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arXiv 2025年

作者： Lorenzo, Guillermo Hormuth, David A. Wu, Chengyue Pash, Graham Chaudhuri, Anirban Lima, Ernesto A.B.F. Okereke, Lois C. Patel, Reshmi Willcox, Karen Yankeelov, Thomas E. Group of Numerical Methods in Engineering Department of Mathematics University of A Coruña Spain Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX United States Livestrong Cancer Institutes The University of Texas at Austin AustinTX United States Department of Imaging Physics The University of Texas MD Anderson Cancer Center HoustonTX United States Department of Breast Imaging The University of Texas MD Anderson Cancer Center HoustonTX United States Department of Biostatistics The University of Texas MD Anderson Cancer Center HoustonTX United States Institute for Data Science in Oncology The University of Texas MD Anderson Cancer Center HoustonTX United States Texas Advanced Computing Center The University of Texas at Austin AustinTX United States Department of Biomedical Engineering The University of Texas at Austin AustinTX United States Department of Diagnostic Medicine The University of Texas at Austin AustinTX United States Department of Oncology The University of Texas at Austin AustinTX United States

Despite advances in methods to interrogate tumor biology, the observational and population-based approach of classical cancer research and clinical oncology does not enable anticipation of tumor outcomes to hasten the discovery of cancer mechanisms and personalize disease management. To address these limitations, individualized cancer forecasts have been shown to predict tumor growth and therapeutic response, inform treatment optimization, and guide experimental efforts. These predictions are obtained via computer simulations of mathematical models that are constrained with data from a patient’s cancer and experiments. This book chapter addresses the validation of these mathematical models to forecast tumor growth and treatment response. We start with an overview of mathematical modeling frameworks, model selection techniques, and fundamental metrics. We then describe the usual strategies employed to validate cancer forecasts in preclinical and clinical scenarios. Finally, we discuss existing barriers in validating these predictions along with potential strategies to address them. © 2025, CC BY-NC-ND.

关键词： Prediction models

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A comparison of deep learning-based techniques for solving partial differential equations

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AIP Conference Proceedings 2024年第1期2905卷

作者： Rabiu Bashir Yunus Nooraini Zainuddin Afza Shafie Muhammad Izzatullah Samsul Ariffin Abdul Karim Fundamental and Applied Sciences Department Universiti Teknologi PETRONAS Bandar Seri Iskandar 32610 Seri Iskandar Perak Darul Ridzuan Malaysia King Abdullah University of Science and Technology (KAUST) Division of Physical Sciences and Engineering Bldg. 1 Desk #0202-WS023 Thuwal 23955-6900 Kingdom of Saudi Arabia Software Engineering Programme Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS 88400 Kota Kinabalu Sabah Malaysia Data Technologies and Applications (DaTA) Research Group Universiti Malaysia Sabah Jalan UMS 88400 Kota Kinabalu Sabah Malaysia

Obtaining the solutions of high-dimensional partial differential equations (PDEs) seems to be difficult by utilizing the classical numerical methods. Recently, deep neural networks (DNNs) techniques have received special attentions in solving high–dimensional problems in PDEs. In this study, our quest is to investigate some newly introduced data-driven deep learning-based approaches and compare their performance in terms of their efficiency and faster training towards high-dimensional PDEs. However, the comparison is carried out based on different activation functions, number of layers and gradient based optimizers. We consider some benchmark problems in our numerical experiments which includes Burgers equation, Diffusion-reaction equation and Allen-Cahn Equations.

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The medical algorithmic audit (vol 4, pg e384, 2022)

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LANCET DIGITAL HEALTH 2022年第6期4卷 E405-E405页

作者： Liu, X. Glocker, B. McCradden, M. M. Ghassemi, M. Denniston, A. K. Oakden-Rayner, L. Academic Unit of Ophthalmology Institute of Inflammation and Ageing College of Medical and Dental Sciences University of Birmingham UK Department of Ophthalmology University Hospitals Birmingham NHS Foundation Trust Birmingham UK Moorfields Eye Hospital NHS Foundation Trust London UK Health Data Research UK London UK Birmingham Health Partners Centre for Regulatory Science and Innovation University of Birmingham Birmingham UK Biomedical Image Analysis Group Department of Computing Imperial College London London UK The Hospital for Sick Children Toronto ON Canada Dalla Lana School of Public Health Toronto ON Canada Institute for Medical Engineering and Science and Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA National Institute of Health Research Biomedical Research Centre for Ophthalmology Moorfields Hospital London NHS Foundation Trust London UK University College London Institute of Ophthalmology London UK Australian Institute for Machine Learning University of Adelaide Adelaide SA Australia. lauren.oakden-rayner@adelaide.edu.au

Artificial intelligence systems for health care, like any other medical device, have the potential to fail. However, specific qualities of artificial intelligence systems, such as the tendency to learn spurious correlates in training data, poor generalisability to new deployment settings, and a paucity of reliable explainability mechanisms, mean they can yield unpredictable errors that might be entirely missed without proactive investigation. We propose a medical algorithmic audit framework that guides the auditor through a process of considering potential algorithmic errors in the context of a clinical task, mapping the components that might contribute to the occurrence of errors, and anticipating their potential consequences. We suggest several approaches for testing algorithmic errors, including exploratory error analysis, subgroup testing, and adversarial testing, and provide examples from our own work and previous studies. The medical algorithmic audit is a tool that can be used to better understand the weaknesses of an artificial intelligence system and put in place mechanisms to mitigate their impact. We propose that safety monitoring and medical algorithmic auditing should be a joint responsibility between users and developers, and encourage the use of feedback mechanisms between these groups to promote learning and maintain safe deployment of artificial intelligence *** © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

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Lightweight and robust representation of economic scales from satellite imagery

arXiv

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arXiv 2019年

作者： Han, Sungwon Ahn, Donghyun Cha, Hyunji Yang, Jeasurk Park, Sungwon Cha, Meeyoung School of Computing KAIST Korea Republic of Institute for Korean Regional Studies SNU Korea Republic of Data Science Group IBS Korea Republic of

Satellite imagery has long been an attractive data source providing a wealth of information regarding human-inhabited areas. While high-resolution satellite images are rapidly becoming available, limited studies have focused on how to extract meaningful information regarding human habitation patterns and economic scales from such data. We present READ, a new approach for obtaining essential spatial representation for any given district from high-resolution satellite imagery based on deep neural networks. Our method combines transfer learning and embedded statistics to efficiently learn the critical spatial characteristics of arbitrary size areas and represent such characteristics in a fixed-length vector with minimal information loss. Even with a small set of labels, READ can distinguish subtle differences between rural and urban areas and infer the degree of urbanization. An extensive evaluation demonstrates that the model outperforms state-of-the-art models in predicting economic scales, such as the population density in South Korea (R2=0.9617), and shows a high use potential in developing countries where district-level economic scales are unknown. Copyright © 2019, The Authors. All rights reserved.

关键词： Satellite imagery

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Learning to Detect Important People in Unlabelled Images for Semi-Supervised Important People Detection

Learning to Detect Important People in Unlabelled Images for...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Fa-Ting Hong Wei-Hong Li Wei-Shi Zheng School of Data and Computer Science Sun Yat-sen University China Accuvision Technology Co. Ltd. Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China VICO Group School of Informatics University of Edinburgh United Kingdom Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Important people detection is to automatically detect the individuals who play the most important roles in a social event image, which requires the designed model to understand a high-level pattern. However, existing methods rely heavily on supervised learning using large quantities of annotated image samples, which are more costly to collect for important people detection than for individual entity recognition (i.e., object recognition). To overcome this problem, we propose learning important people detection on partially annotated images. Our approach iteratively learns to assign pseudo-labels to individuals in un-annotated images and learns to update the important people detection model based on data with both labels and pseudo-labels. To alleviate the pseudo-labelling imbalance problem, we introduce a ranking strategy for pseudo-label estimation, and also introduce two weighting strategies: one for weighting the confidence that individuals are important people to strengthen the learning on important people and the other for neglecting noisy unlabelled images (i.e., images without any important people). We have collected two large-scale datasets for evaluation. The extensive experimental results clearly confirm the efficacy of our method attained by leveraging unlabelled images for improving the performance of important people detection.

关键词： data models Training Task analysis Computational modeling Noise measurement Face Supervised learning

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Corrigendum to “Molecular subtypes classification of breast cancer in DCE-MRI using deep features” [Expert Syst. Appl. 236 (2024) 121371]

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Expert Systems with Applications 2025年 270卷

作者： Ali M. Hasan Noor K.N. Al-Waely Hadeel K. Aljobouri Hamid A. Jalab Rabha W. Ibrahim Farid Meziane College of Medicine Al-Nahrain University Baghdad Iraq Biomedical Engineering Department College of Engineering Al-Nahrain University Iraq Faculty of Computer Science and Information Technology University of Malaya 50603 Kuala Lumpur Malaysia Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Nile Street 64001 Thi-Qar Iraq Department of Computer Science and Mathematics Lebanese American University Beirut 1102 2801 Lebanon Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Nicosia/Mersin 10 Turkey Data Science Research Centre School of Computing and Engineering University of Derby United Kingdom

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Review of e-learning service framework based on cloud computing

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AIP Conference Proceedings 2023年第1期2706卷

作者： Purwono Hendradi Moh Khanapi Abd Ghani Siti Nur Mahfuzah Mohamad Karima Aissaoui Uky Yudatama Teknik Informatika Universitas Muhammadiyah Magelang Magelang Indonesia Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia Department of Interactive Media Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia Artificial Intelligence Data Science and Emergent Systems Laboratory ENSAF Sidi Mohammed Ben Abdellah University FEZ Morocco

The impact of Covid-19 on education is the result of online learning from home as part of the Work from Home (WFH) protocol. There are at least three essential roles in WFH, namely Institutions, Teachers and Students, each of which uses personal resources. This literature begins with a review of the primary literature with the theme of e-learning frameworks and cloud computing. It is found that two crucial things are Integration and measuring the level of success. On that basis, in this literature review research, keywords are taken from the theme, including the word equation by using AND OR operators on the research database site over a span of the last five years. Each literature will be explored in stages focusing on the problem of Integration and or Measurement Level measurement. These results become material for systematic discussion so that an overview is obtained for the development of an E-learning Service Framework based on Cloud computing using a six-layer model with the Business Process layer being the layer used in system integration, both with other service systems and similar systems in the education ecosystem.

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Generating Approximate Ground States of Molecules Using Quantum Machine Learning

arXiv

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arXiv 2022年

作者： Ceroni, Jack Stetina, Torin F. Kieferová, Mária Marrero, Carlos Ortiz Arrazola, Juan Miguel Wiebe, Nathan Xanadu TorontoONM5G 2C8 Canada Department of Mathematics University of Toronto TorontoONM5S 3E1 Canada Simons Institute for the Theory of Computing BerkeleyCA94704 United States Berkeley Quantum Information and Computation Center University of California BerkeleyCA94720 United States Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology SydneyNSW2007 Australia AI & Data Analytics Division Pacific Northwest National Laboratory RichlandWA99354 United States Department of Electrical & Computer Engineering North Carolina State University RaleighNC27607 United States Department of Computer Science University of Toronto ONM5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory RichlandWA99354 United States

The potential energy surface (PES) of molecules with respect to their nuclear positions is a primary tool in understanding chemical reactions from first principles. However, obtaining this information is complicated by the fact that sampling a large number of ground states over a high-dimensional PES can require a vast number of state preparations. In this work, we propose using a generative quantum machine learning model to prepare quantum states at arbitrary points on the PES. The model is trained using quantum data consisting of ground-state wavefunctions associated with different classical nuclear coordinates. Our approach uses a classical neural network to convert the nuclear coordinates of a molecule into quantum parameters of a variational quantum circuit. The model is trained using a fidelity loss function to optimize the neural network parameters. We show that gradient evaluation is efficient and numerically demonstrate our method’s ability to prepare wavefunctions on the PES of hydrogen chains, water, and beryllium hydride. In all cases, we find that a small number of training points are needed to achieve very high overlap with the groundstates in practice. From a theoretical perspective, we further prove limitations on these protocols by showing that if we were able to learn across an avoided crossing using a small number of samples, then we would be able to violate Grover’s lower bound. Additionally, we prove lower bounds on the amount of quantum data needed to learn a locally optimal neural network function using arguments from quantum Fisher information. This work further identifies that quantum chemistry can be an important use case for quantum machine learning. © 2022, CC BY.

关键词： Molecules

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Correction to: development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

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Journal of Cloud computing 2023年第1期12卷 1-1页

作者： Kumar, Ankit Khan, Surbhi Bhatia Pandey, Saroj Kumar Shankar, Achyut Maple, Carsten Mashat, Arwa Malibari, Areej A. Department of Computer Engineering & Applications GLA University Mathura India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Data Science School of Science Engineering and Environment University of Salford Manchester Byblos UK WMG University of Warwick Coventry UK Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK Faculty of Computing and Information Science King Abdulaziz University Rabigh Saudi Arabia Department of Industrial and Systems Engineering College of Engineering Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

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3D human pose machines with self-supervised learning

arXiv

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arXiv 2019年

作者： Wang, Keze Lin, Liang Jiang, Chenhan Qian, Chen Wei, Pengxu School of Data and Computer Science Sun Yat-sen University Guangzhou China Department of Computing Hong Kong Polytechnic University Hong Kong SenseTime Group

Driven by recent computer vision and robotic applications, recovering 3D human poses has become increasingly important and attracted growing interests. In fact, completing this task is quite challenging due to the diverse appearances, viewpoints, occlusions and inherently geometric ambiguities inside monocular images. Most of the existing methods focus on designing some elaborate priors /constraints to directly regress 3D human poses based on the corresponding 2D human pose-aware features or 2D pose predictions. However, due to the insufficient 3D pose data for training and the domain gap between 2D space and 3D space, these methods have limited scalabilities for all practical scenarios (e.g., outdoor scene). Attempt to address this issue, this paper proposes a simple yet effective self-supervised correction mechanism to learn all intrinsic structures of human poses from abundant images. Specifically, the proposed mechanism involves two dual learning tasks, i.e., the 2D-to-3D pose transformation and 3D-to-2D pose projection, to serve as a bridge between 3D and 2D human poses in a type of "free" self-supervision for accurate 3D human pose estimation. The 2D-to-3D pose implies to sequentially regress intermediate 3D poses by transforming the pose representation from the 2D domain to the 3D domain under the sequence-dependent temporal context, while the 3D-to-2D pose projection contributes to refining the intermediate 3D poses by maintaining geometric consistency between the 2D projections of 3D poses and the estimated 2D poses. Therefore, these two dual learning tasks enable our model to adaptively learn from 3D human pose data and external large-scale 2D human pose data. We further apply our self-supervised correction mechanism to develop a 3D human pose machine, which jointly integrates the 2D spatial relationship, temporal smoothness of predictions and 3D geometric knowledge. Extensive evaluations on the Human3.6M and HumanEva-I benchmarks demonstrate the superior

关键词： Geometry

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