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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Boddam, Megana Kim, Wooyoung University of Washington Bothell Division of Computing and Software Systems School of Stem BothellWA United States

ISBN: (纸本)9798350337488

Deep Learning models, such as convolutional neural networks (CNN), are hard to interpret due to their complex, nonlinear, and high-dimensional algorithms. We focused on interpreting CNNs for predicting Hepatocellular Carcinoma (HCC), the most common primary liver cancer, using histopathology images. We used a 50-layer residual neural network (RNN50), an inception CNN, and a recent innovation called concept whitening (CW). The results showed that adding CW layers to CNN models did not significantly affect model accuracy. However, post-hoc analysis methods, including inter-concept similarity rating, intra-concept similarity rating, concept importance rating, and feature vector displays, revealed improved interpretability. © 2023 IEEE.

关键词： Concept Vectors Concept Whitening Convolutional Neural Networks Deep Learning Feature Vectors Interpretive Machine Learning Residual Neural Networks

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iCare - An AI - Powered Virtual Assistant for Mental Health

iCare - An AI - Powered Virtual Assistant for Mental Health

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Remya Mavila Sugam Jaiswal Raghav Naswa Weichao Yuwen Bill Erdly Dong Si Computing and Software Systems Division University of Washington Bothell USA

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

Currently, mental health issues are increasing among individuals. Sharing feelings with someone who cares plays a key role in resolving these issues. Virtual assistants that can simulate human conversations using artificial intelligence can be effectively used to communicate with individuals facing challenges. This paper describes iCare, a conversational AI that uses machine learning to supply support to individuals suffering from mental health issues. iCare supplies a safe, private, virtual environment for users to share their feelings, and get empathetic response that improves their mental health. It combines multiple techniques to provide the best responses to the users. iCare is designed to provide support for a range of users including those who are suffering from anxiety or depression, and individuals who are unhappy and need help to improve their present feeling.

关键词： Technological innovation Virtual assistants Anxiety disorders Virtual environments Medical treatment Mental health Oral communication

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BEYOND CURRENT BOUNDARIES: INTEGRATING DEEP LEARNING AND ALPHAFOLD FOR ENHANCED PROTEIN STRUCTURE PREDICTION FROM LOW-RESOLUTION CRYO-EM MAPS

arXiv

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arXiv 2024年

作者： Ma, Xin Si, Dong Division of Computing and Software Systems University of Washington Bothell BothellWA98011 United States

Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and graph neural networks (GNNs), have spurred the development of sophisticated map-to-model tools like DeepTracer and ModelAngelo, their efficacy notably diminishes with low-resolution maps beyond 4 Å. To address this shortfall, our research introduces DeepTracer-LowResEnhance, an innovative framework that synergizes a deep learning-enhanced map refinement technique with the power of AlphaFold. This methodology is designed to markedly improve the construction of models from low-resolution cryo-EM maps. DeepTracer-LowResEnhance was rigorously tested on a set of 37 protein cryo-EM maps, with resolutions ranging between 2.5 to 8.4 Å, including 22 maps with resolutions lower than 4 Å. The outcomes were compelling, demonstrating that 95.5% of the low-resolution maps exhibited a significant uptick in the count of total predicted residues. This denotes a pronounced improvement in atomic model building for low-resolution maps. Additionally, a comparative analysis alongside Phenix’s auto-sharpening functionality delineates DeepTracer-LowResEnhance’s superior capability in rendering more detailed and precise atomic models, thereby pushing the boundaries of current computational structural biology methodologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Agent-Based Parallelization of a Multi-Dimensional Semantic Database Model

Agent-Based Parallelization of a Multi-Dimensional Semantic ...

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IEEE International Conference on Information Reuse and Integration (IRI)

作者： Alex Li Munehiro Fukuda Division of Computing and Software Systems University of Washington Bothell Bothell WA

Responses to database queries that may be even identical should vary if they are given under a different user context. For instance, queries for wild animals in the context of the ocean versus mountains should be different. Announced in 1993 [1], Mathematical Model of Meaning (MMM) provides users with capabilities to extract data items tightly coupled under different semantic spaces. Such a space is created dynamically with user-defined impression words to compute semantic equivalence and similarity between data items. MMM computes semantic correlations between the key and other data items to achieve dynamic data querying. However, a semantic space creation and a data correlative calculation are computationally demanding. We consider MMM as a practical database application of multi-agent technologies, construct a space over a cluster system, and have multi-agents explore for a given target and its surrounding data items. We use the Multi-Agent Spatial Simulation (MASS) library to implement an agent-based semantic database system and to measure its parallel execution. Compared to a sequential MMM implementation, MASS-based parallelization yielded a 22-time speedup when creating a space, mainly achieved with matrix multiplication. MASS also reduced the time required for distance sorting of multi-dimensional vectors by 23.7%.

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Graph-based Modeling and Simulation of Emergency Services Communication systems

Graph-based Modeling and Simulation of Emergency Services Co...

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International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication systems (MASCOTS)

作者： Jardi Martinez Jordan Michael Stiber Computing and Software Systems Division School of Science Technology Engineering and Mathematics University of Washington Bothell Bothell WA USA

ISBN: (数字)9798331531300

ISBN: (纸本)9798331531317

Emergency Services Communication systems (ESCS) are evolving into Internet Protocol based communication networks, promising enhancements to their function, availability, and resilience. This increase in complexity and cyber-attack surface demands better understanding of these systems’ breakdown dynamics under extreme circumstances. Existing ESCS research largely overlooks simulation and the little work that exists focuses primarily on cybersecurity threats and neglects critical factors such as non-stationarity of call arrivals. This paper introduces a robust, adaptable graph-based simulation framework and essential mathematical models for ESCS simulation. The framework uses a representation of ESCSes where each vertex is a communicating finite-state machine that exchanges messages along edges and whose behavior is governed by a discrete event queuing model. Call arrival burstiness and its connection to emergency incidents is modeled through a cluster point process. Model applicability is demonstrated through simulations of the Seattle Police Department ESCS. Ongoing work is developing GPU implementation and exploring use in cybersecurity tabletop exercises.

关键词： Adaptation models Protocols Law enforcement Computational modeling Stochastic processes Emergency services Mathematical models Telecommunications Internet Resilience

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Interpretable Deep Learning Models With Concept Whitening Layers

Interpretable Deep Learning Models With Concept Whitening La...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Megana Boddam Wooyoung Kim Division of Computing and Software Systems School of STEM University of Washington Bothell Bothell WA USA

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Assessing Empathy in Mental Health Caregivers using Conversational AI

Assessing Empathy in Mental Health Caregivers using Conversa...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Raghav Naswa Sugam Jaiswal Remya Mavila Weichao Yuwen Bill Erdly Dong Si Computing and Software Systems Division University of Washington Bothell USA School of Nursing and Healthcare Leadership University of Washington Tacoma USA

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

Mental health issues are a significant concern in the United States, with a considerable proportion of adults experiencing some form of mental illness. Unfortunately, a significant portion of these adults do not receive any mental health treatment. Effective communication characterized by empathy is essential for enhancing the well-being of individuals with mental health issues, and the burden of care for these folks falls largely upon family caregivers. However, many caregivers lack the necessary skills to engage with these people in a compassionate and understanding manner. Caregiver training can empower family members and friends to provide in-home treatment to people with mental health issues. To address this challenge, a conversational AI chatbot was developed to train caregivers in empathetic communication. The chatbot engages in interactive conversations with caregivers and offers guidance on compassionate and empathetic communication. Our study demonstrates that conversational AI can serve as a valuable tool for training caregivers, leading to improved patient outcomes through enhanced communication skills.

关键词： Training Machine learning algorithms Data analysis Text categorization Mental health Oral communication Medical services

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DeepTracer-Denoising: Deep Learning for 3D Electron Density Map Denoising

DeepTracer-Denoising: Deep Learning for 3D Electron Density ...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Guan, Haowen Si, Dong New York University Center of Data Science New York City United States University of Washington Bothell Division of Computing and Software Systems Bothell United States

ISBN: (纸本)9781665468190

Cryo-electron microscopy (Cryo-EM) is widely used in molecular structure determination and drug discovery. Experimental cryo-EM images suffer from the noises introduced by electron beam dose and sample preparation. Although many approaches have been proposed to improve the signal-to-noise ratio (SNR) for cryo-EM image denoising, the noises are still presented after 3D reconstruction and can obstruct the analysis and visualization of the 3D density map. Here we present DeepTracer-Denoising, a method for 3D electron density map denoising. We employ a 3D Neural Network to learn the pattern of noises and the biological structure from density maps. Our method is designed to work on medium to high-resolution maps ranging from 2.5 A to 10.0A. It is configurated with two modes to tackle both background noise and structural noise in a 3D density map. Our method can correctly identify 97.70% background noise while preserving 96.46% density of the native structure. For the maps that contain structural noise, DeepTracer-Denoising achieves an overall accuracy of 98.95%. © 2022 IEEE.

关键词： Image denoising

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Graph-based Modeling and Simulation of Emergency Services Communication systems

arXiv

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arXiv 2024年

作者： Jordan, Jardi Martinez Stiber, Michael Computing and Software Systems Division School of Science Technology Engineering and Mathematics University of Washington Bothell BothellWA98011 United States

关键词： Queueing theory

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Delay-aware Dual-Interface Gateway Activation for Energy-Efficient Data Collection in IoT

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Qin, Hua Chen, Hao Li, Ni Deng, Yaqi Yang, Gelan Peng, Yang Hunan City University College of Information and Electronic Engineering Yiyang413000 China University of Washington Bothell Division of Computing and Software Systems BothellWA98011 United States

The proliferation of the Internet of Things (IoT) has elevated the demand for energy-efficient gateway solutions, particularly for battery-powered Wi-Fi devices functioning as gateways in IoT data collection. Traditional approaches either neglect gateway energy consumption or struggle to balance energy efficiency with delay requirements. To address this, we introduce the Dual-interface Gateway Activation (DGA) scheme, a significant advancement enhancing IoT gateway energy management via interactive cooperation between ZigBee and Wi-Fi. DGA's core innovation lies in its dual-interface design and heterogeneity-aware activation strategy. By leveraging coexisting low-power ZigBee communications, DGA dynamically activates the gateway's high-power Wi-Fi interface only when necessary for data reception, effectively decoupling power consumption from continuous Wi-Fi operation. Meanwhile, DGA conservatively schedules high-reliability Wi-Fi sessions to augment unreliable ZigBee communications, thereby ensuring prompt gateway activation for delay-bounded data delivery under varying network conditions. This innovative approach not only guarantees high energy efficiency but also low latency, overcoming the limitations of single-interface gateways. Experimental validation of our prototype system demonstrates DGA's superior performance, achieving up to 62.1% and 45.9% reductions in gateway energy consumption compared to state-of-the-art single-interface and dual-interface solutions, respectively. Additionally, DGA attains lower node energy consumption, further highlighting its potential to enable more energy-efficient IoT networks. © 2017 IEEE.

关键词： Wireless local area networks (WLAN)

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