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arXiv 2025年

作者： Li, Mingrui Liu, Shuhong Deng, Tianchen Wang, Hongyu the Department of Computer Science Dalian University of Technology Dalian China the Department of Mechano-informatics Information Science and Technology The University of Tokyo Tokyo Japan Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education China

Gaussian SLAM systems excel in real-time rendering and fine-grained reconstruction compared to NeRF-based systems. However, their reliance on extensive keyframes is impractical for deployment in real-world robotic systems, which typically operate under sparse-view conditions that can result in substantial holes in the map. To address these challenges, we introduce DenseSplat, the first SLAM system that effectively combines the advantages of NeRF and 3DGS. DenseSplat utilizes sparse keyframes and NeRF priors for initializing primitives that densely populate maps and seamlessly fill gaps. It also implements geometry-aware primitive sampling and pruning strategies to manage granularity and enhance rendering efficiency. Moreover, DenseSplat integrates loop closure and bundle adjustment, significantly enhancing frame-to-frame tracking accuracy. Extensive experiments on multiple large-scale datasets demonstrate that DenseSplat achieves superior performance in tracking and mapping compared to current state-of-the-art methods. © 2025, CC BY.

关键词： Real time systems

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HPC-Atlas:Computationally Constructing A Comprehensive Atlas of Human Protein Complexes

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Genomics, Proteomics & Bioinformatics 2023年第5期21卷 976-990页

作者： Yuliang Pan Ruiyi Li Wengen Li Liuzhenghao Lv Jihong Guan Shuigeng Zhou Department of Computer Science and Technology College of Electronic and Information EngineeringTongji UniversityShanghai 201804China Translational Medical Center for Stem Cell Therapy Shanghai East HospitalSchool of MedicineTongji UniversityShanghai 200120China Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China

A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of *** a complete understanding of human cellular functions,it is crucial to have a comprehensive atlas of human protein ***,we still lack such a comprehensive atlas of experimentally validated protein complexes,which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes,as well as the underlying biological *** fill this gap,we built Human Protein Complexes Atlas(HPC-Atlas),as far as we know,the most accurate and comprehensive atlas of human protein complexes available to *** integrated two latest protein interaction networks,and developed a novel computational method to identify nearly 9000 protein complexes,including many previously uncharacterized *** with the existing methods,our method achieved outstanding performance on both testing and independent ***,with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)-affected human protein complexes,and 456 multifunctional proteins that contain many potential moonlighting *** results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources,but also a valuable resource for exploring new biological *** HPCAtlas webserver is freely available at http://***/HPC-Atlas.

关键词： Human protein complex Protein interaction network SARS-CoV-2-affected complex Multifunctional protein Complex identification method

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Exploring the Significance of Feature Analysis in AI/ML Modeling

Exploring the Significance of Feature Analysis in AI/ML Mode...

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Information technology (OCIT), OITS International Conference on

作者： Smriti Rout Rishikant Mallick Santosh Kumar Sahu Department of Computer Science Siksha’O’Anusandhan Bhubaneswar India Department of Computer Science Kalinga Institute of Industrial Technology Bhubaneswar India Geodata Processing and Interpretation Center Oil and Natural Gas Corporation Dehradun India

Feature selection (FS) is one of the important tasks of data preprocessing in data analytics. The data with a large number of features will affect the computational complexity, and increase a huge amount of resource usage and time consumption for data analytics. In the era of burgeoning data, the need to streamline datasets for analysis while maintaining predictive accuracy has become paramount. This study presents an investigation into the efficacy of feature selection techniques in reducing the dimensionality of datasets while preserving predictive accuracy. Our primary objective is to curate a subset of relevant attributes from an original dataset and ascertain if the predictive performance remains comparable to the full dataset. Through systematic experimentation and analysis, we aim to illustrate the advantages of this feature selection approach for enhancing the efficiency of datasets. In this study, we will explore various feature selection methods and assess their impact on both computational efficiency and model accuracy. By examining the reduced dataset’s ability to produce results akin to those of the complete dataset, we aim to demonstrate the potential benefits of feature selection in optimizing data analysis processes. Our findings are expected to shed light on the practical utility of feature selection techniques and provide insights into their relevance for future data-driven endeavors. This research represents a crucial step towards harnessing the power of reduced, yet effective, datasets in data science and machine learning applications.

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Effect of the processing route on the microstructure and mechanical behavior of superlight Mg-9Li-1Zn alloy via friction stir processing

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Journal of Magnesium and Alloys 2022年第11期10卷 3064-3081页

作者： Mengran Zhou Zhuoran Zeng Chun Cheng Yoshiaki Morisada Qingyu Shi Jian-Yih Wang Hidetoshi Fujii Joining and Welding Research Institute(JWRI) Osaka UniversityOsakaJapan Department of Mechanical Engineering Tsinghua UniversityBeijing 100084China State Key Laboratory of Tribology Tsinghua UniversityBeijingChina Key Laboratory for Advanced Materials Processing Technology Ministry of Education of China Tsinghua UniversityBeijing 100084China College of Engineering and Computer Science Australian National UniversityACT 2601Australia Department of Materials Science and Engineering National Dong-Hwa UniversityHwa-LianTaiwanChina

In this study, the effect of the processing route using a friction stir processing(FSP) method on the microstructure and mechanical behavior of a Mg-9Li-1Zn alloy was systematically investigated. In the FSP method, the odd-numbered(1st and 3rd) process directions and even-numbered(2nd and 4th) passes were alternated to distribute the strain throughout the whole processed zone uniformly. Consequently, the processed zone had a much more uniform microstructure and hardness distribution than the processed zone obtained using the conventional FSP method. Using this method, the grain size of a Mg-9Li-1Zn sheet alloy was refined from ~31 μm to ~0.21 μm with uniformly distributedα and β phases. The processed alloy exhibited a high strength-ductility synergy with an ultimate tensile strength(UTS) of 220.1 MPa and total elongation of 70.0% at a strain rate of 10^(-3)s^(-1), overwhelmingly higher than those of the base metal, 155.6 MPa in UTS and 36.0%in elongation. The in-situ SEM-DIC analysis and TEM observation demonstrated that such an outstanding ductility with moderate strength is caused by grain boundary sliding, the dominant deformation mechanism of the ultra-fine-grained sample after FSP. The processing route with reverse processing direction was proven to be efficient in producing the ultrafine grain size microstructure and improving the mechanical properties of superlight Mg-9Li-1Zn alloy.

关键词： Mg-Li-Zn alloy Friction stir processing Microstructure In-situ SEM-DIC Mechanical properties

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PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming Graph Pattern Mining 23

PSMiner: A Pattern-Aware Accelerator for High-Performance St...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Hao Qi Yu Zhang Ligang He Kang Luo Jun Huang Haoyu Lu Jin Zhao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China and Zhejiang-HUST Joint Research Center for Graph Processing Zhejiang Lab China Department of Computer Science University of Warwick United Kingdom

ISBN: (纸本)9798350323481

Streaming Graph Pattern Mining (GPM) has been widely used in many application fields. However, the existing streaming GPM solution suffers from many unnecessary explorations and isomorphism tests, while the existing static GPM ones require many repetitive operations to compute the full graph. In this paper, we propose a pattern-aware incremental execution approach and design the first streaming GPM accelerator called PSMiner, which integrates multiple optimizations to reduce redundant computation and improve computing efficiency. We have conducted extensive experiments. The results show that compared with the state-of-the-art software and hardware solutions, PSMiner achieves the average speedups of 770.9× and 60.4×, respectively.

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Effects of Near-field Sources on Ambisonics Recording and Playback 151

Effects of Near-field Sources on Ambisonics Recording and Pl...

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151st Audio Engineering Society Convention 2021

作者： Gonzalez, Raimundo Politis, Archontis Lokki, Tapio Department of Signal Processing and Acoustics Aalto University Espoo Finland Department of Computer Science Aalto University Espoo Finland Audio and Speech Processing Group Tampere University of Technology Tampere Finland

ISBN: (纸本)9781713839453

Ambisonic recording with spherical microphone arrays (SMAs) is based on a far-field assumption which determines how microphone signals are encoded into Ambisonic signals. In the presence of a near-field source, low-frequency distance-dependent boosts arise in SMAs in similar nature to proximity effects in far-field equalized directional microphones. In this study, the effects of near-field sources on Ambisonic signals are modelled analytically, their interaction with regularization stages is observed, and then traced further across to two basic ambisonic processing operations: virtual microphones, and binaural decoding. © 151st Audio Engineering Society Convention 2021.

关键词： Audio recordings

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Selection of Cancer Biomarkers from Microarray Gene Expression Data Utilizing the Bi-Objective Optimization Method

Selection of Cancer Biomarkers from Microarray Gene Expressi...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jiang, Hanjing Lin, Jianqing Zhu, Huachao Huang, Yabing Huazhong University of Science and Technology Key Lab. of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Wuhan430074 China Wuhan University School of Computer Science Wuhan430064 China Renmin Hospital of Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350337488

Cancer-associated biomarker genes play an indispensable role in the intricate tapestry of cancer development and manifestation. The expression of biomarkers in different types of tumor cells has beneficial implications for shedding light on the development of various cancers, guiding clinical diagnosis, and treatment. Microarray technology enables the expression levels of thousands of genes in samples to be sequenced simultaneously. However, sparse and high-dimensional microarray data present a formidable challenge in identifying biomarker genes. This study presents EREF-NSGA2, a novel method for cancer biomarker selection from microarray data, employing a hybrid gene selection approach. Firstly, the combination of the wrapper and embedded gene selection methods is proposed to filter the microarray data, which efficiently decreases the search space of the algorithm. After that, the improved NSGA-II algorithm is used to search the genes subset obtained from the previous step to reach the optimal subset of cancer biomarker genes. The proposed EREF-NSGA2 is compared with other reported methods on six cancer benchmark gene expression datasets. A detailed biological analysis is performed to analyze the relationship between the selected genes and the cancer data sets they belong to. To summarize, EREF-NSGA2 proves its effectiveness in selecting a feature subset comprising the fewest genes while maintaining the highest classification accuracy. © 2023 IEEE.

关键词： Bi-objective optimization Cancer biomarker Gene selection NSGA-II

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Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques

arXiv

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arXiv 2023年

作者： Qazanfari, Hamed Alyan Nezhadi, Mohammad M. Khoshdaregi, Zohreh Nozari Image Processing and Data Mining Lab Shahrood University of Technology Shahrood Iran Faculty of Science University of Science and Technology of Mazandaran Behshahr Iran Department of Computer Engineering University of Bojnord Iran

Content-based image retrieval (CBIR) systems have emerged as crucial tools in the field of computer vision, allowing for image search based on visual content rather than relying solely on metadata. This survey paper presents a comprehensive overview of CBIR, emphasizing its role in object detection and its potential to identify and retrieve visually similar images based on content features. Challenges faced by CBIR systems, including the semantic gap and scalability, are discussed, along with potential solutions. It elaborates on the semantic gap, which arises from the disparity between low-level features and high-level semantic concepts, and explores approaches to bridge this gap. One notable solution is the integration of relevance feedback (RF), empowering users to provide feedback on retrieved images and refine search results iteratively. The survey encompasses long-term and short-term learning approaches that leverage RF for enhanced CBIR accuracy and relevance. These methods focus on weight optimization and the utilization of active learning algorithms to select samples for training classifiers. Furthermore, the paper investigates machine learning techniques and the utilization of deep learning and convolutional neural networks to enhance CBIR performance. This survey paper plays a significant role in advancing the understanding of CBIR and RF techniques. It guides researchers and practitioners in comprehending existing methodologies, challenges, and potential solutions while fostering knowledge dissemination and identifying research gaps. By addressing future research directions, it sets the stage for advancements in CBIR that will enhance retrieval accuracy, usability, and effectiveness in various application domains. © 2023, CC BY.

关键词： Object detection

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Sequential Topic Selection Model with Latent Variable for Topic-Grounded Dialogue

arXiv

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arXiv 2022年

作者： Wen, Xiaofei Wei, Wei Mao, Xian-Ling Cognitive Computing and Intelligent Information Processing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology China China Department of Computer Science and Technology Beijing Institute of Technology China

Recently, topic-grounded dialogue system has attracted significant attention due to its effectiveness in predicting the next topic to yield better responses via the historical context and given topic sequence. However, almost all existing topic prediction solutions focus on only the current conversation and corresponding topic sequence to predict the next conversation topic, without exploiting other topic-guided conversations which may contain relevant topic-transitions to current conversation. To address the problem, in this paper we propose a novel approach, named Sequential Global Topic Attention (SGTA) to exploit topic transition over all conversations in a subtle way for better modeling post-to-response topic-transition and guiding the response generation to the current conversation. Specifically, we introduce a latent space modeled as a Multivariate Skew-Normal distribution with hybrid kernel functions to flexibly integrate the global-level information with sequence-level information, and predict the topic based on the distribution sampling results. We also leverage a topic-aware prior-posterior approach for secondary selection of predicted topics, which is utilized to optimize the response generation task. Extensive experiments demonstrate that our model outperforms competitive baselines on prediction and generation tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys

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Zoological Research 2023年第5期44卷 967-980页

作者： Chuxi Li Zifan Xiao Yerong Li Zhinan Chen Xun Ji Yiqun Liu Shufei Feng Zhen Zhang Kaiming Zhang Jianfeng Feng Trevor W.Robbins Shisheng Xiong Yongchang Chen Xiao Xiao School of Information Science and Technology Micro Nano System Center Fudan UniversityShanghai 200433China Department of Anesthesiology Huashan HospitalKey Laboratory of Computational Neuroscience and Brain-Inspired IntelligenceMinistry of EducationBehavioral and Cognitive Neuroscience CenterInstitute of Science and Technology for Brain-Inspired IntelligenceMOE Frontiers Center for Brain ScienceFudan UniversityShanghai 200433China Kuang Yaming Honors School Nanjing UniversityNanjingJiangsu 210023China Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China State Key Laboratory of Primate Biomedical Research Institute of Primate Translational MedicineKunming University of Science and TechnologyKunmingYunnan 650500China New Vision World LLC. Aliso ViejoCalifornia 92656USA Behavioural and Clinical Neuroscience Institute University of CambridgeCambridgeCB21TNUK

Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and ***,action recognition currently used in non-human primate(NHP)research relies heavily on intense manual labor and lacks standardized *** this work,we established two standard benchmark datasets of NHPs in the laboratory:Monkeyin Lab(Mi L),which includes 13 categories of actions and postures,and MiL2D,which includes sequences of two-dimensional(2D)skeleton ***,based on recent methodological advances in deep learning and skeleton visualization,we introduced the Monkey Monitor Kit(Mon Kit)toolbox for automatic action recognition,posture estimation,and identification of fine motor activity in *** the datasets and Mon Kit,we evaluated the daily behaviors of wild-type cynomolgus monkeys within their home cages and experimental environments and compared these observations with the behaviors exhibited by cynomolgus monkeys possessing mutations in the MECP2 gene as a disease model of Rett syndrome(RTT).Mon Kit was used to assess motor function,stereotyped behaviors,and depressive phenotypes,with the outcomes compared with human manual *** Kit established consistent criteria for identifying behavior in NHPs with high accuracy and efficiency,thus providing a novel and comprehensive tool for assessing phenotypic behavior in monkeys.

关键词： Action recognition Fine motor identification Two-stream deep model 2D skeleton Non-human primates Rett syndrome

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