19th century.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

Jena : Fischer, 1897.

Wien : J. Safar, 1896.

Stuttgart : Enke, 1904.

Gottingae : Typis Jo. Christian. Dieterich, [1793]

Wien : C. Ueberreuter, 1860.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

Primary season is now upon us. Here are three ideas for teaching in the wake of the Iowa caucus fallout.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

London : Stacy Bias, 2019.

One of the common tasks in unsupervised learning is dimensionality reduction, where the goal is to find meaningful low-dimensional structures hidden in high-dimensional data. Sometimes referred to as manifold learning, this problem is closely related to the problem of localization, which aims at embedding a weighted graph into a low-dimensional Euclidean space. Several methods have been proposed for localization, and also manifold learning. Nonetheless, the robustness property of most of them is little understood. In this paper, we obtain perturbation bounds for classical scaling and trilateration, which are then applied to derive performance bounds for Isomap, Landmark Isomap, and Maximum Variance Unfolding. A new perturbation bound for procrustes analysis plays a key role.

David A. C. Mollinedo.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 1, 188--201.

Abstract:
We consider the stochastic transport equation with a possibly unbounded Hölder continuous vector field. Well-posedness is proved, namely, we show existence, uniqueness and strong stability of $W^{1,p}$-weak solutions.

Eric Beutner, Laurent Bordes, Laurent Doyen.

Source: Bernoulli, Volume 26, Number 1, 557--586.

Abstract:
Virtual age models are very useful to analyse recurrent events. Among the strengths of these models is their ability to account for treatment (or intervention) effects after an event occurrence. Despite their flexibility for modeling recurrent events, the number of applications is limited. This seems to be a result of the fact that in the semiparametric setting all the existing results assume the virtual age function that describes the treatment (or intervention) effects to be known. This shortcoming can be overcome by considering semiparametric virtual age models with parametrically specified virtual age functions. Yet, fitting such a model is a difficult task. Indeed, it has recently been shown that for these models the standard profile likelihood method fails to lead to consistent estimators. Here we show that consistent estimators can be constructed by smoothing the profile log-likelihood function appropriately. We show that our general result can be applied to most of the relevant virtual age models of the literature. Our approach shows that empirical process techniques may be a worthwhile alternative to martingale methods for studying asymptotic properties of these inference methods. A simulation study is provided to illustrate our consistency results together with an application to real data.

Stefan Klöppel
Mar 3, 2010; 30:3271-3275
BRIEF COMMUNICATION

Sebastian Onasch
Apr 15, 2020; 40:3186-3202
Systems/Circuits

Italian version of BIS Press Release - Big tech in finance: opportunities and risks, 23 June 2019

French version of BIS Press Release - Big tech in finance: opportunities and risks, 23 June 2019

Spanish version of BIS Press Release - Big tech in finance: opportunities and risks, 23 June 2019

In what could be a trial run for more of the same, Red Hat last week held a first-ever virtual technical summit to spread the word about its latest cloud tech offerings. CEO Paul Cormier welcomed online viewers to the conference, which attracted more than 80,000 virtual attendees. The company made several key announcements during the online gathering and highlighted customer innovations.

Neurons and circuits each with a distinct balance of intrinsic and synaptic conductances can generate similar behavior but sometimes respond very differently to perturbation. Examining a large family of circuit models with non-identical neurons and synapses underlying rhythmic behavior, we analyzed the circuits' response to modifications in single and multiple intrinsic conductances in the individual neurons. To summarize these changes over the entire range of perturbed parameters, we quantified circuit output by defining a global stability measure. Using this measure, we identified specific subsets of conductances that when perturbed generate similar behavior in diverse individuals of the population. Our unbiased clustering analysis enabled us to quantify circuit stability when simultaneously perturbing multiple conductances as a nonlinear combination of single conductance perturbations. This revealed surprising conductance combinations that can predict the response to specific perturbations, even when the remaining intrinsic and synaptic conductances are unknown. Therefore, our approach can expose hidden variability in the balance of intrinsic and synaptic conductances of the same neurons across different versions of the same circuit solely from the circuit response to perturbations. Developed for a specific family of model circuits, our quantitative approach to characterizing high-dimensional degenerate systems provides a conceptual and analytic framework to guide future theoretical and experimental studies on degeneracy and robustness.

SIGNIFICANCE STATEMENT Neural circuits can generate nearly identical behavior despite neuronal and synaptic parameters varying several-fold between individual instantiations. Yet, when these parameters are perturbed through channel deletions and mutations or environmental disturbances, seemingly identical circuits can respond very differently. What distinguishes inconsequential perturbations that barely alter circuit behavior from disruptive perturbations that drastically disturb circuit output remains unclear. Focusing on a family of rhythmic circuits, we propose a computational approach to reveal hidden variability in the intrinsic and synaptic conductances in seemingly identical circuits based solely on circuit output to different perturbations. We uncover specific conductance combinations that work similarly to maintain stability and predict the effect of changing multiple conductances simultaneously, which often results from neuromodulation or injury.

The National Cherry Blossom Festival has moved online due to the novel coronavirus pandemic

Museums are closing their doors amid the coronavirus crisis, but many offer digital exhibitions visitors can browse from the comfort of home

The California landmark is closed, but you can explore its bizarre architectural features from afar

Visitors can get up close and personal with augmented reality versions of historic globes recently digitized by the British Library