variations. We show that the classical estimation procedure based on the log-linearity of the variogram of dilated series is made more robust to outliers and/or an additive noise by considering sample quantiles and trimmed means of the squared series or differences of empirical variances. These different procedures are compared and discussed through a large simulation study and are implemented in the R package dvfBm.

Reducing domain divergence is a key step in transfer learning problems. Existing works focus on the minimization of global domain divergence. However, two domains may consist of several shared subdomains, and differ from each other in each subdomain. In this paper, we take the local divergence of subdomains into account in transfer. Specifically, we propose to use low-dimensional manifold to represent subdomain, and align the local data distribution discrepancy in each manifold across domains. A Manifold Maximum Mean Discrepancy (M3D) is developed to measure the local distribution discrepancy in each manifold. We then propose a general framework, called Transfer with Manifolds Discrepancy Alignment (TMDA), to couple the discovery of data manifolds with the minimization of M3D. We instantiate TMDA in the subspace learning case considering both the linear and nonlinear mappings. We also instantiate TMDA in the deep learning framework. Extensive experimental studies demonstrate that TMDA is a promising method for various transfer learning tasks.

A free manuscripts workshop for PhD students at Wellcome Collection, 01 June 2018 Engaging with an artefact from the past is often a powerful experience, eliciting emotional and sensory, as well as analytical, responses. Researchers in the library at Wellcome… Continue reading

Stephen Berg, Jun Zhu, Murray K. Clayton, Monika E. Shea, David J. Mladenoff.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2312--2340.

Abstract:
The Wisconsin Public Land Survey database describes historical forest composition at high spatial resolution and is of interest in ecological studies of forest composition in Wisconsin just prior to significant Euro-American settlement. For such studies it is useful to identify recurring subpopulations of tree species known as communities, but standard clustering approaches for subpopulation identification do not account for dependence between spatially nearby observations. Here, we develop and fit a latent discrete Markov random field model for the purpose of identifying and classifying historical forest communities based on spatially referenced multivariate tree species counts across Wisconsin. We show empirically for the actual dataset and through simulation that our latent Markov random field modeling approach improves prediction and parameter estimation performance. For model fitting we introduce a new stochastic approximation algorithm which enables computationally efficient estimation and classification of large amounts of spatial multivariate count data.

Binyan Jiang, Ziqi Chen, Chenlei Leng.

Source: Bernoulli, Volume 26, Number 2, 1234--1268.

Abstract:
High-dimensional data that evolve dynamically feature predominantly in the modern data era. As a partial response to this, recent years have seen increasing emphasis to address the dimensionality challenge. However, the non-static nature of these datasets is largely ignored. This paper addresses both challenges by proposing a novel yet simple dynamic linear programming discriminant (DLPD) rule for binary classification. Different from the usual static linear discriminant analysis, the new method is able to capture the changing distributions of the underlying populations by modeling their means and covariances as smooth functions of covariates of interest. Under an approximate sparse condition, we show that the conditional misclassification rate of the DLPD rule converges to the Bayes risk in probability uniformly over the range of the variables used for modeling the dynamics, when the dimensionality is allowed to grow exponentially with the sample size. The minimax lower bound of the estimation of the Bayes risk is also established, implying that the misclassification rate of our proposed rule is minimax-rate optimal. The promising performance of the DLPD rule is illustrated via extensive simulation studies and the analysis of a breast cancer dataset.

Reaction and analysis from Fox News contributor Byron York and former Florida Attorney General Pam Bondi.

Zhi-Qiang Wang, Nian-Sheng Tang.

Source: Bayesian Analysis, Volume 15, Number 2, 579--604.

Abstract:
Bayesian inference on quantile regression (QR) model with mixed discrete and non-ignorable missing covariates is conducted by reformulating QR model as a hierarchical structure model. A probit regression model is adopted to specify missing covariate mechanism. A hybrid algorithm combining the Gibbs sampler and the Metropolis-Hastings algorithm is developed to simultaneously produce Bayesian estimates of unknown parameters and latent variables as well as their corresponding standard errors. Bayesian variable selection method is proposed to recognize significant covariates. A Bayesian local influence procedure is presented to assess the effect of minor perturbations to the data, priors and sampling distributions on posterior quantities of interest. Several simulation studies and an example are presented to illustrate the proposed methodologies.

John D. Cahoy
Jan 2, 2008; 28:264-278
Cellular

Ye Zhang
Sep 3, 2014; 34:11929-11947
Cellular

Benjamin Meyer
Jul 3, 2019; 39:5326-5335
Systems/Circuits

JS Taube
Feb 1, 1990; 10:420-435
Articles

Zhengui Xia
Sep 1, 1996; 16:5425-5436
Articles

Jamie D. Roitman
Nov 1, 2002; 22:9475-9489
Behavioral

John D. Cahoy
Jan 2, 2008; 28:264-278
Cellular

Ye Zhang
Sep 3, 2014; 34:11929-11947
Cellular

Spanish translation of "Basel III: Finalising post-crisis reforms", December 2017.

Cracked or broken mobile device screens can be costly to fix, but a few inexpensive DIY strategies can eliminate a repair shop visit and salvage your tablet or phone. It is relatively easy and cheap to replace the glass on a phone once you get the hang of it. Tablets are a bit more involved because of the larger size and added components. Tools might require an additional monetary outlay.

Despite their opposing actions on food intake, POMC and NPY/AgRP neurons in the arcuate nucleus of the hypothalamus (ARH) are derived from the same progenitors that give rise to ARH neurons. However, the mechanism whereby common neuronal precursors subsequently adopt either the anorexigenic (POMC) or the orexigenic (NPY/AgRP) identity remains elusive. We hypothesize that POMC and NPY/AgRP cell fates are specified and maintained by distinct intrinsic factors. In search of them, we profiled the transcriptomes of developing POMC and NPY/AgRP neurons in mice. Moreover, cell-type-specific transcriptomic analyses revealed transcription regulators that are selectively enriched in either population, but whose developmental functions are unknown in these neurons. Among them, we found the expression of the PR domain-containing factor 12 (Prdm12) was enriched in POMC neurons but absent in NPY/AgRP neurons. To study the role of Prdm12 in vivo, we developed and characterized a floxed Prdm12 allele. Selective ablation of Prdm12 in embryonic POMC neurons led to significantly reduced Pomc expression as well as early-onset obesity in mice of either sex that recapitulates symptoms of human POMC deficiency. Interestingly, however, specific deletion of Prdm12 in adult POMC neurons showed that it is no longer required for Pomc expression or energy balance. Collectively, these findings establish a critical role for Prdm12 in the anorexigenic neuron identity and suggest that it acts developmentally to program body weight homeostasis. Finally, the combination of cell-type-specific genomic and genetic analyses provides a means to dissect cellular and functional diversity in the hypothalamus whose neurodevelopment remains poorly studied.

SIGNIFICANCE STATEMENT POMC and NPY/AgRP neurons are derived from the same hypothalamic progenitors but have opposing effects on food intake. We profiled the transcriptomes of genetically labeled POMC and NPY/AgRP neurons in the developing mouse hypothalamus to decipher the transcriptional codes behind the versus orexigenic neuron identity. Our analyses revealed 29 transcription regulators that are selectively enriched in one of the two populations. We generated new mouse genetic models to selective ablate one of POMC-neuron enriched transcription factors Prdm12 in developing and adult POMC neurons. Our studies establish a previously unrecognized role for Prdm12 in the anorexigenic neuron identity and suggest that it acts developmentally to program body weight homeostasis.

The new findings raises questions about the authenticity of a collection of texts known as the "post-2002" scrolls

The marble slab appears to be Greek in origin and may have been written in response to the death of a tyrant on the island of Kos

Many retailers are eager to hear more details from the province about when and how they should reopen after weeks of being closed to the public. The next phase of recovery is expected to start Friday.

The premier’s office confirmed the news in an email statement, blaming visibility issues under "very specific lighting conditions."

Before COVID-19 hit, Hemliss Eloïse Konan had plans for how she'd spend her summer in Sudbury. After finishing her first year at Laurentian University, Konan planned to stay in residence, and get a job for the summer.

As the Montreal area continues to be the Canadian epicentre of the COVID-19 outbreak, anyone showing symptoms of the virus is being asked to get tested.

We had to be set apart in order to feel together.

UFC 249 will proceed as planned Saturday night despite Ronaldo "Jacare" Souza being ruled out Friday following a positive test for the coronavirus. He was scheduled to oppose Uriah Hall in Jacksonville, Fla.

Poor visibility from winter-like weather has put a halt on the Snowbirds aerobatics team's plans to fly over southern Ontario on Saturday.

Mary Ellen Dalgleish, a poultry expert at Purity Feeds in Kamloops, B.C., believes the increased interest in backyard chickens follows concerns about food security when consumers saw grocery store shelves cleared out early in the COVID-19 pandemic in B.C.

The general field of quantitative biology has advanced significantly on the back of recent improvements in both sequencing technology and proteomics methods. The development of high-throughput, short-read sequencing has revolutionized RNA-based expression studies, while improvements in proteomics methods have enabled quantitative studies to attain better resolution. Here we introduce methods to undertake global analyses of gene expression through RNA and protein quantification in Xenopus embryos and tissues.

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Wink customers will soon have to pay a monthly subscription fee to access any of the smart home hardware that they have purchased.

Legislative “reform” is hurting legitimate inventors.