A novel application of a microaccelerometer for target classification

This paper presents a novel method of target classification by means of a microaccelerometer. Its principle is that the seismic signals from moving vehicle targets are detected by a microaccelerometer, and targets are automatically recognized by the advanced signal processing method. The detection system based on the microaccelerometer is small in size, light in weight, has low power consumption and low cost, and can work under severe circumstances for many different applications, such as battlefield surveillance, traffic monitoring, etc. In order to extract features of seismic signals stimulated by different vehicle targets and to recognize targets, seismic properties of typical vehicle targets are researched in this paper. A technique of artificial neural networks (ANNs) is applied to the recognition of seismic signals for vehicle targets. An improved back propagation (BP) algorithm and ANN architecture have been presented to improve learning speed and avoid local minimum points in error curve. The improved BP algorithm has been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. Through experiments, it can be proven that target seismic properties acquired are correct, ANN is effective to solve the problem of classification and recognition of moving vehicle targets, and the microaccelerometer can be used in vehicle target recognition. <br /

Adversarial Training Towards Robust Multimedia Recommender System

With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advance on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in https://github.com/duxy-me/AMR.Comment: TKD

Research on seismic signals for vehicle targets and recognition by data fusion

This paper researches seismic signals of typical vehicle targets in order to extract features and to recognize vehicle targets. As a data fusion method, the technique of artificial neural networks combined with genetic algorithm(ANNCGA) is applied for recognition of seismic signals that belong to different kinds of vehicle targets. The technique of ANNCGA and its architecture have been presented. The algorithm had been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. Through experiments, it can be proven that seismic properties of target acquired are correct, ANNCGA data fusion method is effective to solve the problem of target recognition. <br /

A Systematic Review on Different Treatment Methods of Bone Metastasis from Cancers

Background and objective Skeletal metastase is one of the most common complications related to advanced cancer. The aim of this study is to analyze the effectiveness and safety of radiotherapy plus intravenous bisphosphonates versus radiotherapy alone for treating bone metastasis. Methods We searched the Cochrane Library, PubMed, EMBASE, CBM, CNKI and VIP, as well as the reference lists of reports and reviews. The quality of included trials was evaluated by the Cochrane Handbook. Data were extracted and evaluated by two reviewers independently. The Cochrane Collaboration’s Rev-Man 5.0 was used for data analysis. Results Twenty-two trials involving 1 585 patients were included. Compared with radiotherapy alone, radiotherapy plus intravenous bisphosphonates was more effective in total effective rate of pain relive (RR=1.21, 95%CI: 1.13-1.30, P < 0.001), average abated time (WMD=16.00, 95%CI: 10.12-21.88, P < 0.001), and quality of life (RR=1.25, 95%CI: 1.08-1.45, P=0.003, with significant differences. Side effects have no significant differences between the two groups except fever (RR=5.61, 95%CI: 3.11-10.13, P < 0.001). Conclusion Current evidence supports more effective of radiotherapy plus intravenous bisphosphonates for bone metastases. The combine treatment is safe and effective

A meta-analysis of Pemetrexed plus Platinum Chemotherapy versus Gemcitabine plus Platinum Chemotherapy for Advanced Non-small Cell Lung Cancer

Background and objective Whether pemetrexed plus platinum (PP) regimen is superior to gemcitabine plus platinum (GP) regimen for patients with advanced non-small cell lung cancer (NSCLC) is unclear. The aim of this study is to evaluate the efficacy and safety of PP versus GP regimens for patients with NSCLC. Methods We searched relevant randomized controlled trials (RCTs) from Pubmed, EMBASE, Cochrane Library, Chinese Journal Full-text Database, Chinese Biomedical Literature Database, Chinese Scientific Journals Full-text Database, and traced the related references to obtain the information that has not been found. We made quality assessment of qualified RCTs assessed by the exclusion and inclusion criteria and used RevMan 5.0 provided by the Cochrane Collaboration to perform meta-analysis. Results Four RCTs involving 2,235 patients were identified. There were no statistical differences between PP and GP regimens in one-year survival rate (OR=1.09, 95%CI: 0.91-1.29), the efficiency of disease (OR=1.00, 95%CI: 0.40-2.52), but overall survival (MD=0.26, 95%CI: 0.21-0.30), alopecia (OR=0.51, 95%CI: 0.39-0.66) and hematologic toxicity were significantly different. Conclusion The clinical efficiency of PP and GP regimens for advanced NSCLC was similar, but the side effects were different. The toxicity of PP regimen has the tendency to be more tolerable

Outer Product-based Neural Collaborative Filtering

In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimensions of the embedding space. In contrast to existing neural recommender models that combine user embedding and item embedding via a simple concatenation or element-wise product, our proposal of using outer product above the embedding layer results in a two-dimensional interaction map that is more expressive and semantically plausible. Above the interaction map obtained by outer product, we propose to employ a convolutional neural network to learn high-order correlations among embedding dimensions. Extensive experiments on two public implicit feedback data demonstrate the effectiveness of our proposed ONCF framework, in particular, the positive effect of using outer product to model the correlations between embedding dimensions in the low level of multi-layer neural recommender model. The experiment codes are available at: https://github.com/duxy-me/ConvNCFComment: IJCAI 201

Effectiveness of small-angle episiotomy on incisional laceration rate, suturing time, and incisional bleeding in primigravida: A meta-analysis

ObjectiveTo investigate the effect of small-angle lateral perineal incision on postoperative perineal rehabilitation in primiparous women.MethodThe Cochrane Library, PubMed, Embase, CINAHL, CNKI, WanFang, VIP, and the Chinese Biomedical Literature Database were searched for randomized controlled trials (RCTs) on the effect of small-angle episiotomy on postoperative maternal perineal wound rehabilitation in puerpera until April 3, 2022. Two researchers independently performed literature screening, data extraction and evaluation of risk of bias in the included literature, and statistical analysis of the data was performed using RevMan 5.4 and Stata 12.0 software.ResultA total of 25 RCTs were included, with a total sample of 6,366 cases. Meta-analysis results showed that the use of small-angle episiotomy reduced incisional tearing [OR = 0.32, 95% CI (0.26, 0.39)], shortened incisional suture time [MD = −4.58 min, 95% CI (−6.02, −3.14)] and reduced incisional bleeding [MD = −19.08 mL, 95% CI (−19.53, −18.63)], with statistically significant differences (all p &lt; 0.05). There was no significant difference in the rate of severe laceration between the two groups [OR = 2.32, 95% CI (0.70, 7.70), p &gt; 0.05].ConclusionThe use of a small-angle episiotomy during vaginal delivery can reduce the incision tear rate without increasing the incidence of severe perineal laceration, while shortening the incisional suturing time and reducing incisional bleeding. It can be used clinically according to birth canal conditions of the maternal, the intrauterine condition of the fetus and maternal needs.Systematic Review RegistrationPROSPERO International Prospective Register of Systematic Reviews [CRD42022369698]; [https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=369698]

Comparing the diagnostic accuracy of five common tumour biomarkers and CA19-9 for pancreatic cancer: a protocol for a network meta-analysis of diagnostic test accuracy

Introduction: Surgical resection is the only curative treatment for patients with resectable pancreatic cancer. Unfortunately, 80%–85% of patients present with locally advanced or metastatic unresectable pancreatic cancer at the time of diagnosis. Detection of pancreatic cancer at early stages remains a great challenge due to lack of accurate detection tests. Recommendations in existing clinical practice guidelines on early diagnosis of pancreatic cancer are inconsistent and based on limited evidence. Most of them endorse measuring serum CA19-9 as a complementary test, but also state that it is not recommended for diagnosing early pancreatic cancer. There are currently no other tumour-specific markers recommended for diagnosing early pancreatic cancer. This study aims to evaluate and compare the accuracy of five common tumour biomarkers (CA242,carcino-embryonic antigen (CEA)), CA125, microRNAs and K-ras gene mutation) and CA19-9 and their combinations for diagnosing pancreatic cancer using network meta-analysis method, and to rank these tests using a superiority index. Methods and analysis: PubMed, EMBASE and the Cochrane Central Register of Controlled Trials will be searched from inception to April 2017. The search will include the above-mentioned tumour biomarkers for diagnosing pancreatic cancer, including CA19-9. The risk of bias for each study will be independently assessed as low, moderate or high using criteria adapted from the Quality Assessment of Diagnostic Accuracy Studies 2. Network meta-analysis will be performed using STATA V.12.0 and R software V.3.4.1. The competing diagnostic tests will be ranked by a superiority index. Ethics and dissemination: Ethical approval and patient consent are not required since this study is a network meta-analysis based on published studies. The results of this network meta-analysis will be submitted to a peer-reviewed journal for publication