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Targeted materials discovery using Bayesian algorithm execution

npj Computational Materials 2024年第1期10卷 1619-1630页

作者： Sathya R.Chitturi Akash Ramdas Yue Wu Brian Rohr Stefano Ermon Jennifer Dionne Felipe Hda Jornada Mike Dunne Christopher Tassone Willie Neiswanger Daniel Ratner SLAC National Accelerator Laboratory Menlo ParkCA94025USA Department of Materials Science and Engineering Stanford UniversityStanfordCA94305USA Department of Computer Science Stanford UniversityStanfordCA94305USA Department of Computer Science University of Southern CaliforniaLos AngelesCA90089USA

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces.A popular strategy is Bayesian optimization,which aims to find candidates that maximize material properties;however,materials design often requires finding specific subsets of the design space which meet more complex or specialized *** present a framework that captures experimental goals through straightforward user-defined filtering *** algorithms are automatically translated into one of three intelligent,parameter-free,sequential data collection strategies(SwitchBAX,InfoBAX,and MeanBAX),bypassing the time-consuming and difficult process of task-specific acquisition function *** framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision *** demonstrate this approach on datasets for TiO2 nanoparticle synthesis and magnetic materials characterization,and show that our methods are significantly more efficient than state-of-the-art ***,our framework provides a practical solution for navigating the complexities of materials design,and helps lay groundwork for the accelerated development of advanced materials.

关键词： synthesis straight consuming

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Security Challenges of Intent-Based Networking

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Communications of the ACM 2024年第7期67卷 56-65页

作者： Kim, Jiwon Okhravi, Hamed Tian, Dave Jing Ujcich, Benjamin E. Department of Computer Science Purdue University West LafayetteIN United States MIT Lincoln Laboratory LexingtonMA United States Department of Computer Science Georgetown University WashingtonDC United States

computer SYSTEMS HAVE evolved over decades to enable more flexible programmability. Unsurprisingly, such programmability has converged more closely to how humans think and speak. This is perhaps best exemplified in the evolution of programming languages: an obtuse set of ones and zeros (machine language), small snippets of instructions (assembly language), early low-level abstractions with human-readable syntax (for example, ALGOL or C), and today’s high-level abstractions with complex operations (for example, Python or Go). With each subsequent generation, programming languages have described richer semantics that enables desired functionality to be expressed more naturally. Not only has this evolution made programming easier, but such evolution allows humans to focus on what they want to do instead of how the machine should be programmed to do it. In other words, the evolution of high-level semantics has allowed humans to focus on the intent instead of the mechanism. ©2024 Copyright is held by the owner/author(s)

关键词： Semantics

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SEOE:an option graph based semantically embedding method for prenatal depression detection

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Frontiers of computer science 2024年第6期18卷 245-258页

作者： Xiaosong HAN Mengchen CAO Dong XU Xiaoyue FENG Yanchun LIANG Xiaoduo LANG Renchu GUAN College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun 130012China Department of Electrical Engineering and Computer Science and Christopher S.Bond Life Sciences Center University of MissouriColumbiaMO 65211USA Department of Computer Science and Technology Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationZhuhai College of Science and TechnologyZhuhai 519041China Jilin Provincial Institute of Population Science and Technology Changchun 130000China

Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing ***,it is essential to detect prenatal depression early and conduct an attribution *** studies have used questionnaires to screen for prenatal depression,but the existing methods lack *** diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires,we present the semantically enhanced option embedding(SEOE)model to represent questionnaire *** can quantitatively determine the relationship and patterns between options and *** first quantifies options and resorts them,gathering options with little difference,since Word2Vec is highly dependent on *** resort task is transformed into an optimization problem involving the traveling salesman ***,all questionnaire samples are used to train the options’vector using ***,an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from *** verify the model,we compare it with other deep learning and traditional machine learning *** experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of *** most relevant factors of depression found by SEOE are also verified in the *** addition,our model is of low computational complexity and strong generalization,which can be widely applied to other questionnaire analyses of psychiatric disorders.

关键词： questionnaire analyses text classification graph representation learning traveling salesman problem prenatal depression

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Automatic Lung Nodule Segmentation in CT Imaging using an Improved 3D-Res2Unet

Automatic Lung Nodule Segmentation in CT Imaging using an Im...

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Bildverarbeitung für die Medizin Workshop, BVM 2023

作者： Tummala, Pavan Hille, Georg Saalfeld, Sylvia Department of Simulation and Graphics Faculty of Computer Science University of Magdeburg Germany

ISBN: (纸本)9783658416560

Lung cancer represents one of the most common and lethal types of cancerous pathologies and lung nodules are an early indicator for pulmonary cancer. Hence, a precise and reliable segmentation of lung nodules could enhance early diagnosis and therapy and thus, increase patients’ survival rates. This work proposes a modified 3D-Res2Unet, combining an Unet-style neural network architecture with residual blocks and attention mechanisms. This network was tested on the publicly available LUNA16 CT dataset and achieved on average 91.27 ± 6.49 %. Therefore, the proposed method indicates state-of-the-art performance and could represent an important tool for early diagnosis of lung cancer. © 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.

关键词： computerized tomography

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rafPS:A shapley-based visual analytics approach to interpret traffic

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Computational Visual Media 2024年第6期10卷 1101-1119页

作者： Zezheng Feng Yifan Jiang Hongjun Wang Zipei Fan Yuxin Ma Shuang-Hua Yang Huamin Qu Xuan Song Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 18055China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong KongChina Department of Computer Science University of ReadingBerkshire RG66AHUK Center for Spatial Information Science the University of TokyoTokyo 113-00331Japan Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and TechnologyShenzhen 518055China

Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic *** predictions are beneficial for understanding the situation and making traffic control ***,most state-of-the-art DL models are consi-dered“black boxes”with little to no transparency of the underlying mechanisms for end *** previous studies attempted to“open the black box”and increase the interpretability of generated ***,handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain *** overcome these challenges,we present TrafPS,a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban *** measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different *** on the task requirements from domain experts,we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow *** real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.

关键词： data visualization model interpretation urban planning urban visual analytics

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End-User Development for Human-Robot Interaction: Results and Trends in an Emerging Field

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Proceedings of the ACM on Human-computer Interaction 2024年第EICS期8卷 1-40页

作者： Vaiani, Giacomo Paternò, Fabio University of Pisa Department of Computer Science Pisa Italy CNR-ISTI HIIS Laboratory Pisa Italy

This paper presents a comprehensive survey on End-User Development for Human-Robot Interaction, examining existing literature to validate findings and identify unexplored areas for future research. It explores the importance of End-User Development in allowing non-expert users to customise robots, covering methodologies, evaluation methods, robot types, and application contexts. The findings reveal various End-User Development approaches, evaluation practices, and robots application domains, leading to discussions on the untapped potential of End-User Development in enhancing Human-Robot Interaction across diverse fields. The document aims to provide groundwork for future studies, highlighting the necessity for new evaluation standards and greater customisation in robotic technologies. © 2024 Owner/Author.

关键词： Human robot interaction

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A Preliminary Comparative Study on the Centering Algorithms for CassiniISS NAC Images

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Research in Astronomy and Astrophysics 2024年第10期24卷 58-65页

作者： T.Liang Q.-F.Zhang G.-M.Liu W.-H.Zhu C.-S.Wang Department of Computer Science Jinan UniversityGuangzhou 510632China Sino-French Joint Laboratory for Astrometry Dynamics and Space ScienceJinan UniversityGuangzhou 510632China School of Computer Science Guangdong University of EducationGuangzhou 510303China

Obtaining high precision is an important consideration for astrometric studies using images from the Narrow Angle Camera(NAC)of the Cassini Imaging science Subsystem(ISS).Selecting the best centering algorithm is key to enhancing astrometric *** this study,we compared the accuracy of five centering algorithms:Gaussian fitting,the modified moments method,and three point-spread function(PSF)fitting methods(effective PSF(ePSF),PSFEx,and extended PSF(x PSF)from the Cassini Imaging Central laboratory for Operations(CICLOPS)).We assessed these algorithms using 70 ISS NAC star field images taken with CL1 and CL2 filters across different stellar *** ePSF method consistently demonstrated the highest accuracy,achieving precision below 0.03 pixels for stars of magnitude *** to the previously considered best,the modified moments method,the e PSF method improved overall accuracy by about 10%and 21%in the sample and line directions,***,the xPSF model provided by CICLOPS had lower precision than the ***,the ePSF exhibits an improvement in measurement precision of 23%and 17%in the sample and line directions,respectively,over the *** discrepancy might be attributed to the xPSF focusing on photometry rather than *** findings highlight the necessity of constructing PSF models specifically tailored for astrometric purposes in NAC images and provide guidance for enhancing astrometric measurements using these ISS NAC images.

关键词： methods:analytical techniques:image processing stars:imaging astrometry

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Leveraging Spark-TensorFlow Distributor for Distributed Deep Convolutional Neural Networks: Accelerating large-scale COVID-19 Detection

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第17期49卷 173-188页

作者： Mezzoudj, Saliha Khelifa, Meriem Saadna, Yasmina Department of Computer Science University of Algiers Algiers Algeria Artificial Intelligence of Information Technologies Department of Computer Science and Information Technologies University of Kasdi Merbah Ouargla Algeria Labstic laboratory Batna 2 University Batna Algeria

The COVID-19 epidemic has been a critical global challenge due to its high mortality rate and rapid spread. Initial diagnostic methods, such as chest X-rays and reverse transcriptase polymerase chain reaction (RT-PCR), can be time-consuming and require enhanced efficiency, especially in scenarios where large-scale testing is necessary. Combining deep learning (DL) and big data analytics tools is crucial for developing accurate and fast systems to detect COVID-19 infections and prevent further transmission. In this study, we introduce a distributed Deep Convolutional Neural Network (DCNN) framework designed for the detection of COVID-19, utilizing a comprehensive dataset of chest X-ray images. Our approach leverages the spark-tensorflow-distributor, which integrates TensorFlow 2.x with Apache Spark 3.x, enabling distributed training within the Spark framework. The system utilizes Spark’s barrier mode to execute distributed training tasks, significantly enhancing the speed of both training and testing phases. We classify chest X-ray images into two categories: COVID-19 and non-COVID-19. The experimental setup involves processing the large-scale dataset of chest X-ray images in a parallelized manner using the Spark framework. Our results demonstrate that the proposed DCNN model achieves a classification accuracy of 95.53%, with a testing time of 0.06 seconds and a training time of 18,909 seconds. Furthermore, when compared to other state-of-the-art methods for COVID-19 classification, our approach proves to be more efficient and effective. This study highlights the potential of combining distributed computing frameworks with deep learning techniques to address largescale medical image analysis challenges, particularly in the context of COVID-19 detection. © 2025 Slovene Society Informatika. All rights reserved.

关键词： COVID-19

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Improved YOLOv8n Model for Detecting Helmets and License Plates on Electric Bicycles

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computers, Materials & Continua 2024年第7期80卷 449-466页

作者： Qunyue Mu Qiancheng Yu Chengchen Zhou Lei Liu Xulong Yu The College of Computer Science and Engineering North Minzu UniversityYinchuan750021China The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu UniversityYinchuan750021China

Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic *** effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric ***,manual enforcement by traffic police is time-consuming and *** methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning *** paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these *** proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional *** modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic *** results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in ***,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.

关键词： YOLOv8 object detection electric bicycle helmet detection electric bicycle license plate detection

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Committed-programming reductions: formalizations,implications and relations

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science China(Information sciences) 2024年第10期67卷 151-171页

作者： Jiang ZHANG Yu YU Dengguo FENG Shuqin FAN Zhenfeng ZHANG State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences

In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some well-known schemes, including the full-domain hash(FDH) signature(Eurocrypt1996) and the Boneh-Franklin identity-based encryption(IBE) scheme(Crypto 2001), are provably secure under CPReds;(2) we prove that a CPRed associated with an instance-extraction algorithm implies a reduction in the quantum ROM(QROM). This unifies several recent results, including the security of the Gentry-Peikert-Vaikuntanathan IBE scheme by Zhandry(Crypto 2012) and the key encapsulation mechanism(KEM) variants using the Fujisaki-Okamoto transform by Jiang et al.(Crypto 2018) in the ***, we show that CPReds are incomparable to non-programming reductions(NPReds) and randomly-programming reductions(RPReds) formalized by Fischlin et al.(Asiacrypt 2010).

关键词： provable security random oracle model quantum random oracle model black-box reduction/separation programmability

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