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Novel Smart N95 Filtering Facepiece Respirator with Real-time Adaptive Fit Functionality and Wireless Humidity Monitoring for Enhanced Wearable Comfort

arXiv

引用

arXiv 2023年

作者： Kwon, Kangkyu Lee, Yoon Jae Jung, Yeongju Soltis, Ira Choi, Chanyeong Na, Yewon Romero, Lissette Kim, Myung Chul Rodeheaver, Nathan Kim, Hodam Lloyd, Michael S. Zhuang, Ziqing King, William Xu, Susan Ko, Seung-Hwan Lee, Jinwoo Yeo, Woon-Hong School of Electrical and Computer Engineering College of Engineering Georgia Institute of Technology AtlantaGA30332 United States Center for Human-Centric Interfaces and Nanoengineering Institute for Electronics and Nanotechnology Georgia Institute of Technology AtlantaGA30332 United States Applied Nano and Thermal Science Lab Department of Mechanical Engineering Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States School of Industrial Design Georgia Institute of Technology AtlantaGA30332 United States Division of Cardiology Emory University School of Medicine AtlantaGA30322 United States National Institute for Occupational Safety and Health National Personal Protective Technology Laboratory PittsburghPA15236 United States Seoul National University Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Department of Mechanical Robotics and Energy Engineering Dongguk University 30 Pildongro 1-gil Jung-gu Seoul04620 Korea Republic of Wallace H. Coulter Department of Biomedical Engineering Parker H. Petit Institute for Bioengineering and Biosciences Neural Engineering Center Institute for Materials Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332 United States

The widespread emergence of the COVID-19 pandemic has transformed our lifestyle, and facial respirators have become an essential part of daily life. Nevertheless, the current respirators possess several limitations such as poor respirator fit because they are incapable of covering diverse human facial sizes and shapes, potentially diminishing the effect of wearing respirators. In addition, the current facial respirators do not inform the user of the air quality within the smart facepiece respirator in case of continuous long-term use. Here, we demonstrate the novel smart N-95 filtering facepiece respirator that incorporates the humidity sensor and pressure sensory feedback-enabled self-fit adjusting functionality for the effective performance of the facial respirator to prevent the transmission of airborne pathogens. The laser-induced graphene (LIG) constitutes the humidity sensor, and the pressure sensor array based on the dielectric elastomeric sponge monitors the respirator contact on the user’s face, providing the sensory information for a closed-loop feedback mechanism. As a result of the self-fit adjusting mode along with elastomeric lining, the fit factor is increased by 3.20 and 5 times at average and maximum respectively. We expect that the experimental proof-of-concept of this work will offer viable solutions to the current commercial respirators to address the *** Codes 92C55 Copyright © 2023, The Authors. All rights reserved.

关键词： Graphene

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Korean-to-Chinese Machine Translation using Chinese Character as Pivot Clue

arXiv

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arXiv 2019年

作者： Park, Jeonghyeok Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

Korean-Chinese is a low resource language pair, but Korean and Chinese have a lot in common in terms of vocabulary. Sino-Korean words, which can be converted into corresponding Chinese characters, account for more then fifty of the entire Korean vocabulary. Motivated by this, we propose a simple linguistically motivated solution to improve the performance of Korean-to-Chinese neural machine translation model by using their common vocabulary. We adopt Chinese characters as a translation pivot by converting Sino-Korean words in Korean sentence to Chinese characters and then train machine translation model with the converted Korean sentences as source sentences. The experimental results on Korean-to-Chinese translation demonstrate that the models with the proposed method improve translation quality up to 1.5 BLEU points in comparison to the baseline models. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural machine translation

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Understanding gender equity in author order assignment

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Proceedings of the ACM on human-computer interaction 2018年第CSCW期2卷 1–21页

作者： Early, Kirstin Hammer, Jessica Hofmann, Megan Rode, Jennifer A. Wong, Anna Mankoff, Jennifer Yahoo Research 701 First Avenue SunnyvaleCA94089 United States Carnegie Mellon University Human-Computer Interaction Institute Entertainment Technology Center 5000 Forbes Avenue PittsburghPA15213 United States University College London Knowledge Lab Gower Street London United Kingdom Carnegie Mellon University Department of Statistics 5000 Forbes Avenue PittsburghPA15213 United States University of Washington Paul G. Allen School of Computer Science and Engineering 185 E Stevens Way NE SeattleWA98195 United States

Academic success and promotion are heavily infuenced by publication record. In many felds, including computer science, multi-author papers are the norm. Evidence from other felds shows that norms for ordering author names can infuence the assignment of credit. We interviewed 38 students and faculty in human-computer interaction (HCI) and machine learning (ML) at two institutions to determine factors related to assignment of author order in collaborative publication in the feld of computer science. Interview outcomes informed metrics for our bibliometric analysis of gender and collaboration in papers published between 1996 and 2016 in three top HCI and ML conferences. Based on our fndings, we make recommendations for assignment of credit in multi-author papers and interpretation of author order, particularly with respect to how these factors afect women. © 2018 Association for Computing Machinery.

关键词： Paper

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Named entity recognition only from word embeddings

arXiv

引用

arXiv 2019年

作者： Luo, Ying Zhao, Hai Zhan, Junlang Department of Computer Science and Engineering Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence Ai Institute Shanghai Jiao Tong University Shanghai China

Deep neural network models have helped named entity (NE) recognition achieve amaz-ing performance without handcrafting features. However, existing systems require large amounts of human annotated training data. Ef-forts have been made to replace human annota-tions with external knowledge (e.g., NE dictio-nary, part-of-speech tags), while it is another challenge to obtain such effective resources. In this work, we propose a fully unsupervised NE recognition model which only needs to take informative clues from pre-trained word embeddings. We first apply Gaussian Hid-den Markov Model and Deep Autoencoding Gaussian Mixture Model on word embeddings for entity span detection and type prediction, and then further design an instance selec-tor based on reinforcement learning to distin-guish positive sentences from noisy sentences and refine these coarse-grained annotations through neural networks. Extensive experiments on CoNLL benchmark datasets demon-strate that our proposed light NE recognition model achieves remarkable performance with-out using any annotated lexicon or corpus. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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Long-term personalization of an in-home socially assistive robot for children with autism spectrum disorders

arXiv

引用

arXiv 2019年

作者： Clabaugh, Caitlyn Mahajan, Kartik Jain, Shomik Pakkar, Roxanna Becerra, David Shi, Zhonghao Deng, Eric Lee, Rhianna Ragusa, Gisele Matarić, Maja Interaction Lab University of Southern California Viterbi School of Engineering Department of Computer Science Los AngelesCA United States STEM Education Research Group University of Southern California Viterbi School of Engineering Division of Engineering Education Los AngelesCA United States

Socially assistive robots (SAR) have shown great potential to augment the social and educational development of children with autism spectrum disorders (ASD). As SAR continues to substantiate itself as an effective enhancement to human intervention, researchers have sought to study its longitudinal impacts in real-world environments, including the home. Computational personalization stands out as a central computational challenge as it is necessary to enable SAR systems to adapt to each child's unique and changing needs. Toward that end, we formalized personalization as a hierarchical human robot learning framework (hHRL) consisting of five controllers (disclosure, promise, instruction, feedback, and inquiry) mediated by a meta-controller that utilized reinforcement learning to personalize instruction challenge levels and robot feedback based on each user's unique learning patterns. We instantiated and evaluated the approach in a study with 17 children with ASD, aged 3 to 7 years old, over month-long interventions in their homes. Our findings demonstrate that the fully autonomous SAR system was able to personalize its instruction and feedback over time to each child's proficiency. As a result, every child participant showed improvements in targeted skills and long-term retention of intervention content. Moreover, all child users were engaged for a majority of the intervention, and their families reported the SAR system to be useful and adaptable. In summary, our results show that autonomous, personalized SAR interventions are both feasible and effective in providing long-term in-home developmental support for children with diverse learning needs. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

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Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

SSRN

引用

SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

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Donut Modes in Space Wavelength Division Multiplexing: Multimode Optical Fiber Transmission based on Electrical Feedback Equalizer

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Journal of Physics: Conference Series 2021年第1期1755卷

作者： Alaan Ghazi S. A. Aljunid Syed Zulkarnain Syed Idrus R. Endut N. Ali Angela Amphawan Alaa Fareed Aras Al-dawoodi Awab Noori School of Human Development & Techno-communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia. Computer Technical Engineering Al-Qalam University College Iraq Advanced Communication Engineering (ACE) Centre of Excellence Universiti Malaysia Perlis Malaysia. School of Microelectronic Engineering (SCCE) Universiti Malaysia Perlis Perlis Malaysia Lightwave Laboratory School of Engineering and Technology Sunway University 47500 Petaling Jaya Selangor Malaysia. School of Computing University Utara Malaysia Malaysia. Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Data Science Research Lab School of Computing Universiti Utara Malaysia Malaysia

This study demonstrates the implementation of multi-input-multi-output (MIMO) transmission donut modes in space wavelength division multiplexing (SWDM) over multi-mode optical fiber (MMOF) and simulating the effect of mode coupling of the transmitted signal. However, during the data transmitted over MMF by using SWDM an issue is encountered, which is the inter symbol interference (ISI) due to mode coupling. ISI has been recognized as a major obstacle to high-speed data transmission. The signal processing in SDM of donut modes over MMOF with electrical feedback equalizer is used to improve the performance for increasing the bandwidth. A data rate 80 Gbit/s up for a distance of 1600 meters is achieved. SWDM topology was designed emitted from a spatial vertical cavity surface-emitting laser (VCSEL) through MMOF in conjunction with the configurations of electrical feedback equalizer. The parameters and values used in Opt-Sim simulation topology were listed. The performance measurement of this study based on bit-error rates (BER) and eye diagram before and after electrical feedback equalization were analysed.

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LIMIT-BERT : Linguistic informed multi-task BERT

arXiv

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arXiv 2019年

作者： Zhou, Junru Zhang, Zhuosheng Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

In this paper, we present a Linguistic Informed Multi-Task BERT (LIMIT-BERT) for learning language representations across multiple linguistic tasks by Multi-Task Learning (MTL). LIMIT-BERT includes five key linguistic syntax and semantics tasks: Part-Of-Speech (POS) tags, constituent and dependency syntactic parsing, span and dependency semantic role labeling (SRL). Besides, LIMITBERT adopts linguistics mask strategy: Syntactic and Semantic Phrase Masking which mask all of the tokens corresponding to a syntactic/semantic phrase. Different from recent Multi-Task Deep Neural Networks (MTDNN) (Liu et al., 2019), our LIMIT-BERT is linguistically motivated and learning in a semi-supervised method which provides large amounts of linguistic-task data as same as BERT learning corpus. As a result, LIMITBERT not only improves linguistic tasks performance, but also benefits from a regularization effect and linguistic information that leads to more general representations to help adapt to new tasks and domains. LIMIT-BERT obtains new state-of-the-art or competitive results on both span and dependency semantic parsing on Propbank benchmarks and both dependency and constituent syntactic parsing on Penn Treebank. Copyright © 2019, The Authors. All rights reserved.

关键词： Syntactics

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Climate, biogeography, and human resilience in the demographic history of the Canary Islands during the Amazigh period

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Scientific reports 2025年第1期15卷 19485页

作者： Jonathan Santana Miguel Del Pino Curbelo Eneko Iriarte Jacob Morales José L Caro Rosa Fregel Jenny Hagenblad Rebeca García González Amelia Rodríguez Rodríguez Tarha Research Group Department of Historical Sciences Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Spain. jonathan.santana@ulpgc.es. Tarha Research Group Department of Historical Sciences Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Spain. Laboratory of Human Evolution-IsoTOPIK Lab Department of History Universidad de Burgos Burgos Spain. Department of Languages and Computer Science University of Málaga Málaga Spain. Evolution Paleogenomics and Population Genetics Group Department of Biochemistry Microbiology Cell Biology and Genetics Universidad de La Laguna San Cristóbal de La Laguna Santa Cruz de Tenerife Spain. IFM Biology Linköping University Linköping Sweden.

This study explores the dynamic interplay between biogeography, climate variability, and human agency in shaping the population trajectories of Amazigh communities in the Canary Islands (1st to fifteenth centuries cal CE). Using radiocarbon dating as a proxy for population size, this research suggests potential links between demographic trends and environmental factors, highlighting how climatic phases influence agricultural productivity and settlement patterns. Favorable conditions during the Roman Warm Period (RWP) facilitated population expansion, whereas climatic stress during positive phases of the North Atlantic oscillation (NAO) (700-800 cal CE) led to significant demographic declines, particularly on smaller and more arid islands. Larger and ecologically more diverse islands, such as Gran Canaria and Tenerife, showed resilience due to their ecological diversity, agricultural innovations, and food security strategies, which supported sustained growth even during challenging periods such as the Early Medieval Climate Anomaly (MCA, 800-1150 cal CE). From 1150 to 1350 cal CE, cooler sea surface temperatures and a prevailing negative NAO phase increased marine productivity, enabling demographic recovery across islands. However, the arrival of Europeans in the fourteenth-fifteenth centuries introduced external disruptions, including slave raids, novel pathogens, and land seizures, leading to societal collapse. Overall, this study highlights the critical role of environmental diversity and agricultural adaptability in supporting human populations through climatic change and offers valuable perspectives on the relationships among climate, biogeography and human societies.

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Span model for open information extraction on accurate corpus

arXiv

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arXiv 2019年

作者： Zhan, Junlang Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

Open Information Extraction (Open IE) is a challenging task especially due to its brittle data basis. Most of Open IE systems have to be trained on automatically built corpus and evaluated on inaccurate test set. In this work, we first alleviate this difficulty from both sides of training and test sets. For the former, we propose an improved model design to more sufficiently exploit training dataset. For the latter, we present our accurately re-annotated benchmark test set (Re- OIE2016) according to a series of linguistic observation and analysis. Then, we introduce a span model instead of previous adopted sequence labeling formulization for n-ary Open IE. Our newly introduced model achieves new state-of-the-art performance on both benchmark evaluation datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

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