Miracle-dz/UniFAR-FTUs huggingface dataset:
Optogenetic “low‐theta” pacing of the septohippocampal circuit is sufficient for spatial goal finding and is influenced by behavioral state and cognitive demand

Hippocampal theta oscillations show prominent changes in frequency and amplitude depending on behavioral state or cognitive demands. How these dynamic changes in theta oscillations contribute to the spatial and temporal organization of hippocampal cells, and ultimately behavior, remain unclear. We used low‐theta frequency optogenetic stimulation to pace coordination of cellular and network activity between the medial septum (MS) and hippocampus during baseline and MS stimulation while rats were at rest or performing a spatial accuracy task with a visible or hidden goal zone. Hippocampal receptivity to pan‐neuronal septal stimulation at low‐theta frequency was primarily determined by speed and secondarily by task demands. Competition between artificial and endogenous field potentials at theta frequency attenuated hippocampal phase preference relative to local theta, but the spike‐timing activity of hippocampal pyramidal cells was effectively driven by artificial septal output, particularly during the hidden goal task. Notwithstanding temporal reorganization by artificial theta stimulation, place field properties were unchanged and alterations to spatial behavior were limited to goal zone approximation. Our results indicate that even a low‐theta frequency timing signal in the septohippocampal circuit is sufficient for spatial goal finding behavior. The results also advance a mechanistic understanding of how endogenous or artificial somatodendritic timing signals relate to displacement computations during navigation and spatial memory.
Research Trend on the Use of IT in Digital Addiction: An Investigation Using a Systematic Literature Review

Despite the negative role of IT in digital addiction development, IT may have a positive role in dealing with digital addiction. The present study undertakes a systematic literature review to explore the state of play and the trend regarding the use of IT in digital addiction research. Using predefined keywords, the Scopus database was searched for relevant literature published from 2017 to 2020. The initial search found 1655 papers. Six stages of study selection were completed using a set of inclusion and exclusion criteria. The study selection and quality assessment process were applied, then 15 papers were selected for further review. The results show that addiction detection using IT is the most researched topic in digital addiction research. The most commonly used IT in the selected studies are AI methods and biosignal recording systems. Various approaches in detection, prevention, and intervention are suggested in the selected studies. The advantages and limitations of each approach are discussed. Based on these results, some future research directions are suggested.
Teacher candidate perceptions of the edTPA in physical and health education

ABSTRACT The purpose of this exploratory case study was to investigate physical and health education teacher candidate’s perceptions of factors influencing effective implementation of edTPA at one teacher preparation program in the Southeastern United States. The participants were six physical and health education teacher candidates who had recently completed the edTPA portfolio. In line with the principles of case study methodology data were collected through the application of a Qualtrics survey, followed by focus group and one-on-one interviews. NVivo 11 Pro software package was employed to analyse data using analytic induction and constant comparison techniques. The analysis revealed that the factors that influenced participant experiences of the edTPA fell into four themes: (a) tandem cooperating teachers and teacher candidates’ edTPA learning, (b) essential faculty support, (c) boot camp workshop support, and (d) effective mock submissions. The study reveals that there are a number of strategic interventions that can improve the effectiveness of edTPA programs. These include, but are not limited to, targeted training for cooperating teachers, involving physical and health education faculty in boot camp workshops, and assigning mock edTPA assignments during methods courses.
Phytochemical screening, total phenolic content, antioxidant, and cytotoxic activity of five peruvian plants on human tumor cell lines

Background: Cancer is considered a serious public health problem in the world; searching alternative treatments of medicinal plants constitute a promissory field to find new anticancer drugs. Objective: The aim of this study is to evaluate the phytochemical screening, total phenolic content (TPC), antioxidant, and cytotoxic activity of ethanol extracts of Waltheria ovata (WO), Piper aduncum (PA), Myrciaria dubia (MD), Physalis peruviana (PP), and Geranium dielsianum (GD) on human tumor cell lines. Materials and Methods: Phytochemical screening was assessed using chemical reactives. TPC was developed using Folin Ciocalteu reactive, and the antioxidant activity was determined against 2,2-diphenyl-1-picrylhydrazyl (DPPH), and 2,2-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radicals by spectrophotometry. The cytotoxic activity was determined on human tumor cell lines followed as: MCF-7, H-460, HT-29, M-14, K-562, and DU-145. Results: Phytochemical study confirmed flavonoids and phenolic compounds in all extracts. TPC was found to be the highest in WO extract (1250 mg of gallic acid equivalent/g of dried extract) rather than other extracts. The highest antioxidant activity was stablished in WO extract for DPPH and ABTS radical scavenging tests (inhibitory concentration [IC50] = 0.89 ± 0.01 μg/mL, IC50 =4.20 ± 1.50 μg/mL). Ethanolic extracts (μg/mL) showed low cytotoxicity on human tumor cell lines (CI50>20 μg/mL) for PA, PP, GD, MD. Meanwhile, WO presented cytotoxicity on H460, MCF-7, and K562 tumor cell lines. Conclusion: In our findings, WO and MD extracts revealed a high antioxidant and TPC. WO exhibited better cytotoxic effect compared with 5-FU. Hence, these medicinal plants could be effective to prevent cancer disease and oxidative stress. Abbreviations Used: IC50: Half inhibitory concentration. TPC: Total phenolic content. DPPH: ,2-Diphenyl-1-picrylhydrazyl. ABTS: 2,2-Azino-bis (3-ethylbenzothiazoline-6-sulfonic acid). AA: Ascorbic acid.
Somaclonal variation in the in vitro regenerated pineapple (Ananas comosus): investigation of the cellular characteristics, biochemical specificities and ISSR markers

Several parameters of the in vitro regenerated, grown and adapted to the greenhouse plantlets were studied and compared (P ≤ 0.05). The mean length of stem and leaves of the in vitro regenerated pineapple significantly decreased under greenhouse conditions. The chlorophyll pigments content in the regenerated plantlets was increased and carotenoid, anthocyanins, carbohydrate, total phenols, and bromelain showed a relatively lower amount in the in vitro plantlets. Cluster analysis was performed on the basis of Dice genetic distance matrix for the thirty regenerated plants, as well as the mother plant. On the basis of the Neighbor Joining Cluster the plantlets were grouped into three main clusters. AMOVA test showed that 46% of the genetic variance is within the groups, while 54% of the rest belongs in-between the three groups. On the basis of this analysis a significant genetic difference was observed within the regenerated plantlets (P=0.01).
Time-Dependent Global Nonsingular Fixed-Time Terminal Sliding Mode Control-Based Speed Tracking of Permanent Magnet Synchronous Motor

This paper studies global nonsingular fixed-time terminal sliding mode control (GNFTSMC) for a second-order uncertain permanent magnet synchronous motor (PMSM) system to further improve its speed tracking performance. The newly proposed GNFTSMC consists of a time-dependent terminal sliding surface and a piecewise continuous sliding mode control law. By a time-dependent function constructed from the initial conditions of the system and a predefined time, the sliding surface is always reached at the initial instant and forced to a traditional fast terminal sliding surface after the predefined time. Based on Filippov’s stability principles, the globally fixed-time stability of the GNFTSMC is proved. Furthermore, a priori time independent of the initial conditions is derived to estimate the boundary of the settling time of the closed control loop. Then, the control law is analyzed to be always nonsingular. Thus, the GNFTSMC-based speed controller for the PMSM speed tracking system is developed. Finally, simulations are conducted for the proposed controller and other terminal sliding mode controllers. The results show that compared to the other controllers, the PMSM system based on GNFTSMC displays improved performance characteristics of faster speed response, smaller chattering and higher current efficiency.
Event Detection and Localization for Sparsely Populated Outdoor Environment Using Seismic Sensor

Accurate event detection and localization is one of the key requirements for multiple smart applications. In this paper, we present a framework for event detection and localization using seismic sensors in an outdoor environment. Seismic sensors have been widely used for event detection. This paper extends the application of seismic sensor for localization. We develop a framework using multiple seismic sensors and fuse the information to detect and localize a target. The proposed framework employs regression and property of seismic waves for localization. We develop a prototype system to verify the proposed methods. The event detection module shows the average detection accuracy of 96% whereas localization modules show average localization error 1.37 meters.
Bi-objective Optimization of Emergency Dispatching and Routing for Multi-mode Network Using Ant Colony Algorithm

Traditionally, emergency passenger retention is evacuated by single-mode like by coach or railway. To reduce the passengers' delay that is closely related to personal time value and systematical fluence, the multi-mode traffic vehicles are supposed to dispatch economically and collaboratively. This study proposed a bi-objective optimization model, intending to minimize passenger delay and dispatching cost simultaneously, thereby obtaining the dispatching vehicle plan and the trip chain through the trade-off analysis. The Pareto solutions, including delay and expense, correspond to passengers allocating on a trip chain and the number of dispatched vehicles for each mode. A super network, combining coach, railway, high-speed train, and airplane subnetworks, is established to control the transfer times, transfer passenger volume, and multi-mode trip chain. The Ant Colony Optimization (ACO) is utilized, which has superiority on path selection on the network to solve the bi-criterion routing. The algorithm is improved in this research to search the optimal passenger evacuation path when the vehicle carriage capacity is uncertain. A case based on the Beijing-Tianjin-Hebei transportation integration shows the following: (1) The economic efficiency of adding spend is dropping on the Pareto front. (2) The reserved emergency vehicles are dispatched uniformly. (3) The railway is occupied mainly by short and medium distance passengers.
A Modeling Method for the Driving Process of Heavy-Haul Train Based on Multi-Mass Model

Unlike the locomotive, heavy-haul trains have large carrying capacity and consist of hundreds of carriages; the longitudinal impulse in the train has significant coupled nonlinear characteristics during its operation. With the continuous increase in traction weight, problems with the existing control models gradually appear, vehicle decoupling and derailment occur, and the trains often fail to operate on schedule. Using the coupler characteristic curve, this paper comprehensively considers uncertain factors such as the train loading weight, line ramps, vehicle dynamic performance differences, and changeable operating environment to accurately model the heavy-haul train coupling force and train longitudinal dynamic multiple points. We optimize the target curve of the ideal running speed of the train and design the corresponding running tracking control strategy. The simulation results show that the ideal target speed curve optimization method designed in this paper can effectively improve key indicators such as safety, punctuality and energy saving in train operation to make trains more safely and efficiently run.
Towards Improved Network Security Requirements and Policy: Domain-Specific Completeness Analysis via Topic Modeling

Network security policies contain requirements – including system and software features as well as expected and desired actions of human actors. In this paper, we present a framework for evaluation of textual network security policies as requirements documents to identify areas for improvement. Specifically, our framework concentrates on completeness. We use topic modeling coupled with expert evaluation to learn the complete list of important topics that should be addressed in a network security policy. Using these topics as a checklist, we evaluate (students) a collection of network security policies for completeness, i.e., the level of presence of these topics in the text. We developed three methods for topic recognition to identify missing or poorly addressed topics. We examine network security policies and report the results of our analysis: preliminary success of our approach.
IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution

The novel coronavirus (COVID-19), declared by the World Health Organization (WHO) as a global pandemic, has brought with it changes to the general way of life. Major sectors of the world industry and economy have been affected and the Internet of Things (IoT) management and framework is no exception in this regard. This article provides an up to date survey on how a global pandemic such as COVID-19 has affected the world of IoT technologies. It looks at the contributions that IoT and associated sensor technologies have made towards virus tracing, tracking and spread mitigation. The associated challenges of deployment of sensor hardware in the face of a rapidly spreading pandemic have been looked into as part of this review article. The effects of a global pandemic on the evolution of IoT architectures and management have also been addressed, leading to the likely outcomes on future IoT implementations. In general, this article provides an insight into the advancement of sensor-based E-health towards the management of global pandemics. It also answers the question of how a global virus pandemic has shaped the future of IoT networks.
Analyzing Traffic Violations through e-challan System in Metropolitan Cities (Workshop Paper)

Given that India is now moving towards automated solutions to curb traffic violations and road accidents, we focus our efforts on characterizing these violations in Indian cities. In this work, we present our characterization of the traffic violations via an Automated e-challan (electronic traffic-violation receipt) issuance system of Ahmedabad and New Delhi. To explore this, we collected an exhaustive dataset of over 6 million e-challans. Characterizing the fine payment behavior, we find that 57% of unique vehicles in Ahmedabad are involved in repeat offenses. The temporal analysis shows a significant difference in e-challans issued during the festivals. Spatially, different violation types are distributed differently with the existence of certain unique hotspots. Finally, we also demonstrate how e-challans can act as a proxy measure to analyze the efficacy of the Motor Vehicles (Amendment) Act 2019. Our work suggests that high penalties may not have a long term impact on decreasing traffic violations.
Robust control for Markov jump linear systems with unknown transition probabilities – an online temporal differences approach

In this paper, an online temporal differences (TD) learning approach is proposed to solve the robust control problem for discrete-time Markov jump linear systems (MJLS) subject to completely unknown transition probabilities (TP). The TD learning algorithm consists of two parts: policy evaluation and policy improvement. In the first part, by observing the mode jumping trajectories instead of solving a set of coupled algebraic Riccati equations, value functions are updated and approximate the TP related matrices. In the second part, new robust controllers can be obtained until value functions converge in the previous part. Moreover, the convergence of the value functions is proved by initializing a feasible control policy. Finally, two examples are presented to illustrate the effectiveness of the proposed approach by comparing with existing results.
Extra‐pair paternity is not driven by inbreeding avoidance and does not affect provisioning rates in a cooperatively breeding bird, the noisy miner (Manorina melanocephala)

&NA; In many socially monogamous bird species, both sexes regularly engage in mating outside their pair bond. Although the benefits of extra‐pair (EP) mating behavior are clear and well established for males, such as an increase in the number of sired offspring, the benefits of EP mating behavior to females are less clear. A dominant theory for the incidence of EP mating is that socially monogamous females can improve the genetic quality of their offspring and avoid the costs of inbreeding through EP mating. In addition, in cooperatively breeding species, the theory of “parental care” predicts that females obtain additional help for their offspring through EP matings. Here, we examined evidence for both the inbreeding avoidance and parental care hypotheses in the cooperatively breeding noisy miner (Manorina melanocephala). Overall, EP mating occurred in 27% of broods, with 14% of offspring sired by males other than the identified pair‐bonded male. There was a strong tendency to avoid pairing with genetically related individuals, with 86% of breeding pairs being significantly less related to each other than the general population. The occurrence of EP paternity was independent of the degree of relatedness between the pair. Provisioning patterns in relation to EP mating was not consistent with the “parental care” hypothesis and EP males did not contribute to the care of broods. These results demonstrate that in this system, there is no evidence that EP mating might function as a mechanism to reduce the costs of inbreeding depression or to gain benefits of extra helpers.
Unravelling Small Sample Size Problems in the Deep Learning World

The growth and success of deep learning approaches can be attributed to two major factors: availability of hardware resources and availability of large number of training samples. For problems with large training databases, deep learning models have achieved superlative performances. However, there are a lot of small sample size or S3 problems for which it is not feasible to collect large training databases. It has been observed that deep learning models do not generalize well on S3 problems and specialized solutions are required. In this paper, we first present a review of deep learning algorithms for small sample size problems in which the algorithms are segregated according to the space in which they operate, i.e. input space, model space, and feature space. Secondly, we present Dynamic Attention Pooling approach which focuses on extracting global information from the most discriminative sub-part of the feature map. The performance of the proposed dynamic attention pooling is analyzed with state-of-the-art ResNet model on relatively small publicly available datasets such as SVHN, C10, C100, and TinyImageNet.
Consensus Control of Position-Constrained Multi-Agent Systems Without the Velocity Information of Neighbors

Currently, most consensus control approaches need each agent to access all its neighbors’ states directly. The distributed consensus issue of second-order multi-agent systems subject to position constraints is studied without such requirements. The only condition for the communication topology is to include a directed spanning tree. An innovative reference position is provided to deal with the position constraints while eliminating the need for neighbors’ velocity variables. An adaptive control method is designed by constructing a sliding-mode-esque variable so that each agent’s transformed position can converge towards the reference position. This new method can guarantee uniform boundedness of each closed-loop signal as well as asymptotic consensus, and the requirements to meet the position constraints are satisfied at all times. Numerical simulation verifies the correctness of the theoretical results.
Phenotypic and resource use partitioning amongst sympatric, lacustrine brown trout, Salmo trutta

Divergence into discrete foraging specialist morphs living in sympatry is relatively well described in lacustrine fishes of the Salmonidae. Although piscivorous forms of Salmo trutta have been widely reported, other trophic foraging specialists are strangely rarely recorded amongst Salmo species. Microsatellite and mitochondrial genetic data segregated S. trutta collected from Loch Laidon, Scotland, into four distinct genetic groups. Three groups analysed in this study showed significant differences in body shape, stomach contents, muscle stable isotope signature, gill raker length and spacing, and habitat use. We conclude that the three genetically defined groups comprise: a generalist foraging morph, a pelagic feeding specialist morph and a profundal macrobenthos feeding morph. The features distinguishing these morphs, however, show a degree of overlap. This appears to be only the second record of invertebrate resource use partitioning associated with expression of alternative morphologies in sympatry this species and the first report of a profundal invertebrate feeding morph. Why such polymorphisms are generally rare in Salmo species remains unclear, but potential explanations are discussed.
Specific Surface Recognition Using Custom Finger Vision

FingerVision is becoming a promising solution to tactile manipulation tasks. This paper introduces a modular custom FingerVision and a new method of specific contact surface recognition implementing KNN (k-Nearest Neighbor) algorithm. This method uses the markers array in the sensor as feature points to describe the deformation in contact, and generates a database recording all simulated conditions to identify surfaces of different shapes. Adjusted parameters of the blob detection algorithm and a frame correction algorithm by linear interpolation in the feature extraction process are proposed as well. Recognition effects of different sample sizes, k values and other factors are discussed with an accuracy test. With optimization, accuracy rate of recognition achieves 100% among 570 test samples with 1140 given samples from 7 surfaces in 19 categories. The conclusions are that the accuracy of this method hardly depends on the k value and the dimension of coordinates analyzed, so the default k value 5 and only abscissas are applied to simplify the calculation. As for sample size, when the given sample number of each category reaches about 40, the accuracy rate is almost 100%, and thus it is unnecessary to continue to increase the sample size.