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arXiv 2024年

作者： Zhao, Haoxiang An, Lixuan Zhang, Daning Yang, Xiong Yao, Huanmin Zhang, Guanjun Mu, Haibao Baumeier, Björn State Key Laboratory of Electrical Insulation and Power Equipment School of Electrical Engineering Xi'an Jiaotong University Xi'An710049 China Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven5600MB Netherlands Department of Applied Physics Eindhoven University of Technology Eindhoven5600MB Netherlands KERMIT Department of Data Analysis and Mathematical Modelling Ghent University Ghent9000 Belgium Institute for Complex Molecular Systems Eindhoven University of Technology Eindhoven5600MB Netherlands

The rapid development of modern energy applications drives an urgent need to enhance the dielectric strength of energy storage dielectrics for higher power density. Interface design is a promising strategy to regulate the crucial charge transport process determining dielectric strength. However, the targeted exploitation of interface effects on charge transport is limited due to a lack of fundamental understanding of the underlying mechanisms involving elementary electronic processes and details of the intricate interplay of characteristics of molecular building blocks and the interfacial morphology - details that cannot fully be resolved with experimental methods. Here we employ a multiscale modeling approach linking the quantum properties of the charge carriers with nano- and mesoscale structural details of complex interfaces. Applied to a prototypical application-proven cellulose-oil composite with interfaces formed between oil, disordered, and crystalline cellulose regions, this approach demonstrates that charges are trapped in the disordered region. Specifically, it unveils this trapping as a synergistic effect of two transport-regulating interface mechanisms: back-transfer to the oil region is suppressed by energetic factors, while forward-transfer to the crystalline cellulose is suppressed by low electronic coupling. The insight into the molecular origins of interface effects via dual-interface regulation offers new development paths for advanced energy materials. © 2024, CC BY.

关键词： Nanocrystals

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Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots

arXiv

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arXiv 2023年

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023, CC0.

关键词： Machine learning

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

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SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

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A Self-adaptive PSO-Based Dynamic Scheduling Method on Hierarchical Cloud Computing 9th

A Self-adaptive PSO-Based Dynamic Scheduling Method on Hiera...

引用

9th EAI International Conference on Cloud Computing, CloudComp 2019 and the 4th EAI International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

作者： Meng, Shunmei Huang, Weijia Xu, Xiaolong Li, Qianmu Dou, Wanchun Liu, Bowen Department of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9783030485122

Edge computing has been envisioned as an emerging and prospective computing paradigm for its advantage of low latency, which uses local resources. However, the edge resources are usually limited and could not meet end-users’ diversified requirement. Cloud computing paradigm could provide scalable and centralized resources with high computational capabilities, but it has latency issues. Thus it is suggested to combine both computing paradigms together to improve the performance of mobile applications. In this paper, we propose a self-adaptive dynamic scheduling approach based on hierarchical heterogeneous clouds. Our scheduling mechanism considers not only schedule planning but also dynamic scheduling on heterogeneous clouds. Firstly, a self-adaptive scheduling mechanism based on a meta-heuristic optimization algorithm, PSO (Particle Swarm Optimization), is presented for schedule planning. Then a dynamic scheduling mechanism on dynamic partial workflow model is proposed for dynamic optimization during the execution. Finally, external experiments compared with other methods are conducted to demonstrate the effectiveness of our proposal. © 2020, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Mobile edge computing

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Spray Process of Multi-Component Gasoline Surrogate Fuel Under Ecn Spray G Conditions

SSRN

引用

SSRN 2023年

作者： Hwang, Joonsik Karathanassis, Ioannis K. Koukouvinis, Phoevos Nguyen, Tuan Tagliante, Fabien Pickett, Lyle M. Sforzo, Brandon A. Powell, Christopher F. Department of Mechanical Engineering Mississippi State University Mississippi StateMS39762 United States Mississippi State University StarkvilleMS39759 United States School of Mathematics Computer Science and Engineering City University of London United Kingdom Combustion Research Facility Sandia National Laboratories LivermoreCA94550 United States Energy Systems Division Argonne National Laboratory LemontIL60439 United States

As modern gasoline direct injection (GDI) engines utilize sophisticated injection strategies, a detailed understanding of the air-fuel mixing process is crucial to further improvements in engine emission and fuel economy. In this study, a comprehensive evaluation of the spray process of single-component iso-octane (IC8) and multi-component gasoline surrogate E00 (36% n-pentane, 46% iso-octane, and 18% n-undecane, by volume) fuels was conducted using an Engine Combustion Network (ECN) Spray G injector. High-speed extinction, schlieren, and microscopy imaging campaigns were carried out under engine-like ambient conditions in a spray vessel. Experimental results including liquid/vapor penetration, local liquid volume fraction, droplet size, and projected liquid film on the nozzle tip were compared under ECN G1 (573 K, 3.5 kg/m3), G2 (333 K, 0.5 kg/ m3), and G3 (333 K, 1.01 kg/ m3) conditions. In addition to the experiments, preferential evaporation process of the E00 fuel was elucidated by Large-Eddy Simulations (LES). The three-dimensional liquid volume fraction measurement enabled by the computed tomographic reconstruction showed substantial plume collapse for E00 under the G2 and G3 conditions having wider plume growth and plume-to-plume interaction due to the fuel high vapor pressure. The CFD simulation of E00 showed an inhomogeneity in the way fuel components vaporized, with more volatile components carried downstream in the spray after the end of injection. The high vapor pressure of E00 also results in ~4 μm smaller average droplet diameter than IC8, reflecting a higher rate of initial vaporization even though the final boiling point temperature is higher. Consistent with high vapor pressure, E00 had a wider plume cone angle and enhanced interaction with the wall to cover the entire surface of the nozzle tip in a film. However, the liquid fuel underwent faster evaporation, so the final projected tip wetting area was smaller than the IC8 under the flash-boiling c

关键词： Evaporation

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Where to Meet a Driver Privately: Recommending Pick-Up Locations for Ride-Hailing Services 17th

Where to Meet a Driver Privately: Recommending Pick-Up Loc...

引用

17th International Workshop on Security and Trust Management, STM 2021, co-located with the 26th European Symposium on Research in computer Security, ESORICS 2021

作者： Chen, Yifei Li, Meng Zheng, Shuli Lal, Chhagan Conti, Mauro Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Delft University of Technology Delft Netherlands Department of Mathematics University of Padua Padua35131 Italy

ISBN: (纸本)9783030918583

Ride-Hailing Service (RHS) has motivated the rise of innovative transportation services. It enables riders to hail a cab or private vehicle at the roadside by sending a ride request to the Ride-Hailing Service Provider (RHSP). Such a request collects rider’s real-time locations, which incur serious privacy concerns for riders. While there are many location privacy-preserving mechanisms in the literature, few of them consider mobility patterns or location semantics in RHS. In this work, we propose a pick-up location recommendation scheme with location indistinguishability and semantic indistinguishability for RHS. Specifically, we give formal definitions of location indistinguishability and semantic indistinguishability. We model the rider mobility as a time-dependent first-order Markov chain and generates a rider’s mobility profile. Next, it calculates the geographic similarity between riders by using the Mallows distance and classifies them into different geographic groups. To comprehend the semantics of a location, it extracts such information through user-generated content from two popular social networks and obtains the semantic representations of locations. Cosine similarity and unified hypergraph are used to compute the semantic similarities between locations. Finally, it outputs a set of recommended pick-up locations. To evaluate the performance, we build our mobility model over the real-world dataset GeoLife, analyze the computational costs of a rider, show the utility, and implement it on an Android smartphone. The experimental results show that it costs less than 0.12 ms to recommend 10 pick-up locations within 500 m of walking distance. © 2021, Springer Nature Switzerland AG.

关键词： Location

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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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Reasoning like human: Hierarchical reinforcement learning for knowledge graph reasoning 29

Reasoning like human: Hierarchical reinforcement learning fo...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Wan, Guojia Pan, Shirui Gong, Chen Zhou, Chuan Haffari, Gholamreza School of Computer Science Institute of Artificial Intelligence National Engineering Research Center for Multimedia Software Wuhan University China Faculty of Information Technology Monash University Australia School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computing Hong Kong Polytechnic University Hong Kong Academy of Mathematics and Systems Science Chinese Academy of Sciences China

ISBN: (纸本)9780999241165

Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge graph completion is to infer missing knowledge by multi-hop reasoning over the information found along other paths connecting a pair of entities. However, multi-hop reasoning is still challenging because the reasoning process usually experiences multiple semantic issue that a relation or an entity has multiple meanings. In order to deal with the situation, we propose a novel Hierarchical Reinforcement Learning framework to learn chains of reasoning from a Knowledge Graph automatically. Our framework is inspired by the hierarchical structure through which a human being handles cognitionally ambiguous cases. The whole reasoning process is decomposed into a hierarchy of two-level Reinforcement Learning policies for encoding historical information and learning structured action space. As a consequence, it is more feasible and natural for dealing with the multiple semantic issue. Experimental results show that our proposed model achieves substantial improvements in ambiguous relation tasks. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Knowledge graph

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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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Identifying common signatures and potential therapeutic biomarkers in COPD and lung cancer using miRNA-mRNA co-expression networks

引用

Informatics in Medicine Unlocked 2022年 34卷

作者： Fathinavid, Amirhossein Mousavian, Zaynab Najafi, Ali Nematzadeh, Sajjad Salimi, Mahdieh Masoudi-Nejad, Ali Department of Computer Engineering College of Engineering Hamedan Branch Islamic Azad University Hamedan Iran Laboratory of Systems Biology and Bioinformatics (LBB) Institute of Biochemistry and Biophysics University of Tehran Tehran Iran Department of Computer Science School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran Molecular Biology Research Center System Biology and Poisoning Institute Tehran Iran Computer Engineering Department Architecture and Engineering Faculty Nisantasi University Istanbul Turkey Department of Medical Genetics Institute of Medical Biotechnology National Institute of Genetic Engineering and Biotechnology (NIGEB) Tehran Iran

The association between non-small cell lung cancer (NSCLC) and chronic obstructive pulmonary disease (COPD) is now well established, as people with COPD are more likely to develop lung carcinoma. However, the evidence for this relationship is inconclusive and there is currently little information on the underlying molecular mechanisms. MicroRNAs (miRNAs) are one of the regulatory factors in NSCLC and COPD that their functions are widely studied in many chronic diseases and cancers. In this study, we applied systems biology approaches to identify and predict miRNAs that potentially play regulatory roles between COPD and NSCLC. We performed differential expression analysis on public miRNA and mRNA expression datasets, for both of diseases, and then we reconstructed two miRNA-mRNA co-expression networks and merged these two co-expression networks into a community co-expression network. Results indicated the existence of very common miRNAs (ex. hsa-miR-326 and hsa-miR-1293) and mRNAs (such as FAT2, ALOX5AP, and LDB2) between the two mentioned diseases. Moreover, we discovered specific miRNAs (hsa-miR-574-3p) that targeted common mRNAs. We utilized drug-target interaction networks to identify candidate drugs for common mRNAs, which can be considered in the treatment of two diseases (e.g. iloperidone). Generally, our study highlighted common miRNAs between COPD and NSCLC that as new signatures or biomarkers for therapeutic purposes. Investigating the miRNA biomarkers in this study improves our understanding about the shared mechanisms between COPD and NSCLC. © 2022

关键词： Co-expression networks Community networks COPD miRNA-mRNA network Non-small cell lung cancer

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