Person reidentification aims to recognize a specific pedestrian from uncrossed surveillance camera views. Most re-id methods perform the retrieval task by comparing the similarity of pedestrian features extracted from deep learning models. Therefore, learning a discriminative feature is critical for person reidentification. Many works supervise the model learning with one or more loss functions to obtain the discriminability of features. Softmax loss is one of the widely used loss functions in re-id. However, traditional softmax loss inherently focuses on the feature (...) separability and fails to consider the compactness of within-class features. To further improve the accuracy of re-id, many efforts are conducted to shrink within-class discrepancy as well as between-class similarity. In this paper, we propose a circle-based ratio loss for person re-identification. Concretely, we normalize the learned features and classification weights to map these vectors in the hypersphere. Then we take the ratio of the maximal intraclass distance and the minimal interclass distance as an objective loss, so the between-class separability and within-class compactness can be optimized simultaneously during the training stage. Finally, with the joint training of an improved softmax loss and the ratio loss, the deep model could mine discriminative pedestrian information and learn robust features for the re-id task. Comprehensive experiments on three re-id benchmark datasets are carried out to illustrate the effectiveness of the proposed method. Specially, 83.12% mAP on Market-1501, 71.66% mAP on DukeMTMC-reID, and 66.26%/63.24% mAP on CUHK03 labeled/detected are achieved, respectively. (shrink)

The rapid improvement of technologies such as artificial intelligence in recent years has resulted in the development of smart technologies that can influence learning performance in different fields. The purpose of study is to explore the link between smart technology and learning performance. Using the S-O-R model as a framework, the researchers argue that smart technology will increase corporate trust, self-efficacy, and well-being, resulting in improved learning performance. The current model regards corporate trust and self-efficacy as relationship factors and investigates (...) their direct influence on employee well-being and learning performance and the mediating role played by these variables. Additionally, the function of employee well-being in moderating the relationship between corporate trust, self-efficacy, and employee learning performance is also explored. The respondents in the present study are made up of employees from 10 logistics companies located in China. The data analysis is conducted using the AMOS software. The results show that that smart technologies can affect learning performance through corporate trust, self-efficacy, and employee well-being. The implementation of smart technology initiatives by corporations may provide positive workplace outcomes for employees, corporations, and the relationship between employees and the companies that employ them. (shrink)

IntroductionDue to the actual limitation of training conditions, the freestyle skiing aerials winter training term is short. Training tasks such as adaptability training and developing new skills are needed in summer training. When facing different training environments, freestyle skiing aerial athletes’ executive control over their abilities could be affected, which can affect their performance. Therefore, we want to research the effect of training conditions on executive control in freestyle skiing aerials athletes and its neural mechanism.Materials and methodsThirty-two freestyle skiing aerials (...) athletes were recruited. We evaluated their executive control and used fNIRS to measure oxygenated hemoglobin concentration changes in the prefrontal cortex during a rapid event-related design go/nogo task with different training condition-activated materials.ResultsAthletes’ behavior control in the summer condition has a lower accuracy than it is in the control condition. Athletes’ behavior control in the summer and winter training conditions had a longer reaction time than that in the control condition. The activation of the bilateral dlPFC and orbitofrontal cortex had a significant main effect across training conditions when freestyle skiing aerial athletes completed executive control tasks. The activation of athletes’ bilateral vlPFC and left dlPFC had an interaction between training conditions and behavioral control.ConclusionDifferent training conditions can lead to freestyle skiing aerial athletes executive control ability to drop, players in different training conditions show less activation on both sides of the vlPFC and orbitofrontal. The bilateral vlPFC and left dlPFC have an integrated effect on behavior inhibition across training conditions. (shrink)

Resource allocation has always been a key technology in wireless sensor networks, but most of the traditional resource allocation algorithms are based on single interface networks. The emergence and development of multi-interface and multichannel networks solve many bottleneck problems of single interface and single channel networks, it also brings new opportunities to the development of wireless sensor networks, but the multi-interface and multichannel technology not only improves the performance of wireless sensor networks but also brings great challenges to the resource (...) allocation of wireless sensor networks. Edge computing changes the traditional centralized cloud computing processing method into a method that reduces computing storage capacity to the edge of the network and faces users and terminals. Realize the advantages of lower latency, higher bandwidth, and fast response. Therefore, this paper proposes a joint optimization algorithm of resource allocation based on edge computing. We establish a wireless sensor allocation model and then propose our algorithm model combined with the advantages of edge computing. Compared with the traditional allocation algorithm, it can further improve the resource utilization, reduce the network energy consumption, increase network capacity, and reduce the complexity of the schemes. (shrink)

This book examines some possible ethical principles to resolve moral dilemmas involving water. Existing problems in current water management practices are discussed in light of these principles. Transformation of human water ethics has the potential to be far more effective, cheaper and acceptable than some existing means of “regulation”, but transformation of personal and societal ethics need time because the changes to ethical values are slow.

Ben shu fen shang xia liang ge bu fen,Shang bian shi zhu zi yu lei wen xian yan jiu,Xia bian shi zhu zi yu lei ci hui yan jiu.Quan shu yi gong fen wei 9 zhang.Di yi zhang xu lun,Shou xian jie shao le zhu xi yu zhu zi yu lei,Qi ci jin xing le xue shu hui gu,Zong jie le jin 20 nian lai(jie zhi 2009 nian)Guo nei wai zhu zi yu lei yan jiu de sheng kuang he qu (...) de de zhu yao cheng jiu,Zui hou ti chu ben shu de yan jiu fang fa he yi yi.Di er zhang yan jiu tong xing ben zhu zi yu lei ji cheng shu zhi qian de ji ge zhong yao ban ben.Di san zhang wei zhong hua ben zhu zi yu lei yu song ke ben hui an xian sheng zhu wen gong yu lu de yi wen bi jiao.Di si zhang wei zhong hua ben zhu zi yu lei yu chao xian gu xie ben zhu zi yu lei yi wen bi jiao.Di wu zhang yan jiu zhu zi yu lei de wen xian xue jia zhi.Di liu zhang he di qi zhang ji zhong yan jiu zhu zi yu lei de fu yin ci.Di ba zhang tao lun zhu zi yu lei zhong de duo yin jie ci yu.Di jiu zhang zong jie chan shu zhu zi yu lei de yu ti te zheng,Yu yan te dian he yan jiu jia zhi. (shrink)

BackgroundCoronary heart disease is closely associated with cognitive impairment, especially in severe cases of heart failure. However, it is unclear whether cardiac systolic function plays a role in the relationship between pre-existing CHD and cognitive impairment in subjects without clinical heart failure.MethodsIn total, 208 subjects from the First Affiliated Hospital of Xi’an Jiaotong University were recruited from June 2014 to January 2015, and were divided into CHD and non-CHD groups according to the inclusion and exclusion criteria. The global cognitive function (...) of all subjects was assessed by the Mini-Mental State Examination and cognitive impairment was defined as the score lower than the cutoff value. Left ventricular ejection fraction was measured using transthoracic echocardiograms. The relationship among pre-existing CHD, LVEF, and cognitive impairment was analyzed by multivariate logistic regression.ResultsIn total, 34 subjects met the criteria of cognitive impairment. Univariate analysis showed that the cognitive impairment prevalence in the CHD group was significantly higher than that in the non-CHD group. Multivariate logistic analysis revealed that CHD was significantly associated with a higher risk of cognitive impairment after adjusting for confounds except for LVEF. However, the OR of CHD decreased when LVEF was further corrected as a continuous variable, and LVEF was negatively associated with the risk of cognitive impairment.ConclusionPre-existing CHD is associated with a higher risk of cognitive impairment, and such an association can be considerably explained by reduced LVEF. An impaired cardiac systolic function may play a key role in the relationship between CHD and cognitive impairment among patients with pre-heart failure conditions. (shrink)

Recently, the pandemic caused by COVID-19 is severe in the entire world. The prevention and control of crimes associated with COVID-19 are critical for controlling the pandemic. Therefore, to provide efficient and convenient intelligent legal knowledge services during the pandemic, we develop an intelligent system for legal information retrieval on the WeChat platform in this paper. The data source we used for training our system is “The typical cases of national procuratorial authorities handling crimes against the prevention and control of (...) the new coronary pneumonia pandemic following the law”, which is published online by the Supreme People’s Procuratorate of the People’s Republic of China. We base our system on convolutional neural network and use the semantic matching mechanism to capture inter-sentence relationship information and make a prediction. Moreover, we introduce an auxiliary learning process to help the network better distinguish the relation between two sentences. Finally, the system uses the trained model to identify the information entered by a user and responds to the user with a reference case similar to the query case and gives the reference legal gist applicable to the query case. (shrink)