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arXiv 2023年

作者： Wang, Alan Q. Karaman, Batuhan K. Kim, Heejong Rosenthal, Jacob Saluja, Rachit Young, Sean I. Sabuncu, Mert R. School of Electrical and Computer Engineering Cornell University Cornell Tech New YorkNY10044 United States Department of Radiology Weill Cornell Medical School New YorkNY10065 United States Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program New YorkNY10065 United States Martinos Center for Biomedical Imaging Harvard Medical School BostonMA02129 United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA02139 United States

Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretability means. Why does the need for interpretability in MLMI arise? What goals does one actually seek to address when interpretability is needed? To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI. By reasoning about real-world tasks and goals common in both medical image analysis and its intersection with machine learning, we identify five core elements of interpretability: localization, visual recognizability, physical attribution, model transparency, and actionability. From this, we arrive at a framework for interpretability in MLMI, which serves as a step-by-step guide to approaching interpretability in this context. Overall, this paper formalizes interpretability needs in the context of medical imaging, and our applied perspective clarifies concrete MLMI-specific goals and considerations in order to guide method design and improve real-world usage. Our goal is to provide practical and didactic information for model designers and practitioners, inspire developers of models in the medical imaging field to reason more deeply about what interpretability is achieving, and suggest future directions of interpretability research. © 2023, CC0.

关键词： Machine learning

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Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

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Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

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Factors affecting the use of Zoom app in online lectures during Covid-19 pandemic

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AIP Conference Proceedings 2023年第1期2714卷

作者： Sukri Syafrudin Kartika Handayani Ranu Agastya Nugraha Siti Lestari Lindung Parningotan Manik Computer Science Graduate Program University of Nusa Mandiri Jakarta Indonesia Research Center for Informatics National Research and Innovation Agency Jakarta Indonesia

The rapid global spread of the Covid-19 led to the utilization of online tools for various lecturing activities. Online learning is a change mandated on lecturers and students to achieve significant educational goals, irrespective of time and location. This research investigates factors affecting the use of the videoconferencing app, Zoom in supporting the online lectures. This study is quantitative research with the unified theory of acceptance and the use of technology (UTAUT) model to analyze online learning software acceptance. The research subjects consist of students and lecturers from the University of Bina Sarana Informatika (UBSI) Indonesia. The result shows that the performance and effort expectancies, as well as the social factors, affect users’ attitude towards the software use and their behavioral intention. Also, the facilitating conditions affect their attitude with no significant effect on intention.

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Multiscale Numerical Modeling for Near-Field Microwave Impedance Microscopy

Multiscale Numerical Modeling for Near-Field Microwave Imped...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Diego Tami Douglas A. A. Ohlberg Jhonattan C. Ramirez Cássio Gonçalves Do Rego Gilberto Medeiros-Ribeiro Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Brazil Microscopy Center Universidade Federal de Minas Gerais Brazil Department of Electronic Engineering Universidade Federal de Minas Gerais Brazil Computer Science Department Universidade Federal de Minas Gerais Brazil

In this work, we analyzed numerically a multiscale nanosystem based on sMIM on TBG. Spontaneous formation of a water-meniscus by the approximation between the tip-sample concentrates the microwave fields, reaching res... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Microscopy Masers Lasers and electrooptics Laser modes Numerical models Impedance Electrooptical waveguides

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Effect of Zircos-E etching as surface treatment on monotonic and fatigue strength of a 4YSZ

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Journal of the mechanical behavior of biomedical materials 2025年 170卷 107084页

作者： Duvan Cala Castillo Luiza Freitas Brum Souza Gabriel Bauken Mutlu Özcan Luiz Felipe Valandro Gabriel Kalil Rocha Pereira MSciD and PhD Post-Graduate Program in Oral Science Faculty of Dentistry Federal University of Santa Maria (UFSM) Santa Maria Rio Grande do Sul Brazil. Electronic address: duvancala@***. MSciD and PhD Post-Graduate Program in Oral Science Faculty of Dentistry Federal University of Santa Maria (UFSM) Santa Maria Rio Grande do Sul Brazil. Electronic address: luizafbrum@***. MSciD and PhD Post-Graduate Program in Oral Science Faculty of Dentistry Federal University of Santa Maria (UFSM) Santa Maria Rio Grande do Sul Brazil. Electronic address: bgabrielbauken@***. University of Zurich Center for Dental Medicine Clinic for Masticatory Disorders and Dental Biomaterials 8032 Zurich Switzerland. Electronic address: mutluozcan@***. MSciD and PhD Post-Graduate Program in Oral Science Faculty of Dentistry Federal University of Santa Maria (UFSM) Santa Maria Rio Grande do Sul Brazil. Electronic address: valandrolf@***. MSciD and PhD Post-Graduate Program in Oral Science Faculty of Dentistry Federal University of Santa Maria (UFSM) Santa Maria Rio Grande do Sul Brazil. Electronic address: gabriel.pereira@ufsm.br.

This study evaluated the effect of Zircos-E etching as a surface treatment on the monotonic and fatigue strength of 4 mol% yttria-stabilized zirconia (4YSZ), with and without resin cement. Disc-shaped specimens (n = 15 per group; Ø = 15 mm × 1.2 mm) were assigned to four surface conditions: control (no treatment), alumina air abrasion (AirAbr), Zircos-E etching for 30 min (ZE30), and for 2 h (ZE2). Each group was tested both with and without resin cement (Multilink N). Monotonic strength was measured using a biaxial flexural test, while fatigue strength was assessed via stepwise loading, beginning at 130 MPa with 30 MPa increments every 10,000 cycles. Surface roughness, topography, and fractography were also analyzed. Statistical analysis showed that surface treatment significantly influenced fatigue strength (p < 0.001), especially in cemented groups. After cementation, the AirAbr (768.2 ± 174.1 MPa) and ZE2 (757.4 ± 155.1 MPa) groups exhibited the highest fatigue strength, while the untreated control group had the lowest values (519.4 ± 88.7 MPa). Monotonic strength was significantly improved by air abrasion (894.2 ± 153.1 MPa), while Zircos-E produced intermediate results. In terms of surface roughness, ZE2 presented the highest Ra (0.99 ± 0.13 μm) and Rz values, significantly higher than the control (Ra = 0.45 ± 0.07 μm), confirming enhanced micro-retentive topography. Zircos-E etching, particularly for 2 h, effectively improved fatigue performance and roughness of 4YSZ ceramics, with outcomes comparable to conventional air abrasion. CLINICAL SIGNIFICANCE: Zircos-E treatment may offer a promising surface conditioning strategy for zirconia restorations, providing improved mechanical performance, particularly when combined with resin cement.

关键词： Air abrasion Dental materials Dental restorations Etching Fatigue strength Resin cement Surface treatment Zirconia Zircos-E

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GNOME and LBM model evaluation on ocean oil spill far-field impacts to highly sensitive areas

arXiv

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arXiv 2021年

作者： Zhang, Zhanyang Schäfer, Tobias Kress, Michael E. Doctoral Program in Computer Science The Graduate Center City University of New York 365 5th Avenue New YorkNY10016 United States Doctoral Program in Physics The Graduate Center City University of New York 365 5th Avenue New YorkNY10016 United States

In case of an ocean oil spill, there are certain areas, e.g. shrimp farms, which are highly sensitive to small amounts of oil pollution while they are geographically far from the spill. We investigate the Lattice Boltzmann Method (LBM) and GNOME, a tool developed and used by NOAA, in terms of far-field impacts to sensitive areas. We present our simulation results of both models in limited scale (a sub area of Gulf of Mexico) under the same oil spill condition using real ocean current data from the Unified Wave Interface-Coupled Model (UWIN-CM). Our study shows that the kinetic theory based LBM model outperforms the stochastic particle based GNOME model in accuracy and computation time due to their fundamental difference in representation of oil pollution advection and diffusion mechanisms. We propose LBM as a viable alternative to the Lagrangian particle calculation component of GNOME in modeling the far-field impacts to highly sensitive *** Codes 37N10, 65M75, 76R50 Copyright © 2021, The Authors. All rights reserved.

关键词： Kinetic theory

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Latent Regression and Ordination Risk of Infectious Disease and Climate

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Procedia computer science 2021年 179卷 25-32页

作者： Rezzy Eko Caraka Rung Ching Chen Youngjo Lee Prana Ugiana Gio Arif Budiarto Bens Pardamean College of Informatics Chaoyang University of Technology 168 Jifong East Road Wufong Dist. Taichung City 41349 Taiwan (R.O.C.). Department of Statistics College of Natural Science Seoul National University Gwanak-gu Seoul 08826 Korea Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan 20222 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Indonesia.

Global warming arising from climate change can increase the spread of deadly diseases. Effort is needed to develop a set of policies for the government to stem or reduce health risks from global warming. The purpose of this paper is to examine more detail and comprehensively about the relationship among climate and event disease count in Taiwan using the partial least square latent regression model. The results obtained that of the 17 types of diseases in Taiwan, that has the most significant loading factor is Amoebiasis, Malaria and Chikungunya. At the same time, climate variables that have the biggest most significant factor are Number day with max temp more than 30, Number day Temp more than 25, and Rainfall PH. Cronbach’s Alpha infectious disease 0.9696 and climate 0.2813. At the same time, the value of Dillon Goldstein’s rho infectious disease 0.974 and climate 0.6404, respectively.

关键词： Disease Counts Climate Latent PLS Regression

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Shun-Cheng Chang Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan NTU Institute of Applied Mathematical Sciences Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan NTU Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages.

关键词： Liver cancer Accuracy Computed tomography Biological system modeling Transformers Planning Biomedical imaging

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Shun-Cheng Chang Hsin-Pei Yu Yi-Hsien Hsieh Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan Institute of Applied Mathematical Sciences NTU Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag.

关键词： Visualization Accuracy Three-dimensional displays Liver Transformers Planning Convolutional neural networks Context modeling Biomedical imaging Tumors

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Topological Learning for Motion Data via Mixed Coordinates

Topological Learning for Motion Data via Mixed Coordinates

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IEEE International Conference on Big Data

作者： Hengrui Luo Jisu Kim Alice Patania Mikael Vejdemo-Johansson Lawrence Berkeley National Laboratory DataShape Team Inria Saclay and LMO Université Paris-Saclay University of Vermont CUNY College of Staten Island and Computer Science Program CUNY Graduate Center

ISBN: (纸本)9781665445993

Topology can extract the structural information in a dataset efficiently. In this paper, we attempt to incorporate topological information into a multiple output Gaussian process model for transfer learning purposes. To achieve this goal, we extend the framework of circular coordinates into a novel framework of mixed valued coordinates to take linear trends in the time series into *** of the major challenges to learn from multiple time series effectively via a multiple output Gaussian process model is constructing a functional kernel. We propose to use topologically induced clustering to construct a cluster based kernel in a multiple output Gaussian process model. This kernel not only incorporates the topological structural information, but also allows us to put forward a unified framework using topological information in time and motion series.

关键词： Measurement Adaptation models Transfer learning Time series analysis Gaussian processes Big Data Market research

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