Miracle-dz/UniFAR-FTUs huggingface dataset:
Bilinearity in Spatiotemporal Integration of Synaptic Inputs

Neurons process information via integration of synaptic inputs from dendrites. Many experimental results demonstrate dendritic integration could be highly nonlinear, yet few theoretical analyses have been performed to obtain a precise quantitative characterization analytically. Based on asymptotic analysis of a two-compartment passive cable model, given a pair of time-dependent synaptic conductance inputs, we derive a bilinear spatiotemporal dendritic integration rule. The summed somatic potential can be well approximated by the linear summation of the two postsynaptic potentials elicited separately, plus a third additional bilinear term proportional to their product with a proportionality coefficient . The rule is valid for a pair of synaptic inputs of all types, including excitation-inhibition, excitation-excitation, and inhibition-inhibition. In addition, the rule is valid during the whole dendritic integration process for a pair of synaptic inputs with arbitrary input time differences and input locations. The coefficient is demonstrated to be nearly independent of the input strengths but is dependent on input times and input locations. This rule is then verified through simulation of a realistic pyramidal neuron model and in electrophysiological experiments of rat hippocampal CA1 neurons. The rule is further generalized to describe the spatiotemporal dendritic integration of multiple excitatory and inhibitory synaptic inputs. The integration of multiple inputs can be decomposed into the sum of all possible pairwise integration, where each paired integration obeys the bilinear rule. This decomposition leads to a graph representation of dendritic integration, which can be viewed as functionally sparse.
Sources of Wage Inequality

Recent theories of firm heterogeneity emphasize between-firm wage differences as a new mechanism through which trade can affect wage inequality. Using linked employer-employee data for Sweden, we show that many of the stylized facts about wage inequality found in Helpman et al. (2012) for Brazil also hold for Sweden. Much of overall wage inequality arises within sector-occupations and for workers with similar observable characteristics. One notable difference is a smaller contribution from between-firm differences in wages in Sweden, which could reflect the influence of Swedish labor market institutions in dampening the scope for variation in wages between firms through collective wage agreements.
Extreme Anomalous Score Clustering Algorithm

A clustering algorithm usually detects outliers as an aftermath of partitioning data points in a finite dimensional continuous dataset such as AGNES, k-means, and DBSCAN. This research makes use of the extreme anomalous score which represents the outlierness of a data point based on the largest radius of a ball containing only that data point. The new clustering algorithm based on this score is proposed called the extreme anomalous score clustering algorithm (ESC). It searches for a cluster representative by combining two data points which are placed with the smallest extreme anomalous score. Then all extreme anomalous scores are updated and the algorithm stops when it reaches the number of clusters defined by a user. Otherwise, it continues to combine two data points having the smallest extreme anomalous scores. The experimental results on three groups of simulated datasets report the superior performance of ESC over AGNES, k-means, and DBSCAN based on the silhouette measurement and the homogeneity measurement.
of cattle IxodId tIcks In khoozestan ProvInce , south-West of Iran

ticks are considered to be the main vectors for transmission of various diseases to animals and human. the present faunistic study aimed to determine species belonging to Ixodidae ticks, infesting cattle of Khoozestan province, south-west of Iran. Totally 1000 ticks were collected randomly from cattle from 9 locations in five geographical regions between January to September 2011, and were identified to the species level using valid identification keys. Eight tick species in fourdifferent genera were identified as Hyalomma anatolicum (39%), Rhipicephalus sanguineus (25.4%), Hyalomma excavatum (14.3%), Hyalomma asiaticum (6.8%), Haemaphysalis sulcata (3.7%), Hyalomma scupense (3.4%), Hyalomma spp. (3.4%), Rhipicephalus (Boophilus) anuulatus (2%) and Hyalomma dromedarii (2%). As some of the collected ticks are potential vectors for transmission of pathogens to animals and humans; more studies with focus onveterinary and public health importance of these species are recommended. KEy wordS: ixodid tick, cattle, Khoozestan, Iran
On Weak epsilon-Nets and the Radon Number

We show that the Radon number characterizes the existence of weak nets in separable convexity spaces (an abstraction of the euclidean notion of convexity). The construction of weak nets when the Radon number is finite is based on Helly’s property and on metric properties of VC classes. The lower bound on the size of weak nets when the Radon number is large relies on the chromatic number of the Kneser graph. As an application, we prove a boosting-type result for weak ǫ-nets.
Statistical Physics Modeling of Disordered Metallic Alloys

The great majority of metallic alloys in use are disordered. The material property of a disordered alloy changes on exposure to thermal, chemical, or mechanical forcing; the changes are often irreversible. We present a new first principle method for modeling disordered metallic alloys suitable for predicting how the morphology, strength, and transport property would evolve under arbitrary forcing conditions. Such a predictive capability is critically important in designing new alloys for applications, such as in new-generation fission and fusion reactors, where unrelenting harsh thermal loading conditions exist. The protocol is developed for constructing a coarse-grained model that can be specialized for the evolution of thermophysical properties of an arbitrary disordered alloy under thermal, stress, nuclear, or chemical forcing scenarios. We model a disordered binary alloy as a randomly close-packed (RCP) assembly of constituent atoms at given composition. As such, a disordered alloy specimen is an admixture of nanocrystallites and glassy matter. For the present purpose, we first assert that interatomic interactions are by repulsion only, but the contributions from the attractive part of the interaction are restored by treating the nanocrystallites as nanoscale pieces of a single crystalline solid composed of the same constituent atoms. Implementation of the protocol is discussed for heating of disordered metals, and results are compared to the known melting point data.
Universal Device Access with FreeMote

With the FreeMote system, we present a solution for easy and intuitive universal device control. Based on the Wii Remote Controller real world device functions can be accessed and controlled via gestures. Gestures include predefined sets apt for standard functions present in many devices as well as user definable gestures. Offering both possibilities, the users are free to choose and adapt the gestures to their needs while reducing the mental load. Along with the gesture interface, FreeMote offers a simple way of enabling everyday devices for FreeMote controlling. FreeMote makes use of the spatial nature of real world environments in device selection and in function controlling via gestures. Thus, FreeMote system supports the user in finding enabled devices, makes selecting devices easy and provides an intuitive way of controlling devices via gestures.
The influence of cobalt on the microstructure and adherence characteristics of enamel on steel sheet

In this study, the influence of cobalt (Co) on the microstructure and adhesion between enamel and steel substrate has been investigated. The result of press test indicated that the adhesion strength was greatly improved by Co inclusion. Scanning electronic microscope (SEM) revealed that the amount of dendrite increases in the interface while its average size decreases. The wavelength dispersive X-ray analysis (WDS) identified the existence of Co in the dendrite, which confirms that the Co was closely related to the change of interface morphology and the improvement of adhesion strength. Finally, the reasons for adhesion improvement were discussed.
Achieve Orgasm? Oxytocin Triggers Ejaculation in Men

The paraventricular nucleus (PVN) of the hypothalamus, which is considered to be a centre for autonomic regulation, has been shown to exhibit physiological and functional sex differences. It is involved in numerous functions, from stress to the appetite, body energy balance, blood pressure, heart rate and sexual activity, including penile erection [1], and is characterised by a very complex architecture. OXT is produced chiefly by magnocellular neurosecretory cells in the PVN and supraoptic nucleus of the hypothalamus and released into the blood from the axon terminals in the neurohypophysis and into the surrounding neuropil from magnocellular dendrites [2,3]. The principal functions attributed to OXT are involved in the regulation of reproductive functions including parturition, milk ejection, and sexual and maternal behaviour in females, so called a feminine hormone (for review, see Ref. [4] ). On the other hand, it is well accepted that a group of OXT-ergic neurones originating in the PVN and projecting to extrahypothalamic areas (e.g., hippocampus, medulla oblongata and spinal cord) control penile erection and sexual behaviour in male rats [5]. It has been reported that OXT positively impacted on a number of components of sexual function, including libido, erection, and orgasm in men [6-8]. The intracerebroventricular administration of OXT also induced a dose-dependent increase in the number of penile erections and yawning episodes in male rats, suggesting a physiological role of hypothalamic OXT in the regulation of such responses [9]. Because penile erection and yawning induced by either OXT or apomorphine in rats were antagonised in a dose-dependent manner by pre-treatment with OXT antagonists, dopamine may induce these responses by releasing OXT in vivo [9].
Cannabidiol Attenuates Cardiac Dysfunction , Oxidative Stress , Fibrosis , and Inflammatory and Cell Death Signaling Pathways in Diabetic Cardiomyopathy

Results Diabetic cardiomyopathy was characterized by declined diastolic and systolic myocardial performance associated with increased oxidative-nitrative stress, nuclear factorB and mitogen-activated protein kinase (c-Jun N-terminal kinase, p-38, p38 ) activation, enhanced expression of adhesion molecules (intercellular adhesion molecule-1, vascular cell adhesion molecule-1), tumor necrosis factor, markers of fibrosis (transforming growth factor, connective tissue growth factor, fibronectin, collagen-1, matrix metalloproteinase-2 and -9), enhanced cell death (caspase 3/7 and poly[adenosine diphosphate-ribose] polymerase activity, chromatin fragmentation, and terminal deoxynucleotidyl transferase dUTP nick end labeling), and diminished Akt phosphorylation. Remarkably, CBD attenuated myocardial dysfunction, cardiac fibrosis, oxidative/nitrative stress, inflammation, cell death, and interrelated signaling pathways. Furthermore, CBD also attenuated the high glucose-induced increased reactive oxygen species generation, nuclear factorB activation, and cell death in primary human cardiomyocytes.
Optimizing and auto-tuning scale-free sparse matrix-vector multiplication on Intel Xeon Phi

Recently, the Intel Xeon Phi coprocessor has received increasing attention in high performance computing due to its simple programming model and highly parallel architecture. In this paper, we implement sparse matrix vector multiplication (SpMV) for scale-free matrices on the Xeon Phi architecture and optimize its performance. Scale-free sparse matrices are widely used in various application domains, such as in the study of social networks, gene networks and web graphs. We propose a novel SpMV format called vectorized hybrid COO+CSR (VHCC). Our SpMV implementation employs 2D jagged partitioning, tiling and vectorized prefix sum computations to improve hardware resource utilization, and thus overall performance. As the achieved performance depends on the number of vertical panels, we also develop a performance tuning method to guide its selection. Experimental results demonstrate that our SpMV implementation achieves an average 3× speedup over Intel MKL for a wide range of scale-free matrices.
Fast spatial correlation acquisition for hybrid precoding using sequential compressive sensing

This paper addresses the critical problem of high overhead in channel information acquisition for hybrid precoding in massive multiple-input multiple-output (MIMO) system. With the employment of large-scale massive antennas in the next generation 5G wireless system, the high overhead in the estimation of channel state information (CSI) for the precoding has been regarded as one of the main challenges that prevent its implementations. On the other hand, other efficient hybrid precoding schemes such as beam sweeping based two stage precoding have limited performance compared with the complete CSI based precoding algorithm. This paper proposes a novel technique to quickly acquire the channel spatial correlation of the large number of antennas, which can be used for hybrid precoding design. This technique can greatly reduce the training overhead while maintain the capability of generating highly user-specific analog beamforming. The simulation results show that the proposed technique can achieve similar performance with the traditional complete CSI based precoding but only require even less reference signal (RS) symbols than the beam sweeping scheme.
Microglial expression of peptidylarginine deiminase 2 in the prenatal rat brain

Peptidylarginine deiminases (PADs) are Ca2t+-dependant post-translational modification enzymes that catalyze the citrullination of protein arginyl residues. PAD type 2 (PAD2) is thought to be involved in some processes of neurodegeneration and myelination in the central nervous system. In this study, we found PAD2-positive cells in rat cerebra in 19-to 21-day old embryos, i.e. at a developmental stage well before myelination begins. Most of the cells were microglial marker-positive cells found mainly in the prospective medulla, and others were microglial marker-negative cells found mainly in the prospective dentate gyrus of the hippocampus. The former seemed to be in an activated state as judged by morphological criteria. The specificity of the enzyme activity, immunoblotting and reverse transcriptase-polymerase chain reaction analyses revealed that these cells expressed PAD2 and not PAD1, PAD3 or PAD4. Our data is indicative of microglial expression of PAD2 in the prenatal developing cerebrum.
An Evolutionary Programming Based Algorithm for HMM training

In this paper, we propose an evolutionary programming (EP) based algorithm for the training of hidden Markov models (HMMs), which are applied to automatic speech recognition. This algorithm (called the EP algorithm) uses specially designed operators of mutation and selection to find the HMM parameters and the number of states. In order to evaluate the recognition capability of the HMMs trained by the EP algorithm, a computational experiment is carried out using speech samples for 20 words. The results indicate that the proposed EP algorithm is a very promising tool for HMM training the EP-HMM approach achieves an average recognition rate of 97.92% and the training process requires quite a small number of iterations of the algorithm.
Active learning with cross-dataset validation in event-based non-intrusive load monitoring

Supervised event-based NILM systems usually require a large set of labeled training data to achieve high classification accuracies. To minimize the cost of labeling a sufficient amount of events, active learning can be employed. By using only a small set of labeled samples for initial training followed by selecting only the most informative samples to be labeled, the total number of labeled training samples can be reduced significantly. The performance of an active learning system strongly depends on the choice of the initial training set and the used query strategy. We thus investigated the impact of different methods to select the dataset for initial training as well as various query strategies on the resulting classification accuracy in an event-based NILM framework. For evaluation we used two datasets, BLUED and ISS kitchen, on which we were able to achieve high classification accuracies with significantly less training samples compared to conventional training without active learning.
Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation

Collaborative Filtering (CF) based recommender systems often suffer from the sparsity problem, particularly for new and inactive users when they use the system. The emerging trend of social networking sites and their accommodation in other sites like e-commerce can potentially help alleviate the sparsity problem with their provided social relation data. In this paper, we have particularly explored a new kind of social relation, the membership, and its combined effect with friendship. The two type of heterogeneous social relations are fused into the CF recommender via a factorization process. Due to the two relations' respective properties, we adopt different fusion strategies: regularization was leveraged for friendship and collective matrix factorization (CMF) was proposed for incorporating membership. We further developed a unified model to combine the two relations together and tested it with real large-scale datasets at five sparsity levels. The experiment has not only revealed the significant effect of the two relations, especially the membership, in augmenting recommendation accuracy in the sparse data condition, but also identified the ability of our fusing model in achieving the desired fusion performance.
Enhancing the Case Handling Paradigm to Support Object-aware Processes

Despite the widespread adoption of process management systems (PrMS) by industry, there exist numerous processes that cannot be adequately supported by PrMS so far. A common characteristic of these processes, which is usually neglected by traditional activity-centric PrMS, is the role of data as driver for process modeling and enactment. To overcome the limitations caused by missing integration of data and process, several data-centric process management approaches have emerged. A popular one is the Case Handling (CH) paradigm. However, previous case studies pointed out that, although it targets some of the limitations from activity-centric PrMS, the integration of processes and data supported by CH is still unsatisfactory. In this paper, we present the lessons learned from previous case studies and discuss the limitations of CH. We then present the PHILharmonicFlows framework, which enhances the power of data-centric approaches such as CH by enforcing a well-defined modeling methodology governing the object-centric specification and execution of processes and based on a formal operational semantics.
ASIC1A in neurons is critical for fear‐related behaviors

Acid‐sensing ion channels (ASICs) have been implicated in fear‐, addiction‐ and depression‐related behaviors in mice. While these effects have been attributed to ASIC1A in neurons, it has been reported that ASICs may also function in nonneuronal cells. To determine if ASIC1A in neurons is indeed required, we generated neuron‐specific knockout (KO) mice with floxed Asic1a alleles disrupted by Cre recombinase driven by the neuron‐specific synapsin I promoter (SynAsic1a KO mice). We confirmed that Cre expression occurred in neurons, but not all neurons, and not in nonneuronal cells including astrocytes. Consequent loss of ASIC1A in some but not all neurons was verified by western blotting, immunohistochemistry and electrophysiology. We found ASIC1A was disrupted in fear circuit neurons, and SynAsic1a KO mice exhibited prominent deficits in multiple fear‐related behaviors including Pavlovian fear conditioning to cue and context, predator odor‐evoked freezing and freezing responses to carbon dioxide inhalation. In contrast, in the nucleus accumbens ASIC1A expression was relatively normal in SynAsic1a KO mice, and consistent with this observation, cocaine conditioned place preference (CPP) was normal. Interestingly, depression‐related behavior in the forced swim test, which has been previously linked to ASIC1A in the amygdala, was also normal. Together, these data suggest neurons are an important site of ASIC1A action in fear‐related behaviors, whereas other behaviors likely depend on ASIC1A in other neurons or cell types not targeted in SynAsic1a KO mice. These findings highlight the need for further work to discern the roles of ASICs in specific cell types and brain sites.
L-Store: A Real-time OLTP and OLAP System

Arguably data is a new natural resource in the enterprise world with an unprecedented degree of proliferation. But to derive real-time actionable insights from the data, it is important to bridge the gap between managing the data that is being updated at a high velocity (i.e., OLTP) and analyzing a large volume of data (i.e., OLAP). However, there has been a divide where specialized solutions were often deployed to support either OLTP or OLAP workloads but not both; thus, limiting the analysis to stale and possibly irrelevant data. In this paper, we present Lineage-based Data Store (L-Store) that combines the real-time processing of transactional and analytical workloads within a single unified engine by introducing a novel lineage-based storage architecture. We develop a contention-free and lazy staging of columnar data from a write-optimized form (suitable for OLTP) into a read-optimized form (suitable for OLAP) in a transactionally consistent approach that also supports querying and retaining the current and historic data. Our working prototype of L-Store demonstrates its superiority compared to state-of-the-art approaches under a comprehensive experimental evaluation.
WRKY2/34–VQ20 Modules in Arabidopsis thaliana Negatively Regulate Expression of a Trio of Related MYB Transcription Factors During Pollen Development

Male gametogenesis in plants is tightly controlled and involves the complex and precise regulation of transcriptional reprogramming. Interactions between WRKY proteins and VQ motif-containing proteins are required to control these complicated transcriptional networks. However, our understanding of the mechanisms by which these complexes affect downstream gene expression is quite limited. In this study, we found that WRKY2 and WKRY34 repress MYB97, MYB101, and MYB120 expression during male gametogenesis. MYB expression was up-regulated in the wrky2-1 wrky34-1 vq20-1 triple mutant during male gametogenesis. The expression levels of six potential targets of the three MYBs increased the most in the wrky2-1 wrky34-1 vq20-1 triple mutant, followed by the wrky2-1 wrky34-1 double mutant, compared with in wild-type. Yeast one-hybrid and dual luciferase reporter assays indicated that WRKY2 and WRKY34 recognized the MYB97 promoter by binding to its W-boxes. MYB97 overexpression caused defects in pollen germination and pollen tube length, which impacted male fertility. Thus, WRKY2/34–VQ20 complexes appear to negatively regulate the expression of certain MYBs during plant male gametogenesis.