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SSRN 2023年

作者： Dumancas, Gerard G. Cobbinah, Elizabeth Generalao, Oliver B. Ke, Guoyi Malaluan, Roberto Lubguban, Arnold Department of Chemistry Loyola Science Center The University of Scranton ScrantonPA18510 United States Center for Informatics University of San Agustin Gen. Luna St Iloilo City5000 Philippines Department of Mathematics and Computer Science Louisiana State University Alexandria South AlexandriaLA71302 United States Center for Sustainable Polymers Mindanao State University Iligan Institute of Technology Iligan City9200 Philippines Department of Chemical Engineering and Technology MSU Mindanao State University Iligan Institute of Technology Iligan City9200 Philippines

Turmeric essential oil (TEO), derived from Turmeric (Curcuma longa L.) herb, is one of several natural products which has seen a rise in demand in recent years. This rising demand increases the tendency for TEO to be adulterated with less expensive alternatives, such as vegetable oils and synthetic compounds – a practice that has significant economic and organoleptic consequences. The goal of this study was to develop a facile, affordable, rapid, and direct analytical method for the authentication of TEO adulterated with sunflower oil (SFO) and soybean oil (SBO) using Fourier transform infrared spectroscopic and chemometric methods, partial least squares (PLS) and principal component regression (PCR). For the PLS model, the best predictive performance in both training and testing sets was observed at 3, 2 and 4 components for TEO, SFO and SBO, respectively. Further, the PLS was more accurate than the PCR in the prediction of TEO in the training, testing, and unknown sets, with minimum root mean square error values of 0.012 and maximum R2=0.999. The developed PLS model could potentially be applied for the rapid and facile detection of vegetable oil adulterants in TEO, a valuable approach to assess the authenticity of such a highly valued commodity. © 2023, The Authors. All rights reserved.

关键词： Essential oils

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Towards Integrated Sensing and Communications for 6G: A Standardization Perspective

arXiv

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arXiv 2023年

作者： Kaushik, Aryan Singh, Rohit Dayarathna, Shalanika Senanayake, rajitha Di Renzo, Marco Dajer, Miguel Ji, Hyoungju Kim, Younsun Sciancalepore, Vincenzo Zappone, Alessio Shin, Wonjae The School of Engineering & Informatics University of Sussex United Kingdom The Department of Electronics & Communication Engineering Dr. B. R. Ambedkar National Institute of Technology India The Department of Electrical & Electronic Engineering University of Melbourne Australia The CentraleSupelec Paris-Saclay University France The Futurewei Technologies United States Samsung Research Samsung Electronics Korea Republic of The NEC Laboratories Europe GmbH Germany The Department of Electrical and Information Engineering University of Cassino and Southern Lazio Italy The Department of Electrical and Computer Engineering Ajou University Korea Republic of The Department of Electrical Engineering Princeton University United States

The radio communication division of the International Telecommunication Union (ITU-R) has recently adopted Integrated Sensing and Communication (ISAC) among the key usage scenarios for IMT-2030/6G. ISAC is envisioned to play a vital role in the upcoming wireless generation standards. In this work, we bring together several paramount and innovative aspects of ISAC technology from a global 6G standardization perspective, including both industrial and academic progress. Specifically, this article provides 6G requirements and ISAC-enabled vision, including various aspects of 6G standardization, benefits of ISAC co-existence, and integration challenges. Moreover, we present key enabling technologies, including intelligent metasurface-aided ISAC, as well as Orthogonal Time Frequency Space (OTFS) waveform design and interference management for ISAC. Finally, future aspects are discussed to open various research opportunities and challenges on the ISAC technology towards 6G wireless communications. © 2023, CC BY.

关键词： Standardization

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Machine Learning Techniques for Analysis of Mars Weather Data

Machine Learning Techniques for Analysis of Mars Weather Dat...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Piyush Pant Anand Singh rajawat S. B. Goyal Baharu Bin Kemat Traian Candin Mihălţan Chaman Verma Maria Simona Răboacă School of Computer Sciences and Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia City Graduate School City University Petaling Jaya Malaysia Faculty of Building Services Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Romania

The exploration of Mars has provided vast amounts of weather data that present unique challenges for analysis and prediction. To address these challenges, this research paper focuses on the application of machine learning techniques for the analysis of Mars weather data. The objective is to develop models that can effectively extract patterns, uncover hidden relationships, and enable accurate predictions in the Martian weather system. The research begins with a comprehensive review of the available Mars weather data. The dataset, consisting of historical records, serves as the foundation for training and evaluating machine learning models. The analysis of the Mars weather dataset reveals the planet's icy and harsh climate, with average maximum temperatures of around -21°C and average minimum temperatures of -80°C. The temperature variations show that the minimum temperature fluctuates within a narrower range of 20-30°C over the course of Mars sols from 0 to 2000, while the maximum temperature experiences larger variations of about 40-50°C. During this time, the atmospheric pressure on Mars fluctuates between 720 Pa and 950 Pa. In addition, using the elbow method revealed that 3 clusters were the ideal number for identifying distinct patterns in the weather data. The linear regression model also attained an accuracy of 85%, demonstrating its efficacy in forecasting weather patterns on Mars.

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What we should learn from pandemic publishing

arXiv

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arXiv 2024年

作者： Sikdar, Satyaki Venturini, Sara Charpignon, Marie-Laure Kumar, Sagar Rinaldi, Francesco Tudisco, Francesco Fortunato, Santo Majumder, Maimuna S. Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Senseable City Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics "Tullio Levi-Civita" University of Padova Padova Italy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Network Science Institute Northeastern University BostonMA United States School of Mathematics The University of Edinburgh Edinburgh United Kingdom School of Mathematics Gran Sasso Science Institute L’Aquila Italy Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States

Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced researchers to operate on an accelerated timeline, as both policymakers and the public relied on the most current evidence to guide their decisions and behaviors. With high demand for rapid pandemic-related insights and lower barriers to entry via preprint servers, the volume of COVID-19 articles skyrocketed2. The pressing need for research triggered the participation of many researchers with expertise in the science of infectious disease outbreaks (‘outbreak scientists’), who were joined by researchers from other disciplines (‘bellwethers’) and more junior researchers still in training (‘newcomers’) with the common goal of advancing the frontiers of pandemic science and informing policy decisions3. Please see Box 1 for details of this taxonomy. © 2024, CC BY-NC-ND.

关键词： COVID-19

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, computer Retrospective Study

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Detecting Cognitive Impairment and Psychological Well-being among Older Adults Using Facial, Acoustic, Linguistic, and Cardiovascular Patterns Derived from Remote Conversations

arXiv

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arXiv 2024年

作者： Mu, Xiaofan Seyedi, Salman Zheng, Iris Jiang, Zifan Chen, Liu Omofojoye, Bolaji Hershenberg, rachel Levey, Allan I. Clifford, Gari D. Dodge, Hiroko H. Kwon, Hyeokhyen Department of Computer Science Emory University AtlantaGA30322 United States Department of Biomedical Informatics Emory University AtlantaGA30322 United States Department of Biomedical Engineering Emory University Georgia Institute of Technology AtlantaGA30332 United States Department of Psychiatry and Behavioral Sciences Emory University AtlantaGA30322 United States Department of Neurology Emory University AtlantaGA30322 United States Department of Neurology Massachusetts General Hospital Harvard Medical School BostonMA United States

The aging society urgently requires scalable methods to monitor cognitive decline and identify social and psychological factors indicative of dementia risk in older adults. Our machine learning (ML) models captured facial, acoustic, linguistic, and cardiovascular features from 39 individuals with normal cognition or Mild Cognitive Impairment derived from remote video conversations and classified cognitive status, social isolation, neuroticism, and psychological well-being. Our model could distinguish Clinical Dementia rating Scale (CDR) of 0.5 (vs. 0) with 0.78 area under the receiver operating characteristic curve (AUC), social isolation with 0.75 AUC, neuroticism with 0.71 AUC, and negative affect scales with 0.79 AUC. Recent advances in machine learning offer new opportunities to remotely detect cognitive impairment and assess associated factors, such as neuroticism and psychological well-being. Our experiment showed that speech and language patterns were more useful for quantifying cognitive impairment, whereas facial expression and cardiovascular patterns using photoplethysmography (PPG) were more useful for quantifying personality and psychological well-being. © 2024, CC BY-NC-SA.

关键词： Photoplethysmography

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Assessing patient similarity through representation learning on medical records

arXiv

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arXiv 2021年

作者： Memarzadeh, Hoda Ghadiri, Nasser Samwald, Matthias Shahreza, Maryam Lotfi Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria Department of Computer Engineering University of Isfahan Shahreza Campus Iran

MSC Codes 68P20Patient similarity assessments, which identify patients similar to a given patient, can help improve medical care. They can be performed using electronic medical records (EMRs). This makes it necessary to convert heterogeneous EMRs into comparable formats to calculate their distance. While versatile document representation learning methods were developed in recent years, it is still unclear how complex EMR data should be processed to create the most useful patient representations. This study presents a new data representation method for EMRs that accounts for information in clinical narratives. To address the limitations of previous approaches, we propose an unsupervised method for building a patient representation that utilizes unstructured data integrated with structured data extracted from patients' EMR. For modeling extracted data, we employ a tree structure that captures the temporal relations of multiple medical events from EMR. We process clinical notes to extract symptoms, signs, and diseases using different tools such as Medspacy, MetaMap, and Scispacy and map entities to UMLS. After creating a tree data structure, we utilize two novel relabeling methods for the non-leaf nodes of the tree to capture two kinds of temporal aspects in extracted events. By traversing the tree, we generate a sequence that can be used to create an embedding vector for each patient. We extensively evaluate the proposed method across patient similarity and prediction tasks, and demonstrate that our methodology leads to lower mean squared error (MSE) and higher precisions and normalized discounted cumulative gain (NDCG) relative to baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Medical computing

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On the robustness of EEG tensor completion methods

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Science China(Technological Sciences) 2021年第9期64卷 1828-1842页

作者： DUAN Feng JIA Hao ZHANG ZhiWen FENG Fan TAN Ying DAI YangYang CICHOCKI Andrzej YANG ZhengLu CAIAFA Cesar F. SUN Zhe SOLE-CASALS Jordi College of Artificial Intelligence Nankai UniversityTianjin 300350China Skolkowo Institute of Science and Technology Moscow 121205Russia College of Computer Science Hangzhou Dianzi UniversityHangzhou 310018China Department of Informatics Nicolaus Copernicus UniversityTorun 87-100Poland Systems Research Institute Polish Academy of SciencesWarsaw 01-447Poland College of Computer Science Nankai UniversityTianjin 300350China Instituto Argentino de Radioastronom´ıa IAR-CCT La Plata CONICET/CIC-PBA/UNLPVilla Elisa 1894Argentina Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKENWako-Shi 351-0106Japan Department of Psychiatry University of CambridgeCambridge CB28AHUK Data and Signal Processing Research Group University of Vic-Central University of CataloniaCatalonia 08500Spain

During the acquisition of electroencephalographic(EEG) signals, data may be missing or corrupted by noise and artifacts. To reconstruct the incomplete data, EEG signals are firstly converted into a three-order tensor(multi-dimensional data) of shape time × channel × trial. Then, the missing data can be efficiently recovered by applying a tensor completion method(TCM).However, there is not a unique way to organize channels and trials in a tensor, and different numbers of channels are available depending on the EEG setting used, which may affect the quality of the tensor completion results. The main goal of this paper is to evaluate the robustness of EEG completion methods with several designed parameters such as the ordering of channels and trials, the number of channels, and the amount of missing data. In this work, the results of completing missing data by several TCMs were compared. To emulate different scenarios of missing data, three different patterns of missing data were ***, the amount of missing data on completion effects was analyzed, including the time lengths of missing data and the number of channels or trials affected by missing data. Secondly, the numerical stability of the completion methods was analyzed by shuffling the indices along channels or trials in the EEG data tensor. Finally, the way that the number of electrodes of EEG tensors influences completion effects was assessed by changing the number of channels. Among all the applied TCMs, the simultaneous tensor decomposition and completion(STDC) method achieves the best performance in providing stable results when the amount of missing data or the electrode number of EEG tensors is changed. In other words, STDC proves to be an excellent choice of TCM, since permutations of trials or channels have almost no influence on the complete results. The STDC method can efficiently complete the missing EEG signals. The designed simulations can be regarded as a procedure to validate whether or

关键词： electroencephalogram corrupted data missing data tensor completion tensor decomposition

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Inverse learning of black-box aggregator for robust Nash equilibrium

arXiv

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arXiv 2024年

作者： Chen, Guanpu Xu, Gehui He, Fengxiang Tao, Dacheng Parisini, Thomas Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Digital Futures Stockholm100 44 Sweden Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom College of Computing & Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an aggregative game given a known aggregator. When the coupling parameters are affected by uncertainty, robust NE need to be computed. We consider a scenario where players' weight in the aggregator is unknown, making the aggregator kind of "a black box". We pursue a suitable learning approach to estimate the unknown aggregator by proposing an inverse variational inequality-based relationship. We then utilize the counterpart to reconstruct the game and obtain first-order conditions for robust NE in the worst case. Furthermore, we characterize the generalization property of the learning methodology via an upper bound on the violation probability. Simulation experiments show the effectiveness of the proposed inverse learning approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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PhenoProfiler: Advancing Phenotypic Learning for Image-based Drug Discovery

arXiv

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arXiv 2025年

作者： Li, Bo Zhang, Bob Zhang, Chengyang Zhou, Minghao Huang, Weiliang Wang, Shihang Wang, Qing Li, Mengran Zhang, Yong Song, Qianqian PAMI Research Group Department of Computer and Information Science University of Macau Taipa China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Faculty of Applied Sciences Macao Polytechnic University Taipa China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong China Department of Cancer Biology Wake Forest School of Medicine Winston SalemNC United States

In the field of image-based drug discovery, capturing the phenotypic response of cells to various drug treatments and perturbations is a crucial step. This process involves transforming high-throughput cellular images into quantitative representations for downstream analysis. However, existing methods require computationally extensive and complex multi-step procedures, which can introduce inefficiencies, limit generalizability, and increase potential errors. To address these challenges, we present PhenoProfiler, an innovative model designed to efficiently and effectively extract morphological representations, enabling the elucidation of phenotypic changes induced by treatments. PhenoProfiler is designed as an end-to-end tool that processes whole-slide multi-channel images directly into low-dimensional quantitative representations, eliminating the extensive computational steps required by existing methods. It also includes a multi-objective learning module to enhance robustness, accuracy, and generalization in morphological representation learning. PhenoProfiler is rigorously evaluated on large-scale publicly available datasets, including over 230,000 whole-slide multi-channel images in end-to-end scenarios and more than 8.42 million single-cell images in non-end-to-end settings. Across these benchmarks, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20%, demonstrating substantial improvements in both accuracy and robustness. Furthermore, PhenoProfiler uses a tailored phenotype correction strategy to emphasize relative phenotypic changes under treatments, facilitating the detection of biologically meaningful signals. UMAP visualizations of treatment profiles demonstrate PhenoProfiler’s ability to effectively cluster treatments with similar biological annotations, thereby enhancing interpretability. These findings establish PhenoProfiler as a scalable, generalizable, and robust tool for phenotypic learning, offering transformative advancement

关键词： Benchmarking

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