Hamburg : Voss, 1886.

Leipzig : O. Wigand, 1837.

Frankfurt : Christian Winter, 1878.

Leipzig : Vogel, 1904.

Berlin : Hirschwald, 1873.

Leipzig : J.B. Hirschfeld, 1869.

Jena : Fischer, 1904.

Berlin : R. Schoetz, 1911.

Stuttgart : A. Bonz, 1879.

Berlin : Hirschwald, 1876.

Breslau : W. Koebner, 1888.

Cassel : T. Fischer, 1884.

Jena : G. Fischer, 1891.

Edinburgh : J. Dunn, 1826.

London : Smith, Elder, 1877.

Viennae : Apud Rudolphum Graeffer, 1783.

Viennae : Apud Rudolphum Graeffer, 1783.

[Glasgow] : [publisher not identified], [1884]

Newcastle : printed by K. Anderson, 1812.

Berlin : A. Hirschwald, 1870.

Wurzburg : Stahel, 1896.

Wien : W. Braumuller, 1867.

Valencia : M. Alufre, 1886.

London : Longmans, Green, Reader, and Dyer, 1877-1878.

London : H.K. Lewis, 1883.

Wien : SafÏar, 1905.

Paris : Masson, 1870.

Representatives from school districts, state education agencies, and institutions of higher education in Idaho convene to discuss teacher recruitment and retention.

The Minnesota court of appeals has again rejected a lawsuit alleging that teacher tenure and seniority rights in public schools saddle students of color with ineffective teachers and therefore violate those students' right to an adequate education.

Recruiters already are offering bonuses, free housing, and airfare to entice teachers to their remote districts—and the competition is about to get worse.

[London?] : [Endell Street Military Hospital?], [1920] (Harlesden, London N.W. 10 : Leveridge & Co.)

A growing number of schools are re-evaluating their policies on religious holidays in response to the changing demographics of their students.

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.

Berlin : Peter Lang [2019]

Beverly Hills : Sage Publications, 1976.

Oregon State guard Mikayla Pivec is the epitome of a versatile player. Her 1,030 career rebounds were the most in school history, and she finished just one assist shy of becoming the first in OSU history to tally 1,500 points, 1,000 rebounds and 500 assists. She'll head to the WNBA looking to showcase her talents at the next level following the 2020 WNBA Draft.

Yong-Chao Zhang, Na Zhang.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 1, 184--191.

Abstract:
We investigate the equivalence of dynamic and static asset allocations in the case where the price process of a risky asset is driven by a Poisson process. Under some mild conditions, we obtain a necessary and sufficient condition for the equivalence of dynamic and static asset allocations. In addition, we provide a simple sufficient condition for the equivalence.

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

Identification of input data points relevant for the classifier (i.e. serve as the support vector) has recently spurred the interest of researchers for both interpretability as well as dataset debugging. This paper presents an in-depth analysis of the methods which attempt to identify the influence of these data points on the resulting classifier. To quantify the quality of the influence, we curated a set of experiments where we debugged and pruned the dataset based on the influence information obtained from different methods. To do so, we provided the classifier with mislabeled examples that hampered the overall performance. Since the classifier is a combination of both the data and the model, therefore, it is essential to also analyze these influences for the interpretability of deep learning models. Analysis of the results shows that some interpretability methods can detect mislabels better than using a random approach, however, contrary to the claim of these methods, the sample selection based on the training loss showed a superior performance.

Søren Wengel Mogensen, Niels Richard Hansen.

Source: The Annals of Statistics, Volume 48, Number 1, 539--559.

Abstract:
Symmetric independence relations are often studied using graphical representations. Ancestral graphs or acyclic directed mixed graphs with $m$-separation provide classes of symmetric graphical independence models that are closed under marginalization. Asymmetric independence relations appear naturally for multivariate stochastic processes, for instance, in terms of local independence. However, no class of graphs representing such asymmetric independence relations, which is also closed under marginalization, has been developed. We develop the theory of directed mixed graphs with $mu $-separation and show that this provides a graphical independence model class which is closed under marginalization and which generalizes previously considered graphical representations of local independence. Several graphs may encode the same set of independence relations and this means that in many cases only an equivalence class of graphs can be identified from observational data. For statistical applications, it is therefore pivotal to characterize graphs that induce the same independence relations. Our main result is that for directed mixed graphs with $mu $-separation each equivalence class contains a maximal element which can be constructed from the independence relations alone. Moreover, we introduce the directed mixed equivalence graph as the maximal graph with dashed and solid edges. This graph encodes all information about the edges that is identifiable from the independence relations, and furthermore it can be computed efficiently from the maximal graph.

Seth Flaxman, Michael Chirico, Pau Pereira, Charles Loeffler.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2564--2585.

Abstract:
We propose a generic spatiotemporal event forecasting method which we developed for the National Institute of Justice’s (NIJ) Real-Time Crime Forecasting Challenge (National Institute of Justice (2017)). Our method is a spatiotemporal forecasting model combining scalable randomized Reproducing Kernel Hilbert Space (RKHS) methods for approximating Gaussian processes with autoregressive smoothing kernels in a regularized supervised learning framework. While the smoothing kernels capture the two main approaches in current use in the field of crime forecasting, kernel density estimation (KDE) and self-exciting point process (SEPP) models, the RKHS component of the model can be understood as an approximation to the popular log-Gaussian Cox Process model. For inference, we discretize the spatiotemporal point pattern and learn a log-intensity function using the Poisson likelihood and highly efficient gradient-based optimization methods. Model hyperparameters including quality of RKHS approximation, spatial and temporal kernel lengthscales, number of autoregressive lags and bandwidths for smoothing kernels as well as cell shape, size and rotation, were learned using cross validation. Resulting predictions significantly exceeded baseline KDE estimates and SEPP models for sparse events.