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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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A REFERENCE-FREE METRIC FOR LANGUAGE-QUERIED AUDIO SOURCE SEPARATION USING CONTRASTIVE LANGUAGE-AUDIO PRETRAINING

arXiv

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arXiv 2024年

作者： Xiao, Feiyang Guan, Jian Zhu, Qiaoxi Liu, Xubo Wang, Wenbo Qi, Shuhan Zhang, Kejia Sun, Jianyuan Wang, Wenwu College of Computer Science and Technology Harbin Engineering University Harbin China University of Technology Sydney Ultimo Australia Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer Science University of Sheffield Sheffield United Kingdom

Language-queried audio source separation (LASS) aims to separate an audio source guided by a text query, with the signal-to-distortion ratio (SDR)-based metrics being commonly used to objectively measure the quality of the separated audio. However, the SDR-based metrics require a reference signal, which is often difficult to obtain in real-world scenarios. In addition, with the SDR-based metrics, the content information of the text query is not considered effectively in LASS. This paper introduces a reference-free evaluation metric using a contrastive language-audio pretraining (CLAP) module, termed CLAPScore, which measures the semantic similarity between the separated audio and the text query. Unlike SDR, the proposed CLAPScore metric evaluates the quality of the separated audio based on the content information of the text query, without needing a reference signal. Experiments show that the CLAPScore provides an effective evaluation of the semantic relevance of the separated audio to the text query, as compared to the SDR metric, offering an alternative for the performance evaluation of LASS systems. The code for evaluation is publicly available1 Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Highly efficient solution-processed organic photovoltaics enabled by improving packing behavior of organic semiconductors

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science China Chemistry 2024年第3期67卷 890-897页

作者： Xinyue Cui Hongxiang Li Hao Lu Yuqiang Liu Guangliu Ran Rui Liu Huarui Zhang Xueqing Ma Dawei Li Yi Lin Jifa Yu Wenkai Zhang Lei Cai Yahui Liu Pei Cheng Andong Zhang Zaifei Ma Guanghao Lu Zhishan Bo College of Textiles and Clothing State Key Laboratory of Bio-fibers and Eco-textilesQingdao UniversityQingdao 266071China State Key Laboratory of Polymer Materials Engineering College of Polymer Science and EngineeringSichuan UniversityChengdu 610065China Department of Physics and Applied Optics Beijing Area Major Laboratory Center for Advanced Quantum StudiesBeijing Normal UniversityBeijing 100875China Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and ElectronicsShandong Normal UniversityJinan 250014China Beijing Key Laboratory of Energy Conversion and Storage Materials College of ChemistryBeijing Normal UniversityBeijing 100875China Center for Advanced Low-Dimension Materials State Key Laboratory for Modification of Chemical Fibers and Polymer MaterialsCollege of Materials Science and EngineeringDonghua UniversityShanghai 201620China Frontier Institute of Science and Technology Xi'an Jiaotong UniversityXi'an 710054China

Solution processability is a unique property of organic semiconductors. The compact and regular π-π stacking between molecules is paramount in the performance of organic optoelectronic devices. However, it is still a challenge to improve their stacking quality without sacrificing the solution-processability from the aspect of materials design. Here, delicately engineered additives are presented to promote the formation of ordered aggregation of conjugated molecules by regulating their nucleation and growth dynamics. Intriguingly, the long-chain BTP-eC9-4F molecules can realize ordered aggregation comparable to short-chain ones without sacrificing processability. The domain size of BTP-eC9-4F aggregation is enlarged from 24.2 to 32.2 nm in blend *** exciton diffusion and charge transport become faster, contributing to the suppression of recombination losses. As a result, a power conversion efficiency of 19.2% is achieved in D18:BTP-eC9-4F based organic photovoltaics. Our findings demonstrate a facile strategy to improve the packing quality of solution-processed organic semiconductors for high-efficiency photovoltaics and beyond photovoltaics.

关键词： organic semiconductor organic photovoltaic charge transport solution processability

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Sparse common feature representation for undersampled face recognition

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IEEE Internet of Things Journal 2021年第7期8卷 5607-5618页

作者： Yang, Shicheng Wen, Ying He, Lianghua Zhou, MengChu Department of Computer Science and Technology Tongji University Shanghai200092 China Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology East China Normal University Shanghai200241 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah21589 Saudi Arabia

This work investigates the problem of undersampled face recognition (i.e., insufficient training data) encountered in practical Internet-of-Things (IoT) applications. Insufficient and uncertain samples captured by IoT devices may include background and facial disguise that makes face recognition more challenging than that with sufficient and reliable images. Many models work well in face recognition on a big data set, but when training data are insufficient, they achieve unsatisfactory performance. This work proposes a novel method named sparse common feature-based representation (SCFR) that provides a unique and stable result and completely avoids very time-consuming training required by a deep learning model. Specially, it constructs a common feature dictionary using both training and test images. Thereinto, a common feature is based on a discriminative common vector and learned by a Gaussian mixture model for both training and test images in a semisupervised learninig manner, which would reduce the difference among samples in each class. In the optimization, the latent indicator of test data is initialized by the estimated label. This can avoid learning invalid information and lead to good prototype images. A new variation dictionary characterizes variables that can be shared by different classes. Finally, this work adopts minimum reconstruction residuals to recognize test images, thus bringing about a substantial improvement in SCFR's performance. Extensive results on benchmark face databases demonstrate that the proposed method is better than the state-of-the-art methods handling undersampled face recognition. © 2014 IEEE.

关键词： Face recognition

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On-Demand Earth System Data Cubes

On-Demand Earth System Data Cubes

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： David Montero César Aybar Chaonan Ji Guido Kraemer Maximilian Söchting Khalil Teber Miguel D. Mahecha Remote Sensing Centre for Earth System Research (RSC4Earth) Leipzig University Institute of Earth System Science & Remote Sensing Leipzig University German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Image Processing Laboratory Universitat de València Image and Signal Processing Group Leipzig University Department of Remote Sensing Helmholtz Centre for Environmental Research (UFZ)

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Advancements in Earth system science have seen a surge in diverse datasets. Earth System Data Cubes (ESDCs) have been introduced to efficiently handle this influx of high-dimensional data. ESDCs offer a structured, intuitive framework for data analysis, organising information within spatio-temporal grids. The structured nature of ESDCs unlocks significant opportunities for Artificial Intelligence (AI) applications. By providing well-organised data, ESDCs are ideally suited for a wide range of sophisticated AI-driven tasks. An automated framework for creating AI-focused ESDCs with minimal user input could significantly accelerate the generation of task-specific training data. Here we introduce cubo, an open-source Python tool designed for easy generation of AI-focused ESDCs. Utilising collections in SpatioTemporal Asset Catalogs (STAC) that are stored as Cloud Optimised GeoTIFFs (COGs), cubo efficiently creates ESDCs, requiring only central coordinates, spatial resolution, edge size, and time range.

关键词： Earth Instruments Training data Geoscience and remote sensing Spatiotemporal phenomena Sensors Task analysis

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A Single-Cell Clustering Algorithm Based on Structure Perturbation Non-Negative Matrix Factorization

A Single-Cell Clustering Algorithm Based on Structure Pertur...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hanjing Jiang Meineng Wang Luping Zhang Yu-An Huang Yangyuan Li Yabing Huang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology China University of Mining and Technology Xuzhou China School of Mathematics and Computer Science Yichun University Yichun China School of Information Engineering East China University of Technology Nanchang China School of Computer Science Northwestern Polytechnical University Xi’an China Department of Pathology Renmin Hospital of Wuhan University Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets.

关键词： Sequential analysis Perturbation methods Clustering methods RNA Noise Clustering algorithms Sparse matrices Gene expression Unsupervised learning Periodic structures

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Decoding Sleep: Microphone-Based Snoring Analysis using Embedded Machine Learning for Obstructive Sleep Apnea Detection

Decoding Sleep: Microphone-Based Snoring Analysis using Embe...

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International Conference on Biosignals, images and Instrumentation (ICBSII)

作者： Delpha Jacob Priyanka Kokil Subramanian S Jayanthi Thiruvengadam Department of Biomedical Engineering College of Engineering and Technology SRM Institute of Science and Technology Chennai Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Chennai Department of Respiratory Medicine SRM Medical College Hospital and Research Centre Kattankulathur Tamil Nadu India

ISBN: (数字)9798350350951

ISBN: (纸本)9798350350968

Snoring, a recurring habit often disregarded within the Indian community, can signal a grave underlying issue of Obstructive Sleep Apnea (OSA). OSA is a severe sleep disorder characterized by recurrent interruptions in breathing for more than 10 seconds during sleep, typically due to partial or complete airway obstructions. Neglecting OSA can lead to a range of significant health risks, including increased likelihood of occupational accidents, motor vehicle accidents, heightened susceptibility to severe depression, cardiac and cerebrovascular diseases, and reduced life expectancy. The main objective of the study is to detect snoring while at sleep and also to classify it as normal snoring and OSA snoring. Arduino nano 33 BLE sense is used to capture the snore signal, it houses a built-in MP34DT05 sensor. The sensor has a signal-to-noise ratio of 64dB and sensitivity of - 26dBFS ± 3dB. This captures the sound signal of the individual, it is further processed to extract the Mel-filter bank energy features, Mel Frequency Cepstral Coefficients and Spectrogram features. The features are further used to build a model and the same is trained using edge impulse to classify the signal. The dataset is divided into training, testing, and validation sets, with 80% of the data allocated to training, 20% to testing, and an additional 20% within the training data set aside for validation purposes. The results for the two class classification (snoring and non snoring) indicate that the spectrogram-based approach achieved an accuracy rate of 96.9%, while the other two methods yielded accuracy rates of 93.8%. The accuracy for three class classification (normal, snoring and OSA snoring) using the Embedded Machine Learning (EML) approach is 88%. The proposed study demonstrates enhanced accuracy in identifying OSA by snoring compared to previous research. This autonomous system can facilitate the detection of OSA through the analysis of snoring patterns, subsequently alerting the subjec

关键词： Training Performance evaluation Accuracy Embedded systems Training data Machine learning Feature extraction

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An Optimal Sequence Reconstruction Algorithm for Reed-Solomon Codes

An Optimal Sequence Reconstruction Algorithm for Reed-Solomo...

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IEEE International Symposium on Information Theory

作者： Shubhransh Singhvi Roni Con Han Mao Kiah Eitan Yaakobi Signal Processing & Communications Research Center IIIT Hyderabad India Department of Computer Science Technion-Israel Institute of Technology Haifa Israel School of Physical and Mathematical Sciences Nanyang Technological University Singapore

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

The sequence reconstruction problem, introduced by Levenshtein in 2001, considers a scenario where the sender transmits a codeword from some codebook, and the receiver obtains $N$ noisy outputs of the codeword. We study the problem of efficient reconstruction using $N$ outputs that are corrupted by substitutions. Specifically, for the ubiquitous Reed-Solomon codes, we adapt the Koetter-Vardy soft-decoding algorithm, presenting a reconstruction algorithm capable of correcting beyond Johnson radius. Furthermore, the algorithm uses $\mathrm{O}(nN)$ field operations, where $n$ is the codeword length.

关键词： Reed-Solomon codes Receivers Reconstruction algorithms Noise measurement Information theory

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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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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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

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.

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Video Quality Assessment: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Zheng, Qi Fan, Yibo Huang, Leilei Zhu, Tianyu Liu, Jiaming Hao, Zhijian Xing, Shuo Chen, Chia-Ju Min, Xiongkuo Bovik, Alan C. Tu, Zhengzhong Fudan University Shanghai200000 China East China Normal University Shanghai200000 China The Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai200240 China The Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States The Department of Computer Science and Engineering Texas A&M University College StationTX77840 United States

Video quality assessment (VQA) is an important processing task, aiming at predicting the quality of videos in a manner highly consistent with human judgments of perceived quality. Traditional VQA models based on natural image and/or video statistics, which are inspired both by models of projected images of the real world and by dual models of the human visual system, deliver only limited prediction performances on real-world user-generated content (UGC), as exemplified in recent large-scale VQA databases containing large numbers of diverse video contents crawled from the web. Fortunately, recent advances in deep neural networks and Large Multimodality Models (LMMs) have enabled significant progress in solving this problem, yielding better results than prior handcrafted models. Numerous deep learning-based VQA models have been developed, with progress in this direction driven by the creation of content-diverse, large-scale human-labeled databases that supply ground truth psychometric video quality data. Here, we present a comprehensive survey of recent progress in the development of VQA algorithms and the benchmarking studies and databases that make them possible. We also analyze open research directions on study design and VQA algorithm architectures. Github link: https://***/taco-group/Video-Quality-Assessment-A-Comprehensive-Survey. © 2024, CC BY-NC-ND.

关键词： Video analysis

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