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Salix-Leaf: Find Main Veins of Signal Clusters for Practical Parallel Decoding

IEEE Transactions on Mobile Computing 2025年

作者： Li, Yajun Zhao, Jumin Li, Dengao Wu, Hejun Xu, Shuang Bai, Ruiqin Taiyuan University of Technology College of Electronic Information and Optical Engineering Taiyuan China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan China Taiyuan University of Technology College of Computer Science and Technology College of Data Science Taiyuan China Sun Yat-sen University Department of Computer Science Guangzhou China

Parallel decoding of backscatter improves communication throughput by enabling concurrent transmission of backscatter tags. In practical applications of parallel decoding, it is extremely difficult to distinguish collided signals in superclusters where multiple signal clusters overlap. Existing methods are usually effective for superclusters with uniformly distributed signals. Nevertheless, there are many more scenarios in which signals in superclusters tend to gather unevenly, and existing methods cannot work. Such uneven clustering of signals occurs due to the following two possible causes: (1) signal-strengthdifferences (SSDs) among tags;or (2) cluster drifting (CD) driven by interferences from other objects within communication environments. This paper proposes a novel scheme called SalixLeaf, which aims to identify the main veins of signal clusters to address this problem of superclusters with unevenly distributed signals. Salix-Leaf identifies the main vein of each signal cluster for fine-grained clustering so that the direction of the main veins can be used to verify the accuracy of clustering. In addition, SalixLeaf employs a supercluster decomposer that divides signals into different segments for clustering analysis, enhancing robustness and practicability. Experimental results show that Salix-Leaf achieves a 1.2-fold increase in throughput and a 25% reduction in bit error rate (BER) compared to the state-of-the-art. © 2002-2012 IEEE.

关键词： Backscattering

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High-Fidelity Editable Portrait Synthesis with 3D GAN Inversion

High-Fidelity Editable Portrait Synthesis with 3D GAN Invers...

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

作者： Jindong Xie Jiachen Liu Yupei Lin Jinbao Wang Xianxu Hou Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Information Engineering Guangdong University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The 3D generative adversarial network (GAN) inversion converts an image into 3D representation to attain high-fidelity reconstruction and facilitate realistic image manipulation within the 3D latent space. However, previous approaches face challenges regarding the trade-off between the reconstruction ability and editability. That is, reversing a real-world image to a low-dimensional latent code would inevitably lead to information loss, and achieving a near-perfect reconstruction using high-rate triplane representation often limits the ability to manipulate the image freely in the latent space. To address these issues, we propose a novel latent conditioning encoder-based framework with the alignment between the low-dimensional latent and high-dimensional triplane. A non-semantic guided editing strategy bridges the intrinsic relation between the latent condition and triplane generation, making it possible to edit the high-dimensional representation by latent manipulation. As a result, our method can achieve high-fidelity reconstruction and editing simultaneously by directly controlling the latent code. Experimental results demonstrate that our approach excels in reconstruction and editing quality compared to previous 3D inversion methods. Furthermore, our method can also edit even real faces with large poses and out-of-domain cases.

关键词： Bridges Three-dimensional displays Codes Signal processing Generative adversarial networks Vectors Acoustics Speech processing Image reconstruction Faces

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Human tau promotes Warburg effect–like glycolytic metabolism under acute hyperglycemia conditions

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Journal of Biological Chemistry 2025年第4期301卷 108376页

作者： Yao, Jinyi Li, keying Fu, Zhenli Zheng, Jingjing Chen, Zicong Xu, Jiahao Lai, Guoqing Huang, Yaomin Huang, Jinsheng You, Guanying Han, Shuangxue He, Zhijun Liu, Qiong Li, Nan Shenzhen Key Laboratory of Marine Biotechnology and Ecology Brain Disease and Big Data Research Institute College of Life Sciences and Oceanography Shenzhen University Shenzhen China National R&D Center for Se-rich Agricultural Products Processing Hubei Engineering Research Center for Deep Processing of Green Se-rich Agricultural Products School of Modern Industry for Selenium Science and Engineering Wuhan Polytechnic University Wuhan China Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions Shenzhen China Instrumental Analysis Center of Shenzhen University Shenzhen University Shenzhen China Shenzhen Bay Laboratory Shenzhen China

The neurofilaments formed by hyperphosphorylated tau is a hallmark of tauopathies. However, the biological functions of tau and the physiological significance of its phosphorylation are still not fully understood. By using human tau (441 a.a.) transgenic (hTau) mice, murine tau KO mice, and C57BL/6J (C57) mice, unexpectedly, we found that under acute hyperglycemia conditions, JNK but not previously reported GSK3β mediated tau phosphorylation. Moreover, Akt, the inhibitory kinase upstream of GSK3β, was activated in a tau-dependent manner. Furthermore, under acute high glucose conditions, the presence of human tau significantly augmented Akt activation but inhibited 4E-BP1 phosphorylation simultaneously, indicating that human tau is also involved in regulating the alternative activation of mTORC1/2. By comparing the hippocampal membrane-associated proteome, we found that human tau influenced the homeostasis of protein-membrane association under acute hyperglycemia conditions. Of note, with respect to C57 and Tau KO mice, the membrane association of oxidative phosphorylation–related proteins was impeded by human tau in the hippocampus. In vitro study consistently showed that aerobic glycolysis was promoted in the presence of human tau under high glucose conditions, which maintained the ratio of NAD+/NADH. On the other hand, human tau restricted the level of oxidative phosphorylation, modulated the activity of SDH, and reduced ROS production upon high glucose challenging. In summary, the current study revealed that human tau played an important role in regulating glycolytic metabolism under acute hyperglycemia conditions, which is similar with the Warburg effect, through influencing the homeostasis of protein-membrane association. © 2025 The Authors

关键词： Mammals

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Jointly Optimizing the Energy and Time for Multi-UAV 3-D Coverage of Terrestrial Regions

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IEEE Transactions on Mobile Computing 2025年

作者： Gong, Hao Huang, Baoqi Jia, Bing Hao, Lifei Shi, Zhenwei Inner Mongolia University Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education College of Computer Science Hohhot010021 China Inner Mongolia University College of Computer Science Hohhot010021 China Beihang University Image Processing Center School of Astronautics Beijing100191 China

Multi-rotor unmanned aerial vehicles (UAVs) have been widely employed in various sensing tasks, e.g., environmental monitoring and disaster rescuing, many of which often require full coverage of terrestrial regions by UAVs. Efforts have been devoted to minimizing one of two objectives, i.e., energy consumptions and time costs of UAVs fulfilling such tasks, whereas it is still challenging to jointly optimize both objectives due to their complicated interdependent relationship. Therefore, this paper deals with the tasks of sensing terrestrial regions with multiple UAVs, and focuses on the three-dimensional (3-D) coverage problem by formulating a multi-objective optimization problem of jointly minimizing both objectives. Specifically, in order to optimize energy consumption effectively, an advanced closed-form energy consumption model for multi-rotor UAVs is developed based on a rigorous theoretical analysis by introducing the influences of torque and acceleration, which are often ignored by existing heuristic models. Moreover, considering the NP-hardness of the problem, an innovative swarm intelligence optimization framework is established by leveraging a multitasking learning pattern to exploit cross-task knowledge transfer and adopting an improved multi-objective salp swarm algorithm. Therein, two novel operators, i.e., a variable characteristic-guided hybrid solution initialization operator and a large-scale search-space-oriented multi-mechanism solution update operator, are designed to handle continuous, discrete and even high-dimensional variables involved. Real-world experiments validate the proposed energy model due to the reduction of power consumption estimation error by up to 59% compared to baselines, and besides, extensive simulations demonstrate that the proposed algorithm significantly outperforms the benchmarks in terms of both energy consumptions and time costs. © 2002-2012 IEEE.

关键词： Energy utilization

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Three-way space structure and clustering of categorical data

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International Journal of Approximate Reasoning 2025年 184卷

作者： Zhang, Ruxiao Zhang, Hongying Qian, Yuhua School of Mathematics and Statistics Xi'an Jiaotong University Shaanxi Xi'an710049 China Institute of Big Data Science and Industry Shanxi University Shanxi Taiyuan030006 China School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi Taiyuan030006 China

Measures of similarity and dissimilarity play a pivotal role in discovering the structural characteristics of categorical data, which is crucial in various fields and applications because many real-world problems involve qualitative information. However, co-occurrence probability, a popular similarity measure widely used to construct a linear representation space for analyzing categorical data, has been verified to be inefficient in describing the global similarity of objects. In this paper, drawing inspiration from three-way concept theory, a novel three-way representation scheme is developed through the tripartite definition of Common Feature Values (CV), Non-common Feature Values (NV), and Respective Feature Values (RV), which enables comprehensive characterization of intrinsic feature relationships while maintaining interpretative granularity. These three concepts correspond to the positive, negative, and boundary domains of the feature universe, respectively, which map a set of categorical objects into three Euclidean spaces. Utilizing the three-way categorical data representation scheme, we develop a unified framework for three-way space structure based categorical clustering algorithms (TWSBC). Finally, to verify the performance of the TWSBC algorithm, which combines the representation scheme with the k-means algorithm, we employ the SBC- type algorithm as a reference. Extensive experiments show that our proposed TWSBC-type algorithms distinctly outperform SBC-type methods in terms of space correlation and clustering performance. © 2025 Elsevier Inc.

关键词： K-means clustering

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Multi-scale hierarchical cross fusion network for hyperspectral image and LiDAR classification

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Journal of the Franklin Institute 2025年第9期362卷

作者： Pan, Haizhu Li, Xuehu Ge, Haimiao Wang, Liguo Yu, Xiaomin College of Computer and Control Engineering Qiqihar University Qiqihar161000 China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar University Qiqihar161000 China College of Information and Communication Engineering Dalian Nationalities University Dalian116000 China

As the satellite sensor technology continues to advance, acquiring hyperspectral images (HSI) and Light Detection and Ranging (LiDAR) data have become increasingly accessible. Consequently, the fusion of these two types of data has become very popular in land cover classification tasks. However, there are still some deficiencies in the current methods, such as insufficient extraction of distinctive features of multi-source data, and inadequate fusion caused by poor use of the complementarity and cooperation in multi-source data. Therefore, we propose a multi-scale hierarchical cross fusion network (MSHCFNet) for the fusion classification of HSI and LiDAR. It includes four parts. Firstly, the multi-scale cascading feature extraction module is used to extract fully multi-scale spatial features. Secondly, the hierarchical fusion module is used to integrate spatial features by cross fusion and group fusion. Thirdly, the spectral signature enhancement module is used to enhance the spectral signature, so that the discriminant information in the spectral signature can be used effectively. Finally, the decision fusion module is used to classify the types of ground objects. A large number of experiments on three publicly available datasets show that the proposed method has the most advanced classification efficacy compared with existing methods. © 2025

关键词： Feature Selection

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DWMFusion: A lightweight medical image fusion network based on discrete wavelet convolution and selective state space models

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Expert Systems with Applications 2025年 286卷

作者： Fan, Chao Yun, Mengyang Chen, Li Zhao, Huijun Peng, Bingcheng School of Artificial Intelligence and Big Data Henan University of Technology Henan Province Zhengzhou City China Ministry of Education Key Laboratory of Grain Information Processing and Control Henan Province Zhengzhou City China School of Information Science and Engineering Henan University of Technology Henan Province Zhengzhou City450001 China

Multimodal medical image fusion improves the accuracy of clinical diagnosis by integrating medical imaging information from different sensors into unified and complementary multi-scale fused information images. However, existing fusion methods still face challenges in capturing modality-specific features, balancing local and global contextual features, and preserving complementary information from different representations. To overcome these shortcomings, this study proposes a lightweight feature complementary fusion network(DWMFusion) to achieve effective fusion of functional and structural medical images under lightweight premise. Specifically, a cross-channel feature extraction unit based on discrete wavelet transform (DWT) and convolutional coupling structure is designed to enhance the modal intrinsic frequency domain features. Meanwhile, a visual state space block (VSSB) and a contextual modeling structure with multiple layers of Mamba are introduced to balance local details with global semantics and reduce computational redundancy. In the image fusion stage, a multilevel cross-fusion unit (CFFM) that introduces position coding is designed to realize deep cross-modal spatio-temporal feature interaction. A joint loss function incorporating wavelet decomposition properties is proposed to effectively adaptively constrain the frequency domain consistency and maintain the structural information. Validation on the Harvard Medical School database shows that DWMFusion is capable of generating visually satisfactory fusion images. And compared with other methods, it improves the SD metrics by an average of 17.59%, the SF metrics by an average of 17.64%, and the AG metrics by an average of 19.96% on the three datasets. These results confirm the robustness and adaptability of the model. © 2025 Elsevier Ltd

关键词： Wavelet decomposition

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Low-rank reduced biquaternion tensor ring decomposition and tensor completion

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Applied Mathematics and Computation 2025年 504卷

作者： Luo, Hui Liu, Xin Liu, Wei Zhang, Yang School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology China School of Artificial Intelligence at Sun Yat-sen University the Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Mathematics University of Manitoba MB Canada

We define the reduced biquaternion tensor ring (RBTR) decomposition and provide a detailed exposition of the corresponding algorithm RBTR-SVD. Leveraging RBTR decomposition, we propose a novel low-rank tensor completion algorithm RBTR-TV integrating RBTR ranks with total variation (TV) regularization to optimize the process. Numerical experiments on color image and video completion tasks indicate the advantages of our method. © 2025 Elsevier Inc.

关键词： Numerical methods

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LLM Knows Geometry Better than Algebra: Numerical Understanding of LLM-Based Agents in A Trading Arena

arXiv

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arXiv 2025年

作者： Ma, Tianmi Du, Jiawei Huang, Wenxin Wang, Wenjie Xie, Liang Zhong, Xian Zhou, Joey Tianyi Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology China Hubei Key Laboratory of Big Data Intelligent Analysis and Application Hubei University China Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Computing National University of Singapore Singapore School of Science Wuhan University of Technology China

Recent advancements in large language models (LLMs) have significantly improved performance in natural language processing tasks. However, their ability to generalize to dynamic, unseen tasks, particularly in numerical reasoning, remains a challenge. Existing benchmarks mainly evaluate LLMs on problems with predefined optimal solutions, which may not align with real-world scenarios where clear answers are absent. To bridge this gap, we design the Agent Trading Arena, a virtual numerical game simulating complex economic systems through zero-sum games, where agents invest in stock portfolios. Our experiments reveal that LLMs, including GPT-4o, struggle with algebraic reasoning when dealing with plain-text stock data, often focusing on local details rather than global trends. In contrast, LLMs perform significantly better with geometric reasoning when presented with visual data, such as scatter plots or K-line charts, suggesting that visual representations enhance numerical reasoning. This capability is further improved by incorporating the reflection module, which aids in the analysis and interpretation of complex data. We validate our findings on NASDAQ STOCK dataset, where LLMs demonstrate stronger reasoning with visual data compared to text. Our code and data are publicly available at https://***/wekjsdvnm/***. © 2025, CC BY.

关键词： Natural language processing systems

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UniAdapter: All-in-One Control for Flexible Video Generation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Cong Hu, Panwen Zhao, Haoyu Guo, Yuanfan Gu, Jiaxi Dong, Xiao Han, Jianhua Xu, Hang Liang, Xiaodan Chinese University of Hong Kong Hong Kong School of Science and Engineering Shenzhen China Fudan University School of Computer Science and Technology Shanghai China Huawei Noah'ark Lab Shanghai201206 China Sun Yat-Sen University School of Artificial Intelligence Zhuhai Campus Zhuhai519082 China Shenzhen Campus of Sun Yat-sen University School of Intelligent Systems Engineering Guangdong No. 66 Gongchang Road Guangming District Shenzhen518107 China Peng Cheng Laboratory and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

Condition-based video generation aims to create video content based on given information that describes specific subjects. However, most existing works can only utilize a single condition to guide the denoising process, thereby limiting their applicability to specific scenarios. Although some works attempt to accommodate multiple conditions within one framework, they often require multiple encoders, leading to inefficiencies in integrating multi-condition features. In this work, we present a framework that, with the support of the proposed Unified Adapter (UniAdapter), enables simultaneous multi-condition control of video generation within a single model. To effectively merge these conditions, we propose a novel Probabilistic Multi-condition Concatenator (PMC) module, which employs a unified encoder to accommodate multiple conditions and concatenate condition features at the pixel level to achieve fine-grained control. Following the PMC module, we employ 2D down-sampling blocks to refine features for injection into the Video Diffusion Model (VDM). Moreover, our UniAdapter is designed to be model-agnostic and compatible with any U-Net-based VDM, offering a versatile solution for improving video generation quality. Experimental results on public benchmarks UCF-101 and MSR-VTT show that our method achieves superior results in both quantitative and qualitative evaluations. © 2025 IEEE.

关键词： Signal encoding

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