Miracle-dz/UniFAR-FTUs huggingface dataset:
Hispanic Consumer Preferences: Relationships with Acculturation and Materialism

Abstract The objective of this study is to examine Hispanic consumers in the U.S. – specifically to examine the effect that acculturation, years in the U.S., and materialism may have on their store environment preferences. A sample of 160 Hispanic individuals in the U.S. responded to a questionnaire at a Hispanic festival. The findings indicate that the preferences of Hispanic individuals residing in the U.S. for store environments previously identified as being favored by the Hispanic market are affected by their level of acculturation and by their level of materialism, but not by the number of years they have lived in the U.S. Introduction Immigration into the United States is presently at record levels. Consequently, immigrants in the U.S. are receiving increasing attention from numerous sources. From a retail perspective, immigration is especially important given the additional diversity it introduces into the marketplace. At present, the largest group of immigrants to the U.S. originates from Hispanic cultures. Indeed, individuals of Hispanic origin now represent the largest minority group in the U.S. (Gonzalez, 2003). Since the Hispanic population in the U.S. is growing at more than ten times the rate experienced by the general population (Vence, 2006), their influence in the marketplace will continue to increase. Despite the size and buying power of this growing market segment, relatively little research has examined the shopping behaviors and preferences of Hispanic consumers in the U.S. Research suggests that the shopping behavior and the store environment preferences of Hispanic consumers differ significantly from the general population (e.g., Gardyn & Fetto, 2003) and likely serves as an effective market segmentation base (Donthu & Cherian, 1994). Differences in shopping behavior and preferences across varying ethnic groups suggest that an individual’s ethnic character may be especially important to retailers (Mulhern & Williams, 1994). To effectively target ethnic markets, retailers must understand these differences (Perkins, 2004). The store environment preferences of immigrant populations tend to differ by the degree that the individuals in those populations have acculturated to the norms of the dominant culture. Dedeoǧlu and Ustundagli (2011) examined immigrants’ acculturation to a new culture and observed the existence of important differences between the consumer orientation and activities
Sentiment Classification Based on Syntax Tree Pruning and Tree Kernel

Sentiment classification is a way to analyze the subjective information in the text and then mine the opinion. We focus on the sentence-level sentiment classification. On the systematically analyzing the importance and difficulties of the sentence-level sentiment classification, this paper proposes a syntax tree pruning and tree kernel-based approach to sentiment classification. In our method, the convolution kernel of SVM is first used to obtain structured information, and then apply syntax tree as a feature in Sentiment Classification. Firstly, we focus on how to apply the structured features from the syntax tree to the sentiment classification and propose a novel approach of sentence-level sentiment classification which apply the tree kernel and composite kernel to the SVM classifier. Secondly, we provide two kinds of syntax tree pruning strategies: adjectives-based and sentiment words-based. The experimental results show that our method can achieve better performance in sentence level Sentiment Classification.
Prediction of protein-protein interaction types using the decision templates

Protein-protein interactions (PPIs) play a key role in many cellular processes. Knowing about the multitude of PPIs can help the biological scientist understand the molecular mechanism of the cell. Unfortunately, it is both time-consuming and expensive to do so solely based on experiments. Therefore, developing computational approaches for predicting PPIs, PPI binding sites and PPI types would be of significant value. Here, we propose a novel method for predicting the PPI types based on decision templates. First, we introduce the concept of tensor product to construct three kinds of feature vectors which are the amino acid composition tensor product, the residue multi-scale conservation energy tensor product and the secondary structure content tensor product. Then, the correlation-based feature selection method was also used to reduce the dimensionality of these feature vectors. So, the protein pair can be represented by our three new kinds of feature vectors and Zhu's six kinds of feature vectors. The nine kinds of feature vectors are further taken as the inputs of individual support vector machine classifier respectively, and the outputs of these classifiers are aggregated with decision templates in decision level. The overall success rate obtained by jackknife cross-validation was 90.95%, indicating our method is very promising for predicting PPI types.
Working Capital Management, Corporate Performance, and Strategic Choices of the Wholesale and Retail Industry in China

We examine the influence of strategic choice on working capital configurations and observe how the relationship between working capital ratio and operational performance differs depending on strategy. By clustering the strategic factors of the wholesale and retail industry, we find three categories of strategies: terminal market strategy, middle market strategy, and hybrid strategy. Using the panel data of the listed companies of the wholesale and retail industry as our sample, we analyze the differences in the ways companies configure working capital, the speed with which working capital adjusts to its target, and the effects of working capital on performance for companies that make different strategic choices. The empirical results suggest that working capital is configured and adjusted to its target in different ways under different competitive strategic choices. This effect is finally transferred to influence the relationship between working capital configuration and operational performance.
Data and computation transformations for Brook streaming applications on multiprocessors

Multicore processors are about to become prevalent in the PC world. Meanwhile, over 90% of the computing cycles are estimated to be consumed by streaming media applications (Rixner et al., 1998). Although stream programming exposes parallelism naturally, we found that achieving high performance on multiprocessors is challenging. Therefore, we develop a parallel compiler for the Brook streaming language with aggressive data and computation transformations. First, we formulate fifteen Brook stream operators in terms of systems of inequalities. Our compiler optimizes the modeled operators to improve memory footprint and performance. Second, the stream computation including both kernels and operators is mapped to the affine partitioning model by modeling each kernel as an implicit loop nest over stream elements. Note that our general abstraction is not limited to Brook. Our modeling and transformations yield high performance on uniprocessors as well. The geometric mean of speedups is 4.7 on ten streaming applications on a Xeon. On multiprocessors, we show that exploiting the standard intra-kernel data parallelism is inferior to our general modeling. The former yields a speedup of 1.5 for ten applications on a 4-way Xeon, while the latter achieves a speedup of 6.4 over the same baseline. We show that our compiler effectively reduces memory footprint, exploits parallelism, and circumvents phase-ordering issues.
A Hybrid Coherence Model for Noise Reduction in Reverberant Environments

In this letter, we propose a novel dual-microphone technique for enhancement of speech degraded by background noise and reverberation. Our algorithm is based on a prediction of the coherence function between the noisy input signals, considering both direct and reverberant speech and noise components received by the sensors, and therefore, is capable of dealing with both coherent and diffuse noise. After predicting the coherence function, the signal to noise ratio (SNR) can be estimated by solving a quadratic equation obtained from the real and imaginary parts of the function. Objective evaluation in a room with reverberation time T60 = 465 ms, demonstrated noticeable improvements in SNR and quality of the outputs processed with the proposed algorithm over the baseline (front microphone), as well as a recently proposed coherence-based noise reduction algorithm.
Agouti-Related Peptide and MC3/4 Receptor Agonists Both Inhibit Excitatory Hypothalamic Ventromedial Nucleus Neurons

Anorexigenic melanocortins decrease food intake by activating MC3/MC4 receptors (MC3/4R); the prevailing view is that the orexigenic neuropeptide agouti-related peptide (AgRP) exerts the opposite action by acting as an antagonist at MC3/MC4 receptors. A total of 370 hypothalamic ventromedial nucleus (VMH) glutamatergic neurons was studied using whole-cell recording in hypothalamic slices from a novel mouse expressing green fluorescent protein (GFP) under control of the vesicular glutamate transporter 2 (vGluT2) promoter. Massive numbers of GFP-expressing VMH dendrites extended out of the core of the nucleus into the surrounding cell-poor shell. VMH dendrites received frequent appositions from AgRP-immunoreactive axons in the shell of the nucleus, but not the core, suggesting that AgRP may influence target VMH neurons. α-MSH, melanotan II (MTII), and selective MC3R or MC4R agonists were all inhibitory, reducing the spontaneous firing rate and hyperpolarizing vGluT2 neurons. The MC3/4R antagonist SHU9119 was excitatory. Unexpectedly, AgRP did not attenuate MTII actions on these neurons; instead, these two compounds showed an additive inhibitory effect. In the absence of synaptic activity, no hyperpolarization or change in input resistance was evoked by either MTII or AgRP, suggesting indirect actions. Consistent with this view, MTII increased the frequency of spontaneous and miniature IPSCs. In contrast, the mechanism of AgRP inhibition was dependent on presynaptic inhibition of EPSCs mediated by Gi/Go-proteins, and was attenuated by pertussis toxin and NF023, inconsistent with mediation by Gs-proteins associated with MC receptors. Together, our data suggest that the mechanism of AgRP actions on these excitatory VMH cells appears to be independent of the actions of melanocortins on MC receptors.
CAD Parts-Based Assembly Modeling by Probabilistic Reasoning

Nowadays, increasing amount of parts and sub-assemblies are publicly available, which can be used directly for product development instead of creating from scratch. In this paper, we propose an interactive design framework for efficient and smart assembly modeling, in order to improve the design efficiency. Our approach is based on a probabilistic reasoning. Given a collection of industrial assemblies, we learn a probabilistic graphical model from the relationships between the parts of assemblies. Then in the modeling stage, this probabilistic model is used to suggest the most likely used parts compatible with the current assembly. Finally, the parts are assembled under certain geometric constraints. We demonstrate the effectiveness of our framework through a variety of assembly models produced by our prototype system.
A Study of Teaching Under the Weather: The Influence of Weather on Participant Mood and Engagement During an Informal Summer Camp Program.

Jodi Haney, Advisor The research question in this thesis was to find out if and how weather influenced students’ positive and negative mood, as well as cognitive, emotional, and behavioral engagement. This study also inquired if these results were similar to what prior research has found regarding how weather affects people’s mood and learning. The results from this study provided information to help teachers make decisions on how to plan instruction for their students based on the weather. Some suggestions include; an impromptu discussion on a sunny day, or allowing students some more time to do a hands-on activity by themselves when it rains. The research took place at the Toledo Zoo and included 26 participants. Participants were surveyed at the end of each day for the five days that they were at the Toledo Zoo Junior Zoo Keeper Summer Camp Program, creating a total of five surveys per participant. The data were analyzed via a series of sequenced ANOVA tests, known as a MANOVA, which compared the students across the 5 days, week-to-week and good-to-bad-weather days. There are five MANOVAs in the series. One for each of the following: positive mood, negative mood, behavioral engagement, cognitive engagement, and emotional engagement. The data collected showed that the only factor found to be statistically significant and likely to be influenced by weather was behavioral engagement. The results of this study showed information that would allow the zoo access to constructive feedback and ideas on how to potentially improve programs. Educational activities should be focused in different ways for different weather, which could lead to increased learning of the students at the zoo and create a higher retention rate in student knowledge as well.
Independent Component Analysis in the Presence of Gaussian Noise

The problem of estimating the statistical model of independent component analysis in the presence of Gaussian noise is considered. Because of the additive noise, a combination of factor analysis and a noise-free ICA algorithm doesn't give a consistent estimator of the mixing matrix. In this paper, following the semiparametric statistical approach to the noise-free ICA model by Amari and Cardoso(1997), we propose a method of estimating the mixing matrix consistently even if the additive noise exists. The proposed algorithm consists of two stages: First nd the factor subspace by means of factor analysis, and then determine the directions of independent components based on an estimating function in this semiparametric model. Department of Mathematical Engineering and Information Physics, Faculty of Engineering, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan. y Lab for Information Synthesis, Brain-Style Information System Group, RIKEN Brain Science Institute, Hirosawa 2-1, Wako, Saitama 351-0198, Japan.
Accelerating Meta Data Checks for Software Correctness and Security

As high GHZ processors become prevalent, adding hardware support to ensure the correctness and security of programs will be just as important, for the average user, as further increases in processor performance. The goal of our research is to focus on developing compiler and hardware support for ecien tly performing software checks that can be left on all of the time, even in production code releases, to provide a signican t increase in the correctness and security of software. In this paper we focus on the performance of checking the correctness of pointers. We focus on pointers since a signican t amount of bugs and security issues (buer overo ws) in programs are due to memory bugs resulting from incorrect usage of pointers. To determine if a pointer reference is correct many techniques require additional information to be kept track of called meta-data. The meta-data is checked when a pointer is dereferenced to verify some property about the pointer or the object. The rst part of our paper focuses on where to ecien tly keep track of this meta-data information and the overheads for performing safety checks like bounds checking and dangling pointer checks. We then focus on architecture extensions to reduce the overhead of these meta-data checks. We examine these optimizations in the presence of two meta-data checking applications { bounds checking and dangling pointer checks and show that we can reduce the overhead of these pointer checks from 148% down to 21% on average.
Brownian Motion, Bridge, Excursion, and Meander Characterized by Sampling at Independent Uniform Times

For a random process $X$ consider the random vector defined by the values of $X$ at times $0 < U_{n,1} < . < U_{n,n} < 1$ and the minimal values of $X$ on each of the intervals between consecutive pairs of these times, where the $U_{n,i}$ are the order statistics of $n$ independent uniform $(0,1)$ variables, independent of $X$. The joint law of this random vector is explicitly described when $X$ is a Brownian motion. Corresponding results for Brownian bridge, excursion, and meander are deduced by appropriate conditioning. These descriptions yield numerous new identities involving the laws of these processes, and simplified proofs of various known results, including Aldous's characterization of the random tree constructed by sampling the excursion at $n$ independent uniform times, Vervaat's transformation of Brownian bridge into Brownian excursion, and Denisov's decomposition of the Brownian motion at the time of its minimum into two independent Brownian meanders. Other consequences of the sampling formulae are Brownian representions of various special functions, including Bessel polynomials, some hypergeometric polynomials, and the Hermite function. Various combinatorial identities involving random partitions and generalized Stirling numbers are also obtained.
XABA: A zero-knowledge anomaly-based behavioral analysis method to detect insider threats

Insider threat is a significant security risk for organizations and hard to detect. Most introduced detection methods need contextual data entries about users, or preprocessed user activity logs to detect insider threats which it is costly and time-consuming. In this paper, we introduce a behavior analysis method that learns its context and detects multiple types of insider threats from raw logs and network traffic in real-time. This method, named XABA, learns user roles and exclusive behaviors, through analyzing raw logs related to each network session of the user. Then it checks for some abnormal patterns, and if so, triggers the appropriate alert. XABA is implemented on the big-stream platform to operate on high rates of network sessions. To evaluate XABA, a real traitor scenario is designed and detected with low false positive. XABA can detect diverse types of scenarios in many contexts without any predefined information or preprocessed activity logs.
Are Disinhibited Social Behaviors Among Internationally Adopted Children Mediated by the Attachment Environment or by Children's Difficulties with Inhibitory Control?

ABSTRACT Internationally adopted children show higher rates of disinhibited social behavior (DSB). Two competing explanations for DSB include difficulties in attachment specifically and deficits in inhibitory control more generally. This four-wave longitudinal study (a) documented the persistence of DSB among internationally adopted children and (b) tested the relative contributions of attachment environment versus inhibitory control difficulties in the persistence of DSB. Improvement in DSB was found 3 to 4 years post-adoption for the sample as a whole, although heterogeneity within the sample was present. Results from structural equation modeling and another test of mediation both indicated that the association between early deprivation and DSB was mediated by general difficulties with inhibitory control, not by attachment environment.
Firm-Speci c Capital and Welfare

What are the consequences for monetary policy design implied by the fact that price setting and investment takes typically place simultaneously at the rm level? To address this question we analyze simple (constrained) optimal interest rate rules in the context of a dynamic New Keynesian model featuring rm-speci c capital accumulation as well as sticky prices and wages à la Calvo. We make the case for Taylor type rules. They are remarkably robust in the sense that their welfare implications do not appear to hinge neither on the speci c assumptions regarding capital accumulation that are used in their derivation nor on the particular de nition of natural output that is used to construct the output gap. On the other hand we nd that rules prescribing that the central bank does not react to any measure of real economic activity are not robust in that sense. Keywords: Monetary Policy, Sticky Prices, Aggregate Investment. JEL Classi cation: E22, E31, E52 Thanks to seminar participants at Norges Bank. Special thanks to Ida Wolden Bache, Øistein Røisland, and Stephanie Schmitt-Grohé. The usual disclaimer applies. The views expressed in this paper are those of the authors and should not be attributed to Norges Bank.
Computational analysis of noncoding RNAs

Noncoding RNAs have emerged as important key players in the cell. Understanding their surprisingly diverse range of functions is challenging for experimental and computational biology. Here, we review computational methods to analyze noncoding RNAs. The topics covered include basic and advanced techniques to predict RNA structures, annotation of noncoding RNAs in genomic data, mining RNA‐seq data for novel transcripts and prediction of transcript structures, computational aspects of microRNAs, and database resources. These authors contributed equally WIREs RNA 2012. doi: 10.1002/wrna.1134
Disruption of Nitric Oxide Synthase 3 Protects Against the Cardiac Injury, Dysfunction, and Mortality Induced by Doxorubicin

Background— Flavoprotein reductases are involved in the generation of reactive oxygen species by doxorubicin. The objective of the present study was to determine whether or not one flavoprotein reductase, endothelial nitric oxide synthase (nitric oxide synthase 3 [NOS3]), contributes to the cardiac dysfunction and injury seen after the administration of doxorubicin. Methods and Results— A single dose of doxorubicin (20 mg/kg) was administered to wild-type (WT) mice, NOS3-deficient mice (NOS3−/−), and mice with cardiomyocyte-specific overexpression of NOS3 (NOS3-TG). Cardiac function was assessed after 5 days with the use of echocardiography. Doxorubicin decreased left ventricular fractional shortening from 57±2% to 47±1% (P<0.001) in WT mice. Compared with WT mice, fractional shortening was greater in NOS3−/− and less in NOS3-TG after doxorubicin (55±1% and 35±2%; P<0.001 for both). Cardiac tissue was harvested from additional mice at 24 hours after doxorubicin administration for measurement of cell death and reactive oxygen species production. Doxorubicin induced cardiac cell death and reactive oxygen species production in WT mice, effects that were attenuated in NOS3−/− and were more marked in NOS3-TG mice. Finally, WT and NOS3−/− mice were treated with a lower dose of doxorubicin (4 mg/kg) administered weekly over 5 weeks. Sixteen weeks after beginning doxorubicin treatment, fractional shortening was greater in NOS3−/− than in WT mice (45±2% versus 28±1%; P<0.001), and mortality was reduced (7% versus 60%; P<0.001). Conclusions— These findings implicate NOS3 as a key mediator in the development of left ventricular dysfunction after administration of doxorubicin.