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Microscopic study of neutron-induced fission process of^(239)Pu via zero-and finite-temperature density functional theory

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Chinese Physics C 2023年第5期47卷 178-187页

作者：陈永静宿阳刘丽乐董国香葛智刚舒能川王小保 China Nuclear Data Center China Institute of Atomic EnergyBeijing 102413China School of Science Huzhou UniversityHuzhou 313000China

To study the neutron-induced fission of^(239)Pu,potential energy surface(PES)calculations were performed using zero and finite-temperature density functional theory(FT-DFT)with the Skyrme *** energy of the incident neutron was simulated by the temperature of the *** variations of the least-energy fission path,fission barrier,total kinetic energy,scission line,and mass distribution of fission fragments with the incident neutron energy were *** was learned that an increase in the temperature lowers the barrier height,the isomericstate energy,and the ridge between symmetric and asymmetric fission ***,the gaps of the single particle levels become smaller with an increase in the *** the temperature increases,the pre-fission region shrinks,and the scission occurs at smaller deformation around the symmetric fission *** low temperatures,the pairing correlations in the collective space are similar to those in zero-temperature DFT,and when the temperature is T>0.3 MeV,the pairing gaps decrease *** different methods were used to calculate the fission yields of the neutron-induced fision^(239)Pu(n,)with different incident neutron energies,in the framework of timedependent generator coordinate method(TDGCM).One way to calculate the fission yield of^(239)Pu(n,f)is to solve the collective equation of the TDGCM by using the PES from the FT-DFT with the corresponding *** other involves using the PES from the zero-temperature DFT and adjusting the initial collective energy of the wave packet in the TDGCM according to the incident neutron *** the cases of the lower incident neutron energies,these two methods gave similar results and reproduced the experimental peak and width of fission fragment ***,for the highest incident neutron energy considered in this study,the results from the TDGCM using the PES from zero-temperature DFT deviated explicitly from the experimental data,whereas thos

关键词： nuclear fission density functional theory potential energy surfaces fission fragment distribution

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Multimodal contrastive learning for radiology report generation

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Journal of Ambient Intelligence and Humanized computing 2023年第8期14卷 11185-11194页

作者： Wu, Xing Li, Jingwen Wang, Jianjia Qian, Quan School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China

Automated radiology report generation can not only lighten the workload of clinicians but also improve the efficiency of disease diagnosis. However, it is a challenging task to generate semantically coherent radiology reports that are also highly consistent with medical images. To meet the challenge, we propose a Multimodal Recursive model with Contrastive Learning (MRCL). The proposed MRCL method incorporates both visual and semantic features to generate "Impression" and "Findings" of radiology reports through a recursive network, in which a contrastive pre-training method is proposed to improve the expressiveness of both visual and textual representations. Extensive experiments and analyses prove the efficacy of the proposed MRCL, which can not only generate semantically coherent radiology reports but also outperform state-of-the-art methods. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Semantics

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Evolutionary Neural Architecture Search for 3D Point Cloud Analysis

Evolutionary Neural Architecture Search for 3D Point Cloud A...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yang, Yisheng Du, Guodong Toa, Chean Khim Tang, Ho-Kin Goh, Sim Kuan School of Computing and Data Science Xiamen University Malaysia Malaysia China School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia

ISBN: (纸本)9781665410205

Neural architecture search (NAS) automates neural network design by using optimization algorithms to navigate architecture spaces, reducing the burden of manual architecture design. While NAS has achieved success, applying it to emerging domains, such as analyzing unstructured 3D point clouds, remains underexplored due to the data lying in non-Euclidean spaces, unlike images. This paper presents Success-History-based Self-adaptive Differential Evolution with a Joint Point Interaction Dimension Search (SHSADE-PIDS), an evolution-ary NAS framework that encodes discrete deep neural network architectures to continuous spaces and performs searches in the continuous spaces for efficient point cloud neural architectures. Comprehensive experiments on challenging 3D segmentation and classification benchmarks demonstrate SHSADE-PIDS's capabilities. It discovered highly efficient architectures with higher accuracy, significantly advancing prior NAS techniques. For segmentation on SemanticKITTI, SHSADE-PIDS attained 64.51% mean IoU using only 0.55M parameters and 4.5GMACs, reducing overhead by over 22-26X versus other top methods. For ModelNet40 classification, it achieved 93.4% accuracy with just 1.31M parameters, surpassing larger models. SHSADE-PIDS provided valuable insights into bridging evolutionary algorithms with neural architecture optimization, particularly for emerging frontiers like point cloud learning. © 2024 IEEE.

关键词： Cloud computing architecture

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Smartphone Fundus Photography Enhancement for Retinopathy of Prematurity Disease Diagnosis Using Deep Learning 6

Smartphone Fundus Photography Enhancement for Retinopathy of...

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6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Mutua, Elizabeth Ndunge Kasamani, Bernard Shwabwabo Reich, Christoph School of Computing & Engineering Sciences Strathmore University Nairobi Kenya Institute for Data Science Cloud Computing & It Society Furtwangen University Furtwangen Germany

ISBN: (纸本)9798350345162

Retinopathy of Prematurity (ROP) is the highest cause of childhood blindness globally with babies born preterm having a higher probability of contracting the disease. The disease diagnosis remains an economic burden to many countries, lack of enough ophthalmologists for the disease diagnosis coupled with non-existent national screening guidelines still remains a challenge. To diagnose the disease, a fundus photography is conducted, printout images are analyzed to determine the presence or absence of the disease. With the increase in the development of smartphones having advanced image capturing and processing features, the utilization of smartphones to capture retina image for disease diagnosis is becoming a common trend. For regions where ophthalmologists are few and/or for low resource regions with few or no retina capturing equipment, the use of smartphones to capture retina images for retina diseases is an effective method. This, however, is challenged: different smartphones produce images of different resolutions;some images are darker others lighter and with different resolution. A smartphone retina image capturing has a smaller field of view ranging between 450-900 which is a major limitation. A lens to support a bigger view can be combined with this approach to provide a wide view of 1300. This enlargement however distorts the image quality and may result in losing some image features. To overcome these challenges, this work develops an improved U-Net model to preprocess images captured using smartphones for ROP disease diagnosis. Our focus is to determine the presence or absence of the disease from smartphone captured images. Because the images are captured under a smaller field of view (FOV), we develop an improved U-Net model by adding patches to enhance image circumference and extract all features from the image and use the extracted features to train a U-Net model for the disease diagnosis. The model results outperformed similar recent developments with

关键词： convolutional neural network fundus photography retina vessel enhancement smartphone photography

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Multi-View Dynamic Kernelized Evidential Clustering

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

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An electron-transfer-tuning strategy at the graphene/metal interface for improving acidic water electrolysis in proton exchange membrane electrolyzers

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Journal of Energy Chemistry 2025年第4期103卷 344-352页

作者： Yue Xu Shuaidong Li Yingjian He Fumiya Shiokawa Samuel Jeong Aimi Asilah Haji Tajuddin Zeyu Xi Yoshikazu Ito Jingzi Zhang Zeyun Cai Xi Lin Kailong Hu School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhen 518055GuangdongChina Institute of Special Environments Physical Sciences Harbin Institute of TechnologyShenzhen 518055GuangdongChina School of Computer Science and Technology Harbin Institute of TechnologyShenzhen 518055GuangdongChina Institute of Applied Physics Graduate School of Pure and Applied SciencesUniversity of TsukubaTsukuba 305-8573Japan

Graphene encapsulation has been shown to be an effective technique for improving the corrosion resistance of non-noble metal catalysts for the acidic water *** key challenge lies in enhancing the electrocatalytic activity of graphene-encapsulated metals while maintaining their durability in acidic ***,an electron-transfer-tuning strategy is investigated at the graphene/NiMo interface,aiming to improve the hydrogen evolution reaction(HER)performance of graphene-encapsulated NiMo *** doping of Ti,a low electronegativity element,into NiMo substrate was confirmed to increase electron transfer from the metal core toward the *** electron-rich state on graphene facilitates the adsorption of positively charged protons on graphene,thereby enabling a Pt/C-comparable performance in 0.5 M H_(2)SO_(4),with only a 3.8%degradation in performance over a 120-h continuous *** proton exchange membrane(PEM)water electrolyzer assembled by the N-doped grapheneencapsulated Ti-doped NiMo exhibits a smaller cell voltage to achieve a current density of 2.0 A cm^(-2),in comparison to the Pt/C based *** study proposes a novel electron-transfer-tuning strategy to improve the HER activity of graphene-encapsulated non-noble metal catalysts without sacrificing durability in acidic electrolytes.

关键词： Hydrogen evolution Acidic media Graphene encapsulation Interface Proton exchange membrane

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HeterMM: applying in-DRAM index to heterogeneous memory-based key-value stores

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Frontiers of Computer science 2024年第4期18卷 215-217页

作者： Yunhong JI Wentao HUANG Xuan ZHOU School of Information Renmin University of ChinaBeijing 100872China School of Computing National University of SingaporeSingapore 117417Singapore School of Data Science and Engineering East China Normal UniversityShanghai 200062China

1 Introduction Emerging byte-addressable storage technologies,such as NVM(Non-Volatile Memory),provide a more cost-effective and larger-capacity alternative to DRAM(Dynamic Random Access Memory)[1],presenting new opportunities to address the high cost,limited capacity,and volatility of in-memory key-value(KV)*** efforts have been dedicated to redesigning conventional structures on ***,they were challenged by the substantial engineering cost and increased complexity to be integrated into existing ***,a general framework to apply existing indexes to KV stores on NVM becomes more attractive[2].

关键词： stores applying value

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Time-Series data Prediction Using Convolutional-LSTM Network 6

Time-Series Data Prediction Using Convolutional-LSTM Network

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6th IEEE International Conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Liang, Jiesong Ran, Yuzhu School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia School of International Studies Chongqing University of Technology Chongqing China

ISBN: (纸本)9798350374315

Time-series data prediction aims to predict future data according to previous data. Following the temporal correlation of data, the data can be forecasted via recurrent network, considering the neighboring data. By leveraging the powerful discriminative capability of convolutional neural networks, we combine convolutional neural networks with recurrent neural networks to perform time series data prediction, using time-series data as the subject of analysis. Our network includes 4 layers fully connected layer and a 2 layer LSTM network, the full connection layer is used to low dimensional feature mapping to high dimensional, LSTM network is used to learn the timing trend. Our method obtains the mean square error (MSE) of 0.0782 in our evaluation data. This paper provides a good benchmark algorithm in the field of data mining, which lays a foundation for the subsequent application of deep learning. © 2024 IEEE.

关键词： Spatio-temporal data

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Towards Safer Roads: A Comprehensive Review of Object Detection Techniques for Autonomous Vehicles

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SN Computer science 2025年第5期6卷 1-20页

作者： Parisapogu, Samson Anosh Babu Narla, Nitya Juryala, Aarthi Ramavath, Siddhu Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amaravati Campus Andhra Pradesh Amaravati 522 503 India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India

Nowadays, people spend a lot of time in travel, and road safety has become an important area of research. As a result, self-driving cars have been gaining popularity in recent years because they have a lot of benefits in terms of reducing traffic congestion and improving road safety, thereby reducing accidents. They also have an environmental benefit as they reduce travel times and hence reduce the usage of fuel. Though a lot of research has already been done in this field, there are always new problems being created and new solutions being found. Apart from self-driving cars, automated navigation also has a huge number of applications. Therefore, it is important to stay up to date about the latest developments in this field. This research is a comprehensive survey that explores and evaluates the different object-detecting algorithms that have been used throughout the years, their advantages, disadvantages, and trade-offs. It also analyses the datasets and benchmarks that have been used for evaluating the models. It aims to identify the gaps in research in this growing field so that it can act as a roadmap for researchers to focus their study on. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Object detection Road safety Self-driving YOLO

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Beware of Road Markings: A New Adversarial Patch Attack to Monocular Depth Estimation 38

Beware of Road Markings: A New Adversarial Patch Attack to M...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Hangcheng Wu, Zhenhu Wang, Hao Han, Xingshuo Guo, Shangwei Xiang, Tao Zhang, Tianwei College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science Beijing University of Posts and Telecommunications China College of Computer Science Chongqing University China

Monocular Depth Estimation (MDE) enables the prediction of scene depths from a single RGB image, having been widely integrated into production-grade autonomous driving systems, e.g., Tesla Autopilot. Current adversarial attacks to MDE models focus on attaching an optimized adversarial patch to a designated obstacle. Although effective, this approach presents two inherent limitations: its reliance on specific obstacles and its limited malicious impact. In contrast, we propose a pioneering attack to MDE models that decouples obstacles from patches physically and deploys optimized patches on roads, thereby extending the attack scope to arbitrary traffic participants. This approach is inspired by our groundbreaking discovery: various MDE models with different architectures, trained for autonomous driving, heavily rely on road regions when predicting depths for different obstacles. Based on this discovery, we design the Adversarial Road Marking (AdvRM) attack, which camouflages patches as ordinary road markings and deploys them on roads, thereby posing a continuous threat within the environment. Experimental results from both dataset simulations and real-world scenarios demonstrate that AdvRM is effective, stealthy, and robust against various MDE models, achieving about 1.507 of Mean Relative Shift Ratio (MRSR) over 8 MDE models. The code is available at this Github Repo. © 2024 Neural information processing systems foundation. All rights reserved.

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