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SSRN 2024年

作者： Randine, Pietro Wolff, Miriam Kopperstad Pocs, Matthias Connell, Ian R.O. Cafazzo, Joseph A. Årsand, Eirik Faculty of Science and Technology Department of Computer Science UiT The Arctic University of Norway Tromsø Norway Faculty of Information Technology and Electrical Engineering Department of ICT and Natural Sciences Norwegian University of Science and Technology Trondheim Norway STELAR Security Technology Law Research Hamburg Germany Centre for Digital Therapeutics Toronto General Hospital University Health Network Toronto Canada Institute of Health Policy Management and Evaluation Institute of Biomedical Engineering Department of Computer Science the Rotman School of Management University of Toronto Toronto Canada Institute of Health Policy Management and Evaluation Dalla Lana School of Public Health University of Toronto Toronto Canada UiT The Arctic University of Norway Tromsø Norway

Background: In today's data-driven era, 'Openness' promotes transparency and accessibility. This trend is particularly relevant in new initiatives like the European Health Data Space or when processing health data. This article focuses on Diabetes, which relies on real-time medical device data, such as continuous glucose monitors (CGMs), insulin pumps or hybrid closed-loop systems. These devices provide vital information for monitoring and adjusting treatment plans, making real-time access to their data critical for effective diabetes ***: We explore real-time data access for third-party applications, emphasising primary (treatment) and secondary (research) use. Our exploration includes analysing how Application Programming Interfaces (APIs) contribute to secure data retrieval. Additionally, we investigate the impact of terms of service and copyright law on patient-driven innovation within open-source ***: We evaluated current diabetes medical devices and software solutions in Norway, emphasising their real-time data provision capabilities and API functionality. The study also addresses the legal frameworks relevant to these devices and software, particularly the needs of open-source DIY communities, and proposes an interoperability ***: Our research identified seven diabetes medical devices and nine regulated software solutions, with only one offering a publicly accessible API. This finding underlines a significant lack of real-time data access options. The analysis compares vendor-specific and open-source software, highlighting substantial limitations in interoperability and data accessibility. The DIY solutions, while innovative, presented technical and legal challenges, limiting their practical ***: Diabetes management needing real-time data involves navigating challenges related to security, transparency, and accessibility. We propose a model for interoperability that necessitates regulatory decisio

关键词： Interoperability

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Convolutional Bi-GRU-based prediction model for Climate Indicators

Convolutional Bi-GRU-based prediction model for Climate Indi...

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International Conference on Electronics Technology (ICET)

作者： Tsung-Ming Lo Hong-Tao Zhang Yun-Bing He Yi-Zhi Liu Wan-Ying Zhang Qiong-Dan Liang Zhen Bei Guangdong-Taiwan College of Industrial Science & Technology Dongguan University of Technology Dongguan China School of Traffic Information Engineering Guangxi Vocational and Technical College of Communications Nanning China Department of Public administration Nanfang College Guangzhou Guangzhou China School of Electrical and Computer Engineering Nanfang College-Guangzhou Guangzhou China Intelligent Manufacturing Engineering Dongguan University of Technology Dongguan China

ISBN: (数字)9798350363951

ISBN: (纸本)9798350363968

Climate prediction is critical to a wide range of fields, from agriculture to public health, but traditional methods face challenges due to the complexity and time-space characteristics of climate data. Machine learning techniques provide a promising solution by effectively extracting features from climate indicator data. In this study, we propose a new method for predicting climate indicators by combining CNN-Bi-GRU models with FNN networks. Through evaluation, our method demonstrates excellent prediction performance in terms of RMSE and MAE on both test and training sets, and robust convergence behavior is observed in model training. In addition, our method achieves high accuracy in predicting key climate metrics such as temperature and rain frequency, providing valuable information for understanding climate dynamics. Comparison experiments with LSTM-FNN, Double-LSTM, BiLSTM, and Bi-GRU models highlight the superiority of the CNNBi-GRU approach, especially in reducing temperature prediction errors. In conclusion, this study presents a promising approach to climate prediction that can enhance our understanding of climate variability and support informed decision making in various industries.

关键词： Training Measurement Accuracy Rain Predictive models Feature extraction Data models

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Score-PA: Score-based 3D Part Assembly

arXiv

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arXiv 2023年

作者： Cheng, Junfeng Wu, Mingdong Zhang, Ruiyuan Zhan, Guanqi Wu, Chao Dong, Hao Department of Electrical and Electronic Engineering Imperial College London London United Kingdom School of CS Peking University Beijing China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Engineering Science University of Oxford Oxford United Kingdom School of Public Affairs Zhejiang University Hangzhou China

Autonomous 3D part assembly is a challenging task in the areas of robotics and 3D computer vision. This task aims to assemble individual components into a complete shape without relying on predefined instructions. In this paper, we formulate this task from a novel generative perspective, introducing the Score-based 3D Part Assembly framework (Score-PA) for 3D part assembly. Knowing that score-based methods are typically time-consuming during the inference stage. To address this issue, we introduce a novel algorithm called the Fast Predictor-Corrector Sampler (FPC) that accelerates the sampling process within the framework. We employ various metrics to assess assembly quality and diversity, and our evaluation results demonstrate that our algorithm outperforms existing state-of-the-art approaches. We release our code at https://***/J-F-Cheng/Score-PA_Score-based-3D-Part-Assembly. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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PAL: Prompting Analytic Learning with Missing Modality for Multi-Modal Class-Incremental Learning

arXiv

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arXiv 2025年

作者： Yue, Xianghu Chen, Yiming Zhang, Xueyi Gao, Xiaoxue Feng, Mengling Lao, Mingrui Zhuang, Huiping Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China Institute for Infocomm Research A*STAR 138632 Singapore Saw Swee Hock School of Public Health National University of Singapore 117583 Singapore Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangdong Guangzhou510006 China Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

Multi-modal class-incremental learning (MMCIL) seeks to leverage multi-modal data, such as audio-visual and image-text pairs, thereby enabling models to learn continuously across a sequence of tasks while mitigating forgetting. While existing studies primarily focus on the integration and utilization of multi-modal information for MMCIL, a critical challenge remains: the issue of missing modalities during incremental learning phases. This oversight can exacerbate severe forgetting and significantly impair model performance. To bridge this gap, we propose PAL, a novel exemplar-free framework tailored to MMCIL under missing-modality scenarios. Concretely, we devise modality-specific prompts to compensate for missing information, facilitating the model to maintain a holistic representation of the data. On this foundation, we reformulate the MMCIL problem into a Recursive Least-Squares task, delivering an analytical linear solution. Building upon these, PAL not only alleviates the inherent under-fitting limitation in analytic learning but also preserves the holistic representation of missing-modality data, achieving superior performance with less forgetting across various multi-modal incremental scenarios. Extensive experiments demonstrate that PAL significantly outperforms competitive methods across various datasets, including UPMC-Food101 and N24News, showcasing its robustness towards modality absence and its anti-forgetting ability to maintain high incremental accuracy. © 2025, CC BY.

关键词： Contrastive Learning

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Diffusion Gaussian Mixture Audio Denoise

arXiv

引用

arXiv 2024年

作者： Wang, Pu Li, Junhui Li, Jialu Guo, Liangdong Zhang, Youshan Department of Mathematics School of Science University of Science and Technology Liaoning Anshan China School of public policy Cornell University IthacaNY United States School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan China Department of Artificial Intelligence and Computer Science Yeshiva University New YorkNY United States

Recent diffusion models have achieved promising performances in audio-denoising tasks. The unique property of the reverse process could recover clean signals. However, the distribution of real-world noises does not comply with a single Gaussian distribution and is even unknown. The sampling of Gaussian noise conditions limits its application scenarios. To overcome these challenges, we propose a DiffGMM model, a denoising model based on the diffusion and Gaussian mixture models. We employ the reverse process to estimate parameters for the Gaussian mixture model. Given a noisy audio signal, we first apply a 1D-U-Net to extract features and train linear layers to estimate parameters for the Gaussian mixture model, and we approximate the real noise distributions. The noisy signal is continuously subtracted from the estimated noise to output clean audio signals. Extensive experimental results demonstrate that the proposed DiffGMM model achieves state-of-the-art performance. © 2024, CC0.

关键词： Diffusion

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Channel capacity of the ribosome

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Physical Review E 2023年第4期108卷 044404-044404页

作者： Daniel A. Inafuku Kay L. Kirkpatrick Onyema Osuagwu Qier An David A. Brewster Mayisha Zeb Nakib Department of Physics University of Illinois Urbana-Champaign Urbana Illinois 61801 USA Department of Mathematics University of Illinois Urbana-Champaign Urbana Illinois 61801 USA Electrical and Computer Engineering Department Morgan State University Baltimore Maryland 21251 USA Cybersecurity Assurance and Policy Center Morgan State University Baltimore Maryland 21251 USA Department of Physics University of California Santa Barbara Santa Barbara California 93106 USA John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA

Translation is one of the most fundamental processes in the biological cell. Because of the central role that translation plays across all domains of life, the enzyme that carries out this process, the ribosome, is required to process information with high accuracy. This accuracy often approaches values near unity experimentally. In this paper, we model the ribosome as an information channel and demonstrate mathematically that this biological machine has information-processing capabilities that have not been recognized previously. In particular, we calculate bounds on the ribosome's theoretical Shannon capacity and numerically approximate this capacity. Finally, by incorporating estimates on the ribosome's operation time, we show that the ribosome operates at speeds safely below its capacity, allowing the ribosome to process information with an arbitrary degree of error. Our results show that the ribosome achieves a high accuracy in line with purely information-theoretic means.

关键词： Biomolecular processes Emergent biological functions from complex interactions Gene expression Nucleic acid replication & information processes Biomolecules Enzymes Nucleic acids Peptides Proteins RNA Stochastic analysis

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Hardware Software Codesign Using an Open-Source Authentication Algorithm

Hardware Software Codesign Using an Open-Source Authenticati...

引用

Applied Computing (URC), Annual Undergraduate Research Conference on

作者： Sadoon L. Albader Haseeb Ur Rehman Ashraf A. Zaher Computer and Information Center Public Authority for Applied Education & Training Adailiya Kuwait Faculty of Engineering University of Ottawa Ottawa Canada Department of Electrical Engineering German International University Cairo Egypt

ISBN: (纸本)9798350346817

This paper explores the use of hardware/software codesign to implement algorithms for open-source message authentication code while investigating the required tools, the problems to be overcome, and satisfying the industry standards for synthesizing the hardware/software codesign. Technical issues related to using the DE10-Nano were carefully analysed, which include the very poor documentation, and the needed specific Linux kernel patches that only work with certain versions of the Linux kernel. This research builds the fundamental blocks for using the DE10-Nano for hardware/software codesign. The results are expected to pave the way to focus on promoting the current trends of hardware/software codesigns and to encourage the hardware manufacturers to offer more help for programmers and engineers.

关键词： Industries Linux Software algorithms Documentation Market research Hardware Software

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Predicting the role of inequalities on human mobility patterns

arXiv

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arXiv 2024年

作者： Boldini, Alain De Lellis, Pietro Imperatore, Salvatore Das, Rishita Ceferino, Luis Heitor, Manuel Porfiri, Maurizio Department of Mechanical Engineering New York Institute of Technology College of Engineering and Computer Science Old WestburyNY11568 United States Center for Urban Science and Progress Department of Mechanical and Aerospace Engineering New York United States University Tandon School of Engineering BrooklynNY11201 United States Department of Electrical Engineering and Information Technology University of Naples Federico II NA Naples80125 Italy Center for Urban Science and Progress Department of Mechanical and Aerospace Engineering New York University Tandon School of Engineering BrooklynNY11201 United States Department of Aerospace Engineering Indian Institute of Science Karnataka Bengaluru560012 India Center for Urban Science and Progress Department of Civil and Urban Engineering New York University Tandon School of Engineering BrooklynNY11201 United States Department of Civil and Environmental Engineering College of Engineering University of California Berkeley BerkeleyCA94720 United States Center for Urban Science and Progress Marron Institute of Urban Management New York United States Center for Innovation Technology and Policy Research Instituto Superior Técnico Technical University of Lisbon Lisbon1049-001 Portugal Center for Urban Science and Progress Department of Mechanical and Aerospace Engineering Department of Biomedical Engineering New York University Tandon School of Engineering BrooklynNY11201 United States

Whether in search of better trade opportunities or escaping wars, humans have always been on the move. For almost a century, mathematical models of human mobility have been instrumental in the quantification of commuting patterns and migratory fluxes. Equity is a common premise of most of these mathematical models, such that living conditions and job opportunities are assumed to be equivalent across cities. Growing inequalities in modern urban economy and pressing effects of climate change significantly strain this premise. Here, we propose a mobility model that is aware of inequalities across cities in terms of living conditions and job opportunities. Comparing results with real datasets, we show that the proposed model outperforms the state-of-the-art in predicting migration patterns in South Sudan and commuting fluxes in the United States. This model paves the way to critical research on resilience and sustainability of urban systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Machine learning approaches and databases for prediction of drug-target interaction: a survey paper (vol 22, pg 247, 2021)

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BRIEFINGS IN BIOINFORMATICS 2021年第1期22卷 606-606页

作者： Bagherian, Maryam Sabeti, Elyas Wang, Kai Sartor, Maureen A. Nikolovska-Coleska, Zaneta Najarian, Kayvan Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI 48109 USA Michigan Institute for Data Science University of Michigan Ann Arbor MI 48109 USA Department of Biostatistics School of Public Health University of Michigan Ann Arbor MI 48109 USA Department of Pathology University of Michigan Ann Arbor MI 48109 USA Department of Emergency Medicine Medical School University of Michigan Ann Arbor MI 48109 USA Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann Arbor MI 48109 USA

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

关键词： Machine learning drug–target interaction prediction DTI software DTI database

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Anticipating for Smart Healthcare System by Deep Ensemble Model Using Multi-Layer Perceptron in Early Human Facial Emotion Recognition

SSRN

引用

SSRN 2024年

作者： Riaz, Waqar Gong, Tao Ji, Jiancheng Zaman, Khalid Ullah, Inam Alwabli, Abdullah Almutairi, Sulaiman Sulmi Institute of Intelligent Manufacturing Shenzhen Polytechnic University 4089 Shahe West Road Shenzhen518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Department of Computer Engineering Gachon University Seongnam13120 Korea Republic of Department of Electrical Engineering College of Engineering and Computing in Al-Qunfudhah Umm al-Qura University Mecca Saudi Arabia Department of Health Informatics College of Public Health and Health Informatics Qassim University Qassim51452 Saudi Arabia

In the advancement of Artificial Intelligence, this study anticipates towards smart healthcare systems by pioneering anticipation in humans (being a patient) emotions classification. Followed by the investigation delves into the challenge of accurately classifying human emotions, a critical factor in understanding patients' recent mood and situation (if critical). Integrating Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Multi-Layer Perceptron (MLP) for comprehensive facial emotion analysis. Initiated with Faster Region-Based Convolutional Neural Network (R-CNN) for face detection, the methodology extract features from three CNN models and employ advanced feature fusion techniques to combine them. These amalgamated features are then used to train an ensemble classification model. The process commences with the deployment of a Faster Region-Based Convolutional Neural Network (Faster R-CNN) for the swift and accurate identification of human’s face in real-time and recorded video feeds, including advanced feature extraction across three CNN models and innovative fusion techniques by strengthened the InceptionV3 for superior accuracy, replacing improved Faster R-CNN feature learning module. This replacement aims to enhance the accuracy and efficiency of face detection in our proposed framework. Validation on EMOTIC, CK+, FER-2013, and AffectNet confirms notable accuracy results of 98.01%, 99.53%, 99.27% and 96.81%, respectively with accuracy rates highlighting its potential to revolutionize medical environment and measures in smart healthcare systems. It is important to note that these datasets were carefully acquired in a simulated environment. © 2024, The Authors. All rights reserved.

关键词： Face recognition

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