Musée d'Orsay -- Exhibitions.

Sacks, Oliver, 1933-2015 -- Interviews.

Dodd, Reg, 1940-

Chomsky, Noam -- Political and social views.

Blainey, Geoffrey, 1930-

Leipzig : Duncker & Humblot, 1868.

Berlin : Gose & Tetzlaff, 1904.

Muenchen : Müller & Steinicke, 1911.

Bingen am Rhein : Selbstverlag des Verfassers, 1885.

Wiesbaden : J.F. Bergmann, 1885.

Stuttgart : Süddeutsches Verlags-Institut, 1888.

Stuttgart : Süddeutsches Verlags-Institut, 1890.

Argentorati : Sumtibus Amandi Koenig, 1786-1787.

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

Athletic forward Satou Sabally is preparing to take the leap to the WNBA level following three productive seasons at Oregon. As a junior, she averaged 16.2 points and 6.9 rebounds per game while helping the Ducks sweep the Pac-12 regular season and tournament titles. At 6-foot-4, she also drained 45 3-pointers for Oregon in 2019-20 while notching a career-best average of 2.3 assists per game.

During Friday's "Pac-12 Perspective," Stanford head coach Tara VanDerveer spoke about Haley Jones' positionless game and how the Cardinal used the dynamic freshman in 2019-20. Download and listen wherever you get your podcasts.

Latent space models are effective tools for statistical modeling and visualization of network data. Due to their close connection to generalized linear models, it is also natural to incorporate covariate information in them. The current paper presents two universal fitting algorithms for networks with edge covariates: one based on nuclear norm penalization and the other based on projected gradient descent. Both algorithms are motivated by maximizing the likelihood function for an existing class of inner-product models, and we establish their statistical rates of convergence for these models. In addition, the theory informs us that both methods work simultaneously for a wide range of different latent space models that allow latent positions to affect edge formation in flexible ways, such as distance models. Furthermore, the effectiveness of the methods is demonstrated on a number of real world network data sets for different statistical tasks, including community detection with and without edge covariates, and network assisted learning.

We present a probabilistic framework for studying adversarial attacks on discrete data. Based on this framework, we derive a perturbation-based method, Greedy Attack, and a scalable learning-based method, Gumbel Attack, that illustrate various tradeoffs in the design of attacks. We demonstrate the effectiveness of these methods using both quantitative metrics and human evaluation on various state-of-the-art models for text classification, including a word-based CNN, a character-based CNN and an LSTM. As an example of our results, we show that the accuracy of character-based convolutional networks drops to the level of random selection by modifying only five characters through Greedy Attack.

The accuracy and complexity of kernel learning algorithms is determined by the set of kernels over which it is able to optimize. An ideal set of kernels should: admit a linear parameterization (tractability); be dense in the set of all kernels (accuracy); and every member should be universal so that the hypothesis space is infinite-dimensional (scalability). Currently, there is no class of kernel that meets all three criteria - e.g. Gaussians are not tractable or accurate; polynomials are not scalable. We propose a new class that meet all three criteria - the Tessellated Kernel (TK) class. Specifically, the TK class: admits a linear parameterization using positive matrices; is dense in all kernels; and every element in the class is universal. This implies that the use of TK kernels for learning the kernel can obviate the need for selecting candidate kernels in algorithms such as SimpleMKL and parameters such as the bandwidth. Numerical testing on soft margin Support Vector Machine (SVM) problems show that algorithms using TK kernels outperform other kernel learning algorithms and neural networks. Furthermore, our results show that when the ratio of the number of training data to features is high, the improvement of TK over MKL increases significantly.

We consider a setting where multiple players sequentially choose among a common set of actions (arms). Motivated by an application to cognitive radio networks, we assume that players incur a loss upon colliding, and that communication between players is not possible. Existing approaches assume that the system is stationary. Yet this assumption is often violated in practice, e.g., due to signal strength fluctuations. In this work, we design the first multi-player Bandit algorithm that provably works in arbitrarily changing environments, where the losses of the arms may even be chosen by an adversary. This resolves an open problem posed by Rosenski et al. (2016).

Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs. This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units by means of machine-learning methods. The aim is to support decision making addressed at reducing the incidence rate of infections. In this field, it is necessary to deal with the problem of building reliable classifiers from imbalanced datasets. We propose a clustering-based undersampling strategy to be used in combination with ensemble classifiers. A comparative study with data from 4616 patients was conducted in order to validate our proposal. We applied several single and ensemble classifiers both to the original dataset and to data preprocessed by means of different resampling methods. The results were analyzed by means of classic and recent metrics specifically designed for imbalanced data classification. They revealed that the proposal is more efficient in comparison with other approaches.

Ilya Pavlyukevich, Georgiy Shevchenko.

Source: Bernoulli, Volume 26, Number 2, 1381--1409.

Abstract:
In this paper, we study the Stratonovich stochastic differential equation $mathrm{d}X=|X|^{alpha }circ mathrm{d}B$, $alpha in (-1,1)$, which has been introduced by Cherstvy et al. ( New J. Phys. 15 (2013) 083039) in the context of analysis of anomalous diffusions in heterogeneous media. We determine its weak and strong solutions, which are homogeneous strong Markov processes spending zero time at $0$: for $alpha in (0,1)$, these solutions have the form egin{equation*}X_{t}^{ heta }=((1-alpha)B_{t}^{ heta })^{1/(1-alpha )},end{equation*} where $B^{ heta }$ is the $ heta $-skew Brownian motion driven by $B$ and starting at $frac{1}{1-alpha }(X_{0})^{1-alpha }$, $ heta in [-1,1]$, and $(x)^{gamma }=|x|^{gamma }operatorname{sign}x$; for $alpha in (-1,0]$, only the case $ heta =0$ is possible. The central part of the paper consists in the proof of the existence of a quadratic covariation $[f(B^{ heta }),B]$ for a locally square integrable function $f$ and is based on the time-reversion technique for Markovian diffusions.

Peter M. Atkinson, Jorge Mateu.

Source: Statistical Science, Volume 34, Number 3, 504--521.

Abstract:
Peter John Diggle was born on February 24, 1950, in Lancashire, England. Peter went to school in Scotland, and it was at the end of his school years that he found that he was good at maths and actually enjoyed it. Peter went to Edinburgh to do a maths degree, but transferred halfway through to Liverpool where he completed his degree. Peter studied for a year at Oxford and was then appointed in 1974 as a lecturer in statistics at the University of Newcastle-upon-Tyne where he gained his PhD, and was promoted to Reader in 1983. A sabbatical at the Swedish Royal College of Forestry gave him his first exposure to real scientific data and problems, prompting a move to CSIRO, Australia. After five years with CSIRO where he was Senior, then Principal, then Chief Research Scientist and Chief of the Division of Mathematics and Statistics, he returned to the UK in 1988, to a Chair at Lancaster University. Since 2011 Peter has held appointments at Lancaster and Liverpool, together with honorary appointments at Johns Hopkins, Columbia and Yale. At Lancaster, Peter was the founder and Director of the Medical Statistics Unit (1995–2001), University Dean for Research (1998–2001), EPSRC Senior Fellow (2004–2008), Associate Dean for Research at the School of Health and Medicine (2007–2011), Distinguished University Professor, and leader of the CHICAS Research Group (2007–2017). A Fellow of the Royal Statistical Society since 1974, he was a Member of Council (1983–1985), Joint Editor of JRSSB (1984–1987), Honorary Secretary (1990–1996), awarded the Guy Medal in Silver (1997) and the Barnett Award (2018), Associate Editor of Applied Statistics (1998–2000), Chair of the Research Section Committee (1998–2000), and President (2014–2016). Away from work, Peter enjoys music, playing folk-blues guitar and tenor recorder, and listening to jazz. His running days are behind him, but he can just about hold his own in mixed-doubles badminton with his family. His boyhoood hero was Stirling Moss, and he retains an enthusiasm for classic cars, not least his 1988 Porsche 924S. His favorite authors are George Orwell, Primo Levi and Nigel Slater. This interview was done prior to the fourth Spatial Statistics conference held in Lancaster, July 2017 where a session was dedicated to Peter celebrating his contributions to statistics.

Christopher K. Wikle, Jay M. Ver Hoef.

Source: Statistical Science, Volume 34, Number 2, 349--359.

Abstract:
Noel Cressie, FAA is Director of the Centre for Environmental Informatics in the National Institute for Applied Statistics Research Australia (NIASRA) and Distinguished Professor in the School of Mathematics and Applied Statistics at the University of Wollongong, Australia. He is also Adjunct Professor at the University of Missouri (USA), Affiliate of Org 398, Science Data Understanding, at NASA’s Jet Propulsion Laboratory (USA), and a member of the Science Team for NASA’s Orbiting Carbon Observatory-2 (OCO-2) satellite. Cressie was awarded a B.Sc. with First Class Honours in Mathematics in 1972 from the University of Western Australia, and an M.A. and Ph.D. in Statistics in 1973 and 1975, respectively, from Princeton University (USA). Two brief postdoctoral periods followed, at the Centre de Morphologie Mathématique, ENSMP, in Fontainebleau (France) from April 1975–September 1975, and at Imperial College, London (UK) from September 1975–January 1976. His past appointments have been at The Flinders University of South Australia from 1976–1983, at Iowa State University (USA) from 1983–1998, and at The Ohio State University (USA) from 1998–2012. He has authored or co-authored four books and more than 280 papers in peer-reviewed outlets, covering areas that include spatial and spatio-temporal statistics, environmental statistics, empirical-Bayesian and Bayesian methods including sequential design, goodness-of-fit, and remote sensing of the environment. Many of his papers also address important questions in the sciences. Cressie is a Fellow of the Australian Academy of Science, the American Statistical Association, the Institute of Mathematical Statistics, and the Spatial Econometrics Association, and he is an Elected Member of the International Statistical Institute. Noel Cressie’s refereed, unrefereed, and other publications are available at: https://niasra.uow.edu.au/cei/people/UOW232444.html.

Sam Behseta.

Source: Statistical Science, Volume 34, Number 2, 334--348.

Abstract:
Rob Kass has been been on the faculty of the Department of Statistics at Carnegie Mellon since 1981; he joined the Center for the Neural Basis of Cognition (CNBC) in 1997, and the Machine Learning Department (in the School of Computer Science) in 2007. He served as Department Head of Statistics from 1995 to 2004 and served as Interim Co-Director of the CNBC 2015–2018. He became the Maurice Falk Professor of Statistics and Computational Neuroscience in 2016. Kass has served as Chair of the Section for Bayesian Statistical Science of the American Statistical Association, Chair of the Statistics Section of the American Association for the Advancement of Science, founding Editor-in-Chief of the journal Bayesian Analysis and Executive Editor of Statistical Science . He is an elected Fellow of the American Statistical Association, the Institute of Mathematical Statistics and the American Association for the Advancement of Science. He has been recognized by the Institute for Scientific Information as one of the 10 most highly cited researchers, 1995–2005, in the category of mathematics. Kass is the recipient of the 2017 Fisher Award and lectureship by the Committee of the Presidents of the Statistical Societies. This interview took place at Carnegie Mellon University in November 2017.

Vladimir Koltchinskii, Richard Nickl, Philippe Rigollet.

Source: Statistical Science, Volume 34, Number 1, 169--175.

Abstract:
Richard Mansfield Dudley (Dick Dudley) was born in 1938. He received the A.B. from Harvard in 1952 and the Ph.D. from Princeton in 1962 (under the supervision of Gilbert Hunt and Edward Nelson). Following an appointment at UC Berkeley as an assistant professor, he joined the Department of Mathematics at MIT in 1967. Dick Dudley has made fundamental contributions to the theory of Gaussian processes and Probability in Banach Spaces. Among his major achievements is the development of a general framework for empirical processes theory, in particular, for uniform central limit theorems. These results have had and continue having tremendous impact in contemporary statistics and in mathematical foundations of machine learning. A more extensive biographical sketch is contained in the preface to the Selected works of R. M. Dudley (editors: E. Giné, V. Koltchinskii and R. Norvaisa) published in 2010. This conversation took place (mostly, via email) in the fall of 2017.

Geurt Jongbloed.

Source: Statistical Science, Volume 34, Number 1, 156--168.

Abstract:
Petrus (Piet) Groeneboom was born in Scheveningen in 1941 and grew up in Voorburg. Both villages are located near The Hague in The Netherlands; Scheveningen actually being part of The Hague. He attended the gymnasium of the Huygens lyceum. In 1959, he entered the University of Amsterdam, where he studied psychology. After his “candidate” exam (comparable to BSc) in 1963, he worked at the psychological laboratory of the University of Amsterdam until 1966. In 1965, he took up mathematics as a part-time study. After having obtained his master’s degree in 1971, he had a position at the psychological laboratory again until 1973, when he was appointed to the Mathematical Center in Amsterdam. There, he wrote between 1975 and 1979 his Ph.D. thesis with Kobus Oosterhoff as advisor, graduating in 1979. After a period of two years as visiting professor at the University of Washington (UW) in Seattle, Piet moved back to the Mathematical Center until he was appointed full professor of statistics at the University of Amsterdam in 1984. Four years later, he moved to Delft University of Technology where he became professor of statistics and stayed until his retirement in 2006. Between 2000 and 2006 he also held a part-time professorship at the Vrije Universiteit in Amsterdam. From 1999 till 2013 he was Affiliate Professor at the statistics department of UW, Seattle. Apart from being visiting professor at the UW in Seattle, he was also visiting professor at Stanford University, Université Paris 6 and ETH Zürich. Piet is well known for his work on shape constrained statistical inference. He worked on asymptotic theory for these problems, created algorithms to compute nonparametric estimates in such models and applied these models to real data. He also worked on interacting particle systems, extreme value analysis and efficiency theory for testing procedures. Piet (co-)authored four books and 64 papers and served as promotor of 13 students. He is the recipient of the 1985 Rollo Davidson prize, a fellow of the IMS and elected member of the ISI. In 2015, he delivered the Wald lecture at the Joint Statistical Meeting in Montreal. Piet and his wife Marijke live in Naarden. He has two sons, Thomas and Tim, and (since June 12, 2018) one grandson, Tarik. This conversation was held at Piet’s house in Naarden, on February 28 and April 24, 2018.

Martin N. Hebart
Jun 13, 2012; 32:8107-8109
Journal Club

Investment of cognitive effort is required in everyday life and has received ample attention in recent neurocognitive frameworks. The neural mechanism of effort investment is thought to be structured hierarchically, with dorsal anterior cingulate cortex (dACC) at the highest level, recruiting task-specific upstream areas. In the current fMRI study, we tested whether dACC is generally active when effort demand is high across tasks with different stimuli, and whether connectivity between dACC and task-specific areas is increased depending on the task requirements and effort level at hand. For that purpose, a perceptual detection task was administered that required male and female human participants to detect either a face or a house in a noisy image. Effort demand was manipulated by adding little (low effort) or much (high effort) noise to the images. Results showed a network of dACC, anterior insula (AI), and intraparietal sulcus (IPS) to be more active when effort demand was high, independent of the performed task (face or house detection). Importantly, effort demand modulated functional connectivity between dACC and face-responsive or house-responsive perceptual areas, depending on the task at hand. This shows that dACC, AI, and IPS constitute a general effort-responsive network and suggests that the neural implementation of cognitive effort involves dACC-initiated sensitization of task-relevant areas.

SIGNIFICANCE STATEMENT Although cognitive effort is generally perceived as aversive, its investment is inevitable when navigating an increasingly complex society. In this study, we demonstrate how the human brain tailors the implementation of effort to the requirements of the task at hand. We show increased effort-related activity in a network of brain areas consisting of dorsal anterior cingulate cortex (dACC), anterior insula, and intraparietal sulcus, independent of task specifics. Crucially, we also show that effort-induced functional connectivity between dACC and task-relevant areas tracks specific task demands. These results demonstrate how brain regions specialized to solve a task may be energized by dACC when effort demand is high.

Many teens have taken on the role, but not every Robin was a "boy" wonder

Speech by Mr Mugur Isărescu, Governor of the National Bank of Romania, Bucharest, 29 April 2020.

On Saturday, October 16, 2019, Concerts from the Library of Congress hosted Afro-Cuban jazz master, Chucho Valdés. Founder and director of the legendary Cuban jazz band Irakere, winner of six GRAMMYs and four Latin GRAMMYs, Chucho Valdés is not just an authority in the jazz world, he is a living legend. As part of his engagement at […]

A young lady in a small Laotian village is pressured to recant her faith or jeopardize her educational future.

Britons stood in silence and Queen Elizabeth addressed the nation on Friday's 75th anniversary of Victory in Europe Day, though the coronavirus dampened commemorations for the end of the Second World War on the continent.

God opens a door in a juvenile correctional home.

OM Ships International celebrates five years of God’s faithfulness through the ministry of Logos Hope around the world.