nils-herrmann/clean_scirepeval huggingface dataset:
Attitudes of Students at a US Medical School Toward Mental Illness and Its Causes
Venue: Academic Psychiatry

ObjectivesStigma among health care providers toward people with mental illness is a worldwide problem. This study at a large US university examined medical student attitudes toward mental illness and its causes, and whether student attitudes change as they progress in their education.MethodsAn electronic questionnaire focusing on attitudes toward people with mental illness, causes of mental illness, and treatment efficacy was used to survey medical students at all levels of training. Exploratory factor analysis was used to establish attitudinal factors, and analysis of variance was used to identify differences in student attitudes among these factors. Independent samples t tests were used to assess attitudes toward efficacy of treatments for six common psychiatric and medical conditions.ResultsThe study response rate was 42.6 (n 289) Exploratory factor analysis identified three factors reflecting social acceptance of mental illness, belief in supernatural causes, and belief in biopsychosocial causes. Stages of student education did not differ across these factors. Students who had completed the psychiatry clerkship were more likely to believe that anxiety disorders and diabetes could be treated effectively. Students reporting personal experiences with mental illness showed significantly more social acceptance, and people born outside the USA were more likely to endorse supernatural causes of mental illness.ConclusionsSociocultural influences and personal experience with mental illness have a greater effect than medical education on attitudes toward people with mental illness. Psychiatric education appears to have a small but significant effect on student attitudes regarding treatment efficacy.
Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade
Venue: Science

The cellular ancestry of tumor antigens One contributing factor in antitumor immunity is the repertoire of neoantigens created by genetic mutations within tumor cells. Like the corresponding mutations, these neoantigens show intratumoral heterogeneity. Some are present in all tumor cells (clonal) and others are present in only a fraction of cells (subclonal) In a study of lung cancer and melanoma, McGranahan et al. found that a high burden of clonal tumor neoantigens correlated with improved patient survival, an increased presence of tumor infiltrating lymphocytes, and a durable response to immunotherapy. Science, this issue p. 1463 Analysis of the cellular ancestry of tumor neoantigens can predict which are most likely to induce an immune response. As tumors grow, they acquire mutations, some of which create neoantigens that influence the response of patients to immune checkpoint inhibitors. We explored the impact of neoantigen intratumor heterogeneity (ITH) on antitumor immunity. Through integrated analysis of ITH and neoantigen burden, we demonstrate a relationship between clonal neoantigen burden and overall survival in primary lung adenocarcinomas. CD8+ tumor infiltrating lymphocytes reactive to clonal neoantigens were identified in early stage non small cell lung cancer and expressed high levels of PD 1. Sensitivity to PD 1 and CTLA 4 blockade in patients with advanced NSCLC and melanoma was enhanced in tumors enriched for clonal neoantigens. T cells recognizing clonal neoantigens were detectable in patients with durable clinical benefit. Cytotoxic chemotherapy induced subclonal neoantigens, contributing to an increased mutational load, were enriched in certain poor responders. These data suggest that neoantigen heterogeneity may influence immune surveillance and support therapeutic developments targeting clonal neoantigens.
Fundamental housing prices in the Baltic States: empirical approach

This paper investigates if the imbalances in the Baltic residential real estate markets that were mounting up prior the crisis of 2008 2009 could have been detected in real time. It develops an empirical framework comprised of various empirical techniques for assessing housing price misalignments from their fundamental prices. For this purpose, several statistical indicators (price to rent ratio, price to income ratio, price deviations from Hodrick Prescott filtered trend) together with the estimates from equilibrium equations are computed. The use of those indicators in a consistent graphical framework reduce the uncertainty associated with using only a single metric and allows to arrive to clearer conclusions about residential real estate price misalignments. It is shown that the framework would have been able to identify the overvaluation in the residential real estate markets in the Baltics as early as in 2005.
Fly AshAn Important Ingredient for use in Hot Mix ASHphalt Concrete

This paper presents an overview of laboratory and field demonstration work with beneficial use of fly ash in hot mix asphalt. The blend of asphalt binder and fly ash is named "ASHphalt" The field demonstration took place at We Energies, an electric, gas and steam utility headquartered in Milwaukee, Wisconsin, USA. Environmental sustainability benefits include conservation of energy and natural resources as well as reducing CO2 and other environmental impacts. Economic and social benefits include development of construction technologies for longer life pavements with reduced maintenance costs, and reduced impacts for pavement users. The benefits of ASHphalt include: improved workability, easier compaction to specified density, improved binder aging resistance, a reduction in age related cracking, extending the asphalt binder or reduction of binder content, a reduction in binder related issues, thermal stress relief, and reduced potential for thermal cracking. This paper shares the results of some laboratory testing and field demonstration work.
ENet: A Deep Neural Network Architecture for Real Time Semantic Segmentation
Venue: ArXiv

The ability to perform pixel wise semantic segmentation in real time is of paramount importance in mobile applications. Recent deep neural networks aimed at this task have the disadvantage of requiring a large number of floating point operations and have long run times that hinder their usability. In this paper, we propose a novel deep neural network architecture named ENet (efficient neural network) created specifically for tasks requiring low latency operation. ENet is up to 18$\times$ faster, requires 75$\times$ less FLOPs, has 79$\times$ less parameters, and provides similar or better accuracy to existing models. We have tested it on CamVid, Cityscapes and SUN datasets and report on comparisons with existing state of the art methods, and the trade offs between accuracy and processing time of a network. We present performance measurements of the proposed architecture on embedded systems and suggest possible software improvements that could make ENet even faster.
Real Time Superpixel Segmentation by DBSCAN Clustering Algorithm
Venue: IEEE Transactions on Image Processing

In this paper, we propose a real time image superpixel segmentation method with 50 frames/s by using the density based spatial clustering of applications with noise (DBSCAN) algorithm. In order to decrease the computational costs of superpixel algorithms, we adopt a fast two step framework. In the first clustering stage, the DBSCAN algorithm with color similarity and geometric restrictions is used to rapidly cluster the pixels, and then, small clusters are merged into superpixels by their neighborhood through a distance measurement defined by color and spatial features in the second merging stage. A robust and simple distance function is defined for obtaining better superpixels in these two steps. The experimental results demonstrate that our real time superpixel algorithm (50 frames/s) by the DBSCAN clustering outperforms the state of the art superpixel segmentation methods in terms of both accuracy and efficiency.
Real Time Three Dimensional Cell Segmentation in Large Scale Microscopy Data of Developing Embryos.
Venue: Developmental cell

We present the Real time Accurate Cell shape Extractor (RACE) a high throughput image analysis framework for automated three dimensional cell segmentation in large scale images. RACE is 55 330 times faster and 2 5 times more accurate than state of the art methods. We demonstrate the generality of RACE by extracting cell shape information from entire Drosophila, zebrafish, and mouse embryos imaged with confocal and light sheet microscopes. Using RACE, we automatically reconstructed cellular resolution tissue anisotropy maps across developing Drosophila embryos and quantified differences in cell shape dynamics in wild type and mutant embryos. We furthermore integrated RACE with our framework for automated cell lineaging and performed joint segmentation and cell tracking in entire Drosophila embryos. RACE processed these terabyte sized datasets on a single computer within 1.4 days. RACE is easy to use, as it requires adjustment of only three parameters, takes full advantage of state of the art multi core processors and graphics cards, and is available as open source software for Windows, Linux, and Mac OS.
VFDB 2016: hierarchical and refined dataset for big data analysis 10 years on
Venue: Nucleic Acids Res.

The virulence factor database (VFDB, http:/www.mgc.ac.cn/VFs/ is dedicated to providing up to date knowledge of virulence factors (VFs) of various bacterial pathogens. Since its inception the VFDB has served as a comprehensive repository of bacterial VFs for over a decade. The exponential growth in the amount of biological data is challenging to the current database in regard to big data analysis. We recently improved two aspects of the infrastructural dataset of VFDB: (i) removed the redundancy introduced by previous releases and generated two hierarchical datasets one core dataset of experimentally verified VFs only and another full dataset including all known and predicted VFs and (ii) refined the gene annotation of the core dataset with controlled vocabularies. Our efforts enhanced the data quality of the VFDB and promoted the usability of the database in the big data era for the bioinformatic mining of the explosively growing data regarding bacterial VFs.
Animal Models of Maladaptive Traits: Disorders in Sensorimotor Gating and Attentional Quantifiable Responses as Possible Endophenotypes
Venue: Front. Psychol.

Traditional diagnostic scales are based on a number of symptoms to evaluate and classify mental diseases. In many cases, this process becomes subjective, since the patient must calibrate the magnitude of his/her symptoms and therefore the severity of his/her disorder. A completely different approach is based on the study of the more vulnerable traits of cognitive disorders. In this regard, animal models of mental illness could be a useful tool to characterize indicators of possible cognitive dysfunctions in humans. Specifically, several cognitive disorders such as schizophrenia involve a dysfunction in the mesocorticolimbic dopaminergic system during development. These variations in dopamine levels or dopamine receptor sensibility correlate with many behavioral disturbances. These behaviors may be included in a specific phenotype and may be analyzed under controlled conditions in the laboratory. The present study provides an introductory overview of different quantitative traits that could be used as a possible risk indicator for different mental disorders, helping to define a specific endophenotype. Specifically, we examine different experimental procedures to measure impaired response in attention linked to sensorimotor gating as a possible personality trait involved in maladaptive behaviors.
Biomimicry as an approach for bio inspired structure with the aid of computation

Biomimicry is the study of emulating and mimicking nature, where it has been used by designers to help in solving human problems. From centuries ago designers and architects looked at nature as a huge source of inspiration. Biomimicry argues that nature is the best, most influencing and the guaranteed source of innovation for the designers as a result of nature's 3.85 billion years of evolution, as it holds a gigantic experience of solving problems of the environment and its inhabitants. The biomimicry emerging field deals with new technologies honed from bio inspired engineering at the micro and macro scale levels. Architects have been searching for answers from nature to their complex questions about different kinds of structures, and they have mimicked a lot of forms from nature to create better and more efficient structures for different architectural purposes. Without computers these complex ways and forms of structures couldn't been mimicked and thus using computers had risen the way of mimicking and taking inspiration from nature because it is considered a very sophisticated and accurate tool for simulation and computing, as a result designers can imitate different nature's models in spite of its complexity.
The Dialog State Tracking Challenge Series: A Review
Venue: Dialogue Discourse

In a spoken dialog system, dialog state tracking refers to the task of correctly inferring the state of the conversation such as the user's goal given all of the dialog history up to that turn. Dialog state tracking is crucial to the success of a dialog system, yet until recently there were no common resources, hampering progress. The Dialog State Tracking Challenge series of 3 tasks introduced the first shared testbed and evaluation metrics for dialog state tracking, and has underpinned three key advances in dialog state tracking: the move from generative to discriminative models; the adoption of discriminative sequential techniques; and the incorporation of the speech recognition results directly into the dialog state tracker. This paper reviews this research area, covering both the challenge tasks themselves and summarizing the work they have enabled.
Statistical inference of for Weibull distribution under Type II progressively hybrid censored data

Type II progressively hybrid censoring is a mixture of Type II progressive and hybrid censoring schemes. In this paper, we consider the estimation of when X and Y are two independent Weibull distributions with different scale parameters, but having the same shape parameter, and the data obtained from both distributions are Type II progressively hybrid censored. Different methods for estimating are applied. The maximum likelihood estimator and the approximate maximum likelihood estimator of are proposed. The observed Fisher information matrix is computed and it is used to construct an asymptotic confidence interval for Also, two bootstrap confidence intervals are proposed. Bayes estimate of and the associated credible interval are also obtained under the assumption of independent suitable priors. Monte Carlo simulations are performed to compare the efficiency of the proposed estimators. One data analysis has been presented for illustrative purposes.
Community Members' Perception of Brand Community Character: Construction and Validation of a New Scale

Brand communities offer effective means to achieve favorable brand outcomes, such as enhanced consumer brand loyalty. But what drives brand community success, and how can managers evaluate their communities' performance? Based on Muniz and O'Guinn (2001) the existing literature suggests high levels of consciousness of kind, shared rituals and traditions, and a sense of moral responsibility among community members, which imply a notion of "brand community character" This study accordingly argues that community members' perceptions of the brand community character (PBCC) should lead to positive outcomes for both the community and the brand. Neither PBCC nor its three characteristics have been measured empirically previously, so this study develops an initial scale. Six consecutive studies to develop the PBCC scale demonstrate its reliability and validity, while a further study affirms that PBCC has a positive relationship with relevant brand outcomes.
Materiality of Online Brand Community

The bourgeoning interest in brand communities (O'Guinn and Muniz 2009; Piller et al. 2005; Schau et al. 2009; Thomas et al. 2011; Veloutsou and Moutinho 2009) has been given a new impetus with the emergence of OBCs (Bagozzi and Dholakia 2002; Ganley and Lampe 2009; Morandin et al. 2013) These technology supported social spaces (Bagozzi and Dholakia 2002) facilitate aggregation of brand consumers allowing them to engage in relationships with each other (Wiertz 2005; Ouwersloot et al. 2005) As "enduring, self selected groups of consumers, who accept and recognize bonds of membership with each other and the brand" (Veloutsou and Moutinho 2009, p. 316) OBCs provide opportunities for collective brand co production and consumption offering new ways to exchange information, practices, and experiences (O'Guinn and Muniz 2009; Wiertz 2013) Given the widespread consumer engagement in OBC, as evidenced by the proliferation and membership of such communities, and the growing recognition of the social aspect of brands in academic literature, the understanding of OBC is at the forefront of consumer research agenda (Mathwick et al. 2008; Trusov et al. 2009)
MARKETING MIX THEORETICAL ASPECTS

Aim of article is to analyze marketing mix theoretical aspects. The article discusses that marketing mix is one of the main objectives of the marketing mix elements for setting objectives and marketing budget measures. The importance of each element depends not only on the company and its activities, but also on the competition and time. All marketing elements are interrelated and should be seen in the whole of their actions. Some items may have greater importance than others; it depends mainly on the company's strategy and its activities. Companies that provide services the provision of services will be a key element. Article arises research questions is marketing mix create added value for enterprises. There are used scientific literature and analysis methods in article. An analysis of the scientific literature, it can be said that the marketing mix measures are the actions and measures necessary to achieve marketing goals. Marketing elements: product, price, place and promotion are used for marketing objectives. These instruments operate most efficiently when all the elements are combined and working together.
From Social to Sale: The Effects of Firm Generated Content in Social Media on Customer Behavior

Given the unprecedented reach of social media, firms are increasingly relying on it as a channel for marketing communication. The objective of this study is to examine the effect of firm generated content (FGC) in social media on three key customer metrics: spending, cross buying, and customer profitability. The authors further investigate the synergistic effects of FGC with television advertising and e mail communication. To accomplish their objectives, the authors assemble a novel data set comprising customers' social media participation data, transaction data, and attitudinal data obtained through surveys. The results indicate that after the authors account for the effects of television advertising and e mail marketing, FGC has a positive and significant effect on customers' behavior. The authors show that FGC works synergistically with both television advertising and e mail marketing and also find that the effect of FGC is greater for more experienced, tech savvy, and social media prone customers. They propose and examine the effect of three characteristics of FGC: valence, receptivity, and customer susceptibility. The authors find that whereas all three components of FGC have a positive impact, the effect of FGC receptivity is the largest. The study offers critical managerial insights regarding how to leverage social media for better returns.
Look, Listen and Learn A Multimodal LSTM for Speaker Identification
Venue: AAAI

Speaker identification refers to the task of localizing the face of a person who has the same identity as the ongoing voice in a video. This task not only requires collective perception over both visual and auditory signals, the robustness to handle severe quality degradations and unconstrained content variations are also indispensable. In this paper, we describe a novel multimodal Long Short Term Memory (LSTM) architecture which seamlessly unifies both visual and auditory modalities from the beginning of each sequence input. The key idea is to extend the conventional LSTM by not only sharing weights across time steps, but also sharing weights across modalities. We show that modeling the temporal dependency across face and voice can significantly improve the robustness to content quality degradations and variations. We also found that our multimodal LSTM is robustness to distractors, namely the non speaking identities. We applied our multimodal LSTM to The Big Bang Theory dataset and showed that our system outperforms the state of the art systems in speaker identification with lower false alarm rate and higher recognition accuracy.
Cloud computing adoption framework: A security framework for business clouds
Venue: Future Gener. Comput. Syst.

This article presents a cloud computing adoption framework (CCAF) security suitable for business clouds. CCAF multilayered security is based on the development and integration of three major security technologies: firewall, identity management, and encryption based on the development of enterprise file sync and share technologies. This article presents the vision, related works, and views on security framework. Core technologies have been explained in detail, and experiments were designed to demonstrate the robustness of the CCAF multilayered security. In penetration testing, CCAF multilayered security could detect and block 99.95% viruses and trojans, and could achieve 85 of blocking for 100 h of continuous attack. Detection and blocking took 0.012 s /trojan or virus. A full CCAF multilayered security protection could block all SQL (structured query language) injection, providing real protection to data. CCAF multilayered security did not report any false alarm. All F measures for CCAF test results were 99.75 The mechanism of blending of CCAF multilayered security with policy, real services, and business activities has been illustrated. Research contributions have been justified and CCAF multilayered security can be beneficial for volume, velocity, and veracity of big data services operated in the cloud. We demonstrate CCAF multi layered security.We explain the mappings between CCAF multi layered architecture and core technologiesWe performed penetration testing and SQL injection on CCAF multi layered security.Results and analysis by CCAF are better than those produced by the other tools.CCAF multi layered security blends with policy, services and business activities.