Miracle-dz/UniFAR-FTUs huggingface dataset:
Tandem internal models execute motor learning in the cerebellum

Significance The formation and dissolution of internal models of the human body occur in the cerebellum, and their logical organization is essential for normal motor learning. Here, we propose that the two known types of internal models, forward and inverse, are combined into one tandem model. We analyzed human motor learning, theoretically and experimentally, in a prism-adaptation hand-reaching task. Remarkably, the behavioral analyses and computational modeling converged to support the existence of a tandem model and reveal its unique features in cerebellum-based motor learning. For example, intentional misreaching induced covert learning in the tandem model. Finally, the tandem model was used to index the nature and degree of cerebellar damage in patients. In performing skillful movement, humans use predictions from internal models formed by repetition learning. However, the computational organization of internal models in the brain remains unknown. Here, we demonstrate that a computational architecture employing a tandem configuration of forward and inverse internal models enables efficient motor learning in the cerebellum. The model predicted learning adaptations observed in hand-reaching experiments in humans wearing a prism lens and explained the kinetic components of these behavioral adaptations. The tandem system also predicted a form of subliminal motor learning that was experimentally validated after training intentional misses of hand targets. Patients with cerebellar degeneration disease showed behavioral impairments consistent with tandemly arranged internal models. These findings validate computational tandemization of internal models in motor control and its potential uses in more complex forms of learning and cognition.
Footprint Placement for Mosaic Imaging by Sampling and Optimization

We consider the problem of selecting a small set (mosaic) of sensor images (footprints) whose union covers a twodimensional Region Of Interest (ROI) on Earth. We take the approach of modeling the mosaic problem as a Mixed-Integer Linear Program (MILP). This allows solutions to this subproblem to feed into a larger remote-sensor collection-scheduling MILP. This enables the scheduler to dynamically consider alternative mosaics, without having to perform any new geometric computations. Our approach to set up the optimization problem uses maximal disk sampling and point-in-polygon geometric calculations. Footprints may be of any shape, even non-convex, and we show examples using a variety of shapes that may occur in practice. The general integer optimization problem can become computationally expensive for large problems. In practice, the number of placed footprints is within an order of magnitude of ten, making the time to solve to optimality on the order of minutes. This is fast enough to make the approach relevant for near real-time mission applications.We provide open source software for all our methods, “GeoPlace. ”
On New Group Popularity Prediction in Event-Based Social Networks

Event-based social networks (EBSN) have recently emerged as an important complement to online social networks. They enjoy the advantages of both online social networks and offline social communities: offline social events can be conveniently organized online, and users interact with each other face-to-face in the organized offline events. Although previous work has shown that member and structural features are important to the future popularity of groups in the EBSN, it is not yet clear how different member roles and the interplay between them contribute to group popularity. In this paper, we study a real-world dataset from Meetup—a popular EBSN platform—and propose a deep-neural-network-based method to predict the popularity of new Meetup groups. Our method uses group-level features specific to EBSNs, such as time and location of events in a group, as well as the structural features internal to a group, such as the inferred member roles in a group and social substructures among members. Empirically, our approach reduces the normalized root-mean-squared error of the popularity prediction (measured in RSVPs) of a group's future events by up to 12%, against the state-of-the-art baselines. Through case studies, our method also identifies member and structure patterns that are most predictive of a group's future popularity. Our study provides new understanding about what makes a group successful in the EBSN.
GEsture: an online hand-drawing tool for gene expression pattern search

Gene expression profiling data provide useful information for the investigation of biological function and process. However, identifying a specific expression pattern from extensive time series gene expression data is not an easy task. Clustering, a popular method, is often used to classify similar expression genes, however, genes with a ‘desirable’ or ‘user-defined’ pattern cannot be efficiently detected by clustering methods. To address these limitations, we developed an online tool called GEsture. Users can draw, or graph a curve using a mouse instead of inputting abstract parameters of clustering methods. GEsture explores genes showing similar, opposite and time-delay expression patterns with a gene expression curve as input from time series datasets. We presented three examples that illustrate the capacity of GEsture in gene hunting while following users’ requirements. GEsture also provides visualization tools (such as expression pattern figure, heat map and correlation network) to display the searching results. The result outputs may provide useful information for researchers to understand the targets, function and biological processes of the involved genes.
Multimodal imaging of Bietti's crystalline dystrophy

Bietti's crystalline dystrophy (BCD) is a rare autosomal recessive retinal dystrophy characterized by deposition of crystals in the retina. The purpose of this article is to describe retinal abnormalities in BCD using multimodal imaging. An 18-year-old girl presented with decrease of vision and nyctalopia. She was assessed with color fundus picture, red-free photographs, short-wave autofluorescence, spectral-domain optical coherence tomography (OCT) and en face OCT and was diagnosed to have BCD based on typical presentation. Retinal crystals were better visualized on en face OCT as compared to conventional B scan OCT.
A method for principal components selection based on stochastic matrix

Principal component analysis (PCA) is a feature extraction method of mapping the original characteristic term space with high-dimensional sparsity into the low-dimensional principal component space, by utilizing the principle of variance maximization. In random matrix theory, principal components are treated as random variables, and data samples are the observations of random vectors. When there are a large amount of data samples, the observed values of principal components obey the normal distribution. We proposed a method of principal components selection based on the differential distribution of principal components in different classes. Experiment on text classification indicated that the method of principal components selection based on random matrix theory can effectively determine the principal components. This not only improves the classification performance, but also reduces the computational cost.
Characterizing Audience Engagement and Assessing Its Impact on Social Media Disclosures of Mental Illnesses

Self-disclosures of mental illnesses have been identified to yield coping and therapeutic benefits. An important construct in the self-disclosure process is the audience with whom the individual interacts and shares their experiences. Mental illness self-disclosures are increasingly happening online. However, unlike online support communities where the audience comprises sympathetic peers with similar experiences, what the discloser gains from an ‘invisible’ audience on a general purpose, public social media platform is less understood. Focusing on a highly stigmatized mental illness, schizophrenia, this paper provides the first investigation characterizing the audience of disclosures of this condition on Twitter and how the audience’s engagement impacts future disclosures. Our results are based on a rich year-long temporal analysis of the data of nearly 400 disclosers and their nearly 400 thousand audiences. First, characterizing and modeling the audience engagement temporally, we find evidence of reciprocity in the disclosure process between the discloser and their audience. Then, situating our work in the Social Penetration Theory and operationalizing the disclosure process via a measure of intimacy, an auto-regressive time series model indicates that the patterns of audience engagement and content can forecast changes in the intimacy of disclosures. We discuss the implications for building socially engaging, supportive online spaces for stigmatized mental illness disclosures.
Image de-noising based on association-prediction model

In contrast to existing image de-noising algorithms with the problems of threshold selection, parameter initialization, and model complexity, the method proposed in this paper is modeled on the fast and efficient characteristics of the optic nerve of human optic de-noising systems. Based on the association-prediction model, the image de-noising method abstracts and simulates the structure of the human brain nerve. It imports de-noising prior knowledge that is useful when the de-noising process separates the image into patches. Unlike generic image de-noising methods, the proposed method can preserve the high frequency characteristics of the image. Furthermore, it does not require the noise intensity or noise models to be estimated, thus, errors caused by noise estimation are avoided. Experiments were carried out on the simulated noisy images and real noisy images from surveillance cameras, and the results suggest that the proposed method outperforms traditional methods, proving its effectiveness and practicality.
Effects of Voluntary Wheel-Running Types on Hippocampal Neurogenesis and Spatial Cognition in Middle-Aged Mice

While increasing evidence demonstrated that voluntary wheel running promotes cognitive function, little is known on how different types of voluntary wheel running affect cognitive function in elderly populations. We investigated the effects of various voluntary wheel-running types on adult hippocampal neurogenesis and spatial cognition in middle-aged mice. Male C57BL6 and Thy1-green fluorescent protein (GFP) transgenic mice (13 months) were equally assigned to one of the following groups: (1) T1: no voluntary wheel running; (2) T2: intermittent voluntary wheel running; and (3) T3: continuous voluntary wheel running. The Thy1-GFP transgenic mice were used to specifically label granule cells, since Thy-1 is a promoter for neuronal expression. Behavioral evaluations suggested that intermittent voluntary wheel running improved Morris water maze performance in middle-aged mice. The number of BrdU-positive cells was significantly higher in both intermittent and continuous voluntary wheel running compared with no voluntary wheel running. However, only intermittent voluntary wheel running facilitated the newborn cells to differentiate into granule cells, while newborn cells tended to differentiate into astrocytes and repopulation of microglia was also enhanced in the continuous voluntary wheel-running group. These results indicated that intermittent voluntary exercise may be more beneficial for enhancing spatial memory. Effective improvement of hippocampal neurogenesis was also caused by intermittent voluntary wheel running in middle-aged mice.
Research on the key technology of big data service in university library

With the development of information technology of university library, the mass data of the university library has the basic characteristics of Big Data. However, the current situation of the university library is the lack of distributed storage and computing model for massive data, the lack of capacity to handl the diverse data sources, including the structured, semi-structured and unstructured data, the lack of a simple, flexible application model of big data service. In order to solve the problems in the service innovation of University Libraries in China, such as the problem of distributed storage and computation of massive data, the distributed management of diverse data sources, the simple and flexible application of big data services, this paper analyzes the research contents of big data processing, Hadoop ecosystem and the demand for big data services in University Libraries, and presents a technology framework for big data service in University Libraries based Hadoop. The framework includes the distributed storage and parallel computing model of mass data, the distributed management model of diverse data sources and the model of diversified service application for university libraries. This framework takes full account of the service innovation change of University Library under the environment of big data, such as data storage and calculation, data management and service applications et al. It can solve the key technical problems of big data service of University Library in a certain extent.
Robustness improvement of short-circuit capability by SiC trench-etched double-diffused MOS (TED MOS)

A 3.3-kV SiC trench-etched double-diffused MOS (TED MOS) is designed and fabricated for robust short-circuit (SC) capability. Because of its low-Vover (Vg — Vth) operation, the TED MOS successfully reduces the drain current in saturation region to less than 700 A/cm2 at SC tests. The low drain current in a saturation region enhances the SC capability of the TED MOS. As a result, the SC endurance time of the TED MOS is 2.8 times longer than that of the conventional SiC DMOS.
Fast image dehazing under driving scenario

Influenced by the bad weather, such as haze, the images acquired from computer vision systems are seriously degraded, especially under driving scenario. The image features such as contrast and color will change, which makes intelligent transportation systems (e.g., auxiliary drive systems, automatic pilot systems) fail to extract image features, and even leads to traffic accidents. Therefore, it is significant to propose an effective and fast image dehazing method to improve the reliability of these systems. In this paper, we propose a novel fast image dehazing algorithm under driving scenario based on the dark channel prior. On the one hand, according to the image features of driving scenario, we simplify the process of estimating the atmospheric light. And on the other hand, in the process of refining the transmission map, we introduce the fast guided filter to our algorithm. Both of the two measures speed up the dehazing process obviously as well as keep a good dehazing effect. Demonstrated by the experiment results, our algorithm achieves a good dehazing effect and a fast speed, and meets the real-time requirement under driving scenario.
Computation of synchronizing sequences for a class of 1-place-unbounded synchronized Petri nets

Identification of a final state after red a test is one of the fundamental testing problems for discrete event systems and synchronizing sequences represents a conventional solution to this problem. In this paper, we consider systems modeled by a special class of synchronized Petri nets, called 1-place-unbounded, that contain a single unbounded place. The infinite reachability spaces of such nets can be characterized by two types of finite graphs, called improved modified coverability graph and weighted automata with safety conditions. In case these two finite graphs are deterministic, we develop novel computation algorithms for synchronizing sequences for this class of nets by decomposing the finite graphs into strongly connected components.
Visual Tracking with Multilevel Sparse Representation and Metric Learning

Visual﻿tracking﻿arises﻿in﻿various﻿real-world﻿tasks﻿where﻿an﻿object﻿should﻿be﻿located﻿in﻿a﻿video. ﻿Sparse﻿ representation﻿can﻿implement﻿tracking﻿problems﻿by﻿linearly﻿representing﻿object﻿with﻿a﻿few﻿templates. ﻿ However,﻿this﻿approach﻿has﻿two﻿main﻿shortcomings. ﻿Namely,﻿setting﻿the﻿templates﻿updating﻿frequency﻿ is﻿difficult﻿and﻿meanwhile﻿it﻿is﻿relatively﻿weak﻿in﻿distinguishing﻿the﻿object﻿from﻿the﻿background. ﻿ For﻿solving﻿these﻿problems,﻿the﻿author﻿models﻿a﻿multilevel﻿object﻿template﻿set﻿that﻿can﻿be﻿stratified﻿ by﻿different﻿updating﻿time﻿spans. ﻿The﻿hierarchical﻿structure﻿and﻿updating﻿strategy﻿promise﻿the﻿ real-timeness,﻿stability,﻿and﻿diversity﻿of﻿object﻿template. ﻿Additionally,﻿metric﻿learning﻿is﻿combined﻿ to﻿evaluate﻿the﻿object﻿candidates﻿and﻿thereby﻿improve﻿the﻿discriminative﻿ability. ﻿Experiments﻿on﻿ well-known﻿visual﻿tracking﻿datasets﻿demonstrate﻿that﻿the﻿proposed﻿method﻿can﻿track﻿an﻿object﻿more﻿ robustly﻿and﻿accurately﻿compared﻿to﻿the﻿state-of-the-art﻿approaches.
An adapted vision measurement method for package volume based on Kinect

In this paper, we present a method for measuring package volume with multi-source vision sensor — Kinect. Firstly, the foreground segmentation and the algorithm of minimum-area bounding rectangle are used to computed the package dimensions. Secondly, the height of package is obtained by background subtraction method. Finally, the package volume is calculated by integration method. The experimental results show that the relative error is less than 4.10%, which improves the efficiency and accuracy of package volume measurement. This method has the features of non-contact and non-stop run. It can be adapted to different applications.
Left temporal lobe language network connectivity in temporal lobe epilepsy

Impairment of naming function is a critical problem for temporal lobe epilepsy patients, yet the neural correlates of the disruption of temporal lobe language networks are poorly understood. Using functional MRI, we investigated the activation and task-related functional connectivity of left temporal lobe language networks and their relation to clinical naming performance and disease characteristics. We studied 59 adult patients with temporal lobe epilepsy (35 left temporal lobe epilepsy) and 32 healthy controls with auditory and visual naming functional MRI tasks. Time series of activation maxima in the left posterior inferior temporal lobe were extracted to create a psychophysiological interaction regressor for subsequent seed-based whole-brain task-related functional connectivity analyses. Correlational analyses were performed to assess the association of functional MRI activation and functional connectivity with clinical naming scores, age of onset of epilepsy, and duration of epilepsy. Auditory naming elicited activation in the left posterior inferior temporal gyrus and visual naming in the left fusiform gyrus across all groups. Activations in the left inferior temporal gyrus, left thalamus and left supplementary motor region during auditory naming as well as left fusiform activations during picture naming correlated with better clinical naming performance. Functional connectivity analyses indicated coupling of left posterior inferior temporal regions to bilateral anterior and posterior temporal lobe regions and the bilateral inferior precentral gyrus as well as contralateral occipital cortex. Stronger functional connectivity was associated with better clinical naming performance in all groups. In patients with left temporal lobe epilepsy only, functional connectivity increased with later age of onset of epilepsy and shorter disease duration. This suggests that onset of seizures early in life and prolonged disease duration lead to disrupted recruitment of temporal lobe networks ipsilateral to the seizure focus, which might account for naming deficits in temporal lobe epilepsy.
Photonuclear Effect on ITER Operation

In this study, neutron and gamma radiation produced during the ITER tokamak operation including the nuclear heating were studied. This is an approach for analyzing the neutron flux by their contributions. The calculations were conducted using Geant4 Monte Carlo method based on the ITER simplified geometry. It was found that for present design of ITER, only 0.1% of neutrons is contributed by photonuclear interaction while, 33% belongs to the (n, xn) reaction. Finally by adding Beryllium element in the coolant leading to the increase in the neutron flux by increasing the rate of (γ, n) reactions was calculated.
A Crucial Caste Regulation Gene Detected by Comparing Termites and Sister Group Cockroaches

Acquisition of a sterile caste is a key step in insect eusocial evolution; however, the molecular mechanisms associated with sterile caste development are unclear. To help resolve the issue, Masuoka et al. focused on soldiers—the first acquired. Sterile castes are a defining criterion of eusociality; investigating their evolutionary origins can critically advance theory. In termites, the soldier caste is regarded as the first acquired permanently sterile caste. Previous studies showed that juvenile hormone (JH) is the primary factor inducing soldier differentiation, and treatment of workers with artificial JH can generate presoldier differentiation. It follows that a shift from a typical hemimetabolous JH response might be required for soldier formation during the course of termite evolution within the cockroach clade. To address this possibility, analysis of the role of JH and its signaling pathway was performed in the termite Zootermopsis nevadensis and compared with the wood roach Cryptocercus punctulatus, a member of the sister group of termites. Treatment with a JH analog (JHA) induced a nymphal molt in C. punctulatus. RNA interference (RNAi) of JH receptor Methoprene tolerant (Met) was then performed, and it inhibited the presoldier molt in Z. nevadensis and the nymphal molt in C. punctulatus. Knockdown of Met in both species inhibited expression of 20-hydroxyecdysone (20E; the active form of ecdysone) synthesis genes. However, in Z. nevadensis, several 20E signaling genes were specifically inhibited by Met RNAi. Consequently, RNAi of these genes were performed in JHA-treated termite individuals. Knockdown of 20E signaling and nuclear receptor gene, Hormone receptor 39 (HR39/FTZ-F1β) resulted in newly molted individuals with normal worker phenotypes. This is the first report of the JH–Met signaling feature in termites and Cryptocercus. JH-dependent molting activation is shared by both taxa and mediation between JH receptor and 20E signalings for soldier morphogenesis is specific to termites.
Do Words with Certain Part of Speech Tags Improve the Performance of Arabic Text Classification?

Feature extraction - the process of choosing feature types that can represent and discriminate between dataset topics - is one of the critical steps in text classification and varies with the language of the texts. Different feature types have been proposed for Arabic text classification, ranging from features based on word orthography (single word and character and word N-grams) to features based on linguistic analysis (roots, stems). To the best of our knowledge, little attention has been paid to investigating the performance of Arabic text classification when Part of Speech (POS) tagging information is used to extract features. In this study, we used a corpus comprising 4900 newspaper texts distributed evenly over seven topics to investigate the effect of using POS tag distribution and words that belong to certain POS tags on Arabic text classification, namely nouns, verbs and adjectives. For feature selection, feature representation and text classification we used Chi-squared, Log-Weighted Term Frequency Inverse Document Frequency with Cosine Normalization (LTC) and support vector machine (SVM) respectively. We used four metrics, namely accuracy, precision, recall and F-measure to measure classification performance. Experiment data suggest that the words achieved the best classification performance when the number of features was low; however, the classification performance can be marginally increased when nouns, verbs and adjectives are used as features, given that the number of features is increased.