Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Supervised Deep Hashing with a Bipartite Graph

Recently, deep learning has been successfully applied to the problem of hashing, yielding remarkable performance compared to traditional method-s with hand-crafted features. However, most of existing deep hashing methods are designed for the supervised scenario and require a large number of labeled data. In this paper, we propose a novel semi-supervised hashing method for image retrieval, named Deep Hashing with a Bipar-tite Graph (BGDH), to simultaneously learn embeddings, features and hash codes. More speciﬁ-cally, we construct a bipartite graph to discover the underlying structure of data, based on which an embedding is generated for each instance. Then, we feed raw pixels as well as embeddings to a deep neural network, and concatenate the resulting features to determine the hash code. Compared to existing methods, BGDH is a universal framework that is able to utilize various types of graphs and losses. Furthermore, we propose an inductive variant of BGDH to support out-of-sample extensions. Experimental results on real datasets show that our BGDH outperforms state-of-the-art hashing meth-ods.
Domain and Facet Personality Predictors of All-Cause Mortality Among Medicare Patients Aged 65 to 100

Objectives: Our objectives were to test whether Conscientiousness, the other 4 domains of the Five-Factor Model, and their facets predicted mortality in older, frail individuals. Methods: Controlling for demographic and health measures, we used Cox regression to test whether the NEO Five-Factor Inventory (NEO-FFI) Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness domains predicted all-cause mortality over 5 years in 1076 65- to 100-year-old participants who took part in a Medicare Demonstration study. Supplementary analyses on 597 participants aged 66 to 102 who were reassessed 2 years later were conducted to determine whether any of the Revised NEO Personality Inventory (NEO-PI-R) facets were related to mortality. Results: When personality domains were treated as continuous variables, NEO-FFI Neuroticism and Agreeableness were significant protective factors. When personality domains were trichotomized, NEO-FFI Conscientiousness was a protective factor. In a third analysis, Agreeableness was not a significant predictor in a model that included the continuous Neuroticism and trichotomized Conscientiousness variables. Analysis of the NEO-PI-R Neuroticism, Agreeableness, and Conscientiousness factors showed that Agreeableness and Conscientiousness were protective and that there was a trend for a similar effect of Neuroticism. Facet-level analyses revealed that the Impulsiveness, Straightforwardness, and Self-Discipline facets of Neuroticism, Agreeableness, and Conscientiousness, respectively, were prospectively related to greater survival over a 3-year interval. Conclusion: The effects of Neuroticism and Agreeableness on mortality are inconsistent across previous studies. This study indicates that, in a sample of older, frail participants, high Neuroticism and Agreeableness scores are protective and that more specific effects are primarily the result of the Impulsiveness and Straightforwardness facet scales. The Conscientiousness findings are consistent with those in earlier studies and demonstrate the importance of the Self-Discipline facet. NEO-PI = NEO Personality Inventory, NEO-PI-R = Revised NEO Personality Inventory, NEO-FFI = NEO Five-Factor Inventory, MMPI = Minnesota Multiphasic Personality Inventory, ADL = activities of daily living, IADL = instrumental activities of daily living, HR = hazards ratio.
Using Machine Translation Evaluation Techniques to Determine Sentence-level Semantic Equivalence

The task of machine translation (MT) evaluation is closely related to the task of sentence-level semantic equivalence classification. This paper investigates the utility of applying standard MT evaluation methods (BLEU, NIST, WER and PER) to building classifiers to predict semantic equivalence and entailment. We also introduce a novel classification method based on PER which leverages part of speech information of the words contributing to the word matches and non-matches in the sentence. Our results show that MT evaluation techniques are able to produce useful features for paraphrase classification and to a lesser extent entailment. Our technique gives a substantial improvement in paraphrase classification accuracy over all of the other models used in the experiments.
RELATIONSHIP BETWEEN ORGANIZATIONAL SPIRITUALITY WITH PSYCHOLOGICAL EMPOWERMENT, CREATIVITY, SPIRITUAL INTELLIGENCE, JOB STRESS, AND JOB SATISFACTION OF UNIVERSITY STAFF

Introduction: The purpose of this study was to determine the relationship between organizational spirituality and psychological empowerment, creativity, spiritual intelligence, stress, and job satisfaction of employees of University. Method: Research group consisted of all employees of the university among whom 308 were selected at convenience. They filled out research tools including Milliman’s Organizational Spirituality Questionnaire, Spritzer’s Psychological Empowerment Questionnaire, Jafari’s Creativity Questionnaire, King’s Spiritual Intelligence Questionnaire, Job Description Questionnaire, and HSE Job Stress Questionnaire. Data were analyzed using Pearson’s correlation coefficient, independent t-test, one-way analysis of variance, and Scheffe's Post Hoc test. Results: The relationship between organizational spirituality and psychological empowerment, creativity, and job stress was positive and significant. Also based on the results of the employees of a job with spirituality organizational significant relationship. Conclusion: Spirituality is one of the most important variables affecting worker empowerment and creative organizations, and policy makers should use its maximum power to achieve organizational goals.
Comparison of Dry Eye and Corneal Sensitivity between Small Incision Lenticule Extraction and Femtosecond LASIK for Myopia

Purpose To investigate the changes in dry eye symptoms and clinical signs and corneal sensitivity after small incision lenticule extraction (SMILE) and femtosecond LASIK (femto-LASIK). Design Prospective, non-randomized comparative study. Methods The study included a total of 71 eyes of 71 patients; the SMILE group comprised 38 eyes of 38 patients, and the femto-LASIK group comprised 33 eyes of 33 patients. Ocular Surface Disease Index (OSDI), Tear film breakup time (TBUT), the Schirmer test without anesthesia (S1T), corneal fluorescein staining, and central corneal sensation were evaluated before surgery and at 1 week, 1 month, 3 months, and 6 months after surgery. Results OSDI scores in both groups were increased immediately and returned to preoperative level at 1 month after surgeries. The TBUT values in both groups were reduced after surgeries relative to their preoperative scores. Patients in SMILE group were less likely to have corneal staining compared with those in the femto-LASIK group ([odds ratio] OR = 0.50, 95% [confidence interval] CI 0.28 to 0.93, P = 0.03). Central corneal sensitivity was decreased at all postoperative time points in both groups. However, the central corneal sensation scores in the SMILE group were greater than that in the femto-LASIK group at all of the postoperative time points (all P<0.05). Conclusions SMILE surgeries resulted in a short-term increase in dry eye symptoms, tear film instability, and loss of corneal sensitivity. Furthermore, SMILE surgeries have superiority over femto-LASIK in lower risk of postoperative corneal staining and less reduction of corneal sensation.
Deadlock avoidance for systolic communication

The nature of the deadlock problem for the systolic model of communication is described. This problem does not exist for special-purpose systolic arrays for which the hardware designer can afford providing as many queues as required by the specific computation that the array intends to implement. However, for programmable systolic arrays, the number of messages crossing the interval between two adjacent cells can be arbitrarily large, depending on the program. As a result, the possibility of deadlock always exists, since the number of queues between adjacent cells is fixed. The problem of avoiding queue-induced deadlocks, for deadlock-free programs, at run time is described, and a solution to the problem is given. Schemes for consistent labeling and compatible queue assignment, for which the solution calls, as also described, as is how to take advantage of the buffering capability provided by queues. <<ETX>>
A Data-driven Approach to Speech/Non-speech Detection

We present a data-driven approach to weighting the temporal context of signal energy to be used in a simple speech/non-speech detector (SND). The optimal weights are obtained using linear discriminant analysis (LDA). Regularization is performed to handle numerical issues inherent to the usage of correlated features. The discriminant so obtained is interpreted as a filter in the modulation spectral domain. Experimental evaluations on the test data set, in terms of average frame-level error rate over different SNR levels, show that the proposed method yields an absolute performance gain of $10.9%$, $17.5%$, $7.9%$ and $8.3%$ over ITU's G.729B, ETSI's AMR1, AMR2 and a state-of-the-art multi-layer perceptron based system, respectively. This shows that even a simple feature such as full-band energy, when employed with a large-enough context, shows promise for applications.
Not All Prisoner’s Dilemma Games Are Equal: Incentives, Social Preferences, and Cooperation

The Prisoner’s Dilemma (PD) is a classic decision problem where 2 players simultaneously must decide whether to cooperate or to act in their own narrow self-interest. The PD game has been used to model many naturally occurring interactive situations, at the personal, organizational, and social levels, in which there exists a tension between individual material gain and the common good. At least 2 factors may influence the emergence of cooperative behavior in this well-known collective action problem: the incentive structure of the game itself, and the intrinsic social preferences of each of the players. We present a framework that integrates these 2 factors in an effort to account for patterns of high or low cooperation from repeated choice interactions. In an experiment using a collection of different PD games, and a measure of individual social preferences, we identify regions of PD games in which (a) cooperation is independent of social preferences; (b) nice people can be exploited; and (c) being nice is consistently rewarded.
A probabilistic risk assessment for the vulnerability of the European carbon cycle to weather extremes: the ecosystem perspective

Abstract. Extreme weather events are likely to occur more often under climate change and the resulting effects on ecosystems could lead to a further acceleration of climate change. But not all extreme weather events lead to extreme ecosystem response. Here, we focus on hazardous ecosystem behaviour and identify coinciding weather conditions. We use a simple probabilistic risk assessment based on time series of ecosystem behaviour and climate conditions. Given the risk assessment terminology, vulnerability and risk for the previously defined hazard are estimated on the basis of observed hazardous ecosystem behaviour. We apply this approach to extreme responses of terrestrial ecosystems to drought, defining the hazard as a negative net biome productivity over a 12-month period. We show an application for two selected sites using data for 1981–2010 and then apply the method to the pan-European scale for the same period, based on numerical modelling results (LPJmL for ecosystem behaviour; ERA-Interim data for climate). Our site-specific results demonstrate the applicability of the proposed method, using the SPEI to describe the climate condition. The site in Spain provides an example of vulnerability to drought because the expected value of the SPEI is 0.4 lower for hazardous than for non-hazardous ecosystem behaviour. In northern Germany, on the contrary, the site is not vulnerable to drought because the SPEI expectation values imply wetter conditions in the hazard case than in the non-hazard case. At the pan-European scale, ecosystem vulnerability to drought is calculated in the Mediterranean and temperate region, whereas Scandinavian ecosystems are vulnerable under conditions without water shortages. These first model-based applications indicate the conceptual advantages of the proposed method by focusing on the identification of critical weather conditions for which we observe hazardous ecosystem behaviour in the analysed data set. Application of the method to empirical time series and to future climate would be important next steps to test the approach.
Adoption of RFID for Enhanced Food Safety Management: A Qualitative and Explorative Approach

In many countries, food safety crisis can negatively impact public welfare as well as political stability. In over the past tow years, three government agencies and major food companies in South Korea have implemented a Radio Frequency Identification (RFID) project intended to explore and investigate the opportunities and challenges of RFID technology in the areas of instant food manufacture and supply management. This article reports on the successful practices and lessons learned of such project and antecedents of both enablers and inhibitors of acceptance and usage of the system through structured group interview. Among the findings are that item level tagging requires detailed and customized guidelines; standardized system development procedures among stakeholders is critical to the success of RFID implementation; information quality and information related system quality are more important than technical system quality in adopting the systems; factors that have been treated as enablers are identified as inhibitors in this context.
Identification of sequences in c-myc mRNA that regulate its steady-state levels

The level of cellular myc proto-oncogene expression is rapidly regulated in response to environmental signals and influences cell proliferation and differentiation. Regulation is dependent on the fast turnover of c-myc mRNA, which enables cells to rapidly alter c-myc mRNA levels. Efforts to identify elements in myc mRNA responsible for its instability have used a variety of approaches, all of which require manipulations that perturb normal cell metabolism. These various approaches have implicated different regions of the mRNA and have led to a lack of consensus over which regions actually dictate rapid turnover and low steady-state levels of c-myc mRNA. To identify these regions by an approach that does not perturb cell metabolism acutely and that directly assesses the effect of a c-myc mRNA region on the steady-state levels of c-myc mRNA, we developed an assay using reverse transcription and PCR to compare the steady-state levels of human myc mRNAs transcribed from two similarly constructed myc genes transiently cotransfected into proliferating C2C12 myoblasts. Deletion mutations were introduced into myc genes, and the levels of their mRNAs were compared with that of a near-normal, reference myc mRNA. Deletion of most of the myc 3' untranslated region (UTR) raised myc mRNA levels, while deletion of sequences in the myc 5' UTR (most of exon 1), exon 2, or the protein-coding region of exon 3 did not, thus demonstrating that the 3' UTR is responsible for keeping myc mRNA levels low. Using a similar reverse transcription-PCR assay for comparing the steady-state levels of two beta-globin-myc fusion mRNAs, we showed that fusion of the myc 3' UTR lowers globin mRNA levels by destabilizing beta-globin mRNA. Surprisingly, fusion of the protein-coding region of myc exon 3 also lowered globin mRNA steady-state levels. Investigating the possibility that exon 3 coding sequences may play some other role in regulating c-myc mRNA turnover, we demonstrated that these sequences, but not myc 3' UTR sequences, are necessary for the normal posttranscriptional downregulation of c-myc mRNA during myoblast differentiation. We conclude that, while two elements within c-myc mRNA can act as instability determinants in a heterologous context, only the instability element in the 3' UTR regulates its steady-state levels in proliferating C2C12 cells.
THE EFFECT OF LEGAL STATUS ON RESPONSES TO BRIEF MOTIVATIONAL INTERVIEWING WITH SUBSTANCE USERS

ix Review of the Literature 1 Substance Use Disorder 1 Prevalence and Diagnosis of Substance Use Disorders 1 Transtheoretical Model of Change. 6 Self Determination Theory 7 Motivational Interviewing (MI) 12 Motivational Enhancement Therapy (MET) 15 Prevalence Rates Among Correctional Populations 17 Existing Approaches with Substance Use in Correctional Populations 19 MI/MET Research 20 Statement of the Problem and Hypotheses 27 Methods 29 Participants 29 Study Procedures 32 Measures 36 Variables for Present Study 38 Data Analysis Plan 43 iii
User Content Generation and Usage Behavior in Multi-media Settings: A Dynamic Structural Model of Learning 1

Consumer adoption and usage of mobile communication and multimedia content services has been growing steadily over the past few years in many countries around the world. In this paper, we develop and estimate a dynamic structural model of user behavior and learning with regard to content generation and usage activities in mobile multi-media environments. We model that users make content choices based on how well the content matches their taste. Users learn about two different categories of content – content from regular Internet social networking and community (SNC) sites and that from mobile portal sites. Then they can choose to engage in the creation (uploading) and consumption (downloading) of multi-media content from these two categories of websites. In our context, users have two sources of learning about content match value – (i) direct experience through their own content creation and usage behavior and (ii) indirect experience through word-of-mouth such as the content creation and usage behavior of their social network neighbors. Our model seeks to explicitly explain the underlying mechanism of user content generation and usage in mobile multi-media settings and examine how direct and indirect experiences influence the content creation and usage behavior of users over time. We develop a dynamic structural model. We estimate this model using a unique dataset of consumers‟ mobile media content creation and usage behavior over a 3-month time period. Our estimates suggest that when it comes to user learning from direct experience, the content downloaded from mobile portal sites has the highest level of mean match value. In contrast, the content downloaded from Internet SNC sites has the lowest level of mean match value. In terms of the magnitude of estimates, the standard deviation of indirect experience signals is higher than the standard deviation of direct experience signals. That is, in the mobile multi-media context, learning based on direct experience is more reliable (has less variability) than learning based on indirect experience. We use our estimates to assess the importance of learning through different counterfactual experiments. Our policy simulations suggest that the impact of an increase in content match value on the propensity to download content from mobile portal sites is higher for the segment that is geographically more mobile. In contrast, the impact of an increase in content match value on the propensity to upload content to mobile portal sites is higher for the segment that is geographically less mobile. Potential implications for mobile phone operators and mobile advertisers are discussed.
Towards a Sense Based Document Representation for Internet Information Retrieval

We describe an attempt to use word sense as an alternate text representation within an information retrieval system in order to enhance retrieval effectiveness. A performance comparison between a term and sense based system was carried out indicating increased retrieval effectiveness using a sense based representation. These increases come about by using a retrieval strategy designed to down rank documents containing query terms identified as being used in an infrequent sense.
Synthesizing object receiving motions of humanoid robots with human motion database

This paper presents a method for synthesizing motions of a humanoid robot that receives an object from a human, with focus on a natural object passing scenario where the human initiates the passing motion by moving an object towards the robot, which continuously adapts its motion to the observed human motion in real time. In this scenario, the robot not only has to recognize and adapt to the human action but also has to synthesize its motion quickly so that the human does not have to wait holding an object. We solve these issues by using a human motion database obtained from two persons performing the object passing task. The rationale behind this approach is that human performance of such a simple task is repeatable, and therefore the receiver (robot) motion can be synthesized by looking up the passer motion in a database. We demonstrate in simulation that the robot can start extending the arm at an appropriate timing and take hand configurations suitable for the object being passed. We also perform hardware experiments of object handing from a human to a robot.
Effective Sampling for Lightweight Data-Race Detection

Data races are one of the most common and subtle causes of pernicious concurrency bugs. Static techniques for preventing data races are overly conservative and do not scale well to large programs. Past research has produced several dynamic data race detectors that can be applied to large programs and are precise in the sense that they only report actual data races. However, these dynamic data race detectors incur a high performance overhead, slowing down a program’s execution by an order of magnitude. In this paper we present FeatherLite, a very lightweight data race detector that samples and analyzes only selected portions of a program’s execution. We show that it is possible to sample a multi-threaded program at a low frequency and yet find infrequently occurring data races. We implemented FeatherLite using Microsoft’s Phoenix compiler. Our experiments with several Microsoft programs show that FeatherLite is able to find more than 75% of data races by sampling less than 5% of memory accesses in a given program execution.