\u3ci\u3eA\u3c/i\u3e-Optimality for Active Learning of Logistic Regression Classifiers

Over the last decade there has been growing interest in pool-based active learning techniques, where instead of receiving an i.i.d. sample from a pool of unlabeled data, a learner may take an active role in selecting examples from the pool. Queries to an oracle (a human annotator in most applications) provide label information for the selected observations, but at a cost. The challenge is to end up with a model that provides the best possible generalization error at the least cost. Popular methods such as uncertainty sampling often work well, but sometimes fail badly. We take the A-optimality criterion used in optimal experimental design, and extend it so that it can be used for pool-based active learning of logistic regression classifiers. A-optimality has attractive theoretical properties, and empirical evaluation confirms that it offers a more robust approach to active learning for logistic regression than alternatives

PennAspect: Two-Way Aspect Model Implementation

The two-way aspect model is a latent class statistical mixture model for performing soft clustering of co-occurrence data observations. It acts on data such as document/word pairs (words occurring in documents) or movie/people pairs (people see certain movies) to produce their joint distribution estimate. This document describes our software immplementation of the aspect model available under GNU Public License (included with the distribution). We call this package PennAspect. The distribution is packaged as Java source and class files. The software comes with no guarantees of any kind. We welcome user feedback and comments. To download PennAspect, visit: http://www.cis.upenn.edu/datamining/software_dist/PennAspect/index.html

Bayesian Example Selection Using BaBiES

Active learning is widely used to select which examples from a pool should be labeled to give best results when learning predictive models. It is, however, sometimes desirable to choose examples before any labeling or machine learning has occurred. The optimal experimental design literature has many theoretically attractive optimality criteria for example selection, but most are intractable when working with large numbers of predictive features. We present the BaBiES criterion, an approximation of Bayesian A-optimal design for linear regression using binary predictors, which is both simple and extremely fast. Empirical evaluations demonstrate that, in spite of selecting all examples prior to learning, BaBiES is competitive with standard active learning methods for a variety of document classification tasks

Magnetically enhanced vacuum arc thruster

A hydrodynamic model of the vacuum arc thruster and its plume is described. Primarily an effect of the magnetic field on the plume expansion and plasma generation is considered. Two particular examples are investigated, namely the magnetically enhanced co-axial vacuum arc thruster (MVAT) and the vacuum arc thruster with ring electrodes (RVAT). It is found that the magnetic field significantly decreases the plasma plume radial expansion under typical conditions. Predicted plasma density profiles in the plume of the MVAT are compared with experimental profiles, and generally a good agreement is found. In the case of the RVAT the influence of the magnetic field leads to plasma jet deceleration, which explains the non-monotonic dependence of the ion current density, on an axial magnetic field observed experimentally.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/49190/2/psst5_4_004.pd

Culture change in elite sport performance teams: Examining and advancing effectiveness in the new era

Reflecting the importance of optimizing culture for elite teams, Fletcher and Arnold (2011) recently suggested the need for expertise in culture change. Acknowledging the dearth of literature on the specific process, however, the potential effectiveness of practitioners in this area is unknown. The present paper examines the activity's precise demands and the validity of understanding in sport psychology and organizational research to support its delivery. Recognizing that sport psychologists are being increasingly utilized by elite team management, initial evidence-based guidelines are presented. Finally, to stimulate the development of ecologically valid, practically meaningful knowledge, the paper identifies a number of future research directions

Change management: The case of the elite sport performance team

The effective and efficient implementation of change is often required for both successful performance and management survival across a host of contemporary domains. However, although of major theoretical and practical significance, research to date has overlooked the application of change management (hereafter CM) knowledge to the elite sport performance team environment. Considering that the success of ‘off-field’ sports businesses are largely dependent on the performances of their ‘on-field’ team, this article explores the application of current CM theorizing to this specific setting and the challenges facing its utility. Accordingly, we identify the need and importance of developing theory specific to this area, with practical application in both sport and business, through examination of current knowledge and identification of the domain's unique, dynamic and contested properties. Markers of successful change are then suggested to guide initial enquiry before the article concludes with proposed lines of research which may act to provide a valid and comprehensive theoretical account of CM to optimize the research and practice of those working in the field

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment