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Computational approaches for circRNA-disease association prediction:a review

Frontiers of Computer Science 2025年第4期19卷 99-113页

作者： Mengting NIU Yaojia CHEN Chunyu WANG Quan ZOU Lei XU Center for Informational Biology University of Electronic Science and Technology of ChinaChengdu 610054China School of Applied Chemistry and Biological Technology Shenzhen Polytechnic UniversityShenzhen 518055China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of ChinaQuzhou 324000China Faculty of Computing Harbin Institute of TechnologyHarbin 150006China

Circular RNA(circRNA)is a covalently closed RNA molecule formed by back *** role of circRNAs in posttranscriptional gene regulation provides new insights into several types of cancer and neurological *** are associated with multiple diseases and are emerging biomarkers in cancer diagnosis and *** associations prediction is one of the current research hotspots in the field of *** research on circRNAs has made great progress,the traditional biological method of verifying circRNA-disease associations is still a great challenge because it is a difficult task and requires much ***,advances in computational methods have made considerable progress in circRNA *** review comprehensively discussed the functions and databases related to circRNA,and then focused on summarizing the calculation model of related predictions,detailed the mainstream algorithm into 4 categories,and analyzed the advantages and limitations of the 4 *** not only helps researchers to have overall understanding of circRNA,but also helps researchers have a detailed understanding of the past algorithms,guide new research directions and research purposes to solve the shortcomings of previous research.

关键词： circular RNA disease association prediction machine learning data mining deep learning

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HEAT: Efficient Vision Transformer Accelerator With Hybrid-Precision Quantization

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IEEE Transactions on Circuits and Systems II: Express Briefs 2025年第5期72卷 758-762页

作者： Zhao, Pan Xue, Donghui Wu, Licheng Chang, Liang Tan, Haining Han, Yinhe Zhou, Jun University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Quantization is an important technique for the acceleration of transformer-based neural networks. Prior related works mainly consider quantization from the algorithm level. Their hardware implementation is inefficient. In this brief, we propose an efficient vision transformer accelerator with retraining-free and finetuning-free hybrid-precision quantization. At the algorithm level, the features and weights are divided into two parts: normal values and outlier values. These two parts are quantized with different bit widths and scaling factors. We use matrix transformation and group-wise quantization policy to improve hardware utilization. At the hardware level, we propose a two-stage FIFO group structure and a hierarchical interleaving data flow to further improve the utilization of the PE array. As a result, the input and weight matrices are quantized to 5.71 bits on average with 0.526 % accuracy loss on Swin-T. The accelerator achieves a frame rate of 118.9 FPS and an energy efficiency of 43.58 GOPS/W on the ZCU102 FPGA board, better than state-of-the-art works. © 2004-2012 IEEE.

关键词： Particle accelerators

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Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations

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Computers, Materials & Continua 2025年第1期82卷 345-369页

作者： Jiaying Shen Donglin Zhu Yujia Liu Leyi Wang Jialing Hu Zhaolong Ouyang Changjun Zhou Taiyong Li School of Computer Science and Technology Zhejiang Normal UniversityJinhua321004China School of Future Technologies Jiangxi Institute of Applied Science and TechnologyNanchang330000China School of Computing and Artificial Intelligence Southwestern University of Finance and EconomicsChengdu611130China

The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real *** development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of *** a specific area,achieving higher signal coverage with fewer base stations has become an urgent ***,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as *** a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by *** also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization *** better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base *** experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches *** results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and *** ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.

关键词： Particle swarm optimization effective coverage area global optimization base station deployment

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Fourier single-pixel spectral imaging via local low-rank tensor nuclear norm and deep tensor priors

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Optics Letters 2025年第4期50卷 1281-1284页

作者： Tang, Zixin Hu, Yexun Duo, Chen Yang, Guowei Jiang, Taixiang Guo, Daqing School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Sichuan Province Chengdu China Kash Institute of Electronics and Information Industry Kash China Engineering Research Center of Intelligent Finance Ministry of Education Southwestern University of Finance and Economics China Chongqing University of Education School of Artificial Intelligence China School of Life Science and Technology University of Electronic Science and Technology of China Sichuan Province Chengdu China Chongqing University Industrial Technology Research Institute Department of Science and Technology Development China

The imaging quality of single-pixel spectral imaging (SSI) methods is poor at a low sampling ratio (SR). To tackle this problem, a new Fourier single-pixel spectral imaging (FSSI) technique is proposed. Firstly, we introduce the low-rank tensor nuclear norm (TNN) to characterize the correlation between spectral images. Compared with the conventional method, TNN reconstructs image details better but brings image artifacts simultaneously. Therefore, local low-rank TNN (LTNN) constraint is proposed to ameliorate global ones and to reduce the distortion caused by TNN and low SR. Secondly, to make full use of the spectral information, the proposed constraint is used as the coarse prior, and the deep tensor prior (DTP) is introduced as the fine one to construct the joint priors. Different from the single prior, the joint method can make the two priors benefit and improve each other and further enhance the imaging quality. Finally, an efficient and high-quality SSI technique is achieved by deducing the closed-form solution algorithm. Experimental results show that our method significantly improves the quality of FSSI as much as 7–10 dB when compared to 3DTV at the SR of 5%. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Tensors

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Threshold Password-Hardening Updatable Oblivious Key Management

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4799-4814页

作者： Jiang, Changsong Xu, Chunxiang Liu, Zhen Dong, Xinfeng Zhang, Wenzheng University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Singapore Management University School of Computing and Information Systems Bras Basah 188065 Singapore Huzhou313001 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Science and Technology on Communication Security Laboratory Chengdu610041 China

We propose a threshold password-hardening updatable oblivious key management system dubbed TPH-UOKM for cloud storage. In TPH-UOKM, a group of key servers share a user-specific secret key for a user, and assist the user in producing her/his password-derived private key in a threshold and oblivious way, where the password is hardened to resist offline dictionary guessing attacks. Anyone can outsource data protected with the user's password-derived public key to the cloud server, and merely the user holding the correct password can recover the password-derived private key for data access. TPH-UOKM can accomplish decryption of N ciphertexts with the complexity O(1) of communication between a user and the key servers, which outperforms existing schemes. TPH-UOKM supports password update. The cloud server can update all protected data of a user with an update token to be accessible only with the new password, which resists password leakage. We present a two-level proactivization mechanism to periodically update user-specific secret key shares and the key servers to thwart perpetual compromise of them, where the renewal of user-specific secret key shares reduces computation and communication costs compared to existing approaches. Provable security and high efficiency of TPH-UOKM are demonstrated by comprehensive analyses and performance evaluations. © 2005-2012 IEEE.

关键词： Information management

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DyRoNet: Dynamic Routing and Low-Rank Adapters for Autonomous Driving Streaming Perception

DyRoNet: Dynamic Routing and Low-Rank Adapters for Autonomou...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Huang, Xiang Cheng, Zhi-Qi He, Jun-Yan Li, Chenyang Xiang, Wangmeng Sun, Baigui Institute of Artificial Intelligence Southwest Jiaotong University Chengdu China School of Engineering and Technology University of Washington TacomaWA United States Institute for Intelligent Computing Alibaba Group China Cmu United States

ISBN: (纸本)9798331510831

The advancement of autonomous driving systems hinges on the ability to achieve low-latency and high-accuracy perception. To address this critical need, this paper introduces Dynamic Routering Network (DyRoNet), a low-rank enhanced dynamic routing framework designed for streaming perception in autonomous driving systems. DyRoNet integrates a suite of pre-trained branch networks, each meticulously fine-tuned to function under distinct environmental conditions. At its core, the framework offers a speed router module, developed to assess and route input data to the most suitable branch for processing. This approach not only addresses the inherent limitations of conventional models in adapting to diverse driving conditions but also ensures the balance between performance and efficiency. Extensive experimental evaluations demonstrating the adaptability of DyRoNet to diverse branch selection strategies, resulting in significant performance enhancements across different scenarios. This work not only establishes a new benchmark for streaming perception but also provides valuable engineering insights for future work.11Project: https://***/DyRoNet/ © 2025 IEEE.

关键词： Object detection

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Understanding the Influence of Extremely High-Degree Nodes on Graph Anomaly Detection 27th

Understanding the Influence of Extremely High-Degree Nodes...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Sun, Xun Xiao, Xi Wang, Zhaoguo Hu, Guangwu Jiang, Xuhui Zhang, Bin Li, Hao Shenzhen International Graduate School Tsinghua University Shenzhen China Ministry of Education Chengdu China Shenzhen China Shenzhen Institute of Information Technology Shenzhen China Institute of Computing Technology CAS Beijing China Peng Cheng Laboratory Shenzhen China National Key Laboratory of Advanced Communication Network Shijiazhuang China

ISBN: (纸本)9783031781827

Graph Anomaly Detection (GAD) has attracted considerable attention for its potential in detecting anomalies. However, an overlooked issue in prior research is the presence of extremely high-degree node, which can introduce noise into GAD, escalate computational costs, and intensify the problem of over-smoothing. To tackle this issue, this paper first presents a novel graph anomaly dataset, NFTGraph, characterized by a notable presence of extremely high-degree nodes. A series of experiments on this dataset sheds light on the influence of such nodes on GAD. Moreover, we introduce a novel model, the Super Node-Aware Graph Neural Network (SNGNN), designed to mitigate the noise emanating from extremely high-degree nodes. Experimental results demonstrate that SNGNN outperforms extant models, achieving an average improvement of over 2% in the Area Under the ROC Curve (AUROC), and effectively reducing noise. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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One Shot is Enough for Sequential Infrared Small Target Segmentation

One Shot is Enough for Sequential Infrared Small Target Segm...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Dan, Bingbing Li, Meihui Tang, Tao Zhang, Jing National Key Laboratory of Optical Field Manipulation Science and Technology Chinese Academy of Sciences Chengdu China Institute of Optics and Electronics Chinese Academy of Sciences Chengdu China University of Chinese Academy of Sciences Beijing China School of Computing Australian National University Canberra Australia

ISBN: (纸本)9798350368741

Infrared small target sequences exhibit strong similarities between frames and contain rich contextual information, which motivates us to achieve sequential infrared small target segmentation (IRSTS) with minimal data. Inspired by the success of Segment Anything Model (SAM) across various downstream tasks, we propose a one-shot and training-free method that perfectly adapts SAM's zero-shot generalization capability to sequential IRSTS. Specifically, we first obtain a confidence map through local feature matching (LFM). The highest point in the confidence map is used as the prompt to replace the manual prompt. Then, to address the over-segmentation issue caused by the domain gap, we design the point prompt-centric focusing (PPCF) module. Subsequently, to prevent miss and false detections, we introduce the triple-level ensemble (TLE) module to produce the final mask. Experiments demonstrate that our method requires only one shot to achieve comparable performance to state-of-the-art IRSTS methods and significantly outperforms other one-shot segmentation methods. Moreover, ablation studies confirm the robustness of our method in the type of annotations and the selection of reference images. Our codes are available at https://***/D-IceIce/one-shot-IRSTS. © 2025 IEEE.

关键词： One-Shot Sequential Infrared Small Target Segmentation Training-Free

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REXIO: Indexing for Low Write Amplification by Reducing Extra I/Os in Key-Value Store Under Mixed Read/Write Workloads 25th

REXIO: Indexing for Low Write Amplification by Reducing Ex...

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Wang, Zizhao Qu, Qiang Han, Nan Deng, Zhelang Ma, Yizhuo Huang, Xiaowen Meng, Jintao Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Shenzhen University Shenzhen China Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9789819605781

In mixed read/write (r/w) workloads, Key-Value stores that utilize Log-Structured Merge trees face significant write amplification (WA) due to frequent writing, resulting in extensive data compaction. While current LSM-based KV stores effectively address WA in write-heavy workloads, they struggle with increased block cache invalidation and increased r/w I/O conflicts in mixed r/w workloads. This paper introduces REXIO, a novel indexing approach to reduce WA. It decouples RAM from Solid State Drives (SSDs) and stores addresses of KV pairs in the In-RAM hashing table, which reduces buffer invalidations and eliminates unnecessary data reorganization to lower extra I/Os, thereby reducing I/O conflicts. Additionally, REXIO stores keys and values in different blocks and employs In-blocking logging to optimize updating and deleting. The experimental results show that REXIO achieves a 68.3% reduction of WA and 3.4x throughput compared to state-of-the-art LSM-Tree approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Amplification

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A multi-target regression-based method for multiple orders remaining completion time prediction in discrete manufacturing workshops

A multi-target regression-based method for multiple orders r...

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作者： Liu, Mingyuan Zhang, Jian Qin, Shengfeng Zhang, Kai Wang, Shuying Ding, Guofu Institute of Advance Design and Manufacturing Technology Southwest Jiaotong University Chengdu610031 China Department of Design Northumbria University Newcastle Upon TyneNE1 8ST United Kingdom State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong University Chengdu China Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu61003l China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu610031 China

Accurate prediction of multiple orders remaining completion time (MORCT) is crucial in the make-to-order production model. It enables managers to keep track of production status, make timely decisions, and ensure on-time delivery of orders. However, dynamic production environments, characterized by constantly changing order quantities and relationships, as well as the special temporal features of the production process, pose challenges to existing prediction methods. To address these issues, this paper proposes a novel framework based on multi-target regression. First, production data are collected and standardized from various sources using multiple data transfer protocols. The input dataset is then constructed and dynamically adjusted to accommodate changes in order quantities and priorities. Finally, a prediction model named DMTR-LSA is developed to effectively handle the specific temporal relationships in the production data by integrating long short-term memory (LSTM) and self-attention mechanisms. A case study in a real production workshop demonstrates that the proposed method supports simultaneous prediction of multiple orders. It outperforms existing methods on several evaluation metrics, reducing the average prediction error by more than 8.9%. These results highlight the practical value of the proposed method for predicting MORCT in dynamic production environments and its potential impact to enhance the production decision-making process. © 2024 Elsevier Ltd

关键词： Prediction models

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