Miracle-dz/UniFAR-FTUs huggingface dataset:
Children with Autism Spectrum Disorder Show Impairments During Dynamic Versus Static Grip‐force Tracking

Impairments in visuomotor integration (VMI) may contribute to anomalous development of motor, as well as social‐communicative, skills in children with autism spectrum disorder (ASD). However, it is relatively unknown whether VMI impairments are specific to children with ASD versus children with other neurodevelopmental disorders. As such, this study addressed the hypothesis that children with ASD, but not those in other clinical control groups, would show greater deficits in high‐VMI dynamic grip‐force tracking versus low‐VMI static presentation. Seventy‐nine children, aged 7–17 years, participated: 22 children with ASD, 17 children with fetal alcohol spectrum disorder (FASD), 18 children with Attention‐Deficit Hyperactivity Disorder (ADHD), and 22 typically developing (TD) children. Two grip‐force tracking conditions were examined: (1) a low‐VMI condition (static visual target) and (2) a high‐VMI condition (dynamic visual target). Low‐frequency force oscillations <0.5 Hz during the visuomotor task were also examined. Two‐way ANCOVAs were used to examine group x VMI and group x frequency effects (α = 0.05). Children with ASD showed a difficulty, above that seen in the ADHD/FASD groups, tracking dynamic, but not static, visual stimuli as compared to TD children. Low‐frequency force oscillations <0.25 Hz were also significantly greater in the ASD versus the TD group. This study is the first to report VMI deficits during dynamic versus static grip‐force tracking and increased proportion of force oscillations <0.25 Hz during visuomotor tracking in the ASD versus TD group. Dynamic VMI impairments may be a core psychophysiologic feature that could contribute to impaired development of motor and social‐communicative skills in ASD.
A Feasibility Study of an Uncertainty Management Intervention for Patient-Partner Dyads Experiencing Breast Cancer.

OBJECTIVES To examine the feasibility, acceptability, and effects of a dyad-based uncertainty management intervention for breast cancer, including tailored information and coping skills training. SAMPLE & SETTING 16 patient-partner dyads experiencing breast cancer were enrolled from a midwestern comprehensive cancer center. METHODS & VARIABLES A single-group pre-/post-test design was used, and descriptive statistics and Cohen's d were calculated. Measures were completed before the intervention and during each treatment cycle. Feasibility, acceptability, fidelity, uptake, and outcome variables (uncertainty, dyadic coping, family functioning) were included. RESULTS 16 dyads were enrolled during a 13-month period; 15 dyads completed the training for the study, and 13 dyads completed all study activities. Overall, participants reported satisfaction with the intervention. Small to medium effect sizes were observed across the outcomes. IMPLICATIONS FOR NURSING This study highlights the need for nurses to help couples manage uncertainty related to new cancer treatment. Tailored interventions can allow nurses to use their time efficiently by focusing on individuals' actual needs.
SARS-CoV-2 infection prevalence on repatriation flights from Wuhan City, China

We estimated SARS-CoV-2 infection prevalence in cohorts of repatriated citizens from Wuhan to be 0.44% (95% CI: 0.19%-1.03%). Although not representative of the wider population we believe these estimates are helpful in providing a conservative estimate of infection prevalence in Wuhan City, China, in the absence of large-scale population testing early in the epidemic.
A functional long‐term 2D serum‐free human hepatic in vitro system for drug evaluation

Human in vitro hepatic models generate faster drug toxicity data with higher human predictability compared to animal models. However, for long‐term studies, current models require the use of serum and 3D architecture, limiting their utility. Maintaining a functional long‐term human in vitro hepatic culture that avoids complex structures and serum would improve the value of such systems for preclinical studies. This would also enable a more straightforward integration with current multi‐organ devices to study human systemic toxicity to generate an alternative model to chronic animal evaluations. A human primary hepatocyte culture system was characterized for 28 days in 2D and serum‐free defined conditions. Under the studied conditions, human primary hepatocytes maintained their characteristic morphology, hepatic markers and functions for 28 days. The acute and chronic administration of known drugs validated the sensitivity of the system for drug testing. This human 2D model represents a realistic system to evaluate hepatic function for long‐term drug studies, without the need of animal serum, confounding variable in most models, and with less complexity and resultant cost compared to most 3D models. The defined culture conditions can easily be integrated into complex multi‐organ in vitro models for studying systemic effects driven by the liver function for long‐term evaluations.
Sampling Topic Representative Users by Integrating Node Degree and Edge Weight

Finding a subset of users to statistically represent the original social network in terms of attributes has received a lot of attention recently. The existing literature had proved that the problem is NP-Hard and proposed some sampling models, but representative degrees aren’t systematically studied. Taking node degree and edge weight into account, we present a topic sampling model for the whole social network in this paper. Firstly, by statistically calculating node degrees for one real dataset, we find that one node is more likely to be a representative user if its degree is high. We then give some definitions about representative degrees. Based on statistical stratified sample, we finally give a sampling model of topic representative users who are from most or all attribute groups in the network. Experimental results show that our algorithm significantly outperforms other six baseline methods and is also effective in terms of time complexity.
An Event Coding Method Based on Frame Images With Dynamic Vision Sensor Modeling

The dynamic vision sensor (DVS) has shown great application potential in the field of real-time object detections due to the advantages of low latency, high dynamic range and low memory usage. DVS utilizes the event-driven principles to extract the features of moving objects and records feature information as the address-event (AE) data. Prior work has shown that DVS-based spike neural networks (SNNs) have obvious advantages on energy efficiency by converting the AE data to spike events. However, most of the prior work mainly relies on specific DVS cameras, which limits the applications of DVS-based SNNs. Then, based on the principles of DVS, this paper presents an event coding method based on frame images. This method generates triggered self-defined AE (SAE) data by using differential comparisons among frame images. The SAE data can be converted to spike events. Compared with pixel-based spike coding methods, the features of moving objects can be represented more efficiently by the proposed method, and the redundant spikes are reduced for low power considerations.
Direction Finding With Partially Corrupted Data Based on OptSpace Algorithm

It is known that, if a low-rank matrix has partial entries in random locations being missing, the matrix completion technique can be used to restore the matrix. However, when the missing elements are distributed in an entire row or column, there is no enough information to directly recover the data. This paper considers such a problem: in the data matrix of a sensor array system for direction finding, the entries in an entire row (possibly several rows) are corrupted or missing, due to, for example, down-sampling or some of the array elements are damaged or turned off. To tackle this problem, we propose to utilize the spatial smoothing scheme to construct an extended data matrix which allows us to apply the matrix completion technique. Then, a gradient descent algorithm, namely OptSpace, is adopted to solve the resulting matrix completion problem. By doing so, the restored data matrix can be obtained, and the autocorrelation matrix can be computed. Finally, the directions of arrival (DOAs) are estimated with the traditional MUSIC algorithm. The experimental results demonstrate the effectiveness of the proposed approach.
Computation Offloading for Distributed Mobile Edge Computing Network: A Multiobjective Approach

Mobile edge computing (MEC) is emerging as a cornerstone technology to address the conflict between resource-constrained smart devices (SDs) and the ever-increasing computational demands of the mobile applications. MEC enables the SDs to offload computational-intensive tasks to the nearby edge nodes for providing better quality-of-services (QoS). The recently proposed offloading strategies, mainly consider a centralized approach for a limited number of SDs. However, with the growing popularity of the SDs, these offloading models may have the scalability issue and can be susceptible to single point failure. Although there are few distributed offloading models in the literature, they ignore the vast computational resources of the cloud, load sharing between the MEC servers, and other optimization parameters. Toward this end, we propose an efficient computation offloading scheme for a distributed load sharing MEC network in cooperation with cloud computing to enhance the capabilities of the SDs. We formulate a nonlinear multiobjective optimization problem by applying queuing theory to model the execution delay, energy consumption, and payment cost for using edge and cloud services. To solve the formulated problem, we propose a stochastic gradient descent (SGD) algorithm based solution approach to jointly optimize the offloading probability and transmission power of the SDs for finding an optimal trade-off between energy consumption, execution delay, and cost of the SDs. Finally, we perform extensive simulations to demonstrate the effectiveness of the proposed offloading scheme. Moreover, compared to the other solutions, the proposed scheme is scalable and outperforms the existing schemes.
A Way of Shadow Zone Autonomous Localization of Deep Sea by a Single Hydrophone

The sound intensity periodic structure in the shadow zone is mainly caused by the interference among the bottom-reflected wave, the bottom-surface-reflected wave, the surface-bottom-reflected wave, and the surface-bottom-surface-reflected wave and the corresponding interference period is related to the location of sound source. The traditional method extracts the interference period manually and matches the interference period with the simulation result to estimate the source localization. Manual extraction process is cumbersome and may introduce large errors. In this paper, the mechanism of interference fringe formation is analyzed in depth, and it is pointed out that the interference period is determined by the multipath delay difference, and the multipath delay difference is related to the source localization, so that source localization can be performed. Finally, a method of autonomous extraction of interference period and source location estimation is proposed, and the feasibility of this method is verified by simulation.
Visual SLAM With Drift-Free Rotation Estimation in Manhattan World

This letter presents an efficient and accurate simultaneous localization and mapping (SLAM) system in man-made environments. The Manhattan world assumption is imposed, with which the global orientation is obtained. The drift-free rotational motion estimation is derived from the structural regularities using line features. In particular, a two-stage vanishing points (VPs) estimation method is developed, which consists of a short-term tracking module to track the clustered line features and a long-term searching module to generate abundant sets of VPs candidates and retrieve the optimal one. A least square problem is constructed and solved to provide refined VPs with the clusters of structural line features every frame. We make full use of the absolute orientation estimation to benefit the whole SLAM process. In particular, we utilize the absolute orientation estimation to increase the localization accuracy in the front end, and formulate a linear batch camera pose refinement problem with the known rotations to improve the real time performance in the back end. Experiments on both synthesized and real-world scenes reveal results with high-precision in the real time camera pose estimation process and high-speed in pose graph optimization process compared with the existing state-of-the-art methods.
Trustful Resource Management for Service Allocation in Fog-Enabled Intelligent Transportation Systems

A fog-enabled intelligent transportation system is constructed using Vehicle-to-Everything (V2X) communications towards the Internet of Vehicles (IoV). In this system, the road side unit (RSU) and vehicle correspond to the fog server and fog device, respectively. In practice, the RSU offers instant infotainment services (e.g., video streaming and traffic assistance) to vehicles. However, the vehicles are considered untrustworthy and could possibly be exploited to attack the system, thereby compromising the service stability and data integrity. To address this problem, this article proposes truthful service allocation using a Vickrey–Clarke–Groves (VCG) auction mechanism. The RSU leads the proposed auction as a seller who sells their computing resources, and the vehicles participate as buyers who purchase these computing resources for the offered services. Consequently, the RSU generates a transaction whenever a service allocation is assigned. To secure the service transactions, we utilize a distributed blockchain system that implements Hyperledger Fabric framework among RSUs for transaction verification. The simulation results demonstrate that the proposed system provides service stability while ensuring service trustfulness.
Radar Signal Processing for Low-observable Marine Target-Challenges and Solutions

Sea clutter analysis, suppression, basic and advanced marine target detection and classification techniques, future development are discussed in this paper. Firstly, challenges of marine radar signal processing will be introduced including the challenges of sea clutter suppression, characteristics of complex marine targets, detection and recognition. Subsequently, we will give an overview of popular signal processing techniques from the point of view of detection mechanism, signal modelling, energy integration, radar resolution, and processing domains. Then, this paper will give a deep discussion on the development and advances of marine target detection, i.e., long-time coherent integration, sparse time-frequency distribution, refined characteristics extraction and estimation via micro-Doppler, and deep learning for detection and classification. Finally, we will provide some real applications and examples of marine target detection for real applications.
Cognitive radar waveform optimization for stealth target RCS estimation

In this paper, the radar cross section (RCS) estimation performance is improved by cognitive radar waveform optimization in both angle and frequency domain. F-35 stealth target is modeled by physical optics (PO) approximation method. The recognition of target characteristics by cognitive radar is carried out from the perspective of maximum observation of RCS. The waveform optimization problem is formulated using maximum a posteriori probability (MAP) and Kalman filtering (KF) to estimate RCS of the stealth target. The minimum mean square error (MMSE) is taken as the objective function which is solved by different nature inspired waveform optimization (NIWO). It is demonstrated through computer simulations that the proposed method can reconstruct RCS of the stealth target and outperforms the traditional semi-definite relaxation (SDR) technique, showing a promising method of waveform optimization for stealth target RCS reconstruction in cognitive radar.
Etude d'une population de muscardins (Muscardinus avellanarius) lors du repos journalier (Mammalia, Gliridae)

Etude a l'aide d'abris artificiels disposes en lisiere de foret pres de Lausanne et d'individus marques et relâches in situ, a intervalles reguliers durant 3 ans (1981-83) dans le but de preciser: les fluctuations d'effectifs au cours de l'ete; les deplacements individuels; les caracteristiques de la reproduction et l'esperance de vie. Les animaux sont generalement solitaires lors du repos mais il y a d'assez nombreux cas de cohabitation
Event-Triggered H∞ State Estimation for Discrete-time T-S Fuzzy Systems

This paper is concerned on the design of H∞ estimation for discrete-time T-S fuzzy system. In order to save limited bandwidth resources, the event-triggered scheme is introduced. By using Lyapunov functionals and integral inequalities, the stability of the error system is proved. In addition, the observer gain parameters are obtained by LMI technique in MATLAB. Finally, the effectiveness of the design method is verified by a simulation example.
Radar High Resolution Range Profile Restoration based on Conditional Generative Adversarial Networks

High Resolution Range Profile (HRRP) is very valuable for radar automatic target recognition. However, the HRRP synthesized by stepped frequency signals may be affected a lot by the speed error. As a result, the performance of radar automatic target recognition would be reduced. To solve this problem, we propose a novel HRRP restoration method based on Conditional Generative Adversarial Networks (CGAN). Simulation results show that this method performs well when the speed error is around 20 m/s.
Neuro-Inspired-in-Memory Computing Using Charge-Trapping MemTransistor on Germanium as Synaptic Device

In this work, we fabricated charge-trapping MemTransistors (CTMTs) on a germanium (Ge) substrate with a single-charge-trapping-layer gate-stack or a double-charge-trapping-layer gate-stack. We first constructed the energy band diagram of two gate stacks using transmission electron microscope (TEM) images and by X-ray photoelectron spectroscopy analysis. We deposited Al<sub>2</sub>O<sub>3</sub> as a tunneling layer and a barrier layer using an atomic layer deposition (ALD) system while depositing HfO<sub>2</sub> by ALD as the charge-trapping layer whose conduction band offset with respect to Al<sub>2</sub>O<sub>3</sub> is 1.74 eV. Next, we demonstrated the memory characteristics of the CTMTs. By implementing the double-charge-trapping-layer gate-stack on the CTMT, we were able to enlarge the memory windows by 372 mV, improve the retention by 2.7%, and reduce the read disturbance. Furthermore, we demonstrated the synaptic device characteristics of the CTMTs. With the optimization of pulse schemes, we reduced the nonlinearity of potentiation (<inline-formula> <tex-math notation="LaTeX">$\alpha _{\text {p}}$ </tex-math></inline-formula>) and depression (<inline-formula> <tex-math notation="LaTeX">$\alpha _{\text {d}}$ </tex-math></inline-formula>) from 8.62 and −6.01 to 0.71 and 0.01, respectively, enlarged the ON/OFF ratio from 10.2 to 66.2, and increased the recognition accuracy from 24.5% to 82.1% simultaneously. With the implementation of the double-charge-trapping-layer gate-stack, we could further enlarge the ON/OFF ratio to 75.3 and increase the recognition accuracy to 86.5% simultaneously.
Convolutional LSTM networks for seawater temperature prediction

The seawater temperature has the characteristics of both spatial and temporal. To better predict seawater temperature, we propose a convolutional long short-term memory recurrent neural network method based on thermohaline data. We use convolutional neural networks to extract features from the thermohaline data in near time domains to obtain corresponding values of these features. These features are input into the LSTM recurrent neural network for the temporal prediction of seawater temperature. The method was empirically evaluated and compared to the RNN methods considering only a single variable and the methods considering temperature and salinity. The results show that the Thermohaline Convolutional LSTM Model has significant advantages over other methods and can predict changes in seawater temperature more accurate at different depths.
PolSAR Image Segment Using Model-Based Decomposition and Energy Minimization

The backscattering response of the target is affected by the relative geometric relationship between the target pose and the line of sight of the target and the radar. Ignoring this property, the traditional scattering model-based decomposition method does not interpret the PolSAR image perfectly. The newly proposed model-based six-component scattering matrix power decomposition method (6SD) fully considers the influence of the relative positional relationship between the radar line of sight and the target. It can more accurately acquire the characteristics of the polarized SAR image. In this paper, a PolSAR image classification algorithm combining 6SD and energy minimization is proposed. After obtaining scattering characteristics by model-based six-component scattering matrix power decomposition, the spatial relationship of different pixels is considered to segment the PolSAR image by energy minimization method. In this paper, the principle and specific steps of the algorithm are elaborated in detail, and the proposed algorithm is verified by the measured AIRSAR data. Finally, the experimental results are compared with the wishart classification algorithm. The results show that the proposed algorithm has obvious advantages over the classification using wishart method. The proposed algorithm can make the classification more accurate and make the classification result smoother.
A Fault Diagnosis Method for Valve Train of Diesel Engine Considering Incomplete Feature Set

Abnormal valve clearance is a common fault of diesel engine, and early warning of abnormal valve clearance plays an important role in the condition based maintenance of diesel engine. Although information fusion technology can improve the accuracy of fault diagnosis, it cannot guarantee that the fused features can perfectly represent the required key information. For the incomplete feature set, a method combining multi-domain feature and improved support vector machine is proposed. Firstly, the extraction of multi-domain feature is carried out to deeply explore the state information of valve train contained in the original vibration signal. The statistical characteristics and waveform characteristics are extracted from time domain vibration signals, and the frequency domain feature similar to time-domain feature is extracted after the Fourier transform of the vibration signal. What’s more, according to the working principle of diesel engine, the energy characteristics in angular frequency domain are extracted. Then, an improved support vector machine method based on multi-domain feature is proposed to further reduce the diagnostic errors caused by incomplete feature set. Finally, the proposed method is compared with other traditional methods about the fault diagnosis of valve train of diesel engine. The results show that the proposed method is applicable to the fault diagnosis of valve train of diesel engine with good accuracy, and the generalization ability of diagnostic model is greatly improved.