Concepts of quantum non-Markovianity: a hierarchy

Markovian approximation is a widely-employed idea in descriptions of the dynamics of open quantum systems (OQSs). Although it is usually claimed to be a concept inspired by classical Markovianity, the term quantum Markovianity is used inconsistently and often unrigorously in the literature. In this report we compare the descriptions of classical stochastic processes and quantum stochastic processes (as arising in OQSs), and show that there are inherent differences that lead to the non-trivial problem of characterizing quantum non-Markovianity. Rather than proposing a single definition of quantum Markovianity, we study a host of Markov-related concepts in the quantum regime. Some of these concepts have long been used in quantum theory, such as quantum white noise, factorization approximation, divisibility, Lindblad master equation, etc.. Others are first proposed in this report, including those we call past-future independence, no (quantum) information backflow, and composability. All of these concepts are defined under a unified framework, which allows us to rigorously build hierarchy relations among them. With various examples, we argue that the current most often used definitions of quantum Markovianity in the literature do not fully capture the memoryless property of OQSs. In fact, quantum non-Markovianity is highly context-dependent. The results in this report, summarized as a hierarchy figure, bring clarity to the nature of quantum non-Markovianity.Comment: Clarifications and references added; discussion of the related classical hierarchy significantly improved. To appear in Physics Report

Attentional capture by meaning: A multi-level modelling study

We present a computational study of attentional capture by meaning, based on Barnard et al's key-distractor attentional blink task. We highlight a sequence of models, from an abstract black-box to a structurally detailed white-box model. Each of these models reproduces the major findings from the key-distractor blink task. We argue that such multi-level modelling gives greater confidence in the theoretical position encapsulated by these models

GliomaPredict: A Clinically Useful Tool for Assigning Glioma Patients to Specific Molecular Subtypes

Background: Advances in generating genome-wide gene expression data have accelerated the development of molecular-based tumor classification systems. Tools that allow the translation of such molecular classification schemas from research into clinical applications are still missing in the emerging era of personalized medicine. Results: We developed GliomaPredict as a computational tool that allows the fast and reliable classification of glioma patients into one of six previously published stratified subtypes based on sets of extensively validated classifiers derived from hundreds of glioma transcriptomic profiles. Our tool utilizes a principle component analysis (PCA)-based approach to generate a visual representation of the analyses, quantifies the confidence of the underlying subtype assessment and presents results as a printable PDF file. GliomaPredict tool is implemented as a plugin application for the widely-used GenePattern framework. Conclusions: GliomaPredict provides a user-friendly, clinically applicable novel platform for instantly assigning gene expression-based subtype in patients with gliomas thereby aiding in clinical trial design and therapeutic decisionmaking. Implemented as a user-friendly diagnostic tool, we expect that in time GliomaPredict, and tools like it, will become routinely used in translational/clinical research and in the clinical care of patients with gliomas

Formal Modeling of Connectionism using Concurrency Theory, an Approach Based on Automata and Model Checking

This paper illustrates a framework for applying formal methods techniques, which are symbolic in nature, to specifying and verifying neural networks, which are sub-symbolic in nature. The paper describes a communicating automata [Bowman & Gomez, 2006] model of neural networks. We also implement the model using timed automata [Alur & Dill, 1994] and then undertake a verification of these models using the model checker Uppaal [Pettersson, 2000] in order to evaluate the performance of learning algorithms. This paper also presents discussion of a number of broad issues concerning cognitive neuroscience and the debate as to whether symbolic processing or connectionism is a suitable representation of cognitive systems. Additionally, the issue of integrating symbolic techniques, such as formal methods, with complex neural networks is discussed. We then argue that symbolic verifications may give theoretically well-founded ways to evaluate and justify neural learning systems in the field of both theoretical research and real world applications

Blockout: Dynamic Model Selection for Hierarchical Deep Networks

Most deep architectures for image classification--even those that are trained to classify a large number of diverse categories--learn shared image representations with a single model. Intuitively, however, categories that are more similar should share more information than those that are very different. While hierarchical deep networks address this problem by learning separate features for subsets of related categories, current implementations require simplified models using fixed architectures specified via heuristic clustering methods. Instead, we propose Blockout, a method for regularization and model selection that simultaneously learns both the model architecture and parameters. A generalization of Dropout, our approach gives a novel parametrization of hierarchical architectures that allows for structure learning via back-propagation. To demonstrate its utility, we evaluate Blockout on the CIFAR and ImageNet datasets, demonstrating improved classification accuracy, better regularization performance, faster training, and the clear emergence of hierarchical network structures

Skeletal Rigidity of Phylogenetic Trees

Motivated by geometric origami and the straight skeleton construction, we outline a map between spaces of phylogenetic trees and spaces of planar polygons. The limitations of this map is studied through explicit examples, culminating in proving a structural rigidity result.Comment: 17 pages, 12 figure

A Gapless, Unambiguous Genome Sequence of the Enterohemorrhagic Escherichia coli O157:H7 Strain EDL933.

Escherichia coli EDL933 is the prototypic strain for enterohemorrhagic E. coli serotype O157:H7, associated with deadly food-borne outbreaks. Because the publicly available sequence of the EDL933 genome has gaps and >6,000 ambiguous base calls, we here present an updated high-quality, unambiguous genome sequence with no assembly gaps

Electronic Cigarette Use and Associated Risk Factors in U.S.-Dwelling Pacific Islander Young Adults.

Background: E-cigarette use is rapidly increasing among US young adults, heightening their risk for vaping-related illnesses. Yet, little is known about e-cigarette use among young adult Native Hawaiians and Pacific Islanders (NHPI): an indigenous-colonized US racial group rarely described in research literature. This exploratory study provides the first known data on e-cigarette use and potential risk factors in NHPI young adults. Method: Self-report data were collected from 143 NHPI young adults (age 18-30 years) living in two large NHPI communities: Samoans in urban Los Angeles County and Marshallese in rural Arkansas. We assessed rates of e-cigarette, cigarette, alcohol, and marijuana use, and positive and negative outcome expectancies from e-cigarettes, that is expected outcomes from e-cigarette use. To identify potential risk factors for NHPI e-cigarette use, regressions explored associations between participants' current e-cigarette use with current cigarette, alcohol, and marijuana use, and e-cigarette outcome expectancies. Results: Among NHPI young adults, lifetime e-cigarette use rate was 53% and current use rate was 39%. Current rate of dual e-cigarette/cigarette, e-cigarette/alcohol, and e-cigarette/marijuana use was 38%, 35%, and 25%, respectively. In our regression models, current marijuana use and positive e-cigarette outcome expectancies were significantly associated with current e-cigarette use. Conclusions: E-cigarette use is common among NHPI young adults, exceeding rates for other at-risk racial groups. Marijuana use and positive expectations about e-cigarette use may represent potential e-cigarette use risk factors. Collectively, findings underscore the need for additional research to further explore the scope of, and risk and protective factors for, e-cigarette use in this understudied high-risk population