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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Obe, Ruth Kaufmann, Brandt Baird, Kaelen Kadel, Sam Soltani, Yasmin Cham, Mostafa Gobbert, Matthias K. Barajas, Carlos A. Jiang, Zhuoran Sharma, Vijay R. Ren, Lei Peterson, Stephen W. Poif, Jerimy C. U. of Houston-Clear Lake Dept. of Computer Science and of Software Engineering United States U. of San Francisco Dept. of Mathematics and Statistics United States Skidmore College Dept. of Computer Science and of Mathematics United States Mount Holyoke College Dept. of Computer Science and of Psychology United States U. of Houston Dept. of Biomedical Engineering United States U. of Maryland Dept. of Information Systems Baltimore County United States U. of Maryland Dept. of Mathematics and Statistics Baltimore County United States Medical Physics Graduate Program Duke University United States U. of Maryland School of Medicine Dept. of Radiation Oncology United States U. of Maryland School of Medicine Department of Radiation Oncology United States U. of Cape Town Dept. of Physics South Africa H3D Inc. United States

ISBN: (纸本)9798350345346

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy. © 2023 IEEE.

关键词： Clas-sification Compton camera Distributed Data Parallelism Proton beam therapy PyTorch Recurrent neural network

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engineering dimensionality of collective dipole-dipole interactions using a nanophotonic environment

Engineering dimensionality of collective dipole-dipole inter...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Ashwin K. Boddeti Yi Wang Xitlali Juarez Alexandra Boltasseva Hadiseh Alaeian Teri W. Odom Zubin Jacob Elmore Family School of Electrical and Computer Engineering and Birck Nanotechnology Center Purdue University West Lafayette Indiana USA Graduate Program in Applied Physics Northwestern University Evanston IL USA Department of Materials Science and Engineering Northwestern University Evanston Illinois USA Department of Chemistry and Department of Materials Science and Engineering Northwestern University Evanston Illinois USA

We experimentally demonstrate reduced dimensionality in a interacting ensemble of emitters. The well-known stretched exponential decay dynamics, I(t)/I 0 = exp(−γ D t)exp(−αt β ) with β = 0.5 in 3D geometries, is... 详细信息

ISBN: (纸本)9781957171258

关键词： Energy transfer Fluorescence Molecules Spontaneous emission Titanium dioxide

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Strategic Planning and Knowledge Management Approaches as input to remote area strategic plans: A Case study in Aru Islands Regency Maluku Province 8

Strategic Planning and Knowledge Management Approaches as in...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Irena Gutandjala, Ilona Harso Supangkat, Suhono Lumban Gaol, Ford Rianti, Benny Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia Bandung Institute of Technology Smart City and Community Innovation Center School of Electrical and Informatics Engineering Bandung Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781665416979

Knowledge is an important asset in an organization. Aru Islands District is one of the districts in Maluku Province. The Government of Aru Islands District Maluku has a vision and mission as outlined in the Regional Strategic Plan or commonly referred to as RENSTRA. This vision and mission are an activity that requires a policy direction that is planned and integrated with the strategic environment of all related aspects. The strategic plan is then realized with the implementation of the government with regional autonomy which is the modern governance in realizing quality public services, fast, easy, fair and unlimited for the welfare and progress of the region. In this research will be used a strategic approach as input from the analysis with knowledge management and assessment of the condition of Aru district. © 2021 IEEE.

关键词： Strategic planning

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Fuzzy Positive Learning for Semi-Supervised Semantic Segmentation

Fuzzy Positive Learning for Semi-Supervised Semantic Segment...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Pengchong Qiao Zhidan Wei Yu Wang Zhennan Wang Guoli Song Fan Xu Xiangyang Ji Chang Liu Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China Department of Automation and BNRist Tsinghua University Beijing China

Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations. Although typical attempts focus on ameliorating the inevitable error-prone pseudo-labeling, we think differently and resort to exhausting informative semantics from multiple probably correct candidate labels. In this paper, we introduce Fuzzy Positive Learning (FPL) for accurate SSL semantic segmentation in a plug-and-play fashion, targeting adaptively encouraging fuzzy positive predictions and suppressing highly-probable negatives. Being conceptually simple yet practically effective, FPL can remarkably alleviate interference from wrong pseudo labels and progressively achieve clear pixel-level semantic discrimination. Concretely, our FPL approach consists of two main components, including fuzzy positive assignment (FPA) to provide an adaptive number of labels for each pixel and fuzzy positive regularization (FPR) to restrict the predictions of fuzzy positive categories to be larger than the rest under different perturbations. Theoretical analysis and extensive experiments on Cityscapes and VOC 2012 with consistent performance gain justify the superiority of our approach. Codes are provided in https://***/qpc1611094/FPL.

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Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning

Out-of-Distributed Semantic Pruning for Robust Semi-Supervis...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yu Wang Pengchong Qiao Chang Liu Guoli Song Xiawu Zheng Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science(AI4S)-Preferred Program Peking University Shenzhen Graduate School China Peng Cheng Laboratory Shenzhen China Department of Automation and BNRist Tsinghua University Beijing China

Recent advances in robust semi-supervised learning (SSL) typically filter out-of-distribution (OOD) information at the sample level. We argue that an overlooked problem of robust SSL is its corrupted information on semantic level, practically limiting the development of the field. In this paper, we take an initial step to explore and propose a unified framework termed OOD Semantic Pruning (OSP), which aims at pruning OOD semantics out from in-distribution (ID) features. Specifically, (i) we propose an aliasing OOD matching module to pair each ID sample with an OOD sample with semantic overlap. (ii) We design a soft orthogonality regularization, which first transforms each ID feature by suppressing its semantic component that is collinear with paired OOD sample. It then forces the predictions before and after soft orthogonality decomposition to be consistent. Being practically simple, our method shows a strong performance in OOD detection and ID classification on challenging benchmarks. In particular, OSP surpasses the previous state-of-the-art by 13.7% on accuracy for ID classification and 5.9% on AUROC for OOD detection on TinyImageNet dataset. The source codes are publicly available at https://***/rain305f/OSP.

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Expectation-maximization contrastive learning for compact video-and-language representations 22

Expectation-maximization contrastive learning for compact vi...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Peng Jin Jinfa Huang Fenglin Liu Xian Wu Shen Ge Guoli Song David A. Clifton Jie Chen School of Electronic and Computer Engineering Peking University China and AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China Department of Engineering Science University of Oxford UK Tencent JARVIS Lab China Peng Cheng Laboratory Shenzhen China Department of Engineering Science University of Oxford UK and Oxford-Suzhou Centre for Advanced Research Suzhou China School of Electronic and Computer Engineering Peking University China and Peng Cheng Laboratory Shenzhen China and AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China

ISBN: (纸本)9781713871088

Most video-and-language representation learning approaches employ contrastive learning, e.g., CLIP [53], to project the video and text features into a common latent space according to the semantic similarities of text-video pairs. However, such learned shared latent spaces are not often optimal, and the modality gap between visual and textual representation can not be fully eliminated. In this paper, we propose Expectation-Maximization Contrastive Learning (EMCL) to learn compact video-and-language representations. Specifically, we use the Expectation-Maximization algorithm to find a compact set of bases for the latent space, where the features could be concisely represented as the linear combinations of these bases. Such feature decomposition of video-and-language representations reduces the rank of the latent space, resulting in increased representing power for the semantics. Extensive experiments on three benchmark text-video retrieval datasets prove that our EMCL can learn more discriminative video-and-language representations than previous methods, and significantly outperform previous state-of-the-art methods across all metrics. More encouragingly, the proposed method can be applied to boost the performance of existing approaches either as a jointly training layer or an out-of-the-box inference module with no extra training, making it easy to be incorporated into any existing methods.

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Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Artificial Intelligence-Generated Content (AIGC) refers to the use of AI to automate the information creation process while fulfilling the personalized requirements of users. However, due to the instability of AIGC models, e.g., the stochastic nature of diffusion models, the quality and accuracy of the generated content can vary significantly. In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. Thus, a dynamic AIGC service provider (ASP) selection scheme is required to enable users to connect to the most suited ASP, improving the users’ satisfaction and quality of generated content. In this article, we first review the AIGC techniques and their applications in wireless networks. We then present the AIGC-as-a-service (AaaS) concept and discuss the challenges in deploying AaaS at the edge networks. Yet, it is essential to have performance metrics to evaluate the accuracy of AIGC services. Thus, we introduce several image-based perceived quality evaluation metrics. Then, we propose a general and effective model to illustrate the relationship between computational resources and user-perceived quality evaluation metrics. To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for the optimal ASP selection. Simulation results show that the proposed algorithm can provide a higher quality of generated content to users and achieve fewer crashed tasks by comparing with four benchmarks, i.e., overloading-avoidance, random, round-robin policies, and the upper-bound schemes. © 2023, CC BY.

关键词： Reinforcement learning

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A Genetic Algorithm approach for optimization of geothermal power plant production: Case studies of direct steam cycle in Kamojang

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South African Journal of Chemical engineering 2023年 45卷 1-9页

作者： Rudiyanto, Bayu Birri, Mochammad Syahrul Widjonarko Avian, Cries Kamal, Dianta Mustofa Hijriawan, Miftah Energy Engineering Laboratory Department of Renewable Energy Engineering Politeknik Negeri Jember Jl. Mastrip No.164 Krajan Timur Jember 68121 Indonesia Graduate School of Department of Renewable Energy Engineering Politeknik Negeri Jember Jl. Mastrip No.164 Krajan Timur Jember 68121 Indonesia Department of Electrical Engineering Universitas Jember Jl. Kalimantan 37 Tegalboto Jember 68121 Indonesia Departement of Electronic and Computer Engineering National Taiwan University of Science and Technology No.43 Keelung Rd. Sec.4 Da'an Dist. Taipei City 106335 Taiwan Department of Mechanical Engineering Politeknik Negeri Jakarta Jl. Prof. Dr. G. A. Siwabessy Depok 16425 Indonesia Graduate Program of Mechanical Engineering Universitas Sebelas Maret Jl. Ir. Sutami 36 Surakarta 57126 Indonesia

Indonesia has enormous geothermal potential, but it only contributes 5% to Indonesia's energy matrix. During 37 years of operation, PT. Pertamina Geothermal Energy Kamojang area has been operating to produce electricity and is currently capable of supplying and distributing electricity to the Java-Bali area with a capacity of 60 MWe. However, this can run into a decrease in the efficiency and effectiveness of system performance due to energy losses in several geothermal power plant components during energy conversion. In this case, exergy analysis at PT. Pertamina Geothermal Energy Kamojang area Unit 4 direct-dry steam cycle was done on each component and state. This aims to know the energy and exergy stream and where it happened irreversibly at the component. The biggest irreversibility value occurred at the turbine and main condenser, with a value of 21,693.890 kW and 21,688.148 kW. The total irreversibility of all systems is 58,326.201 kW, while the total exergy inlet systems is 119,308.457 kW, so the value efficiency exergy obtained is 51.13%. Based on the environment as dead state analysis, an efficiency exergy value is inversely proportional to the irreversibility value and ascending environment temperature. System optimization was done with the genetic algorithm method, with variable values at the pressure wellhead and inlet turbine for the overall exergy efficiency value. The value obtained from optimization is 11.98 bar at the wellhead and 10.023 bar at the inlet turbine, and the overall efficiency exergy increased by 51.22%. © 2023 The Author(s)

关键词： Dry steam cycle Exergy Genetic Algorithm Geothermal power plant Optimization

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Big data in healthcare:Conceptual network structure,key challenges and opportunities

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Digital Communications and Networks 2023年第4期9卷 856-868页

作者： Leonardo B.Furstenaua Pedro Leivas Michele Kremer Sott Michael S.Dohan Jose Ricardo Lopez-Robles Manuel J.Cobo Nicola Luigi Bragazzi Kim-Kwang Raymond Choo Post-Graduate Program in Industrial Engineering Federal University of Rio Grande Do SulPorto Alegre90035-190Brazil University of Santa Cruz Do Sul Av.Independencia 2293Santa Cruz Do SulRSBrazil Business School Unisinos UniversityAv.Dr.Nilo Peçanha 160091330-002Porto AlegreBrazil Faculty of Business Administration Lakehead UniversityThunder BayOntarioCanada Autonomous University of Zacatecas Av.Ramon Lopez Velarde Col.CentroZacatecas98000Mexico Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain York University ON M3J 1P3Canada Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249USA

Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources *** data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease *** research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare *** this goal in mind,4857 documents from the Web of science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT *** bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research *** strategic diagram yielded 20 clusters and their relative importance in terms of centrality and *** thematic evolution structure presented the most important themes and how it changes over ***,we presented the main challenges and future opportunities of big data in healthcare.

关键词： Big data Healthcare digitalization Bibliometric Strategic intelligence Co-word analysis SciMAT

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Reducing Effective System Dimensionality with Long-Range Collective Dipole-Dipole Interactions

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Physical Review Letters 2024年第17期132卷 173803-173803页

作者： Ashwin K. Boddeti Yi Wang Xitlali G. Juarez Alexandra Boltasseva Teri W. Odom Vladimir Shalaev Hadiseh Alaeian Zubin Jacob Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA Birck Nanotechnology Center Purdue University West Lafayette Indiana 47907 USA Graduate Program in Applied Physics Northwestern University Evanston Illinois 60208 USA Department of Materials Science and Engineering Northwestern University Evanston Illinois 60208 USA Department of Chemistry Northwestern University Evanston Illinois 60208 USA School of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA

Dimensionality plays a crucial role in long-range dipole-dipole interactions (DDIs). We demonstrate that a resonant nanophotonic structure modifies the apparent dimensionality in an interacting ensemble of emitters, as revealed by population decay dynamics. Our measurements on a dense ensemble of interacting quantum emitters in a resonant nanophotonic structure with long-range DDIs reveal an effective dimensionality reduction to d¯=2.20(12), despite the emitters being distributed in 3D. This contrasts with the homogeneous environment, where the apparent dimension is d¯=3.00. Our work presents a promising avenue to manipulate dimensionality in an ensemble of interacting emitters.

关键词： Light-matter interaction Long-range interactions Metamaterials Optics & lasers Photonics Plasmonics Quantum optics

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