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arXiv 2025年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas. However, extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks. This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle (UAV) to ensure data security at the physical layer. Since such a system requires trade-off policies that balance the secrecy rate and energy consumption of the UAV to meet evolving scenario demands, we formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP). In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process. We then propose a transformer-enhanced soft actor critic (TransSAC) algorithm, which is a generative artificial intelligence-enable deep reinforcement learning approach to solve the reformulated problem, so that capturing global dependencies and diversely exploring weights. Simulation results demonstrate that the TransSAC outperforms various baselines, and achieves an optimal secrecy rate while effectively minimizing the energy consumption of the UAV. Moreover, the results find more suitable constraint values for the system. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

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IEEE Transactions on Affective Computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

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Mamba Adapter: Efficient Multi-Modal Fusion for Vision-Language Tracking

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Shi, Liangtao Zhong, Bineng Liang, Qihua Hu, Xiantao Mo, Zhiyi Song, Shuxiang Ministry of Education Guangxi Normal University Key Laboratory of Education Blockchain and Intelligent Technology Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Wuzhou University School of Data Science and Software Engineering Wuzhou543002 China

Utilizing the high-level semantic information of language to compensate for the limitations of vision information is a highly regarded approach in single-object tracking. However, most existing vision-language (VL) trackers employ full-parameter fine-tuning, which can easily lead to catastrophic forgetting. Therefore, they fail to fully exploit the prior knowledge of pre-trained models from upstream tasks, resulting in unsatisfactory tracking performance. To alleviate the above problem, we propose a simple yet effective Vision-Language Tracking pipeline based on Mamba Adapter, named MAVLT, which adopts the idea of parameter-efficient fine-tuning (PEFT) to realize the interaction between vision-language modalities. This novel approach offers the following advantages: (1)The knowledge of the upstream pre-trained model is efficiently inherited by freezing its parameters. This ensures that the VL tracking framework only learns the modules for vision and language interaction, with a focus on the fusion between modalities. (2)The modal interaction between language and vision encoders is flexibly bridged in each encoder layer via proposed mamba adapter, enabling efficient interaction of visual and language information at multiple levels. Extensive experiments on five popular vision-language tracking benchmarks validate the effectiveness of the proposed MAVLT. Particularly, the MAVLT achieves 73.4% AUC score on the LaSOT benchmarks with only 0.18%(0.32M) of the total parameters updates. Code and models are available at https://***/GXNU-Zhonglab/MAVLT. © 1991-2012 IEEE.

关键词： Semantics

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In-situ synthesized SiC/TiC/SiTiOC hybrid nanofiber mats for broad-range temperature microwave absorption

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Science China Technological Sciences 2025年第6期68卷 1-14页

作者： Huo, Yashan Qian, Yuetong Zhao, Minshan Zhang, Lei Tan, Yujia He, Zhihui Miao, Peng Zhang, Fuchun Cui, Wei Che, Renchao School of Physics and Electronic Information Yan’an University Yan’an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan’an China Laboratory of Advanced Materials Shanghai Key Lab of Molecular Catalysis and Innovative Materials Academy for Engineering & Technology Advanced Coatings Research Center of Ministry of Education of China Fudan University Shanghai China School of Materials and Chemical Engineering Xi’an Technological University Xi’an China School of Materials Science & Engineering Tongji University Shanghai China College of Physics Donghua University Shanghai China

Efficient, temperature-insensitive electromagnetic attenuation materials are critical for high microwave absorption at high temperatures. This study presents SiC/TiC/SiTiOC hybrid nanofiber mats constructed using an in-situ synthesis method; these mats incorporate SiC/TiC as the microwave loss unit and SiTiOC as the antioxidant unit, offering an innovative approach to high-temperature microwave absorption. The introduction of high-conductivity TiC not only optimizes the dielectric constant and impedance matching of the material but also enhances its thermal cycling stability. This design ensures steady electromagnetic attenuation, enabling the hybrid nanofiber mats to maintain robust absorption performance in the X-band, even at high temperatures. At 873 K, the material demonstrates an effective absorption bandwidth of 3.6 GHz and a minimum reflection loss of −45.55 dB. Compared to single-system absorbers, such as SiC/C nanofiber materials, with a single system, the multi-component SiC/TiC/SiTiOC hybrid nanofiber mats deliver reliable absorption performance in the temperature range of 293–873 K. This study confirms that highly conductive titanium-based ceramic materials used for dielectric property regulation effectively address the challenges of poor temperature stability and limited reusability in high-temperature carbon-ceramic absorbing materials, providing valuable insights and guidance for designing efficient microwave absorbers that can operate across a wide temperature range.

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Revealing the significant role of band structure asymmetry on thermoelectric bipolar conduction

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Physical Review B 2025年第4期111卷 045203-045203页

作者： Jiamin Qiu Shizhen Zhi Peng Zhao Jian Wang Xiaojing Ma Sheng Ye Chenhao Lin Xuanhe Zhang Zuoxu Wu School of Materials Science and Engineering and Institute of Materials Genome and Big Data Harbin Institute of Technology Shenzhen 518055 People's Republic of China School of Science and Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen 518055 China

Bipolar conduction, which is widely observed in various materials, plays a deleterious role in the thermoelectric properties. Traditionally, the single-band model is often applied to understand the transport properties of thermoelectric materials. However, it ignores the contribution from minority carriers and is incapable of quantifying the bipolar conduction. Herein, a general and feasible approach for calculating all the temperature-dependent transport properties based on the two-band model has been developed. In addition to the reduced electron Fermi energy and reduced band-gap energy, band structure asymmetry has been identified to play a significant role in bipolar conduction. Importantly, the effect of band structure asymmetry on bipolar conduction has been highlighted by using the Mg3Bi2−xSbx alloy as a typical example. Our results demonstrate that the band structure asymmetry is of great significance to the materials' thermoelectric performance.

关键词： Energy gap

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SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking 25

SoAy: A Solution-based LLM API-using Methodology for Academi...

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Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and data Mining V.1

作者： Yuanchun Wang Jifan Yu Zijun Yao Jing Zhang Yuyang Xie Shangqing Tu Yiyang Fu Youhe Feng Jinkai Zhang Jingyao Zhang Bowen Huang Yuanyao Li Huihui Yuan Lei Hou Juanzi Li Jie Tang School of Information Renmin University of China Beijing China & Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China Institute of Education Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China School of Information Renmin University of China Beijing China & Engineering Research Center of Database and Business Intelligence MOE Beijing China School of Information Renmin University of China Beijing China Zhipu AI Beijing China

ISBN: (纸本)9798400712456

Applying large language models (LLMs) to academic API usage shows promise in reducing researchers' efforts to seek academic information. However, current LLM methods for using APIs struggle with the complex API coupling commonly encountered in academic queries. To address this, we introduce SoAy, a solution-based LLM methodology for academic information seeking. SoAy enables LLMs to generate code for invoking APIs, guided by a pre-constructed API calling sequence referred to as a solution. This solution simplifies the model's understanding of complex API relationships, while the generated code enhances reasoning efficiency. LLMs are aligned with this solution-oriented, code-based reasoning method by automatically enumerating valid API coupling sequences and transforming them into queries and executable *** evaluate SoAy, we introduce SoAyBench, an evaluation benchmark accompanied by SoAyEval, built upon a cloned environment of APIs from AMiner. Experimental results demonstrate a 34.58-75.99% performance improvement compared to state-of-the-art LLM API-based baselines. All datasets, codes, tuned models, and deployed online services are publicly accessible at https://***/RUCKBReasoning/SoAy.

关键词： academic information seeking

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Movable Antenna-Assisted Integrated Sensing and Communication Systems

arXiv

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arXiv 2025年

作者： Jiang, Chengjun Zhang, Chensi Huang, Chongwen Ge, Jianhua Niyato, Dusit Yuen, Chau The State Key Lab. of Integrated Service Networks Xidian University Xi’an China China The College of Computing and Data Science Nanyang Technological University Singapore The School of Electrical and Electronics Engineering Nanyang Technological University Singapore

Movable antennas (MAs) enhance flexibility in beamforming gain and interference suppression by adjusting position within certain areas of the transceivers. In this paper, we propose an MA-assisted integrated sensing and communication framework, wherein MAs are deployed for reconfiguring the channel array responses at both the receiver and transmitter of a base station. Then, we develop an optimization framework aimed at maximizing the sensing signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the receive beamforming vector, the transmit beamforming matrix, and the positions of MAs while meeting the minimum SINR requirement for each user. To address this nonconvex problem involving complex coupled variables, we devise an alternating optimization-based algorithm that incorporates techniques including the Charnes-Cooper transform, second-order Taylor expansion, and successive convex approximation (SCA). Specifically, the closed form of the received vector and the optimal transmit matrix can be first obtained in each iteration. Subsequently, the solutions for the positions of the transmit and receive MAs are obtained using the SCA method based on the second-order Taylor expansion. The simulation results show that the proposed scheme has significant advantages over the other baseline schemes. In particular, the proposed scheme has the ability to match the performance of the fixed position antenna scheme while utilizing fewer resources. Copyright © 2025, The Authors. All rights reserved.

关键词： Signal to noise ratio

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MTMD: Multi-Scale Temporal Memory Learning and Efficient Debiasing Framework for Stock Trend Forecasting

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年

作者： Wang, Mingjie Tian, Juanxi Zhang, Mingze Guo, Jianxiong Jia, Weijia BNU-HKBU United International College Guangdong Key Lab of Ai and Multi-Modal Data Processing Department of Computer Science Zhuhai519087 China Gonzaga University School of Engineering and Applied Sciences SpokaneWA99258 United States Beijing Normal University Advanced Institute of Natural Sciences Zhuhai519087 China BNU-HKBU United International College Guangdong Key Lab of Ai and Multi-Modal Data Processing Zhuhai519087 China

The endeavor of stock trend forecasting is principally focused on predicting the future trajectory of the stock market, utilizing either manual or technical methodologies to optimize profitability. Recent advancements in machine learning technologies have showcased their efficacy in discerning authentic profit signals within the realm of stock trend forecasting, predominantly employing temporal data derived from historical stock price patterns. Nevertheless, the inherently volatile and dynamic characteristics of the stock market render the learning and capture of multi-scale temporal dependencies and stable trading opportunities a formidable challenge. This predicament is primarily attributed to the difficulty in distinguishing real profit signal patterns amidst a plethora of mixed, noisy data. In response to these complexities, we propose a Multi-Scale Temporal Memory Learning and Efficient Debiasing (MTMD) model. This innovative approach encompasses the creation of a learnable embedding coupled with external attention, serving as a memory module through self-similarity. It aims to mitigate noise interference and bolster temporal consistency within the model. The MTMD model adeptly amalgamates comprehensive local data at each timestamp while concurrently focusing on salient historical patterns on a global scale. Furthermore, the incorporation of a graph network, tailored to assimilate global and local information, facilitates the adaptive fusion of heterogeneous multi-scale data. Rigorous ablation studies and experimental evaluations affirm that the MTMD model surpasses contemporary state-of-the-art methodologies by a substantial margin in benchmark datasets. © 2025 IEEE.

关键词： Profitability

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Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haonan Miao Peizheng Zhao Yuqi Sun Fang Nan Xiaolong Zhang Yaqiang Wu Feng Tian School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students’ gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods.

关键词： Three-dimensional displays Estimation Focusing Signal processing Acoustics Speech processing

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Hierarchical Alignment-enhanced Adaptive Grounding Network for Generalized Referring Expression Comprehension

arXiv

引用

arXiv 2025年

作者： Wang, Yaxian Ding, Henghui He, Shuting Jiang, Xudong Wei, Bifan Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University China Institute of Big Data Fudan University China Shanghai University of Finance and Economics China Nanyang Technological University Singapore School of Continuing Education Xi'an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University China

In this work, we address the challenging task of Generalized Referring Expression Comprehension (GREC). Compared to the classic Referring Expression Comprehension (REC) that focuses on single-target expressions, GREC extends the scope to a more practical setting by further encompassing notarget and multi-target expressions. Existing REC methods face challenges in handling the complex cases encountered in GREC, primarily due to their fixed output and limitations in multi-modal representations. To address these issues, we propose a Hierarchical Alignment-enhanced Adaptive Grounding Network (HieA2G) for GREC, which can flexibly deal with various types of referring expressions. First, a Hierarchical Multi-modal Semantic Alignment (HMSA) module is proposed to incorporate three levels of alignments, including word-object, phrase-object, and text-image alignment. It enables hierarchical cross-modal interactions across multiple levels to achieve comprehensive and robust multi-modal understanding, greatly enhancing grounding ability for complex cases. Then, to address the varying number of target objects in GREC, we introduce an Adaptive Grounding Counter (AGC) to dynamically determine the number of output targets. Additionally, an auxiliary contrastive loss is employed in AGC to enhance object-counting ability by pulling in multi-modal features with the same counting and pushing away those with different counting. Extensive experimental results show that HieA2G achieves new state-of-the-art performance on the challenging GREC task and also the other 4 tasks, including REC, Phrase Grounding, Referring Expression Segmentation (RES), and Generalized Referring Expression Segmentation (GRES), demonstrating the remarkable superiority and generalizability of the proposed HieA2G. Copyright © 2025, The Authors. All rights reserved.

关键词： Electric grounding

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