Amsterdam : Uitgegeven by Frans Buffa en Zonen, [between 1843 and 1845]

[Paris] : [Pillet aîné], [between 1827 and 1839?]

London : Longmans, Green, Reader and Dyer, 1870.

London : [Published not identified], 1871.

London : [Published not identified], 1872.

London : [Published not identified], 1873.

London : [Published not identified], 1874.

London : [Published not identified], 1875.

London : [Published not identified], 1876.

[London] : [Published not identified], [1877]

[Place of publication not identified] : [Publisher not identified], 1895.

[London] : Waterlow Bros. & Layton, Limited, [1893]

[The Netherlands] : [Romeyn de Hooghe?], [1678?]

London : Printed & sold by Thos. & John Bowles, printsellers, [1728]

Brian Whiston since 2015 has led the state's education department while it put together its plan under the Every Student Succeeds Act and took control of the state's school takeover district.

Unless they come to an agreement with the district, Chicago Teachers Union members plan to stop work Oct. 17. And the fight is about more than just pay.

Teachers in the nation's third-largest school system are fighting for salary increases, class-size caps, and a written commitment for more nurses, social workers, and librarians—as well as investments some say are outside the scope of collective bargaining.

It's been two years since Kuzy sent the Caps to the Eastern Conference Final with a legendary goal in overtime.

The win was the first of four straight for the Flyers, culminating in a historic comeback over the Bruins.

Capitals forward Evgeny Kuznetsov doesn't consider his series-winning goal against the Penguins in 2018 is the biggest goal of his life.

The Boston Bruins had planned on opening up their 2020-21 NHL regular season overseas against the Nashville Predators in Prague as part of the NHL Global Series, but that plan to visit the Czech Republic has now been postponed.

National Hockey League regular-season games that were scheduled for early in the 2020-21 campaign in Helsinki and Prague have been called off in the wake of the coronavirus pandemic. With concern about when or if the halted 2019-20 campaign might resume due to the deadly virus outbreak, the NHL and the NHL Players Association announced the decision to scrap the matchups with an eye to a 2021 return. "The NHLPA and the NHL remain committed to maintaining and growing our international presence," a joint statement said.

The Washington Capitals on Friday placed Brendan Leipsic on unconditional waivers to terminate his contract after he made disparaging comments about women and teammates in a private social media chat. In a conversation involving his brother and Florida Panthers minor leaguer Jack Rodewald, Leipsic commented on the physical appearances of Vancouver forward Tanner Pearson's wife and Edmonton captain Connor McDavid's girlfriend. The NHL called it ''inexcusable conduct'' and said it would address the matter with the Capitals and Panthers.

Edinburgh, Scotland : ANSR, 1993.

Edinburgh, Scotland : ANSR, 1993.

[Place of publication not identified] : Asian Age, 1997.

England : League of Nations, 1925.

Rockville, Maryland : National Institute on Drug Abuse, 1976.

Rockville, Maryland : U.S. Dept. of Health, Education, and Welfare, Public Health Service, Alcohol, Drug Abuse and Mental Health Administration, 1976.

Rockville, Maryland : National Institute on Drug Abuse, 1979.

Rockville, Maryland : National Institute on Drug Abuse, 1979.

Rockville, Maryland : National Institute on Drug Abuse, 1979.

[London] : [publisher not identified], [2019]

[Hong Kong] : [Art In Hospitals], [2019]

Norwich : University of East Anglia : Social Work Today, 1984.

CHICAGO (AP) -- Chicago State women’s coach Misty Opat resigned Thursday after two seasons and a 3-55 record.

During Wednesday's "Pac-12 Perspective" podcast, Natalie Chou shared why she is using her platform to speak out against racism she sees in her community related to the novel coronavirus.

On the day Kobe Bryant suddenly passed away, the Beavers embraced their rivals at midcourt in a moment of strength to support the Ducks, many of whom had personal connections to Bryant and his daughter, Gigi. For this, Oregon State is the 2020 recipient of the Pac-12 Sportsmanship Award.

Recurrent Neural Networks have been widely used to process sequence data, but have long been criticized for their biological implausibility and training difficulties related to vanishing and exploding gradients. This paper presents a novel algorithm for training recurrent networks, target propagation through time (TPTT), that outperforms standard backpropagation through time (BPTT) on four out of the five problems used for testing. The proposed algorithm is initially tested and compared to BPTT on four synthetic time lag tasks, and its performance is also measured using the sequential MNIST data set. In addition, as TPTT uses target propagation, it allows for discrete nonlinearities and could potentially mitigate the credit assignment problem in more complex recurrent architectures.

We study the misclassification error for community detection in general heterogeneous stochastic block models (SBM) with noisy or partial label information. We establish a connection between the misclassification rate and the notion of minimum energy on the local neighborhood of the SBM. We develop an optimally weighted message passing algorithm to reconstruct labels for SBM based on the minimum energy flow and the eigenvectors of a certain Markov transition matrix. The general SBM considered in this paper allows for unequal-size communities, degree heterogeneity, and different connection probabilities among blocks. We focus on how to optimally weigh the message passing to improve misclassification.

A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to obtain a global posterior approximation. While being conceptually and computationally appealing, this method involves the problematic need to also split the prior for the local inferences; these weakened priors may not provide enough regularization for each separate computation, thus eliminating one of the key advantages of Bayesian methods. To resolve this dilemma while still retaining the generalizability of the underlying local inference method, we apply the idea of expectation propagation (EP) as a framework for distributed Bayesian inference. The central idea is to iteratively update approximations to the local likelihoods given the state of the other approximations and the prior. The present paper has two roles: we review the steps that are needed to keep EP algorithms numerically stable, and we suggest a general approach, inspired by EP, for approaching data partitioning problems in a way that achieves the computational benefits of parallelism while allowing each local update to make use of relevant information from the other sites. In addition, we demonstrate how the method can be applied in a hierarchical context to make use of partitioning of both data and parameters. The paper describes a general algorithmic framework, rather than a specific algorithm, and presents an example implementation for it.

We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate rapid prototyping and promote reproducible research. We also provide modular APIs with flexible building blocks to enable efficient customization. Leveraging the MXNet ecosystem, the deep learning models in GluonCV and GluonNLP can be deployed onto a variety of platforms with different programming languages. The Apache 2.0 license has been adopted by GluonCV and GluonNLP to allow for software distribution, modification, and usage.

We study the identity testing problem in the context of spin systems or undirected graphical models, where it takes the following form: given the parameter specification of the model $M$ and a sampling oracle for the distribution $mu_{M^*}$ of an unknown model $M^*$, can we efficiently determine if the two models $M$ and $M^*$ are the same? We consider identity testing for both soft-constraint and hard-constraint systems. In particular, we prove hardness results in two prototypical cases, the Ising model and proper colorings, and explore whether identity testing is any easier than structure learning. For the ferromagnetic (attractive) Ising model, Daskalakis et al. (2018) presented a polynomial-time algorithm for identity testing. We prove hardness results in the antiferromagnetic (repulsive) setting in the same regime of parameters where structure learning is known to require a super-polynomial number of samples. Specifically, for $n$-vertex graphs of maximum degree $d$, we prove that if $|eta| d = omega(log{n})$ (where $eta$ is the inverse temperature parameter), then there is no polynomial running time identity testing algorithm unless $RP=NP$. In the hard-constraint setting, we present hardness results for identity testing for proper colorings. Our results are based on the presumed hardness of #BIS, the problem of (approximately) counting independent sets in bipartite graphs.

pyts is an open-source Python package for time series classification. This versatile toolbox provides implementations of many algorithms published in the literature, preprocessing functionalities, and data set loading utilities. pyts relies on the standard scientific Python packages numpy, scipy, scikit-learn, joblib, and numba, and is distributed under the BSD-3-Clause license. Documentation contains installation instructions, a detailed user guide, a full API description, and concrete self-contained examples.