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28th ACM SIGSPATIAL International Conference on Advances in Geographic Information systems, SIGSPATIAL GIS 2020

作者： Wang, Shuo Li, Yanran Zhang, Jiang Meng, Qingye Meng, Lingwei Gao, Fei School of Systems Science Normal University Beijing China Department of Computing Hong Kong Polytechnic University Hong Kong ColorfulClouds Pacific Technology Co. Ltd. Beijing China Atmospheric and Oceanic Sciences Program Princeton University Princeton United States

ISBN: (纸本)9781450380195

When predicting PM2.5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period. In this paper, we identify a set of critical domain knowledge for PM2.5 forecasting and develop a novel graph based model, PM2.5-GNN, being capable of capturing long-term dependencies. On a real-world dataset, we validate the effectiveness of the proposed model and examine its abilities of capturing both fine-grained and long-term influences in PM2.5 process. The proposed PM2.5-GNN has also been deployed online to provide free forecasting service. © 2020 Owner/Author.

关键词： Domain Knowledge

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Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs

Research Square

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Research Square 2020年 rs3rs-28592页

作者： Karunakaran, Kalyani B. Balakrishnan, N. Ganapathiraju, Madhavi K. Supercomputer Education and Research Centre Indian Institute of Science Bangalore560 012 India Department of Biomedical Informatics School of Medicine and Intelligent Systems Program School of Computing and Information University of Pittsburgh Pittsburgh United States Intelligent Systems Program School of Computing and Information University of Pittsburgh Pittsburgh United States

World over, people are looking for solutions to tackle the pandemic coronavirus disease (COVID-19) caused by the virus SARS-CoV-2/nCoV-19. Notable contributions in biomedical field have been characterizing viral genomes, host transcriptomes and proteomes, repurposable drugs and vaccines. In one such study, 332 human proteins targeted by nCoV19 were identified. We expanded this set of host proteins by constructing their protein interactome, including in it not only the known protein-protein interactions (PPIs) but also novel, hitherto unknown PPIs predicted with our High-precision Protein-Protein Interaction Prediction (HiPPIP) model that was shown to be highly accurate. In fact, one of the earliest discoveries made possible by HiPPIP is related to activation of immunity upon viral infection. We found that several interactors of the host proteins are differentially expressed upon viral infection, are related to highly relevant pathways, and that the novel interaction of NUP98 with CHMP5 may activate an antiviral mechanism leading to disruption of viral budding. We are making the interactions available as downloadable files to facilitate future systems biology studies and also on a web-server at http://***/corona that allows not only keyword search but also queries such as "PPIs where one protein is associated with ‘virus’ and the interactors with 'pulmonary'". © 2020, CC BY.

关键词： Coronavirus

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Improving dynamic collision frequencies: impacts on dynamic structure factors and stopping powers in warm dense matter

arXiv

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arXiv 2023年

作者： Hentschel, Thomas W. Kononov, Alina Olmstead, Alexandra Cangi, Attila Baczewski, Andrew D. Hansen, Stephanie B. School of Applied & Engineering Physics Cornell University IthacaNY United States Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States Nanoscience and Microsystems Engineering Program University of New Mexico AlbuquerqueNM United States Center for Advanced Systems Understanding Helmholtz-Zentrum Dresden-Rossendorf Görlitz Germany Department of Physics and Astronomy University of New Mexico AlbuquerqueNM United States Pulsed Power Sciences Center Sandia National Laboratories AlbuquerqueNM United States

Simulations and diagnostics of high-energy-density plasmas and warm dense matter rely on models of material response properties, both static and dynamic (frequency-dependent). Here, we systematically investigate variations in dynamic electron-ion collision frequencies ν(ω) in warm dense matter using data from a self-consistent-field average-atom model. We show that including the full quantum density of states, strong collisions, and inelastic collisions lead to significant changes in ν(ω). These changes result in red shifts and broadening of the plasmon peak in the dynamic structure factor, an effect observable in x-ray Thomson scattering spectra, and modify stopping powers around the Bragg peak. These changes improve the agreement of computationally efficient average-atom models with first-principles time-dependent density functional theory in warm dense aluminum, carbon, and deuterium. © 2023, CC BY.

关键词： Quantum theory

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Source separation-based data augmentation for improved joint beat and downbeat tracking

arXiv

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arXiv 2021年

作者： Chiu, Ching-Yu Ching, Joann Hsiao, Wen-Yi Chen, Yu-Hua Su, Alvin Wen-Yu Yang, Yi-Hsuan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan Research Center for IT Innovation Academia Sinica Taiwan Yating Music Team Taiwan AI Labs Taiwan Dept. CSIE National Cheng Kung University

Due to advances in deep learning, the performance of automatic beat and downbeat tracking in musical audio signals has seen great improvement in recent years. In training such deep learning based models, data augmentation has been found an important technique. However, existing data augmentation methods for this task mainly target at balancing the distribution of the training data with respect to their tempo. In this paper, we investigate another approach for data augmentation, to account for the composition of the training data in terms of the percussive and non-percussive sound sources. Specifically, we propose to employ a blind drum separation model to segregate the drum and non-drum sounds from each training audio signal, filtering out training signals that are drumless, and then use the obtained drum and non-drum stems to augment the training data. We report experiments on four completely unseen test sets, validating the effectiveness of the proposed method, and accordingly the importance of drum sound composition in the training data for beat and downbeat tracking. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Perioperative management of direct oral anticoagulants in patients having a high-bleed-risk surgery or neuraxial procedure: the Perioperative Anticoagulant Use for Surgery Evaluation (PAUSE)-2 pilot randomized trial

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Journal of Thrombosis and Haemostasis 2025年第7期23卷 2226-2234页

作者： Douketis, James D. Li, Na St. John, Melanie Nixon, Joanne Moffat, Karen Shaw, Joseph Syed, Summer Schulman, Sam Gross, Peter L. Spyropoulos, Alex C. Department of Medicine St. Joseph's Healthcare Hamilton Thrombosis and Atherosclerosis Research Institute McMaster University Hamilton ON Canada Department of Community Health Sciences University of Calgary Calgary AB Canada Department of Computing and Software McMaster University Hamilton ON Canada Department of Medicine Michael G. DeGroote Centre for Transfusion Research McMaster University Hamilton ON Canada Hamilton Regional Laboratory Medicine Program McMaster University Hamilton ON Canada Department of Medicine Ottawa Hospital Research Institute University of Ottawa Ottawa ON Canada Department of Anesthesia Halton Healthcare Oakville Trafalgar Hospital Oakville ON Canada Department of Anesthesia McMaster University Hamilton ON Canada Department of Medicine Hamilton Health Sciences General Hospital Thrombosis and Atherosclerosis Research Institute McMaster University Hamilton ON Canada Department of Medicine University Health Network Toronto ON Canada Department of Medicine Anticoagulation and Clinical Thrombosis Service Northwell Health at Lenox Hill Hospital New York NY United States Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Hempstead New York NY United States Institute of Health Systems Science at The Feinstein Institutes for Medical Research Manhasset NY United States

Background: There is uncertainty about the perioperative management of patients who are receiving a direct oral anticoagulant (DOAC) and require elective high-bleed-risk surgery, including those who are undergoing a neuraxial or deep peripheral nerve block procedure. Objectives: The objectives of this pilot trial were to provide preliminary data as to pre-operative residual DOAC levels with different management strategies andro assess the feasibility of a larger trial. Methods: The Perioperative Anticoagulant Use for Surgery Evaluation (PAUSE)-2 pilot trial was a proof-of-concept, open-label, randomized controlled trial that assessed the feasibility of comparing perioperative DOAC management using an American Society of Regional Anesthesia (ASRA)-based or PAUSE-based approach in patients with atrial fibrillation who needed an elective high-bleed-risk surgery/procedure and/or any neuraxial anesthesia/procedure. Results: There were 159 patients enrolled (86 on apixaban, 12 on dabigatran, and 61 on rivaroxaban), of whom 141 had preoperative DOAC-level testing done. The median (IQR) residual DOAC level was 19 (19-24) ng/mL in the ASRA group and 20 (19-24) ng/mL in the PAUSE group (standardized difference = −0.02). The percentage of patients in the ASRA and PAUSE groups with preoperative residual DOAC levels < 30 ng/mL was 95.6% and 94.4%, respectively;the percentage with residual DOAC levels of 30 to 50 ng/mL was 1.4% and 2.8%, respectively, and the percentage with levels > 50 ng/mL was 2.9% and 2.8%, respectively. Conclusion: In this pilot trial, we found that recruitment of DOAC-treated patients who needed an elective high-bleed-risk surgery or neuraxial procedure was feasible and that preoperative residual DOAC levels appeared similar according to ASRA-based and PAUSE-based management approaches, providing the foundation for a larger trial comparing ASRA- and PAUSE-based perioperative DOAC management. © 2025 International Society on Thrombosis and Haemostasis

关键词： direct oral anticoagulants neuraxial anesthesia perioperative medicine surgery surgical procedure

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Malignant Peritoneal Mesothelioma Interactome with 417 Novel Protein-Protein Interactions

Research Square

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Research Square 2021年

作者： Karunakaran, Kalyani B. Ganapathiraju, Madhavi K. Supercomputer Education and Research Centre Indian Institute of Science Bengaluru560012 India Department of Biomedical Informatics School of Medicine Intelligent Systems Program School of Computing and Information University of Pittsburgh 5607 Baum Blvd 5th Floor PittsburghPA15206 United States

Malignant peritoneal mesothelioma (MPeM) is an aggressive cancer affecting the peritoneal lining of the abdominal cavity and intra-abdominal organs, with a median survival of ~2.5 years. We constructed an ‘MPeM interactome’ with over 400 computationally predicted protein-protein interactions (PPIs) and over 4,700 known PPIs of 59 literature-curated genes whose activity affects MPeM. Known PPIs of the 59 MPeM-associated genes were derived from BioGRID and HPRD databases. Novel PPIs were predicted by applying the HiPPIP algorithm, which computes features of protein pairs such as cellular localization, molecular function, biological process membership, genomic location of the gene, and gene expression in microarray experiments, and classifies the pairwise features as interacting or non-interacting based on a random forest model. 75.6% of the interactome and 65% of the novel interactors in it were supported by transcriptomic evidence in rodent and human peritoneal mesothelioma/mesothelial cell lines and tumor specimens. 152 drugs targeted 427 proteins in the MPeM interactome. Comparative transcriptome analysis of peritoneal mesothelioma-associated versus drug-induced gene expression profiles revealed 39 repurposable drugs, out of which 29 were effective against peritoneal/pleural mesothelioma and/or peritoneal metastasis/primary peritoneal cancer in clinical trials, animal models or cell lines. Functional modules of chromosomal segregation, transcriptional deregulation, positive regulation of IL-6 production and hematopoiesis were identified from the interactome. Genes with tissue-specific expression in 2 sites of extramedullary hematopoiesis (spleen and thymus) and those correlated with unfavorable prognosis in liver, renal, pancreatic and lung cancers were noted. MPeM interactome showed extensive overlap with the malignant pleural mesothelioma (MPM) interactome and MPM cell line expression profiles. Our findings demonstrate the utility of the MPeM interactome in disco

关键词： Proteins

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Learning Pairwise Preferences from Movie Ratings

Learning Pairwise Preferences from Movie Ratings

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International Conference on ICT For Smart Society (ICISS)

作者： Nunung Nurul Qomariyah Ahmad Nurul Fajar Faculty of Computing and Media Bina Nusantara University Jakarta Indonesia BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781665404228

ISBN: (纸本)9781665404235

Preference Elicitation is now considered a crucial stage in recommender system. That is the stage where we collect and query preferences of the users as part of interactive decision support system. Preference elicitation can be performed using many ways, such as asking the user explicitly to give ratings or feedbacks for some products, or implicitly observing their behaviour. Once this preference has been collected by the system, it can then be used to generate item recommendation. Using explicit feedback itself, such as rating only, has several drawbacks. Using ratings is very subjective. Two users with similar taste might give different rating to the same item. A user also may not be consistent in giving number to express their liking. In other case, a user may like an item more than the other items, but feels difficult to give lower rating on the other item, because it means that the particular item is just as bad as the one he/she does not like. By showing two different items as a pair, it will be easier for the users to pick which one is the best. In this paper, we show the result of our experiment in predicting user pairwise preferences by using their movie ratings data. We evaluated five different machine learning algorithms to predict the data in pairwise preference format. The result shows that K-Nearest Neighbour and Random Forest outperformed the other algorithms.

关键词： Motion pictures Predictive models Recommender systems Social networking (online) Industries Encoding Task analysis

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Drum-aware ensemble architecture for improved joint musical beat and downbeat tracking

arXiv

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arXiv 2021年

作者： Chiu, Ching-Yu Su, Alvin Wen-Yu Yang, Yi-Hsuan The Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan The Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan

This paper presents a novel system architecture that integrates blind source separation with joint beat and downbeat tracking in musical audio signals. The source separation module segregates the percussive and non-percussive components of the input signal, over which beat and downbeat tracking are performed separately and then the results are aggregated with a learnable fusion mechanism. This way, the system can adaptively determine how much the tracking result for an input signal should depend on the input's percussive or non-percussive components. Evaluation on four testing sets that feature different levels of presence of drum sounds shows that the new architecture consistently outperforms the widely-adopted baseline architecture that does not employ source separation. Copyright © 2021, The Authors. All rights reserved.

关键词： Blind source separation

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Explainable and Adaptable Augmentation in Knowledge Attention Network for Multi-Agent Deep Reinforcement Learning systems

Explainable and Adaptable Augmentation in Knowledge Attentio...

引用

Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Joshua Ho Chien-Min Wang Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9781728187082

ISBN: (纸本)9781728187099

The scale of modem Artificial Intelligence systems has been growing and entering more research territories by incorporating Deep Learning (DL) and Deep Reinforcement Learning (DRL) methods. More specifically, multi-agent DRL methods have been widely applied to address the problems of high-dimensional computation, which interpret the conditions that real-world systems mainly encounter and the issues that require resolving. However, the current approaches of DL and DRL are often challenged for their untransparent and time-consuming modeling processes in their attempt to achieve a practical and applicable inference based on human-level perspective and acceptance. This paper presents an explainable and adaptable augmented knowledge attention network for multi-agent DRL systems, which uses game theory simulation to tackle the problem of non-stationarity at the beginning, while improving the learning exploration built upon the strategic ontology to achieve the learning convergence more efficiently for autonomous agents. We anticipate that our approach will facilitate future research studies and potential research inspections of emerging multi-agent DRL systems for increasingly complex and autonomous environments.

关键词： Knowledge engineering Adaptation models Reinforcement learning Ontologies Inspection Modems Game theory

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Converging on mutual harmony of knowledge-based expert system and technology consultant

引用

IOP Conference Series: Earth and Environmental Science 2021年第1期729卷

作者： M Siek T W Urian Business Information Systems Program Information Systems Department Faculty of Computing and Media Bina Nusantara University Jakarta Indonesia 11480

In the Society 5.0, the adoption of advanced technologies in many fields has large positive impacts on the way people think and behave as well as how the companies provide services for any other businesses. In adopting new technology, however, the businesses require significant efforts and deep insights on how to implement such technology that offers large benefits for their businesses. This attempt is typically in line with the one conducted by many consulting firms which provide solution services on how the new innovation and technologies can be implemented and aligned with business goals. With regards to that, this paper focuses on the implementation of a knowledge-based expert system as a technology consultant to recommend suitable technological solutions in supporting the existing teaching and learning activities at educational institutions. A number of tasks have been executed in the research in order to build a reliable expert system, such as properly using design knowledge ontology, formulating if-then rules and reasoning, expert system structure and user interface, and so forth. The resulting knowledge-based expert system demonstrates promising performance and permits to its integration with machine learning techniques for automated knowledge acquisition. This knowledge-based expert system can be a mutual supports in harmony to the human consultants on providing their technology consultancy services.

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