Information visualization.

Women journalists -- Fiction.

Sex discrimination against women.

UK : Institute of Ophthalmology, 1951.

UK : Institute of Ophthalmology, 1951.

The ICRC’s collections of sound recordings, film footage, videos and photos illustrate and document the activities of the ICRC and of the International Red Cross and Red Crescent Movement as a whole from the end of the 19th century up to the present day, in all operational contexts

U.S. Secretary of Education Betsy DeVos will hold a workforce event at a South Carolina drug manufacturer that employs hundreds of cash-strapped teachers in second jobs.

Wiesbaden : C. Limbarth, 1882.

London : printed for the Council by Spottiswoode, 1903.

London : Layton, 1903.

London : Longman, Brown, Green, and Longmans, 1842.

Edinburgh : Young J. Pentland, 1887.

Edinburgh : Young J. Pentland, 1889.

Oxford : printed by W. Baxter, 1843.

London : John J. Griffin, 1851.

London, 1826.

London : Bailliere, Tindall, & Cox, 1880.

London : Macmillan, 1878.

London : Longmans, Green, 1902.

London : Taylor and Walton, 1841.

Edinburgh : Young J. Pentland, 1887.

Florida's governor signed a bill that will allow schools to arm classroom teachers, part of a longer list of school safety changes made after a school shooting in Parkland, Fla., last year.

Governors' races in Kentucky and Mississippi took center stage, testing the political muscle of teacher activists and yielding possible policy implications for everything from public employee pensions to teacher pay.

London (No. 4, Hanover Street) : Published ... for the proprietor by T.G. March ; [London] (Threadneedle Street) : Sold also by F. G. Moon ; [London] (Strand) : [Sold also by] Ackermann & Co., April 10, 1842 ([London?] : Printed by R. Lloyd)

Berlin (Louisen-Str. 53) : Published by F. Silber, [between 1800 and 1899]

[Rome?]

Kerfoot discussing his first Maple Leafs season spurred a larger discussion.

England : Harrogate Corporation, Wells and Baths Department, 1945.

New York : Experimental Manpower Laboratory at Mobilization for Youth, Inc., [1971]

Washington : Dept. of Transportation, National Highway Traffic Safety Administration, 1976.

Washington, D.C. : Wynne Associates, 1974.

Washington, D.C. : Wynne Associates, [1974]

London : London Research Centre, 1991.

[Dorchester] : Dorset Community NHS Trust, 1997.

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.

This paper considers a Bayesian approach to graph-based semi-supervised learning. We show that if the graph parameters are suitably scaled, the graph-posteriors converge to a continuum limit as the size of the unlabeled data set grows. This consistency result has profound algorithmic implications: we prove that when consistency holds, carefully designed Markov chain Monte Carlo algorithms have a uniform spectral gap, independent of the number of unlabeled inputs. Numerical experiments illustrate and complement the theory.

Application and use of deep learning algorithms for different healthcare applications is gaining interest at a steady pace. However, use of such algorithms can prove to be challenging as they require large amounts of training data that capture different possible variations. This makes it difficult to use them in a clinical setting since in most health applications researchers often have to work with limited data. Less data can cause the deep learning model to over-fit. In this paper, we ask how can we use data from a different environment, different use-case, with widely differing data distributions. We exemplify this use case by using single-sensor accelerometer data from healthy subjects performing activities of daily living - ADLs (source dataset), to extract features relevant to multi-sensor accelerometer gait data (target dataset) for Parkinson's disease classification. We train the pre-trained model using the source dataset and use it as a feature extractor. We show that the features extracted for the target dataset can be used to train an effective classification model. Our pre-trained source model consists of a convolutional autoencoder, and the target classification model is a simple multi-layer perceptron model. We explore two different pre-trained source models, trained using different activity groups, and analyze the influence the choice of pre-trained model has over the task of Parkinson's disease classification.

Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep learning algorithms typically results in black box models, where the path leading to individual predictions cannot be followed in detail. In order to address this issue, we propose the novel "Cyclic Boosting" machine learning algorithm, which allows to efficiently perform accurate regression and classification tasks while at the same time allowing a detailed understanding of how each individual prediction was made.

In this paper we introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning. Plan2vec constructs a weighted graph on an image dataset using near-neighbor distances, and then extrapolates this local metric to a global embedding by distilling path-integral over planned path. When applied to control, plan2vec offers a way to learn goal-conditioned value estimates that are accurate over long horizons that is both compute and sample efficient. We demonstrate the effectiveness of plan2vec on one simulated and two challenging real-world image datasets. Experimental results show that plan2vec successfully amortizes the planning cost, enabling reactive planning that is linear in memory and computation complexity rather than exhaustive over the entire state space.

Increasing number of sectors which affect human lives, are using Machine Learning (ML) tools. Hence the need for understanding their working mechanism and evaluating their fairness in decision-making, are becoming paramount, ushering in the era of Explainable AI (XAI). In this contribution we introduced a few intrinsically interpretable models which are also capable of dealing with missing values, in addition to extracting knowledge from the dataset and about the problem. These models are also capable of visualisation of the classifier and decision boundaries: they are the angle based variants of Learning Vector Quantization. We have demonstrated the algorithms on a synthetic dataset and a real-world one (heart disease dataset from the UCI repository). The newly developed classifiers helped in investigating the complexities of the UCI dataset as a multiclass problem. The performance of the developed classifiers were comparable to those reported in literature for this dataset, with additional value of interpretability, when the dataset was treated as a binary class problem.

Anru Zhang, Lawrence D. Brown, T. Tony Cai.

Source: The Annals of Statistics, Volume 47, Number 5, 2538--2566.

Abstract:
We propose a general semi-supervised inference framework focused on the estimation of the population mean. As usual in semi-supervised settings, there exists an unlabeled sample of covariate vectors and a labeled sample consisting of covariate vectors along with real-valued responses (“labels”). Otherwise, the formulation is “assumption-lean” in that no major conditions are imposed on the statistical or functional form of the data. We consider both the ideal semi-supervised setting where infinitely many unlabeled samples are available, as well as the ordinary semi-supervised setting in which only a finite number of unlabeled samples is available. Estimators are proposed along with corresponding confidence intervals for the population mean. Theoretical analysis on both the asymptotic distribution and $ell_{2}$-risk for the proposed procedures are given. Surprisingly, the proposed estimators, based on a simple form of the least squares method, outperform the ordinary sample mean. The simple, transparent form of the estimator lends confidence to the perception that its asymptotic improvement over the ordinary sample mean also nearly holds even for moderate size samples. The method is further extended to a nonparametric setting, in which the oracle rate can be achieved asymptotically. The proposed estimators are further illustrated by simulation studies and a real data example involving estimation of the homeless population.

Sébastien Gadat, Sébastien Gerchinovitz, Clément Marteau.

Source: Bernoulli, Volume 26, Number 3, 1797--1831.

Abstract:
We consider the binary supervised classification problem with the Gaussian functional model introduced in ( Math. Methods Statist. 22 (2013) 213–225). Taking advantage of the Gaussian structure, we design a natural plug-in classifier and derive a family of upper bounds on its worst-case excess risk over Sobolev spaces. These bounds are parametrized by a separation distance quantifying the difficulty of the problem, and are proved to be optimal (up to logarithmic factors) through matching minimax lower bounds. Using the recent works of (In Advances in Neural Information Processing Systems (2014) 3437–3445 Curran Associates) and ( Ann. Statist. 44 (2016) 982–1009), we also derive a logarithmic lower bound showing that the popular $k$-nearest neighbors classifier is far from optimality in this specific functional setting.