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MS-EBDL: Reliable Glaucoma Assessment via Sufficient Epistemic Uncertainty

MS-EBDL: Reliable Glaucoma Assessment via Sufficient Epistem...

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2023 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhao, Rongchang Ma, Rui Zhou, Liang Jia, Xiaoliang Liu, Jin Central South University School of Computer Science and Engineering Changsha China Central South University The Second Xiangya Hospital Changsha China

ISBN: (纸本)9798350337488

Existing computer-aided diagnosis models only focus on the statistical accuracy for glaucoma diagnosis, but ignore its reliability for the predictions. Reliability refers to the degree how the model's prediction can be trusted when be used to assess glaucoma. Predictions with higher reliability can help the clinician make a confident decision, while the prediction with lower reliability will confuse the decision-making. Researches indicate that reliability is high related to the uncertainty both from model and data. In this paper, a method, multi-sample evidential deep learning (MS-EBDL), is proposed to enable the glaucoma assessment model with high reliability. The proposed MS-EBDL gives the disease prediction with quantitative confidence to indicate its reliability by capturing sufficient epistemic. Therefore, the proposed MS-EBDL consists of two parts: evidential deep learning for fundamental epistemic uncertainty and multi-sample dropout for additional epistemic uncertainty. Experimental results on two glaucoma datasets shown that the proposed MS-EBDL outperforms the benchmark with 97.75%(LAG) and 92.20%(RIM-ONE-R2) of accuracy, and provides reliable prediction confidence, which helps clinician make the right decision. © 2023 IEEE.

关键词： Confident prediction Evidence-based deep learning Retinal images assessment Trustworthy medical imaging

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DRGame: Diversified Recommendation for Multi-category Video Games with Balanced Implicit Preferences 29th

DRGame: Diversified Recommendation for Multi-category Video ...

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29th International conference on Database Systems for Advanced Applications (DASFAA)

作者： Liu, Kangzhe Ma, Jianghong Feng, Shanshan Zhang, Haijun Zhang, Zhao Harbin Inst Technol Shenzhen Peoples R China City Univ Hong Kong Kowloon Hong Kong Peoples R China ASTAR Ctr Frontier AI Res Singapore Singapore ASTAR Inst High Performance Comp Singapore Singapore HITSZ J&A Joint Lab Digital Design & Intelligent Shenzhen Peoples R China Hefei Univ Technol Hefei Peoples R China

ISBN: (纸本)9789819755745;9789819755752

The growing popularity of subscription services in video game consumption has emphasized the importance of offering diversified recommendations. Providing users with a diverse range of games is essential for ensuring continued engagement and fostering long-term subscriptions. We propose a novel framework, named DRGame, to obtain diversified recommendation. It is centered on multi-category video games, consisting of two components: Balance-driven Implicit Preferences Learning for data pre-processing and Clustering-based Diversified Recommendation Module for final prediction. The first module aims to achieve a balanced representation of implicit feedback in game time, thereby discovering a view of player interests across different categories. The second module adopts category-aware representation learning to cluster and select players and games based on balanced implicit preferences, and then employs asymmetric neighbor aggregation to achieve diversified recommendations. Experimental results on a real-world dataset demonstrate the superiority of our proposed method over existing approaches in terms of game diversity recommendations.

关键词： Adversarial machine learning

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Cortical and Subcortical Articulatory Representations of Two Languages Revealed by Speech Decoding Based on sEEG 24

Cortical and Subcortical Articulatory Representations of Two...

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9th International conference on Biomedical Signal and Image Processing, ICBIP 2024

作者： Wu, Yunying Li, Chenyang Zhang, Haiyan Chen, Feiyan Chen, Weidong Zhang, Shaomin Qiushi Academy for Advanced Studies Department of Biomedical Engineering Zhejiang University China Bio-X Laboratory School of Physics Zhejiang University China Qiushi Academy for Advanced Studies Department of Computer Science and Technology Zhejiang University China

ISBN: (纸本)9798400717970

Numerous studies have suggested that the sensorimotor area may play a role in speech decoding due to its involvement in controlling the movement of vocal organs and its close link to speech. However, due to limitations in surface neural recording methods such as EEG and ECoG, it remains uncertain whether deep brain regions are capable of speech decoding. In this study, we utilized two sets of stereo electroencephalography (sEEG) data to investigate how speech is represented in both cortical and subcortical areas when people speak two different languages. The first sEEG dataset involved 10 subjects speaking Dutch, while the second dataset involved 5 subjects speaking Chinese. Consistent with previous studies, specific brain gyrus locations were closely linked to speech. We also discovered that high decoding performance could be achieved in many deep regions when subjects speak two languages. Furthermore, despite the two languages sharing similar articulation representation, they exhibited differences in neural representation, particularly in terms of high and low-frequency differences of audio. The differences of Chinese speakers were greater than those of Dutch speakers. We anticipate that future studies analyzing more data will uncover more precise articulatory representations of cortical and subcortical structures. © 2024 Copyright held by the owner/author(s).

关键词： Electroencephalography

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Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition

Decoupling and Recoupling Spatiotemporal Representation for ...

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IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhou, Benjia Wang, Pichao Wan, Jun Liang, Yanyan Wang, Fan Zhang, Du Lei, Zhen Li, Hao Jin, Rong Macau Univ Sci & Technol Taipa Macao Peoples R China Alibaba Grp Hangzhou Peoples R China Chinese Acad Sci Inst Automat NLPR Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Decoupling spatiotemporal representation refers to decomposing the spatial and temporal features into dimension-independent factors. Although previous RGB-D-based motion recognition methods have achieved promising performance through the tightly coupled multi-modal spatiotemporal representation, they still suffer from (i) optimization difficulty under small data setting due to the tightly spatiotemporal-entangled modeling;(ii) information redundancy as it usually contains lots of marginal information that is weakly relevant to classification;and (iii) low interaction between multi-modal spatiotemporal information caused by insufficient late fi4sion. 'lb alleviate these drawbacks, we propose to decouple and recouple spatiotemporal representation for RGB-D-based motion recognition. Specifically, we disentangle the task of learning spatiotemporal representation into 3 sub-tasks: (1) Learning high-quality and dimension independent features through a decoupled spatial and temporal modeling network. (2) Recoupling the decoupled representation to establish stronger space-time dependency. (3) Introducing a Cross-modal Adaptive Posterior Fusion (CAPF) mechanism to capture cross-modal spatiotemporal information from RGB-D data. Seamless combination of these novel designs forms a robust spatiotemporal representation and achieves better performance than state-of-the-art methods on four public motion datasets.

关键词： computer vision Codes Fuses Redundancy Feature extraction Data models Spatiotemporal phenomena

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Predictive Preload at Fixed Preemption Points for Microcontrollers with Hard Real-Time Requirements 43rd

Predictive Preload at Fixed Preemption Points for Microcon...

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43rd Real-Time Systems conference on Automation in Everyday Life, Real-Time 2022

作者： Jungklass, Philipp Grieger, Folkhart Berekovic, Mladen IAV GmbH Rockwellstraße 12 Gifhorn38518 Germany University of Lübeck Institute of Computer Engineering Ratzeburger Allee 160 Lübeck23562 Germany

ISBN: (纸本)9783031326998

Modern microcontrollers for safety-critical applications with a hard real-time requirement use scratchpad memories assigned directly to the respective processor core to increase the performance. The advantage of scratchpad memories is their deterministic and high access speed. The disadvantage, on the other hand, is the low memory capacity, which means that efficient use is essential for maximum execution speed. For this reason, this article presents a concept that predictively preloads the program scratchpads at fixed preemption points in order to effectively compensate the low memory capacity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Real time systems

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A Dense Retrieval System and Evaluation Dataset for Scientific Computational Notebooks 19

A Dense Retrieval System and Evaluation Dataset for Scientif...

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19th IEEE International conference on e-Science, e-Science 2023

作者： Li, Na Zhang, Yangjun Zhao, Zhiming Multiscale Networked System Informatics Institute University of Amsterdam Netherlands School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Amsterdam Netherlands

ISBN: (纸本)9798350322231

The discovery and reutilization of scientific codes are crucial in many research activities. Computational notebooks have emerged as a particularly effective medium for sharing and reusing scientific codes. Nevertheless, effectively locating relevant computational notebooks is a significant challenge. First, computational notebooks encompass multi-modal data comprising unstructured text, source code, and other media, posing complexities in representing such data for retrieval purposes. Second, the absence of evaluation datasets for the computational notebook search task hampers fair performance assessments within the research community. Prior studies have either treated computational notebook search as a code-snippet search problem or focused solely on content-based approaches for searching computational notebooks. To address the aforementioned difficulties, we present DeCNR, tackling the information needs of researchers in seeking computational notebooks. Our approach leverages a fused sparse-dense retrieval model to represent computational notebooks effectively. Additionally, we construct an evaluation dataset including actual scientific queries, computational notebooks, and relevance judgments for fair and objective performance assessment. Experimental results demonstrate that the proposed method surpasses baseline approaches in terms of F1@5 and NDCG@5. The proposed system has been implemented as a web service shipped with REST APIs, allowing seamless integration with other applications and web services. © 2023 IEEE.

关键词： Web services

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GRNNLink: Predicting gene regulatory links from single-cell RNA-seq data using graph recurrent neural network

GRNNLink: Predicting gene regulatory links from single-cell ...

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2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bai, Liang Wang, Shulin Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

ISBN: (纸本)9798350386226

Single-cell RNA sequencing (scRNA-seq) technology offers unprecedented opportunities for inferring gene regulatory networks (GRNs) at the genome level. However, scRNA-seq data is highly sparse and has a low signal-to-noise ratio with significant dropout. Many unsupervised or self-supervised models have been proposed to infer GRNs from large RNA-seq datasets, but few are suitable for scRNA-seq data. Recent research confirms that transcription factor (TF)-DNA binding data enables supervised GRN inference. In this paper, we propose a novel framework called GRNNLink, which leverages known GRNs to infer potential regulatory relationships between genes. First, we preprocess the raw scRNA-seq data. Then, we introduce an interactive graph encoder based on a graph recurrent neural network (GRNN) to refine gene features by capturing the correlations between network nodes. Finally, matrix completion is performed using the node correlation features to predict GRNs. To evaluate model performance, we compare GRNNLink with six existing GRN reconstruction methods across seven scRNA-seq datasets. The results demonstrate that our method exhibits high robustness and accuracy. © 2024 IEEE.

关键词： Recurrent neural networks

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Efficiency and Accuracy of high-performance Calculations of the Electrostatic Energy of Thin Films Atomistic Clusters 9th

Efficiency and Accuracy of High-Performance Calculations of ...

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9th Russian Supercomputing Days International conference, RuSCDays 2023

作者： Grigoriev, Fedor Sulimov, Vladimir Tikhonravov, Alexander Research Computing Center M.V. Lomonosov Moscow State University Moscow119234 Russia

ISBN: (纸本)9783031494314

A study of the efficiency and accuracy of calculations of the electrostatic energy of atomistic clusters of thin films of silicon dioxide is presented. Electrostatic energy is calculated using the Particle Mesh Ewald method using the GROMACS program. The parallel efficiency is calculated for the number of cores from 8 to 64. The dependence of the results on two main parameters of the Particle Mesh Ewald method - fourierspacing and interpolation order - is studied. It is found that the density profiles of growing films are sensitive to the interpolation order. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Interpolation

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VM performance Optimization Virtual Machine Migration Method Based on Ant Colony Optimization in Cloud 24

VM Performance Optimization Virtual Machine Migration Method...

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24th IEEE International conference on high performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhao, Hui Li, Shangshu Wang, Jing Li, Jianhua Xidian University China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province China

ISBN: (纸本)9798350319934

Virtual machine migration (VMM) is one of the most commonly used technologies in cloud platforms. However, the existing VMM methods did not try to optimize VM performance for cloud users when migrating VMs, so as to affect the users' experiences. In this paper, a VM performance Optimization Virtual Machine Migration method (POVMM) is proposed, which can bring benefits to both cloud users and cloud service providers. It first takes the workload of PM into account to establish an improved workload-based VM performance model, and then uses the trained model to predict VM performance after migration. Then it formulates VMM as a multi-objective optimization problem, whose optimization objectives include maximizing VM performance, minimizing migration costs of all migrat-ed VMs and reducing the number of working PMs in cloud. Lastly, an ACO-based (ant colony optimization) algorithm, POVMM, is proposed to obtain the approximate optimal solution of the VMM problem. The simulation experiment is completed on the cloud simulation software, CloudSim. Through comparing with the other VMM algorithms, the POVMM algorithm has better results, which proves the effectiveness of the POVMM algorithm. © 2022 IEEE.

关键词： Ant colony optimization

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A Preliminary Result of Food Object Detection using Swin Transformer 22

A Preliminary Result of Food Object Detection using Swin Tra...

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8th International conference on computer Technology Applications, ICCTA 2022

作者： Jung, Daeil Shim, Simon Choo, Charles Hwang, Doosung Nah, Yunmook Oh, Sejong Dept. of Computer Science Dankook University Korea Republic of Dept. of Computer Engineering San Jose State University United States Dept. of Electrical Engineering San Jose State University United States Dept. of Software Science Dankook University Korea Republic of Dept. of Applied Computer Engineering Dankook University Korea Republic of

ISBN: (纸本)9781450396226

An inappropriate diet is one of the main causes of poor health. However, it is difficult to sustain a quantitative diet assessment in the general living environment. Food object detection is a key method for solving this problem;still, it is difficult to find studies that apply recent object detection techniques. In addition, the currently used high-performance food object detection models have a special architecture that combines two deep learning models-food localization and food classification-in series to achieve high accuracy. The disadvantage of this architecture is that it is difficult to predict the scalability of a model. In this study, we built an end-to-end food object detection model using the Swin Transformer, which is one of the latest backbone models. The experiment was conducted to compare the performance of the UECFOOD dataset with other food object detection studies. For the UECFOOD-100 dataset, a mAP(mean Average Precision) of 0.522 was obtained;also, a mAP of 0.52 was obtained for the UECFOOD-256 dataset. The findings show that the proposed model that uses only end-to-end object detection produces better performance than previous studies using a combination of food localization and food classification. © 2022 ACM.

关键词： Object detection

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