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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ziyan Wang Chengzhi Hong Xuan Liu Zhankun Xiong Feng Liu Wen Zhang School of Computer Science Wuhan University Wuhan China College of Informatics Huazhong Agricultural University Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Agricultural Bioinformatics Key Laboratory of Hubei Province Key Laboratory of Smart Farming for Agricultural Animals Ministry of Agriculture Huazhong Agricultural University Wuhan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Exploring the transcriptional response after employing chemical compounds assists in treating gene-related diseases and understanding biological activity of compounds. Calculating the similarity of drug transcriptional response can help to discover novel compounds that have the similar biological activity to known drugs for treating the same disease. Considering the transcriptional profiles of compounds are limited and harder to get than the structure of compounds, it is worth modeling the structure-transcriptional response similarity relationship. In this paper, we propose a signed graph convolutional network (SGCN)-based method, namely SGCN-DTRS, to predict drug transcriptional response similarity, which is quantitatively measured by Connectivity Map (CMap) scores, from their structures. SGCNDTRS constructs a CMap signed network from compounds and their CMap scores in the training data, which takes compounds as nodes, molecular structural representations of compounds as the attributes of nodes, and similarity relations between compounds as edges. Then SGCN-DTRS learns the CMap compound embeddings to predict CMap scores of pairwise compounds. Extensive experiments verify the superiority of the proposed method against the compared state-of-the-art methods and reveal that the relational information of the CMap data, which is learned from CMap signed network, is important for the CMap score prediction. SGCN-DTRS can not only work for the compounds in the training set but also is applicable to unseen compounds.

关键词： Drugs Training databases Training data Predictive models Biology Graph neural networks

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Improving Incremental Learning: A Closer Look at the Softmax Function

SSRN

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SSRN 2024年

作者： Zhai, Zheng Zhang, Jiali Wang, Haiyu Wu, Mingxin Yang, Keshun Qiao, Xiaoyan Sun, Qiang Beijing Normal University No.18 Jinfeng Road Guangdong Zhuhai519087 China Shandong Technology and Business University Shandong Yantai China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Shandong China Immersion Technology and Evaluation Shandong Engineering Research Center Shandong China School of Mathematics Sichuan University Chengdu China College of Liberal Arts and Sciences University of Illinois Urbana-Champaign IL United States Department of Statistical Sciences University of Toronto ON Canada Department of Computer Science University of Toronto ON Canada Department of Statistics and Data Science MBZUAI Abu Dhabi United Arab Emirates

This paper investigates the limitations of the widely adopted softmax cross-entropy loss in incremental learning problems. Specifically, we highlight how the shift-invariant property of this loss function can lead to multiple optimal solutions and imbalanced weights across different tasks, which exacerbates catastrophic forgetting. To address these issues, we propose two key modifications to the objective function: (1) regularizing or replacing the original joint distillation loss with a summation of task-specific distillation losses, and/or (2) utilizing a shift-sensitive prediction loss function, such as the binary cross-entropy loss. Numerical experiments validate the effectiveness of these proposals, demonstrating improved prediction accuracy and a significant reduction in forgetting. © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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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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Progressive Dual Priori Network for Generalized Breast Tumor Segmentation

arXiv

引用

arXiv 2023年

作者： Wang, Li Wang, Lihui Kuai, Zixiang Tang, Lei Ou, Yingfeng Ye, Chen Zhu, Yuemin Wu, Min Shi, Tianliang Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Imaging Center Harbin Medical University Cancer Hospital Harbin150081 China Radiology Department Guizhou Provincial People’s Hospital Guiyang550002 China Radiology Department Tongren People’s Hospital Tongren554300 China Univ Lyon INSA Lyon CNRS Inserm CREATIS UMR 5220 U1206 LyonF-69621 France

To promote the generalization ability of breast tumor segmentation models, as well as to improve the segmentation performance for breast tumors with smaller size, low-contrast and irregular shape, we propose a progressive dual priori network (PDPNet) to segment breast tumors from dynamic enhanced magnetic resonance images (DCE-MRI) acquired at different centers. The PDPNet first cropped tumor regions with a coarse-segmentation based localization module, then the breast tumor mask was progressively refined by using the weak semantic priori and cross-scale correlation prior knowledge. To validate the effectiveness of PDPNet, we compared it with several state-of-the-art methods on multi-center datasets. The results showed that, comparing against the suboptimal method, the DSC and HD95 of PDPNet were improved at least by 5.13% and 7.58% respectively on multi-center test sets. In addition, through ablations, we demonstrated that the proposed localization module can decrease the influence of normal tissues and therefore improve the generalization ability of the model. The weak semantic priors allow focusing on tumor regions to avoid missing small tumors and low-contrast tumors. The cross-scale correlation priors are beneficial for promoting the shape-aware ability for irregular tumors. Thus integrating them in a unified framework improved the multicenter breast tumor segmentation performance. The source code and open data can be accessed at https://github. com/wangli100209/PDPNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance

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Incorporating edge sharpening and covariance attention for named entity recognition

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Neurocomputing 2025年 643卷

作者： Yang, Caiwei Chen, Yanping Yu, Shuai Huang, Ruizhang Qin, Yongbin Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guizhou Guiyang550025 China State Key Laboratory of Public Big Data Guizhou university Guizhou Guiyang550025 China College of Computer Science and Technology Guizhou University Guiyang550025 China

Named Entity Recognition (NER) is a key application in the field of Artificial Intelligence and Natural Language Processing, which automatically identifies and categorizes entities in text by intelligent algorithms. In NER, all spans of a sentence can be organized into a two-dimensional representation. The semantic plane has the advantage to represent the semantic structure of a sentence and to learn the interaction between spans. One of an important phenomenon of this representation is that neighboring elements of the semantic plane are spans denoted to overlapped subsequences in a sentence. Because they share the same contextual features and semantic dependencies, it is difficult to distinguish true entities from the backgrounds. Therefore, refining span representations and building the semantic dependency between spans is helpful for the entity recognition task. In this paper, we propose an Edge Sharpening and Covariance Attention (ES&CA) model to support recognizing named entities from the semantic plane representation. The edge sharpening (ES) module adopts a differential convolution to sharpen the semantic gradients in the semantic plane, which has the ability to gather semantic information from neighborhoods. In the covariance attention (CA) module, the covariance between spans are applied to weight the attention of spans relevant to task-relevant learning objective. Establishing semantic relationships across spans is a highly successful approach. The ES&CA model is assessed on five public datasets for nested and flattened named entity recognition. The evaluation results demonstrate the effectiveness of our strategy in distinguishing entity spans from the backgrounds, hence significantly enhancing the final performance. © 2025 Elsevier B.V.

关键词： Natural language processing systems

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The DUDFTO Attack: Towards Down-to-UP Timeout Probing and Dynamically Flow Table Overflowing in SDN

引用

IEEE Transactions on Network and Service Management 2025年

作者： Li, Jiasong Cui, Yunhe Chen, Yi Shen, Guowei Guo, Chun Qian, Qing Guizhou University State Key Laboratory of Public Big Data Engineering Research Center of Text Computing and Cognitive Intelligence Ministry of Education College of Computer Science and Technology Guizhou Provincial Characteristic Key Laboratory of Software Engineering and Information Security Guiyang550000 China Guizhou University of Finance and Economics School of Information Guiyang550000 China

As a new network structure, the decoupling of the control plane and forwarding plane makes Software-Defined Networking (SDN) widely used in large-scale network scenarios. However, the decoupling network architecture also brings new vulnerabilities. The flow table overflow attack is an attack strategy that can overwhelm SDN switches. Nevertheless, the existing flow table overflow attacks may fail in probing timeouts and match fields of flow entries, due to link failure, measurement of the round-trip time (RTT) of different packets, interference of hard-timeout and idle-timeout. Meanwhile, the stealthiness of the existing attacks may also reduce, as these attacks use fixed attack rate. To improve the timeout probing accuracy and the stealthiness of attack, a new flow table overflow attack strategy, DUDFTO, is proposed to accurately probe timeout settings and match fields, then stealthily overflow SDN flow tables. Firstly, it probes the match fields by measuring the one-sided transmission delay of the packets. After that, DUDFTO designs a down-to-up feedback-based timeout probing algorithm to eliminate the issues caused by high RTT, link failure, interference between hard-timeout and idle-timeout. Then, DUDFTO designs a dynamic attack packets sending algorithm to improve its stealthiness. Finally, DUDFTO probes the flow table state to stop sending new attack packets. The evaluation results demonstrate that DUDFTO outperforms the existing attacks in terms of match fields probing ability, timeout probing relative error, number of packet_in and flow_mod messages generated by the attack, rate distribution of packet_in and flow_mod messages generated during the attack, and number of detected attack packets. © 2004-2012 IEEE.

关键词： flow table overflow attack information probing SDN switchs

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B2e-Cdg: Artifact Removal for Enhanced Optical Coherence Tomography Angiography Using Conditional Guidance Diffusion

SSRN

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SSRN 2024年

作者： Xu, Jing Fu, Suzhong Xing, Jiwei Xue, Linyan Wang, Cheng Tuchin, Valery V. Zhao, Qingliang State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics Organ Transplantation Institute China Institute of Artificial Intelligence Xiamen University Xiamen361102 China State Key Laboratory of Vaccines for Infectious Diseases Xiang An Biomedicine Laboratory School of Public Health Xiamen University Xiamen361102 China Shenzhen518172 China College of Quality and Technical Supervision Hebei University Baoding071002 China School of Informatics Computer Science and Technology Department Xiamen University Xiamen361005 China Institute of Physics and Science Medical Center Saratov State University Saratov410012 Russia Laboratory of Laser Molecular Imaging and Machine Learning Tomsk State University Tomsk634050 Russia Institute of Precision Mechanics and Control FRS "Saratov Scientific Centre of the RAS" Saratov410028 Russia China

Optical Coherence Tomography Angiography (OCTA) is a revolutionary technology widely used to diagnose and manage fundus, skin and cardiovascular diseases. However, unavoidable movements, such as breathing, cause motion artifacts in images, which can significantly degrade image quality, obscure critical vascular details, and reduce the diagnostic reliability of the modality. Although recent advances in learning-based image inpainting methods for OCTA enface images have made notable progress in artifact removal, these methods still require the collection of large amounts of accurately labeled data and the generation of pseudo stripes to create paired training sets. Furthermore, the abundant structural information and flow intensity signals present in OCTA B-scans are nonnegligible. Hence we proposed B-scans to Enface Conditional Diffusion Guidance (B2E-CDG) for translation from signal-void B-scans to available B-scans. We introduce the normal B-scans in a connection manner and the specified reference B-scans in a gradient-based manner as style feature guidance into the diffusion model. Conditional guidance facilitates a more controllable and precise generation process for B-scans flow signal recovery. The requirement for labeled image collection and pseudo stripes is obviated, as the repetitive scanning nature of OCTA naturally results in paired datasets comprising signal-void and normal B-scans. The experimental results demonstrated that B2E-CDG effectively removes artifacts and restores vascular and structural information obscured by artifacts. This work shows superior vascular recovery and artifact removal capabilities in metrics and downstream tasks, enhancing the usability of OCTA in diagnostics. © 2024, The Authors. All rights reserved.

关键词： Optical coherence tomography

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On layer normalization in the transformer architecture 37

On layer normalization in the transformer architecture

引用

37th International Conference on Machine Learning, ICML 2020

作者： Xiong, Ruibin Yang, Yunchang He, Di Zheng, Kai Zheng, Shuxin Xing, Chen Zhang, Huishuai Lan, Yanyan Wang, Liwei Liu, Tie-Yan CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technolog Chinese Academy of Sciences China University of Chinese Academy of Sciences China Center for Data Science Peking University Beijing Institute of Big Data Research China Key Laboratory of Machine Perception MOE School of EECS Peking University China Microsoft Research College of Computer Science Nankai University China

ISBN: (纸本)9781713821120

The Transformer is widely used in natural language processing tasks. To train a Transformer however, one usually needs a carefully designed learning rate warm-up stage, which is shown to be crucial to the final performance but will slow down the optimization and bring more hyperparameter tunings. In this paper, we first study theoretically why the learning rate warm-up stage is essential and show that the location of layer normalization matters. Specifically, we prove with mean field theory that at initialization, for the original-designed Post-LN Transformer, which places the layer normalization between the residual blocks, the expected gradients of the parameters near the output layer are large. Therefore, using a large learning rate on those gradients makes the training unstable. The warm-up stage is practically helpful for avoiding this problem. On the other hand, our theory also shows that if the layer normalization is put inside the residual blocks (recently proposed as Pre-LN Transformer), the gradients are well-behaved at initialization. This motivates us to remove the warm-up stage for the training of Pre-LN Transformers. We show in our experiments that Pre-LN Transformers without the warm-up stage can reach comparable results with baselines while requiring significantly less training time and hyper-parameter tuning on a wide range of applications. Copyright 2020 by the author(s).

关键词： Mean field theory

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METER: An Ensemble DWT-based Method for Identifying Low-rate DDoS Attack in SDN

METER: An Ensemble DWT-based Method for Identifying Low-rate...

引用

IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC

作者： Cong Wang Yunhe Cui Qing Qian Guowei Shen Hongfeng Gao Saifei Li Guizhou Provincial Key Laboratory of Public Big Data School of Computer Science and Technology Guizhou University Guiyang China School of information GuiZhou University of Finance and Economics Guiyang China School of Information Science and Technology Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781665400374

As one of the next generation of network architectures, Software-Defined Networking (SDN) decouples the forwarding and control function of the traditional network device. However, it also faces new threats from network attacks, such as the Low-rate Distributed Denial of Service (L-DDoS) attack. To resist L-DDoS attack in SDN, this work proposes METER, an enseMble discrEte wavelet Transform-based method for idEntifying low-Rate DDoS attack in SDN. The rationale for METER is to identify L-DDoS attacks based on a new metric referred to as the Ensemble Wavelet Energy Entropy set (EWEEs), which is calculated by a novel ensemble DWT method. Firstly, the ensemble wavelet coefficients matrix is attained by METER using the ensemble DWT method. After that, METER combines the related entropy values with wavelet energy of the ensemble wavelet coefficients matrix to obtain EWEEs. Furthermore, to increase the precision of the L-DDoS detection method, machine learning methods are used to mine the correlation between EWEEs and L-DDoS attacks for identifying L-DDoS. The experiments were implemented on the RYU controller and Mininet, which demonstrates that METER outperforms the baseline method, in terms of precision, accuracy, F -score, recall, ROC curve, and P-R curve.

关键词： Meters Measurement Support vector machines Resists Denial-of-service attack Ubiquitous computing Entropy

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Development of an Artificial Intelligence Model for Individualized FSH Daily Dosage Guidance During Ovulation Stimulation

SSRN

引用

SSRN 2023年

作者： Duan, Liyan Xia, Yu Mei, Jie Zhang, Hui Zhu, YingChun Huang, ChenYnag Yan, Guijun Kong, Na Li, Wujun Sun, Haixiang Center for Reproductive Medicine and Obstetrics and Gynecology Drum Tower Clinic Medical College Nanjing Medical University Nanjing210008 China Center for Molecular Reproductive Medicine Nanjing University Nanjing210008 China National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210008 China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China Center for Medical Big Data Nanjing Drum Tower Hospital Affifiliated Drum Tower Hospital Medical School of Nanjing University Jiangsu Nanjing210008 China State Key Laboratory of Reproductive Medicine China International Joint Research Center on Environment and Human Health Center for Global Health School of Public Health Nanjing Medical University Nanjing211166 China Department of Reproductive Medicine Center Nanjing Drum Tower Hospital State Key Laboratory of Pharmaceutical Biotechnology Nanjing University Nanjing210023 China

Background: We aim to develop a real-time, interpretable artificial intelligence (AI) system, using an algorithm for estimating follicle stimulating hormone (FSH) daily dose in individualized controlled ovarian stimulation (COS).Methods: Between January 2018 and December 2020, a total of 13,788 cycles of GnRHa long-protocol ovulation stimulation cycles were included from the Reproductive Medicine Centre of Nanjing Drum Tower Hospital. We used a method of cross-temporal and cross-feature joint encoding to learn the time series information X=X1, X2,..., XN of the N days to get its hidden layer representation H. Then, we applied K-nearest neighbors (KNN) with the hidden layer representation H to obtain the ***: Our proposed CTFE model obtained Accuracy and Weighted-F1 metrics of 0.737 and 0.733, to predict the daily FSH dose respectively, which are the highest among other popular models for processing time series data. On days 1 and 5, Lasso regression predicted the FSH daily dosage with an accuracy of 0.696 and 0.522, respectively, whereas our technique predicted it with 0.834 and 0.819. The introduction of a sliding window mechanism after 13 days helps to maintain good performance, although accuracy drops due to insufficient ***: Our study demonstrated that the CTFE model can be used to calculate the daily FSH dose with high accuracy. This model may be easily applied in daily clinical practice by ***: This work was supported by the National Natural science Foundation of China (NO.82030040), Scientific research project of Jiangsu Province Association of Maternal and Child Health (No. FYX202203).Declaration of Interest: HX S, WJ L,GJ Y, N K, J M, LY D and Y X are listed as inventors of patents relating to this work. The authors affirm that they have no known financial or interpersonal conflicts that would have seemed to have an impact on the research presented in this *** Approval: The study had ethical approval fro

关键词： Hospitals

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