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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information sciences 2021年第2期5卷 7-7页

作者： Yilmaz, Alper Wegner, Jan Dirk Remondino, Fabio Fuse, Takashi Toschi, Isabella Dept. of Civil Environmental and Geodetic Engineering The Ohio State University ColumbusOH United States EcoVision Lab Photogrammetry and Remote Sensing ETH Zurich Zurich8093 Switzerland Institute for Computational Science University of Zurich Zurich8057 Switzerland Trento Italy Dept. of Civil Engineering University of Tokyo Hongo 7-3-1Bunkyo-ku Tokyo113-8656 Japan

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computational Approaches to Explainable Artificial Intelligence: Advances Intheory, Applications and Trends

SSRN

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SSRN 2023年

作者： Górriz, Juan Manuel Álvarez-Illán, I. Álvarez-Marquina, A. Arco, J.E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S.B. Contreras, R. Cuadra, J.M. Domínguez, E. Domínguez-Mateos, F. Duro, R.J. Elizondo, D. Fernández-Caballero, A. Fernandez-Jover, E. Formoso, M.A. Gallego-Molina, N.J. Gamazo, J. García González, J. Garcia-Rodriguez, J. Garre, C. Garrigós, J. Gómez-Rodellar, A. Gómez-Vilda, P. Graña, M. Guerrero-Rodriguez, B. Hendrikse, S.C.F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J.J. Martinez-Murcia, F.J. Martínez-Tomás, R. Mekyska, J. Nalepa, G.J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M.A. Ponticorvo, M. Psarrou, A. Ramírez, J. Rincón, M. Rodellar-Biarge, V. Rodríguez-Rodríguez, I. Roelofsma, P.H.M.P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S.H. Wang, W. Zhang, Y.D. Zhu, H. Zhu, Z. Ferrández-Vicente, J.M. SiPBA at Data Science and Computational Intelligence Institute University of Granada Granada Spain University of Granada Granada Spain Dept. of Psychiatry University of Cambridge Cambridge United Kingdom Dept. of Communications Engineering University of Malaga Malaga Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Semantic Information Systems Group Osnabrück University Osnabrück Germany Osnabrück Germany Dept. of Electronics Computer Technology and Projects Universidad Politécnica de Cartagena Cartagena Spain Universitat Politècnica de València València Spain Institute of Applied Computer Science Jagiellonian University Kraków Poland Dept. of Information and Communication Engineering University of Murcia Murcia Spain Dept. of Computer Science and Information Technologies University of A Coruña A Coruña Spain Dept. of Computer Science University of Oviedo Oviedo Spain Buenos Aires Argentina Buenos Aires Argentina Eindhoven University of Technology Eindhoven5612 AZ Netherlands Dept. of Computer Science University of Malaga Malaga Spain De Montfort University Leicester United Kingdom Dept. of Computer Science Faculty of Engineering Universidad de Concepción Concepción Chile Neurocognition and Emotion Research Unit Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain Dept. of Educational Informatics Universidad de Concepción Concepción Chile Madrid Spain Dept. of Humanistic Studies University of Naples Federico II Napoli Italy Escuela Técnica Superior de Ingeniería Informática Universidad Rey Juan Carlos Campus de Móstoles Móstoles Madrid28933 Spain Neuromorphic Speech Processing Lab Center for Biomedical Technology Universidad Politécnica de Madrid Campus de Montegancedo Pozuelo de Alarcón Madrid28223 Spain Biomedical Research Networking Center in Mental Health Instituto de Salud Carlos III Madrid28016 Spain Usher Institute Faculty o

Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). DL algorithms are groundedin non-linear and complex artificial neural systems that progressively extract higher-level features from *** is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. Itis also a turning point in solving otherwise intractable problems in the design of virtual agents or robotics;orin providing novel insight into disease progress in genomics and Neuroimaging (NI), understanding humanbrain function, the automation of industrial processes based on computer vision, and so on. In this paper, themost relevant advances from the last few years in AI and several applications to neuroscience, neuroimaging,computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-ofthe-art in AI methods, models and applications within a collection of works presented at the 9th InternationalConference on the Interplay between Natural and Artificial Computation (IWINAC). The works presentedin this paper are excellent examples of new scientific discoveries made in laboratories that are successfullytransferred to real-life applications. © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

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Preface: Technical commission II

International Archives of the Photogrammetry, Remote Sensing...

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International Archives of the Photogrammetry, Remote Sensing and Spatial Information sciences - ISPRS Archives 2021年第B2-2021期43卷 7-7页

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ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data

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BRIEFINGS IN BIOINFORMATICS 2022年第6期23卷 bbac448页

作者： Yao, Yuhua Lv, Yaping Tong, Ling Liang, Yuebin Xi, Shuxue Ji, Binbin Zhang, Guanglu Li, Ling Tian, Geng Tang, Min Hu, Xiyue Li, Shijun Yang, Jialiang School of Mathematics and Statistics Hainan Normal University Haikou 570100 China Key Laboratory of Data Science and Intelligence Education Ministry of Education Hainan Normal University Haikou China Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University Haikou China Genies Beijing Co. Ltd. Beijing 100102 China Chifeng Municipal Hospital Chifeng Inner Mongolia 024000 China Qingdao Geneis Institute of Big Data Mining and Precision Medicine Qingdao 266000 China Basic Courses Department Zhejiang Shuren University Hangzhou 310000 China School of Life Sciences Jiangsu University Zhenjiang 212013 China Dept. of Colorectal Surgery National Cancer Center/ Cancer Hospital Chinese Academy of Medical Science 17 Panjiayuan Nanli Chaoyang District Beijing China 100021

Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patient is essential for the development of precision treatment. In this study, we proposed a novel multi-modal deep learning prediction model by integrating hematoxylin & eosin (H&E)-stained histopathological images, clinical information and gene expression data. Specifically, we segmented tumor regions in H&E into image blocks (256 x 256 pixels) and encoded each image block into a 1D feature vector using a deep neural network. Then, the attention module scored each area of the H&E-stained images and combined image features with clinical and gene expression data to predict the risk of recurrence and metastasis for each patient. To test the model, we downloaded all 196 breast cancer samples from the Cancer Genome Atlas with clinical, gene expression and H&E information simultaneously available. The samples were then divided into the training and testing sets with a ratio of 7: 3, in which the distributions of the samples were kept between the two datasets by hierarchical sampling. The multi-modal model achieved an area-under-the-curve value of 0.75 on the testing set better than those based solely on H&E image, sequencing data and clinical data, respectively. This study might have clinical significance in identifying high-risk breast cancer patients, who may benefit from postoperative adjuvant treatment.

关键词： breast cancer recurrence and metastasis multi-modality residual network attention module

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The Landscape of Receptor-Mediated Precision Cancer Combination Therapy: A Single-Cell Perspective

Research Square

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Research Square 2021年

作者： Ahmadi, Saba Sukprasert, Pattara Vegesna, Rahulsimham Sinha, Sanju Schischlik, Fiorella Artzi, Natalie Khuller, Samir Schäffer, Alejandro A. Ruppin, Eytan Dept. of Computer Science University of Maryland College ParkMD20742 United States Dept. of Computer Science Northwestern University EvanstonIL60208 United States Cancer Data Science Laboratory National Cancer Institute BethesdaMD20892 United States Dept. of Medicine Engineering in Medicine Division Brigham and Women’s Hospital Harvard Medical School BostonMA02139 United States Broad Institute of Harvard and MIT CambridgeMA02139 United States Institute for Medical Engineering and Science MIT CambridgeMA02139 United States

The availability of single-cell transcriptomics data opens new opportunities for rational design of combination cancer treatments. Mining such data, we employed combinatorial optimization techniques to explore the landscape of optimal combination therapies in solid tumors including brain, head and neck, melanoma, lung, breast and colon cancers. We assume that each individual therapy can target any one of 1269 genes encoding cell surface receptors, which may be targets of CAR-T, conjugated antibodies or coated nanoparticle therapies. As a baseline case, we studied the killing of at least 80% of the tumor cells while sparing more than 90% of the non-tumor cells in each patient, as a putative regimen. We find that in most cancer types, personalized combinations composed of at most four targets are then sufficient. However, the number of distinct targets that one would need to assemble to treat all patients in a cohort accordingly would be around 10 in most cases. Further requiring that the target genes be also lowly expressed in healthy tissues uncovers qualitatively similar trends. However, as one asks for more stringent and selective killing beyond the baseline regimen we focused on, we find that the number of targets needed rises rapidly. Emerging individual promising receptor targets include PTPRZ1, which is frequently found in the optimal combinations for brain and head and neck cancers, and EGFR, a recurring target in multiple tumor types. In sum, this systematic single-cell based characterization of the landscape of combinatorial receptor-mediated cancer treatments establishes first of their kind estimates on the number of targets needed, identifying promising ones for future development. © 2021, CC BY.

关键词： Combinatorial optimization

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GPU-Based Static data-Flow Analysis for Fast and Scalable Android App Vetting

GPU-Based Static Data-Flow Analysis for Fast and Scalable An...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Xiaodong Yu Fengguo Wei Xinming Ou Michela Becchi Tekin Bicer Danfeng Yao Dept. of Computer Science Virginia Tech Blacksburg VA Dept. of Computer Science and Engineering University of South Florida Tampa FL Dept. of Electrical and Computer Engineering North Carolina State University Raleigh NC Data Science and Learning Division Argonne National Laboratory Lemont IL

ISBN: (数字)9781728168760

ISBN: (纸本)9781728168777

Many popular vetting tools for Android applications use static code analysis techniques. In particular, Interprocedural data-Flow Graph (IDFG) construction is the computation at the core of Android static data-flow analysis and consumes most of the analysis time. Many analysis tools use a worklist algorithm, an iterative fixed-point approach, to construct the IDFG. In this paper, we observe that a straightforward GPU parallelization of the worklist algorithm leads to significant underutilization of the GPU resources. We identify four performance bottlenecks, namely, frequent dynamic memory allocations, high branch divergence, workload imbalance, and irregular memory access patterns. Accordingly, we propose GDroid, a GPU-based worklist algorithm implementation with multiple fine-grained optimizations tailored to common characteristics of Android applications. The optimizations considered are: matrix-based data structure, memory access-based node grouping, and worklist merging. Our experimental evaluation, performed on 1000 Android applications, shows that the proposed optimizations are beneficial to performance, and GDroid can achieve up to 128X speedups against a plain GPU implementation.

关键词： Graphics processing units Androids Humanoid robots Optimization Smart phones Tools Security

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Timber-concrete composite slab using notched connections and nails in plywood units: Experimental investigation and γ-method validation

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Construction and Building Materials 2025年 487卷

作者： Ahn, Kyung-Sun Oh, Jaewon Kim, Jiyong Kim, Min-Jeong Lee, Gwang-Ryul Pang, Sung-Jun Kim, Chul-Ki Hwang, Sung-Wook Bang, Junsik Yeo, Hwanmyeong Kwak, Hyo Won Oh, Jung-Kwon Dept. of Agriculture Forestry and Bioresources Seoul National University Gwanak-gu Seoul Korea Republic of Dept. of Wood Science University of British Columbia Vancouver Canada Dept. of Wood Science and Engineering Chonnam National University Buk-gu Gwangju Korea Republic of Wood Engineering Division National Institute of Forest Science Dongdaemun-gu Seoul Korea Republic of Human Resource Development Center for Big Data-based Glocal Forest Science 4.0 Kyungpook National University Buk-gu Daegu Korea Republic of Research Institute of Agriculture and Life Sciences Seoul National University Gwanak-gu Seoul Korea Republic of

This study investigated a novel timber-concrete composite (TCC) slab system utilizing notched connections and nails in plywood units. The TCC slab system uses multiple plywood units with grooves, which are connected to timber laminae using common nails. It was developed to address the limitations of existing TCC slabs, where the use of existing mass timber products and conventional notched connections reduces their manufacturing efficiency. The TCC slab design has a hollow-core configuration which can accommodate electrical and telecommunication wiring systems to enhance the aesthetics of the timber ceiling design. The research aims to validate the γ-method for the structural design of the TCC system. The γ-method estimation and experimental investigation of bending performance were conducted in three different configurations to increase the reliability of validation. Compared to the results of four-point bending tests, the analytical results showed good agreement while predicting accurate failure modes, with differences of up to 4.73 %, 7.53 %, and 5.22 % for the effective bending stiffness, load-bearing capacity, and composite efficiency, respectively. The minor differences and accurate prediction of failure modes validated the applicability of the γ-method for predicting the structural performance of the novel TCC system. © 2025 The Authors

关键词： Concrete slabs

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Impact of baseline drift removal on ECG beat classification and alignment

Impact of baseline drift removal on ECG beat classification ...

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2021 Computing in Cardiology, CinC 2021

作者： Bear, L.R. Svehlikova, J. Bergquist, J.A. Good, W.W. Rababah, A. Coll-Font, J. MacLeod, R.S. Van Dam, E. Dubois, R. IHU-LIRYC Fondation Bordeaux Université France Université de Bordeaux Inserm U1045 Y CRCTB France Institute of Measurement Science Slovak Academy of Sciences Bratislava Slovakia University of Utah Salt Lake City Dept. of Biomedical Engineering and SCI Institute UT United States Acutus Medical CarlsbadCA United States School of Engineering Ulster University Northern Ireland United Kingdom Computational Radiology Laboratory Children's Hospital BostonMA United States Peacs BV Nieuwerbrug aan den Rijn Netherlands

ISBN: (纸本)9781665479165

Accurate beat classification and alignment is fundamental to any signal averaging method. The objective of this study was to investigate the accuracy of different beat classification and alignment methods, and the impact of pre-processing methods on these algorithms. Experimental data came from a human-shaped torso tank, with 256 body surface ECG recorded during sinus rhythm (SR) and left ventricular pacing (LVP) (n=4). 'Gold-standard' classification and alignment were defined from recorded cardiac electrograms. Six different methods of baseline drift removal (BDR) were applied to ECG. Subsequently, 3 different beat segmentation methods were used to extract QRS complexes and align them, and four different beat classification methods. Pre-processing methods had only a small impact on beat classification and alignment compared to the segmentation and classification methods themselves. However, baseline drift removal over the whole QRS does appears to be important in providing the most accurate final averaged beat. © 2021 Creative Commons.

关键词： Alignment

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Expected uncertainty reduction for sequential kriging-based reliability analysis

Expected uncertainty reduction for sequential kriging-based ...

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ASME 2020 International Design engineering Technical Conferences and Computers and Information in engineering Conference, IDETC-CIE 2020

作者： Li, Meng Shen, Sheng Barzegar, Vahid Sadoughi, Mohammadkazem Laflamme, Simon Hu, Chao Department of Mechanical Engineering Iowa State University AmesIA United States Corporate Engineering Data Science National Oilwell Vargo HoustonTX United States Dept. of Civil Construction and Environmental Engineering and Electrical and Computer Egineering Iowa State University AmesIA United States Departments of Mechanical Engineering and Electrical and Computer Engineering Iowa State University AmesIA United States

ISBN: (纸本)9780791884003

Several acquisition functions have been proposed to identify an optimal sequence of samples in sequential kriging-based reliability analysis. However, no single acquisition function provides better performance over the others in all cases. To address this problem, this paper proposes a new acquisition function, namely expected uncertainty reduction (EUR), that serves as a meta-criterion to select the best sample from a set of optimal samples, each identified from a large number of candidate samples according to the criterion of an acquisition function. EUR directly quantifies the expected reduction of the uncertainty in the prediction of limit-state function by adding an optimal sample. The uncertainty reduction is quantified by sampling over the kriging posterior. In the proposed EUR-based sequential sampling framework, a portfolio that consists of four acquisition functions is first employed to suggest four optimal samples at each iteration of sequential sampling. Then, EUR is employed as the meta-criterion to identify the best sample among those optimal samples. The results from two mathematical case studies show that (1) EUR-based sequential sampling can perform as well as or outperform the single use of any acquisition function in the portfolio, and (2) the best-performing acquisition function may change from one problem to another or even from one iteration to the next within a problem. © 2020 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Reliability analysis

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Advancing eosinophilic esophagitis diagnosis and phenotype assessment with deep learning computer vision 8

Advancing eosinophilic esophagitis diagnosis and phenotype a...

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8th International Conference on Bioimaging, BIOIMAGING 2021 - Part of the 14th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2021

作者： Adorno, William Catalano, Alexis Ehsan, Lubaina von Eckstaedt, Hans Vitzhum Barnes, Barrett McGowan, Emily Syed, Sana Brown, Donald E. Dept. of Engineering Systems and Environment University of Virginia CharlottesvilleVA United States College of Dental Medicine Columbia University New York CityNY United States School of Medicine University of Virginia CharlottesvilleVA United States Department of Pediatrics School of Medicine University of Virginia CharlottesvilleVA United States Department of Medicine University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia CharlottesvilleVA United States

ISBN: (纸本)9789897584909

Eosinophilic Esophagitis (EoE) is an inflammatory esophageal disease which is increasing in prevalence. The diagnostic gold-standard involves manual review of a patient's biopsy tissue sample by a clinical pathologist for the presence of 15 or greater eosinophils within a single high-power field (400× magnification). Diagnosing EoE can be a cumbersome process with added difficulty for assessing the severity and progression of disease. We propose an automated approach for quantifying eosinophils using deep image segmentation. A U-Net model and post-processing system are applied to generate eosinophil-based statistics that can diagnose EoE as well as describe disease severity and progression. These statistics are captured in biopsies at the initial EoE diagnosis and are then compared with patient metadata: clinical and treatment phenotypes. The goal is to find linkages that could potentially guide treatment plans for new patients at their initial disease diagnosis. A deep image classification model is further applied to discover features other than eosinophils that can be used to diagnose EoE. This is the first study to utilize a deep learning computer vision approach for EoE diagnosis and to provide an automated process for tracking disease severity and progression. © 2021 by SCITEPRESS - science and Technology Publications, Lda. All rights reserved

关键词： Image segmentation

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