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arXiv 2025年

作者： Guo, Zhendong Ong, Yew-Soon He, Tiantian Liu, Haitao The Data Science and Artificial Intelligence Research Center School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Energy and Power Engineering Dalian University of Technology Dalian116024 China

Bayesian optimization (BO) is well known to be sample-efficient for solving black-box problems. However, the BO algorithms can sometimes get stuck in suboptimal solutions even with plenty of samples. Intrinsically, such suboptimal problem of BO can attribute to the poor surrogate accuracy of the trained Gaussian process (GP), particularly that in the regions where the optimal solutions locate. Hence, we propose to build multiple GP models instead of a single GP surrogate to complement each other and thus resolving the suboptimal problem of BO. Nevertheless, according to the bias-variance tradeoff equation, the individual prediction errors can increase when increasing the diversity of models, which may lead to even worse overall surrogate accuracy. On the other hand, based on the theory of Rademacher complexity, it has been proved that exploiting the agreement of models on unlabeled information can help to reduce the complexity of the hypothesis space, and therefore achieving the required surrogate accuracy with fewer samples. Such value of model agreement has been extensively demonstrated for co-training style algorithms to boost model accuracy with a small portion of samples. Inspired by the above, we propose a novel BO algorithm labeled as co-learning BO (CLBO), which exploits both model diversity and agreement on unlabeled information to improve the overall surrogate accuracy with limited samples, and therefore achieving more efficient global optimization. Through tests on five numerical toy problems and three engineering benchmarks, the effectiveness of proposed CLBO has been well demonstrated. © 2025, CC BY.

关键词： Semi-supervised learning

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Any-Angle Routing Algorithm for Microfluidic Biochips Driven by Flow Path

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Jisuanji Yanjiu yu Fazhan/computer Research and Development 2025年第4期62卷 978-988页

作者： Youlin, Pan Shuai, Guo Xing, Huang Genggeng, Liu College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Fuzhou University Ministry of Education Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

Continuous-flow microfluidic biochips (CFMBs) have become a hot research topic in recent years due to their ability to perform biochemical assays automatically and efficiently. For the first time, PathDriver+ takes the requirements of the actual fluid transportation into account in the design process of CFMBs and implements the actual fluid transport and removal, and plans separate flow paths for each transport task, which have been neglected in previous work. However, PathDriver+ does not take full advantage of the flexibility of CFMBs routing because it only considers the optimization of flow channel length for the global routing in the mesh model, except for the detailed routing. In addition, PathDriver+ only considers the X architecture, while the existing work shows that the any-angle routing can utilize the routing resources more efficiently and shorten the flow channel length. To address the above issues, we propose a flow path-driven arbitrary angle routing algorithm, which can improve the utilization of routing resources and reduce the flow channel length while considering the actual fluid transportation requirements. The proposed algorithm constructs a search graph based on constrained Delaunay triangulation to improve the search efficiency of routing solutions while ensuring the routing quality. Then, a Dijkstra-based flow path routing method is used on the constructed search graph to generate a routing result with a short channel length quickly. In addition, in the routing process, channel reuse strategy and intersection optimization strategy are proposed for the flow path reuse and intersection number optimization problems, respectively, to further improve the quality of routing results. The experimental results show that compared with the latest work PathDriver+, the length of channels, the number of ports used, and the number of channel intersections are significantly reduced by 33.21%, 11.04%, and 44.79%, respectively, and the channel reuse rate is i

关键词： High level synthesis

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A Lightweight Aerial Image Object Detector Based on Mask Information Enhancement

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhou, Liming Zhao, Shuai Liu, Zhehao Zhang, Wanjun Qiao, Baojun Liu, Yang Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan Kaifeng China Henan University School of Computer and Information Engineering Henan Kaifeng China

Object detection in UAV imagery is highly valuable in both military and civilian domains. However, deploying conventional detectors is challenging due to limited computing resources on UAV platforms. Additionally, UAV images often contain densely arranged objects, demanding higher accuracy from detectors. In response to these challenges, we proposed the lightweight aerial object detection method based on mask feature enhancement, named BFDet. First, to enhance the feature extraction capability of the model under limited parameters, we designed the Balanced Channel Attention Layer (BCA Layer). The BCA Layer improves the extraction of high-quality channel information during feature processing. Second, to enhance model performance while minimizing parameters, we designed a feature extraction network utilizing the Efficient Feature Extraction Module (EFEM) and the Downsampling Module (DM) as its backbone. This design reduces the number of parameters while improving the extraction of discriminative features. Third, to enrich the semantic information of features, we proposed a Progressive Space Pyramid Pooling (PSPP) module, obtaining features with rich semantic information to enhance model performance. Finally, to tackle the challenge of detecting densely packed objects, we proposed a Mask Information Enhancement Module (MIEM) to obtain the fine-grained features, enhancing the model's discriminative ability in dense scenes. The proposed BFDet underwent extensive experimentation on both the Vis-Drone and UAVDT datasets. The mAP0.5 and FPS values of BFDet reach 51.4% and 33 respectively on the VisDrone dataset, and the parameter number is only 5.6M. © 1963-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Radial Adaptive Node Embedding Hashing for cross-modal retrieval

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Knowledge-Based Systems 2025年 319卷

作者： Chen, Yunfei Xia, Renwei Yang, Zhan Long, Jun Big Data Institute Central South University Hunan ChangSha410000 China School of Computer Science and Engineering Central South University Hunan ChangSha410000 China

With the rapid growth of multimedia data on social networks, efficient and accurate cross-modal retrieval has become essential. Cross-modal hashing methods offer advantages such as fast retrieval speed and low storage cost. However, unsupervised deep cross-modal hashing methods often struggle with semantic misalignment and noise, limiting their effectiveness in capturing fine-grained relationships across modalities. To address these challenges, we propose Radial Adaptive Node Embedding Hashing (RANEH), designed to enhance semantic consistency and retrieval efficiency. Specifically, the semantic meta-similarity construction module reconstructs identity semantics using a similarity matrix, ensuring that hash codes retain modality-specific features. The radial adaptive hybrid coding method employs FastKAN as an encoder to map features into a shared hash space, maintaining semantic consistency across modalities. Lastly, the broadcasting node embedding unit leverages the Fast Kolmogorov–Arnold network to capture deep modality relationships, improving semantic alignment and node embedding accuracy. Experiments on the NUS-WIDE, MIRFlickr, and MSCOCO datasets show that RANEH method consistently outperforms state-of-the-art unsupervised cross-modal hashing methods in accuracy and efficiency. The codes are available at https://***/YunfeiChenMY/RANEH. © 2025 Elsevier B.V.

关键词： Network coding

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Control Logic Routing for Continuous-Flow Microfluidic Biochips Based on Deep Reinforcement Learning

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Jisuanji Yanjiu yu Fazhan/computer Research and Development 2025年第4期62卷 950-962页

作者： Cai, Huayang Huang, Xing Liu, Genggeng College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Fuzhou University Ministry of Education Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

With the advancement of electronic design automation, continuous-flow microfluidic biochips have become one of the most promising platforms for biochemical experiments. This chip manipulates fluid samples in milliliters or nanoliters by utilizing internal microvalves and microchannels, and thus automatically performs basic biochemical experiments, such as mixing and detection. To achieve the correct bioassay function, the microvalves deployed inside the chip are usually managed by a multiplexer-based control logic, and valves receive control signals from a core input through the control channel for accurate switching. Since biochemical reactions typically require high sensitivity, the length of control paths connecting each valve needs to be reduced to ensure immediate signal propagation, and thus to reduce the signal propagation delay. In addition, to reduce the fabrication cost of chips, a vital issue to be addressed in the logic architecture design is how to effectively reduce the total channel length within the control logic. To address the above issues, we propose a deep reinforcement learning-based control logic routing algorithm to minimize the signal propagation delay and total control channel length, thereby automatically constructing an efficient control channel network. The algorithm employs the dueling deep Q-network architecture as the agent of the deep reinforcement learning framework to evaluate the tradeoff between signal propagation delay and total channel length. Besides, the diagonal channel routing is implemented for the first time for control logic, thus fundamentally improving the efficiency of valve switching operations and reducing the fabrication cost of the chip. The experimental results demonstrate that the proposed algorithm can effectively construct a high-performance and low-cost control logic architecture. © 2025 Science Press. All rights reserved.

关键词： Deep reinforcement learning

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From Pixels to Trajectory: Universal Adversarial Example Detection via Temporal Imprints

arXiv

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arXiv 2025年

作者： Gao, Yansong Peng, Huaibing Ma, Hua Dai, Zhiyang Wang, Shuo Hu, Hongsheng Fu, Anmin Xue, Minhui The School of Computer Science and Software Engineering The University of Western Australia Australia The School of Computer Science and Engineering Nanjing University of Science and Technology China Data61 CSIRO Australia The School of Cyber Science and Engineering Shanghai Jiao Tong University China The School of Information and Physical Sciences The University of Newcastle Australia

For the first time, we unveil discernible temporal (or historical) trajectory imprints resulting from adversarial example (AE) attacks. Standing in contrast to existing studies all focusing on spatial (or static) imprints within the targeted underlying victim models, we present a fresh temporal paradigm for understanding these attacks. Of paramount discovery is that these imprints are encapsulated within a single loss metric, spanning universally across diverse tasks such as classification and regression, and modalities including image, text, and audio. Recognizing the distinct nature of loss between adversarial and clean examples, we exploit this temporal imprint for AE detection by proposing TRAIT (Traceable Adversarial Temporal Trajectory Imprints). TRAIT operates under minimal assumptions without prior knowledge of attacks, thereby framing the detection challenge as a one-class classification problem. However, detecting AEs is still challenged by significant overlaps between the constructed synthetic losses of adversarial and clean examples due to the absence of ground truth for incoming inputs. TRAIT addresses this challenge by converting the synthetic loss into a spectrum signature, using the technique of Fast Fourier Transform to highlight the discrepancies, drawing inspiration from the temporal nature of the imprints, analogous to time-series signals. Across 12 AE attacks including SMACK (USENIX Sec’2023), TRAIT demonstrates consistent outstanding performance across comprehensively evaluated modalities (image, text, audio), tasks (classification and regression), datasets (nine datasets), and model architectures (e.g., ResNeXt50, BERT, RoBERTa, AudioNet). In all scenarios, TRAIT achieves an AE detection accuracy exceeding 97%, often around 99%, while maintaining a false rejection rate of 1%. TRAIT remains effective under the formulated strong adaptive attacks. Copyright © 2025, The Authors. All rights reserved.

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Performance Analysis of Differential Amplifiers Using Cadence with GPDK45, GPDK90, and GPDK180

Performance Analysis of Differential Amplifiers Using Cadenc...

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Devices for Integrated Circuit (DevIC)

作者： Tomal Suvro Sannyashi Sk Babul Akhtar Tanmay Sinha Roy Biswajit Jana Soumen Ghosh Electronics and Communication Engineering Swami Vivekananda University Kolkata Electrical and Electronics Engineering Institute of Engineering and Management Kolkata School of Computer Science Engineering and Technology Bennett University UP Computer Science and Engineering (Data Science) Haldia Institute of Technology West Bengal

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

This paper presents a comprehensive analysis of differential amplifier circuits designed using three process technologies: GPDK45, GPDK90, and GPDK180. Utilizing Cadence design and simulation tools, we perform detailed simulations to evaluate and compare key performance parameters of these architectures. The analysis focuses on critical metrics such as propagation delay, gain, power consumption, and bandwidth, which play a pivotal role in determining the overall efficiency and suitability of differential amplifiers for various applications. The simulation results highlight the impact of technology scaling on the performance characteristics, offering insights into trade-offs and optimization strategies. By systematically comparing the outputs across these process nodes, the study provides valuable guidelines for the selection of process technology based on specific design requirements and application domains.

关键词： Performance evaluation Power demand Simulation Bandwidth Very large scale integration Differential amplifiers Performance analysis Optimization Propagation delay Guidelines

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Channel Gain Map Estimation for Wireless Networks Based on Scatterer Model

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IEEE Transactions on Wireless Communications 2025年

作者： Sun, He Zhu, Lipeng Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore The Chinese University of Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Channel gain map (CGM) contains crucial large-scale fading information regarding wireless channels at specific frequency bands in wireless networks. Traditional CGM construction methods require either detailed propagation environment information or numerous channel measurements, rendering them practically cumbersome to implement. To overcome such difficulties, we propose in this paper a novel scatterer-based CGM construction framework to characterize the large-scale channel fading in a spatial region by estimating the scatterer responses from a few channel gain measurements at designated locations in the region. In particular, the region is divided into equally-spaced grids so as to smooth out the small-scale fading by averaging the channel power gain within each grid. As the average channel power gain within each grid can be expressed as a function of large-scale multi-path channel parameters such as the scatterer response coefficients and path-loss coefficients, an iterative algorithm is proposed to estimate these parameters based on only a sufficient number of channel gain measurements taken at random locations around each scatterer. Specifically, the proposed algorithm decomposes the parameter estimation problem into a set of univariate or linear estimation subproblems, which can be efficiently solved by one-dimensional (1D) line search and least-squares methods. Based on the estimated channel parameters, the CGM over the whole region can be estimated by calculating the average channel power gain for each grid. Simulation results under both the two-dimensional (2D) multipath channel model and the practical three-dimensional (3D) ray-tracing channel model verify that the proposed scatterer-based channel gain characterization can accurately capture the large-scale channel gain spatial distribution within a region, and the proposed CGM estimation framework outperforms other channel-measurement-based benchmarks in terms of estimation accuracy, while requiring a sign

关键词： Least squares approximations

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Gold Price Forecasting Using Machine Learning Techniques 1st

Gold Price Forecasting Using Machine Learning Techniques

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1st International Research Conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： John, Binu Nidhina, K. Artificial Intelligence and Data Science SCMS School of Engineering and Technology Vidya Nagar Kerala Cochin683572 India Computer Science and Engineering SCMS School of Engineering and Technology Vidya Nagar Kerala Cochin683572 India

ISBN: (纸本)9783031823824

This project aims to create an advanced machine learning system for accurate gold price prediction in response to the growing importance of gold prices in reflecting sentiment in the global economy and helping informed decision-making in international trade. By utilizing state-of-the-art machine learning techniques such as random forest regression, decision trees, and support vector machines in addition to large historical datasets of market indices and related commodities, the project seeks to identify complex patterns and trends influencing the dynamics of gold prices. From a methodological standpoint, the research focuses on optimizing both predictive accuracy and generalizability through extensive data preprocessing, exploratory analysis, and model training. Essential metrics like the root mean square error (RMSE) and R2 score are used in performance evaluation. The results show how well the random forest regression model outperforms the other two techniques with an R2 score of 0.990 and a low RMSE, demonstrating remarkable accuracy and reliability. The project’s goal is to provide organizations with practical insights for proactive risk management and well-informed decision-making in the dynamic world of international commerce and finance by combining state-of-the-art technology with empirical research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector regression

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A temporal knowledge graph reasoning model based on recurrent encoding and contrastive learning

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PeerJ computer Science 2025年 11卷 e2595-e2595页

作者： Liu, Weitong Hasikin, Khairunnisa Khairuddin, Anis Salwa Mohd Liu, Meizhen Zhao, Xuechen School of Data and Computer Science Shandong Women’s University Shandong China Department of Electrical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia Department of Biomedical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia

Temporal knowledge graphs (TKGs) are critical tools for capturing the dynamic nature of facts that evolve over time, making them highly valuable in a broad spectrum of intelligent applications. In the domain of temporal knowledge graph extrapolation reasoning, the prediction of future occurrences is of great significance and presents considerable obstacles. While current models consider the fact changes over time and recognize that historical facts may recur, they often overlook the influence of past events on future predictions. Motivated by these considerations, this work introduces a novel temporal knowledge graph reasoning model, named Temporal Reasoning with Recurrent Encoding and Contrastive Learning (TRCL), which integrates recurrent encoding and contrastive learning techniques. The proposed model has the ability to capture the evolution of historical facts, generating representations of entities and relationships through recurrent encoding. Additionally, TRCL incorporates a global historical matrix to account for repeated historical occurrences and employs contrastive learning to alleviate the interference of historical facts in predicting future events. The TKG reasoning outcomes are subsequently derived through a time decoder. A quantity of experiments conducted on four benchmark datasets demonstrate the exceptional performance of the proposed TRCL model across a range of metrics, surpassing state-of-the-art TKG reasoning models. When compared to the strong baseline Time-Guided Recurrent Graph Network (TiRGN) model, the proposed TRCL achieves 1.03% improvements on ICEWS14 using mean reciprocal rank (MRR) evaluation metric. This innovative proposed method not only enhances the accuracy of TKG extrapolation, but also sets a new standard for robustness in dynamic knowledge graph applications, paving the way for future research and practical applications in predictive intelligence systems. © 2025 Liu et al.

关键词： Contrastive Learning

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