2期

最新文章

• CYTOPT: OPTIMAL TRANSPORT WITH DOMAIN ADAPTATION FOR INTERPRETING FLOW CYTOMETRY DATA AI翻译
  作者：Freulon, P. A. U. L.; Bigot, J. E. R. E. M. I. E.; Hejblum, Boris p.
  作者单位：Universite de Bordeaux; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Bordeaux
  摘要：The automated analysis of flow cytometry measurements is an active research field. We introduce a new algorithm, referred to as CytOpT, using regularized optimal transport to directly estimate the different cell population proportions from a biological sample characterized with flow cytometry measurements. We rely on the regularized Wasserstein metric to compare cytometry measurements from different samples, thus accounting for possible misalignment of a given cell population across samples (d...
• LATENT VARIABLE MODELS FOR MULTIVARIATE DYADIC DATA WITH ZERO INFLATION: ANALYSIS OF INTERGENERATIONAL EXCHANGES OF FAMILY SUPPORT AI翻译
  作者：Kuha, Jouni; Zhang, Siliang; Steele, Fiona
  作者单位：University of London; London School Economics & Political Science; East China Normal University
  摘要：Understanding the help and support that is exchanged between family members of different generations is of increasing importance, with research questions in sociology and social policy focusing on both predictors of the levels of help given and received, and on reciprocity between them. We pro-pose general latent variable models for analysing such data, when helping tendencies in each direction are measured by multiple binary indicators of specific types of help. The model combines two continu...
• KNOCKOFFS WITH SIDE INFORMATION AI翻译
  作者：Ren, Zhimei; Candes, Emmanuel
  作者单位：University of Chicago; Stanford University
  摘要：We consider the problem of assessing the importance of multiple variables or factors from a dataset when side information is available. In principle, using side information can allow the statistician to pay attention to variables with a greater potential which, in turn, may lead to more discoveries. We introduce an adaptive knockoff filter, which generalizes the knockoff procedure (2018) 551-577), in that it uses both the data at hand and side information to adaptively order the variables unde...
• LAGGED COUPLINGS DIAGNOSE MARKOV CHAIN MONTE CARLO PHYLOGENETIC INFERENCE AI翻译
  作者：Kelly, Luke J.; Ryder, Robin J.; Clarte, Gregoire
  作者单位：Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite PSL; Universite Paris-Dauphine; University of Helsinki
  摘要：Phylogenetic inference is an intractable statistical problem on a complex space. Markov chain Monte Carlo methods are the primary tool for Bayesian phylogenetic inference, but it is challenging to construct efficient schemes to explore the associated posterior distribution or assess their performance. Existing approaches are unable to diagnose mixing or convergence of Markov schemes jointly across all components of a phylogenetic model. Lagged couplings of Markov chain Monte Carlo algorithms h...
• ROBUST SENSIBLE ADVERSARIAL LEARNING OF DEEP NEURAL NETWORKS FOR IMAGE CLASSIFICATION AI翻译
  作者：Kim, Jungeum; Wang, Xiao
  作者单位：Purdue University System; Purdue University
  摘要：The idea of robustness is central and critical to modern statistical analymany studies have shown that DNNs are vulnerable to adversarial attacks. Making imperceptible changes to an image can cause DNN models to make the wrong classification with high confidence, such as classifying a benign mole as a malignant tumor and a stop sign as a speed limit sign. The tradeoff between robustness and standard accuracy is common for DNN models. In this paper we introduce sensible adversarial learning and...
• CO-CLUSTERING OF SPATIALLY RESOLVED TRANSCRIPTOMIC DATA AI翻译
  作者：Sottosanti, Andrea; Risso, Davide
  作者单位：University of Padua
  摘要：Spatial transcriptomics is a groundbreaking technology that allows the where the activity occurs. This technology has enabled the study of the spatial variation of the genes across the tissue. Comprehending gene functions and interactions in different areas of the tissue is of great scientific interest, as it might lead to a deeper understanding of several key biological mechanisms, such as cell-cell communication or tumor-microenvironment interaction. To do so, one can group cells of the same...
• LOCUS: A REGULARIZED BLIND SOURCE SEPARATION METHOD WITH LOW-RANK STRUCTURE FOR INVESTIGATING BRAIN CONNECTIVITY AI翻译
  作者：Wang, Yikai; Guo, Ying
  作者单位：Emory University
  摘要：Network-oriented research has been increasingly popular in many sci-entific areas. In neuroscience research, imaging-based network connectiv-ity measures have become the key for understanding brain organizations, potentially serving as individual neural fingerprints. There are major chal-lenges in analyzing connectivity matrices, including the high dimensionality of brain networks, unknown latent sources underlying the observed connec-tivity, and the large number of brain connections leading t...
• IDENTIFICATION OF IMMUNE RESPONSE COMBINATIONS ASSOCIATED WITH HETEROGENEOUS INFECTION RISK IN THE IMMUNE CORRELATES ANALYSIS OF HIV VACCINE STUDIES AI翻译
  作者：Kang, Chaeryon; Huang, Ying
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Fred Hutchinson Cancer Center; Fred Hutchinson Cancer Center
  摘要：In HIV vaccine/prevention research, probing into the vaccine-induced immune responses that can help to predict the risk of HIV infection provides valuable information for the development of vaccine regimens. Previous cor-relate analysis of the Thai vaccine trial aided the discovery of interesting immune correlates related to the risk of developing an HIV infection. The present study aimed to identify the combinations of immune responses asso-ciated with the heterogeneous infection risk. We exp...
• ESTIMATION OF GAUSSIAN DIRECTED ACYCLIC GRAPHS USING PARTIAL ORDERING INFORMATION WITH APPLICATIONS TO DREAM3 NETWORKS AND DAIRY CATTLE DATA AI翻译
  作者：Rahman, Syed; Khare, Kshitij; Michailidis, George; Martinez, Carlos; Carulla, Juan
  作者单位：Apple Inc; State University System of Florida; University of Florida; Corporacion Colombiana de Investigacion Agropecuaria, AGROSAVIA; Universidad Nacional de Colombia
  摘要：Estimating a directed acyclic graph (DAG) from observational data rep-resents a canonical learning problem and has generated a lot of interest in recent years. Research has focused mostly on the following two cases: when no information regarding the ordering of the nodes in the DAG is available and when a domain-specific complete ordering of the nodes is available. In this paper, motivated by a recent application in dairy science, we develop a method for DAG estimation for the middle scenario,...
• SIMULATING FLOOD EVENT SETS USING EXTREMAL PRINCIPAL COMPONENTS AI翻译
  作者：Rohrbeck, Christian; Cooley, Daniel
  作者单位：University of Bath; Colorado State University System; Colorado State University Fort Collins
  摘要：Hazard event sets, a collection of synthetic extreme events over a given period, are important for catastrophe modelling. This paper addresses the is-sue of generating event sets of extreme river flow for northern England and southern Scotland, a region which has been particularly affected by severe flooding over the past 20 years. We start by analysing historical extreme river flow across 45 gauges, using methods from extreme value analysis, includ-ing the concept of extremal principal compon...