841 research outputs found
Interview with Anthony F. Janson
No full textAnthony F. Janson is a retired professor and former Department Chair for the UNCW Department of Art and Theatre [retired December 2002]. This interview covers his complete life and career. He discusses his relationship with his art historian father, H.W. Janson, including his relationship as son and co-author and editor of the Janson texts on art history. The interview covers Tony's career as a scholar, book editor, author, art museum curator [at Indianapolis Art Museum and North Carolina Art Museum], and as a professor. Throughout, he comments on important artists in history and his philosophy of art history. He also includes stories of his time in the Vietnam War
Interview with Anthony F. Janson
No full textAnthony F. Janson is a retired professor and former Department Chair for the UNCW Department of Art and Theatre [retired December 2002]. This interview covers his complete life and career. He discusses his relationship with his art historian father, H.W. Janson, including his relationship as son and co-author and editor of the Janson texts on art history. The interview covers Tony's career as a scholar, book editor, author, art museum curator [at Indianapolis Art Museum and North Carolina Art Museum], and as a professor. Throughout, he comments on important artists in history and his philosophy of art history. He also includes stories of his time in the Vietnam War
Les tragédies et les théories dramatiques de Voltaire, par Henri Lion, docteur ès lettres, professeur au lycée Janson-de-Sailly. — Paris, Hachette, 1896
No full textRocheblave Samuel. Les tragédies et les théories dramatiques de Voltaire, par Henri Lion, docteur ès lettres, professeur au lycée Janson-de-Sailly. — Paris, Hachette, 1896. In: Revue internationale de l'enseignement, tome 31, Janvier-Juin 1896. pp. 509-510
Gösta Gustaf-Janson och nazismen
No full textGösta Gustaf-Janson (1902−1993) was a prolific and successful Swedish author, whose novels through frequent film versions also reached a larger audience. Although growing up in a very fashionable suburb of Stockholm he did not belong to a rich family, his father being an author too. Gustaf-Janson describes this upper class environment in an ambiguous way, both satirically and apologetically. The theme here is Swedish Nazism as it is depicted in his novels, from the early thirties and some forty years ahead. During this long period the author changes his attitude from contemporary witness to ageing historian telling young generations about a strange and distant past. All the time Gustaf-Janson is a steadfast and consequent adversary of Nazi ideology, but he also seeks to understand the human beings behind the ideological fanaticism. The affluent suburb is described in its own right, but at the same time also serves as a representative symbol of the whole country.</p
Finding Common Ground: an Eco-Feminist Reading of Christa Wolf\u27s Work
No full textMany ideas expressed in the work of the German author Christa Wolf can be compared fruitfully with central tenets of American eco-feminism. Wolf, arguably the former GDR\u27s best known writer, often posited in her texts a socialist vision for a future society that was based on cooperation, community, and recognition of the intrinsic value of each individual. Such a vision corresponds closely to the ecofeminist concept of a partnership-based society that advances communication and mutual respect; pursues the development of life-sustaining rather than life-destroying technologies; embraces life\u27s connecting spirit; and emphasizes relationships rather than hierarchies, linking rather than ranking. In my presentation, I will examine works that Wolf has written over a span of more than thirty years including several penned since German unification-thereby demonstrating the ongoing relevance that her ideas hold for eco-feminism today.
Deborah Janson is an associate professor of German in West Virginia University\u27s Department of Foreign Languages. Her current scholarly interests focus on GDR and post-Wende literature, including the theme of national and personal identity in works by minority and East German writers. She is also currently writing about Christa Wolf and has published articles on works from the German Enlightenment and Romantic periods and on German literature from an eco-feminist perspective
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Exact Asymptotics of Linear Quadratic Adaptive Control and Population-level Comorbidity Analysis
No full textWe present two self-contained topics in this thesis: exact asymptotics of linear quadratic adaptive control (LQAC) and population-level comorbidity analysis.
LQAC is perhaps the simplest non-bandit reinforcement learning problem. Existing work on LQAC is focused almost exclusively on characterizing rates of regret and their ability to learn the underlying system dynamics, with little attention paid to the constants multiplying those rates that can be critically important in practice. By carefully combining recent finite-sample performance bounds for the LQAC problem with a particular (less-recent) martingale central limit theorem, we are able to derive asymptotically-exact expressions for the regret, estimation error, and prediction error of
a rate-optimal stepwise-updating LQAC algorithm. In simulations on both stable and unstable systems, we find that our asymptotic theory also describes the algorithm’s finite-sample behavior remarkably well. In the same LQAC setting, we closes a log(T) rate gap between regret upper bound and lower bound by establishing a novel regret upper-bound of Op(√T).
Population-level comorbidity analysis can be conducted with Electronic health records (EHRs), which have more data, lower cost, and less ethical concern comparing with clinical trials. With nationwide insurance claims data of 85.97 million enrollees across 8 years, our study quantifies the bi-directional causal effect: getting one of cancers and autoimmune diseases could increase the risk of getting the other. We identified a significantly increased risk of developing autoimmune diseases among patients receiving immunotherapy agents in all seven cancer types commonly treated with immunotherapy. On the reversal direction, we suggested that the underlying immune system dysregulation of rheumatoid arthritis (a common autoimmune disease), rather than its treatments, implicates the development of subsequent cancers. We applied the matching methods to balance the treatment and control group patients by sex, race, age, and inferred health and economic status. Our method is extensible to investigating the connections among drugs, diseases, and comorbidities at scale
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Reliable and Flexible Inference for High Dimensional Data
No full textHigh-dimensional data are now widely collected in many areas to make scientific discoveries or build complicated predictive models.
The high dimensionality of such data requires analyses to have greater flexibility in modeling while ensuring the reproducibility of discoveries.
This thesis contains three self-contained chapters that adjust different aspects of high dimensional analysis.
Chapter 1.
A catalytic prior distribution is designed to stabilize a high-dimensional ``working model'' by shrinking it toward a ``simplified model.'' The shrinkage is achieved by supplementing the observed data with a small amount of ``synthetic data'' generated from a predictive distribution under the simpler model. We apply this framework to generalized linear models, where we propose various strategies for the specification of a tuning parameter governing the degree of shrinkage and study resultant theoretical properties. In simulations, the resulting posterior estimation using such a catalytic prior outperforms maximum likelihood estimation from the working model and is generally comparable or superior to existing competitive methods in terms of frequentist prediction accuracy of point estimation and coverage accuracy of interval estimation.
The catalytic priors have simple interpretations and are easy to formulate.
Chapter 2.
A crucial task in many scientific studies is to select important covariates, often from a massive collection of candidates, that determine a response of interest.
The recently developed \emph{model-X knockoffs} framework selects important covariates and provides provable and finite-sample control on the false discovery rate (FDR).
Though the original framework does not require any assumptions on the conditional distribution of the response given the covariates, it requires the distribution of the covariates to be known.
In this work, we show that the exact same guarantees can be made \emph{without} knowing the covariate distribution fully, but instead knowing it only up to a parametric model with as many as parameters, where is the dimension and is the number of covariate samples (which may exceed the usual sample size of labeled samples when unlabeled samples are also available).
The key is to treat the covariates as if they are drawn conditionally on their observed value for a sufficient statistic of the model.
Although this idea is simple, even in Gaussian models conditioning on a sufficient statistic leads to a distribution supported on a set of zero Lebesgue measure, requiring techniques from topological measure theory to establish valid algorithms.
We demonstrate how to do this for three models of interest, with simulations showing the new approach remains powerful under the weaker assumptions.
Chapter 3.
In many statistical applications, exploring nonlinear dependence of a response on multivariate predictors is challenging.
%Researchers often assume only a low-rank projection of the predictors affect the response and are interested in estimating such a projection.
Researchers are often interested in finding a low-rank projection from the predictors that truly influences the response.
The central subspace is the minimal subspace such that Y\indp X | P_{\mathcal{S}} X, where is the projection into .
Sliced inverse regression (SIR) is a widely applicable method to estimate the central subspace, but knowledge about its optimality is limited.
In this work, we study the rate-optimality of SIR under the multiple index model.
We consider a large class of models depending on the smallest non-zero eigenvalue of and the central dimension , and show a lower bound on the minimax risk of .
This lower bound characterizes the essential difficulty of estimating the central space in terms of , , , and .
We show that the risk for SIR is at the same rate as the lower bound, and thus SIR is rate-optimal.
When is larger than or comparable to , we assume that there are at most active predictors and show that an aggregate estimator based on SIR achieves the optimal rate.Statistic
Samuelis Jansonii in Holländischer Sprache heraus gegebenes Flagellum Veneris oder Abhandlung der Venus-Kranckheit darinne derselben Ursprung, Zufälle und Methode solche ohne Salivation zu curiren tractiret, und die von dem Autore gut befundenen Medicamenta communiciret werden ; anjetzo Ins Hochteutsche übersetzet
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