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An unsupervised auditory scene analysis system using incremental low-dimensional embedding

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An unsupervised auditory scene analysis system using increme...

IEEE/SICE International Symposium on System Integration

作者： Kenta Shinzato Ryosuke Kojima Undergraduate School of Informatics and Mathematical Science Undergraduate Course Program of Applied Mathematics and Physics Kyoto University 54 Kawahara-cho Shogoin Sakyo-ku Kyoto Japan Graduate School of Medicine Kyoto University 54 Kawahara-cho Shogoin Sakyo-ku Kyoto Japan

ISBN: (数字)9781728176581

ISBN: (纸本)9781728176598

This paper addresses the low-dimensional embedding of sounds towards unsupervised auditory scene analysis. Summarizing long-time recordings by mapping them into low-dimensional space is an essential task in long-time environmental monitoring. In this paper, we propose a novel low-dimensional embedding system using an incremental embedding algorithm. To analyze long-time recordings, we design an incremental system consisting of recording, feature extraction, low-dimensional embedding, and visualization. Recently, many low-dimensional embedding methods for acoustic scenes have been studied; however, applicability of these methods to the long-time recording is not adequately evaluated. Thus, this paper describes the construction of the scene analysis system and evaluates the performance of this system. In this paper, we especially focus on two important viewpoints in long-time monitoring: incremental methods and effects of noisy data. To realize an incremental system, we use Self-Organizing Nebulous Growths (SONG), which can incrementally construct a low-dimensional embedding space. Also, in our experiments, we apply our system to bird song analysis under noise conditions. By the preliminary experiments using benchmark datasets, we discover noise sensitivity of our system and applicability to environmental monitoring.

关键词： Training Image analysis Feature extraction Birds Real-time systems Environmental monitoring Task analysis

High-resolution spatio-temporal model for county-level COVID-19 activity in the U.S.

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arXiv 2020年

作者： Zhu, Shixiang Bukharin, Alexander Xie, Liyan Santillana, Mauricio Yang, Shihao Xie, Yao School of Industrial and Systems Engineering Georgia Institute of Technology Harvard Medical School Computational Health Informatics Program Harvard University School of Engineering & Applied Sciences

We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases one-week ahead of the current time, at the county-level and weekly aggregated, in the United States. A notable feature of our spatio-temporal model is that it considers the (a) temporal auto- and pairwise correlation of the two local time series (confirmed cases and death of the COVID-19), (b) dynamics between locations (propagation between counties), and (c) covariates such as local within-community mobility and social demographic factors. The within-community mobility and demographic factors, such as total population and the proportion of the elderly, are included as important predictors since they are hypothesized to be important in determining the dynamics of COVID-19. To reduce the model’s high-dimensionality, we impose sparsity structures as constraints and emphasize the impact of the top ten metropolitan areas in the nation, which we refer (and treat within our models) as hubs in spreading the disease. Our retrospective out-of-sample county-level predictions were able to forecast the subsequently observed COVID-19 activity accurately. The proposed multi-variate predictive models were designed to be highly interpretable, with clear identification and quantification of the most important factors that determine the dynamics of COVID-19. Ongoing work involves incorporating more covariates, such as education and income, to improve prediction accuracy and model interpretability. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamics

An aerodynamic analysis of energy saving car based on computational fluid dynamic using solidworks

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AIP Conference Proceedings 2024年第1期3222卷

作者： R. F. Naryanto A. Setiyawan A. Roziqin S. Maulana F. B. Darsono I. G. M. Andika M. K. Delimayanti R. A. Widodo Mechanical Engineering Department Universitas Negeri Semarang Semarang Indonesia Automotive Engineering Education Universitas Negeri Semarang Semarang Indonesia Computer and Informatics Engineering Department Politeknik Negeri Jakarta Depok Indonesia Master Program in Applied Electrical Engineering Politeknik Negeri Jakarta Depok Indonesia

Improved transportation increased the amount of fuel consumption required. Transportation in Indonesia is the largest oil-consuming sector, with a percentage of 40.1% of the total. Vehicle manufacturers are required to produce a vehicle that has a high level of fuel efficiency. This study aims to determine the aerodynamics of energy-efficient vehicle bodies based on computational fluid dynamics (CFD). This research was conducted using SolidWorks to implement the model. This study examined the variations in airflow velocity at 25, 40, 55, and 70 km/hour. It can be found that the coefficient of drag (Cd) values of the latest body designs with variations in airflow velocity were increased value. The calculation results of the coefficient of drag values were 0.149, 0.154, 0.162, and 0.162, respectively, and the average value was 0.156. In this research, we have found that the energy-saving car body can reduce the drag coefficient compared with the previous study.

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WaiterBot: Comparison of Fuzzy Logic Approaches for Obstacle Avoidance in Dynamic Unmapped Environments Using a Laser Scanning System (LiDAR)

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WaiterBot: Comparison of Fuzzy Logic Approaches for Obstacle...

IEEE Latin American Robotics Symposium, LARS

作者： Jonathan Cerbaro Dieisson Martinelli André Schneider de Oliveira João Alberto Fabro ederal University of Technology - Paraná (UTFPR) Graduate Program on Electrical Engineering and Applied Informatics (CPGEI) Advanced Laboratory of Embedded Systems and Robotics (LASER) Curitiba Paraná Brazil

ISBN: (数字)9780738111537

ISBN: (纸本)9781665419857

Fuzzy logic is a control technique commonly applied in mobile robotics. This paper contains the analysis of three implementations of control systems for obstacle avoidance of a mobile robot. The simulation environment simulate situations with both fixed and dynamic obstacles - like people walking - aiming to compare the fuzzy control approach most suitable to avoid different kinds of obstacles. The analyzed approaches differ by segmenting the sensor data (provided by a LiDAR sensor) into three regions, five regions or by considering only the position of the nearest obstacle. The three approaches were tested in scenarios containing different obstacles and in a simulation where the robot delivers to tables in a dynamic environment, simulating a social event as an application example of a waiter robot.

关键词： Robot sensing systems Robots Collision avoidance Robot kinematics Laser radar Fuzzy logic Input variables

Quantum annealing for vehicle routing problem with weighted segment

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AIP Conference Proceedings 2023年第1期2906卷

作者： Toufan Diansyah Tambunan Andriyan Bayu Suksmono Ian Joseph Matheus Edward Rahmat Mulyawan Doctoral Program of Electrical Engineering and Informatics School of Electrical Engineering and Informatics Institut Teknologi Bandung. Jl. Ganesha 10 Bandung 40132 Jawa Barat Indonesia School of Applied Science Telkom University. Jl. Telekomunikasi no.1 Bandung Jawa Barat Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Jl. Ganesha 10 Bandung 40132 Jawa Barat Indonesia

Quantum annealing technologies aim to solve combinatorial optimization and sampling problems. Studies on combi-natorial optimization problems include the selection of vehicle routes on road traffic, known as the NP-hard problem. We propose a vehicle route problem that can be formulated in the Quadratic Unconstrained Binary Optimization (QUBO) model as a combina-torial problem to optimize vehicle route selection. The solution aims to optimize the vehicle’s journey to reach a destination. The experiment presents a formulation to solve traffic congestion problems on certain roads. The selection of vehicle routes results in optimizing the distribution of alternative road vehicle flows. The level of road density is a cost function that will be optimized, and the constraint is a limitation on the route choice for each vehicle. This vehicle routing problem considers the weight parameter (as distance in actual road conditions) of the road segment that affects the cost function. The simulations on the D-Wave quantum annealer show optimal results on the route deployment of several vehicles. The cost value on route choice with weighted segments gives better results than in previous studies. Each vehicle can go through different road options and accurately reduce road conges-tion. This solution provides an opportunity to develop QUBO modeling on more complex vehicle routing problems to reduce road congestion.

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Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives

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arXiv 2023年

作者： Jeon, Hyeongseon Xie, Juan Jeon, Yeseul Jung, Kyeong Joo Gupta, Arkobrato Chang, Won Chung, Dongjun Department of Biomedical Informatics The Ohio State University ColumbusOH United States Pelotonia Institute for Immuno-Oncology The James Comprehensive Cancer Center The Ohio State University ColumbusOH43210 United States The Interdisciplinary PhD program in Biostatistics The Ohio State University ColumbusOH United States Department of Statistics and Data Science Yonsei University Seoul Korea Republic of Department of Applied Statistics Yonsei University Seoul Korea Republic of Department of Computer Science and Engineering The Ohio State University ColumbusOH United States Division of Statistics and Data Science University of Cincinnati CincinnatiOH United States

Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis. © 2023, CC BY-NC-ND.

关键词： RNA

The association between social vulnerability and oncologic stage and treatment in the United States

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Surgical Oncology Insight

Surgical Oncology Insight 2024年第2期1卷 100044-100044页

作者： Christina M. Stuart Adam R. Dyas Michael R. Bronsert Catherine G. Velopulos William G. Henderson Richard D. Schulick Robert A. Meguid Surgical Outcomes and Applied Research Program Department of Surgery University of Colorado School of Medicine Aurora CO USA Adult and Child Center for Health Outcomes Research and Delivery Science University of Colorado School of Medicine Aurora CO USA Department of Biostatistics and Informatics Colorado School of Public Health Aurora CO USA

Objective Growing evidence supports the impact of sociodemographics on cancer outcomes. The objective of this study was to examine the Social Vulnerability Index (SVI) and its association with oncologic presentation and subsequent treatments across 8 major cancers. Methods This was a retrospective-cohort study using one institution’s contribution to the National Cancer Database (2011–2021). Patients were grouped into low SVI (<75th percentile) and high SVI (≥75th percentile) cohorts. Un-adjusted comparison between groups was performed followed by multivariable regression to control for the effect of demographic characteristics on oncologic presentation, and for demographic and oncologic characteristics on subsequent treatments. A subgroup analysis was performed comparing cancers that have national screening protocols versus those without. Results Of 12,712 cases, 2842 (22.4%) were in the high SVI group and 9870 (77.6%). After risk-adjustment, high SVI patients presented at more advanced T-stage (odds ratio 1.09, 95% confidence interval 1.00–1.19); N-stage (1.11, 1.01–1.23); M stage (1.16, 1.03–1.30); and overall stage (1.14, 1.04–1.24) and were more frequently not recommended for surgery (1.15, 1.01–1.32) or chemotherapy (1.20, 1.07–1.38). Screening protocols tended to increase the association between high SVI and advanced oncologic presentation. After adjustment high SVI remained significantly associated with decreased odds of survival (0.85, 0.79 - 0.91). Conclusions High SVI is associated with advanced stage presentation and decreased likelihood of being recommended surgery or chemotherapy even after risk-adjustment. Differences in presentation stage are predominantly driven by cancers with screening protocols and ultimately high SVI is associated with decreased odds of survival.

关键词： Social vulnerability Cancer Clinical stage Social determinants of health

A General-Purpose Self-Supervised Model for Computational Pathology

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arXiv 2023年

作者： Chen, Richard J. Ding, Tong Lu, Ming Y. Williamson, Drew F.K. Jaume, Guillaume Chen, Bowen Zhang, Andrew Shao, Daniel Song, Andrew H. Shaban, Muhammad Williams, Mane Vaidya, Anurag Sahai, Sharifa Oldenburg, Lukas Weishaupt, Luca L. Wang, Judy J. Williams, Walt Le, Long Phi Gerber, Georg Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Systems Biology Harvard University CambridgeMA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using over 100 million tissue patches from over 100,000 diagnostic haematoxylin and eosin-stained WSIs across 20 major tissue types, and evaluated on 33 representative CPath clinical tasks in CPath of varying diagnostic difficulties. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree code classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient AI models that can generalize and transfer to a gamut of diagnostically-challenging tasks and clinical workflows in anatomic pathology. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

A Foundational Multimodal Vision Language AI Assistant for Human Pathology

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arXiv 2023年

作者： Lu, Ming Y. Chen, Bowen Williamson, Drew F.K. Chen, Richard J. Ikamura, Kenji Gerber, Georg Liang, Ivy Phi Le, Long Ding, Tong Parwani, Anil V. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Pathology Wexner Medical Center Ohio State University ColumbusOH United States Harvard Data Science Initiative Harvard University CambridgeMA United States

The field of computational pathology has witnessed remarkable progress in the development of both taskspecific predictive models and task-agnostic self-supervised vision encoders. However, despite the explosive growth of generative artificial intelligence (AI), there has been limited study on building general purpose, multimodal AI assistants tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology using an in-house developed foundational vision encoder pretrained on 100 million histology images from over 100,000 patient cases and 1.18 million pathology image-caption pairs. The vision encoder is then combined with a pretrained large language model and the whole system is finetuned on over 250,000 diverse disease agnostic visual language instructions. We compare PathChat against several multimodal vision language AI assistants as well as GPT4V, which powers the commercially available multimodal general purpose AI assistant ChatGPT-4. When relevant clinical context is provided with the histology image, PathChat achieved a diagnostic accuracy of 87% on multiple-choice questions based on publicly available cases of diverse tissue origins and disease models. Additionally, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive and general vision language AI assistant that can flexibly handle both visual and natural language inputs, PathChat can potentially find impactful applications in pathology education, research, and human-in-the-loop clinical decision making. © 2023, CC BY-NC-ND.

关键词： Pathology