Miracle-dz/UniFAR-FTUs huggingface dataset:
Cost and Probability Biases in Social Phobia: Evaluating Their Relation to Attention Bias and Treatment Outcome

Social phobia is maintained in part by judgmental biases concerning the probability and cost of negative social events. One hypothesized mechanism of action of cognitive behavioral therapy (CBT) for anxiety disorders is its reduction in the exaggerated probabilities and costs associated with feared out‐ comes, termed the " cognitive mediation hypothesis " (Foa & Kozak, 1986). A number of studies have ex‐ amined the cognitive mediation hypothesis; some investigations find cost bias to be more important to treatment outcome, whereas others find probability bias to be more important. However, methodologi‐ cal limitations of several of these studies leave open the possibility that changes in judgmental biases are simply correlates or consequences of social anxiety reduction. Attentional processes, which mark the first discrimination of incoming information, may serve as precursors to cognitive processes like proba‐ bility and cost estimates. Though intuitively linked, whether social phobics' pattern of attending to ex‐ ternal threat cues is correlated with their appraisals of the cost and probability of negative events has yet to be examined empirically. The current project examines cost and probability biases and their rela‐ tion to attention bias and treatment outcome in a randomized controlled trial of CBT for social phobia. It was found that, contrary to hypotheses, greater attentional bias for threat in either direction (vigilance or avoidance) did not predict higher cost and probability estimates. However, a significant relation was observed between attentional vigilance and outcome probability estimates, such that greater vigilance for threat predicted greater estimates of the likelihood that negative social events will occur. As hypoth‐ esized, early changes in cost and probability biases predicted later changes in social anxiety symptoms (and not vice versa). Changes in probability estimates were a stronger predictor of treatment outcome than changes in cost estimates. Broadly, findings provide support for the cognitive mediation hypothesis of social phobia and point to both outcome cost and outcome probability as potential treatment mech‐ anisms. Findings are discussed in the context of extant theories of social phobia, and directions for fu‐ ture research are proposed.
Adaptive Learning, Endogenous Inattention, and Changes in Monetary Policy

This paper develops an adaptive learning formulation of an extension to the Ball, Mankiw, and Reis (2005) sticky information model that incorporates endogenous inattention. We show that, following an exogenous increase in the policymaker's preferences for price vs. output stability, the learning process can converge to a new equilibrium in which both output and price volatility are lower.
How Well Can a CNN Marginalize Simple Nuisances It is Designed for?

We conduct an empirical study to test the ability of convolutional neural networks (CNNs) to reduce the effects of nuisance transformations of the input data, such as location, scale and aspect ratio. We isolate factors by adopting a common convolutional architecture either deployed globally on the image to compute class posterior distributions, or restricted locally to compute class conditional distributions given location, scale and aspect ratios of bounding boxes determined by proposal heuristics. As explained in the paper, averaging the latter should in principle yield performance inferior to proper marginalization. Empirical tests yield the converse, however, leading us to conclude that - at the current level of complexity of convolutional architectures and scale of the data sets used to train them - CNNs are not very effective at marginalizing nuisance variability. We also quantify the effects of context on the overall classification task and its impact on the performance of CNNs, and propose improved sampling techniques for heuristic proposal schemes that improve end-to-end performance to state-of-the-art levels. We test our hypothesis on classification task using the ImageNet Challenge benchmark, on detection and on wide-baseline matching using the Oxford and Fischer matching datasets.
Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitat

In a method of producing a plate-like polystyrene resin foam which comprises extruding a molten mixture of a polystyrene resin and a volatile blowing agent from an extruder into a zone kept at a lower pressure than the inside of the extruder, the improvement wherein the volatile blowing agent is a mixture consisting of 50 to 80% by weight of dichlorodifluoromethane and 50 to 20% by weight of ethyl chloride and is present in said molten mixture in a proportion of 8 to 17 parts by weight per 100 parts by weight of the polystyrene resin.
The Rhetorical Construction of the AD/HD Subject: Managing the Self

As the diagnosis of AD/HD becomes more frequent in our schools, and as many individuals across ages embrace an AD/HD identity, scholars need to account for the lived experience of the disorder to understand the meanings individuals give to it. This paper analyses the relationship between ethical practices of self-formation, power, and knowledge (subjectification) in the case of the North American AD/HD subject. Specifically, it builds on recent medicalization scholarship to analyze how individuals make meaning of their disorder and its corresponding drug regimen in light of the “cultural meanings” made available by an increasingly prevalent neuropsychological discourse and ontology. By examining the first-person online accounts of AD/HD subjects from popular Internet message boards, this essay demonstrates how these individuals position themselves on the side of knowledge (the officially sanctioned AD/HD discourse) in creating self-interested and productive identities within newly emergent discursive configurations that diverge from earlier constructions that focused on the eradication of undesirable behaviors. It finds that the AD/HD drug regimen can no longer be understood solely in the terms of deviancy and the “social control” of behavior and both confirms and complicates the argument that these regimens reflect “cosmetic” self-improvement or coping.
Reactive Power Compensation Game Under Prospect-Theoretic Framing Effects

Reactive power compensation is an important challenge in smart power systems. However, in the context of reactive power compensation, most existing studies assume that customers can assess their compensation value (their Var unit) objectively. In this paper, customers are assumed to make decisions that pertain to reactive power coordination. In consequence, the way in which those customers evaluate the compensation value resulting from their individual decisions will impact the overall grid performance. In particular, a behavioral framework, based on the framing effect of prospect theory (PT), is developed to study the impact of both objective value and subjective evaluation in a reactive power compensation game. For instance, PT’s framing effect allows customers to optimize a subjective value of their utility which essentially frames the objective utility with respect to a reference point. This game enables customers to coordinate the use of their electrical devices to compensate reactive power. For the proposed game, both the objective case using expected utility theory and the PT consideration are solved via a learning algorithm that converges to a mixed-strategy Nash equilibrium. In addition, several key properties of this game are derived analytically. Simulation results show that, under PT, customers are likely to make decisions that differ from those predicted by classical models. For instance, using an illustrative two-customer case, we show that a PT customer will increase the adoption of a conservative strategy (achieving a high power factor) by 29% compared to a conventional customer. Similar insights are also observed for a case with three customers.
Use of a Tetracycline-Inducible System for Conditional Expression in Mycobacterium tuberculosis and Mycobacterium smegmatis

ABSTRACT A number of essential genes have been identified in mycobacteria, but methods to study these genes have not been developed, leaving us unable to determine the function or biology of the genes. We investigated the use of a tetracycline-inducible expression system in Mycobacterium tuberculosis and Mycobacterium smegmatis. Using a reporter gene which encodes an unstable variant of GFP, we showed that tetracycline-inducible expression occurred in M. smegmatis and that expression levels were titratable to some extent by varying the concentration of tetracycline. The removal of tetracycline led to cessation of GFP expression, and we showed that this was a controllable on/off switch for fluorescence upon addition and removal of the antibiotic inducer. The system also functioned in M. tuberculosis, giving inducible expression of the reporter gene. We used homologous recombination to construct a strain of M. tuberculosis that expressed the only copy of the tryptophan biosynthetic enzyme, TrpD, from the tetracycline-inducible promoter. This strain was conditionally auxotrophic, showing auxotrophy only in the absence of tetracycline, confirming that trpD was tightly controlled by the foreign promoter. This is the first demonstration of the use of an inducible promoter to generate a conditional auxotroph of M. tuberculosis. The ability to tightly regulate genes now gives us the possibility to define the functions of essential genes by switching them off under defined conditions and paves the way for in vivo studies.
Do private labels increase retailer bargaining power?

Like any new product, private label entry increases competition within a category leading to downward pressure on both wholesale and retail prices. But, given the higher margins for private labels and potential bargaining benefits for retailers, they have incentives to help private labels gain market share. The paper addresses two questions: First, do private labels enhance a retailer’s bargaining power with respect to manufacturers? Second, given the higher profitability and potential increase in bargaining power, does the retailer strategically set retail prices to favor and strengthen the private label? We find support for the “bargaining power” hypothesis, but qualified support for the “strategic retailer pricing” hypothesis. Retailers gain bargaining power through lower wholesale prices on imitated national brands. But the gain is greater in niche categories than in mass categories, suggesting that niche national brands with limited “pull” power lose greater bargaining power. In terms of strategic pricing, the retailer, on initially introducing the private label, strategically sets prices to help private labels gain market share in high volume mass market categories. But retail prices revert to the category profit maximizing price after a year when the private label gains a stable market share.
DISTINCT CHEMICAL REGIONS IN THE “PRESTELLAR” INFRARED DARK CLOUD G028.23−00.19

We have observed the Infrared Dark Cloud (IRDC) G028.23−00.19 at 3.3 mm using the Combined Array for Research in Millimeter-wave Astronomy. In its center, the IRDC hosts one of the most massive (∼1520 M☉) quiescent, cold (12 K) clumps known (MM1). The low temperature, high NH2D abundance, narrow molecular line widths, and absence of embedded infrared sources (from 3.6 to 70 μm) indicate that the clump is likely prestellar. Strong SiO emission with broad line widths (6–9 km s−1) and high abundances ((0.8–4) × 10−9) is detected in the northern and southern regions of the IRDC, unassociated with MM1. We suggest that SiO is released to the gas phase from the dust grains through shocks produced by outflows from undetected intermediate-mass stars or clusters of low-mass stars deeply embedded in the IRDC. A weaker SiO component with narrow line widths (∼2 km s−1) and low abundances (4.3 × 10−11) is detected in the center-west region, consistent with either a “subcloud–subcloud” collision or an unresolved population of a few low-mass stars. We report widespread CH3OH emission throughout the whole IRDC and the first detection of extended narrow methanol emission (∼2 km s−1) in a cold, massive prestellar clump (MM1). We suggest that the most likely mechanism releasing methanol into the gas phase in such a cold region is the exothermicity of grain-surface reactions. HN13C reveals that the IRDC is actually composed of two distinct substructures (“subclouds”) separated in velocity space by ∼1.4 km s−1. The narrow SiO component arises where the subclouds overlap. The spatial distribution of C2H resembles that of NH2D, which suggests that C2H also traces cold gas in this IRDC.
Two Multi-Terminal Communication Problems: Distributed Estimation and Source-Channel Broadcast

Two Multi-Terminal Communication Problems: Distributed Estimation and Source-Channel Broadcast by Vinod Malathi Prabhakaran Doctor of Philosophy in Engineering-Electrical Engineering and Computer Sciences Designated Emphasis in Communication, Computation, and Statistics University of California, Berkeley Professors Kannan Ramchandran and David Tse, Co-chairs A major success of information theory is a more or less satisfactory understanding of point-to-point communication (where a single source of information wants to convey this information to a single sink). However, many questions remain open when there is more than one source or sink of information. This dissertation addresses two classes of such problems. The first set of problems is motivated by wireless sensor networks where a large number of sensors collect potentially noisy data about an underlying physical phenomenon and communicate a compressed form of the data to a fusion centre which attempts to reconstruct some aspects of the original source of data. One model for
LSDM: Improving the Performance of Mobile Storage with a Log-Structured Address Remapping Device Driver

Mobile devices use low-cost SSDs such as microSD cards and eMMC devices for persistent data storage. However, The controllers of low-cost SSDs are optimized for reads and for sequential writes and they perform poorly under random writes. In this paper, we show that it is possible to overcome this limitation using a novel device driver on the host. Our driver, called LSDM, uses design techniques normally used in the firmware (FTL) of high-end SSD to transform random writes to sequential ones. Our driver is a generic kernel module that interfaces an existing file system to the block device that represents the SSD, transforming the arbitrary read/write request sequence of the file system to a sequence with long streams of sequential writes, even low-cost SSDs perform well on such sequences. The use of an existing unmodified file system allows users and administrators to benefit from mature, feature-complete file systems. Our prototype implementation speeds up all file bench workloads used, by up to a factor of 6. Our experiments show that a flash-friendly file system that generates long streams of sequential writes delivers performance that is similar to a conventional file system mounted on LSDM. The high complexity of a completely new file system, relative to the simplicity of LSDM, favors our solution.
Min-hash sketch construction via nonparametric clustering

In partial duplicate image retrieval systems, min-Hash algorithms are widely used because of its high efficiency and robustness. In most of min-Hash algorithms, min-Hash functions are considered independent and grouped into tuples called sketches, the discriminative power of sketches are limited. By modeling correlations of min-Hash functions, we propose a novel sketch construction method called Nonpara-metric Clustering min-Hash (NCmH). In NCmH, the randomly generated min-Hash functions are clustered before grouping them into sketches, while spatial information is fully used in this process. The constructed sketches preserve abundant spatial information between visual words, thus NCmH achieves higher retrieval accuracy compared to the standard min-Hash. Furthermore, our method can be combined with other min-Hash algorithms such as GVP mH [1], PmH [2] and TmH [3] to further improve accuracy. In experiments, we show that our method outperforms the standard min-Hash and improves the state-of-the-art min-Hash algorithm on Oxford 5K dataset and University of Kentucky dataset.
Creating a Knowledge Base from a Collaboratively Generated Encyclopedia

We present our work on using Wikipedia as a knowledge source for Natural Language Processing. We first describe our previous work on computing semantic relatedness from Wikipedia, and its application to a machine learning based coreference resolution system. Our results suggest that Wikipedia represents a semantic resource to be treasured for NLP applications, and accordingly present the work directions to be explored in the future.
Putting the ‘Human’ Back in Genetics: Modeling the Extended Kinships of Twins

Abstract Few papers have attempted to address the potential of extended kinships of twins to resolve the complexities of biological and cultural inheritance in humans. Since the mid-80s these issues have largely been buried beneath the quest for specific genes that contribute to individual differences in complex traits and liability to disease. The articles of Keller et al. and Medland and Keller (current issue) revisit these issues. History will decide whether these new papers represent the final gasp of a paradigm superceded by the ‘new’ genetics or contain the timely seeds of new birth in the face of a 30-year genetic research program otherwise poised on the cusp of degeneration (Lakatos & Musgrave, 1970).
Evaluating Quantitative and Qualitative Types of Change

In this research, the authors present theoretical explanations for quantitative and qualitative types of changes to evaluate the malleability of general self-efficacy (GSE) and specific self-efficacy (SSE) measures due to domain-specific training. First, the authors hypothesize that the change in the GSE measure due to sales training is a quantitative mean-difference (alpha) change. Second, they hypothesize that the change in SSE due to sales training is a qualitative (beta or gamma) change. Results of latent variable cross-lagged analysis on sales trainee data (N = 417) support the authors’ hypothesis that the difference in GSE was quantitative. Findings also provide partial support of the second hypothesis that a change in a SSE measure was qualitative. The qualitative change is attributed to the novelty and complexity of the particular SSE tasks (i.e., direct selling). Additional analysis of rank-order consistency further validates the measures and demonstrates the GSE measure was consistent before and after training whereas the SSE scales were not as consistent. Theoretical and practical implications of the findings are discussed.
The development and validation of an instrument assessing student-institution fit

While past research has focused on how student background characteristics and university experiences predict student retention and achievement, very few studies have examined the role that student-institution ‘fit’ might play in this process. In this study, we developed and validated a student-institution fit instrument that assesses the correspondence between student and institutional characteristics. We then examined the extent to which student-institution fit and friendship groups predict indicators of student success (e.g. academic adjustment and disengagement), which in turn predict future intentions. Using independent samples of undergraduate students from two Australian universities, exploratory and confirmatory factor analyses provide evidence for a unidimensional model of student-institution fit. Moreover, structural equation modelling analyses indicate that student-institution fit predicts greater academic adjustment and less disengagement, which in turn predict intentions regarding retention, transferring to another university, and changing major or career choice.
Generalized Imaging Conditions for Wave-Equation Migration

We generalize imaging conditions and angle decompositions for wave-equation migration of primary and converted waves. Prestack images are described as functions of space-shifts or time-shifts between source and receiver wavefields at every image location. Angle transformations allow construction of common-image gathers for both primary (P) and converted (C) waves.
A simplified statistical analysis of the Affine Projection Algorithm

This paper develops a statistical analysis of the Affine Projection Algorithm (APA) for adaptive filters to theoretically calculate filter convergence behavior. Assuming that the number of tap weights of the adaptive filter is sufficiently large, we derive simple difference equations that only require matrix computations. In experiments with some examples, it is observed that simulated and theoretically calculated filter convergence curves are generally in good agreement that justifies the approximations used and shows the validity of the analysis for practical use.
Assessment and Treatment of Depression in Gay and Bisexual Men in Emergency Settings

Gay and bisexual men are at higher risk for certain mental health disorders, including anxiety, depression, and suicide. These individuals often present to emergency settings in need of evaluation and treatment. Despite the utilization of emergency mental health services by this population, clinicians are often underprepared in responding to their unique needs. This article discusses depression in gay and bisexual men and provides an evidence-based approach in treating these men in emergency settings. Directives for future research and advanced practice nursing education are also presented.
Fast Approximate Matrix Multiplication by Solving Linear Systems

In this paper, we present novel deterministic algorithms for multiplying two $n \times n$ matrices approximately. Given two matrices $A,B$ we return a matrix $C'$ which is an \emph{approximation} to $C = AB$. We consider the notion of approximate matrix multiplication in which the objective is to make the Frobenius norm of the error matrix $C-C'$ arbitrarily small. Our main contribution is to first reduce the matrix multiplication problem to solving a set of linear equations and then use standard techniques to find an approximate solution to that system in $\tilde{O}(n^2)$ time. To the best of our knowledge this the first examination into designing quadratic time deterministic algorithms for approximate matrix multiplication which guarantee arbitrarily low \emph{absolute error} w.r. t. Frobenius norm.
Differential Effects of Right–Wing Authoritarianism and Social Dominance Orientation on Dimensions of Generalized Prejudice in Brazil

Previous research within a dual–process cognitive–motivational theory of ideology and prejudice has indicated that dimensions of generalized prejudice are structured around attitudes towards dangerous, derogated and dissident groups, and that these prejudice dimensions are differentially predicted by the ideological attitudes of Right–Wing Authoritarianism (RWA) and Social Dominance Orientation (SDO). However, to date, these findings have been restricted to New Zealand samples. We describe two studies examining whether the structure of prejudiced attitudes and the differential prediction by RWA and SDO replicate in the Brazilian context, incorporating context–relevant examples of each group—politicians, those from the northeast region of Brazil, and environmentalists. Results broadly supported the three–factor structure of dangerous, derogated, and dissident groups. Consistent with previous research, regression and structural equation analyses showed that RWA explained prejudice against dangerous groups, SDO explained prejudice against derogated groups, and both RWA and SDO explained prejudice against dissident groups. This research provides some evidence for the generalizability of the three–dimensional structure of generalized prejudice and differential prediction by RWA and SDO. Copyright © 2014 European Association of Personality Psychology