Miracle-dz/UniFAR-FTUs huggingface dataset:
A hitting set construction, with application to arithmetic circuit lower bounds

A polynomial identity testing algorithm must determine whether a given input polynomial is identically equal to 0. We give a deterministic black-box identity testing algorithm for univariate polynomials of the form $\sum_{j=0}^t c_j X^{\alpha_j} (a + b X)^{\beta_j}$. From our algorithm we derive an exponential lower bound for representations of polynomials such as $\prod_{i=1}^{2^n} (X^i-1)$ under this form. It has been conjectured that these polynomials are hard to compute by general arithmetic circuits. Our result shows that the "hardness from derandomization" approach to lower bounds is feasible for a restricted class of arithmetic circuits. The proof is based on techniques from algebraic number theory, and more precisely on properties of the height function of algebraic numbers.
Engagement with online mental health interventions: an exploratory clinical study of a treatment for depression

Online mental health interventions can benefit people experiencing a range of psychological difficulties, but attrition is a major problem in real-world deployments. We discuss strategies to reduce attrition, and present SilverCloud, a platform designed to provide more engaging online experiences. The paper presents the results of a practice-based clinical study in which 45 clients and 6 therapists used an online Cognitive Behavioural Therapy programme for depression. Pre and post-treatment assessments, using the Beck Depression Inventory, indicate a statistically significant improvement in depressive symptoms, with a large effect size, for the moderate-to-severe clinical sub-sample receiving standalone online treatment (n=18). This group was the primary target for the intervention. A high level of engagement was also observed compared to a prior online intervention used within the same service. We discuss strategies for design in this area and consider how the quantitative and qualitative results contribute towards our understanding of engagement.
To Disguise or to Disclose? The Influence of Disclosure Recognition and Brand Presence on Readers' Responses Toward Native Advertisements in Online News Media

ABSTRACT While several studies have focused on native advertisements' disclosures, little research has been conducted on native advertisements' content. Therefore, this study investigated the influence of both disclosure recognition and brand presence on readers' persuasion knowledge (PK) and subsequent evaluations. Results of an online study (N = 290) showed that disclosure recognition resulted in higher conceptual PK, yet this had no effect on readers' attitudinal PK and evaluations. Conversely, high brand presence did not increase readers' conceptual PK, but it did increase attitudinal PK, which subsequently decreased evaluations of the ad, brand, and news medium. Implications are discussed.
The viewing-from-above bias and the silhouette illusion

The silhouette illusion published online a number of years ago by the Japanese Flash designer Nobuyuki Kayahara has received substantial attention from the online community. One feature that seems to make it interesting is an apparent rotational bias: Observers see it spinning more often clockwise than counter-clockwise. Here, we show that this rotational bias is in fact due to the visual system's preference for viewpoints from above rather than from below.
Low dose vs standard dose of antipsychotics for relapse prevention in schizophrenia: meta-analysis.

BACKGROUND It remains unknown as to whether the antipsychotic dose needed for the acute-phase treatment of schizophrenia is also necessary for relapse prevention. AIM To compare the efficacy between standard dose [(World Health Organization daily defined dose (DDD)] vs low dose (≥50% to <1 DDD) or very low dose (<50% DDD) for relapse prevention in schizophrenia. DATA SOURCE Double-blind, randomized, controlled trials with a follow-up duration of ≥24 weeks, including ≥2 dosage groups of the same antipsychotic drug for relapse prevention in schizophrenia, were searched using MEDLINE, the Cochrane Central Register of Controlled Trials, and EMBASE (last search: August 2009). DATA EXTRACTION Data on overall treatment failure, hospitalization, relapse, and dropouts due to side effects were extracted and combined in a meta-analysis. DATA SYNTHESIS Thirteen studies with 1395 subjects were included in this meta-analysis. Compared with the standard-dose treatment, the low-dose therapy did not show any statistically significant difference in overall treatment failure or hospitalization, while the standard dose showed a trend-level (P = .05) superiority in risk of relapse. The very low-dose group was inferior to the standard-dose group in all efficacy parameters. No significant difference was found in the rate of dropouts due to side effects between either standard dose vs low dose or very low dose. CONCLUSIONS Although antipsychotic treatment with ≥50% to <1 DDD may be as effective as standard-dose therapy, there are insufficient clinical trial data to draw firm conclusions on standard- vs low-dose maintenance antipsychotic therapy for schizophrenia.
Modularities for bipartite networks

Real-world relations are often represented as bipartite networks, such as paper-author networks and event-attendee networks. Extracting dense subnetworks (communities) from bipartite networks and evaluating their qualities are practically important research topics. As the attempts for evaluating divisions of bipartite networks, Guimera and Barber propose bipartite modularities. This paper discusses the properties of these bipartite modularities and proposes another bipartite modularity that allows one-to-many correspondence of communities of different vertex types. Preliminary experimental results for the bipartite modularities are also described.
Managerial Opportunism: Monitoring Financial Risk of Malaysian Shariah-compliant Companies

Managerial opportunism, such as earnings management, is a rampant, but often discreet, among certain managers. Agency theory and the notion of conflict of interest are used as bases to assume that managers would resolve to engage with inappropriate activities for their personal benefits. Distortion of earnings would erode investor confidence and is detrimental to the capital market. The Islamic capital market with Shariah-compliant firms is expected not to engage in activities that go against the religion code of conduct. The main purpose of this study is to investigate managerial opportunism among Shariah-compliant companies, specifically the relationship between real earnings management and the risk of financial distress, leverage, and free cash flow. This study employs 4,115 data from 694 sample companies for five years (2009–2013). Findings conclude that the risk of financial distress, leverage, and free cash flow have a significant relationship with earnings management among Shariah-compliant companies. This study provides evidence that further improvements on corporate governance are necessary to ensure that Shariah-compliant companies adhere with the Shariah requirement.
Cognitive and emotional factors as predictors of performance indicators in young volleyball players

In recent years, there has been much discussion about the need for using the key performance indicators, in order to guide training and consequently, competition. Some studies used the game action efficacy, the match result or the classification as a reference, while others used player’s or coach’s perception of performance. Thus, a predictive study with the aim to analyze several performance indicators in volleyball formative stages, was realized. The sample consisted of 164 players from all 16 teams (male and female) of an Under-16 volleyball league. Dependent variables: game action efficacy, classification and player’s perceived performance. Independent variables: procedural knowledge, decision-making, motivation and basic psychological needs. The results of the regression analysis showed that game action efficacy and classification were predicted only by cognitive variables (knowledge and decision-making), while players’ perceived performance was predicted by the basic need of relatedness. The different performance indicators appear to measure different realities, being predicted by different variables. Mixed methods could provide a better understanding of performance than a single analytic approach alone, combining descriptions from the inside and from the outside.
Brain Representations and Mechanisms of Pain Experience and Regulation: a Multivariate Pattern Approach

Despite great promise in mapping brain to mind, neuroimaging has had minimal impact on clinical practice and public health. However, a developing synergy between emerging analysis techniques and data-sharing initiatives has the potential to revolutionize neuroimaging’s role in clinical applications. We review the state of translational neuroimaging and outline an approach to developing brain signatures that can be shared, tested in multiple contexts, and used in applied settings. The approach rests on three pillars: (1) the use of multivariate pattern recognition techniques from statistics and machine learning to develop brain signatures for clinical symptoms and relevant mental processes; (2) assessment and optimization of the diagnostic value of these signatures; and (3) a program of broad exploration followed by increasingly rigorous assessment of generalizability across samples, research contexts, and populations. This approach integrates ideas from machine learning, ‘big data,’ replicability in research, and open science to bring translational goals within reach. Introduction A central goal of human neuroscience is mapping brain to mind. Effective mappings would allow us to infer mental states (i.e., thoughts and feelings) based on patterns of brain activity, and thereby understand the brain representations that underlie those states. This goal is critical for both basic neuroscience and for translational neuroimaging, which seeks to map health-related emotions and behaviors onto measures of brain structure and function (Erickson,
Nutraceuticals and Their Potential to Treat Duchenne Muscular Dystrophy: Separating the Credible from the Conjecture

In recent years, complementary and alternative medicine has become increasingly popular. This trend has not escaped the Duchenne Muscular Dystrophy community with one study showing that 80% of caregivers have provided their Duchenne patients with complementary and alternative medicine in conjunction with their traditional treatments. These statistics are concerning given that many supplements are taken based on purely “anecdotal” evidence. Many nutraceuticals are thought to have anti-inflammatory or anti-oxidant effects. Given that dystrophic pathology is exacerbated by inflammation and oxidative stress these nutraceuticals could have some therapeutic benefit for Duchenne Muscular Dystrophy (DMD). This review gathers and evaluates the peer-reviewed scientific studies that have used nutraceuticals in clinical or pre-clinical trials for DMD and thus separates the credible from the conjecture.
Heat-induced ribosome pausing triggers mRNA co-translational decay in Arabidopsis thaliana

The reprogramming of gene expression in heat stress is a key determinant to organism survival. Gene expression is downregulated through translation initiation inhibition and release of free mRNPs that are rapidly degraded or stored. In mammals, heat also triggers 5′-ribosome pausing preferentially on transcripts coding for HSC/HSP70 chaperone targets, but the impact of such phenomenon on mRNA fate remains unknown. Here, we provide evidence that, in Arabidopsis thaliana, heat provokes 5′-ribosome pausing leading to the XRN4-mediated 5′-directed decay of translating mRNAs. We also show that hindering HSC/HSP70 activity at 20°C recapitulates heat effects by inducing ribosome pausing and co-translational mRNA turnover. Strikingly, co-translational decay targets encode proteins with high HSC/HSP70 binding scores and hydrophobic N-termini, two characteristics that were previously observed for transcripts most prone to pausing in animals. This work suggests for the first time that stress-induced variation of translation elongation rate is an evolutionarily conserved process leading to the polysomal degradation of thousands of ‘non-aberrant’ mRNAs.
Should I Bridge or Should I Bond? Social Capital Strategies and Contingencies

My dissertation expands the line of inquiry of the contingent value of social capital to individual performance by raising three novel questions. In the first essay of my dissertation I focus on “How to bridge and how to bond” and propose a new theoretical framework for analyzing social capital, which deconstructs its major function beyond bridging or bonding into its substance as social relations versus position in network structure. Considering these two dimensions of social network analysis I propose four distinct sources of social capital that have different predictive value for individual achievements: bridging network, bridging relations, bonding network and bonding relations. The lead question in the second chapter of my dissertation is “When to bridge and when to bond”. Joining the research on the contingent value of social capital, I look at organization and individual level factors to predict the value of each social capital source to performance and theorize about the strategies individuals should pursue in order to achieve better performance. In the third essay I ask “Should one start with bridging or with bonding? ” Building on the categorization proposed in the first chapter I investigate the most successful social capital path to on-the-job performance.
A Multi-Task Deep Network for Person Re-Identification

Person re-identification (ReID) focuses on identifying people across different scenes in video surveillance, which is usually formulated as a binary classification task or a ranking task in current person ReID approaches. In this paper, we take both tasks into account and propose a multi-task deep network (MTDnet) that makes use of their own advantages and jointly optimize the two tasks simultaneously for person ReID. To the best of our knowledge, we are the first to integrate both tasks in one network to solve the person ReID. We show that our proposed architecture significantly boosts the performance. Furthermore, deep architecture in general requires a sufficient dataset for training, which is usually not met in person ReID. To cope with this situation, we further extend the MTDnet and propose a cross-domain architecture that is capable of using an auxiliary set to assist training on small target sets. In the experiments, our approach outperforms most of existing person ReID algorithms on representative datasets including CUHK03, CUHK01, VIPeR, iLIDS and PRID2011, which clearly demonstrates the effectiveness of the proposed approach.
Antibacterial Activities of Rhodamine B-Conjugated Gelsolin-Derived Peptides Compared to Those of the Antimicrobial Peptides Cathelicidin LL37, Magainin II, and Melittin

ABSTRACT The growing number of antibiotic-resistant bacteria necessitates the search for new antimicrobial agents and the principles by which they work. We report that cell membrane-permeant rhodamine B (RhB)-conjugated peptides based on the phosphatidylinositol-4,5-bisphosphate binding site of gelsolin can kill the gram-negative organisms Escherichia coli and Pseudomonas aeruginosa and the gram-positive organism Streptococcus pneumoniae. RhB linkage to the QRLFQVKGRR sequence in gelsolin was essential for the antibacterial function, since the unconjugated peptide had no effect on the bacteria tested. Because RhB-QRLFQVKGRR (also termed PBP10), its scrambled sequence (RhB-FRVKLKQGQR), and PBP10 synthesized from d-isomer amino acids show similar antibacterial properties, the physical and chemical properties of these derivatives appear to be more important than specific peptide folding for their antibacterial functions. The similar activities of PBP10 and all-d-amino-acid PBP10 also indicate that a specific interaction between RhB derivatives and bacterial proteins is unlikely to be involved in the bacterial killing function of PBP10. By using a phospholipid monolayer system, we found a positive correlation between the antibacterial function of PBP10, as well as some naturally occurring antibacterial peptides, and the intrinsic surface pressure activity at the hydrophobic-hydrophilic interface. Surprisingly, we observed little or no dependence of the insertion of these peptides into lipid monolayers on the phospholipid composition. These studies show that an effective antimicrobial agent can be produced from a peptide sequence with specificity to a phospholipid not found in bacteria, and comparisons with other antimicrobial agents suggest that the surface activities of these peptides are more important than specific binding to bacterial proteins or lipids for their antimicrobial functions.
Option pricing and trading with artificial neural networks and advanced parametric models with implied parameters

We combine parametric models and feedforward artificial neural networks to price and trade European S&P500 Index options. Artificial neural networks are optimized on a hybrid target function consisted by the standardized residual term between the actual market price and the option estimate of a certain parametric model. Parametric models include: (i) the Black and Scholes model that assumes a geometric Brownian motion process (GBM); (ii) the Corrado and Su that additionally allows for excess skewness and kurtosis via a Gram-Charlier series expansion; (iii) analytic models that extend the GBM by incorporating multiple sources of Poisson distributed jumps; and (vi) stochastic volatility and jump models. Daily average implied parameters of these models are estimated with options transaction data via an unconstraint process optimized by the non-linear least squares Levenberg-Marquardt algorithm. This structural average implied parameters are used to validate the out-of sample pricing and trading (with transaction costs) ability of all models developed.
Self-assessment accuracy in higher education: The influence of gender and performance of university students

Self-assessment activities are increasingly popular in the classrooms. But regarding self-assessment accuracy, mixed results are reported without clear reasons for this. This article addresses the impact of gender and performance on self-assessment accuracy. To evaluate self-assessment accuracy, peer and professor assessments are used as reference. A research project, relating to the assessment of oral presentations using scoring rubrics, is conducted in two university degrees. Data from 155 self-assessments and more than 12,000 peer and professor assessments are gathered. The analysis differentiates by degree, gender and level of performance in the assessed competency. The results show that self-assessment accuracy is low and related to the student’s gender, that men rate themselves higher than women do, and that even using a scoring rubric, students receiving higher marks from professors are more accurate than students receiving lower marks.
Caregiver and Provider Experiences With Coaching and Embedded Intervention

Despite an emphasis in the field on triadic approaches to the delivery of early intervention (EI), remarkably little is known about the shared experiences of caregivers (CGs) and providers during home visits. Within the context of developing, refining, and evaluating the Embedded Practices and Intervention With Caregivers (EPIC) approach, two studies were undertaken with 11 EI providers and 19 CGs of infants/toddlers with moderate–severe delays/disabilities to gather their perspectives about and experiences with the EPIC approach. The EPIC intervention has two components: CG coaching and a five-question (5Q) process for supporting embedded practices. Interview, focus group, and rating scale data were collected in both studies and aggregated for reporting purposes in the present article. Findings are described with regard to the coaching and 5Q components and the extent to which participants found the intervention to be feasible, acceptable, and useful. Implications for future research and professional development on coaching and embedded intervention practices are discussed.
Improving teachers’ pedagogical and instructional practice through action research: potential and problems

Abstract This conceptual paper reviews the extant literature on action research/teacher led inquiry and answers the question: How and in what circumstances can action research improve teachers’ classroom practice and therefore what might be the implications for school improvement? A critical analysis of the nature and purpose of action research as a mechanism for school improvement is explored via close examination of definitions, the characteristics of action research and models of action research. From the literature reviewed, it is noted that action research, whether conducted individually or collaboratively, has been found to contribute to teachers’ ability to investigate their practice with a view to improving students’ outcomes and for school improvement. Nevertheless, the success of action research is predicated on a number of conditions such as motivation, trust, mutual respect, and resources, particularly time spent within the situational context. Additionally, in educational contexts, action research is generally externally mandated, and tends to take place as a fulfillment for programs of higher education which are targeted at improving practice in settings such as schools and classrooms. Under these circumstances action research does lead to school and classroom improvement. Still, these circumstances can be called ideal and as such the question of institutionalizing action research as part of the routine of schools globally remains a challenge. Still, however, action research has the potential for improvement at both the individual and institutional level once the conditions are right.
Cross Lingual Adaptation: An Experiment on Sentiment Classifications

In this paper, we study the problem of using an annotated corpus in English for the same natural language processing task in another language. While various machine translation systems are available, automated translation is still far from perfect. To minimize the noise introduced by translations, we propose to use only key 'reliable" parts from the translations and apply structural correspondence learning (SCL) to find a low dimensional representation shared by the two languages. We perform experiments on an English-Chinese sentiment classification task and compare our results with a previous cotraining approach. To alleviate the problem of data sparseness, we create extra pseudo-examples for SCL by making queries to a search engine. Experiments on real-world on-line review data demonstrate the two techniques can effectively improve the performance compared to previous work.