Miracle-dz/UniFAR-FTUs huggingface dataset:
A parallel Lauritzen-Spiegelhalter algorithm for probabilistic inference

Probabilistic inference in belief networks is a promising technique for diagnosis, forecasting and decision analysis tasks. Unfortunately, exact inference can be very expensive computationally. We examine whether probabilistic inference can be speeded up effectively through parallel computation on real multiprocessors. Our experiments are performed on a 32-processor Stanford DASH multiprocessor, a cache-coherent shared-address-space machine with physically distributed main memory. We find that the major part of the calculation can be moved outside the actual propagation through the network, and yields good speedups. Speedups for the propagation itself depend on the structure of the network and the size of the cliques that the algorithm creates. We demonstrate good speedup on a CPCS subnetwork used for medical diagnosis. This result as well as a tendency for the speedup to increase with the size of the network invites practical application of parallel techniques for large Bayesian networks in expert systems. <<ETX>>
The Limited Impact of Exposure Duration on Holistic Word Processing

The current study explored the impact of stimuli exposure duration on holistic word processing measured by the complete composite paradigm (CPc paradigm). The participants were asked to match the cued target parts of two characters which were presented for either a long (600 ms) or a short duration (170 ms). They were also tested by two popular versions of the CPc paradigm: the “early-fixed” task where the attention cue was visible from the beginning of each trial at a fixed position, and the “delayed-random” task where the cue showed up after the study character at random locations. The holistic word effect, as indexed by the alignment × congruency interaction, was identified in both tasks and was unaffected by the stimuli duration in both tasks. Meanwhile, the “delayed-random” task did not bring about larger holistic word effect than the “early-fixed” task. These results suggest the exposure duration (from around 150 to 600 ms) has a limited impact on the holistic word effect, and have methodological implications for experiment designs in this field.
Palmprint recognition based on Kernel Locality Preserving Projections

Locality Preserving Projections (LPP) is a linear projective map that optimally preserves the neighborhood structure of the data set. Though LPP has been applied in many fields, it has limits to solve recognition problem. Thus, a new palmprint recognition method is proposed based on Kernel Locality Preserving Projections (KLPP). Different from LPP method, KLPP not only describes the nonlinear correlations between pixels, but also preserves the local structure of the palmprint image space. In this way, the unwanted variations resulting from in lighting may be eliminated or reduced. We compare our proposed approach with Principal Component Analysis (PCA), LPP and Kernel Principal Component Analysis (KPCA) methods on PolyU palmprint database. Experiment results demonstrate that KLPP achieves better recognition rate as the dimension of the palmprint subspace changes.
A NEW APPROACH FOR OPTICAL AND SAR SATELLITE IMAGE REGISTRATION

Over the last years several research studies have shown the high geometric accuracy of high resolution radar satellites like TerraSARX. Due to this fact, the impact of high resolution SAR images for image registration has increased. An aim of high accuracy image registration is the improvement of the absolute geometric accuracy of optical images by using SAR images as references. High accuracy image registration is required for different remote sensing applications and is an on-going research topic. The registration of images acquired by different sensor types, like optical and SAR images, is a challenging task. In our work, a novel approach is proposed, which is a combination of the classical feature-based and intensity-based registration approaches. In the first step of the method, spatial features, here roundabouts, are detected in the optical image. In the second step, the detected features are used to generate SAR like roundabout templates. In the third step, the templates are matched with the corresponding parts of the SAR image by using an intensitybased matching process. The proposed method is tested for a pair of TerraSAR-X and QuickBird images and a pair of TerraSAR-X and WorldView-2 images of a suburban area. The results show that the proposed method offers an alternative approach compared to the common optical and SAR images registration methods and it can be used for the geometric accuracy improvement of optical images.
The Effect of Hospital Nurse Basic Psychological Needs Satisfaction on Turnover Intention and Compassion Fatigue

The Effect of Hospital Nurse Basic Psychological Needs Satisfaction on Turnover Intention and Compassion Fatigue by David S. Klein MSW, New York University, 1980 BS, Bowling Green State University, 1968 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Organizational Psychology Walden University May 2017
Using Educational Resources to Improve the Efficiency of Web Searches for Additional Learning Material

The Internet is an invaluable source of information that can and should be used to help education. One of the possibilities of Internet in this area is the search for resources that complement the learning process, usually done with the support of the search engines. This search is generally performed using keywords, which implies on contextless results. This work uses information extraction techniques applied to educational resources to expand the queries done by students, adding contextual information in the search and thus recovering more appropriate educational resources. A prototype was developed according to the proposed architecture and a case study conducted in a Brazilian university presented results showing that this proposal can be used in an educational environment to improve the search of educational resources.
GIF-LR:GA-based informative feature for lipreading

In this paper, we propose a general and discriminative feature “GIF” (GA-based Informative Feature), and apply the feature to lipreading (visual speech recognition). The feature extraction method consists of two transforms, that convert an input vector to GIF for recognition. The transforms can be computed using training data and Genetic Algorithm (GA). For lipreading, we extract a fundamental feature as an input vector from an image; the vector consists of intensity values at all the pixels in an input lip image, which are enumerated from left-top to right-bottom. Recognition experiments of continuous digit utterances were conducted using an audio-visual corpus including more than 268,000 lip images. The recognition results show that the GIF-based method is better than the baseline method using eigenlip features.
A scalable approach for malware detection through bounded feature space behavior modeling

In recent years, malware (malicious software) has greatly evolved and has become very sophisticated. The evolution of malware makes it difficult to detect using traditional signature-based malware detectors. Thus, researchers have proposed various behavior-based malware detection techniques to mitigate this problem. However, there are still serious shortcomings, related to scalability and computational complexity, in existing malware behavior modeling techniques. This raises questions about the practical applicability of these techniques. This paper proposes and evaluates a bounded feature space behavior modeling (BOFM) framework for scalable malware detection. BOFM models the interactions between software (which can be malware or benign) and security-critical OS resources in a scalable manner. Information collected at run-time according to this model is then used by machine learning algorithms to learn how to accurately classify software as malware or benign. One of the key problems with simple malware behavior modeling (e.g., n-gram model) is that the number of malware features (i.e., signatures) grows proportional to the size of execution traces, with a resulting malware feature space that is so large that it makes the detection process very challenging. On the other hand, in BOFM, the malware feature space is bounded by an upper limit N, a constant, and the results of our experiments show that its computation time and memory usage are vastly lower than in currently reported, malware detection techniques, while preserving or even improving their high detection accuracy.
Minimization of the energy of the nonrelativistic one-electron Pauli-Fierz model over quasifree states

In this article the existence of a minimizer for the energy for the nonrelativistic one-electron Pauli-Fierz model wi thin the class of quasifree states is established. To this end it is shown that the minimum of the energy on quasifree states coincides with the minimum of the energy on pure quasifree states, where existence and uniqueness of a minimizer holds. Infrared and ultraviolet cutoffs are assumed, along with sufficiently small coupling constant and momentum of the dressed electron. A perturbative expression of the minimum of the energy on quasifree states for a small momentum of the dressed electron and small coupling constant is given. We also express the Lagrange equation for the minimizer in terms of the generalized one particle density matrix of the pure quasifr ee state.
DEPOSITOR BEHAVIOR AND MARKET DISCIPLINE IN COLOMBIA Adolfo Barajas , International Monetary Fund

This empirical study examines how the public makes decisions to place deposits among different banks and over time in the Colombian banking system. Recent studies suggest that in several Latin American countries, depositors exhibit significant responsiveness to performance indicators. With our specification, we set out to control for a more comprehensive set of risk/return factors, and thus conduct a stronger test for market discipline. We incorporate variables reflecting return to deposits (interest rate, noninterest services provided, etc.) as well as those reflecting risk (non-performing loans, capital-to-asset ratio, etc). This specification allows us to answer two types of questions of particular interest in rapidly evolving and liberalizing financial systems: (1) How do banks gain or lose market share? Which specific return or risk factors are the most important in explaining a bank's deposit growth? (2) How well do depositors discriminate between well and poorly managed banks? Using semi-annual data for 1985-1999 we find evidence that the rate of growth of deposits is related to a wide range of proxies for a bank’s probability of default. Furthermore, there is evidence that depositors’ choices effectively discipline banks; following “fundamental-driven” deposit losses, banks tend to respond by improving their capitalization and coverage ratios. Finally, banks with strong fundamentals appear to benefit from lower interest costs. This allows us to conclude that in the case of Colombia, market discipline is strong and that moral hazard stemming from deposit insurance ís limited, perhaps a consequence of certain design features of the insurance scheme. JEL Classification Number: G21
Daily salivary cortisol profile: Insights from the Croatian Late Adolescence Stress Study (CLASS)

Introduction The aim of the study was to examine basal hypothalamic-pituitary-adrenal (HPA) axis activity and to determine associations of various covariates (gender, sleep-wake rhythm, demographic, academic, life style and health-related characteristics) with altered daily salivary cortisol profiles in late adolescence. Materials and methods The total analytic sample consisted of 903 Croatian secondary school students aged 18 - 21 years (median 19 years). Salivary cortisol was sampled at home at three time points over the course of one week and its concentrations were measured by using the enzyme immunoassay. Results In comparison to males, female students had a higher cortisol awakening response (CAR) (median 4.69, IQR 10.46 and median 3.03, IQR 8.94, respectively; P < 0.001), a steeper (“healthier”) diurnal cortisol slope (DCS) (median 0.51, IQR 0.55 and median 0.44, IQR 0.51, respectively; P = 0.001), and a greater area under curve with respect to ground (AUCG) (median 206.79, IQR 111.78 and median 191.46, IQR 104.18, respectively; P < 0.001). Those students who woke-up earlier and were awake longer, had a higher CAR (P < 0.001), a flatter (“less healthy”) DCS (P < 0.001), and a greater AUCG (P < 0.001), than students who woke-up later and were awake shorter. Less consistent but still significant predictors of salivary cortisol indexes were age, school behaviour, friendship, diet healthiness and drug abuse. Conclusion Gender and sleep-wake up rhythm were major determinants of the altered daily salivary cortisol profiles in late adolescence. The predictive power of other covariates, although less clear, has a potential for identifying vulnerable subgroups such as male drug users and females without a best friend.
Clonal dynamics in western North American aspen ( Populus tremuloides )

Clonality is a common phenomenon in plants, allowing genets to persist asexually for much longer periods of time than ramets. The relative frequency of sexual vs. asexual reproduction determines long-term dominance and persistence of clonal plants at the landscape scale. One of the most familiar and valued clonal plants in North America is aspen (Populus tremuloides). Previous researchers have suggested that aspen in xeric landscapes of the intermountain west represent genets of great chronological age, maintained via clonal expansion in the near absence of sexual reproduction. We synthesized microsatellite data from 1371 ramets in two large sampling grids in Utah. We found a surprisingly large number of distinct genets, some covering large spatial areas, but most represented by only one to a few individual ramets at a sampling scale of 50 m. In general, multi-ramet genets were spatially cohesive, although some genets appear to be fragmented remnants of much larger clones. We conclude that recent sexual reproduction in these landscapes is a stronger contributor to standing genetic variation at the population level than the accumulation of somatic mutations, and that even some of the spatially large clones may not be as ancient as previously supposed. Further, a striking majority of the largest genets in both study areas had three alleles at one or more loci, suggesting triploidy or aneuploidy. These genets tended to be spatially clustered but not closely related. Together, these findings substantially advance our understanding of clonal dynamics in western North American aspen, and set the stage for a broad range of future studies.
The LExR Collection for Expertise Retrieval in Academia

Expertise retrieval has been the subject of intense research over the past decade, particularly with the public availability of benchmark test collections for expertise retrieval in enterprises. Another domain which has seen comparatively less research on expertise retrieval is academic search. In this paper, we describe the Lattes Expertise Retrieval (LExR) test collection for research on academic expertise retrieval. LExR has been designed to provide a large-scale benchmark for two complementary expertise retrieval tasks, namely, expert profiling and expert finding. Unlike currently available test collections, which fully support only one of these tasks, LExR provides graded relevance judgments performed by expert judges separately for each task. In addition, LExR is both cross-organization and cross-area, encompassing candidate experts from all areas of knowledge working in research institutions all over Brazil. As a result, it constitutes a valuable resource for fostering new research directions on expertise retrieval in an academic setting.
Household Optimism and Borrowing

A unique Finnish household-level data from 1994 to 2009 allow us to measure how households financial expectations are related to the sub- sequent outcomes. We use the difference between the two to measure forecast errors and household optimism and link the errors to households´ borrowing behaviour. We find that households making greatest optimistic forecast errors carry greater levels of debt and are most likely to suffer from excessive debt loads (overindebtedness). They also are less attentive to forecast errors than their pessimistic counterparts when forming their expectations for a subsequent period JEL: D21, L20 Key words: forecast errors, ex ante optimism, borrowing
Modeling Adoption and Usage of Competing Products

The emergence and wide-spread use of online social networks has led to a dramatic increase on the availability of social activity data. Importantly, this data can be exploited to investigate, at a microscopic level, some of the problems that have captured the attention of economists, marketers and sociologists for decades, such as, e.g., product adoption, usage and competition. In this paper, we propose a continuous-time probabilistic model, based on temporal point processes, for the adoption and frequency of use of competing products, where the frequency of use of one product can be modulated by those of others. This model allows us to efficiently simulate the adoption and recurrent usages of competing products, and generate traces in which we can easily recognize the effect of social influence, recency and competition. We then develop an inference method to efficiently fit the model parameters by solving a convex program. The problem decouples into a collection of smaller subproblems, thus scaling easily to networks with hundred of thousands of nodes. We validate our model over synthetic and real diffusion data gathered from Twitter, and show that the proposed model does not only provides a good fit to the data and more accurate predictions than alternatives but also provides interpretable model parameters, which allow us to gain insights into some of the factors driving product adoption and frequency of use.
Motivations of Vietnamese Students in Study Abroad: A Case of Vietnamese Graduate Students in the Philippines

This paper investigates the motivational factors of Vietnamese graduate students choose to study abroad and why they select the Philippines as their academic destination. Descriptive data using mixed method from 56 Vietnamese graduate students (VNGSs) in six universities in Metro Manila were collected through questionnaires and semi-structured interview. The findings reveal that VNGSs respectively identify “professional development”, “English improvement and “better educational quality” as the most important motivational factors to study abroad. The factors of affordable tuition fees and living costs, English speaking environment, convenience for travelling and the closeness of the Philippines are considered as very significant factors influence VNGSs’ coming to study in this country. From the findings, the paper discusses their implications for Vietnam graduate education.
Mining and computing phrase weight in texts

Phrase identification using frequencies is employed in text mining studies. In this work, we propose that all phrases occur in a document are not equal as the semantic relevance between the phrases and text varies. We proposed to identify the relevance of phrases in natural language texts using phrase weights. The pilot study results are worth to investigate further.
JamaisVu: Robust Scheduling with Auto-Estimated Job Runtimes

JamaisVu is a new end-to-end cluster scheduling system that automatically generates and robustly exploits job runtime predictions. Using runtime knowledge allows it to more effectively pack jobs with diverse time concerns (e.g., deadline vs. latency) and soft placement constraints on heterogeneous cluster resources. JamaisVu’s job run time predictor, JVuPredict uses a new black-box approach that tracks job run time history as a function of multiple job submission features (e.g., user ID and program name), and then adaptively uses the most effective feature subset for each submitted job. Analysis of a 1-month Google cluster trace shows JVuPredict predicts reasonably well for complex real-world job mixes; for example, 90% of predictions are within a factor of two of actual runtime. But, because predictions cannot be perfect, JamaisVu includes new techniques for mitigating the effects of such real misprediction proﬁles. Experiments with workloads derived from the trace show that JamaisVu performs nearly as well as a hypothetical scheduler with perfect job runtime information, outperforming runtime-unaware scheduling by reducing SLO miss rate, increasing goodput, and maintaining comparable latency for best effort jobs. Acknowledgements: We thank the member companies of the PDL Consortium ( Broadcom, Citadel, Dell EMC, Facebook, Google, Hewlett-Packard Labs, Hitachi, Intel, Microsoft Research, MongoDB, NetApp, Oracle, Samsung, Seagate Technology, Tintri, Two Sigma, Uber, Veritas, Western Digital) for their interest, insights, feedback, and support. This research is supported in part by Intel as part of the Intel Science and Technology Center for Cloud Computing (ISTC-CC), by an NSERC Postgraduate Fellowship, by a Samsung Scholarship, and by National Science Foundation under awards CSR-1116282, 0946825 and CNS-1042537
Heading Disorientation in Amnestic Mild Cognitive Impairment is Correlated with Hypometabolism in the Posterior Cingulate Cortex

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