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8th International Conference on computer and Communication engineering, ICCCE 2021

作者： Ab Aziz, Siti Hajar Nor, Norhanis Aida M. Zabidi, Suriza Ahmad Universiti Kuala Lumpur British Malaysian Institute Communication Technology Section Selangor53100 Malaysia International Islamic University Malaysia Kuliyyah of Engineering Department of Science in Engineering Kuala Limpur53100 Malaysia International Islamic University Malaysia Kuliyyah of Engineering Department of Electrical and Computer Engineering Department Kuala Limpur53100 Malaysia

ISBN: (纸本)9781728110646

Free space optical (FSO) networking has emerged as a promising technology for bridging the last mile gap in current high-rate fibre networks as well as for high-rate next-generation broadband wireless communication networks. This paper theoretically investigates the performance of the Free Space Optic system under rainy weather conditions using Optisystem software. Quality factor and eye-diagram analysis are used to analyse the FSO performance for several rain intensity and precipitation, following the Carbonneau model for rain attenuation. The results show that the link is completely lost when the distances are at 1.5 km, 1.2 km, and 0.8 km for light rain, moderate rain, and heavy rain, respectively. An optical amplifier is then introduced to improve the system performances and increase the signal intensity, which helps to mitigate the atmospheric effects easily. As a result, data transmission is sustained without interruptions, and overall connectivity is enhanced. Results show that the transmission range improves up to several meters, specifically under moderate rain weather condition when using the optical amplifier gain is applied. © 2021 IEEE.

关键词： Rain

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Characterization of Quantum Frequency Processors

arXiv

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arXiv 2023年

作者： Lu, Hsuan-Hao Peters, Nicholas A. Weiner, Andrew M. Lukens, Joseph M. Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Research Technology Office and Quantum Collaborative Arizona State University TempeAZ85287 United States

Frequency-bin qubits possess unique synergies with wavelength-multiplexed lightwave communications, suggesting valuable opportunities for quantum networking with the existing fiber-optic infrastructure. Although the coherent manipulation of frequency-bin states requires highly controllable multi-spectral-mode interference, the quantum frequency processor (QFP) provides a scalable path for gate synthesis leveraging standard telecom components. Here we summarize the state of the art in experimental QFP characterization. Distinguishing between physically motivated "open box" approaches that treat the QFP as a multiport interferometer, and "black box" approaches that view the QFP as a general quantum operation, we highlight the assumptions and results of multiple techniques, including quantum process tomography of a tunable beamsplitter—to our knowledge the first full process tomography of any frequency-bin operation. Our findings should inform future characterization efforts as the QFP increasingly moves beyond proof-of-principle tabletop demonstrations toward integrated devices and deployed quantum networking experiments. © 2023, CC BY.

关键词： Tomography

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A Machine Learning Approach for Analyzing Texture and Morphometry Markers of Glioblastoma TERT Promoter Mutations in DAPI Fluorescence Images

A Machine Learning Approach for Analyzing Texture and Morpho...

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Medical Imaging 2025: Digital and Computational Pathology

作者： Afsari, Fatemeh Naglah, Ahmed Gupta, Akshita Lohaan, Harshit Onubogu, Ugoma Janiszewska, Michalina Sarder, Pinaki Division of Nephrology Hypertension and Renal Transplantation-Quantitative Health Section Department of Medicine College of Medicine University of Florida GainesvilleFL32603 United States Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida GainesvilleFL32611 United States Department of Computer and Information Science and Engineering University of Florida GainesvilleFL32611 United States Department of Molecular Medicine The Herbert Wertheim UF Scripps Institute for Biomedical Innovation and Technology JupiterFL33458 United States

ISBN: (纸本)9781510686045

Advanced molecular and imaging techniques, such as STAR-FISH, are frequently required to identify mutations in tissue specimens to properly diagnose Glioblastoma (GBM), an aggressive type of brain tumor. Mutations to the telomerase reverse transcriptase promoter (TERTp) are common in cancer and present in approximately 80% of all GBM tumors. We hypothesize that machine learning can stratify cells by their TERTp mutation status using DAPI fluorescence images since the STAR-FISH technique can be costly and inaccessible for some healthcare facilities. In this study, we present a machine learning-based approach to recognize nuclei harboring TERTp mutations solely based on the DAPI fluorescence images of GBM tissues. The proposed pipeline integrates a convolutional neural network (CNN)-based deep learning model to segment cells, extract hand-engineered features, and feed them into fully connected (FC) layers within the neural network or other machine learning classifiers to identify TERTp mutant cells. This approach involves extracting DAPI features, including both morphological and textural image features, followed by an ensemble of machine learning modules to predict TERTp mutant cells. We evaluate our approach on a cohort of 18 patients (70 pairs of DAPI and STAR-FISH processed images). Our experimental results show that the FC layers within the neural network achieve the highest AUC, F1-Score, and recall of 0.77, 0.69, and 0.86, respectively, with the integrated CNN model achieving a Dice score of 0.83, accurately segmenting cells. Our work is one of the earliest attempts that we are aware of to use machine-learning models for TERTp mutation identification using DAPI fluorescence images. Our results indicate the possibility of improved GBM diagnosis accuracy. To improve patient diagnosis and treatment planning for GBM, future research aims will expand the cohort and enhance the models to provide more precise and diagnostically valuable models for the interpretation o

关键词： Image segmentation

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Improving Entity Recognition Using Ensembles of Deep Learning and Fine-tuned Large Language Models: A Case Study on Adverse Event Extraction from Multiple Sources

arXiv

引用

arXiv 2024年

作者： Li, Yiming Viswaroopan, Deepthi He, William Li, Jianfu Zuo, Xu Xu, Hua Tao, Cui McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Department of Electrical & Computer Engineering Pratt School of Engineering Duke University 305 Tower Engineering Building DurhamNC27708 United States Department of Artificial Intelligence and Informatics Mayo Clinic JacksonvilleFL32224 United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT06510 United States

Objective Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of immunizations, identifying potential risks and ensuring the safe use of these products. Traditional deep learning models are adept at learning intricate feature representations and dependencies in sequential data, but often require extensive labeled data. In contrast, large language models (LLMs) excel in understanding contextual information, but exhibit unstable performance on named entity recognition (NER) tasks, possibly due to their broad but unspecific training. This study aims to evaluate the effectiveness of LLMs and traditional deep learning models in AE extraction, and to assess the impact of ensembling these models on performance. Methods In this study, we utilized reports and posts from the Vaccine Adverse Event Reporting System (VAERS) (n=621), Twitter (n=9,133), and Reddit (n=131) as our corpora. Our goal was to extract three types of entities: vaccine, shot, and adverse event (ae). We explored and fine-tuned (except GPT-4) multiple LLMs, including GPT-2, GPT-3.5, GPT-4, Llama-2 7b, and Llama-2 13b, as well as traditional deep learning models like Recurrent neural network (RNN) and Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT). To enhance performance, we created ensembles of the three models with the best performance. For evaluation, we used strict and relaxed F1 scores to evaluate the performance for each entity type, and micro-average F1 was used to assess the overall performance. Results The ensemble model achieved the highest performance in "vaccine," "shot," and "ae," with strict F1-scores of 0.878, 0.930, and 0.925, respectively, along with a micro-average score of 0.903. These results underscore the significance of fine-tuning models for specific tasks and demonstrate the effectiveness of ensemble methods in enhancing performance. Conclusion In conclusion,

关键词： Vaccines

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Bayesian quantum state reconstruction with a learning-based tuned prior

Bayesian quantum state reconstruction with a learning-based ...

引用

Optica Quantum 2.0 Conference and Exhibition, Quantum 2.0

作者： Regmi, Sangita Blackwell, Ashley N. Khannejad, Amirali Lohani, Sanjaya Lukens, Joseph M. Glasser, Ryan T. Kirby, Brian T. Searles, Thomas A. Dept. of Electrical & Computer Engineering University of Illinois Chicago ChicagoIL60607 United States Research Technology Office and Quantum Collaborative Arizona State University TempeAZ85287 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States Tulane University New OrleansLA70118 United States DEVCOM Army Research Laboratory AdelphiMD20783 United States

We demonstrate machine-learning-enhanced Bayesian quantum state tomography on near-term intermediate-scale quantum hardware. Our approach to selecting prior distributions leverages pre-trained neural networks incorpor... 详细信息

ISBN: (纸本)9781957171272

关键词： Machine learning Neural networks Photonic entanglement Quantum computation Quantum technology Remote sensing

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Absolute Localization in Feature-Poor Industrial Confined Spaces

Absolute Localization in Feature-Poor Industrial Confined Sp...

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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： Rune Y. Brogaard Robert A. Hewitt Sarah Etter Arash Kalantari Evangelos Boukas Faculty of Electrical Engineering Technical University of Denmark Kgs Lyngby Denmark Mobility and Robotic Systems Section NASA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Department of Computer Science University of Texas Austin TX USA

Autonomous inspection of dark, confined, and feature-poor spaces requires robotic platforms to utilize accurate and reliable localization systems for safe and reliable operation. This paper presents an absolute localization system for highly feature-poor spaces, using visual inertial odometry and GPU-based point cloud registrations for limited field-of-view sensors. The extracted structural elements from sensor scans, along side IMU measurements, are used to limit the search area for the GPU-based point cloud registrations. We employ Stein-ICP which is an uncertainty aware variant of ICP. The 3D registrations are then fused with a visual-inertial odometry estimate in an Extended Kalman Filter to provide a fast and accurate absolute pose estimate. The proposed localization system is tested in both a simulated environment and in a mock-up model of a chemical distillation column - both highly feature-poor areas.

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Procrustean entanglement concentration in dense wavelength-division multiplexing

Procrustean entanglement concentration in dense wavelength-d...

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IEEE Photonics Conference (IPC)

作者： Hsuan-Hao Lu Muneer Alshowkan Jude Alnas Joseph M. Lukens Nicholas A. Peters Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge Tennessee USA School of Electrical and Computer Engineering Duke University Durham North Carolina USA Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona USA

We employ polarization-dependent loss emulators for Procrustean filtering of a noisy quantum state affected by classical crosstalk, resulting in a substantial improvement of two-qubit entangled state fidelities from a...

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Optimizing Variable-Strength Combinatorial Test Suite Generation via Fuzzy Adaptive Sine Cosine Algorithm (FASCA)

Optimizing Variable-Strength Combinatorial Test Suite Genera...

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Cyber Security and Computing (CyberComp), International Conference on

作者： Nur Syabila Zabidi Noraini Ibrahim Mazidah Mat Rejab MasrulEhsan Mamat Sumaira Nazir Nurul Hawani Mat Tuselim Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia (UTHM) Parit Raja Batu Pahat MALAYSIA Department of Software Engineering National University of Modern Languages Islamabad PAKISTAN Business Intelligence Section Industrial Research Management Centre SIRIM Berhad Shah Alam MALAYSIA

ISBN: (数字)9798350387728

ISBN: (纸本)9798350387735

Combinatorial test suite generation is a critical aspect of software testing, particularly for systems with variable-strength interactions. Traditional optimization algorithms often struggle to efficiently generate minimal test suites while maintaining coverage. This research introduces the Fuzzy Adaptive Sine Cosine Algorithm (FASCA), a variant of the Sine Cosine Algorithm (SCA), tailored specifically for optimizing variable-strength combinatorial test suite generation. FASCA incorporates fuzzy adaptation mechanisms to dynamically balance exploration and exploitation, addressing the limitations of standard SCA in handling diverse interaction strengths. Experimental evaluations demonstrate that FASCA significantly reduces test suite size and execution time compared to the original SCA, offering a more efficient solution for combinatorial test optimization. These results highlight FASCA's potential as a robust tool for enhancing the efficiency of software testing processes.

关键词： Fuzzy logic Heuristic algorithms Software algorithms Metaheuristics Switches Minimax techniques Linguistics Inference algorithms Standards Gravity

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Measuring the social influence for retweet behavior prediction in microblogging network

Measuring the social influence for retweet behavior predicti...

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International Symposium on Telecommunications, IST

作者： Rahebeh Mojtahedi Safari Amir Masoud Rahmani Sasan H. Alizaeh Department of Computer Engineering Lahijan Branch Islamic Azad University Lahijan Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section3 Douliou Yunlin Taiwan Faculty of Information Technology ICT Research Institute (ITRC) Tehran Iran

ISBN: (数字)9798350356250

ISBN: (纸本)9798350356267

Nowadays, retweeting behavior is considered a substantial behavior to analyze information diffusion in the microblogging network. To understand the underlying mechanism of information diffusion and analyze the evolution of the diffusion corresponding to a given original microblog, user retweeting behavior prediction has recently gained considerable attention. In this paper firstly, we introduce the most influential factors which affect user retweeting behavior and propose an effective measurement based on the random walk model to measure the social influence. Secondly, by incorporating our defined influential factors into a logistic regression classifier model, we predict the future activated user set concerning the diffusion of a given original microblog at the next time point. Unlike other comparative models, our prediction model can illustrate the evolution of diffusion and predict the retweeting behavior of all potential users through the diffusion of any original microblog in the microblogging network. Finally, based on the real Twitter microblogging dataset, the experimental results show by the contribution of our defined influential factors and presenting an effective measure of the social influence factor, we can obtain more accurate predictions in comparison to other baseline methods.

关键词： Logistic regression Accuracy Blogs Predictive models Telecommunications Information diffusion

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Complete frequency-bin Bell basis synthesizer

arXiv

引用

arXiv 2022年

作者： Seshadri, Suparna Lu, Hsuan-Hao Leaird, Daniel E. Weiner, Andrew M. Lukens, Joseph M. Elmore Family School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States

We report the experimental generation of all four frequency-bin Bell states in a single versatile setup via successive pumping of spontaneous parametric downconversion with single and dual spectral lines. Our scheme utilizes intensity modulation to control the pump configuration and offers turn-key generation of any desired Bell state using only off-the-shelf telecommunication equipment. We employ Bayesian inference to reconstruct the density matrices of the generated Bell states, finding fidelities ≥97% for all cases. Additionally, we demonstrate the sensitivity of the frequency-bin Bell states to common-mode and differential-mode temporal delays traversed by the photons comprising the state-presenting the potential for either enhanced resolution or nonlocal sensing enabled by our complete Bell basis synthesizer. Copyright © 2022, The Authors. All rights reserved.

关键词： Bayesian networks

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