Miracle-dz/UniFAR-FTUs huggingface dataset:
Switching edges to randomize networks: what goes wrong and how to fix it

The switching model is a well-known random network model that randomizes a network while keeping its degree sequence fixed. The idea behind the switching model is simple: a network is randomized by repeatedly rewiring pairs of edges. In this paper we demonstrate that despite its simple description, and in part due to it, much can go wrong when implementing the switching model. Specifically, we show that the model needs to be implemented carefully, to avoid biased sampling. We propose a precise definition of the switching model which guides its implementation. Furthermore, we argue that we should refer to the switching model with respect to a specific network class, and in fact define a family of switching models. This formalizes previous use of the switching model to randomize networks from numerous network classes. We show that the properties of these models depend on the network class, and in particular that their stationary distributions differ. Hence it is important to take into account which class of networks is being randomized. We derive conditions, and where possible adjust the models, such that sampling is unbiased for the switching model with respect to eight common network classes. These unbiased null-models are important, since common network analysis techniques such as motif finding and community detection rely on them.
Abciximab in the management of acute myocardial infarction with ST-segment elevation: evidence-based treatment, current clinical use, and future perspectives

Introduction of antiplatelet agents has contributed substantially to improve the outcome of patients with acute coronary syndromes. Meta-analysis of the studies on abciximab administration during primary percutaneous coronary intervention (PCI) for acute ST-segment elevation myocardial infarction (STEMI) has clearly confirmed the mortality benefit associated with intravenous bolus and infusion of abciximab compared to placebo. Recently, introduction of new oral P2Y12 inhibitors (prasugrel, ticagrelor), with a faster and more pronounced antiplatelet effect, have decreased the use of abciximab even in patients with STEMI. However, recent studies have shown a delayed onset of antiplatelet effect of new oral antiplatelet drugs in the setting of STEMI, especially in patients with hemodynamic compromise. Thus, the use of abciximab as an intravenous agent should be strongly considered when oral P2Y12 inhibitors might fail or cannot be given before primary PCI for STEMI. An additional benefit of abciximab administration was reported when abciximab was given early, before primary PCI, compared to typical periprocedural use. To the contrary, no clear clinical benefit was confirmed for intracoronary administration of abciximab compared with intravenous administration. Future studies should focus on the role of abciximab given on top of new oral P2Y12 inhibitor (prasugrel, ticagrelor) or used as an alternative to an intravenous P2Y12 inhibitor (cangrelor). Undoubtedly, the results of these studies will change everyday practice of STEMI treatment.
Personality Characteristics of Business Majors as Defined by the Big Five and Narrow Personality Traits

Using data from 347 undergraduate business majors and 2,252 nonbusiness majors at a large Southeastern university, the authors drew on J. L. Holland's (1985) vocational theory and investigated whether the 2 groups differed on the Big Five model of personality (B. De Raad, 2000; agreeableness, conscientiousness, emotional stability, extraversion, openness) and 4 narrow personality traits. For business majors, the authors also examined the relations between personality traits and life satisfaction. Business majors scored higher for conscientiousness, emotional stability, extraversion, assertiveness, and tough-mindedness, but they scored lower on agreeableness and openness. All of the traits except for agreeableness and tough-mindedness correlated significantly and positively with life satisfaction. The authors discuss results in terms of similar relations in business occupations and support of vocational theory.
Integrated analysis of multi-source data in drug discovery experiments using structural equation models

The drug discovery and development processes are typically costly and time consuming. Hence, it is crucial to identify early failure of candidate compounds and thereby save time and investment in a later stage. We propose structural equation modeling (SEM) based approach for an integrated analysis which combines information from three data sources: (1) bioactivity variables, (2) variables representing the chemical structure of the compounds, and (3) gene expression data. The proposed model allows to estimate the effects of the gene expression on the biological activity variable and furthermore, it allows to decompose the effect of the chemical structure on the biological activity into direct and indirect (i.e. the effect via the gene expression) effects.
The effects of self-emotion, counterpart emotion, and counterpart behavior on negotiator behavior: a comparison of individual-level and dyad-level dynamics

This study expands the negotiation literature by examining how negotiator behavior is predicted by various emotions felt by the negotiators and their counterparts and by counterpart negotiation behavior. Using hierarchical linear modeling, we also compare individual- and dyad-level processes that lead to negotiator behavior and outcomes. The results from a dyadic negotiation simulation showed that both the valence and agency of negotiator and counterpart emotions need to be considered to understand the roles of emotion in negotiator behavior. Negotiators tend to reciprocate counterparts' integrating, compromising, and dominating behaviors, but they also offer complementary (or matching) responses to the counterparts' dominating and yielding behaviors. Integrating behavior was more dependent on dyad-level interpersonal dynamics than were the other behaviors. The comparison of negotiator-level and dyad-level results suggests that negotiation needs to be understood in the context of collective exchanges as well as individual-level cognitive processes. Copyright © 2005 John Wiley & Sons, Ltd.
A functional analysis of deception detection of a mock crime using infrared thermal imaging and the Concealed Information Test

The purpose of this study was to utilize thermal imaging and the Concealed Information Test to detect deception in participants who committed a mock crime. A functional analysis using a functional ANOVA and a functional discriminant analysis was conducted to decrease the variation in the physiological data collected through the thermal imaging camera. Participants chose between a non-crime mission (Innocent Condition: IC), or a mock crime (Guilty Condition: GC) of stealing a wallet in a computer lab. Temperature in the periorbital region of the face was measured while questioning participants regarding mock crime details. Results revealed that the GC showed significantly higher temperatures when responding to crime relevant items compared to irrelevant items, while the IC did not. The functional ANOVA supported the initial results that facial temperatures of the GC elevated when responding to crime relevant items, demonstrating an interaction between group (guilty/innocent) and relevance (relevant/irrelevant). The functional discriminant analysis revealed that answering crime relevant items can be used to discriminate guilty from innocent participants. These results suggest that measuring facial temperatures in the periorbital region while conducting the Concealed Information Test is able to differentiate the GC from the IC.
Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation

The growth of data sharing initiatives for neuroimaging and genomics represents an exciting opportunity to confront the “small N” problem that plagues contemporary neuroimaging studies while further understanding the role genetic markers play in the function of the brain. When it is possible, open data sharing provides the most benefits. However, some data cannot be shared at all due to privacy concerns and/or risk of re-identification. Sharing other data sets is hampered by the proliferation of complex data use agreements (DUAs) which preclude truly automated data mining. These DUAs arise because of concerns about the privacy and confidentiality for subjects; though many do permit direct access to data, they often require a cumbersome approval process that can take months. An alternative approach is to only share data derivatives such as statistical summaries—the challenges here are to reformulate computational methods to quantify the privacy risks associated with sharing the results of those computations. For example, a derived map of gray matter is often as identifiable as a fingerprint. Thus alternative approaches to accessing data are needed. This paper reviews the relevant literature on differential privacy, a framework for measuring and tracking privacy loss in these settings, and demonstrates the feasibility of using this framework to calculate statistics on data distributed at many sites while still providing privacy.
Emergency Department Staff Attitudes Toward People Presenting in Chronic Pain: A Qualitative Study.

OBJECTIVE Patients who experience their nonmalignant chronic pain as intolerable sometimes present at Emergency Departments (EDs). However, as emergency medical services are set up to provide rapid treatment for acute injury or illness; there is potential for misunderstanding and disappointment. Literature on the topic of ED staff attitudes toward chronic pain patients is minimal, USA-based and methodologically unsatisfying. We carried out an in-depth, qualitative study identifying the attitudes and narratives of ED staff around people in chronic pain. DESIGN Focus groups with ED staff; qualitative analysis of the group transcripts. SETTING Regional trauma centre in the UK. SUBJECTS Three focus groups, 20 ED clinicians, mean ED experience 8.1 years. RESULTS The clinical challenge of treating patients in the ED stemmed from a mismatch between patients' needs and what the setting can deliver. Participants reported frustration with the system and with chronic pain patients' perceived inconsistencies and requirements. However, they also highlighted good practice and acknowledged their frustration around not being able to help this group. CONCLUSIONS ED staff found people presenting at ED with chronic pain to be a challenging and frustrating population to treat. Staff was constrained by the fast-paced nature of their jobs as well as the need to prioritise emergency cases, and so were unable to spend the time needed by chronic pain patients. This was seen as being bad for staff, and for the patient experience. Staff suggested that care could be improved by appropriate information, signposting and with time invested in communication with the patient.
Human computing and crowdsourcing methods for knowledge acquisition

Ambiguity, complexity, and diversity in natural language textual expressions are major hindrances to automated knowledge extraction. As a result state-of-the-art methods for extracting entities and relationships from unstructured data make incorrect extractions or produce noise. With the advent of human computing, computationally hard tasks have been addressed through human inputs. While textbased knowledge acquisition can benefit from this approach, humans alone cannot bear the burden of extracting knowledge from the vast textual resources that exist today. Even making payments for crowdsourced acquisition can quickly become prohibitively expensive. In this thesis we present principled methods that effectively garner human computing inputs for improving the extraction of knowledge-base facts from natural language texts. Our methods complement automatic extraction techniques with human computing to reap benefits of both while overcoming each other’s limitations. We present the architecture and implementation of HIGGINS , a system that combines an information extraction (IE) engine with a human computing (HC) engine to produce high quality facts. Using automated methods, the IE engine compiles dictionaries of entity names and relational phrases. It further combines statistics derived from large Web corpora with semantic resources like WordNet and ConceptNet to expand the dictionary of relational phrases. It employs specifically designed statistical language models for phrase relatedness to come up with questions and relevant candidate answers that are presented to human workers. Through extensive experiments we establish the superiority of this approach in extracting relation-centric facts from text. In our experiments we extract facts about fictitious characters in narrative text, where the issues of diversity and complexity in expressing relations are far more pronounced. Finally, we also demonstrate how interesting human computing games can be designed for knowledge acquisition tasks.
Cultural weapons and national stages: theatres and politics in South Africa and elsewhere

"Preparing ourselves for freedom," Albie Sachs's critical revision of the ANC line on "culture as a weapon of struggle" was released more than a decade ago and may seem out of date in a post-apartheid economy of scarcity, which seems to have little place for culture, political or otherwise. Nonetheless, Sachs's polemic against the production of political slogans and for a cultural practice that engages with the complexity and diversity of lives in South Africa continues to resonate in ongoing debates about the role of culture in a post-apartheid environment, in which the clear lines of conflict of the anti-apartheid struggle have given way to a much more ambiguous picture of mixed allegiances and uncertain futures. Institutions once beholden to the apartheid state have been closed or restructured out of existence and those that once played a role in the struggle, such as the Market Theatre, now seem at sea, if not yet on the rocks. Clearly, theatre has yet to cross into a definitively post-apartheid world, but, like other cultural practices in South Africa, inhabits a moment that might be called post-anti-apartheid. This condition may not be completely dominated by apartheid . as such but, as Ian Steadman suggests, it is still governed by the legacy of apartheid (Steadman 1999:25).
Implementation of a High Performance Barrel Shifter Architecture using Three Different Logic Design Styles

Barrel shifters are often required for performing data shifting and rotation in many key computer operations from address decoding to computer arithmetic. In this paper we present a comparative study of various parameters like delay, power and area, for a high performance 16-bit barrel shifter VLSI implementations using three different logic design styles (conventional CMOS, transmission gate CMOS and Dual rail Domino CMOS logic) in 0.6mm, N-well CMOS process. The proposed barrel shifter implementations shows better performance as compared to implementation by R. Pereira [3].
Stable operating region in a harmonically actively mode-locked fiber laser

We theoretically study the recovery of a harmonically actively mode-locked soliton fiber laser from pulse dropout. In such lasers, a large number of pulses propagate simultaneously in the cavity. In order to obtain stable operation, pulses that are dropped due to changes in environmental conditions should recover, while other pulses that propagate in the cavity should remain stable. Soliton perturbation theory is used to find stability conditions for the noise in a time slot where a steady state pulse exists and in a time slot where a pulse is dropped. In the stable operating region of the laser, noise is stable in the presence of a pulse while noise becomes unstable in time slots where a pulse is dropped. Such a stability condition ensures that the laser can recover from accidental pulse dropout. A good agreement between the results of a reduced model and the results of a comprehensive numerical simulation was obtained. The results of this paper may enable to improve the stability of actively mode-locked fiber lasers.
Memories of childhood neglect and abuse: corroboration in a series of sisters.

Reports from 87 community-based sister-pairs, selected for high rates of neglect or abuse in childhood, have been used to establish validity of the CECA, a retrospective interview measure of childhood experience. Corroboration was based on independent assessments of sisters' accounts of what happened to each other in childhood. Corroboration of scales assessing parental neglect, physical abuse in the household and sexual abuse (either household or nonhousehold) was satisfactory, with a mean correlation for the three experiences of . 60 (weighted kappa [Kw]). Concordance reflected the degree to which experience was shared and was judged by comparing the sisters' accounts of their own experience. Among sisters with shared (concordant) experience for neglect or abuse, corroboration was high (mean of .74), but for those with nonshared (nonconcordant) experience it was largely absent (mean of .01). The degree to which the experiences of neglect or abuse were concordant was related to whether the perpetrator was a member of the household. Neglect and physical abuse were by definition from household members (mainly parents) and involved high concordance of experience. Sexual abuse occurred from many sources and in practice was commonly from an adult living outside the household and such experiences were less likely to be shared by sisters. An overall index indicating the presence of at least one abusive experience before the age of 17 showed a concordance of . 64 with corroboration between sisters as high as . 70. Issues involving retrospective recall and measurement are discussed.
The Effects of Persuasion Knowledge on Click-Through of Keyword Search Ads: Moderating Role of Search Task and Perceived Fairness

This study examines the effects of-persuasion knowledge and conditions that influence its impact in the context of keyword search ads. Students participated in an experiment featuring a 2 (persuasion knowledge: primed vs. not primed) x 2 (search tasks: complex vs. less complex) between-subjects design with a covariate (perceived fairness). Results reveal online users are less likely to click through keyword search ads when they are aware that advertisers' persuasion attempts are at work. However, this negative impact is attenuated when the search task is complex and level of perceived fairness of the ad practice is high.
Detection of intrusions in information systems by sequential change-point methods

Sequential multi-chart detection procedures for detecting changes in multichannel sensor systems are developed. In the case of complete information on pre-change and post-change distributions, the detection algorithm represents a likelihood ra tio-based multichannel generali zation of Page’s cumulative sum (CUSUM) test that is applied to general stochastic models that may include correlated and nonstationary observations. There are many potential application areas where it is necessary to consider multichannel generalizations and general statistical models. In this paper our main motivation for doing so is network security: rapid anomaly detection for an early detectio n of attacks in computer networks that lead to changes in network traffic. Moreover, this kind of application encourages the development of a nonparametric multichannel detection test that does not use exact pre-change (legitimate) and post-change (attack) traffic models. The proposed nonparametric method can be effectively applied to detect a wide variety of attacks such as denial-of-service attacks, worm-based att acks, port-scanning, and man-in-the-middle attacks. In addition, we propose a multichannel CUSUM procedur that is based on binary quantized data; this procedure turns out to be more efficient than the previous two algorithms in certain scenarios. All proposed detection algorithms are based on the change-point detection theory. They utilize the thresholding of test statistics to achieve a fixed rate of false alarms, while allowing changes in statistical models to be detected “as soon as possible”. Theoretical frameworks for the performance analysis of detection procedures, as well ∗ Corresponding author. Tel. : +1 213 74
Automatic speaker recognition using dynamic Bayesian network

This paper presents a novel approach to automatic speaker recognition using dynamic Bayesian network (DBN). DBNs have a precise and well-understand probabilistic semantics, and it has the ability to incorporate prior knowledge, to represent arbitrary non-linearities, and to handle hidden variables and missing data in a principled way with high extensibility. Experimental evaluation over YOHO corpus shows promising results compared to other classical methods.
Why Should We Group Students Within-Class for Learning?

The purpose of this article is threefold. First, we bring to the attention of the European and international research community the research evidence concerning within-class grouping. In a previous quantitative review, we computed 103 independent effect sizes from studies comparing within-class instruction and whole class instruction. The mean weighted effect size (ES) of within-class versus no grouping on student achievement was 0.17, which was significantly greater than zero (p < .05). We also reported that the findings were statistically heterogeneous and explored which of 26 study features accounted for the variability. Second, we address the issues related to our research integration raised by Prais (1998, 1999). We argue that our review focused only on grouping effects on students, not other features of classroom organization or teacher outcomes. We also believe that our data suggest that Continental Education would be further enhanced by the use of small group instruction. In particular, our findings are: useful; not so variable as to be meaningless; provide evidence of beneficial effects for students of all relative abilities; are thorough and detailed; and provide a rather complete picture of the available evidence. Third, we elaborate further on the pedagogical techniques which encourage effective small group instruction. We describe cooperative learning in general, the important elements of positive interdependence and individual accountability in particular, and summarize reviews of research on its effectiveness. We remain convinced that educators should group students within-class for learning and rely on the research evidence for guidance in doing so.
New Pselaphidae from New Hampshire (Coleoptera)

Two species of undescribed Pselaphidae were discovered during a comparison of the fauna of an uncut and a 40 year-old forest. The species are described here to provide names for a forthcoming paper comparing the pselaphid fauna of these two sites. Holotypes were cleared, disarticulated, and mounted on slides in Canada Balsam. Both are placed in the Field Museum of Natural History, Chicago. All measurements of specimens are in millimeters. I would like to thank certain individuals for the loan of specimens, greatly extending the known ranges of these two new species. The abbreviations used to indicate specimen deposition follows the individual’s affiliation: Rickard Baranowski, Lund University, Sweden (RBC); J. Milton Campbell, Biosystematics Research Institute, Ottawa, Canada (CNCI); Michael A. Ivie, Montana State University, Bozeman (DZEC); and Alfred F. Newton, Jr., Field Museum of Natural History, Chicago (FMNH). Specimens otherwise lacking an indication of deposition are in the collections of the author and the University of New Hampshire. I would like to thank J. F. Burger and R. Marcel Reeves, University of New Hampshire, for reviewing the manuscript.