Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying the Factors That Influence Change in SEBD Using Logistic Regression Analysis.

Multiple linear regression and ANOVA models are widely used in applications since they provide effective statistical tools for assessing the relationship between a continuous dependent variable and several predictors. However these models rely heavily on linearity and normality assumptions and they do not accommodate categorical dependent variables. The seminal contribution of John Nelder and Robert Wedderburn (1972) introduced the concept of Generalized Linear Models. GLMs overcome the limitations of Normal regression models and accommodate any distribution which is a member of the exponential family. Moreover, these models relate the dependent variable to the linear predictor (non-random component) through any invertible link function. Logistic regression models are GLMs that accommodate categorical dependent variables. They assume a Binomial distribution and Logit canonical link function. The iteratively re-weighted least squares algorithm using the Fisher scoring technique is employed to maximize the log-likelihood function in GLMs and estimate the model parameters. In this paper, Logistic regression analysis was used to identify the dominant factors that influence change in social, emotional and behaviour difficulties (SEBD) of Maltese children. The study comprised 486 pupils whose SEBD was assessed by both teachers and parents using the Strengths and Difficulties Questionnaire (Goodman 1997) when the children were aged 6 and 9 years old.
A Two‐Sided Matching Model of Monitored Finance

We analyse a model of two-sided matching and incentive contracts where expert investors (venture capitalists) with different monitoring capacities are matched with firms with different levels of initial wealth. Firms do not have sufficient start-up capital to cover their project costs and hence, seek external financing. In equilibrium, the matching and the payoffs of the venture capitalists and the firms are determined simultaneously. More effective VCs and higher-wealth firms consume higher payoffs. We also show that, in equilibrium VCs with higher monitoring ability invest in firms with lower initial wealth following a negatively assortative matching pattern.
PREDICTING CO-RELAPSE AMONG NUTRITION AND EXERCISE FROM ADOLESCENCE THROUGH YOUNG ADULTHOOD

Despite significant public health efforts, obesity remains a stagnant outcome comprised of multiple health behaviors including fruit and vegetable intake (FV) and physical activity (PA). Existing research has provided correlates of FV and PA behaviors over development, with secular trends towards acquiring negative health behaviors (e.g., sedentary behaviors). This study examined demographic and dynamic predictors of FV and PA regression at one-year post-intervention within three samples including middle school, high school, and college. Regression is defined using the Transtheoretical Model (TTM) as being in Action or Maintenance at baseline and regressing to a pre-Action stage at one-year follow-up. The novel phenomenon of coregression, the likelihood of regressing on a second behavior given a change in the first, was explored within each sample. Results of univariate and multivariate logistic regressions produced odds ratios that suggest inconsistent demographic but reliable dynamic predictors of FV and PA regression within each sample. Univariate logistic regressions revealed co-regression for FV and PA among middle school and college samples, but not the high school sample. These results indicate that existing behaviors, decisional balance variables (i.e., “Pros”), and self-efficacy are the most salient predictors of regression. The exploration of co-regression as a novel phenomenon provides a foundation for future research in the field. Implications for this study include the tailoring of individualized evidence-based interventions and new directions for public health research.
1 TN United Kingdom FOREIGN AID AND DOMESTIC ABSORPTION

This paper introduces a new ‘supply-push’ instrument for foreign aid, to be used together with an instrumental variable estimator that filters out unobserved common factors. We use this instrument to study the effects of aid on macroeconomic ratios, and especially the ratios of consumption, investment, imports and exports to GDP. We cannot reject the hypothesis that aid is fully absorbed rather than used to build foreign reserves or exiting as capital flight, nor do we find evidence of Dutch Disease effects. Aid leads to higher consumption, while the evidence that it promotes investment is less robust. JEL Classifications: F35
Major depression and coronary flow reserve detected by positron emission tomography.

BACKGROUND Major depressive disorder (MDD) is associated with coronary heart disease (CHD), but the mechanisms are unclear. The presence of MDD may increase CHD risk by affecting microvascular circulation. It is also plausible that genetic factors influencing MDD may overlap with those for CHD. We sought to examine the relationship between MDD and coronary flow reserve (CFR), the ratio of maximum flow during stress to flow at rest measured in milliliters per minute per gram of tissue. METHODS We examined 289 male middle-aged twins, including 106 twins (53 twin pairs) discordant for a lifetime history of MDD and 183 control twins (unrelated to any twins in the experimental group) without MDD. To calculate CFR, we used positron emission tomography with nitrogen 13 ((13)N) ammonia to evaluate myocardial blood flow at rest and after adenosine stress. A standard perfusion defect score was also used to assess myocardial ischemia. RESULTS There was no difference in myocardial ischemia between twins with and without MDD. Among the dizygotic twin pairs discordant for MDD, the CFR was 14% lower in the twins with MDD than in their brothers without MDD (2.36 vs 2.74) (P = .03). This association was not present in the monozygotic discordant pairs who were genetically matched (2.86 vs 2.64) (P = .19). The zygosity-MDD interaction after adjustment was significant (P = .006). The CFR in the dizygotic twins with MDD was also lower than in the control twins. CONCLUSIONS Our results provide evidence for a shared genetic pathway between MDD and microvascular dysfunction. Common pathophysiologic processes may link MDD and early atherosclerosis.
Running title : Mutations in cold-adapted isocitrate dehydrogenases

Title Analysis of amino acid residues involved in cold activity of monomeric isocitrate dehydrogenase from psychrophilic bacteria, Colwellia maris and Colwellia psychrerythraea Author(s) Yasuda, Wataru; Kobayashi, Miyuki; Takada, Yasuhiro Citation Journal of bioscience and bioengineering, 116(5): 567-572 Issue Date 2013-11 Doc URL http://hdl.handle. net/2115/54722 Right © [2013] The Society for Biotechnology, Japan
DeepProduct

Features extracted by deep networks have been popular in many visual search tasks. This article studies deep network structures and training schemes for mobile visual search. The goal is to learn an effective yet portable feature representation that is suitable for bridging the domain gap between mobile user photos and (mostly) professionally taken product images while keeping the computational cost acceptable for mobile-based applications. The technical contributions are twofold. First, we propose an alternative of the contrastive loss popularly used for training deep Siamese networks, namely robust contrastive loss, where we relax the penalty on some positive and negative pairs to alleviate overfitting. Second, a simple multitask fine-tuning scheme is leveraged to train the network, which not only utilizes knowledge from the provided training photo pairs but also harnesses additional information from the large ImageNet dataset to regularize the fine-tuning process. Extensive experiments on challenging real-world datasets demonstrate that both the robust contrastive loss and the multitask fine-tuning scheme are effective, leading to very promising results with a time cost suitable for mobile product search scenarios.
Determining Reheating Temperature at LHC with Axino or Gravitino Dark Matter

After a period of inflationary expansion, the Universe reheated and reached full thermal equilibrium at the reheating temperature TR. In this talk, based on [1], we point out that, in the context of effective low‐energy supersymmetric models, LHC measurements may allow one to determine TR as a function of the mass of the dark matter particle assumed to be either an axino or a gravitino. An upper bound on their mass and on TR may also be derived.
Dispositional Mindfulness Moderates the Relationship between Occupational Stressors and Stress Outcomes among Law Enforcement Personnel

Law enforcement personnel (LEPs) experience diverse and substantial stressors (McCraty & Atkinson, 2012; Burke, 1998; Gelderen et al., 2007; Tuckey, Winwood, & Dollard, 2012). These stressors result in significant negative impacts for the personnel themselves (Anshel & Brinthaupt, 2014; Ma et al., 2015; Marmar et al., 2006; Rees & Smith, 2008; Wang et al., 2010) and for society (Arslan, 2010; Rajaratnam et al., 2011; Kligyte, 2013). In order to assess the degree to which these stressors manifest amongst LEPs, biomarkers such as salivary cortisol (Taverniers, Smeets, Ruyssereld, Syroit, & von Grumbkow, 2011) and psychological measures, such as perceived stress (Gershon et al., 2009; Christopher et al., 2015) have been commonly used. Research among the general population shows that dispositional traits such as mindfulness and reactivity to stress moderate the relationship between stressors and stress outcomes (Miller & Chen, 2006; Feizi, Aliyari, & Roohafza, 2012). Additionally, dispositional mindfulness has been shown to negatively predict perceived stress (Zimmaro et al., 2016), mitigate the impact of stressors on negative stress-related outcomes (Chiesa et al., 2012; Marks, Sobanski, & Hine, 2010), and moderate the relationship between stressors and the CAR (Daubenmier, Hayden, Chang, & Epel, 2014). We hypothesize that dispositional mindfulness may be a similarly important protective factor for high-stress occupations such as LEPs.
A Model of Distributed Query Computation in Client-Server Scenarios on the Semantic Web

This paper provides an overview of a model for capturing properties of client-server-based query computation setups. This model can be used to formally analyze different combinations of client and server capabilities, and compare them in terms of various fine-grain complexity measures. While the motivations and the focus of the presented work are related to querying the Semantic Web, the main concepts of the model are general enough to be applied in other contexts as well.
Maximum entropy regularized group collaborative representation for face recognition

While sparse representation is heavily emphasized in many recent literatures, the importance of collaborative representation is usually ignored. In this paper, we exploit the advantage of collaborative representation and propose a maximum entropy regularized group collaborative representation (MECR) algorithm for face recognition. MECR takes the group structure of the face data into consideration under the framework of collaborative representation, and uses maximum entropy principle to obtain discriminative coding for classification. Experiments show that MECR outperforms several state-of-the-art coding methods and dictionary learning methods on some benchmark face databases.
Reconstructing evolving signalling networks by hidden Markov nested effects models

Inferring time-varying networks is important to understand the development and evolution of interactions over time. However, the vast majority of currently used models assume direct measurements of node states, which are often difficult to obtain, especially in fields like cell biology, where perturbation experiments often only provide indirect information of network structure. Here we propose hidden Markov nested effects models (HM-NEMs) to model the evolving network by a Markov chain on a state space of signalling networks, which are derived from nested effects models (NEMs) of indirect perturbation data. To infer the hidden network evolution and unknown parameter, a Gibbs sampler is developed, in which sampling network structure is facilitated by a novel structural Metropolis--Hastings algorithm. We demonstrate the potential of HM-NEMs by simulations on synthetic time-series perturbation data. We also show the applicability of HM-NEMs in two real biological case studies, in one capturing dynamic crosstalk during the progression of neutrophil polarisation, and in the other inferring an evolving network underlying early differentiation of mouse embryonic stem cells.
Implementation of an interpreter for abstract equations

This paper summarizes a project whose goal is the implementation of a useful interpreter for abstract equations that is absolutely faithful to the logical semantics of equations. The interpreter was first distributed to Berkeley UNIX VAX sites in May 1983. The main novelties of the interpreter are strict adherence to semantics based on logical consequences, ‘lazy’ (outermost) evaluation applied uniformly, an implementation based on table‐driven pattern matching, with no run‐time penalty for large sets of equations, and strict separation of syntactic and semantic processing, so that different syntaxes may be used for different problems.
Comparison of Performances of Spectral Based Approaches on Fabric Defect Detection

In this paper, it is aimed to compare the performance of spectral based fault detection methods in quality control by testing on the same environment. The most widely used spectral based approaches as Fourier Transform, Wavelet Transform, Gabor Transform were used to extract features of the faulty fabric samples. By using statistical functions feature selection was done so huge dimensionality of features was decreased. The selected features are taken as inputs for feed-forward network (with the back propagation algorithm) to classify faulty fabrics in categories; weft, wrap and oil. All computations were performed in Matlab program so as to satisfy all conditions as the same. The analyses’ results show the Wavelet transform in terms of classification for three type defects was more efficient than the others, on other hand; Fourier transform in terms of processing time is faster than the others. Keywords-fabric detection techniques, Fourier transform, Wavelet transform, Gabor transform, classification
Molecular Origins of the Mechanical Behavior of Hybrid Glasses

Hybrid organic‐inorganic glasses exhibit unique electro‐optical properties along with excellent thermal stability. Their inherently mechanically fragile nature, however, which derives from the oxide component of the hybrid glass network together with the presence of terminal groups that reduce network connectivity, remains a fundamental challenge for their integration in nanoscience and energy technologies. We report on a combined synthesis and computational strategy to elucidate the effect of molecular structure on mechanical properties of hybrid glass films. We first demonstrate the importance of rigidity percolation to elastic behavior. Secondly, using a novel application of graph theory, we reveal the complex 3‐D fracture path at the molecular scale and show that fracture energy in brittle hybrid glasses is fundamentally governed by the bond percolation properties of the network. The computational tools and scaling laws presented provide a robust predictive capability for guiding precursor selection and molecular network design of advanced hybrid organic‐inorganic materials.
Does Telling White Lies Signal Pro-Social Preferences?

The opportunity to tell a white lie (i.e., a lie that benefits another person) generates a moral conflict between two opposite moral dictates, one pushing towards telling the truth always and the other pushing towards helping others. Here we study how people resolve this moral conflict. What does telling a white lie signal about a person’s pro-social tendencies? To answer this question, we conducted a two-stage 2x2 experiment. In the first stage, we used a Deception Game to measure aversion to telling a Pareto white lie (i.e., a lie that helps both the liar and the listener), and aversion to telling an altruistic white lie (i.e., a lie that helps the listener at the expense of the liar). In the second stage we measured altruistic tendencies using a Dictator Game and cooperative tendencies using a Prisoner’s dilemma. We found three major results: (i) both altruism and cooperation are positively correlated with aversion to telling a Pareto white lie; (ii) both altruism and cooperation are negatively correlated with aversion to telling an altruistic white lie; (iii) men are more likely than women to tell an altruistic white lie, but not to tell a Pareto white lie. Our results shed light on the moral conflict between prosociality and truth-telling. In particular, the first finding suggests that a significant proportion of people have non-distributional notions of what the right thing to do is, irrespective of the economic consequences, they tell the truth, they cooperate, they share their money.
Selective sparse coding based coupled dictionary learning algorithm for single image super-resolution

In this paper a new strategy of multiple dictionary learning is proposed for the problem of super-resolution. A two way clustering mechanism is proposed for classification. Dictionaries are obtained for each cluster by coupled dictionary learning with mapping functions. Clustering of training data is carried out by using two approximate scale invariant features. This is followed by coupled dictionary and mapping learning which further helps in making the sparse representation invariant to resolution blur. This mechanism provides a selective sparse coding over multiple dictionaries. At the reconstruction phase each patch is recovered by selective sparse coding and dictionary learning. Experiment results indicate that the proposed algorithm is on par with existing state-of-the-art algorithms. The proposed algorithm is able to recover directional features more accurately.
Effect of Salt Inclusion Level on Commercial Bacon Processing and Slicing Yields

The objectives were to determine effects of salt inclusion on production yields, commercial slicing yields, sensory characteristics, and lipid oxidation of bacon. A total of 144 bellies that ranged in weight from 5.8 to 6.6 kg were selected from 2 different suppliers. Fresh bellies were weighed to determine an initial weight (green weight). Then, bellies were randomly assigned to salt levels of 1.2, 1.5, or 1.8% in the final product and manufactured into bacon. Bacon was stored frozen, in aerobic packages, for approximately 0 d, 30 d, 60 d, or 90 d and analyzed for lipid oxidation. Sensory analysis was conducted approximately 14 d after slicing and again 90 d later. Cook yield was increased (P ≤ 0.05) in 1.2% bacon compared with 1.5 and 1.8% bacon, but slicing yield was 1% unit greater (P ≤ 0.05) in 1.8% bacon compared with 1.2% bacon. Increasing salt content from 1.5 to 1.8% increased the number of bacon slices generated from a slab of bacon by 12 slices and by nearly 16 slices when compared with the 1.2% treatment. Sensory saltiness increased (P ≤ 0.05) as intended salt level increased. Lipid oxidation and oxidized odor and flavor intensity was not different among salt treatment levels within any storage period. Reducing salt from 1.8 to 1.2% in bacon can adversely affect slicing yield, but was not detrimental to cook yield and did not reduce the rate of lipid oxidation of bacon.
Oncogenic BRAF(V600E) Promotes Stromal Cell-Mediated Immunosuppression Via Induction of Interleukin-1 in Melanoma

Purpose: In this study, we assessed the specific role of BRAF(V600E) signaling in modulating the expression of immune regulatory genes in melanoma, in addition to analyzing downstream induction of immune suppression by primary human melanoma tumor-associated fibroblasts (TAF). Experimental Design: Primary human melanocytes and melanoma cell lines were transduced to express WT or V600E forms of BRAF, followed by gene expression analysis. The BRAF(V600E) inhibitor vemurafenib was used to confirm targets in BRAF(V600E)-positive melanoma cell lines and in tumors from melanoma patients undergoing inhibitor treatment. TAF lines generated from melanoma patient biopsies were tested for their ability to inhibit the function of tumor antigen-specific T cells, before and following treatment with BRAF(V600E)-upregulated immune modulators. Transcriptional analysis of treated TAFs was conducted to identify potential mediators of T-cell suppression. Results: Expression of BRAF(V600E) induced transcription of interleukin 1 alpha (IL-1α) and IL-1β in melanocytes and melanoma cell lines. Further, vemurafenib reduced the expression of IL-1 protein in melanoma cell lines and most notably in human tumor biopsies from 11 of 12 melanoma patients undergoing inhibitor treatment. Treatment of melanoma-patient–derived TAFs with IL-1α/β significantly enhanced their ability to suppress the proliferation and function of melanoma-specific cytotoxic T cells, and this inhibition was partially attributable to upregulation by IL-1 of COX-2 and the PD-1 ligands PD-L1 and PD-L2 in TAFs. Conclusions: This study reveals a novel mechanism of immune suppression sensitive to BRAF(V600E) inhibition, and indicates that clinical blockade of IL-1 may benefit patients with BRAF wild-type tumors and potentially synergize with immunotherapeutic interventions. Clin Cancer Res; 18(19); 5329–40. ©2012 AACR.
A user-tracing architecture for modeling interaction with the world wide web

We have developed a methodology for studying and analyzing the psychology of users performing ecologically valid WWW tasks. A user trace is a record of all significant states and events in the user-WWW interaction based on eye tracking data, application-level logs, and think-aloud protocols. A user-tracing architecture has been implemented for developing simulation models of user-WWW interaction and for comparing a simulation model (SNIF-ACT) against user-trace data. The user tracing architecture compares each action of the SNIF-ACT simulation directly against observed user actions. The model and architecture have been used to successfully match detailed user trace data from four users working on two tasks each.
Regulating "Hate Spin": The Limits of Law in Managing Religious Incitement and Offense

As democracies try to manage the risks arising from religious vilification, questions are being raised about free speech and its limits. This article clarifies key issues in that debate. It centers on the phenomenon of “hate spin”—the giving or taking of offense as a political strategy. Any policy response must try to distinguish between incitement to actual harms and expression that becomes the object of manufactured indignation. An analysis of the use of hate spin by right-wing groups in India and the United States demonstrates that laws against incitement, while necessary, are insufficient for dealing with highly organized hate campaigns. As for laws against offense, these are counterproductive, because they tend to empower the most intolerant sections of society.