Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervised Learning in SNN via Reward-Modulated Spike-Timing-Dependent Plasticity for a Target Reaching Vehicle

Spiking neural networks (SNNs) offer many advantages over traditional artificial neural networks (ANNs) such as biological plausibility, fast information processing, and energy efficiency. Although SNNs have been used to solve a variety of control tasks using the Spike-Timing-Dependent Plasticity (STDP) learning rule, existing solutions usually involve hard-coded network architectures solving specific tasks rather than solving different kinds of tasks generally. This results in neglecting one of the biggest advantages of ANNs, i.e., being general-purpose and easy-to-use due to their simple network architecture, which usually consists of an input layer, one or multiple hidden layers and an output layer. This paper addresses the problem by introducing an end-to-end learning approach of spiking neural networks constructed with one hidden layer and reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) synapses in an all-to-all fashion. We use the supervised reward-modulated Spike-Timing-Dependent-Plasticity learning rule to train two different SNN-based sub-controllers to replicate a desired obstacle avoiding and goal approaching behavior, provided by pre-generated datasets. Together they make up a target-reaching controller, which is used to control a simulated mobile robot to reach a target area while avoiding obstacles in its path. We demonstrate the performance and effectiveness of our trained SNNs to achieve target reaching tasks in different unknown scenarios.
Voluntary physical activity prevents insulin resistance in a tissue specific manner

Physical inactivity and a sedentary lifestyle are risk factors for the development of type 2 diabetes. Here, we identified the effects 8 weeks of voluntary physical activity had on the prevention of insulin resistance in mouse skeletal muscles and liver (a hallmark of T2D). To do this, 8 week old C57BL/6J mice with (RUN) and without (SED) voluntary access to running wheels were fed a standard rodent chow ad libitum for 8 weeks. In the liver, there was a 2.5‐fold increase in insulin stimulated AktSER473 phosphorylation, and a threefold increase in insulin‐stimulated (0.5 U/kg) GSK3βSER9 phosphorylation in RUN compared to SED mice. Although not induced in skeletal muscles, there was a twofold increase in SOCS3 expression in SED compared to RUN mice in the liver. There was no difference in the glucose tolerance test between groups. This study was the first to show differences in liver insulin sensitivity after 8 weeks of voluntary physical activity, and increased SOCS3 expression in the liver of sedentary mice compared to active mice. These findings demonstrate that even in young mice that would normally be considered healthy, the lack of physical activity leads to insulin resistance representing the initial pathogenesis of impaired glucose metabolism leading to type 2 diabetes.
A semantics of Security Protocol Language (SPL) using a class of composable high-level Petri nets

This paper aims at introducing a Petri net semantics of security protocols allowing to study their properties formally. This is obtained by means of an economic but expressive class of composable high-level Petri nets, called S-nets, inspired from works about the relationship between Petri nets and process algebras. S-nets are applied then to give a compositional high-level Petri net semantics to SPL The Needham-Schroder protocol is employed to illustrate how this semantics can be used in order to establish the violation of the authentication property.
Catch me if you can: performance bug detection in the wild

Profilers help developers to find and fix performance problems. But do they find performance bugs -- performance problems that real users actually notice? In this paper we argue that -- especially in the case of interactive applications -- traditional profilers find irrelevant problems but fail to find relevant bugs. We then introduce lag hunting, an approach that identifies perceptible performance bugs by monitoring the behavior of applications deployed in the wild. The approach transparently produces a list of performance issues, and for each issue provides the developer with information that helps in finding the cause of the problem. We evaluate our approach with an experiment where we monitor an application used by 24 users for 1958 hours over the course of 3-months. We characterize the resulting 881 issues, and we find and fix the causes of a set of representative examples.
Diagnosis and Treatment of Adrenocortical Oncocytoma: Case Report of Five Cases and Review of the Literature

Objective: To investigate the diagnosis and treatment of adrenocortical oncocytoma, and have a literature of review. Materials and Methods: The clinical data of 5 cases of adrenocortical oncocytoma treated in our hospital was retrospectively analyzed. The clinical manifestations, imaging examination, endocrine examination, and pathological results were analyzed respectively. Results: Oncocytic adrenocortical neoplasms are extremely rare. Oncocytic adrenocortical neoplasms are usually discovered incidentally, only the tumors with endocrine function could exhibit specific manifestations. No specific imageological features of oncocytic adrenocortical neoplasms have been found. Conclusions: The diagnosis of adrenocortical oncocytoma mainly depends on the pathological examination. Surgical resection is the main treatment method.
Exploring Changes in Two Types of Self-Efficacy Following Participation in a Chronic Disease Self-Management Program

Chronic conditions and falls are related issues faced by many aging adults. Stanford’s Chronic Disease Self-Management Program (CDSMP) added brief fall-related content to the standardized 6-week workshop; however, no research had examined changes in Fall-related self-efficacy (SE) in response to CDSMP participation. This study explored relationships and changes in SE using the SE to manage chronic disease scale (SEMCD Scale) and the Fall Efficacy Scale (FallE Scale) in participants who successfully completed CDSMP workshops within a Southern state over a 10-month period. SE scale data were compared at baseline and post-intervention for 36 adults (mean age = 74.5, SD = ±9.64). Principal component analysis (PCA), using oblimin rotation was completed at baseline and post-intervention for the individual scales and then for analysis combining both scales as a single scale. Each scale loaded under a single component for the PCA at both baseline and post-intervention. When both scales were entered as single meta-scale, the meta-scale split along two factors with no double loading. SEMCD and FallE Scale scores were significantly correlated at baseline and post-intervention, at least p < 0.05. A significant proportion of participants improved their scores on the FallE Scale post-intervention (p = 0.038). The magnitude of the change was also significant only for the FallE Scale (p = 0.043). The SEMCD Scale scores did not change significantly. Study findings from the exploratory PCA and significant correlations indicated that the SEMCD Scale and the FallE Scale measured two distinct but related types of SE. Though the scale scores were correlated at baseline and post-intervention, only the FallE Scale scores significantly differed post-intervention. Given this relationship and CDSMP’s recent addition of a 10-min fall prevention segment, further exploration of CDSMP’s possible influence on Fall-related SE would provide useful understanding for health promotion in aging adults.
School Bullying and Social and Moral Orders

This article provides a theoretical consideration of the ways in which school bullying relates to social and moral orders and the relations of power that are central to the upholding of such orders. Moving away from the focus on individual aggressive intentionality that has hitherto dominated school bullying research, the article argues that understanding the social processes of bullying requires not only understanding bullying as a group interactional process but also how such interaction is part of power relations within both the immediate context of the school and the wider society.
Bovine immunodeficiency virus and bovine leukemia virus and their mixed infection in Iranian Holstein cattle.

INTRODUCTION Bovine immunodeficiency virus (BIV) and bovine leukemia virus (BLV) have worldwide distributions, but their prevalences in Iran are unknown. We investigated the presence of infections in Iranian Holstein cattle and determined changes in hematological values for infected animals. METHODOLOGY Nested PCR was used on blood samples from 143 animals Holstein cattle to detect proviral BIV and BLV gag sequences. Flow cytometric analysis was performed using monoclonal antibodies (mAbs) against CD4, CD8, and CD21 bovine T lymphocyte subsets. RESULTS Proviral BIV and BLV gag sequences were detected in 20.3% and 17% of the animals, respectively. BIV-BLV confection was also detected in 4.2% of the study population but this was not statistically significant. Flow cytometric analysis showed that both BIV-infected cows and non-infected ones had CD4/CD8 ratios of 2.45 and 1.43, respectively, and this difference was significant. BLV infected and non-infected animals had no significant differences in their CD4/CD8 ratio. In comparison to non-infected cattle, those with both BIV and BLV had a significant decrease in their CD4/CD8 ratios (1.5 % vs. 2.3; P = 0.01). CONCLUSION This is the first report of BIV and BLV infections in Iran. We found no evidence that infection with one agent predisposed an animal to infection with the other. BIV infection may have a role in decreasing T CD8 counts, but this may depend on the genetics of the cattle and virus strains involved.
A Process of Change

Abstract The GLBT individual experiences familial and societal constraints that can impede and disrupt personal and relational health. This article explores those constraints and the impact within the family of origin. It also addresses issues surrounding the GLBT individual's awareness and discovery of sexual orientation and gender identity and the decision-making process surrounding disclosure. The family process of change, through adaptation, transition, and sometimes transformation, also is outlined. Clinical implications and future trends in research are included.
Dual Convolutional Neural Networks With Attention Mechanisms Based Method for Predicting Disease-Related lncRNA Genes

A lot of studies indicated that aberrant expression of long non-coding RNA genes (lncRNAs) is closely related to human diseases. Identifying disease-related lncRNAs (disease lncRNAs) is critical for understanding the pathogenesis and etiology of diseases. Most of the previous methods focus on prioritizing the potential disease lncRNAs based on shallow learning methods. The methods fail to extract the deep and complex feature representations of lncRNA-disease associations. Furthermore, nearly all the methods ignore the discriminative contributions of the similarity, association, and interaction relationships among lncRNAs, disease, and miRNAs for the association prediction. A dual convolutional neural networks with attention mechanisms based method is presented for predicting the candidate disease lncRNAs, and it is referred to as CNNLDA. CNNLDA deeply integrates the multiple source data like the lncRNA similarities, the disease similarities, the lncRNA-disease associations, the lncRNA-miRNA interactions, and the miRNA-disease associations. The diverse biological premises about lncRNAs, miRNAs, and diseases are combined to construct the feature matrix from the biological perspectives. A novel framework based on the dual convolutional neural networks is developed to learn the global and attention representations of the lncRNA-disease associations. The left part of the framework exploits the various information contained by the feature matrix to learn the global representation of lncRNA-disease associations. The different connection relationships among the lncRNA, miRNA, and disease nodes and the different features of these nodes have the discriminative contributions for the association prediction. Hence we present the attention mechanisms from the relationship level and the feature level respectively, and the right part of the framework learns the attention representation of associations. The experimental results based on the cross validation indicate that CNNLDA yields superior performance than several state-of-the-art methods. Case studies on stomach cancer, lung cancer, and colon cancer further demonstrate CNNLDA's ability to discover the potential disease lncRNAs.
Predicting Change: A Study of the Value Frequency Model for Change of Practice

Information systems (IS) researchers have made considerable progress on defining and formalizing structured methods to support collaborative development of information systems. Concepts and methods for transferring IS artifacts to practitioners in ways that give rise to sustained use nonetheless remain more an art than a science. A better understanding of the mechanisms that give rise to willingness or unwillingness to change could help IS developers predict whether intended users would be willing to embrace a new system if it were offered to them. Such understanding might also help IS researchers to develop more effective and reliable deployment concepts and methods for their solutions. This paper reports the findings of an action research study of change-of-work-practice among 17 groups at the headquarters of a 3,000-person organization. All of the groups considered changing to a new project-tracking-and-status system which supported a new collaborative project management approach. Five of the groups adopted the new system while 12 either did not adopt, or adopted but then abandoned, the new IS. The goal of this study was to explore whether the change-of-practice behaviors and choices that manifested in the field were consistent or inconsistent with the Value Frequency Model (VFM), a new causal theory for willingness-to-change that emerged from the Collaboration Engineering literature. Observed outcomes were consistent with the theory, which suggests that the constructs and relationships proposed by the VFM may provide useful insights to help explain and predict willingness to change to new information technologies and the work practices in which they are embedded.
Ultra-Wideband Positioning System Based on ESP32 and DWM3000 Modules

In this paper, an Ultra-Wideband (UWB) positioning system is introduced, that leverages six identical custom-designed boards, each featuring an ESP32 microcontroller and a DWM3000 module from Quorvo. The system is capable of achieving localization with an accuracy of up to 10 cm, by utilizing Two-Way-Ranging (TWR) measurements between one designated tag and five anchor devices. The gathered distance measurements are subsequently processed by an Extended Kalman Filter (EKF) running locally on the tag board, enabling it to determine its own position, relying on fixed, a priori known positions of the anchor boards. This paper presents a comprehensive overview of the systems architecture, the key components, and the capabilities it offers for indoor positioning and tracking applications.
KLF11 is an Epigenetic Mediator of DRD2/Dopaminergic Signaling in Endometriosis

Endometriosis is a heterogeneous, recalcitrant disease that affects 10% of reproductive-age women. Resistance to conventional therapy critically raises the need for novel treatment options that target specific, dysregulated underlying molecular mechanisms. Dopamine receptor 2 (DRD2) has been shown to be associated with vascularity and fibrosis in endometriosis. Transcription factor KLF11 has been implicated in the pathogenesis of several human endocrine and reproductive tract diseases including endometriosis. KLF11 recruits epigenetic cofactors for regulation of target genes; dysregulation of critical target genes and associated signaling pathways results in diverse disease phenotypes. KLF11 regulates the expression of DRD2 in neurons. We investigated the regulation of DRD2 by KLF11 in the established eutopic and ectopic endometrial cell lines as well as in an animal model of endometriosis. KLF11 binding and activation of the DRD2 promoter was conserved across species. Promoter activation was reflected in correspondingly increased gene expression in an endometrial cell line and in primary endometriotic cells. In vivo, disease relevance was further evaluated in a surgically induced murine endometriotic model using Klf11−/− and wild-type mice. Consistent with loss of Klf11-mediated activation, lesions in Klf11−/− animals were associated with progressive fibrosis and decreased Drd2 expression. KLF11 binds specific epigenetic corepressors to repress several target genes. Activation of DRD2 by KLF11 could not be explained simply by loss of corepressor binding and is thus likely due to selective coactivator recruitment; identification of the precise pathway is the focus of ongoing investigation. Characterization of pharmacologically reversible epigenetic regulatory mechanisms has translational relevance in health and disease.
An Investigation on Impact of Free Primary Education on Quality of Education in Kenya Primary Schools

Primary education in Kenya has become a constitutional right for every school going child. This was in fulfillment of international obligations like Education for All by 2015, in order to achieve this Kenya government introduced Free Primary Education (FPE) in 2003 to achieve universal primary education (UPE) by 2005. This noble policy in Kenya benefited 1.3 million school going children who would not have afforded many levies charged by primary schools. This notwithstanding the programme was implemented without proper preparation compromising the quality of education. The study looked at the implication of text book ratio, teacher’s pupils’ ratio and teachers’ perception on the quality of FPE in Kenya. The study used survey design whereby quantitative and qualitative data was generated. The target population was 71 public primary schools in Embu East Sub- County in Kenya. Simple random sampling procedure was used to select a sample of 30% of target population. The instruments used included open and closed ended questionnaires which were administered by the researcher to the respondents mainly schools head teachers and teachers. The data was analyzed through statistical programme where percentages were got and deductions made. The study found out that FPE had improved enrolment by 68%. Over 50% reported that the text book ratio was 1:3, teacher pupil ratio was observed to be more than 1:40 in 31.8% of the sampled cases and the perception on quality was found to be good at 86.4% of the respondents. The findings recommended adequate funds should be availed for FPE programme to ensure adequate teachers and textbooks to improve quality of education in Kenya Primary schools. Keywords : Free Primary Education (FPE), quality education, teacher-pupil ratio, text book ratio perception, pupil, teacher, head teacher
Inhibition of E2F‐mediated transcription by p202.

Many of the antimicrobial, immunomodulatory and cell growth inhibitory activities of the interferons are mediated by interferon‐inducible proteins. Earlier we characterized an interferon‐inducible murine protein, p202, whose expression in transfected cells inhibits cell proliferation and which can form a complex with retinoblastoma protein (pRb). Here we report that in transfected cells expression of p202 inhibits E2F‐stimulated transcription of a reporter gene and of endogenous genes. Inhibition of the transcriptional activity of E2F by p202 does not depend on fully functional pRb and is correlated with inhibition of the sequence‐specific DNA binding of E2F. p202 interacts with the transcription factor E2F (E2F‐1/DP‐1) in vitro and in vivo. Inhibition of E2F activity by p202 may contribute to growth inhibition by the interferons.
Time-Frequency Jointed Imperceptible Adversarial Attack to Brainprint Recognition with Deep Learning Models

EEG-based brainprint recognition with deep learning models has garnered much attention in biometric identification. Yet, studies have indicated vulnerability to adversarial attacks in deep learning models with EEG inputs. In this paper, we introduce a novel adversarial attack method that jointly attacks time-domain and frequency-domain EEG signals by employing wavelet transform. Different from most existing methods which only target time-domain EEG signals, our method not only takes advantage of the time-domain attack's potent adversarial strength but also benefits from the imperceptibility inherent in frequency-domain attack, achieving a better balance between attack performance and imperceptibility. Extensive experiments are conducted in both white- and grey-box scenarios and the results demonstrate that our attack method achieves state-of-the-art attack performance on three datasets and three deep-learning models. In the meanwhile, the perturbations in the signals attacked by our method are barely perceptible to the human visual system.
Design and implementation of video image acquisition and processing system based on CPLD

A system of image acquisition and processing for video signal is introduced in this paper. This system is based on CPLD, it includes CCD camera, video decoding chip SAA7111 and FIFO memory. The system could complete the image acquisition, processing and display, and satisfy most of the applications on video image acquisition and processing by some modifications. This paper discusses the systemic entire structure, the hardware and software design scheme. The experimental results are also given.
Action Functional as Early Warning Indicator in the Space of Probability Measures

In neuroscience, scientists have some hypotheses on the brain's critical dynamics, which means the brain may stay in a phase transition state between ordered and disordered activities. Some tipping points, as a past of no return, can be critical for neural diseases. Therefore, a key question is how the critical brain hypothesis relates to pathological conditions compared with normal brain functionality. Action functional between two meta-stable states in stochastic dynamical systems is a good tool to study the critical transition and tipping. Here we extend the conventional Onsager-Machlup action functional of finding the most probable transition pathway to be looking for the evolutionary transition dynamics between two meta-stable invariant sets. Hence a rich theory from Schr\"odinger Bridge and Optimal Transport is brought in to solve this problem. Furthermore, different from various early warning indicators via statistics, bifurcation theory, information theory, statistical physics, topology, graph theory, we propose a novel viewpoint of early warning indicators in the space of probability measure, which facilitates us to build indicators based on action functional. To validate our framework, we apply this methodology to a Morris-Lecar model introduced to analyze the transition dynamics between a meta-stable state and the homo-clinic orbit. Besides, the real Alzheimer's data from ADNI database is also investigated to study the early warning signals of transition from healthy to pre-AD states. This framework not only extends the transition path to be pathway measures between two given densities on invariant sets but also shows potential ability for early warning indicators or biomarkers in complex diseases.