Miracle-dz/UniFAR-FTUs huggingface dataset:
Calcium-Induced Calcium Release Contributes to Action Potential-Evoked Calcium Transients in Hippocampal CA1 Pyramidal Neurons

Calcium-induced calcium release (CICR) is a mechanism by which local elevations of intracellular calcium (Ca2+) are amplified by Ca2+ release from ryanodine-sensitive Ca2+ stores. CICR is known to be coupled to Ca2+ entry in skeletal muscle, cardiac muscle, and peripheral neurons, but no evidence suggests that such coupling occurs in central neurons during the firing of action potentials. Using fast Ca2+ imaging in CA1 neurons from hippocampal slices, we found evidence for CICR during action potential-evoked Ca2+ transients. A low concentration of caffeine enhanced Ca2+ transient amplitude, whereas a higher concentration reduced it. Simultaneous Ca2+ imaging and whole-cell recordings showed that membrane potential, action potential amplitude, and waveform were unchanged during caffeine application. The enhancement of Ca2+ transients by caffeine was not affected by the L-type channel blocker nifedipine, the phosphodiesterase inhibitor IBMX, the adenylyl cyclase activator forskolin, or the PKA antagonist H-89. However, thapsigargin or ryanodine, which both empty intracellular Ca2+stores, occluded this effect. In addition, thapsigargin, ryanodine, and cyclopiazonic acid reduced action potential-evoked Ca2+ transients in the absence of caffeine. These results suggest that Ca2+ release from ryanodine-sensitive stores contributes to Ca2+signals triggered by action potentials in CA1 neurons.
Computational analysis of the binding free energy of H3K9me3 peptide to the tandem tudor domains of JMJD2A

JMJD2A is a histone lysine demethylase enzyme which plays a prominent role in the development of prostate and esophageal squamous cancers. Consisting of a JmjC, a JmjN, two PHD and two tandem tudor domains, JMJD2A recognizes and binds to four different methylated histone peptides: H3K4me3, H4K20me3, H4K20me2 and H3K9me3, via its tudor domains. Of the four histone peptides, only recognition of the H3K4me3 and H4K20me3 by JMJD2A-tudor has been identified. In this study, we investigated the recognition of trimethylated H3K9 by the tandem tudor domains of JMJD2A. Using the molecular dynamics simulations, we performed normal mode and molecular mechanics - Poisson Boltzmann / generalized born - surface area (MM-PB/GB-SA) analysis to find the entropic and enthalpic contributions to binding free energy respectively. We showed that binding of the ligand is mainly driven by favorable van der Waals interactions made after complexation. Our findings suggest that, upon complex formation, H3K9me3 peptide adopts a similar binding mode and the same orientation with H3K4me3 peptide.
Optimal Battery Sizing in Photovoltaic Based Distributed Generation Using Enhanced Opposition-Based Firefly Algorithm for Voltage Rise Mitigation

This paper presents the application of enhanced opposition-based firefly algorithm in obtaining the optimal battery energy storage systems (BESS) sizing in photovoltaic generation integrated radial distribution network in order to mitigate the voltage rise problem. Initially, the performance of the original firefly algorithm is enhanced by utilizing the opposition-based learning and introducing inertia weight. After evaluating the performance of the enhanced opposition-based firefly algorithm (EOFA) with fifteen benchmark functions, it is then adopted to determine the optimal size for BESS. Two optimization processes are conducted where the first optimization aims to obtain the optimal battery output power on hourly basis and the second optimization aims to obtain the optimal BESS capacity by considering the state of charge constraint of BESS. The effectiveness of the proposed method is validated by applying the algorithm to the 69-bus distribution system and by comparing the performance of EOFA with conventional firefly algorithm and gravitational search algorithm. Results show that EOFA has the best performance comparatively in terms of mitigating the voltage rise problem.
Cross-calibration of the X-ray instruments onboard the Chandra, INTEGRAL, RXTE, Suzaku, Swift, and XMM- Newton observatories using G21.5-0.9

Context. The Crab nebula has been used as a celestial calibration source of the X-ray flux and spectral shape for many years by X-ray astronomy missions. However, the object is often too bright for current and future missions equipped with instruments with improved sensitivity. Aims. We use G21.5-0.9 as a viable, fainter substitute to the Crab, which is another pulsar-wind nebula with a time-constant powerlaw spectrum with a flux of a few milli Crab in the X-ray band. Using this source, we conduct a cross-calibration study of the instruments onboard currently active observatories: Chandra ACIS, Suzaku XIS, Swift XRT, XMM-Newton EPIC (MOS and pn) for the soft-band, and INTEGRAL IBIS-ISGRI, RXTE PCA, and Suzaku HXD-PIN for the hard band. Methods. We extract spectra from all the instruments and fit them under the same astrophysical assumptions. We compare the spectral parameters of the G21.5-0.9 model: power-law photon index, H-equivalent column density of the interstellar photoelectric absorption, flux in the soft (2-8 keV) or hard (15-50 keV) energy band. Results. We identify the systematic differences in the best-fit parameter values unattributable to the statistical scatter of the data alone. We interpret these differences as due to residual cross-calibration problems. The differences can be as large as 20% and 9% for the soft-band flux and power-law index, respectively, and 46% for the hard-band flux. The results are plotted and tabulated as a useful reference for future calibration and scientific studies using multiple missions.
Regret Analysis of Block Coordinate Gradient Methods for Online Convex Programming

In this paper, we propose two block coordinate gradient (BCG) methods for an online convex optimization problem with a separable structure: the BCG method with the cyclic rule and the BCG method with the random rule. The proposed methods solve a low dimensional problem on each iteration, and hence they are efficient for large scale problems. For the proposed methods, under usual assumptions, we show that their regret bounds are O( √ T ), where T is the number of time steps. These results are shown to be natural extensions of that for the greedy projection method by Zinkevich.
Thinking dispositions and cognitive reflection performance in schizotypy

Schizotypy refers to the continuum of normal variability of psychosis-like characteristics and experiences, often classified as positive schizotypy (‘unusual experiences’; UE) and negative schizotypy (‘introvertive anhedonia’; IA). Here, we investigated the link between schizotypy and cognitive processing style and performance. A particular focus was on whether schizotypy is associated more with Type 1 (automatic/heuristic) than Type 2 (reflective/effortful) processes, as may be expected from findings of impaired top-down control in schizophrenia. A large sample (n = 1,512) completed online measures pertaining to schizotypy (Oxford-Liverpool Inventory for Feelings and Experiences; O-LIFE), thinking style (Rational Experiential Inventory-10, Actively Open-Minded Thinking Scale), and reasoning performance (Cognitive Reflection Test). Higher positive (UE) and negative (IA) schizotypy were associated with more pronounced Type 1 processing, i.e. greater self-reported Faith in Intuition (FI), lower Need for Cognition (NFC), lower Actively Open-Minded Thinking (AOT), and lower cognitive reflection test (CRT) scores. Canonical correlation analysis confirmed a significant association between UE and increased FI, lower AOT and lower CRT performance, accounting for 12.38% of the shared variance between schizotypy and thinking dispositions. IA was more highly associated with reduced NFC. These findings suggest that schizotypy may be associated with similar thinking dispositions to those reported in psychosis, with different patterns of associations for positive and negative schizotypy. This result informs research on reasoning processes in psychosis and has clinical implications, including potential treatment targets and refinements for cognitive therapies.
Heterogeneity in capture-recapture: Bayesian methods to balance realism and model complexity

Capture-recapture experiments are important for monitoring many endangered animal populations, such as salmon threatened by over-harvesting and migratory songbirds impacted by habitat loss. An important consideration in the analysis of capture-recapture data is potential variation in the probabilities of capture and survival. Failure to account for this variation can lead to incorrect inference, but traditional models incorporating heterogeneity may be very complex. This thesis presents three Bayesian methods that balance realistical modelling of variation in the capture and survival probabilities and increasing model complexity. In the first project, I consider the analysis of data from two-sample experiments used in estimating the number of juvenile salmon leaving their spawning grounds. These migrations may last for several weeks and standard models may require many parameters to account for variations over time. My solution is to model the population size as a smooth function of time by fitting a Bayesian penalised spline. The method is applied to two datasets from the migration of juvenile salmon and provides more precise estimates of the population size that are less affected by outliers in the data than previous methods. My second project addresses estimation of the size of an open population when individual capture or survival probabilities are functions of a time-dependent, continuous covariate. The main challenge is that these covariates can only be observed on occasions when an individual is captured. I develop a two-stage Bayesian method that first examines the covariate’s effect by analysing the capture of marked individuals, and then applies the results to estimate the total population size. The model is used to study the dynamics of a population of Soay sheep (Ovis aries) whose survival is affected by body mass. Finally, I develop a method to allow more flexibility in modelling the relationship between a covariate and individual survival probabilities. Standard methods assume that the
Photorealistic 2D audiovisual text-to-speech synthesis using active appearance models

Audiovisual text-to-speech (AVTTS) synthesizers are capable of generating a synthetic audiovisual speech signal based on an input text. A possible approach to achieve this is model-based synthesis, where the talking head consists of a 3D model of which the polygons are varied in accordance with the target speech. In contrast with these rule-based systems, data-driven synthesizers create the target speech by reusing pre-recorded natural speech samples. The system we developed at the Vrije Universiteit Brussel is a data-based 2D photorealistic synthesizer that is able to create a synthetic visual speech signal that is similar to standard 'newsreader-style' television recordings.
TAPE: thermal-aware agent-based power economy for multi/many-core architectures

A growing challenge in embedded system design is coping with increasing power densities resulting from packing more and more transistors onto a small die area, which in turn transform into thermal hotspots. In the current late silicon era silicon structures have become more susceptible to transient faults and aging effects resulting from these thermal hotspots. In this paper we present an agent-based power distribution approach (TAPE) which aims to balance the power consumption of a multi/many-core architecture in a pro-active manner. By further taking the system's thermal state into consideration when distributing the power throughout the chip, TAPE is able to noticeably reduce the peak temperature. In our simulation we provide a fair comparison with the state-of-the-art approaches HRTM [19] and PDTM [9] using the MiBench benchmark suite [18]. When running multiple applications simultaneously on a multi/many-core architecture, we are able to achieve an 11.23% decrease in peak temperature compared to the approach that uses no thermal management [14]. At the same time we reduce the execution time (i.e. we increase the performance of the applications) by 44.2% and reduce the energy consumption by 44.4% compared to PDTM [9]. We also show that our approach exhibits higher scalability, requiring 11.9 times less communication overhead in an architecture with 96 cores compared to the state-of-the-art approaches.
Question-answer topic model for question retrieval in community question answering

The major challenge for Question Retrieval (QR) in Community Question Answering (CQA) is the lexical gap between the queried question and the historical questions. This paper proposes a novel Question-Answer Topic Model (QATM) to learn the latent topics aligned across the question-answer pairs to alleviate the lexical gap problem, with the assumption that a question and its paired answer share the same topic distribution. Experiments conducted on a real world CQA dataset from Yahoo! Answers show that combining both parts properly can get more knowledge than each part or both parts in a simple mixing way and combining our QATM with the state-of-the-art translation-based language model, where the topic and translation information is learned from the question-answer pairs at two different grained semantic levels respectively, can significantly improve the QR performance.
High order cascade multibit σΔ modulator for wide bandwidth applications

A novel 2-1-1 cascaded sigma-delta I/Q modulator suitable for WCDMA applications is presented. The modulator employs multibit quantizer in the last stage and dynamic element matching (DEM) to attain high linearity over a wide band-width at an oversampling ratio (OSR) of 16. The DEM algorithm is introduced in such a way as to suppress in-band digital-to-analog converter (DAC) errors in the feedback of the last stage, thereby enabling the use of the highest resolution quantizer yet reported in a multibit cascaded sigma-delta modulator.
AN APPROACH TO INTEGRATE QUALITY FUNCTION DEPLOYMENT WITH COMPETITIVE TECHNICAL INTELLIGENCE : APPLICATION ON A NEW FOOD PRODUCT

This paper proposes the integration of Quality Function Deployment with Competitive Technical Intelligence in a model focused on the planning phase of product development. This model originated from the Total Quality Management state of the art, based on what is known as the " Customer Voice " as well as the " Technician Voice " , under a competitive technical intelligence approach. While the Japanese methodology of Quality Function Deployment is applied to build links between the organization and its clients, Competitive Technical Intelligence is applied to monitor the entire environment, pursuing the identification of future opportunities to innovate. This paper proposes the integration of the two methodologies. Résumé : Ce document nous présente l'intégration de Déploiement de Fonction de Qualité avec l'Intelligence Technologique Compétitive grâce à un modèle basé sur la phase de planification de développement de produits nouveaux. Ce modèle provient de la Direction de Qualité Total, particulièrement au sujet connu sous le nom de «Voix des Clients » ainsi que celui de « Voix de Technicien », mais sous une approche d'Intelligence Technologique Compétitive. Tandis que la méthodologie japonaise de Déploiement de Fonction de Qualité est appliquée pour établir des liens entre organisation et clients, l'Intelligence Technologique Compétitive est appliquée pour surveiller l'environnement entier, visant l'identification d'opportunités futures à innover. L'intégration de ces deux méthodologies est ainsi exposée.
SOPHIE - A Conceptual Model for a Semantic Choreography Framework

Choreography models the external visible behavior of services as a set of message exchanges that realize Message Exchange Patterns (MEP). A number of language exist that try to tackle the problem. However they constitute incomplete solutions, either because they lack of support of semantics, have a tight relation to the underlying communication framework, or mix structural, behavioral and operational aspects resulting in confusing specifications. This position paper defines a conceptual model for SOPHIE, a choreography engine for Semantic Web Services following a Semantic Service-Oriented Architecture. The conceptual model of SOPHIE is especially suitable for supporting the fine grained interaction among services with different structural, behavioral or operational models. A case study in the Telecommunication sector is presented.
QUADRATIC STABILIY OF A CLASS OF SWITCHED SYSTEMS

Quadratic stability of a class of switched non-linear systems is studied in this paper. We first transform quadratic stability problem into an equivalent nonlinear programming problem. Then, with the help of Fritz John condition for nonlinear programming problems, we derive a necessary and sufficient condition for switched systems to be quadratically stable. This necessary and sufficient condition is consisted of a set of algebraic equations and inequalities that are amenable to computer algebra evaluation.
Concatenated edge and co-occurrence feature extracted from Curvelet Transform for human detection

An efficient feature extraction method based on the Curvelet Transform for detecting human in static images is proposed in this paper. The edge features can be extracted with the block-based statistical information of each sub-band coefficients, and then the texture feature can be extracted from the co-occurrence of the lowest sub-band coefficients, all the extracted features are concatenated as the final feature vector of the images. All the training and test data are from the INRIA and MIT human dataset. The classification results with test data show that the proposed feature extraction method is suitable for human detection. From the detection results, it can be seen that, the detection accuracy of the proposed method is higher, meanwhile the false alarms are lower than the Histograms of Oriented Gradients(HOG) method.
Mouse Pose Estimation From Depth Images

We focus on the challenging problem of efficient mouse 3D pose estimation based on static images, and especially single depth images. We introduce an approach to discriminatively train the split nodes of trees in random forest to improve their performance on estimation of 3D joint positions of mouse. Our algorithm is capable of working with different types of rodents and with different types of depth cameras and imaging setups. In particular, it is demonstrated in this paper that when a top-mounted depth camera is combined with a bottom-mounted color camera, the final system is capable of delivering full-body pose estimation including four limbs and the paws. Empirical examinations on synthesized and real-world depth images confirm the applicability of our approach on mouse pose estimation, as well as the closely related task of part-based labeling of mouse.
Unsupervised learning of object affordances for planning in a mobile manipulation platform

In this paper, we use the notion of affordances, proposed in cognitive science, as a framework to propose a developmental method that would enable a robot to ground symbolic planning mechanisms in the continuous sensory-motor experiences of a robot. We propose a method that allows a robot to learn the symbolic relations that pertain to its interactions with the world and show that they can be used in planning. Specifically, the robot interacts with the objects in its environment using a pre-coded repertoire of behaviors and records its interactions in a triple that consist of the initial percept of the object, the behavior applied and its effect, defined as the difference between the initial and the final percept. The method allows the robot to learn object affordance relations which can be used to predict the change in the percept of the object when a certain behavior is applied. These relations can then be used to develop plans using forward chaining. The method is implemented and evaluated on a mobile robot system with limited object manipulation capabilities. We have shown that the robot is able to learn the physical affordances of objects from range images and use them to build symbols and relations that can be used in making multi-step predictions about the affordances of objects and achieve complex goals.
Soluble HLA-G and HLA-E Levels in Bone Marrow Plasma Samples Are Related to Disease Stage in Neuroblastoma Patients

The role of nonclassical HLA-class Ib molecules HLA-G and HLA-E in the progression of Neuroblastoma (NB), the most common pediatric extracranial solid tumor, has been characterized in the last years. Since BM infiltration by NB cells is an adverse prognostic factor, we have here analyzed for the first time the concentration of soluble (s)HLA-G and HLA-E in bone marrow (BM) plasma samples from NB patients at diagnosis and healthy donors. sHLA-G and sHLA-E are present in BM plasma samples, and their levels were similar between NB patients and controls, thus suggesting that these molecules are physiologically released by resident or stromal BM cell populations. This hypothesis was supported by the finding that sHLA-G and sHLA-E levels did not correlate with BM infiltration and other adverse prognostic factors (MYCN amplification and age at diagnosis). In contrast, BM plasma levels of both molecules were higher in patients with metastatic disease than in patients with localized NB, thus suggesting that concentration of these molecules might be correlated with disease progression. The prognostic role of sHLA-G and sHLA-E concentration in the BM plasma for NB patients will be evaluated in future studies, by analyzing the clinical outcome of the same NB patients at follow-up.
Algorithm of the Text Copy Detection Based on Topic Bag

In order to resolve the current problem about seriously academic plagiarism in the web environment, this article proposes an algorithm of the text copy detection on the topic bag and the algorithm uses the idea of semantic clustering and multi-instance learning. Firstly, a paper is divided into three layers construction tree: a leaf node denotes a sentence; a branch node represents a topic bag, and the topic bag formed by semantic clustering of several paragraphs; the uppermost a root node is a text. Secondly, the similarities of topic bags are calculated by the similarities of sentences; then we can get the similarity of two papers by similarities and weights of topic bags. Experiments show that the proposed algorithm has higher accuracy.
AUTH-Atypon at BioASQ 3: Large-Scale Semantic Indexing in Biomedicine

In this paper we present the methods and the approaches employed in terms of our participation to the BioASQ Challenge 2015 and more specifically in task 3a, concerning the automatic semantic annotation of scientific abstracts. Based on the successful approaches of the previous years we considered a variety of ensembles, incorporated journalspecific semantic information and developed an approach to handle the concept drift within the BioASQ corpus. The official results demonstrate a consistent advantage of our approaches against the BioASQ and the National Library of Medicine (NLM) baselines. Specifically, the systems proposed by our team ranked among the top tier ones along the competition, obtaining the second place in 10 out of 15 weeks.