Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Wireless Communication and Entertainment Devices on Simulated Driving Performance

An analysis of the effect of wireless telephone communication using text and voice modalities as well as an Apple iPod on lane keeping, speed, speed variability, lateral speed, and lane position variability was conducted with a driving simulator. Participants (young adult licensed drivers) drove in an unusually curvy simulated driving environment while using wireless devices, controlling an iPod, and participating in conversations and word games. As expected on the basis of previous research, lane-keeping performance was robust for voice communication tasks; however, the text messaging and iPod tasks that required significant manual manipulation of the device resulted in significant decrements in lane-keeping performance. In addition, all wireless communication tasks and the iPod task resulted in significant increases in speed variability throughout the driving scenario. Lateral speed increases occurred for all wireless communication tasks other than the cellular phone conversation as well as the iPod task. Increases in lane position variability were observed for the text messaging conditions. In addition to establishing the dramatic performance decrement caused by text messaging tasks, this experiment suggests that driving performance may be affected by distraction in ways not captured by lane-keeping measures alone and explores potential alternative measures of driving performance that may be useful for identifying and quantifying the effects of distracted driving.
Application of hierarchical neural networks to pattern recognition for quality control analysis in steel-industry plants

Our paper focuses on the classification of surface defects in flat rolled strips in steel industry. Since this work aims at the classification of samples organized in a hierarchical way it seems natural to use a hierarchical approach. We choose a hierarchical neural architecture, based on the multilayer perceptron, which, to some extent, combines classification trees with neural network approaches. We exhaustively tested the proposed architecture in the classification of surface defects in flat rolled strips on real plant data, obtaining a higher classification accuracy with respect to the state-of-the-art technologies. This approach can be generalized to many other industrial classification problems.
Transcriptomic fingerprint of bacterial infection in lower extremity ulcers

Clinicians and researchers utilize subjective, clinical classification systems to stratify lower extremity ulcer infections for treatment and research. The purpose of this study was to examine whether these clinical classifications are reflected in the ulcer's transcriptome. RNA sequencing (RNA‐seq) was performed on biopsies from clinically infected lower extremity ulcers (n = 44). Resulting sequences were aligned to the host reference genome to create a transcriptome profile. Differential gene expression analysis and gene ontology (GO) enrichment analysis were performed between ulcer severities as well as between sample groups identified by k‐means clustering. Lastly, a support vector classifier was trained to estimate clinical infection score or k‐means cluster based on a subset of genes. Clinical infection severity did not explain the major sources of variability among the samples and samples with the same clinical classification demonstrated high inter‐sample variability. High proportions of bacterial RNA were identified in some samples, which resulted in a strong effect on transcription and increased expression of genes associated with immune response and inflammation. K‐means clustering identified two clusters of samples, one of which contained all of the samples with high levels of bacterial RNA. A support vector classifier identified a fingerprint of 20 genes, including immune‐associated genes such as CXCL8, GADD45B, and HILPDA, which accurately identified samples with signs of infection via cross‐validation. This study identified a unique, host‐transcriptome signature in the presence of infecting bacteria, often incongruent with clinical infection‐severity classifications. This suggests that stratification of infection status based on a transcriptomic fingerprint may be useful as an objective classification method to classify infection severity, as well as a tool for studying host–pathogen interactions.
Simulations and Measurements of Failure Modes in SiC Cascode JFETs under Short Circuit Conditions

Using experimental measurements and finite element simulations, this paper investigates the failure mode of SiC Cascode JFETs under short circuit (SC) conditions. Unlike SiC MOSFETs, where failure results in a shorted gate-source terminal (resulting from gate oxide failure), in SiC Cascode JFETs, failure usually occurs in drain-source short with the gate of the Low Voltage (LV) silicon MOSFET still operational. Measurements show that after the LV Si MOSFET gate is turned OFF, a high tail current results in the Cascode drain terminal with the drain-source voltage falling to approximately half of the blocking voltage, suggesting that the JFET is in linear mode. Finite element simulations show that thermally generated carriers in the JFET depletion region cause high leakage currents through the internal JFET gate terminal capable of turning-ON the JFET (after turn-OFF of the LV Si MOSFET) into linear mode. The measured short circuit withstand time (SCWT) is shown to be independent of temperature unlike silicon MOSFETs where the SC withstand time increases with the initial temperature.
Neural network model for video-based facial expression recognition in-the-wild on mobile devices

In this paper, we propose to solve the problem of facial expression recognition in videos by implementing a two-stage procedure, in which, firstly, facial features are extracted from all frames using an EfficientNet-based model. The latter is pre-trained to identify facial attributes and further fine-tuned on an external dataset for the emotion classification task. Secondly, multiple statistical functions are calculated and used in the aggregation process to create a single video representation. Furthermore, we propose a new technique for sequence, frame-level attention models, and 1D convolutions by concatenating the output of a statistical function with the facial features. It was experimentally shown that the proposed approach leads to state-of-the-art results on the AFEW 8.0 dataset.
Analysis of Heat Dissipation of Nanocrystalline Diamond Capping Layers for GaN HEMTs Applications

The routes and mechanism of heat dissipation of nanocrystalline diamond capping layers for GaN HEMTs applications is systematically investigated by using three dimensional simulations with the finite element method. The distribution of heat for nanocrystalline diamond capping layers device is performed and those results demonstrate the path and mechanism of heat transfer for nanocrystalline diamond capped devices. Meanwhile, the effects of nanocrystalline diamond capping layers structures on the thermal performance of GaN HEMTs are investigated in detail based on temperature distribution around heat sources, and those results indicate geometric parameters of nanocrystalline diamond capping layers have crucial importance influence on heat dissipation, especially the distance between gate to nanocrystalline diamond. In addition, the analysis of temperature gradient illustrates the effect of geometric parameters on junction temperature, and highlight that nanocrystalline diamond capping layers provide a high thermal conductivity path for GaN devices.
Dynamic Model of the Cardiorespiratory System in Healthy Humans by using Electrocardiogram signals as Input

In this article, a dynamic model of the cardio-respiratory system is developed, which uses an electrocardiogram (ECG) signal as input. Identification of two models of the Hammerstein − Wiener type is carried out, in order to obtain the mass flow of air in the respiratory process and the blood flow, starting from the ECG signal. These flows are coupled in turn with a phenomenological-based dynamic model that describes respiratory dynamics. From the developed model, it is possible to obtain as a result the concentrations of oxygen (O2) and carbon dioxide (CO2) in different parts of the body for different ECG signals.
An efficient technique for the computation of the input resistance of rectangular microstrip antenna elements with thick substrates

Several formulas based on the cavity model and the transmission-line model are combined to produce closed-form expressions for calculating the input resonant resistance of probe-fed rectangular microstrip antenna elements with thick substrates. These expressions take into account the patch and substrate physical properties and the effect of surface wave, radiation, conductor, and dielectric losses with their equivalent resistances. Experimental results are compared with computed results to demonstrate the accuracy and versatility of this expressions. © 1996 John Wiley & Sons, Inc.
Blockage Effects on Scouring Downstream of Box Culverts Under Unsteady Flow

Abstract This experimental study is concerning scouring at the outlet of partially blocked box culverts under unsteady flow condition. The blockage of culverts is considered as an important factor on the scouring pattern at the outlet of the blocked box culverts. To investigate the effect of blockage on scouring pattern downstream of a box culvert under unsteady flow, some experimental tests were carried out in a laboratory flume located in the Hydraulics Laboratory of the University of Technology Sydney. The experimental tests were carried on in both partially blocked and non-blocked conditions and consequentially the effect of blockage on scour pattern is investigated. It was found that 88% to 98% of maximum scour depth occurs in the rising limb of the hydrograph.
Simulation of the flapping wing aerial vehicle using flexible multibody dynamics

An insect-type flapping wing micro aerial vehicle offers high aerodynamic efficiency and maneuverability in confined spaces. The complicated aerodynamic/structural behavior of flapping wing micro aerial vehicle, however, causes difficulties regarding the dynamic control and parametric design. This paper develops a moderately accurate numerical framework taking into account the passive motion of the main wings. Finite-element-based multibody dynamics and two-dimensional unsteady aerodynamics are combined to simulate the hover of a flapping wing micro aerial vehicle. In addition, flexible and rigid wings are compared through numerical simulation considering the flexibility. In terms of the average thrust, numerical simulation by fluid–structure interaction shows good agreements against the experimental results within 5% discrepancy.
An Aging Focused Unobtrusive and Privacy-Preserving Digital Behaviorome

Digital measures are increasingly used as objective health measures in remote-monitoring settings. In addition to their use in purely clinical research, such as in clinical trials, one promising application area for sensor-derived digital measures is in technology-assisted ageing and ageing-related research. In this context, digital measures may be used to measure the risk of certain adverse events such as falls, and also to provide novel research insights into ageing and ageing-related conditions, like cognitive impairment. While major emphasis has been placed on deriving one or more digital measures from wearable devices, a more holistic approach inspired by systems biology that leverages large, non-exhaustive sets of digital measures may prove highly beneficial. Such an approach would be useful if combined with modern big data approaches like machine learning. As such, extensive sets of digital measures, which may be referred to as digital behavioromes, could help characterise new phenotypes in deep phenotyping efforts. These measures could also assist in the discovery of novel digital biomarkers or in the creation of digital clinical outcome assessments. While clinical research into digital measures focuses primarily on measures derived from wearable devices, proven technology used for long-term remote monitoring of older adults is generally contactless, unobtrusive, and privacy-preserving. In this context, we introduce and describe a digital behaviorome: a large, non-exhaustive set of digital measures based entirely on contactless, unobtrusive, and privacy-preserving sensor technologies. We also demonstrate how such a behaviorome can be used to build digital clinical outcome assessments that are relevant to ageing and derived from machine learning. These outcomes included fall risk, frailty, mild cognitive impairment, and late-life depression. With the exception of late-life depression, all digital outcome assessments demonstrated a promising ability (ROC AUC [≥] 0.7) to discriminate between positive and negative health outcomes, often in the range of comparable work with wearable devices. Finally, we highlight the possibility of using these digital behaviorome-based outcome assessments to discover novel potential digital biomarkers for each outcome. Here, we found reasonable contributors but also some potentially interesting new candidates regarding fall risk and mild cognitive impairment.
Automated Analysis of Road Safety with Video Data

Traffic safety analysis has often been undertaken with historical collision data. However, well-recognized availability and quality problems are associated with collision data. In addition, the use of collision records for safety analysis is reactive: a significant number of collisions has to be recorded before action is taken. Therefore, the observation of traffic conflicts has been advocated as a complementary approach in the analysis of traffic safety. However, incomplete conceptualization and the cost of training observers and collecting conflict data have been factors inhibiting extensive application of the traffic conflict technique. The goal of this research is to develop a method for automated analysis of road safety with video sensors to address the problem of dependency on the deteriorating collision data. The method automates the extraction of traffic conflicts from video sensor data. This method should address the main shortcomings of the traffic conflict technique. A comprehensive system is described for traffic conflict detection in video data. The system is composed of a feature-based vehicle tracking algorithm adapted for intersections and a traffic conflict detection method based on the clustering of vehicle trajectories. The clustering uses a K-means approach with hidden Markov models and a simple heuristic to find the number of clusters automatically. Traffic conflicts can then be detected by identifying and adapting pairs of models of conflicting trajectories. The technique is demonstrated on real-world video sequences of traffic conflicts.
Arithmetic Coding-Based 5-bit Weight Encoding and Hardware Decoder for CNN Inference in Edge Devices

Convolutional neural networks (CNNs) have gained a huge attention for real-world artificial intelligence (AI) applications such as image classification and object detection. On the other hand, for better accuracy, the size of the CNNs’ parameters (weights) has been increasing, which in turn makes it difficult to enable on-device CNN inferences in resource-constrained edge devices. Though weight pruning and 5-bit quantization methods have shown promising results, it is still challenging to deploy large CNN models in edge devices. In this paper, we propose an encoding and hardware-based decoding technique which can be applied to 5-bit quantized weight data for on-device CNN inferences in resource-constrained edge devices. Given 5-bit quantized weight data, we employ arithmetic coding with range scaling for lossless weight compression, which is performed offline. When executing on-device inferences with underlying CNN accelerators, our hardware decoder enables a fast in-situ weight decompression with small latency overhead. According to our evaluation results with five widely used CNN models, our arithmetic coding-based encoding method applied to 5-bit quantized weights shows a better compression ratio by 9.6× while also reducing the memory data transfer energy consumption by 89.2%, on average, as compared to the case of uncompressed 32-bit floating-point weights. When applying our technique to pruned weights, we obtain better compression ratios by 57.5×–112.2× while reducing energy consumption by 98.3%–99.1% as compared to the case of 32-bit floating-point weights. In addition, by pipelining the weight decoding and transfer with the CNN execution, the latency overhead of our weight decoding with 16 decoding unit (DU) hardware is only 0.16%–5.48% and 0.16%–0.91% for non-pruned and pruned weights, respectively. Moreover, our proposed technique with 4-DU decoder hardware reduces system-level energy consumption by 1.1%–9.3%.
Vesselplasty versus vertebroplasty in the treatment of osteoporotic vertebral compression fractures with posterior wall rupture

Objective This study was performed to compare the effectiveness and safety of vesselplasty versus vertebroplasty in the treatment of osteoporotic compression fractures with posterior wall rupture. Methods Patients who underwent treatment of a single osteoporotic vertebral compression fracture with posterior wall rupture from January 2016 to February 2020 were retrospectively reviewed. They were divided into a vesselplasty group (n = 17) and a vertebroplasty group (n = 43). Pain relief, radiographic outcomes, and bone cement leakage were compared between the two groups. Results There were no significant differences in the operation time, postoperative pain relief, vertebral compression recovery, or local Cobb angle improvement between the two groups. However, the overall bone cement leakage rate (29.4% vs. 67.4%) and spinal canal leakage rate (0.0% vs. 30.2%) were significantly lower in the vesselplasty group than vertebroplasty group. Conclusions Vesselplasty offers similar pain relief and vertebral compression recovery but lower spinal canal leakage compared with vertebroplasty. Vesselplasty is thus a better option than vertebroplasty for patients with osteoporotic compression fractures with posterior wall rupture.
Accelerate Logic Re-simulation on GPU via Gate/Event Parallelism and State Compression

In this paper, we propose a logic re-simulation method on GPU via gate/event parallelism and state compression. We achieve 2-dimensional parallelism on GPU through grouping gates and splitting events. Furthermore, we compress the states to reduce the communication overhead. Asynchronous communication between GPU and CPU is used to hide the latency of dumping results. Compared with the first place of problem C of ICCAD contest 2020, the proposed method can be 47.1 % better on the speedup of single design and 10.5% better on the geometric mean of speedup for all the benchmarks.
Terrorist Use of Smuggling Tunnels

This article examines terrorists' use of smuggling tunnels to finance terrorism and/or launch terrorist operations. Israel's Operation Pillar of Cloud and Hamas' Operation Stones of Shale are used to illustrate both the physical structure of smuggling tunnels and social networks of the people who build and use smuggling tunnels. The sociological factors of terrorists and criminals engaged in smuggling in the same country, such as the Gaza Strip and Egypt during peaceful times and the Gaza Strip and Israel during a state of war, is examined. The article proposes the application of open-source social-network models applicable to regional and transnational tunnel analysis during times of peace and war, employing intelligence from law enforcement and national security sources to improve border security.
A Comparative Study on Voice Distribution in English Abstracts between Chinese and English Power Papers

As many international scholars and editors currently advocated to use more active voice and less passive voice, this new trend aroused many Chinese reaearchers to conduct a comparative study on voice distribution in English abstracts between Chinese and foreign countries. To explore the voice distribution in English abstracts in Chinese and foreign power papers, we randomly sampled 200 English abstracts respectively from international and domestic power papers and analyzed the voice distribution in the abstracts. The results show that Chinese writers and translators tend to use more passive voice than foreign writers, especially in English-speaking countries, while the latter more frequently use active voice in their writings. To futher this study, the following gives a detailed explaination of the reasons of the results. In summary, to better exchange mutual latest achievements in power industry, it is crucial to keep pace with international new trend, obey international common practice, particularly the rules in English abstracts writing. Index Terms - power papers, English abstract, voice distribution, active voice, passive voice 1. Introduction Abstract is an indispensable part of a paper, which even can decide the paper whether can be included. "In the process of including scientific papers by international retrieval systems, the English abatract is regarded as a key to success. For instance, EI Compendex including papers is decided by English abstract and keywords(1). " This shows how important the abstract is. Meanwhile, as a key industry in China, the development and research of power have to keep track of international new trend, and its English abstract is a crucial hub for communication between domestic and abroad. Therefore, it is of great significance to study the English abstract of power papers. Divided into active and passive voice, the proper use of voice is one of the key factors in the writing of papers. The famaous linguist Quark pointed out that the frequency of using passive voice in English science and technology is ten times higher than that of active voice(2). The passive voice is regarded as one of the characteristics of scientific papers. However, with the constant changes of language, the era of passive voice dominatied in English has changed. International researchers start to advocate widely use active voice in scientific and technological papers.
New control scheme of three-phase PWM AC/DC converter without phase angle detection under the unbalanced input voltage conditions

In general, three-phase PWM AC/DC power converters have been implemented in the synchronous frame model to eliminate steady state errors effectively and to obtain fast transient response characteristics. However, controllers designed in such way would have input current harmonics and DC-link voltage ripples under the unbalanced input voltage conditions due to the assumption of the balanced input voltage conditions. This paper describes a new control scheme to minimize harmonic distortions of the input current and DC-link voltage in the converter under the unbalanced input voltage. conditions. The synchronous frame input voltage, which is considered as the input side back-EMF component, is regulated pertinently according to the input voltage conditions. The current command is selected to eliminate the reactive power and the second order harmonic component of active power. In this case, the analysis of the input voltage is implemented in the synchronous frame without detecting the phase angle and magnitude of each phase voltage. The proposed control scheme is simple and effectively minimizing the harmonic distortions in the input and output system under the unbalanced input voltage conditions.