Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual place categorization in maps

Categorizing areas such as rooms and corridors using a discrete set of labels has been of long-standing interest to the robotics community. A map with labels such as kitchen, lab, copy room etc provides a basic amount of semantic information that can enable a robot to perform a number of tasks specified in human-centric terms rather than just map coordinates. In this work, we propose a method to label areas in a pre-built map using information from camera images. In contrast to most existing approaches, our method labels the area that is viewed in the camera image rather than just the current robot location. Place labels are generated from the image input using the PLISS system [14]. The label information on the viewed areas is integrated in a Conditional Random Field (CRF) that also considers higher level semantics such as adjacency and place boundaries. We demonstrate our technique on maps built using from laser and visual SLAM systems. We obtain the correct place categorization of a very high percentage of the map areas even when the place categorization system is trained using images only from the internet.
Large-Scale Learning of Structure-Activity Relationships Using a Linear Support Vector Machine and Problem-Specific Metrics

The goal of this study was to adapt a recently proposed linear large-scale support vector machine to large-scale binary cheminformatics classification problems and to assess its performance on various benchmarks using virtual screening performance measures. We extended the large-scale linear support vector machine library LIBLINEAR with state-of-the-art virtual high-throughput screening metrics to train classifiers on whole large and unbalanced data sets. The formulation of this linear support machine has an excellent performance if applied to high-dimensional sparse feature vectors. An additional advantage is the average linear complexity in the number of non-zero features of a prediction. Nevertheless, the approach assumes that a problem is linearly separable. Therefore, we conducted an extensive benchmarking to evaluate the performance on large-scale problems up to a size of 175000 samples. To examine the virtual screening performance, we determined the chemotype clusters using Feature Trees and integrated this information to compute weighted AUC-based performance measures and a leave-cluster-out cross-validation. We also considered the BEDROC score, a metric that was suggested to tackle the early enrichment problem. The performance on each problem was evaluated by a nested cross-validation and a nested leave-cluster-out cross-validation. We compared LIBLINEAR against a Naïve Bayes classifier, a random decision forest classifier, and a maximum similarity ranking approach. These reference approaches were outperformed in a direct comparison by LIBLINEAR. A comparison to literature results showed that the LIBLINEAR performance is competitive but without achieving results as good as the top-ranked nonlinear machines on these benchmarks. However, considering the overall convincing performance and computation time of the large-scale support vector machine, the approach provides an excellent alternative to established large-scale classification approaches.
Opposing Role of Dopamine D1 and D2 Receptors in Modulation of Rat Nucleus Accumbens Noradrenaline Release

The role of dopamine receptors in the modulation of nucleus accumbens noradrenaline release was investigated in superfused rat brain slices. At concentrations of ≤1 μm, dopamine enhanced, whereas at higher concentrations dopamine inhibited electrically evoked [3H]noradrenaline release. The D1 receptor agonist SKF-38393 increased, whereas the D2 agonist quinpirole inhibited evoked [3H]noradrenaline release. These effects were attenuated by the D1 antagonist SCH-23390 and the D2 antagonist (−)-sulpiride, respectively, indicating that accumbens noradrenaline release is regulated by stimulatory D1 and inhibitory D2 receptors. Whereas (−)-sulpiride enhanced, SCH-23390 did not reduce evoked accumbens [3H]noradrenaline release, indicating a tonic activation of D2 receptors only. Given the similar apparent affinity of dopamine for D1 and D2 receptors in striatal slices, the lack of tonic D1 receptor activation suggests that D1, unlike D2, receptors are extrasynaptically localized. No dopaminergic modulation of noradrenaline release was observed in rat medial prefrontal cortex or amygdala slices. To examine the regulation of accumbens noradrenaline release under conditions of increased dopaminergic activity, measurements were made using slices of amphetamine-pretreated rats. In these slices, the electrically evoked release of [3H]dopamine and [3H]noradrenaline was enhanced. The increasing effect of (−)-sulpiride on noradrenaline release was augmented, and SCH-23390 almost completely reversed this enhancement of [3H]noradrenaline release. These data suggest that whereas although under a moderate dopaminergic tone, accumbens noradrenaline release is mainly regulated by inhibitory D2 receptors, under circumstances of increased dopaminergic activity, recruitment of extrasynaptic stimulatory D1 receptors contributes to enhancement of noradrenaline release.
Revisiting cyberbullying in schools using the quality circle approach

An earlier study reported the use of Quality Circles (QC) in a UK school in the context of understanding and reducing bullying and cyberbullying. Here, we report further work in the same school setting. The QC approach allows explorative analysis of problems in school settings, whereby students embark on a problem-solving exercise over a period of time. The process involves identifying key issues and prioritizing concerns, analysing problems and generating solutions, through participation in a series of workshops. The purpose of this research was to explore further the use of QCs as an effective means of gathering information on bullying and cyberbullying in school, and how these might have changed over one academic year; as well as to examine the use of QCs in empowering pupils and in producing pupil-led solutions. This study validated the use of QCs as an engaging process for pupils (N = 30) which encourages a range of suggested solutions to problems. The information gained from the QCs supported a transitory notion of bullying behaviour, whereby forms of bullying and cyberbullying continue to alter over time, thus prevention programmes must adapt to the changeable nature of this behaviour to remain effective.
PRODUCTS OF SPECIAL SETS OF REAL NUMBERS

We describe a simple machinery which translates results on algebraic sums of sets of reals into the corresponding results on their cartesian product. Some consequences are: 1. The product of a meager/null-additive set and a strong measure zero/strongly meager set in the Cantor space has strong measure zero/is strongly meager, respectively.
Embedding-based subsequence matching in time-series databases

We propose an embedding-based framework for subsequence matching in time-series databases that improves the efficiency of processing subsequence matching queries under the Dynamic Time Warping (DTW) distance measure. This framework partially reduces subsequence matching to vector matching, using an embedding that maps each query sequence to a vector and each database time series into a sequence of vectors. The database embedding is computed offline, as a preprocessing step. At runtime, given a query object, an embedding of that object is computed online. Relatively few areas of interest are efficiently identified in the database sequences by comparing the embedding of the query with the database vectors. Those areas of interest are then fully explored using the exact DTW-based subsequence matching algorithm. We apply the proposed framework to define two specific methods. The first method focuses on time-series subsequence matching under unconstrained Dynamic Time Warping. The second method targets subsequence matching under constrained Dynamic Time Warping (cDTW), where warping paths are not allowed to stray too much off the diagonal. In our experiments, good trade-offs between retrieval accuracy and retrieval efficiency are obtained for both methods, and the results are competitive with respect to current state-of-the-art methods.
Graph spectral analysis of protein interaction network evolution

We present an analysis of protein interaction network data via the comparison of models of network evolution to the observed data. We take a Bayesian approach and perform posterior density estimation using an approximate Bayesian computation with sequential Monte Carlo method. Our approach allows us to perform model selection over a selection of potential network growth models. The methodology we apply uses a distance defined in terms of graph spectra which captures the network data more naturally than previously used summary statistics such as the degree distribution. Furthermore, we include the effects of sampling into the analysis, to properly correct for the incompleteness of existing datasets, and have analysed the performance of our method under various degrees of sampling. We consider a number of models focusing not only on the biologically relevant class of duplication models, but also including models of scale-free network growth that have previously been claimed to describe such data. We find a preference for a duplication-divergence with linear preferential attachment model in the majority of the interaction datasets considered. We also illustrate how our method can be used to perform multi-model inference of network parameters to estimate properties of the full network from sampled data.
Assessing Personality and Cognitive Psychopathology in Psychiatric Patients

In the present study, we focused on the dimension of psychoticism (P), deriving from Eysenckian theory, on which cognitive psychopathology can presumably be mapped. In particular, we looked at one side of this continuum, ranging between the affective disorders, through paranoid schizophrenia to schizophrenia, exploring hypothesized differences in both P as well as in measures assessing syncretic and symbolic thinking. Schizophrenics were indeed found to score high on P; but an even better differentiation was found when scores on the Lie Scale (L) were also inspected. A clear partition of the bivariate P-L plane was apparent, with a marked differentiation between the paranoid and non-paranoid schizophrenics, and with the manic depressives lying in between, in line with the Eysenckian hypothesis.
Sinonasal Malignancies and Charged Particle Radiation Treatment: A Systematic Literature Review

Background. Paranasal and nasal cavity malignancies are rare tumors that frequently present at advanced stages. Tumor extension and anatomic complexity pose a challenge for their treatment. Due to their peculiar physical and biological properties particle radiation therapy, i.e. protons and ions can have a role in their management. We performed a systematic literature review to gather clinical evidence about their use to treat sinonasal malignancies. Materials and Methods. We searched the browsers PubMed and Medline as well as specific journals and conference proceedings. Inclusion criteria were: at least 10 patients, English language, reporting outcome and/or toxicity data. Results. We found six studies with data on clinical outcome. Carbon and helium ions were each used in one study, protons in four. Toxicity was specifically described in five studies. One reported acute toxicity of carbon ions, one dealt with brain toxicity from both carbon ions and protons. Three papers reported on visual toxicity: one from carbon ions, one from protons and one from both. Specific data were extracted and compared with the most pertinent literature. Conclusion. Particle radiation therapy is in its early phase of development. Promising results achieved so far must be confirmed in further studies.
Head Start, 4 years after completing the program

This paper studies the effect of the Head Start program on children's achievements in reading and math tests during their first 4 years of schooling after completing the program. Using nationally representative data from the Early Childhood Longitudinal Study, I found large measurement error in the parental reports of Head Start attendance, which is new in the literature. Further I found that after accounting for measurement error and potential selection bias, black Head Start children make significant progress toward third grade, whereas white and Hispanic children reap little gain from the program relative to their peers who were exposed to other types of programs and care.
Disruption of Hypoxia-Inducible Factor 1 in Adipocytes Improves Insulin Sensitivity and Decreases Adiposity in High-Fat Diet–Fed Mice

OBJECTIVE Obesity, insulin resistance, and type 2 diabetes form a tightly correlated cluster of metabolic disorders in which adipose is one of the first affected tissues. The role of hypoxia and hypoxia-inducible factor 1 (HIF1) in the development of high-fat diet (HFD)–induced obesity and insulin resistance was investigated using animal models. RESEARCH DESIGN AND METHODS Mice with adipocyte-specific targeted disruption of the genes encoding the HIF1 obligatory subunits Hif1α or Arnt (Hif1β) were generated using an aP2-Cre transgene with the Cre/LoxP system. The mice were fed an HFD for 12 weeks and their metabolic phenotypes were determined. Gene expression patterns in adipose tissues were also determined by microarray and quantitative PCR. RESULTS On an HFD, adipocyte-specific ARNT knockout mice and adipocyte-specific HIF1α knockout mice exhibit similar metabolic phenotypes, including reduced fat formation, protection from HFD-induced obesity, and insulin resistance compared with similarly fed wild-type controls. The cumulative food intake remained similar; however, the metabolic efficiency was lower in adipocyte-specific HIF1α knockout mice. Moreover, indirect calorimetry revealed respiratory exchange ratios were reduced in adipocyte-specific HIF1α knockout mice. Hyperinsulinemic-euglycemic clamp studies demonstrated that targeted disruption of HIF1α in adipocytes enhanced whole-body insulin sensitivity. The improvement of insulin resistance is associated with decreased expression of Socs3 and induction of adiponectin. CONCLUSIONS Inhibition of HIF1 in adipose tissue ameliorates obesity and insulin resistance. This study reveals that HIF1 could provide a novel potential therapeutic target for obesity and type 2 diabetes.
Asynchronous Task-Based Polar Decomposition on Single Node Manycore Architectures

This paper introduces the first asynchronous, task-based formulation of the polar decomposition and its corresponding implementation on manycore architectures. Based on a new formulation of the iterative <inline-formula> <tex-math notation="LaTeX">$QR$</tex-math><alternatives><inline-graphic xlink:href="sukkari-ieq1-2755655.gif"/> </alternatives></inline-formula> dynamically-weighted Halley algorithm (QDWH) for the calculation of the polar decomposition, the proposed implementation replaces the original and hostile LU factorization for the condition number estimator by the more adequate <inline-formula><tex-math notation="LaTeX">$QR$</tex-math><alternatives> <inline-graphic xlink:href="sukkari-ieq2-2755655.gif"/></alternatives></inline-formula> factorization to enable software portability across various architectures. Relying on fine-grained computations, the novel task-based implementation is also capable of taking advantage of the identity structure of the matrix involved during the QDWH iterations, which decreases the overall algorithmic complexity. Furthermore, the artifactual synchronization points have been weakened compared to previous implementations, unveiling look-ahead opportunities for better hardware occupancy. The overall QDWH-based polar decomposition can then be represented as a directed acyclic graph (DAG), where nodes represent computational tasks and edges define the inter-task data dependencies. The StarPU dynamic runtime system is employed to traverse the DAG, to track the various data dependencies and to asynchronously schedule the computational tasks on the underlying hardware resources, resulting in an out-of-order task scheduling. Benchmarking experiments show significant improvements against existing state-of-the-art high performance implementations (i.e., Intel MKL and Elemental) for the polar decomposition on latest shared-memory vendors’ systems (i.e., Intel Haswell/Broadwell/Knights Landing, NVIDIA K80/P100 GPUs and IBM Power8), while maintaining high numerical accuracy.
Hybrid coaxial/substrate integrated waveguide eight-way power divider

A hybrid coaxial-substrate integrated waveguide (SIW)-microstrip line power divider is proposed in this paper. The coaxial to oversized coaxial transition and SIW to microstrip line transition is utilized to realize the power divider. The input signal can be divided into arbitrary N ways equally by a set of oversized coaxial to SIW transition structure uniformly distributed along the circumference of the oversized coaxial waveguide. The simulated results of the presented eight-way power divider show that the amplitude and phase balance is good. In addition, compared with the conventional N-way power dividers, this novel power divider has low insertion loss.
DESIGN AND EVALUATION OF CELL SCHEDULINGALGORITHMS FOR ATM SWITCHESW

An ATM switch is a high speed xed-size packet switching architecture; it is a basic element for the ATM protocol which has been widely accepted as the ultimate solution for the BISDN. In this paper, we rst successfully transform the cell scheduling problem of a window-based input queueing ATM switch to the maximum matching problem on a bipartite graph. An optimal solution can thus be found in O(W N 2) time, where N is the number of input/output ports, and W is the window size; or in O(W N 3=2) time if the maximum matching problem is further transformed to a maximum network ow problem. Next, we study and evaluate four existing approximation algorithms, including a simple heuristic algorithm and three neural network methods: the Hoppeld memory neural network algorithm, the McCulloch-Pitts neural algorithm, and an approximate neural algorithm based on McCulloch-Pitts neural network. Their performance in terms of throughput, mean waiting time, and neuron convergence time, are evaluated using computer simulations. We found that the optimal algorithm provides the highest throughput, and more signiicantly, a much shorter mean waiting time than the other four approximation schemes.
Detectability of the Warm/Hot Intergalactic Medium through Emission Lines of O VII and O VIII

Most of cosmic baryons predicted by the big-bang nucleosynthesis has evaded the direct detection. Recent numerical simulations indicate that approximately 30 to 50 percent of the total baryons in the present universe is supposed to take a form of warm/hot intergalactic medium (WHIM) whose X-ray continuum emission is very weak. To identify those missing baryons, we consider in detail the detectability of WHIM directly through emission lines of OVII (561, 568, 574, 665eV) and OVIII (653eV). For this purpose, we create mock spectra of the emission lines of WHIM using a light-cone output of the cosmological hydrodynamic simulations. Since the predicted fluxes are generally below the current detection limit, the unambiguous detection requires a dedicated X-ray satellite mission that we also discuss in detail. We find that our proposed mission is sensitive to the WHIM with gas temperature $T=10^{6-7}$K and overdensity $\delta=10-100$ up to a redshift of 0.3 without being significantly contaminated by the cosmic X-ray background and the Galactic emissions. Thus such a mission provides a unique and important tool to identify a large fraction of otherwise elusive baryons in the universe.
Determinants of Transparency for Singaporean Listed Companies

The extent to which companies disclose information about their activities, corporate transparency, is universally regarded as a vital prerequisite for the efficient functioning, vibrancy, and fairness of stock markets. If companies are completely open and frank, so the accepted wisdom goes, then investors stand to be able to make well informed investment decisions. Countries around the world rely upon a mixture of securities regulation and market driven measures to achieve the desired level of transparency. Many countries use awards schemes to promote high standards of disclosure. Recently, Singapore witnessed the introduction of an index to help boost what many feel to be low levels of transparency amongst local listed companies. The Corporate Transparency Index (CTI) was introduced by Singapore financial daily, The Business Times. The CTI measures a company's degree of openness by focusing on the release of the interim results of listed companies. Unlike other measures of transparency it looks not only at the content of the results release but also how that content is communicated to the markets. This paper seeks to identify the key determinants of a company's transparency score. The provisional findings suggest company size and profitability correlate positively with corporate transparency.
Relationships Between Creative Thinking Abilities and a Measure of the Creative Personality

Persons with creative abilities might be expected to have creative personality characteristics. The purpose of this study was to ascertain the relationships between a creative personality measure, What Kind of Person Are You? Test, and three verbal and four figural creative ability measures derived from the Torrance Tests of Creative Thinking. The subjects were 65 males and 164 female undergraduates enrolled in the introductory course in educational psychology at the University of Georgia. For the males, multiple coefficients of correlation were . 57 for all seven creative ability measures, . 42 for the three verbal measures, and . 51 for the four figural measures (all significant at the .01 level). For the females, multiple coefficients of correlation were . 33 for all seven measures, . 22 for the three verbal measures, and . 30 for the four figural measures (all significant at or beyond the .05 level).