Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Alignment of Fractions and Decimals with Discrete Versus Continuous Entities: A Cross-national Comparison

Previous work has shown that adults in the United States selectively use fractions and decimals to model discrete and continuous entities, respectively. However, it is unclear whether this apparent semantic alignment between the format of rational numbers and quantitative ontology is specific to the American education system, the English language, or measuring conventions (primarily imperial measures). Here we test whether similar alignments hold for Korean college students who differ from American students in educational background, language, and measurement conventions. Across three experiments, we found that the alignments found in the United States were generally replicated in South Korea. Relative to Americans, Korean students showed an overall bias towards using continuous representations, perhaps related to their exclusive use of the metric measurement system and to differences in instructional practice identified in a textbook analysis.
Effects of the dry season on the vegetation canopy of some river basins of West Africa as deduced from NOAA-AVHRR data

Abstract Thirteen watersheds were selected in the Sudanese area of West Africa. Nine of them are situated in the transition zone between the Sudanese and the Sahelian area, three contributing to the Senegal river and six to the Niger river. The other four are included in the humid tropical area, facing the Atlantic Ocean. The vegetation status was characterized over the 13 river basins using the Normalized Difference Vegetation Index (NDVI), computed from channels 1 and 2 of NOAA-AVHRR imagery, and the radiometric behaviour in the middle thermal infrared, channel 3. The frequency distribution of radiometric values, both NDVI and thermal infrared, within the watersheds clearly shows the effect of the dry season on the vegetation canopy. Thermal infrared data seem to be more efficient than NDVI data for characterizing changes in density and vigour of the vegetation canopy, at least at that stage in the year. Channel 1 of AVHRR, being very sensitive to atmospheric conditions, introduces a masking effect on t.
Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy—EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants’ BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants’ performance with a mean error of less than 3 points. This study determined how users’ profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user.
Hard pion and prompt photon at RHIC, from single to double inclusive production

Single pion and prompt photon large transverse momentum spectra in p-p and Au-Au collisions are computed in perturbative QCD at RHIC energy, √ s NN = 200 GeV. Next-to-leading order calculations are discussed and compared with p-p scattering data. Subsequently, quenching factors are computed to leading order for both pions and photons within the same energy loss model. The good agreement with PHENIX preliminary data allows for a lower estimate of the energy density reached in central Au-Au collisions, ǫ RHIC & 10 GeV/fm 3 . Double inclusive γ-π 0 production in p-p and Au-Au collisions is then addressed. Next-to-leading order corrections prove rather small in p-p scattering. In Au-Au collisions, the quenching of momentum-correlation spectra is seen to be sensitive to parton energy loss processes, which would help to understand how the fragmentation dynamics is modified in nuclear collisions at RHIC.
RAPID COEVAL BLACK HOLE AND HOST GALAXY GROWTH IN MRC 1138-262: THE HUNGRY SPIDER

We present a detailed study of the infrared spectral energy distribution of the high-redshift radio galaxy MRC 1138-26 at z = 2.156, also known as the Spiderweb Galaxy. By combining photometry from Spitzer, Herschel, and LABOCA we fit the rest-frame 5–300 μm emission using a two-component, starburst, and active galactic nucleus (AGN) model. The total infrared (8–1000 μm) luminosity of this galaxy is (1.97 ± 0.28) × 1013 L☉ with (1.17 ± 0.27) and (0.79 ± 0.09) × 1013 L☉ due to the AGN and starburst components, respectively. The high derived AGN accretion rate of ∼20% Eddington and the measured star formation rate (SFR) of 1390 ± 150 M☉ yr−1 suggest that this massive system is in a special phase of rapid central black hole and host galaxy growth, likely caused by a gas-rich merger in a dense environment. The accretion rate is sufficient to power both the jets and the previously observed large outflow. The high SFR and strong outflow suggest that this galaxy could potentially exhaust its fuel for stellar growth in a few tens of Myr, although the likely merger of the radio galaxy with nearby satellites suggests that bursts of star formation may recur again on timescales of several hundreds of Myr. The age of the radio lobes implies the jet started after the current burst of star formation, and therefore we are possibly witnessing the transition from a merger-induced starburst phase to a radio-loud AGN phase. We also note tentative evidence for [C ii]158 μm emission. This paper marks the first results from the Herschel Galaxy Evolution Project (Project HeRGÉ), a systematic study of the evolutionary state of 71 high-redshift, 1 < z < 5.2, radio galaxies.
Metric tensor and Christoffel symbols based 3D object categorization

In this paper we propose to address the problem of 3D object categorization. We model the 3D object as a 2D Riemannian manifold and propose metric tensor and Christoffel symbols as a novel set of features. The proposed set of features capture the local and global geometry of 3D objects by exploiting the positional dependence of the features. The categorization of 3D objects is carried out using polynomial kernel SVM classifier. The effectiveness of the proposed framework is demonstrated on 3D objects obtained from different datasets and achieve comparable results.
What pieces of limit order book information are informative

This paper studies the importance of different pieces of limit order book information in characterizing order aggressiveness and the timing of trades, order submissions and cancellations. Using limit order book information on a representative sample of Spanish stocks, we evidence that most of the explanatory power of the book concentrates on the best quotes. However, the book beyond the best quotes also matters in explaining the aggressiveness of traders. In particular, liquidity providers (limit-order traders) benefit more from an increased degree of pre-trade transparency than liquidity consumers (market-order traders). Finally, no piece of book information matters in explaining the timing of orders.
Disparities in HRQOL of Cancer Survivors and Non-Cancer Managed Care Enrollees

Health plan member survey and cancer registry data were analyzed to understand differences in health-related quality of life (HRQOL) among cancer survivors and those without a cancer diagnosis enrolled in Medicare managed care. HRQOL was measured by the physical component summary score (PCS) and mental component summary score (MCS) of the Medical Outcomes Study SF-36®, version 1.0. Cancer survivors enrolled in Medicare managed care have lower PCS and MCS scores than those enrollees who have never been diagnosed with cancer. PCS scores are worse than the MCS scores, and lowest for cancer survivors who are Hispanic, Medicaid enrollees, and those who have low income or education. HRQOL disparities are greatest among cancer survivors diagnosed with lung cancer and those with multiple primary cancer diagnoses. The influence of these variables persists when controlling for multiple variables including comorbidity status. Health plans should focus on addressing these disparities.
Impact of the Environment

Hispanic-Serving Institutions (HSIs) enroll the majority of Latina/o students in higher education; however, it is unclear how HSIs influence Latina/os’ postsecondary experiences. In this study, we examined how the Latina/o student experience differed between students who did and did not attend 4-year HSIs. The results suggest that HSIs generally have positive, but modest, effects on Latina/o’s student engagement and self-perceived gains. The differences were more pronounced for first-year students than seniors.
Warm-Up Simulation Methodology for HW/SW Co-Designed Processors

Evaluation techniques in microprocessor design are mostly based on simulating selected application samples using a cycle-accurate simulator. In order to achieve accurate results, microarchitectural structures are warmed-up for a few million instructions prior to statistics collection. Unfortunately, this strategy cannot be applied to HW/SW co-designed processors, in which a Transparent Optimization software Layer (TOL) translates and optimizes code on-the-fly from a guest ISA to an internal host custom microarchitecture. We show that the warm-up period in this case needs to be 3-4 orders of magnitude longer than what is needed for traditional microprocessor designs because the TOL state needs to be warmed-up as well. In this paper, we propose a novel simulation technique for HW/SW co-designed processors based on adapting the optimization promotion thresholds using high level application statistics in order to find the best trade-off between accuracy and simulation cost. In particular, the proposed technique reduces the simulation cost by 65X with an average error of just 0.75%. Furthermore, as opposed to other alternatives, the proposed technique satisfies the additional requirement of allowing evaluation using different TOL and microarchitectural configurations.
Energy-consumption clustering in cloud data centre

In order to perform optimal Virtual Machine (VM) consolidation under QoS constraints based on energy consumption in Cloud Data Centres (CDCs) containing heterogeneous physical resources, one must build a framework that combines many subsystem algorithms, including prediction, selection, placement, etc. Several energy minimization strategies can be used in CDCs, but the most importantly is one in which minimization is done through switching off selected host underload servers after reallocating all VMs on the selected server. Predicting the resources needed in a given time period is the first and the most important step in dynamic provisioning to meet QoS expectations in the presence of variable workloads. In other words, we use previous usage patterns to estimate future VM request workloads in a data centre. The first step in the prediction process of the energy-consumption framework is to cluster the historical data. In this paper, we proposed a clustering for both user and VM requests. Real Google traces that feature over 25 million tasks collected over a 29-day period are used as an example in this paper.
Motion correction of cardiac PET using mass-preserving registration

Cardiac motion leads to image quality degradation in positron emission tomography (PET). In the literature gated listmode acquisition along with motion estimation techniques, such as optical flow or image registration, proved successful for respiratory motion correction. Cardiac gated PET images, however, are affected by the partial volume effect (PVE) which is expressed in strongly varying local intensities. This fact complicates the motion estimation process. To overcome this problem, the mass-preserving nature of PET images is identified and included into the image registration problem. We show that our mass-preserving registration approach allows cardiac motion correction with high accuracy due to realistic motion estimates. In addition, neglecting the mass-preserving property of PET is proven to entail unrealistic results.
MillWheel: Fault-Tolerant Stream Processing at Internet Scale

MillWheel is a framework for building low-latency data-processing applications that is widely used at Google. Users specify a directed computation graph and application code for individual nodes, and the system manages persistent state and the continuous flow of records, all within the envelope of the framework's fault-tolerance guarantees. This paper describes MillWheel's programming model as well as its implementation. The case study of a continuous anomaly detector in use at Google serves to motivate how many of MillWheel's features are used. MillWheel's programming model provides a notion of logical time, making it simple to write time-based aggregations. MillWheel was designed from the outset with fault tolerance and scalability in mind. In practice, we find that MillWheel's unique combination of scalability, fault tolerance, and a versatile programming model lends itself to a wide variety of problems at Google.
The causality of causal attributions in depression: a cross-lagged panel correlational analysis.

The attributional reformulation of the learned helplessness model of depression proposes that causal attributions about negative outcomes play a causal role in reactive depression. This research tested this hypothesis by studying the causal role of attributions in depression in 180 college students. On two occasions separated by 1 month, students were administered a battery of tests that included an attributional style questionnaire and the Beck Depression Inventory. The attributional dimensions of internality, stability, and globality were found to be correlated with depression; when the possible causal role of attributions was tested through the use of cross-lagged panel correlational analysis, the hypothesis that stability and globality attributions for bad outcomes might be causes of depression was supported. There was no support, however, for the hypothesis that internal attributions for bad outcomes are a cause of depression. Evidence was also found that unstable attributions for good outcomes may function as a cause of depression.
A novel method to measure and correct the odometry errors in mobile robots

This paper presents a novel and still simple method for odometry errors measurement and compensation. For a typical differential drive mobile robot, the three dominant and systematic error sources (unequal wheelspsila diameter, imprecise average wheels diameter and uncertainty about the effective wheelbase) are investigated. It is shown that the mobile robot, calibrated by using the proposed algorithm, has noticeable Odometric accuracy. Calibration coefficients are obtained by accurate measurements of the wheelspsila diameter ratios and the effective wheelbase. Then these coefficients are applied to the mobile robot through the driverpsilas controller software.
Changes in Neural Activity Underlying Working Memory after Computerized Cognitive Training in Older Adults

Computerized cognitive training (CCT) may counter the impact of aging on cognition, but both the efficacy and neurocognitive mechanisms underlying CCT remain controversial. In this study, 35 older individuals were randomly assigned to Cogmed adaptive working memory (WM) CCT or an active control CCT, featuring five weeks of five ∼40 min sessions per week. Before and after the 5-week intervention, event-related potentials were measured while subjects completed a visual n-back task with three levels of demand (0-back, 1-back, 2-back). The anterior P3a served as an index of directing attention and the posterior P3b as an index of categorization/WM updating. We hypothesized that adaptive CCT would be associated with decreased P3 amplitude at low WM demand and increased P3 amplitude at high WM demand. The adaptive CCT group exhibited a training-related increase in the amplitude of the anterior P3a and posterior P3b in response to target stimuli across n-back tasks, while subjects in the active control CCT group demonstrated a post-training decrease in the anterior P3a. Performance did not differ between groups or sessions. Larger overall P3 amplitudes were strongly associated with better task performance. Increased post-CCT P3 amplitude correlated with improved task performance; this relationship was especially robust at high task load. Our findings suggest that adaptive WM training was associated with increased orienting of attention, as indexed by the P3a, and the enhancement of categorization/WM updating processes, as indexed by the P3b. Increased P3 amplitude was linked to improved performance; however. there was no direct association between adaptive training and improved performance.
Luminosity-metallicity relation for dIrr galaxies in the near-infrared

Context. The luminosity-metallicity relation is one of the fundamental constraints in the study of galaxy evolution; yet none of the relations available today has been universally accepted by the community. Aims. The present work is a first step to collect homogeneous abundances and near-infrared (NIR) luminosities for a sample of dwarf irregular (dIrr) galaxies, located in nearby groups. The use of NIR luminosities is intended to provide a better proxy to mass than the blue luminosities commonly used in the literature; in addition, selecting group members reduces the impact of uncertain distances. Accurate abundances are derived to assess the galaxy metallicity. Methods. Optical spectra are collected for $\ion{H}{ii}$ regions in the dIrrs, allowing the determination of oxygen abundances by means of the temperature-sensitive method. For each dIrr galaxy H -band imaging is performed and the total magnitudes are measured via surface photometry. Results. This high-quality database allows us to build a well-defined luminosity-metallicity relation in the range $-13\geq M_{H}\geq-20$. The scatter around its linear fit is reduced to 0.11 dex, the lowest of all relations currently available. There might exist a difference between the relation for dIrrs and the relation for giant galaxies, although a firm conclusion should await direct abundance determinations for a significant sample of massive galaxies. Conclusions. This new dataset provides an improved luminosity-metallicity relation, based on a standard NIR band, for dwarf star-forming galaxies. The relation can now be compared with some confidence to the predictions of models of galaxy evolution. Exciting follow-ups of this work are (a) exploring groups with higher densities, (b) exploring nearby galaxy clusters to probe environmental effects on the luminosity-metallicity relation, and (c) deriving direct oxygen abundances in the central regions of star-forming giant galaxies, to settle the question of a possible dichotomy between the chemical evolution of dwarfs and that of massive galaxies.
Corporate Governance, Female Directors and Quality of Financial Information

The aim of this study is to examine whether gender diversity on audit committees (hereinafter, ACs) influences financial reporting quality by using panel data of Spanish listed firms. The financial reporting quality of firms is measured by the type of opinion received in the audit report. We estimate various panel data models of audit opinions and control for factors that are traditionally found to impact audit opinions. This study provides evidence to support the hypotheses that the percentage of females on ACs reduces the probability of qualifications due to errors, non‐compliance or the omission of information. Furthermore, the results also find that the percentage of female directors on ACs, the percentage of independent female directors on ACs and ACs chaired by females increase the likelihood of further transparency by disclosing audit reports with uncertainties and scope limitation qualifications.
ADAPTIVE CREDIT SCORING WITH ANALYTIC HIERARCHY PROCESS

Credit risk assessment for consumers has been a cor nerstone of risk management in financial institutio ns and constitutes a component of the three pillars of Basel II. Traditionally, the concept of 5 ‘C’s was widely adopted by financial institutions as the key basis for credit risk assessment for loan applicat ions by prospective borrowers. With the evolution of the c redit risk management practices, more quantitative methods such as credit scorecards have been develop ed, which is implemented through the use of logisti c regression, decision trees and neural networks. How ever, such approaches proved to be inadequate with the validity and effectiveness of the approaches do ubted especially in the light of the 2008 sub-prime financial crisis in US, which was partly triggered by poor quantitative modeling as well as over-relia nce on mathematical modeling resulting in a divorce bet ween reality and model. To remedy the problem of over-reliance on pure quantitative models, Clark Ab rahams and Mingyuan Zhang introduced the Comprehensive Credit Assessment Framework (CCAF) that attempts to address weaknesses in the existing credit risk assessment system, and provide s flexibility with better accuracy, transparency an d simplicity for the various stakeholders in the cred it lending business. Unlike the 5 ‘C’s of credit assessment which provides very coarse segregation o f potential borrower and focus more on past performance which can be a reflection of better cre dit environment and no longer represent the current environment, CCAF caters for a more fine-grain segmentation that allow for specific action for specifi c groups of borrower which results in an adaptive fra mework that takes in new inputs to improve its predictiveness. This paper proposes to implement th e CCAF by utilizing Analytic Hierarchy Process (AHP) for consumer credit segment to establish a ne w hybrid version of the framework ‐ AHP-CCAF model. The new model is first tested on 3 classica l credit risk data to illustrate the feasibility of the model. The paper compares the performance of the new model against traditional method of Decision Tree Analysis. Results shows that the proposed mod el is feasible and has better forecasting capabilit y than the traditional methods.