Greenland -- Discovery and exploration.

Fascism -- History -- 21st century.

1954.

As teachers across the country grapple with the challenges that come with remote learning due to the coronavirus pandemic, an elementary school teacher on Maryland’s Eastern Shore faces the added challenge of a lack of internet access at home. 

European Sources Online (ESO) is an online database and information service which provides access to information on the institutions and activities of the European Union, the countries, regions and other international organisations of Europe, and on issues of importance to European researchers, citizens and stakeholders.

Early Modern Letters Online is a combined finding aid and editorial interface for basic descriptions of early modern correspondence: a collaboratively populated union catalogue of sixteenth, seventeenth, and eighteenth-century letters.

Brill Online Books and Journals is the delivery platform that provides online access to the full text of individual Brill journals. The Wellcome Library offers access to selected titles from this publisher.

As a new index in the Web of Science™ Core Collection, Emerging Sources Citation Index (ESCI) expands the citation universe and reflects the growing global body of science and scholarly activity. Coverage is from 2015-

Wiley Online Library hosts a multidisciplinary collection of online resources covering life, health and physical sciences, social science, and the humanities. The Wellcome Library offers access to selected titles in this collection.

Wiley Online Library hosts a multidisciplinary collection of online resources covering life, health and physical sciences, social science, and the humanities. The Wellcome Library offers access to selected titles in this collection.

This is a subset of full text journal backfiles in the arts, humanities, and social sciences from the Periodicals Archive Online collection, dating from 1891-.

Berlin : Fischer's Medicin. Buchhandlung, H. Kornfeld, 1897.

Berlin : A. Hirschwald, 1845.

Leipzig : Deuticke, 1905.

Wien : W. Braumuller, 1867.

Stuttgart : F. Enke, 1883.

Berlin : Hirschwald, 1887.

Jena : G. Fischer, 1900.

Braunschweig : G. Westermann, 1874.

Berlin : A. Hirschwald, 1884.

Heidelberg : C. Winter, 1878.

Bamberg : Verlag des literarisch-artistischen Instituts, 1837.

Leipzig : W. Engelmann, 1870.

Leipzig : W. Engelmann, 1863.

Stuttgart : F. Enke, 1894.

Weimar : F. Voigt, 1874-1882.

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

Tubingen : C.F. Osiander, 1820.

Wien : W. Braumuller, 1867.

Leipzig : J.A. Barth, 1899.

London : Macmillan, 1896.

London : printed for P. M'Queen, J. Laikington [sic], J. Mathews; Wilson and Spence, York; J. Binns, Leeds; Jo. Clarke and Co. Manchester [and 1 in Gainsborough, 1 in Leith], M.DCC.LXXXVIII [1788]

Mannheim : T. Loeffler, 1812.

[Paris?] : Mac. S. [le tout dirigé & mis au jour, par les soins de Eugene Mac-Swiny], [1741?]

München [Munich] : Jos. Albert K.B. Hof-Photograph, [1860]

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

We may have eliminated a suspect from the mystery of the missing puck from Game 6 of the 2010 Stanley Cup Final.

A 24-team Stanley Cup Playoffs? Here's a look at the latest NHL buzz and a hypothetical Flyers matchup. By Jordan Hall

Halle (Saale) : Deutsche Akademie der Naturforscher Leopoldina - Nationale Akademie der Wissenschaften ; Stuttgart : Wissenschaftliche Verlagsgesellschaft, 2019.

This paper considers online convex optimization over a complicated constraint set, which typically consists of multiple functional constraints and a set constraint. The conventional online projection algorithm (Zinkevich, 2003) can be difficult to implement due to the potentially high computation complexity of the projection operation. In this paper, we relax the functional constraints by allowing them to be violated at each round but still requiring them to be satisfied in the long term. This type of relaxed online convex optimization (with long term constraints) was first considered in Mahdavi et al. (2012). That prior work proposes an algorithm to achieve $O(sqrt{T})$ regret and $O(T^{3/4})$ constraint violations for general problems and another algorithm to achieve an $O(T^{2/3})$ bound for both regret and constraint violations when the constraint set can be described by a finite number of linear constraints. A recent extension in Jenatton et al. (2016) can achieve $O(T^{max{ heta,1- heta}})$ regret and $O(T^{1- heta/2})$ constraint violations where $ hetain (0,1)$. The current paper proposes a new simple algorithm that yields improved performance in comparison to prior works. The new algorithm achieves an $O(sqrt{T})$ regret bound with $O(1)$ constraint violations.

Sliced inverse regression is an effective paradigm that achieves the goal of dimension reduction through replacing high dimensional covariates with a small number of linear combinations. It does not impose parametric assumptions on the dependence structure. More importantly, such a reduction of dimension is sufficient in that it does not cause loss of information. In this paper, we adapt the stationary sliced inverse regression to cope with the rapidly changing environments. We propose to implement sliced inverse regression in an online fashion. This online learner consists of two steps. In the first step we construct an online estimate for the kernel matrix; in the second step we propose two online algorithms, one is motivated by the perturbation method and the other is originated from the gradient descent optimization, to perform online singular value decomposition. The theoretical properties of this online learner are established. We demonstrate the numerical performance of this online learner through simulations and real world applications. All numerical studies confirm that this online learner performs as well as the batch learner.

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.

People learn to discriminate between classes without explicit exposure to negative examples. On the contrary, traditional machine learning algorithms often rely on negative examples, otherwise the model would be prone to collapse and always-true predictions. Therefore, it is crucial to design the learning objective which leads the model to converge and to perform predictions unbiasedly without explicit negative signals. In this paper, we propose a Collectively loss function to learn from only Positive and Unlabeled data (cPU). We theoretically elicit the loss function from the setting of PU learning. We perform intensive experiments on the benchmark and real-world datasets. The results show that cPU consistently outperforms the current state-of-the-art PU learning methods.

Eric D. Kolaczyk, Lizhen Lin, Steven Rosenberg, Jackson Walters, Jie Xu.

Source: The Annals of Statistics, Volume 48, Number 1, 514--538.

Abstract:
It is becoming increasingly common to see large collections of network data objects, that is, data sets in which a network is viewed as a fundamental unit of observation. As a result, there is a pressing need to develop network-based analogues of even many of the most basic tools already standard for scalar and vector data. In this paper, our focus is on averages of unlabeled, undirected networks with edge weights. Specifically, we (i) characterize a certain notion of the space of all such networks, (ii) describe key topological and geometric properties of this space relevant to doing probability and statistics thereupon, and (iii) use these properties to establish the asymptotic behavior of a generalized notion of an empirical mean under sampling from a distribution supported on this space. Our results rely on a combination of tools from geometry, probability theory and statistical shape analysis. In particular, the lack of vertex labeling necessitates working with a quotient space modding out permutations of labels. This results in a nontrivial geometry for the space of unlabeled networks, which in turn is found to have important implications on the types of probabilistic and statistical results that may be obtained and the techniques needed to obtain them.

Hongbin Zhang, Lang Wu.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2140--2157.

Abstract:
For a time-to-event outcome with censored time-varying covariates, a joint Cox model with a linear mixed effects model is the standard modeling approach. In some applications such as AIDS studies, mechanistic nonlinear models are available for some covariate process such as viral load during anti-HIV treatments, derived from the underlying data-generation mechanisms and disease progression. Such a mechanistic nonlinear covariate model may provide better-predicted values when the covariates are left censored or mismeasured. When the focus is on the impact of the time-varying covariate process on the survival outcome, an accelerated failure time (AFT) model provides an excellent alternative to the Cox proportional hazard model since an AFT model is formulated to allow the influence of the outcome by the entire covariate process. In this article, we consider a nonlinear mixed effects model for the censored covariates in an AFT model, implemented using a Monte Carlo EM algorithm, under the framework of a joint model for simultaneous inference. We apply the joint model to an HIV/AIDS data to gain insights for assessing the association between viral load and immunological restoration during antiretroviral therapy. Simulation is conducted to compare model performance when the covariate model and the survival model are misspecified.