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arXiv 2025年

作者： Lai, Haoran Jiang, Zihang Yao, Qingsong Wang, Rongsheng He, Zhiyang Tao, Xiaodong Wei, Wei Lv, Weifu Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Stanford University Palo AltoCA94305 United States Medical Business Department iFlytek Co.Ltd. Anhui Hefei230088 China The First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230001 China Department of Radiology The First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230001 China Suzhou Institute for Advance Research USTC Jiangsu Suzhou215123 China State Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Anhui Hefei230026 China Institute of Computing Technology CAS Beijing100190 China

3D medical images such as Computed tomography (CT) are widely used in clinical practice, offering a great potential for automatic diagnosis. Supervised learning-based approaches have achieved significant progress but rely heavily on extensive manual annotations, limited by the availability of training data and the diversity of abnormality types. Vision-language alignment (VLA) offers a promising alternative by enabling zero-shot learning without additional annotations. However, we empirically discover that the visual and textural embeddings after alignment endeavors from existing VLA methods form two well-separated clusters, presenting a wide gap to be bridged. To bridge this gap, we propose a Bridged Semantic Alignment (BrgSA) framework. First, we utilize a large language model to perform semantic summarization of reports, extracting high-level semantic information. Second, we design a Cross-Modal Knowledge Interaction (CMKI) module that leverages a cross-modal knowledge bank as a semantic bridge, facilitating interaction between the two modalities, narrowing the gap, and improving their alignment. To comprehensively evaluate our method, we construct a benchmark dataset that includes 15 underrepresented abnormalities as well as utilize two existing benchmark datasets. Experimental results demonstrate that BrgSA achieves state-of-the-art performances on both public benchmark datasets and our custom-labeled dataset, with significant improvements in zero-shot diagnosis of underrepresented abnormalities. © 2025, CC BY.

关键词： Zero-shot learning

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SGBA: A Stealthy Scapegoat Backdoor Attack against Deep Neural Networks

arXiv

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arXiv 2021年

作者： He, Ying Shen, Zhili Xia, Chang Hua, Jingyu Tong, Wei Zhong, Sheng The Computer Science and Technology Department Nanjing University Nanjing210023 China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

Outsourced deep neural networks have been demonstrated to suffer from patch-based trojan attacks, in which an adversary poisons the training sets to inject a backdoor in the obtained model so that regular inputs can be still labeled correctly while those carrying a specific trigger are falsely given a target label. Due to the severity of such attacks, many backdoor detection and containment systems have recently, been proposed for deep neural networks. One major category among them are various model inspection schemes, which hope to detect backdoors before deploying models from non-trusted third-parties. In this paper, we show that such state-of-the-art schemes can be defeated by a so-called Scapegoat Backdoor Attack, which introduces a benign scapegoat trigger in data poisoning to prevent the defender from reversing the real abnormal trigger. In addition, it confines the values of network parameters within the same variances of those from clean model during training, which further significantly enhances the difficulty of the defender to learn the differences between legal and illegal models through machine-learning approaches. Our experiments on 3 popular datasets show that it can escape detection by all five state-of-the-art model inspection schemes. Moreover, this attack brings almost no side-effects on the attack effectiveness and guarantees the universal feature of the trigger compared with original patch-based trojan attacks. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings

arXiv

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arXiv 2024年

作者： Wang, Yue Cao, Xu Hu, Yaojun Ying, Haochao Xu, Hongxia Wu, Ruijia Rehg, James Matthew Sun, Jimeng Wu, Jian Chen, Jintai The College of Computer Science and Technology Zhejiang University Hangzhou310012 China State Key Laboratory of Transvascular Implantation Devices The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou310009 China Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence Hangzhou310058 China Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL United States Antai College of Economics and Management Shanghai Jiao Tong University Shanghai China School of Computer Science University of Illinois at Urbana-Champaign UrbanaIL United States Guangzhou511458 China University of Illinois at Urbana-Champaign UrbanaIL United States The School of Public Health Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou310058 China

Electrocardiogram (ECG), a non-invasive and affordable tool for cardiac monitoring, is highly sensitive in detecting acute heart attacks. However, due to the lengthy nature of ECG recordings, numerous machine learning methods have been developed for automated heart disease detection to reduce human workload. Despite these efforts, performance remains suboptimal. A key obstacle is the inherent complexity of ECG data, which includes heterogeneity (e.g., varying sampling rates), high levels of noise, demographic-related pattern shifts, and intricate rhythm-event associations. To overcome these challenges, this paper introduces AnyECG, a foundational model designed to extract robust representations from any real-world ECG data. Specifically, a tailored ECG Tokenizer encodes each fixed-duration ECG fragment into a token and, guided by proxy tasks, converts noisy, continuous ECG features into discrete, compact, and clinically meaningful local rhythm codes. These codes encapsulate basic morphological, frequency, and demographic information (e.g., sex), effectively mitigating signal noise. We further pre-train the AnyECG to learn rhythmic pattern associations across ECG tokens, enabling the capture of cardiac event semantics. By being jointly pre-trained on diverse ECG data sources, AnyECG is capable of generalizing across a wide range of downstream tasks where ECG signals are recorded from various devices and scenarios. The experimental results show that AnyECG achieves an average performance improvement of 6% across four critical tasks—anomaly detection, arrhythmia classification, corrupted lead generation, and ultra-long ECG recognition. AnyECG learns common ECG rhythm from data and significantly outperforms state-of-the-art methods in each of these tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Heart

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Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning

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npj Computational Materials 2020年第1期6卷 46-57页

作者： Qi Wang Jun Ding Longfei Zhang Evgeny Podryabinkin Alexander Shapeev Evan Ma Department of Materials Science and Engineering Johns Hopkins UniversityBaltimoreMD 21218USA Center for Advancing Materials Performance from the Nanoscale(CAMP-Nano) State Key Laboratory for Mechanical Behavior of MaterialsXi’an Jiaotong UniversityXi’an 710049China Department of Computer Science Johns Hopkins UniversityBaltimoreMD 21218USA Center for Energy Science and Technology Skolkovo Institute of Science and Technology3 Nobel StreetMoscow 143026Russia

The elementary excitations in metallic glasses(MGs),i.e.,β processes that involve hopping between nearby sub-basins,underlie many unusual properties of the amorphous alloys.A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions,however,has remained a daunting ***,employing well-designed site environment descriptors and machine learning(ML),notable progress has been made in predicting the propensity for stress-activated β processes(i.e.,shear transformations)from the static structure.

关键词： structure glasses metallic

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Software Defect Prediction via Deep Belief Network

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Chinese Journal of Electronics 2019年第5期28卷 925-932页

作者： WEI Hua SHAN Chun HU Changzhen ZHANG Yu YU Xiao School of Computer Science and Technology Beijing Institute of Technology China Information Technology Security Evaluation Center Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology School of Electrical and Information Engineering and Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture State Key Laboratory in China for Geomechanics and Deep Underground Engineering (Beijing) China University of Mining and Technology School of Computer Science and Technology Shandong University of Technology

Defect distribution prediction is a meaningful topic because software defects are the fundamental cause of many attacks and data loss. Building accurate prediction models can help developers find bugs and prioritize their testing efforts. Previous researches focus on exploring different machine learning algorithms based on the features that encode the characteristics of *** problem of data redundancy exists in software defect data set, which has great influence on prediction *** propose a defect distribution prediction model(Deep belief network prediction model, DBNPM), a system for detecting whether a program module contains defects. The key insight of DBNPM is Deep belief network(DBN)technology, which is an effective deep learning technique in image processing and natural language processing,whose features are similar to defects in source *** results show that DBNPM can efficiently extract and process the data characteristics of source program and the performance is better than Support vector machine(SVM), Locally linear embedding SVM(LLE-SVM), and Neighborhood preserving embedding SVM(NPE-SVM).

关键词： Defect prediction Software security Deep belief network(DBN)

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BANANA: When behavior ANAlysis meets social network alignment 29

BANANA: When behavior ANAlysis meets social network alignmen...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Ren, Fuxin Zhang, Zhongbao Zhang, Jiawei Su, Sen Sun, Li Zhu, Guozhen Guo, Congying State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China IFM Lab Department of Computer Science Florida State University FL United States

ISBN: (纸本)9780999241165

Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users' behavior information during the aligning procedure and thus still suffer from the poor learning performance. In fact, we observe that social network alignment and behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment problem and user behavior analysis problem. We design a novel end-to-end framework named BANANA. In this framework, to leverage behavior analysis for social network alignment at the distribution level, we design an earth mover's distance based alignment component to fuse users' behavior information for more comprehensive user representations. To further leverage social network alignment for behavior analysis, in turn, we design a temporal graph neural network component to fuse behavior information in different social networks based on the alignment result. Two models above can work together in an end-to-end manner. Through extensive experiments on real-world datasets, we demonstrate that our proposed approach outperforms the state-of-the-art methods in the social network alignment task and the user behavior analysis task, respectively. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Alignment

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Distinct genetic alterations in CD10-negative MUM1-positive follicular lymphoma

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Pathology 2025年

作者： Yang, Yan Wang, Wei Zhang, Liang Lu, Xu Li, Huihui Liu, Yixiong Xu, Zhouyi Zhao, Danhui Chai, Jia Shao, Longfei Fan, Linni Li, Mingyang Yang, Yanru Gao, Chuanshu Wu, Haibo Wang, Zhe Intelligent Pathology Institute The First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei China School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei China BGI Research Shenzhen China State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers Department of Pathology School of Basic Medicine and Xijing Hospital. Fourth Military Medical University Xi'an China

Conventional follicular lymphoma (FL) is positive for CD10 and negative for multiple myeloma oncogene 1 (MUM1). However, a subset of FL shows CD10–MUM1+ features, and the genetic alterations of this unique FL subtype are poorly understood. Thus, we conducted whole-exome sequencing in 20 cases of CD10–MUM1+ FL and analysed the genetic and clinicopathological features of these patients. The results suggested that CD10−MUM1+ FL patients were predominantly elderly women, lacked BCL2 rearrangement (17/17, 100%) and showed a higher grade (17/20, 85% grade 3) than patients with conventional FL. The most frequently mutated genes in these tumours were CD79B, GOLGA6L2, IGLL5, KMT2D, NFKBIE, ROBO1, BTG2, DTX1, LRP3 and PTPN6. In contrast to conventional FL, CD10–MUM1+ FL exhibited an elevated mutation frequency of CD79B and NFKBIE and decreased mutation frequencies of CREBBP, BCL2, KMT2D and TNFRSF14. No significant difference was found in the overall survival between CD10–MUM1+ FL and conventional FL patients. In summary, this study demonstrated that CD10–MUM1+ FL has unique clinical, pathological and genetic features. © 2025

关键词： CD10 follicular lymphoma genetic mutation MUM1 whole-exome sequencing

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Microstructure and Mechanical Performance Study of Crmnfeconi High-Entropy Alloys after Cryogenic Treatment

SSRN

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SSRN 2023年

作者： Qin, Yulin Wu, Changgui Long, Xiaojiang Qiu, Wenbin Zhou, Lvjun Tang, Jiafeng Zhang, Ning Yin, Ming Chen, Longqing Zhu, Jun College of Physics Sichuan University Sichuan Chengdu610065 China Kunming Metallurgical Research Institute Co. Ltd Yunnan Kunming650031 China Key Laboratory of Micro Nano Optoelectronic Devices and Intelligent Perception Systems Yangtze Normal University Chongqing Fuling408100 China Department of Fundamental Courses Wuxi Institute of Technology Wuxi214121 China Key Laboratory of Radiation Physics and Technology Ministry of Education Institute of Nuclear Science and Technology Sichuan University Chengdu610064 China School of Mechanical Engineering Sichuan University Chengdu610064 China

In recent years, significant progress has been made in the development of high-entropy alloys (HEAs) that possess both high strength and high ductility. The application of the metastable engineering strategy to enhance the strength and ductility of materials has also gradually been employed, and cryogenic treatment is one of the important techniques in this regard. Through cryogenic liquid nitrogen treatment, this study significantly enhanced the strength and ductility of (CrMnFeCoNi)45Fe55 and (CrMnFeCoNi)40Fe60 HEAs prepared by laser melting deposition (LMD). Compared to the initial samples, the fracture strength of (CrMnFeCoNi)45Fe55 increased from 480 MPa (at room temperature) to 980 MPa (after cryogenic treatment), and the fracture elongation alloy increased from 60% (at room temperature) to 62% (after cryogenic treatment). The fracture strength of (CrMnFeCoNi)40Fe60 alloy increased from 550 MPa (at room temperature) to 1150 MPa (after cryogenic treatment), and the fracture strength decreased from 55% (at room temperature) to 45% (after cryogenic treatment). This is attributed to the fact that cryogenic liquid nitrogen treatment reduces the stacking fault energy of the alloy system, facilitating the formation of stacking faults. The formation of stacking faults, in turn, provides nucleation sites for phase transformations, promoting the transformation of the matrix from face-centered cubic (FCC) phase to body-centered cubic (BCC) phase or other phases. This activates phase transformation-induced plasticity effects (TRIP). Simultaneously, the bidirectional coexistence mechanism achieves a favorable balance between strength and ductility. This provides a new ideas and method for obtaining high-entropy alloys with excellent comprehensive mechanical properties. © 2023, The Authors. All rights reserved.

关键词： Microstructure

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Adaptive Variational Mode Time-frequency Analysis of Ship Radiated Noise 7

Adaptive Variational Mode Time-frequency Analysis of Ship Ra...

引用

7th International Conference on Information science and Control Engineering, ICISCE 2020

作者： Zhang, Haoyi Junejo, Naveed Ur Rehman Sun, Weitao Chen, Hailan Yan, Jiaquan Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou China Ilma University Department of Computer Science Karachi Pakistan Northwestern Polytechnical University School of Marine Science and Technology Xi'an China Jimei University School of Science Xiamen China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China

ISBN: (纸本)9781728164069

In this paper, we have proposed a novel time-frequency analysis method implementing adaptive variational mode decomposition (AVMD) and Wigner-Ville Distribution (WVD) to visualize the local features of ship radiated noise. Unlike the traditional methods are invalid under low signal to noise ratio (SNR), the proposed method is suitable for constructing time-frequency (TF) image with the capability of mining the local feature information accurately. Firstly, we employ complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) on the raw signal to determine the numbers of variational modes which is equal to intrinsic mode functions (IMFs) numbers. Secondly, we perform VMD algorithm to extract the local features absolutely by decomposing the raw signal into IMFs, where we set the IMFs gained by CEEMDAN as the input parameter of VMD. Finally, we adopt Wigner-Ville distribution (WVD) to draw TF signatures of all the IMFs, then superpose them to reconstruct the objected signal that can solve the cross-item problem. We have conducted the comparative experiments by the real oceanic data. Under the different SNRs, we have verified the effectiveness and robustness of the proposed method, also obtained better performance in terms of resolution than compared algorithm. © 2020 IEEE.

关键词： Intrinsic mode functions

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Ambient Backscatter Communications over NOMA Downlink Channels

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China Communications 2020年第6期17卷 80-100页

作者： Weiyu Chen Haiyang Ding Shilian Wang Daniel Benevides daCosta Fengkui Gong Pedro Henrique Juliano Nardelli College of Electronic Science and Technology National University of Defense TechnologyChangsha 410073China College of Information and Communication National University of Defense TechnologyXi’an 710106China Department of Computer Engineering Federal University of CearáSobral 62010-560Brazil State Key Laboratory of Integrated Service Networks Xidian UniversityXi’an 710071China School of Energy Systems Lappeenranta University of TechnologyLappeenranta 53850Finland

In this paper,we investigate the performance of commensal ambient backscatter communications(AmBC)that ride on a non-ortho go nal multiple access(NOMA)downlink transmission,in which a backscatter device(BD)splits part of its received signals from the base station(BS)for energy harvesting,and backscatters the remaining received signals to transmit information to a cellular ***,under the power consumption constraint at BD and the peak transmit power constraint at BS,we derive the optimal reflection coefficient at BD,the optimal total transmit power at BS,and the optimal power allocation at BS for each transmission block to maximize the ergodic capacity of the ambient backscatter transmission on the premise of preserving the outage performance of the NOMA downlink ***,we consider a scenario where the BS is restricted by a maximum allowed average transmit power and the reflection coefficient at BD is fixed due to BD’s low-complexity *** algorithm is developed to determine the optimal total transmit power and power allocation at BS for this ***,a low-complexity algorithm is proposed for this scenario to reduce the computational complexity and the signaling ***,the performance of the derived solutions are studied and compared via numerical simulations.

关键词： power-domain NOMA ambient backscatter communications IoT wireless-powered devices optimization

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