Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Rapid Extraction and Recognition Based on Depth Learning Method in Regional Nomination Mode

The advent of the large data era, the development of deep learning theory helps to create a good condition. Data mining generally refers to the process of automatically searching for information that has special relevance hidden from a large amount of data. Data mining is usually related to computer science and it is achieved through statistical, on-line analytical processing, information retrieval, machine learning, expert systems which is relying on past rule of thumb and pattern recognition. This paper introduces the background of the deep learning development, and it mainly discusses the self-encoding method in the depth learning, and implements the simulation application from the self-coding method. The latest developments in information fusion technology make it possible to easily capture images of color and depth information in order to improve the image of object recognition. This paper also introduces a BP neural network model for feature learning and image classification. Our model achieves better artistic expression on standard image object sets, finally the results are more accurate and quicker in training and testing compared to other comparable architectures.
Dynamic Modeling and Control of High-Speed Automated Vehicles for Lane Change Maneuver

Lane change maneuver of high-speed automated vehicles is complicated, since it involves highly nonlinear vehicle dynamics, which is critical for the driving safety and handling stability. Addressing this challenge, we present the dynamic modeling and control of high-speed automated vehicles for lane change maneuver. A nonlinear single-track vehicle dynamics model and a multisegment lane change process model are employed. Variable time steps are utilized for the vehicle model discretization to ensure a long enough prediction horizon, while maintaining model fidelity and computational feasibility. Accordingly, the control of lane change maneuver is addressed in two successive stages. First, by considering the lane change maneuver as primarily a longitudinal control problem, velocity profiles are determined to ensure the longitudinal safety of this maneuver. Then, the associated lateral control is generated with a model-predictive controller, taking the handling stability envelope, coupled tire forces, and environmental constraints into account. Simulations demonstrate the real-time ability and stable-handling capability of the proposed approach.
Gender impact on children's knowledge and perceptions regarding cardiovascular disease risk factors: A school-based survey in Greece

BACKGROUND: Cardiovascular disease (CVD) risk factors are adopted during childhood and adolescence. Health literacy at these ages remains the cornerstone of a healthy adult life. The aim of the study was to examine the role of gender regarding CVD risk factors’ awareness and to develop an evaluation tool for the assessment of CVD risk factors’ knowledge and perception among children. METHODS: During the school years 2014–2015 and 2015–2016, 1728 students aged 10–12 years (5th and 6th grade), from 5 Greek cities (including Athens metropolitan area), were enrolled; nearly 45% were boys (participation rate varied from 95% to 100% from school to school). Students and their parents completed an anonymous questionnaire; students’ somatometric characteristics were also recorded. Schools were randomly selected. Linear regression models were applied to evaluate the impact of children's gender on knowledge and perceptions about CVD risk factors. RESULTS: Significant higher percentage of correct answers, among girls compared to boys, was revealed regarding the weekly consumption of legumes, the breakfast weekly consumption, and the effects of soft drinks on health (all P < 0.05). As far as CVD risk factors’ knowledge, significantly higher percentage of girls than boys also answered that high blood pressure and television viewing are bad for health and particularly for heart-related problems (all P < 0.05). Girls had a significantly higher mean score of 0.304 than boys, after adjusting for several confounders (P = 0.029). CONCLUSION: Health education programs should take into account gender differences in children's perception and attitudes toward CVD risk factors, in order to increase awareness of children and eventually reduce CVD risk during adulthood.
A high efficiency Selective Harmonic Elimination technique for multilevel converters

This paper proposes a selective harmonic elimination (SHE) technique capable to remove all harmonics except those having order $n=4sk\pm 1, k=1,2,3,\ldots$ from the output voltage of a $l$-level inverter, $l=2s+1$, where $s$ is the number of the dc sources. It is shown that a very low total harmonic distortion (T H D) factor is obtained. The proposed technique produces fundamental frequency pulses, therefore high efficiency is ensured. Experimental results, obtained for an 9-level inverter, confirm the validity of the proposed modulation.
The Prevalence of Dietary Supplement Use Among Elementary, Junior High, and High School Students: A Nationwide Survey in Japan

The prevalence of dietary supplement use, such as vitamins, minerals, or fish oil, has increased among children in Japan; however, whether children are using dietary supplements appropriately remains unclear. This study aimed to determine dietary supplement use among children. In August 2017, a nationwide internet preliminary survey of 265,629 mothers aged from 25 to 59 years old was undertaken. Of these, 19,041 mothers of children attending either elementary school, junior high school, or high school were selected. Among them, 16.4% were currently providing their children with dietary supplements and 5.2% had previously given dietary supplements to their children. The prevalence of dietary supplement use was higher in boys than in girls, and the prevalence increased according to their grade. A total of 2439 participants were eligible to undertake a targeted survey on dietary supplement use. Dietary supplements were being taken to maintain health, supplement nutrients, and enhance growth in both boys and girls, and many children (37.5%) were provided with vitamin and mineral supplements. Mothers mainly obtained information concerning dietary supplements via the internet, and supplements were purchased in drug stores or via the internet. The prevalence of dietary supplement use in mothers was 65.4% and may be associated with the prevalence rates in children. Some mothers reported adverse events (3.6%) in their children, such as stomachache, diarrhea, nausea and vomiting, and constipation. The cause-and-effect relationships for adverse events were not clear, but some children were given products for adults. Children are more influenced by dietary supplements compared to adults. To prevent adverse events due to inappropriate use, parental education concerning dietary supplements is essential.
An optimal control strategy for High-Speed Micro Gas Turbine Permanent-Magnet Synchronous Generator

The use of the micro-gas turbine devices is an important solution for distributed power generation and cogeneration. A cogenerating micro gas turbine of 30kW, operating with a recuperated thermal cycle, is examined under the point of view of the transient developments, induced by variations in electrical loads. The result is defined by the fuel supply law and by the response delay of the rotating and stationary components. In the paper, an optimization method in order to reduce the fuel consumption by varying load is presented. The control strategy adopted is able to determine the optimal reference value of the rotational speed and the ratio between the inlet air and fuel mass flow rate by a simple PI controller. Some results have been reported in terms of TIT temperature, global efficiency and outlet recuperator temperature to evaluate the respond time during a transient operation.
Bridging Knowledge Gaps in Neural Entailment via Symbolic Models

Most textual entailment models focus on lexical gaps between the premise text and the hypothesis, but rarely on knowledge gaps. We focus on filling these knowledge gaps in the Science Entailment task, by leveraging an external structured knowledge base (KB) of science facts. Our new architecture combines standard neural entailment models with a knowledge lookup module. To facilitate this lookup, we propose a fact-level decomposition of the hypothesis, and verifying the resulting sub-facts against both the textual premise and the structured KB. Our model, NSNet, learns to aggregate predictions from these heterogeneous data formats. On the SciTail dataset, NSNet outperforms a simpler combination of the two predictions by 3% and the base entailment model by 5%.
The Effortless Assessment of Risk States (EARS) Tool: An Interpersonal Approach to Mobile Sensing

Background To predict and prevent mental health crises, we must develop new approaches that can provide a dramatic advance in the effectiveness, timeliness, and scalability of our interventions. However, current methods of predicting mental health crises (eg, clinical monitoring, screening) usually fail on most, if not all, of these criteria. Luckily for us, 77% of Americans carry with them an unprecedented opportunity to detect risk states and provide precise life-saving interventions. Smartphones present an opportunity to empower individuals to leverage the data they generate through their normal phone use to predict and prevent mental health crises. Objective To facilitate the collection of high-quality, passive mobile sensing data, we built the Effortless Assessment of Risk States (EARS) tool to enable the generation of predictive machine learning algorithms to solve previously intractable problems and identify risk states before they become crises. Methods The EARS tool captures multiple indices of a person’s social and affective behavior via their naturalistic use of a smartphone. Although other mobile data collection tools exist, the EARS tool places a unique emphasis on capturing the content as well as the form of social communication on the phone. Signals collected include facial expressions, acoustic vocal quality, natural language use, physical activity, music choice, and geographical location. Critically, the EARS tool collects these data passively, with almost no burden on the user. We programmed the EARS tool in Java for the Android mobile platform. In building the EARS tool, we concentrated on two main considerations: (1) privacy and encryption and (2) phone use impact. Results In a pilot study (N=24), participants tolerated the EARS tool well, reporting minimal burden. None of the participants who completed the study reported needing to use the provided battery packs. Current testing on a range of phones indicated that the tool consumed approximately 15% of the battery over a 16-hour period. Installation of the EARS tool caused minimal change in the user interface and user experience. Once installation is completed, the only difference the user notices is the custom keyboard. Conclusions The EARS tool offers an innovative approach to passive mobile sensing by emphasizing the centrality of a person’s social life to their well-being. We built the EARS tool to power cutting-edge research, with the ultimate goal of leveraging individual big data to empower people and enhance mental health.
Seed bank and growth comparisons of native (Virgilia divaricata) and invasive alien (Acacia mearnsii and A. melanoxylon) plants: implications for conservation

Background Invasive alien plants with long-lived dormant seed banks and fast growth rates are difficult to manage. Acacia mearnsii and Acacia melanoxylon are two such invaders in the southern Cape of South Africa which occasionally co-occur with a native, ecologically analogous species, Virgilia divaricata. We compared the performance of these three species to determine potential for the native species to be used in management of the invasives. Methods We compared the study species in terms of (i) soil seed bank densities, their vertical distribution, and the viability of seeds underneath the canopies of mature trees; (ii) seedling growth from planted seeds over a period of three months; and (iii) growth rates of saplings over a period of 10 months in stands that have naturally regenerated in the field (these stands were dominated by A. mearnsii) and where saplings have been exposed to varying levels of competition from surrounding saplings. Results Seed bank densities differed significantly among species but not among soil depth classes. Acacia mearnsii had the highest seed bank densities (mean of 7,596 seeds m−2), followed by V. divaricata (938 seeds m−2) and A. melanoxylon (274 seeds m−2). Seed viability was high (87–91%) in all three study species and did not differ significantly among species or soil depth classes. As seedlings, V. divaricata significantly outgrew A. mearnsii in terms of height, root and shoot dry mass, and root:shoot ratio. Relative growth (the relationship between growth in height and initial height) was negative in the seedlings of both species. Trends during the sapling stage were opposite to those during the seedling stage; A. mearnsii (but not A. melanoxylon) saplings significantly outgrew V. divaricata saplings in height, while relative growth rates were positive in all species. Sapling growth of all species was furthermore uninfluenced by the collective biomass of surrounding competitors. Discussion Our findings suggest that amongst the measures considered, A. mearnsii’s success as an invader is primarily attributable to its large seed banks, and secondly to its vigorous growth in height as saplings. However, the superior growth performance of V. divaricata seedlings and no apparent negative effect of competition from the acacias on sapling growth show promise for its use in integrated management of the acacias.
Loop Closure Detection for Visual SLAM Based on Deep Learning

Simultaneous Localization and Mapping(SLAM) is used to solve the problem of mobile robot navigation and map building in an unknown environment. Loop closure detection is the key part of SLAM, which determines the precision and stability of SLAM to a great extent. Closed loop detection is affected by changes in light and dynamic environment. In order to improve the accuracy of the loop closure detection, this paper introduces the model of deep learning to transform the visual SLAM process. On the basis of Sparse Autoencoder(SAE), the activation function of the output layer is changed to the identity function to solve the problem that the input sample needs to be scaled and does not apply to the color image. Then, the input image and the trained network do convolution and pooling operation. Not only can significantly reduce the feature dimension, but also can improve the effect of feature description. The test conducted by using the open data set shows that the proposed method can effectively solve the problem of visual SLAM loop closure detection.
SAR Targets Classification Based on Deep Memory Convolution Neural Networks and Transfer Parameters

Deep learning has obtained state-of-the-art results in a variety of computer vision tasks and has also been used to solve SAR image classification problems. Deep learning algorithms typically require a large amount of training data to achieve high accuracy. In contrast, the size of SAR image datasets is often comparatively limited. Therefore, this paper proposes a novel method, deep memory convolution neural networks (M-Net), to alleviate the problem of overfitting caused by insufficient SAR image samples. Based on the convolutional neural networks (CNN), M-Net adds an information recorder to remember and store samples’ spatial features, and then it uses spatial similarity information of the recorded features to predict unknown sample labels. M-Net's use of this information recorder may cause difficulties for convergence if conventional CNN training methods were directly used to train M-Net. To overcome this problem, we propose a transfer parameter technique to train M-Net in two steps. The first step is to train a CNN, which has the same structure as the part of CNN incorporated in M-Net, to obtain initial training parameters. The second step applies the initialized parameters to M-Net and then trains the entire M-Net. This two-step training approach helps us to overcome the nonconvergence issue, and also reduces training time. We evaluate M-Net using the public benchmark MSTAR dataset, and achieve higher accuracy than several other well-known SAR image classification algorithms.
Noise Figure of a Balanced Amplifier

Noise figure expression of a balanced amplifier built with lossy divider and combiner and two imperfectly noise-matched component amplifiers is derived analytically using the method of noise waves in terms of the noise parameters of the amplifiers. We present analytical expressions for the three noise parameters of the balanced amplifier. We show that a low-noise amplifier optimized to be used as a stand-alone amplifier is not the optimum choice for the component amplifiers of a balanced amplifier. The derived expressions can be used to choose or optimize the component amplifiers.
Why Do Neural Response Generation Models Prefer Universal Replies?

Recent advances in sequence-to-sequence learning reveal a purely data-driven approach to the response generation task. Despite its diverse applications, existing neural models are prone to producing short and generic replies, making it infeasible to tackle open-domain challenges. In this research, we analyze this critical issue in light of the model's optimization goal and the specific characteristics of the human-to-human dialog corpus. By decomposing the black box into parts, a detailed analysis of the probability limit was conducted to reveal the reason behind these universal replies. Based on these analyses, we propose a max-margin ranking regularization term to avoid the models leaning to these replies. Finally, empirical experiments on case studies and benchmarks with several metrics validate this approach.
Evaluating the Utility of Hand-crafted Features in Sequence Labelling

Conventional wisdom is that hand-crafted features are redundant for deep learning models, as they already learn adequate representations of text automatically from corpora. In this work, we test this claim by proposing a new method for exploiting handcrafted features as part of a novel hybrid learning approach, incorporating a feature auto-encoder loss component. We evaluate on the task of named entity recognition (NER), where we show that including manual features for part-of-speech, word shapes and gazetteers can improve the performance of a neural CRF model. We obtain a F 1 of 91.89 for the CoNLL-2003 English shared task, which significantly outperforms a collection of highly competitive baseline models. We also present an ablation study showing the importance of auto-encoding, over using features as either inputs or outputs alone, and moreover, show including the autoencoder components reduces training requirements to 60%, while retaining the same predictive accuracy.
Hardware Compilation of Deep Neural Networks: An Overview

Deploying a deep neural network model on a reconfigurable platform, such as an FPGA, is challenging due to the enormous design spaces of both network models and hardware design. A neural network model has various layer types, connection patterns and data representations, and the corresponding implementation can be customised with different architectural and modular parameters. Rather than manually exploring this design space, it is more effective to automate optimisation throughout an end-to-end compilation process. This paper provides an overview of recent literature proposing novel approaches to achieve this aim. We organise materials to mirror a typical compilation flow: front end, platform-independent optimisation and back end. Design templates for neural network accelerators are studied with a specific focus on their derivation methodologies. We also review previous work on network compilation and optimisation for other hardware platforms to gain inspiration regarding FPGA implementation. Finally, we propose some future directions for related research.
Burnout symptoms in forensic mental health nurses: Results from a longitudinal study

Burnout in nursing staff is a major cause for turnover and absenteeism. Identifying risk and protective factors may be helpful in decreasing burnout symptoms. Moreover, research indicates that ambulatory assessments of the autonomic nervous system might be helpful in detecting long-term stress and burnout symptoms. One hundred and ten forensic nursing staff members completed questionnaires measuring experiences with aggressive behaviour, emotional intelligence, personality, and job stress during four waves of data collection across a 2-year period. Multilevel analyses were used to test the predicted associations and moderation effects with (the development of) burnout symptoms. Burnout was predicted by a combination of emotional intelligence, job stress, aggression, personality factors, and skin conductance, but no moderation effects over time were found. Over a period of 2 years, the model approximately predicts a change in one burnout category on the Maslach Burnout Inventory. The amount of burnout symptoms in nurses might be used as an indicator to predict turnover and absenteeism considering the increase in symptoms over time. Nursing staff who experience severe aggression and who have relatively low levels of emotional intelligence and altruism and high levels of neuroticism and job stress should be monitored and supported to decrease the risk of burnout. Staff members can be trained to increase their emotional intelligence and relieve stress to decrease their burnout symptoms and turnover and absenteeism on the long term. Ambulatory assessment might be helpful as a nonintrusive way to detect increasing levels of burnout.
A Soft Gripper Based on Dielectric Elastomer Actuator

Dielectric elastomer (DE) is known as a promising candidate for artificial muscle due to its inherent flexibility, large strain and high efficiency. This paper introduces a soft gripper based on dielectric elastomer actuators (DEAs), which utilizes electro-adhesion to enhance the holding force and ensure the adaptability to various objects. Comprised of pre-stretched VHB 4910 membrane and covered with silicone membrane, the gripper is capable of bending motion and holding objects with different structure and material. The design, fabrication and experimental result of the soft gripper are demonstrated in this paper.
Parameters of LCD BCH codes with two lengths

In this paper, we study LCD BCH codes over the finite field GF \begin{document}$(q)$\end{document} with two types of lengths \begin{document}$n$\end{document} , where \begin{document}$n = q^l+1$\end{document} and \begin{document}$n = (q^l+1)/(q+1)$\end{document} . Several classes of LCD BCH codes are given and their parameters are determined or bounded by exploring the cyclotomic cosets modulo \begin{document}$n$\end{document} . For \begin{document}$n = q^l+1$\end{document} , we determine the dimensions of the codes with designed distance \begin{document}$δ$\end{document} , where \begin{document}$q^{\lfloor\frac{l+1}{2}\rfloor}+1 ≤ δ ≤q ^{\lfloor\frac{l+3}{2}\rfloor}+1$\end{document} . For \begin{document}$n = (q^l+1)/(q+1)$\end{document} , the dimensions of the codes with designed distance \begin{document}$δ$\end{document} are presented, where \begin{document}$2 ≤ δ ≤q ^\frac{l-1}{2}+1$\end{document} .
Elliptic Curve Cryptography (ECC) Cipher Suites for Transport Layer Security (TLS) Versions 1.2 and Earlier

This document describes key exchange algorithms based on Elliptic Curve Cryptography (ECC) for the Transport Layer Security (TLS) protocol. In particular, it specifies the use of Ephemeral Elliptic Curve Diffie-Hellman (ECDHE) key agreement in a TLS handshake and the use of Elliptic Curve Digital Signature Algorithm (ECDSA) and Edwards Digital Signature Algorithm (EdDSA) as authentication mechanisms. This document obsoletes and replaces RFC 4492.