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Self-attention Multi-view Representation Learning with Diversity-promoting Complementarity

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Self-attention Multi-view Representation Learning with Diver...

第32届中国控制与决策会议

作者： jian-wei liu Xi-hao Ding Run-kun Lu Xionglin LUO Department of automation School of Information Science and EngineeringChina University of Petroleum

Multi-view learning attempts to generate a model with a better performance by exploiting the consensus and/or complementarity among multi-view ***,in terms of complementarity,most existing approaches only can find representations with single complementarity rather than complementary information with *** this paper,to utilize both complementarity and consistency simultaneously,give free rein to the potential of deep learning in grasping diversity-promoting complementarity for multi-view representation learning,we propose a novel supervised multi-view representation learning algorithm,called Self-Attention Multi-View network with Diversity-Promoting Complementarity（SAMVDPC）,which exploits the consistency by a group of encoders,uses self-attention to find complementary information entailing *** experiments conducted on eight real-world datasets have demonstrated the effectiveness of our proposed method,and show its superiority over several baseline methods,which only consider single complementary information.

关键词： Multi-view Learning Self-attention Mechanism Complementary Information with Diversity

Domain generalization with semi-supervised learning for people-centric activity recognition

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Science China(Information Sciences) 2025年第1期68卷 171-188页

作者： Jing liu wei ZHU Di LI Xing HU Liang SONG Academy for Engineering & Technology Fudan University Shanghai East-bund Research Institute on Networking Systems of AI School of Optoelectronic Information and Computer Engineering University of Shanghai for Science & Technology

People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.

关键词： activity recognition deep learning domain generalization semi-supervised learning adversarial training

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酿酒酵母感受态细胞的低温保存及酵母菌落PCR-快速筛选鉴定

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生物化学与生物物理进展 2003年第6期30卷 956-959页

作者：叶玲刘建伟刘静中国人民解放军总医院老年医学研究所北京100853

异源蛋白在酵母中的表达涉及到大量重组子的筛选鉴定 .以直接煮沸 2～ 5min后的酵母菌落水悬浮液为模板 ,建立了酵母克隆的PCR快速筛选方法 .介绍了PCR反应中减少引物二聚体、提高反应特异性及

关键词： PCR筛选转化酿酒酵母感受态细胞低温保存

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Discovery of a new family member of arsenic–vanadium polyoxometalates with high catalytic performance for one-pot coupling of CO2, epoxides and amines

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Tungsten 2025年第1期7卷 80-87页

作者： Fang-Qian Wang Yin Zhang wei-wei Wang Mei-Qian Fu Hong-Bing Mo wei-Dong Yu Chao liu Jun Yan College of Chemistry and Chemical Engineering Central South University Junior Education Department Changsha Normal University School of Science Hunan University of Technology and Business

Arsenic-vanadium polyoxometalates（POMs） represent a family of tremendous potential catalysts owing to their peculiar physical and chemical *** in CO2conversion,using POMs to catalyze the conversion of CO2and epoxide into chemical intermediates is the research ***,A new family number of arsenic-vanadium POMs,[N（CH3）4]4[As8V12O38（H2O）]·2H2O({As8V12O38}),was reported and characterized by single-crystal X-ray diffraction,fourier transform infrared spectroscopy,X-ray photoelectron spectroscopy,*** from other allotropicity,{As8V12O38}shows a different structure pattern with an I-43d *** obtained {As8V12O38} was employed as a catalyst for the *** initial catalytic reaction confirmed that {As8V12O38} can catalyze the formation of epoxy carbonate by using 1.01 kPa CO2and epoxide at 70℃.Furtherly,when added amine in the catalytic system,oxazolidinone could be obtained by high yields,which provides an efficient way to convert CO2into profitable ***,a possible catalytic reaction mechanism was proposed by using different characterization analyses.{As8V12O38} first catalyzes the epoxide and CO2to form an epoxy carbonate and then catalyzes epoxy carbonate and benzylamine into oxazolidinone.

关键词： Arsenic CO2 conversion Oxazolidinone Polyoxometalates Vanadium

Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss

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Multi-View Non-negative Matrix Factorization Discriminant Le...

第32届中国控制与决策会议

作者： jian-wei liu Yuan-Fang Wang Run-Kun Lu Xiong-Lin Luo Department of Automation China University of PetroleumBeijing Campus(CUP)

Multi-view learning accomplishes the task objectives of classification by leveraging the relationships between different views of the same *** existing methods usually focus on consistency and complementarity between multiple *** not all of this information is useful for classification ***,it is the specific discriminating information that plays an important *** Zhang et al explores the discriminative and non-discriminative information existing in common and view-specific parts among different views via joint non-negative matrix *** this paper,we improve this algorithm on this basis by using the cross entropy loss function to constrain the objective function *** last,we implement better classification effect than original on the same data sets and show its superiority over many state-of-the-art algorithms.

关键词： Multi-View learning Matrix factorization Cross entropy loss Discriminative learning Classification

Multi-view classification via Multi-view Partially Common Feature Latent Factor Learning

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Multi-view classification via Multi-view Partially Common Fe...

第32届中国控制与决策会议

作者： jian-wei liu Hao-Jie Xie Run-Kun Lu Xiong-Lin Luo Department of Automation China University of PetroleumBeijing Campus

In the real world,multi-view data usually consists of different representations or *** are two key factors in multi-view data:consistency and *** most multi-view learning algorithms based on nonnegative matrix factorization（NMF）,the proposed method can make full use of the consistency and complementarity of *** paper presents a new semi-supervised multi-view learning algorithm,called Multi-View Partially Common Feature Latent Factor（MVPCFLF） *** is an extended learning form based on Partially Shared Latent Factor（PSLF） learning,which can make full use of common and special features to obtain latent *** key idea of MVPCFLF is to increase the constraints on common feature matrix so as to maintain the consistency of common *** experimental results show that MVPCFLF is more effective than the existing multi-view learning algorithms.

关键词： Multi-view Learning Common Feature Special Feature Latent Representation Nonnegative Matrix Factorization Complementarity and Consistency

Multi-view classifier based on Probabilistic Collaborative Representation and Latent Representation

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Multi-view classifier based on Probabilistic Collaborative R...

第32届中国控制与决策会议

作者： jian wei liu Hui dan Zhao Run-kun Lu Xiong lin Luo China University of Petroleum

Multi-view is quite more effective at improving the training of model than merely using single ***,most existing multi-view learning algorithms only either pay attention to consistency or complementary principle among views,not making full use of multi-view *** to its high complexity,algorithm considering both complementarity and consistency has limited ability to process large-scale *** the basis of Probabilistic Collaborative Representa-tion based Classifier（ProCRC）,we propose Probabilistic Collaborative Representation based Classifier for Multi-View（ProCRC-MV）,which jointly maximizes the likelihood that a test example belongs to the cosubspace of each *** subspace in the process of collaborative representation,considering consistency and complementarity concur-rently,ProCRC-MV can achieve promising classification ***,it has low computational complexity,fast running speed,and can still maintain good performance when dealing with large-scale ***-MV has the ability for subspace learning based on self-representation,so we combain latent representation learning for better search-ing subspace with ProCRC-MV to construct a novel classifier called LProCRC-MV,the ability of LProCRC-MV to process complex data is further enhanced comparing with ProCRC-MV.

关键词： Probabilistic Collaborative Representation Multi-View Learning Complementarity and Consistency Subspace Learning,Latent Representation

Gonadotropin-releasing hormone agonists cotreatment during chemotherapy in borderline ovarian tumor and ovarian cancer patients

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Chinese Medical Journal 2013年第4期126卷 688-691页

作者： ZHU Hong-lan WANG Yan LI Xiao-ping WANG Chao-hua WANG Yue CUI Heng WANG jian-liu wei Li-hui Department of Obstetrics and Gynecology Peking University People's Hospital Beijing 100044 China

Background Recently, conservative surgery is acceptable in young patients with borderline ovarian tumor and ovarian cancer. The preservation of these patients＇ future fertility has been the focus of recent interest. This study aimed to observe the effect of gonadotropin-releasing hormone agonists （GnRHa） cotreatment during chemotherapy in borderline ovarian tumor and ovarian cancer patients. Methods Sixteen patients who were treated with fertility preservation surgery for borderline ovarian tumor and ovarian cancer and then administered GnRHa during chemotherapy in Peking University People＇s Hospital from January 2006 to July 2010 were retrospectively analyzed. This group was compared with a control group of 16 women who were treated concurrently with similar chemotherapy （n=5） without GnRHa or were historical controls （n=11）. The disease recurrence, the menstruation status and reproductive outcome were followed up and compared between the two groups. Results There were no significant differences between both groups regarding age, body weight, height, marriage status, classification of the tumors, stage of the disease, as were the cumulative doses of each chemotherapeutic agent. One （1/16） patient in the study group while 2 （2/16） patients in the control group relapsed 2 years after conclusion of the primary treatment （P 〉0.05）. All of the 16 women in the study group compared with 11 of the 16 patients in the control group resumed normal menses 6 months after the termination of the treatment （P 〈0.05）. There were 4 spontaneous pregnancies in the study group while 2 in the control group, all of the neonates were healthy. Conclusions GnRHa administration before and during chemotherapy in borderline ovarian tumor and ovarian cancer patients who had undergone fertility preservation operation may bring up higher rates of spontaneous resumption of menses and a better pregnancy rate. Long-term follow up and large scale clinical studies are required.

关键词： gonadotropin-releasing hormone agonists fertility preservation," chemotherapy," borderline ovarian tumor, ovarian cancer

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PI3K/AKT signaling and neuroprotection in ischemic stroke:molecular mechanisms and therapeutic perspectives

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Neural Regeneration Research 2025年第10期20卷 2758-2775页

作者： Tianlong liu Xiaolin Li Xiaowei Zhou wei Chen Aidong Wen Minna liu Yi Ding Department of Pharmacy Xijing HospitalFourth Military Medical UniversityXi’anShaanxi ProvinceChina Department of Pharmacy The 940 Hospital of Joint Logistics Support Forces of Chinese PLALanzhouGansu ProvinceChina Department of Pharmacy The 944 Hospital of Joint Logistics Support Forces of Chinese PLAJiuquanGansu ProvinceChina Department of Nephrology The 940 Hospital of Joint Logistics Support Forces of Chinese PLALanzhouGansu ProvinceChina

It has been reported that the PI3K/AKT signaling pathway plays a key role in the pathogenesis of ischemic *** a result,the development of drugs targeting the PI3K/AKT signaling pathway has attracted increasing attention from *** article reviews the pathological mechanisms and advancements in research related to the signaling pathways in ischemic stroke,with a focus on the PI3K/AKT signaling *** key findings include the following:(1)The complex pathological mechanisms of ischemic stroke can be categorized into five major types:excitatory amino acid toxicity,Ca^(2+)overload,inflammatory response,oxidative stress,and apoptosis.(2)The PI3K/AKT-mediated signaling pathway is closely associated with the occurrence and progression of ischemic stroke,which primarily involves the NF-κB,NRF2,BCL-2,mTOR,and endothelial NOS signaling pathways.(3)Natural products,including flavonoids,quinones,alkaloids,phenylpropanoids,phenols,terpenoids,and iridoids,show great potential as candidate substances for the development of innovative anti-stroke medications.(4)Recently,novel therapeutic techniques,such as electroacupuncture and mesenchymal stem cell therapy,have demonstrated the potential to improve stroke outcomes by activating the PI3K/AKT signaling pathway,providing new possibilities for the treatment and rehabilitation of patients with ischemic *** investigations should focus on the direct regulatory mechanisms of drugs targeting the PI3K/AKT signaling pathway and their clinical translation to develop innovative treatment strategies for ischemic stroke.

关键词： apoptosis autophagy inflammation ischemic stroke neuroprotection oxidative stress pathogenesis phosphatidylinositol 3-kinase protein kinase B therapeutics

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