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arXiv 2024年

作者： Ding, Xiaohan Carik, Buse Gunturi, Uma Sushmitha Reyna, Valerie Rho, Eugenia H. Department of Computer Science Virginia Tech Blacksburg United States Human Neuroscience Institute Cornell University New York United States

We introduce a multi-step reasoning framework using prompt-based LLMs to examine the relationship between social media language patterns and trends in national health outcomes. Grounded in fuzzy-trace theory, which emphasizes the importance of "gists" of causal coherence in effective health communication, we introduce Role-Based Incremental Coaching (RBIC), a prompt-based LLM framework, to identify gists at-scale. Using RBIC, we systematically extract gists from subreddit discussions opposing COVID-19 health measures (Study 1). We then track how these gists evolve across key events (Study 2) and assess their influence on online engagement (Study 3). Finally, we investigate how the volume of gists is associated with national health trends like vaccine uptake and hospitalizations (Study 4). Our work is the first to empirically link social media linguistic patterns to real-world public health trends, highlighting the potential of prompt-based LLMs in identifying critical online discussion patterns that can form the basis of public health communication strategies. © 2024, CC BY.

关键词： Public health

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Why do microwaves heat oil faster than water?

Why do microwaves heat oil faster than water?

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2023 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2023

作者： Zhou, Xu Zhang, Shuang Takhar, Pawan Pedrow, Patrick Sablani, Shyam Tang, Juming Department of Biological Systems Engineering Washington State University PullmanWA99164 United States Department of Food Science and Human Nutrition University of Illinois Urbana-ChampaignIL61801 United States School of Electrical Engineering and Computer Science Washington State University PullmanWA99164 United States

ISBN: (纸本)9781713885887

It is generally believed that oil does not absorb microwave energy due to the small dielectric loss factor (only 1/100 that of water). Here, we compared the heating rates of four cooking oils versus water in a microwave oven under the same experimental conditions. Contrary to general belief, we found that oils heated 1.4 ~ 2.0 times faster than water in microwaves. We developed a theoretical explanation to show that a 10-fold stronger electric field was created in oil compared to water, resulting in approximately equal amounts of microwave power being absorbed by oil and water. After microwave energy had been absorbed, oil heated faster due to a smaller specific heat value (1/2 that of water). This study creates opportunities for electrifying industrial heating of oils and oil-rich biomass with microwaves, which would move the processes toward carbon-neutral operations. © 2023 ASABE Annual International Meeting. All Rights Reserved.

关键词： Microwave heating

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Multiview Attention Fusion for Explainable Body Language Behavior Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Madan, Surbhi Jain, Rishabh Subramanian, Ramanathan Dhall, Abhinav Indian Institute of Technology Ropar Department of Computer Science India University of Canberra Human-Centred Technology Research Centre Faculty of Science and Technology ACT Australia Monash University Faculty of Information & Technology Australia

Body language behavior, including gestures and fine-grained movements not only reflects human emotions, but also serves as a versatile cue for enhancing emotional intelligence and creating responsive technologies. In this work, we explore the efficacy of multiview-multimodal cues for explainable prediction of bodily behavior. This paper proposes an attention fusion method that combines features extracted from (1) multiview videos termed "RGB", (2) their multiview Discrete Cosine Transform representations termed "DCT"and (3) three stream skeleton features termed "Skeleton", via a transformer-based approach. We evaluate our approach on the diverse BBSI [1] and Drive&Act [2] datasets. Empirical results confirm that the RGB, DCT and Skeleton features enable discovery of multiple class-specific behaviors resulting in explainable predictions. Our key findings are: (a) Multimodal approaches outperform unimodal counterparts in categorizing bodily behavioral classes;(b) Efficient class predictions and plausible explanations are achieved with both unimodal and multimodal approaches;and (c) Empirical results confirm the superiority of our approach compared to state-of-the-art methods on both datasets. Our implementation code is available at: https://***/surbhimadan92/MAGIC_TBR_Extended. © 2010-2012 IEEE.

关键词： Musculoskeletal system

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Enhancing Cancer Gene Prediction through Aligned Fusion of Multiple PPI Networks Using Graph Transformer Models

Enhancing Cancer Gene Prediction through Aligned Fusion of M...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zebei Han Gufeng Yu Yang Yang Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China

Despite the considerable progress that has been made in cancer research, identifying cancer genes remains a significant challenge due to the intricate nature of the disease. Given the importance of incorporating gene interaction relationships in identifying potential cancer genes, Graph Neural Networks (GNNs) have garnered increasing attention for their ability to model gene associations effectively. Particularly, recent studies have demonstrated the superiority of GNNs in deciphering the knowledge embedded in Protein-Protein interaction (PPI) networks for cancer gene prediction. However, these studies primarily focused on a single PPI network, overlooking the valuable insights encapsulated within other PPI networks. Additionally, previous endeavors often centered around pan-cancer datasets, neglecting the importance of predicting specific cancer genes. To address these limitations, we present a novel method called MPIT, which employs Graph Transformer Networks (GTNs) to identify specific cancer driver genes. MPIT effectively integrates data from diverse PPI and multi-omics data via the alignment and fusion of gene representations learned from different PPI networks. We collect three distinct cancer cell line datasets to assess the model performance. Our experimental findings demonstrate the superiority of MPIT over the existing methods, achieving the state-of-the-art performance across all three datasets.

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Finding Our Joy: Queer Perspectives on HCI Research

Finding Our Joy: Queer Perspectives on HCI Research

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2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： O’Reilly-Kime, Molly Feng, Yifan Cipelli, Francesca Osipova, Ekat Kender, Kay Steeds, Madeleine Clinch, Sarah Rubegni, Elisa Rode, Jennifer School of Computing & Communications Lancaster University Lancaster United Kingdom Knowledge Lab. University College London London United Kingdom Creative Computing Institute University of the Arts London London United Kingdom Catholic University of the Sacred Heart Milan Italy Crip Collective Human-Computer Interaction Group TU Wien Vienna Austria Smurfit Business School University College Dublin Dublin Ireland Department of Computer Science The University of Manchester Manchester United Kingdom School of Computing and Communication Lancaster University Lancaster United Kingdom

ISBN: (纸本)9798400713958

How do we make space for joy in Queer HCI? This work draws on our authors’ experiences as Queer researchers and allies, navigating a field that is ostensibly receptive to our work, yet provides a narrow framework for acceptable research. We relate personal accounts of contending with identity, practice, and publication;we discuss what joy means to us, and the gap we see in Queer HCI where joy should exist. We believe that Queer HCI and research can itself be subversive, and assert that it needs to be in order for our research community to flourish. Our work emerges from a need for Queer joy in our research practice, and we approach Queerness as a multifaceted experience that by nature resists the categorisation that we often see in HCI. Finally, we present reflective guidelines for future work, that will allow us achieve inclusive, intentional, and joyful research. © 2025 Copyright held by the owner/author(s).

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Firing Pattern of Cells in the Hippocampus and Basolateral Amygdala during Novelty Recognition

Firing Pattern of Cells in the Hippocampus and Basolateral A...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xiaoxin Ren Yimeng Wang Ling Wang Jiajia Yang Chenguang Zheng Academy of Medical Engineering and Translational Medicine Medical College Tianjin University Tianjin China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Novelty recognition is essential for exploring and collecting unknown but interesting information, in which multiple brain regions related to cognitive memory are involved. Dorsal hippocampus (dHPC) is revealed to be closely associated with spatial memory and basolateral amygdala (BLA) is reported recently to be related to state-dependent encoding of exploratory behavior. However, how the firing pattern of cells in these two brain regions responds to novelty dynamically remains unclear. In this study, we observed that the representations of cells in BLA exhibited shift towards novelty, whereas dHPC cells maintained strong stability after the introduction of novelty, which are both engaged in the execution of novel recognition. Our study may provide new evidence for underlying the internal mechanisms of perception of novelty in the brain.

关键词： Brain In vivo Firing Cells (biology) Encoding Pattern recognition Object recognition Engineering in medicine and biology Hippocampus

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A Comparison of Three Types of computer-Based Procedures: An Experiment Using the Rancor Microworld Simulator 67th

A Comparison of Three Types of Computer-Based Procedures: An...

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67th International Annual Meeting of the human Factors and Ergonomics Society, HFES 2023

作者： Hall, Anna Boring, Ronald L. Ulrich, Thomas A. Lew, Roger Velazquez, Michelle Xing, Jinding Whiting, Tim Makrakis, Georgios Michail Human Factors and Reliability Department Idaho National Laboratory Idaho FallsID United States Virtual Technology and Design Program University of Idaho MoscowID United States Psychology Department Idaho State University Pocatello Idaho United States Civil and Environmental Engineering Carnegie Mellon University PittsburghPA United States Computer Science Department Brigham Young University ProvoUT United States Computer Science Department University of Idaho Idaho FallsID United States

The nuclear power industry has historically used paper-based procedures, but a shift towards computer-based procedures (CBPs) has the potential to reduce human errors, alleviate mental workload, and improve work performance. Twentyseven participants were randomly assigned to one of three CBP types using the Rancor Microworld Simulator, and each performed two different types of operational scenarios (startup and loss of feedwater). The three CBP types varied in levels of digitalization. It was hypothesized that there would be less favorable impressions of the most basic procedures, and that these would demonstrate lower usability than types with greater digitalization. Overall, our predictions were partially supported with some interesting caveats, specifically with some performance benefits for CBPs that provided indicators but not embedded controls. We discuss our findings in terms of optimal levels of digitalization/automation within nuclear operations and suggest pathways for future directions. © 2023 human Factors and Ergonomics Society.

关键词： human-system integration Interface evaluation System design and analysis Usability

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The Role of AI in Healthcare Policy Development and Management 1

The Role of AI in Healthcare Policy Development and Manageme...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Yadav, Sapna Mane, Pravin Swarnkar, Virendra Kumar Rawandale, Shital Kumar Patil, Alaknanda S. Katke, Kiran S. Bhat, Niyati Entrepreneurship Mindset Curriculum State Council of Educational Research and Training Delhi India Institute of Management and Entrepreneurship Development Pune India Bharti Vishwavidyalaya Department of Computer Science & Engineering Chhattisgarh Durg India Dean Industry Institute Interaction PCET's Pimpri Chinchwad College of Engineering Maharashtra Pune India Jspm Ntc E&tc Department Pune India Sanjivani College of Engineering Kopergaon Ahmednagar Department of Computer Engineering India Software Developer Master of Science California State University Long Beach CA90840 United States

ISBN: (纸本)9798350330915

This study looks at how artificial intelligence (AI) is incorporated into healthcare policy, with an emphasis on how it affects administrative procedures, treatment optimization, especially clinical choice support. Using a deductive methodology and interpretive thinking as a philosophy, an exploratory design with secondary data collecting was used. In the present-day environment, AI is being used more frequently in healthcare legislation, especially within medical settings. Clinical decision-making systems driven by AI have shown to offer a great promise for lowering diagnostic mistakes and increasing treatment precision. Improving patient outcomes through individualized AI-driven treatments for treatment improvement appears promising. But there are issues with algorithm prejudices as well as information privacy, among other ethical and legal issues. Establishing thorough guidelines, ongoing education, and auditing procedures are among the suggestions that are made. In order to guarantee fair and efficient integration of health care legislation, future research should prioritize longitudinal research and improving artificial intelligence algorithms © 2023 IEEE.

关键词： Artificial Intelligence Clinical Decision Support Ethical Considerations Healthcare Policy Treatment Optimization

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Challenges and Emerging Issues for Generative AI and Hyper Intelligence

Challenges and Emerging Issues for Generative AI and Hyper I...

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Cyber science and Technology Congress (CyberSciTech), IEEE

作者： Jianhua Ma Qun Jin Hui-Huang Hsu John Paul C. Vergara Antonio Guerrieri Claudio Miceli Ao Guo Faculty of Computer and Information Sciences Hosei University Japan Department of Human Informatics and Cognitive Sciences Waseda University Japan Department of Computer Science and Information Engineering Tamkang University Taiwan Department of Information Systems and Computer Science Ateneo de Manila University Philippines ICAR-CNR Italy Graduate Program in Systems Engineering and Computer Science Federal University of Rio de Janeiro Brazil Graduate School of Informatics Nagoya University Japan

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

Generative Artificial Intelligence (GenAI) represents a significant milestone in the development of artificial intelligence, bringing sophisticated AI capabilities into daily life and work. As we approach the era of Hyper Intelligence (Hyper-I), a variety of critical challenges and emerging issues have come to light, ranging from computational complexity to ethical concerns. This paper explores the evolution of AI from the perspective of human learning, comparing machine and human intelligence, and identifying key considerations for the development of future AI systems. It also highlights the growing importance of regulating advanced AI models, such as Reinforcement Learning-based Long-Term Planning Agents, to ensure that Hyper-I remains under human control. Additionally, the paper discusses the computational complexity of transformer-based models, their applicability to intractable problems, and their role in cognitive building systems and resource-constrained environments through TinyML. By analyzing these pressing challenges, this work provides insights into the future of AI and the path toward responsible innovation in generative and hyper-intelligent systems.

关键词： Hyper-intelligent systems Ethics Technological innovation Generative AI Computational modeling Tiny machine learning Pressing Transformers Regulation Computational complexity

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TripletGO: Integrating Transcript Expression Profiles with Protein Homology Inferences for Gene Function Prediction

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 1013-1027页

作者： Yi-Heng Zhu Chengxin Zhang Yan Liu Gilbert S.Omenn Peter L.Freddolino Dong-Jun Yu Yang Zhang School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Computational Medicine and Bioinformatics University of MichiganAnn ArborMI 48109USA Departments of Internal Medicine and Human Genetics and School of Public HealthUniversity of MichiganAnn ArborMI 48109USA Department of Biological Chemistry University of MichiganAnn ArborMI 48109USA

Gene Ontology(GO)has been widely used to annotate functions of genes and gene ***,we proposed a new method,Triplet GO,to deduce GO terms of protein-coding and noncoding genes,through the integration of four complementary pipelines built on transcript expression profile,genetic sequence alignment,protein sequence alignment,and naīve *** GO was tested on a large set of 5754 genes from 8 species(human,mouse,Arabidopsis,rat,fly,budding yeast,fission yeast,and nematoda)and 2433 proteins with available expression data from the third Critical Assessment of Protein Function Annotation challenge(CAFA3).Experimental results show that Triplet GO achieves function annotation accuracy significantly beyond the current state-of-the-art *** analyses show that the major advantage of Triplet GO lies in the coupling of a new triplet network-based profiling method with the feature space mapping technique,which can accurately recognize function patterns from transcript expression ***,the combination of multiple complementary models,especially those from transcript expression and protein-level alignments,improves the coverage and accuracy of the final GO annotation *** standalone package and an online server of Triplet GO are freely available at https://***/Triplet GO/.

关键词： Gene function annotation Gene Ontology Transcript expression profile Triplet network Protein-level alignment

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