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International Conference on Informatics and Computational sciences (ICICoS)

作者： Cuk Tho Harco Leslie Hendric Spits Warnars Benfano Soewito Ford Lumban Gaol BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia 11480

ISBN: (数字)9781728195261

ISBN: (纸本)9781728195278

Code-mixed language is ubiquitous. Having been commonly practiced among bilingual communities, code-mixed language has emerged as a common language among social media users. Despite its popularity, the analysis of a code-mixed text is challenging as the text does not typically comply with the monolingual grammar. Therefore, the popularity of social media in the past ten years has raised wide attention to develop methods for analyzing code-mixed text such as extracting popularity sentiment from the text. Machine learning-based classifier such as Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression have been widely used to analyze the sentiment. This paper intends to further explore machine learning classifiers, their performances, variables, and most common classifiers for the code-mixed sentiment analysis. Prisma Methodology was used in this paper, extracting 12 from 230 papers that met predefined required criteria, including publication year within the last 5 years. Our findings suggested that the most common classifiers found in the papers were Support Vector Machine, Naïve Bayes, and Logistic Regression. By using the accuracy and F1 as the performance measures, the Support Vector Machine exhibited a better performance compared to Naïve Bayes and Logistic Regression. Thus, this study supported the use of Support Vector Machine, Naïve Bayes and Logistic Regression as the main classifiers for the code-mixed sentiment analysis.

关键词： Support vector machines Sentiment analysis Systematics Social networking (online) Machine learning Regression tree analysis Logistics

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Picosecond optical-to-x-ray (Cd,Mg)Te radiation detectors

Picosecond optical-to-x-ray (Cd,Mg)Te radiation detectors

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Conference on Lasers and Electro-Optics (CLEO)

作者： J. Cheng I. Komissarov G. Chen D. Chakraborty S. Kutcher H. Chen S. Trivedi Roman Sobolewski Materials Science Graduate Program University of Rochester Rochester New York USA Laboratory for Laser Energetics University of Rochester Rochester New York USA Department of Electrical and Computer Engineering University of Rochester Rochester New York USA Brimrose Technology Corp Sparks Maryland USA

We developed a dual optical/x-ray ultrafast photodetector based on in-house grown Cd 0.97 Mg 0.03 Te single crystals. The detector is characterized by ~200 ps full-width-at-half-maximum, readout-electronics limited p... 详细信息

ISBN: (纸本)9781957171258

关键词： Femtosecond lasers Photodetectors Photon counting Pump probe spectroscopy Radiation detectors Ti:sapphire lasers

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Dynamic Healthcare Embeddings for Improving Patient Care

Dynamic Healthcare Embeddings for Improving Patient Care

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Hankyu Jang Sulyun Lee D. M. Hasibul Hasan Philip M. Polgreen Sriram V. Pemmaraju Bijaya Adhikari Department of Computer Science University of Iowa Iowa City Iowa USA Interdisciplinary Graduate Program in Informatics University of Iowa Iowa City Iowa USA Department of Internal Medicine University of Iowa Iowa City Iowa USA

As hospitals move towards automating and integrating their computing systems, more fine-grained hospital operations data are becoming available. These data include hospital architectural drawings, logs of interactions between patients and healthcare professionals, prescription data, procedures data, and data on patient admission, discharge, and transfers. This has opened up many fascinating avenues for healthcare-related prediction tasks for improving patient care. However, in order to leverage off-the-shelf machine learning software for these tasks, one needs to learn structured representations of entities involved from heterogeneous, dynamic data streams. Here, we propose DECENT, an auto-encoding heterogeneous co-evolving dynamic neural network, for learning heterogeneous dynamic embeddings of patients, doctors, rooms, and medications from diverse data streams. These embeddings capture similarities among doctors, rooms, patients, and medications based on static attributes and dynamic interactions. DECENT enables several applications in healthcare prediction, such as predicting mortality risk and case severity of patients, adverse events (e.g., transfer back into an intensive care unit), and future healthcare-associated infections. The results of using the learned patient embeddings in predictive modeling show that DECENT has a gain of up to 48.1% on the mortality risk prediction task, 12.6% on the case severity prediction task, 6.4% on the medical intensive care unit transfer task, and 3.8% on the Clostridioides difficile (***) Infection (CDI) prediction task over the state-of-the-art baselines. In addition, case studies on the learned doctor, medication, and room embeddings show that our approach learns meaningful and interpretable embeddings.

关键词： Hospitals Social networking (online) Neural networks Machine learning Predictive models Back Software

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New Frontiers for Wave Engineering Using Metamaterials

New Frontiers for Wave Engineering Using Metamaterials

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2022年全国微波毫米波会议

作者： Andrea Alù Photonics Initiative Advanced Science Research Center City University of New York Physics Program Graduate Center City University of New York Department of Electrical and Computer Engineering City College of New York

Metamaterials are engineered materials with properties that go well beyond what offered by nature, providing unprecedented opportunities to tailor and enhance the control of waves. In this talk, I discuss our recent activity at microwaves and THz frequencies, showing how suitably tailored meta-atoms and their arrangements open exciting avenues for wave manipulation, including metasurfaces with enhanced wavefront manipulation, and magnet-free nonreciprocity and topological phenomena. Insights into the underlying physics and new devices based on these concepts will be presented.

关键词： New Frontiers for Wave Engineering Using Metamaterials

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MEC deployment with distributed cloud in 4G network for 5G success 6

MEC deployment with distributed cloud in 4G network for 5G s...

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6th International Conference on Information Technology, computer and Electrical Engineering, ICITACEE 2019

作者： Hairuman, Andika Zahra, Amalia Kusuma, Gede Putra Murad, Dina Fitria Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9781728126890

The purpose of this paper is to understand the deployment model of MEC with distributed cloud in 4G network before rolling out 5G and make the most successful deployment without creating conflict with NFV as part of digital transformation. MEC brings flexibility and agility to meet the application demands of low latency, high reliability with high scalability, bandwidth and battery savings. It explains few reasons why MEC should be adopted before 5G deployment and what would be the benefits for all the actors like operators and public cloud service providers. MEC architecture is designed by ETSI and there are some known challenges with MEC use scenarios like AR/VR, IoT and connected vehicles. MEC offerings few deployment options which available for 4G and take that deployment model as a reference for MEC deployment on 5G include the migration options. This paper provides list of recommendations for key successes deploying MEC in 4G network and 5G and help the stakeholders understand the way to realize the MEC benefits. © 2019 IEEE.

关键词： Distributed cloud

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Air Medical Blood Transfusion as a Trigger of Massive Transfusion Protocol

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Air Medical Journal 2023年第5期42卷 353-357页

作者： Cornelius, Brian Thompson, Dennis Kilgore, Phillip Cvek, Urska Trutschl, Marjan Samra, Navdeep Cornelius, Angela Graduate Program in Nurse Anesthesia Texas Wesleyan University Fort Worth TX United States Department of Anesthesia John Peter Smith Hospital Fort Worth TX United States Department of Anesthesia Ochsner LSU Health Shreveport LA United States Department of Emergency Medicine Louisiana State University Health Sciences Center Shreveport LA United States Laboratory for Advanced Biomedical Informatics Department of Computer Science Louisiana State University Shreveport LA Department of Trauma and Surgical Critical Care Louisiana State University Health Sciences Center Shreveport LA United States Department of Emergency Medicine John Peter Smith Hospital Fort Worth TX United States

Air medical services can improve access to blood products at the point of injury. Studies have shown that early activation of mass transfusion protocols (MTPs) can improve the survival of trauma patients by up to 25%. There are several scoring systems to guide early activation, but the use of a single criterion has been elusive. Our study sought to determine if air medical administration of blood products was a risk factor for massive transfusion activation and utilization of prehospital vital signs for calculation of the shock index. In our retrospective study, we evaluated adult trauma patients transfused by helicopter emergency medical services (HEMS) and as a control all patients in our institution receiving the MTP. Our study found HEMS blood transfusion was not a reliable trigger for MTP, although the sample size may have limited our findings. We found that HEMS care resulted in an overall reduction in the volume of transfusion and an improvement in hemodynamic parameters upon trauma center arrival. HEMS transfusion and a higher rate of tranexamic acid administration may have contributed to these findings. Of note, the assessment of blood consumption score and shock index were nonspecific in the study populations. © 2023 Air Medical Journal Associates

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Lightweight Learner for Shared Knowledge Lifelong Learning

arXiv

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arXiv 2023年

作者： Ge, Yunhao Li, Yuecheng Wu, Di Xu, Ao Jones, Adam M. Rios, Amanda Sofie Fostiropoulos, Iordanis Wen, Shixian Huang, Po-Hsuan Murdock, Zachary William Sahin, Gozde Ni, Shuo Lekkala, Kiran Sontakke, Sumedh Anand Itti, Laurent Thomas Lord Department of Computer Science University of Southern California United States Neuroscience Graduate Program University of Southern California United States Intel Labs United States Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Dornsife Department of Psychology University of Southern California United States

In Lifelong Learning (LL), agents continually learn as they encounter new conditions and tasks. Most current LL is limited to a single agent that learns tasks sequentially. Dedicated LL machinery is then deployed to mitigate the forgetting of old tasks as new tasks are learned. This is inherently slow. We propose a new Shared Knowledge Lifelong Learning (SKILL) challenge, which deploys a decentralized population of LL agents that each sequentially learn different tasks, with all agents operating independently and in parallel. After learning their respective tasks, agents share and consolidate their knowledge over a decentralized communication network, so that, in the end, all agents can master all tasks. We present one solution to SKILL which uses Lightweight Lifelong Learning (LLL) agents, where the goal is to facilitate efficient sharing by minimizing the fraction of the agent that is specialized for any given task. Each LLL agent thus consists of a common task-agnostic immutable part, where most parameters are, and individual task-specific modules that contain fewer parameters but are adapted to each task. Agents share their task-specific modules, plus summary information ("task anchors") representing their tasks in the common task-agnostic latent space of all agents. Receiving agents register each received task-specific module using the corresponding anchor. Thus, every agent improves its ability to solve new tasks each time new task-specific modules and anchors are received. If all agents can communicate with all others, eventually all agents become identical and can solve all tasks. On a new, very challenging SKILL-102 dataset with 102 image classification tasks (5,033 classes in total, 2,041,225 training, 243,464 validation, and 243,464 test images), we achieve much higher (and SOTA) accuracy over 8 LL baselines, while also achieving near perfect parallelization. Code and data can be found at https://***/gyhandy/Shared-Knowledge-Lifelong-Learning Copyr

关键词： Statistical tests

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NTRK3 exhibits a pro-oncogenic function in upper tract urothelial carcinomas

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Kaohsiung Journal of Medical sciences 2024年第5期40卷 445-455页

作者： Lim, Lee-Moay Lee, Yi-Chen Lin, Ting-Wei Hong, Zi-Xuan Hsu, Wei-Chi Ke, Hung-Lung Hwang, Daw-Yang Chung, Wen-Yu Li, Wei-Ming Lin, Hui-Hui Kuo, Hung-Tien Huang, A-Mei Graduate Institute of Clinical Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Division of Nephrology Department of Internal Medicine Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Graduate Institute of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Anatomy School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Urology School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Urology Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan Department of Urology Kaohsiung Municipal Ta-Tung Hospital Kaohsiung Medical University Kaohsiung Taiwan National Institute of Cancer Research National Health Research Institute Tainan Taiwan Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Urology Ministry of Health and Welfare Pingtung Hospital Pingtung Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan Doctoral Degree Program in Toxicology College of Pharmacy Kaohsiung Medical University Kaohsiung Taiwan Department of Biochemistry School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Research Center for Environmental Medicine Kaohsiung Medical University Kaohsiung Taiwan

Neurotrophic receptor tyrosine kinase 3 (NTRK3) has pleiotropic functions: it acts not only as an oncogene in breast and gastric cancers but also as a dependence receptor in tumor suppressor genes in colon cancer and neuroblastomas. However, the role of NTRK3 in upper tract urothelial carcinoma (UTUC) is not well documented. This study investigated the association between NTRK3 expression and outcomes in UTUC patients and validated the results in tests on UTUC cell lines. A total of 118 UTUC cancer tissue samples were examined to evaluate the expression of NTRK3. Survival curves were generated using Kaplan–Meier estimates, and Cox regression models were used for investigating survival outcomes. Higher NTRK3 expression was correlated with worse progression-free survival, cancer-specific survival, and overall survival. Moreover, the results of an Ingenuity Pathway Analysis suggested that NTRK3 may interact with the PI3K-AKT-mTOR signaling pathway to promote cancer. NTRK3 downregulation in BFTC909 cells through shRNA reduced cellular migration, invasion, and activity in the AKT-mTOR pathway. Furthermore, the overexpression of NTRK3 in UM-UC-14 cells promoted AKT-mTOR pathway activity, cellular migration, and cell invasion. From these observations, we concluded that NTRK3 may contribute to aggressive behaviors in UTUC by facilitating cell migration and invasion through its interaction with the AKT-mTOR pathway and the expression of NTRK3 is a potential predictor of clinical outcomes in cases of UTUC. © 2024 The Authors. The Kaohsiung Journal of Medical sciences published by John Wiley & Sons Australia, Ltd on behalf of Kaohsiung Medical University.

关键词： AKT-mTOR pathway neurotrophic receptor tyrosine kinase 3 (NTRK3) prognosis tropomyosin-related kinase (TrK) upper tract urothelial carcinoma (UTUC)

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Symbolic-numeric algorithm for parameter estimation in discrete-time models with exp

arXiv

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arXiv 2024年

作者： Berman, Yosef Forrest, Joshua Grote, Matthew Ovchinnikov, Alexey Rueda, Sonia Ph.D. Program in Mathematics CUNY Graduate Center New York United States Ph.D. Program in Computer Science CUNY Graduate Center New York United States Department of Mathematics CUNY Queens College Queens United States Departamento de Matemática Aplicada E.T.S. Arquitectura Universidad Politécnica de Madrid Spain School of Mathematics and Statistics University of Melbourne VIC Australia

Determining unknown parameter values in dynamic models is crucial for accurate analysis of the dynamics across the different scientific disciplines. Discrete-time dynamic models are widely used to model biological processes, but it is often difficult to determine these parameters. In this paper, we propose a robust symbolic-numeric approach for parameter estimation in discrete-time models that involve non-algebraic functions such as exp. We illustrate the performance (precision) of our approach by applying our approach to the flour beetle (LPA) model, an archetypal discrete-time model in biology. Unlike optimization-based methods, our algorithm guarantees to find all solutions of the parameter values given time-series data for the measured *** Codes 92B05, 68W30, 14Q20, 39A60, 13P15 © 2024, CC BY-NC-SA.

关键词： Parameter estimation

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Implementing the Haversine Formula for Detecting Societal Issues and Delivering Relevant Preaching Content

Implementing the Haversine Formula for Detecting Societal Is...

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International Conference on Electrical Engineering and Informatics (ICon EEI)

作者： Rahmad Kurniawan Fatayat Tisha Melia Evfi Mahdiyah Abdul Somad Batubara Imam Ghozali Ilyas Husti Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Riau Pekanbaru Indonesia Bandar Seri Begawan Brunei Darussalam Universiti Islam Sultan Sharif Ali Brunei Darussalam Sekolah Tinggi Agama Islam Negeri (STAIN) Bengkalis Bengkalis Indonesia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (数字)9798331540791

ISBN: (纸本)9798331540807

The effectiveness of Islamic preaching is often hindered by the need for localized data on societal issues, which leads to lectures that may not address the community’s specific needs. This study seeks to bridge this gap by developing a system that provides relevant preaching outlines tailored to issues detected within a 1 km radius of a mosque. The system incorporates data from 185 mosques and is enhanced by integrating societal issues as a feature. The Haversine formula was employed for precise distance calculations to identify nearby issues accurately. Blackbox testing verified that the system functions correctly. In addition, response time experiments showed an average time of 0.17 seconds. This result highlights the system's overall efficiency in delivering quick responses. Furthermore, a user acceptance test involving eight Islamic preachers revealed high satisfaction with the system’s ease of understanding, as indicated by a mean score of 4.75. These findings suggest that the system effectively supports preachers in delivering contextually relevant preaching and enhances the impact of their preaching efforts by aligning them with the specific concerns of the communities. This innovative approach marks a significant step forward in using geospatial intelligence for religious outreach, with future developments planned to integrate AI to generate dynamic content for various Islamic occasions.

关键词： Electrical engineering Bridges Closed box Geospatial analysis Time factors Artificial intelligence Informatics Testing

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