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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Minsung Yoon Heechan Shin Jeil Jeong Sung-Eui Yoon School of Computing at Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Robotics Program at the Same Institute KAIST

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

A quadruped robot faces balancing challenges on a six-degrees-of-freedom moving platform, like subways, buses, airplanes, and yachts, due to independent platform motions and resultant diverse inertia forces on the robot. To alleviate these challenges, we present the Learning-based Active Stabilization on Moving Platforms (LAS-MP), featuring a self-balancing policy and system state estimators. The policy adaptively adjusts the robot’s posture in response to the platform’s motion. The estimators infer robot and platform states based on proprioceptive sensor data. For a systematic training scheme across various platform motions, we introduce platform trajectory generation and scheduling methods. Our evaluation demonstrates superior balancing performance across multiple metrics compared to three baselines. Furthermore, we conduct a detailed analysis of the LAS-MP, including ablation studies and evaluation of the estimators, to validate the effectiveness of each component.

关键词： Training Measurement Systematics Propioception Trajectory Steady-state Safety Quadrupedal robots Public transportation Intelligent robots

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Identification of Key Genes as Potential Drug Targets for Gastric Cancer

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Tsinghua Science and technology 2023年第4期28卷 649-664页

作者： Md.Tofazzal Hossain Md.Selim Reza Yin Peng Shengzhong Feng Yanjie Wei Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Statistics Bangabandhu Sheikh Mujibur Rahman Science and Technology UniversityGopalganj 8100Bangladesh Guangdong Provincial Key Laboratory for Genome Stability and Disease Prevention and Regional Immunity and Diseases Department of PathologyShenzhen UniversityShenzhen 518060China

Gastric cancer(GC)is one of the most common cancers and ranks the third in cancer mortality all over the *** goal of this study was to identify potential hub-genes,highlighting their functions,signaling pathways,and candidate drugs for the treatment of GC *** used publicly available next generation sequencing(NGS)data to identify differentially expressed(DE)*** top DE genes were mapped to STRING database to construct the protein-protein interaction(PPI)network and top hub genes were selected for further *** found a total of 1555 DE genes with 870 upregulated and 685 downregulated genes in *** selected the top 400(200 upregulated and 200 downregulated)genes to construct a PPI network and extracted the top 15 hub *** gene ontology(GO)term and kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analyses of the 15 hub genes exposed some important functions and signaling pathways that were significantly associated with GC *** survival analysis of the hub genes disclosed that the lower expressions of the three hub genes CDH2,COL4A1,and COL5A2 were associated with better survival of GC *** three genes might be the candidate biomarkers for the diagnosis and treatment of ***,we considered 3 key proteins(genomic biomarkers)(COL4A1,CDH2,and CO5A2)as the drug target proteins(receptors),performed their docking analysis with the 102 meta-drug agents,and found Everolimus,Docetaxel,Lanreotide,Venetoclax,Temsirolimus,and Nilotinib as the top ranked 6 candidate drugs with respect to our proposed target proteins for the treatment against GC ***,the proposed drugs might play vital role for the treatment against GC patients.

关键词： gastric cancer hub genes candidate genes molecular docking candidate drugs

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Empirical Study of Rectifier and Dropout in Feedforward Neural Networks

Empirical Study of Rectifier and Dropout in Feedforward Neur...

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International Conference on Information and Communication technology Convergence (ICTC)

作者： Sungpil Woo Sunhwan Lim Daeyoung Kim Electronics and Telecommunications Research Institute (ETRI) School of Computing Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea

In this paper, we analyze the unique sparsity properties of dropout, rectified activation functions, and other activation functions through simple experiments. We explore how the distinctive membership functions of ea... 详细信息

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

关键词： Rectifiers Information and communication technology Feedforward neural networks Convergence

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Empirical Study of Attention Mechanism on Neural Ordinary Differential Equations

Empirical Study of Attention Mechanism on Neural Ordinary Di...

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International Conference on Information and Communication technology Convergence (ICTC)

作者： Sungpil Woo Muhammad Zubair Sunhwan Lim Daeyoung Kim Electronics and Telecommunications Research Institute (ETRI) School of Computing Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

This paper presents a thorough empirical study on arrhythmia detection using Neural Ordinary Differential Equations (NODE) enhanced with an attention mechanism. We implement NODE and ACE-NODE to extract target-specific information for classifying ECG beats. Following the Association for the Advancement of Medical Instrumentation (AAMI) guidelines for beat labeling, our experimental results show that the inclusion of an attention mechanism significantly improves the model's classification performance.

关键词： Attention mechanisms Heart beat Arrhythmia Instruments Ordinary differential equations Electrocardiography Mathematical models Information and communication technology Data mining Labeling

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Interacting plexcitons for designed ultrafast optical nonlinearity in a monolayer semiconductor

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Light(Science & Applications) 2022年第5期11卷 877-887页

作者： Yuxiang Tang Yanbin Zhang Qirui Liu Ke Wei Xiang’ai Cheng Lei Shi Tian Jiang College of Advanced Interdisciplinary Studies National University of Defense Technology410073 ChangshaChina Key Laboratory of Micro-and NanoPhotonic Structures(Ministry of Education) and State Key Laboratory of Surface PhysicsDepartment of PhysicsFudan University200433 ShanghaiChina State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology410073 ChangshaChina Beijing Institute for Advanced Study National University of Defense Technology100000 BeijingChina

Searching for ideal materials with strong effective optical nonlinear responses is a long-term task enabling remarkable breakthroughs in contemporary quantum and nonlinear ***,hybridized light-matter quasiparticles,are an appealing candidate to realize such ***,we explore a class of peculiar polaritons,named plasmon–exciton polaritons(plexcitons),in a hybrid system composed of silver nanodisk arrays and monolayer tungsten-disulfide(WS2),which shows giant room-temperature nonlinearity due to their deep-subwavelength localized ***,comprehensive ultrafast pump–probe measurements reveal that plexciton nonlinearity is dominated by the saturation and higher-order excitation-induced dephasing interactions,rather than the well-known exchange interaction in traditional microcavity ***,we demonstrate this giant nonlinearity can be exploited to manipulate the ultrafast nonlinear absorption properties of the solid-state *** findings suggest that plexcitons are intrinsically strongly interacting,thereby pioneering new horizons for practical implementations such as energy-efficient ultrafast all-optical switching and information processing.

关键词： nonlinearity monolayer nonlinear

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A Framework for Tile Processing on Edge Servers for Roadside Traffic Surveillance 23

A Framework for Tile Processing on Edge Servers for Roadside...

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Proceedings of the 7th International Conference on Algorithms, computing and Systems

作者： Saumya Jaipuria Arani Bhattacharya Ansuman Banerjee Advanced Computing and Microelectronics Unit Indian Statistical Institute India Department of Computer Science Indraprastha Institute of Information Technology India

ISBN: (纸本)9798400709098

Roadside traffic monitoring is increasingly performed by deploying roadside high-resolution video cameras and then running Computer Vision (CV) models on the video data. Since computer vision models are compute-intensive as they utilize Deep Neural Networks (DNNs), the data is usually sent to one or more edge servers located adjacent to mobile base stations. Recent techniques propose running CV models on tiles of videos separately to detect and track small objects. Several CV models exist, each with different requirements of compute and memory. Since more compute and memory-intensive CV models provide higher accuracy, a key challenge of such techniques is to determine which vision model should be used on which tile. This becomes even more challenging if multiple videos are processed by the same edge server. In this paper, we first formulate this problem of model selection on edge devices as an Integer Linear Programming (ILP) problem, and then propose a heuristic to solve it. Our experiments show that it is quite effective in practice.

关键词： Computer Vision Edge computing Integer Linear Programming

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FedSM: A Practical Federated Shared Mobility System 50th

FedSM: A Practical Federated Shared Mobility System

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50th International Conference on Very Large Data Bases, VLDB 2024

作者： Wei, Shuyue Zhang, Yuanyuan Zhou, Zimu Zhang, Tianlong Xu, Ke SKLCCSE Lab FBPC Beijing Advanced Innovation Center Beihang University China North China Institute of Computing Technology China School of Data Science City University of Hong Kong Hong Kong SKLCCSE Lab Beihang University China

Shared mobility leverages under-utilized vehicles to offer on-demand transport services by sharing vehicles among users. It strives to match supply with demand via a series of data-intensive opera- tions such as supply prediction and task assignment. However, its full potential is often compromised in practice as most shared mobility platforms operate in isolation, leading to sub-optimal re- source utilization. In this demonstration, we advocate a federated approach to shared mobility, which enhances its effectiveness by enabling optimizations across platforms while retaining their au- tonomy. We develop privacy-preserving operators and incentive mechanisms dedicated to supply prediction and task assignment in shared mobility and implement generic interfaces that support diverse prediction and assignment algorithms. We showcase the shared mobility system with real-world ride-hailing applications. © 2024, VLDB Endowment. All rights reserved.

关键词： Arctic vehicles

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A comparative stratification of Automated Indagation on Apple Disease Using DeepSpectral Generative Adversarial and Densenet Convolutional Neural Network

A comparative stratification of Automated Indagation on Appl...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： V. Subha K. Kasturi Department of Computer Science Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

A prominent and rife among fruit is apple, one of the significant fruits that are a globally established source of human *** growing demand for improved quality of crops and augmenting the resilience of diseases is a field of expertise researched in multiple dimensions. The existing studies in the chosen area of apple fruit disease have incorporated diverse domains such as machine learning and other algorithmic effectuations. However, the persisting glitches in terms of ineffective disease segmentation, less sensitivity, and specificity leading to constraints in enhanced classification results are the impetus to the proposed research. The proposed study provides a juxtaposed scrutinization of two deep-learning-based solutions, such as the Deep Spectral Generative Adversarial Network (DSGANs), to categorize the diseases that are triggered in apples and the DenseNet Recursive Convolutional Neural Network (DNRCNN) for optimized classification. The rendered solutions are effectuated through different phases such as preprocessing, segmentation, feature extraction from the apple fruit images, and algorithm performance metrics. However, the results explicitly indicate that the DenseNet Recursive Convolutional Neural Network (DNRCNN) provides higher stratified throughput than the former due to the influence of optimized feature extraction. The simulations for this study are implemented using Python, and the results are successfully procured.

关键词： Accuracy Biological system modeling Feature extraction Generative adversarial networks Throughput Real-time systems Classification algorithms

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Pea Hash: A Performant Extendible Adaptive Hashing Index

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Proceedings of the ACM on Management of Data 2023年第1期1卷 1-25页

作者： Zhuoxuan Liu Shimin Chen SKLP and Center for Advanced Computer Systems Institute of Computing Technology CAS University of Chinese Academy of Sciences Beijing China

Hashing index is widely used to support efficient point operations. We observe that there is a conflict between performance and memory utilization goals. Existing hashing indices often have to trade off hash table access latency for better memory utilization. Moreover, many designs support only unique keys, and their performance is often suboptimal with skew *** this paper, we propose Pea Hash with two techniques to address the above two problems: (i) adaptive hashing strategy that holistically optimizes both access latency and memory utilization, and (ii) data-aware adaptive buckets that accommodate unique keys, and keys with various numbers of duplicates. We develop both an NVM-optimized Pea Hash and a DRAM-based Pea Hash index. Experiments on a machine equipped with Intel Optane DC Persistent memory show that compared to state-of-the-art NVM-optimized hashing indices, the NVM-optimized Pea Hash achieves up to 13.8x performance improvements with similar memory utilization. The DRAM-based Pea Hash outperforms existing in-DRAM hashing index designs, showing the generality of the proposed techniques.

关键词： duplicate keys hash index hashing strategy persistent memory

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MixUNet: Mix the 2D and 3D Models for Robust Medical Image Segmentation

MixUNet: Mix the 2D and 3D Models for Robust Medical Image S...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Jiawei Chen, Shizhan Ma, Shiqiang Guo, Fei Tang, Jijun Tianjin University College of Intelligence and Computing Tianjin China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350337488

Brain tumor segmentation is pivotal in the diagnosis and treatment of brain tumors. As functional imaging technologies like CT and MR advance, analyzing 3D medical image data becomes more time-consuming. Several challenges exist in 3D medical image segmentation: 1) 2D networks, when applied to 3D segmentation tasks, suffer from a lack of 3D structural information. 2) Pure 3D networks, due to their vast parameter count and smaller training sample, are susceptible to overfitting. 3) Current 2.5D networks do not fully leverage the available 3D structural information. In this study, we introduce the Mix-UNet, a multi-branch network that synergizes 2D and 3D networks. This design preserves essential 3D structural details for precise segmentation while ensuring computational efficiency. Our model comprises two main branches and a fusion module: a 2D branch for coarse segmentation without 3D structural information, a 3D branch to capture comprehensive 3D structural details, and a fusion module for pixel-level integration to produce the final segmentation. Experimental results demonstrate the model's ability to reduce parameter count, increase robustness, and maintain high precision. When tested on the BraTS 2020 validation dataset, our model achieved mean dice coefficients of 90.4%, 80.7%, and 71.2% for the whole tumor, tumor core, and enhancing tumor, respectively, with only 2.2M parameters. © 2023 IEEE.

关键词： BraTS Medical Image Segmentation Mix-UNet UNet

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