Miracle-dz/UniFAR-FTUs huggingface dataset:
The relationship between shame and guilt: cultural comparisons between Ireland and the United Arab Emirates

ABSTRACT The current study examines whether proneness to shame and guilt is related to the cultural dimensions of collectivism and individualism. Two groups of participants from Ireland (n = 120) and the United Arab Emirates (UAE) (n = 115) completed measures assessing collectivism, individualism, and shame and guilt proneness. Results indicated that both samples displayed similar levels of individualism and collectivism. The UAE sample reported significantly higher levels of guilt proneness and shame proneness characterised by negative self-evaluation. In contrast, the Irish sample displayed significantly higher levels of shame characterised by withdrawal tendencies. Guilt was positively correlated with individualism, but shame was not correlated with either scores on collectivism or individualism. Young Arab women appear to experience higher levels of guilt and shame characterised by negative self-evaluation in comparison to their Irish counterparts who displayed higher levels of guilt proneness.
Crossmodal Interactions during Affective Picture Processing

"Natural" crossmodal correspondences, such as the spontaneous tendency to associate high pitches with high spatial locations, are often hypothesized to occur preattentively and independently of task instructions (top-down attention). Here, we investigate bottom-up attentional engagement by using emotional scenes that are known to naturally and reflexively engage attentional resources. We presented emotional (pleasant and unpleasant) or neutral pictures either below or above a fixation cross, while participants were required to discriminate between a high or a low pitch tone (experiment 1). Results showed that despite a robust crossmodal attentional capture of task-irrelevant emotional pictures, the general advantage in classifying the tones for congruent over incongruent visual-auditory stimuli was similar for emotional and neutral pictures. On the other hand, when picture position was task-relevant (experiment 2), task-irrelevant tones did not interact with pictures with regard to their combination of pitch and visual vertical spatial position, but instead they were effective in minimizing the interference effect of emotional picture processing on the ongoing task. These results provide constraints on our current understanding of natural crossmodal correspondences.
Diffusion Synthetic Acceleration of S{sub n} Linear Nodal Schemes in Weighted Difference Form

The consistent formulation of a diffusion synthetic acceleration (DSA) equation, which is derived directly from the discretized form of a transport equation following the methods of Alcouffe and Larsen, encounters problems in practical application to the higher-order multidimensional differencing schemes due to the relatively complex form of the resulting equation and to the difficulties in solving it efficiently. Alternative approaches, like the method of Khalil, in which the starting point is the continuous transport equation, or that of Azmy, in which the cell-centered discretization is used, yield DSA equations in a form much easier to solve. Several authors have developed transport-based synthetic acceleration methods, in which the acceleration equation is derived directly from a discretized transport equation and can be solved efficiently with minimal programming effort. However, for diffuse-mode-dominated problems, the use of transport acceleration may not be the optimal solution because the diffusion equation can be solved faster. Azmy has shown that arbitrarily high-order multidimensional nodal methods can be cast in a weighted-difference form that is suitable for the formulation of a DSA algorithm. The stability and convergence of such a DSA scheme has been proved for the lowest-order nodal method, i.e., constant-surface flux, but no attempt has beenmore » made to apply this algorithm to higher-order differencing schemes. Here, we describe the implementation of DSA to the linear (LN) and bilinear (BL) nodal approximations taking into account linearly isotropic scattering. We have implemented consistently formulated DSA equations for multidimensional nodal methods in weighted-difference form. The final acceleration equation retains a relatively simple form of interface current equations with matrices that are inexpensive to compute. Further work is needed for more efficient methods to solve the DSA equation. The preliminary results of the implementations of the multigrid methods to solve the acceleration equation are encouraging. « less
On the learning of nonlinear visual features from natural images by optimizing response energies

The operation of V1 simple cells in primates has been traditionally modelled with linear models resembling Gabor filters, whereas the functionality of subsequent visual cortical areas is less well understood. Here we explore the learning of mechanisms for further nonlinear processing by assuming a functional form of a product of two linear filter responses, and estimating a basis for the given visual data by optimizing for robust alternative of variance of the nonlinear model outputs. By a simple transformation of the learned model, we demonstrate that on natural images, both minimization and maximization in our setting lead to oriented, band-pass and localized linear filters whose responses are then nonlinearly combined. In minimization, the method learns to multiply the responses of two Gabor-like filters, whereas in maximization it learns to subtract the response magnitudes of two Gabor-like filters. Empirically, these learned nonlinear filters appear to function as conjunction detectors and as opponent orientation filters, respectively. We provide a preliminary explanation for our results in terms of filter energy correlations and fourth power optimization.
Default Network and Intelligence Difference

In the last few years, many studies in the cognitive and system neuroscience found that a consistent network of brain regions, referred to as the default network, showed high levels of activity when no explicit task was performed. Some scientists believed that the resting state activity might reflect some neural functions that consolidate the past, stabilize brain ensembles, and prepare us for the future. Here, we modeled the default network as undirected weighted graph, and then used graph theory to investigate the topological properties of the default network of the two groups of people with different intelligence levels. We found that, in both groups, the posterior cingulate cortex showed the greatest degree in comparison to the other brain regions in the default network, and that the medial temporal lobes and cerebellar tonsils were topologically separations from the other brain regions in the default network. More importantly, we found that the strength of some functional connectivities and the global efficiency of the default network were significantly different between the superior intelligence group and the average intelligence group, which indicates that the functional integration of the default network might be related to the individual intelligent performance.
Conditional Random Fields for XML Applications

XML tree labeling is the problem of classifying elements in XML documents. It is a fundamental task for applications like XML transformation, schema matching, and information extraction. In this paper we propose XCRFs, conditional random fields for XML tree labeling. Dealing with trees often raises complexity problems. We describe optimization methods by means of constraints and combination techniques that allow XCRFs to be used in real tasks and in interactive machine learning programs. We show that domain knowledge in XML applications easily transfers in XCRFs thanks to constraints and combination of XCRFs. We describe an approach based on XCRF to learn tree transformations. The approach allows to solve xml data integration tasks and restructuration tasks. We have developed an open source toolbox for XCRFs. We use it to propose a Web service for the generation of personalized RSS feeds from HTML pages.
THE HORIZON RUN N-BODY SIMULATION: BARYON ACOUSTIC OSCILLATIONS AND TOPOLOGY OF LARGE-SCALE STRUCTURE OF THE UNIVERSE

In support of the new III survey, which will measure the baryon oscillation scale using the luminous red galaxies (LRGs), we have run the largest N-body simulation to date using 41203 = 69.9 billion particles, and covering a volume of (6.592 h−1 Gpc)3. This is over 2000 times the volume of the Millennium Run, and corner-to-corner stretches all the way to the horizon of the visible universe. LRGs are selected by finding the most massive gravitationally bound, cold dark matter subhalos, not subject to tidal disruption, a technique that correctly reproduces the three-dimensional topology of the LRGs in the Sloan Survey. We have measured the covariance function, power spectrum, and the three-dimensional topology of the LRG distribution in our simulation and made 32 mock surveys along the past light cone to simulate the Sloan III survey. Our large N-body simulation is used to accurately measure the nonlinear systematic effects such as gravitational evolution, redshift space distortion, past light cone space gradient, and galaxy biasing, and to calibrate the baryon oscillation scale and the genus topology. For example, we predict from our mock surveys that the baryon acoustic oscillation peak scale can be measured with the cosmic variance-dominated uncertainty of about 5% when the SDSS-III sample is divided into three equal volume shells, or about 2.6% when a thicker shell with 0.4 < z < 0.6 is used. We find that one needs to correct the scale for the systematic effects amounting up to 5.2% to use it to constrain the linear theories. The uncertainty in the amplitude of the genus curve is expected to be about 1% at 15 h−1 Mpc scale. We are making the simulation and mock surveys publicly available.
Method for continuous nondisturbing monitoring of blood pressure by magnetoelastic skin curvature sensor and ECG

This paper concerns continuous nondisturbing estimation of blood pressure using mechanical plethysmography in connection with standard electrocardiography (ECG). The plethysmography is given by a novel magnetoelastic skin curvature sensor (SC-sensor) applied on the neck over the carotid artery. The sensor consists of a magnetoelastic bilayer partly enclosed by a coil. Bending the bilayer causes large changes of magnetic permeability which can be measured by the coil. The SC-sensor signal and the ECG signal are adaptively processed in order to estimate blood pressure according to a specifically established theoretical model. The model uses estimated vessel radius changes and pulse transit time as parameters. The results show cross correlation coefficients in the range 0.8 up to 0.9 between reference and estimated values of systolic blood pressure, diastolic blood pressure, and systolic/diastolic blood pressure change, whereas the estimation error was below 4 +/- 7 mmHg at rest and increased with the stress level. Limitations of the model applicability are given by a hysteretic behavior of both model parameters due to inert changes in artery stiffness. The SC-sensor and the ECG electrodes cause minimal inconvenience to the patient and offer an approach for a continuous nondisturbing monitoring of blood pressure changes, as being relevant for sleep monitoring or biomechanic feedback
Real‐time global flood estimation using satellite‐based precipitation and a coupled land surface and routing model

A widely used land surface model, the Variable Infiltration Capacity (VIC) model, is coupled with a newly developed hierarchical dominant river tracing‐based runoff‐routing model to form the Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE) model, which serves as the new core of the real‐time Global Flood Monitoring System (GFMS). The GFMS uses real‐time satellite‐based precipitation to derive flood monitoring parameters for the latitude band 50°N–50°S at relatively high spatial (∼12 km) and temporal (3 hourly) resolution. Examples of model results for recent flood events are computed using the real‐time GFMS (http://flood.umd.edu). To evaluate the accuracy of the new GFMS, the DRIVE model is run retrospectively for 15 years using both research‐quality and real‐time satellite precipitation products. Evaluation results are slightly better for the research‐quality input and significantly better for longer duration events (3 day events versus 1 day events). Basins with fewer dams tend to provide lower false alarm ratios. For events longer than three days in areas with few dams, the probability of detection is ∼0.9 and the false alarm ratio is ∼0.6. In general, these statistical results are better than those of the previous system. Streamflow was evaluated at 1121 river gauges across the quasi‐global domain. Validation using real‐time precipitation across the tropics (30°S–30°N) gives positive daily Nash‐Sutcliffe Coefficients for 107 out of 375 (28%) stations with a mean of 0.19 and 51% of the same gauges at monthly scale with a mean of 0.33. There were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input.
Probability maximization model of 0–1 knapsack problem with random fuzzy variables

This paper considers a new model of 0-1 knapsack problem including probabilistic coefficients with ambiguous expected returns assumed as random fuzzy variables. Since the random fuzzy 0-1 knapsack problem is not well-defined integer programming problem due to involve random fuzzy variables, it is hard to construct the efficient solution method to solve this problem directly. In this paper, using chance constraints, possibility measure and fuzzy goal based on both stochastic and fuzzy programming approaches, the main problem is transformed into a deterministic equivalent quadratic integer programming. Then, the efficient solution method to find a strict optimal solution based on dynamic programming is constructed.
2015 Ieee International Conference on Big Data (big Data) Graph Analytics Using Vertica Relational Database

—Graph analytics is becoming increasingly popular, with a number of new applications and systems developed in the past few years. In this paper, we study Vertica relational database as a platform for graph analytics. We show that vertex-centric graph analysis can be translated to SQL queries, typically involving table scans and joins, and that modern column-oriented databases are very well suited to running such queries. Furthermore , we show how developers can trade memory footprint for significantly reduced I/O costs in Vertica. We present an experimental evaluation of the Vertica relational database system on a variety of graph analytics, including iterative analysis, a combination of graph and relational analyses, and more complex 1-hop neighborhood graph analytics, showing that it is competitive to two popular vertex-centric graph analytics systems, namely Giraph and GraphLab. I. INTRODUCTION Recent years have seen growing interest in the area of graph data management. This focus on graphs arises from their use in a number of new applications, including social network analytics, transportation, ad and e-commerce recommendation systems, and web search. Given the popular demand for graph analytics, a natural question is whether or not traditional database systems really are a bad fit for these graph analytics workloads? This question arises because, in many real-world scenarios, graph data is collected and stored in a relational database in the first place and it is expensive to move data around. Given this, if it is avoidable, users may prefer not to export their data from the relational database into a graph database. Rather, they would like to perform the graph analyt-ics (with comparable performance) directly with the relational engine, without the expensive step of copying data into a file system (or distributed storage system like HDFS), in order to be processed by a graph system, and then (possibly) back into the relational system for further storage and analyses.
K-134, a Phosphodiesterase 3 Inhibitor, Prevents Brain Damage by Inhibiting Thrombus Formation in a Rat Cerebral Infarction Model

Background K-134 is a more potent antiplatelet drug with a selective inhibitory effect on phosphodiesterase 3 (PDE3) compared with its analogue, cilostazol. Objectives This study was performed to compare the ameliorating effects of K-134 and cilostazol on brain damage in an experimental photothrombotic cerebral infarction model. Methods and Results We investigated the effects of oral preadministration of PDE3 inhibitors in a rat stroke model established by photothrombotic middle cerebral artery (MCA) occlusion. K-134 significantly prolonged MCA occlusion time at doses >10 mg/kg, and reduced cerebral infarct size at 30 mg/kg in the stroke model (n = 12, 87.5±5.6 vs. 126.8±7.5 mm3, P<0.01), indicating its potent antithrombotic effect. On the other hand, the effects of cilostazol on MCA occlusion time and cerebral infarct size are relatively weak even at the high dosage of 300 mg/kg. Furthermore, K-134 blocked rat platelet aggregation more potently than cilostazol in vitro. Also in an arteriovenous shunt thrombosis model, K-134 showed an antithrombotic effect greater than cilostazol. Conclusions These findings suggest that K-134, which has strong antithrombotic activity, is a promising drug for prevention of cerebral infarction associated with platelet hyperaggregability.
Preference Parameters and Behavioral Heterogeneity: An Experimental Approach in the Health and Retirement Survey

Individuals' preferences underlying most economic behavior are likely to display substantial heterogeneity. This paper reports on direct measures of preference parameters relating to risk tolerance, time preference, and intertemporal substitution. These experimental measures are based on survey respondents' choices in hypothetical situations. The questions are constructed with as little departure from the theorist's concept of the underlying parameter as possible. The individual measures of preference parameters display substantial heterogeneity. The majority of respondents fall into the least risk-tolerant group, but a substantial minority display higher risk tolerance. The individual measures of intertemporal substitution and time preference also display substantial heterogeneity. The mean risk tolerance is 0.25; the mean elasticity of intertemporal substitution is 0.2. Estimated risk tolerance and the elasticity of intertemporal substitution are essentially uncorrelated across individuals. Because the risk tolerance measure is obtained as part of the main questionnaire of a large survey, it can be related to a number of economic behaviors. Measured risk tolerance is positively related to a number of risky behaviors, including smoking, drinking, failing to have insurance, and holding stocks rather than Treasury bills. Although measured risk tolerance explains only a small fraction of the variation of the studied behaviors, these estimates provide evidence about the validity and usefulness of the measures of preference parameters.
Intercultural Communicative Competence of Novice-Level Japanese Learners

World-Readiness Standards for Learning Languages (ACTFL, 2017) articulate the development of the linguistic and intercultural communicative competence as important outcomes of world language education in secondary and post-secondary schools. The present body of literature lacks research on the topic of intercultural communicative competence of Japanese language learners in the novice level of proficiency. This study attempted to discover if the Observe State Explore Evaluate (OSEE) Tool (Deardorff, 2009) increased the intercultural communicative competence among novice level high school Japanese language learners in a quantitative nonequivalent groups, pretest-posttest quasi-experimental design. Can-Do Statements for Intercultural Communication (ACTFL, 2017) were used as the pretest and posttest measurements. Study participants were 44 high school students in their second semester of second year Japanese class during the weather report unit. Twenty-one participants in the treatment group received the OSEE Tool intervention. Analyses of the independent and paired ttests showed that the increase of mean scores between pretest and posttest was bigger and the standard deviation figures were smaller in the treatment group than the control group. However, these figures were not statistically significant. Continued investigation and documentation on this construct is needed to ensure all language learners are developing intercultural communicative competence.
A distributed and scalable person tracking system for robotic visual servoing with 8 dof in virtual reality TV studio automation

In this paper, a distributed and scalable person tracking system for visual servoing using Industrial Robot Arms for virtual reality television studios (VR-TV) is presented. The system robustly tracks the moderator while freely moving, sitting or walking around the studio, and the estimation result can be used to drive the main broadcasting camera mounted on a large robotic arm connected with a pan tilt unit. The system consists of a person tracking system operating on the TV camera, which localizes the moderator in its field of view, and a overhead tracking system which localizes the moderator over the complete studio environment. The system is completely scalable in scenarios where a single scene is to be shot from multiple angles using multiple TV cameras. A common overhead tracking system monitors the moderator over the complete studio environment allowing the individual camera systems to initialize themselves and re-initialize in case of target loss. Application of the proposed tracking system to real-time VR-TV results in a robot cameraman, able to keep the moderator inside the screen with jitter-free viewpoint adjustments, as required by the VR scene rendering engine.
Pleiotropy of Glycogen Synthase Kinase-3 Inhibition by CHIR99021 Promotes Self-Renewal of Embryonic Stem Cells from Refractory Mouse Strains

Background Inhibition of glycogen synthase kinase-3 (GSK-3) improves the efficiency of embryonic stem (ES) cell derivation from various strains of mice and rats, as well as dramatically promotes ES cell self-renewal potential. β-catenin has been reported to be involved in the maintenance of self-renewal of ES cells through TCF dependent and independent pathway. But the intrinsic difference between ES cell lines from different species and strains has not been characterized. Here, we dissect the mechanism of GSK-3 inhibition by CHIR99021 in mouse ES cells from refractory mouse strains. Methodology/Principal Findings We found that CHIR99021, a GSK-3 specific inhibitor, promotes self-renewal of ES cells from recalcitrant C57BL/6 (B6) and BALB/c mouse strains through stabilization of β-catenin and c-Myc protein levels. Stabilized β-catenin promoted ES self-renewal through two mechanisms. First, β-catenin translocated into the nucleus to maintain stem cell pluripotency in a lymphoid-enhancing factor/T-cell factor–independent manner. Second, β-catenin binds plasma membrane-localized E-cadherin, which ensures a compact, spherical morphology, a hallmark of ES cells. Further, elevated c-Myc protein levels did not contribute significantly to CH-mediated ES cell self-renewal. Instead, the role of c-Myc is dependent on its transformation activity and can be replaced by N-Myc but not L-Myc. β-catenin and c-Myc have similar effects on ES cells derived from both B6 and BALB/c mice. Conclusions/Significance Our data demonstrated that GSK-3 inhibition by CH promotes self-renewal of mouse ES cells with non-permissive genetic backgrounds by regulation of multiple signaling pathways. These findings would be useful to improve the availability of normally non-permissive mouse strains as research tools.