检索结果-内蒙古大学图书馆

10B-doped MCP detector developed for neutron resonance imaging at Back-n white neutron source

Nuclear science and Techniques 2024年第8期35卷 58-68页

作者： Qiang Li Li‑Jiao Wang Jing‑Yu Tang Xiang‑Biao Qiu Zhen Chen Mao‑Yuan Zhao Chang‑Jun Ning Kai Pan Wei Xu Tao Li Su‑Peng Lu Han Yi Rui‑Rui Fan Chang‑Qing Feng Rong Zhang Xiao‑Yang Sun Qi An Hao‑Fan Bai Jiang‑Bo Bai Jie Bao Ping Cao Qi‑Ping Chen Yong‑Hao Chen Zeng‑Qi Cui An‑Chuan Fan Fan‑Zhen Feng Min‑Hao Gu Chang‑Cai Han Zi‑Jie Han Guo‑Zhu He Yong‑Cheng He Yang Hong Yi‑Wei Hu Han‑Xiong Huang Wei Jiang Zhi‑Jie Jiang Zheng‑Yao Jin Ling Kang Bo Li Gong Li Xiao Li Yang Li Jie Liu Rong Liu Shu‑Bin Liu Yi‑Na Liu Guang‑Yuan Luan Jie Ren Zhi‑Zhou Ren Xi‑Chao Ruan Zhao‑Hui Song Kang Sun Zhi‑Xin Tan Sheng‑Da Tang Jin‑Cheng Wang Peng‑Cheng Wang Zhao‑Hui Wang Zhong‑Wei Wen Xiao‑Guang Wu Xuan Wu Cong Xia Yong‑Ji Yu Guo‑Hui Zhang Hang‑Chang Zhang Lin‑Hao Zhang Qi‑Wei Zhang Xian‑Peng Zhang Yu‑Liang Zhang Yue Zhang Zhi‑Yong Zhang Zhi‑Hao Zhou Ke‑Jun Zhu Chong Zou Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China State Key Laboratory of Particle Detection and Electronics Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China Dongguan Songshan Lake Future School Dongguan 523808China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230027China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China North Night Vision Science and Technology(Nanjing)Research Institute Co.Ltd Nanjing 211106China Department of Modern Physics University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China Institute of Modern Physics Fudan UniversityShanghai 200433China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China China Academy of Engineering Physics Institute of Nuclear Physics and ChemistryMianyang 621900China USTC archaeometry lab University of Science and Technology of ChinaHefei 230026China Northwest Institute of Nuclear Technology Xi’an 710024China University of Chinese Academy of Sciences Beijing 100049China China Shipbuilding Trading Co. LtdBeijing 100044China

Neutron resonance imaging(NRI)has recently emerged as an appealing technique for neutron *** complexity surpasses that of conventional transmission imaging,as it requires a high demand for both a neutron source and ***,the progression of NRI technology has been sluggish since its inception in the 1980s,particularly considering the limited studies analyzing the neutron energy range above *** white neutron source(Back-n)at the China Spallation Neutron Source(CSNS)provides favorable beam conditions for the development of the NRI technique over a wide neutron energy range from eV to ***-sensitive microchannel plates(MCP)have emerged as a cutting-edge tool in the field of neutron detection owing to their high temporal and spatial resolutions,high detection efficiency,and low *** this study,we report the development of a 10B-doped MCP detector,along with its associated electronics,data processing system,and NRI experiments at the *** heavy elements such as gold,silver,tungsten,and indium can be easily identified in the transmission images by their characteristic resonance peaks in the 1–100 eV energy range;the more difficult medium-weight elements such as iron,copper,and aluminum with resonance peaks in the 1–100 keV energy range can also be *** particular,results in the neutron energy range of dozens of keV(Aluminum)are reported here for the first time.

关键词： Neutron resonance imaging 10B doped MCP detector White neutron source Sample nuclide identification

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A novel hybrid butterfly optimization algorithm for feature selection with sine cosine velocity in the high-dimensional classification data

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 369-391页

作者： Zhang, Li Chen, Xiaobo Key Laboratory of Data Science and Intelligence Education Hainan Normal University Ministry of Education Haikou Hainan China School of Computer Engineering Jiangsu University of Technology Changzhou Jiangsu China Changzhou City Center Branch People's Bank of China Changzhou Jiangsu China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

Aiming at the shortcomings of the traditional butterfly optimization algorithm in solving the high-dimensional classification feature selection problem, which has low convergence and is prone to fall into local optimal solutions, a new hybrid butterfly optimization algorithm is proposed, i.e., HBOA-SCV (A novel hybrid butterfly optimization algorithm with sine cosine velocity). The algorithm is applied to solve a high-dimensional classification feature selection problem. Firstly, the algorithm's global exploration and local exploitation ability can be dynamically balanced by introducing inertia weight coefficients w based on multiple learning strategies. Secondly, using the updated speed position formula of the sine-cosine acceleration strategy, individual butterflies' autonomous search ability and convergence speed can be further improved. Finally, according to the fitness value of each butterfly individual, the moving step length and direction of the butterfly individual are automatically adjusted better to fit the actual search process of the butterfly individual, increase the search ability in the global range, and avoid the algorithm from falling into the local optimum. To verify the algorithm's effectiveness, 18 high-dimensional classification numbers are selected to carry out simulation and comparison experiments between HBOA-SCV and traditional BOA algorithm, five improved BOA algorithms and other comparative algorithms for high-dimensional classification data successively. The experimental results show that the average fitness value and classification accuracy of the HBOA-SCV algorithm are better than the comparison algorithm, thus verifying the superiority of the HBOA-SCV algorithm. © 2024 - IOS Press. All rights reserved.

关键词： Optimization algorithms

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Towards Efficient and Diverse Generative Model for Unconditional Human Motion Synthesis 24

Towards Efficient and Diverse Generative Model for Unconditi...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yu, Hua Liu, Weiming Bai, Jiapeng Gui, Xu Hou, Yaqing Ong, Yewsoon Zhang, Qiang Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education School of Computer Science and Technology Dalian University of Technology Dalian China College of Computer Science Zhejiang University Hangzhou China National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian China College of Computing and Data Science Nanyang Technological University Singapore Singapore Yonsei University Seoul Korea Republic of

ISBN: (纸本)9798400706868

Recent generative methods have revolutionized the way of human motion synthesis, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DMs). These methods have gained significant attention in human motion fields. However, there are still challenges in unconditionally generating highly diverse human motions from a given distribution. To enhance the diversity of synthesized human motions, previous methods usually employ deep neural networks (DNNs) to train a transport map that transforms Gaussian noise distribution into real human motion distribution. According to Figalli's regularity theory, the optimal transport map computed by DNNs frequently exhibits discontinuities. This is due to the inherent limitation of DNNs in representing only continuous maps. Consequently, the generated human motions tend to heavily concentrate on densely populated regions of the data distribution, resulting in mode collapse or mode mixture. To address the issues, we propose an efficient method called MOOT for unconditional human motion synthesis. First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space. Next, we employ convex optimization to match the noise distribution with the latent space distribution of human motions through the Optimal Transport (OT) map. Then, we combine the extended OT map with the generator of reconstruction network to generate new human motions. Thereby overcoming the issues of mode collapse and mode mixture. MOOT generates a latent code distribution that is well-behaved and highly structured, providing a strong motion prior for various applications in the field of human motion. Through qualitative and quantitative experiments, MOOT achieves state-of-the-art results surpassing the latest methods, validating its superiority in unconditional human motion generation. © 2024 ACM.

关键词： Generative adversarial networks

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DeepPointMap2: Accurate and Robust LiDAR-Visual SLAM with Neural Descriptors 24

DeepPointMap2: Accurate and Robust LiDAR-Visual SLAM with Ne...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Xiaze Ding, Ziheng Jing, Qi Cheng, Ying Ding, Wenchao Feng, Rui Fudan University Shanghai China The School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China The Academy for Engineering and Technology Fudan University China The Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

ISBN: (纸本)9798400706868

Simultaneous Localization and Mapping (SLAM) plays a pivotal role in autonomous driving and robotics. Existing methods often rely on hand-craft feature extraction and cross-modal fusion techniques, resulting in limited feature representation capability and reduced robustness. To address this challenge, we introduce DeepPointMap2, a novel learning-based LiDAR-Visual SLAM architecture that leverages neural descriptors to tackle multiple SLAM sub-tasks in a unified manner. Our approach employs neural networks to extract multi-modal tokens, which are then adaptively fused by the Visual-Point Fusion Module to generate sparse 3D neural descriptors, ensuring precise and robust performance. As a pioneering work, our method achieves state-of-the-art localization performance among various Visual-, LiDAR-, and Visual-LiDAR-based methods in widely-used benchmarks, as shown in the experiment results. Furthermore, the approach proves to be robust in scenarios involving camera failure and LiDAR obstruction. © 2024 ACM.

关键词： SLAM robotics

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A Location Recommendation Model Based on User Behavior and Sequence Influence 1

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8th International Conference on Internet on Things, ICIOT 2023, held as part of Services Conference Federation SCF 2023

作者： Mao, Weixuan Wen, Ruwang Cheng, Zhengxiang Mei, Zhuolin Wang, Haibin Wu, Bin Shi, Jiaoli School of Computer and Big Data Science Jiangxi Jiujiang China Jiujiang Key Laboratory of Network and Information Security Jiangxi Jiujiang China Institute of Information Security Jiujiang University Jiangxi Jiujiang China

ISBN: (数字)9783031517341

ISBN: (纸本)9783031517334

There are evident sequential patterns in human visits to different locations in daily life, which has led to the emergence of numerous sequence-based geographic location recommendation methods. However, previous research has mainly focused on considering the sequential patterns of check-ins as a whole, while overlooking the fact that users’ interactive behaviors at check-in locations are likely to influence their preferences for those locations, and subsequently impact their future check-in decisions. The problem of how to fully leverage a user’s overall location sequence and location interaction data to provide more personalized recommendations is still unresolved. To address this issue, we propose a geographic location recommendation model called UBSI (User Behavior and Sequence Influence) in this article. Our model is constructed based on check-in sequence, weight model, collaborative filtering, and n th-order additive Markov chain. First, UBSI collects users’ check-in sequence and interaction behavior data, generates interest weight using a weight model, integrates personalized data using collaborative filtering method, and finally applies the weighted n th-order additive Markov chain to mine sequential pattern of user check-ins for location recommendations. Finally, we conducted extensive experiments to demonstrate the effectiveness of our approach. The experimental results indicate that our UBSI solution can effectively improve the accuracy of recommendations. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Collaborative filtering

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Once read is enough: domain-specific pretraining-free language models with cluster-guided sparse experts for long-tail domain knowledge 24

Once read is enough: domain-specific pretraining-free langua...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Fang Dong Mengyi Chen Jixian Zhou Yubin Shi Yixuan Chen Mingzhi Dong Yujiang Wang Dongsheng Li Xiaochen Yang Rui Zhu Robert Dick Qin Lv Fan Yang Tun Lu Ning Gu Li Shang China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Oxford Suzhou Centre for Advanced Research Suzhou China University of Bath and China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Microsoft Research Asia Shanghai China School of Mathematics & Statistics University of Glasgow Bayes Business School City St George's University of London Department of Electrical Engineering and Computer Science University of Michigan Department of Computer Science University of Colorado Boulder School of Microelectronics Fudan University

ISBN: (纸本)9798331314385

Language models (LMs) only pretrained on a general and massive corpus usually cannot attain satisfying performance on domain-specific downstream tasks, and hence, applying domain-specific pretraining to LMs is a common and indispensable practice. However, domain-specific pretraining can be costly and time-consuming, hindering LMs' deployment in real-world applications. In this work, we consider the incapability to memorize domain-specific knowledge embedded in the general corpus with rare occurrences and "long-tail" distributions as the leading cause for pretrained LMs' inferior downstream performance. Analysis of Neural Tangent Kernels (NTKs) reveals that those long-tail data are commonly overlooked in the model's gradient updates and, consequently, are not effectively memorized, leading to poor domain-specific downstream performance. Based on the intuition that data with similar semantic meaning are closer in the embedding space, we devise a Cluster-guided Sparse Expert (CSE) layer to actively learn long-tail domain knowledge typically neglected in previous pretrained LMs. During pretraining, a CSE layer efficiently clusters domain knowledge together and assigns long-tail knowledge to designate extra experts. CSE is also a lightweight structure that only needs to be incorporated in several deep layers. With our training strategy, we found that during pretraining, data of long-tail knowledge gradually formulate isolated, "outlier" clusters in an LM's representation spaces, especially in deeper layers. Our experimental results show that only pretraining CSE-based LMs is enough to achieve superior performance than regularly pretrained-finetuned LMs on various downstream tasks, implying the prospects of domain-specific-pretraining-free language models.

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Liu, Yuanbin Zhang, Hongyi Liu, Jiacheng Lu, Liangjun Du, Jiangbing Li, Yu He, Zuyuan Chen, Jianping Zhou, Linjie Poon, Andrew W. State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

ISBN: (纸本)9781957171395

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s × 9 on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and a cascaded microring resonator-based reconfigurable dispersion compensator. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Optical fibers

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DeepRisk:A deep learning approach for genome-wide assessment of common disease risk

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Fundamental Research 2024年第4期4卷 752-760页

作者： Jiajie Peng Zhijie Bao Jingyi Lia Ruijiang Han Yuxian Wang Lu Han Jinghao Peng Tao Wang Jianye Hao Zhongyu Wei Xuequn Shang AI for Science Interdisciplinary Research Center Schoolof Computer ScienceNorthwestern Polyechnical UniversityXi'an 129China Key Laboratoryof Bg Data Sorage and Management Northwestern Polytechnical UniversityMinisty of Industy and Information TechnologyX'an 129Chin College of Intelligence and Computing Tianjin UniversityTianjin 300072China School of Data Science Fudan UniversityShanghai 200433China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518000China

The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant *** widely applied,traditional polygenic risk scoring methods fall short,as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms(SNPs).This presents a limitation,as genetic diseases often arise from complex interactions between multiple *** address this challenge,we developed DeepRisk,a biological knowledge-driven deep learning method for modeling these complex,nonlinear associations among SNPs,to provide a more effective method for scoring the risk of common diseases with genome-wide genotype *** demonstrated that DeepRisk outperforms existing PRs-based methods in identifying individuals at high risk for four common diseases:Alzheimer's disease,inflammatory bowel disease,type 2diabetes,and breast cancer.

关键词： Disease risk prediction Deep learning Polygenic risk score Common disease risk Disease prevention

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MAGIC: detecting advanced persistent threats via masked graph representation learning 24

MAGIC: detecting advanced persistent threats via masked grap...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Zian Jia Yun Xiong Yuhong Nan Yao Zhang Jinjing Zhao Mi Wen Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Software Engineering Sun Yat-sen University China National Key Laboratory of Science and Technology on Information System Security China Shanghai University of Electric Power China

ISBN: (纸本)9781939133441

Advance Persistent Threats (APTs), adopted by most delicate attackers, are becoming increasing common and pose great threat to various enterprises and institutions. data provenance analysis on provenance graphs has emerged as a common approach in APT detection. However, previous works have exhibited several shortcomings: (1) requiring attack-containing data and a priori knowledge of APTs, (2) failing in extracting the rich contextual information buried within provenance graphs and (3) becoming impracticable due to their prohibitive computation overhead and memory *** this paper, we introduce MAGIC, a novel and flexible self-supervised APT detection approach capable of performing multi-granularity detection under different level of supervision. MAGIC leverages masked graph representation learning to model benign system entities and behaviors, performing efficient deep feature extraction and structure abstraction on provenance graphs. By ferreting out anomalous system behaviors via outlier detection methods, MAGIC is able to perform both system entity level and batched log level APT detection. MAGIC is specially designed to handle concept drift with a model adaption mechanism and successfully applies to universal conditions and detection scenarios. We evaluate MAGIC on three widely-used datasets, including both real-world and simulated attacks. Evaluation results indicate that MAGIC achieves promising detection results in all scenarios and shows enormous advantage over state-of-the-art APT detection approaches in performance overhead.

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Identification of an immune-related gene signature for predicting prognosis and immunotherapy efficacy in liver cancer via cell-cell communication

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World Journal of Gastroenterology 2024年第11期30卷 1609-1620页

作者： Jun-Tao Li Hong-Mei Zhang Wei Wang Dong-Qing Wei College of Mathematics and Information Science Henan Normal UniversityXinxiang 453007Henan ProvinceChina College of Computer and Information Engineering Henan Normal UniversityXinxiang 453007Henan ProvinceChina State Key Laboratory of Microbial Metabolism and School of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China

BACKGROUND Liver cancer is one of the deadliest malignant tumors *** has provided hope to patients with advanced liver cancer,but only a small fraction of patients benefit from this treatment due to individual *** immune-related gene signatures in liver cancer patients not only aids physicians in cancer diagnosis but also offers personalized treatment strategies,thereby improving patient survival *** several methods have been developed to predict the prognosis and immunotherapeutic efficacy in patients with liver cancer,the impact of cell-cell interactions in the tumor microenvir-onment has not been adequately *** To identify immune-related gene signals for predicting liver cancer prognosis and immunotherapy *** Cell grouping and cell-cell communication analysis were performed on single-cell RNA-sequencing data to identify highly active cell groups in immune-related *** active immune cells were identified by intersecting the highly active cell groups with B cells and T *** significantly differentially expressed genes between highly active immune cells and other cells were subsequently selected as features,and a least absolute shrinkage and selection operator(LASSO)regression model was constructed to screen for diagnostic-related *** genes that were selected more than 5 times in 10 LASSO regression experiments were included in a multivariable Cox regression ***,3 genes(stathmin 1,cofilin 1,and C-C chemokine ligand 5)significantly associated with survival were identified and used to construct an immune-related gene *** The immune-related gene signature composed of stathmin 1,cofilin 1,and C-C chemokine ligand 5 was identified through cell-cell *** effectiveness of the identified gene signature was validated based on experi-mental results of predictive immunotherapy response,tumor mutation burden analysis,immune cell infiltrat

关键词： Liver cancer Cell-cell communication Gene signature Prognosis Immunotherapy Single-cell RNA sequencing

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