the proceedings contain 17 papers. the special focus in this conference is on Clinical Image-Based Procedures. the topics include: DCL preface;LL-COVID-19 preface;PPML preface;intestine Segmentation with Small Computa...
ISBN:
(纸本)9783030908737
the proceedings contain 17 papers. the special focus in this conference is on Clinical Image-Based Procedures. the topics include: DCL preface;LL-COVID-19 preface;PPML preface;intestine Segmentation with Small Computational Cost for Diagnosis Assistance of Ileus and Intestinal Obstruction;multi-task Federated Learning for Heterogeneous Pancreas Segmentation;federated Learning in the Cloud for Analysis of Medical Images - Experience with Open Source Frameworks;on the Fairness of Swarm Learning in Skin Lesion Classification;Lessons Learned from the Development and Application of Medical Imaging-Based AI Technologies for Combating COVID-19: Why Discuss, What Next;the Role of Pleura and Adipose in Lung Ultrasound AI;DuCN: Dual-Children Network for Medical Diagnosis and Similar Case Recommendation Towards COVID-19;data Imputation and Reconstruction of Distributed Parkinson’s Disease Clinical Assessments: A Comparative Evaluation of Two Aggregation algorithms;defending Medical Image Diagnostics Against Privacy Attacks Using Generative Methods: Application to Retinal Diagnostics;generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning;Bayesian Graph Neural Networks for EEG-Based Emotion Recognition;ViTBIS: Vision Transformer for Biomedical Image Segmentation;Attention-Guided Pancreatic Duct Segmentation from Abdominal CT Volumes;development of the Next Generation Hand-Held Doppler with Waveform Phasicity Predictive Capabilities Using Deep Learning;learning from Mistakes: An Error-Driven Mechanism to Improve Segmentation Performance Based on Expert Feedback.
For digital twin in current time, some applications have gradually matured. But there are still some unsatisfactory, such as t oversized production line models, the hardware requirements have also increased, so this a...
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the growing dependence on eTextbooks and Massive Open Online Courses (MOOCs) has led to an increase in the amount of students’ learning data. By carefully analyzing this data, educators can identify difficult exercis...
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ISBN:
(纸本)9781665464642
the growing dependence on eTextbooks and Massive Open Online Courses (MOOCs) has led to an increase in the amount of students’ learning data. By carefully analyzing this data, educators can identify difficult exercises, and evaluate the quality of the exercises when teaching a particular topic. In this study, an analysis of log data from the semester usage of the OpenDSA eTextbook was offered to identify the most difficult data structure course exercises and to evaluate the quality of the course exercises. Our study is based on analyzing students’ responses to the course exercises. We applied Item Response theory (IRT) analysis and a Latent Trait Mode (LTM) to identify the most difficult exercises. To evaluate the quality of the course exercises we applied the IRT theory. Our findings showed that the exercises that related to algorithm analysis topics represented the most difficult exercises, and there existing six exercises were classified as poor exercises which could be improved or need some attention.
the proceedings contain 8 papers. the topics discussed include: accelerating domain propagation: an efficient GPU-parallel algorithm over sparse matrices;parallelizing irregular computations for molecular docking;redu...
ISBN:
(纸本)9780738110905
the proceedings contain 8 papers. the topics discussed include: accelerating domain propagation: an efficient GPU-parallel algorithm over sparse matrices;parallelizing irregular computations for molecular docking;reducing queuing impact in irregular data streaming applications;supporting irregularity in throughput-oriented computing by SIMT-SIMD integration;DistDGL: distributed graph neural network training for billion-scale graphs;labeled triangle indexing for efficiency gains in distributed interactive subgraph search;distributed memory graph coloring algorithms for multiple GPU;and performance evaluation of the vectorizable binary search algorithms on an FPGA platform.
In this paper, we investigate the following problem: "given a set S of n homothetic polygons, preprocess S to efficiently report all the polygons of S containing a query point." A set of polygons is said to ...
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ISBN:
(数字)9783031343476
ISBN:
(纸本)9783031343469;9783031343476
In this paper, we investigate the following problem: "given a set S of n homothetic polygons, preprocess S to efficiently report all the polygons of S containing a query point." A set of polygons is said to be homothetic if each polygon in the set can be obtained from any other polygon of the set using scaling and translating operations. the problem is the counterpart of the homothetic range search problem discussed by Chazelle and Edelsbrunner (Chazelle, B., and Edelsbrunner, H., Linear space datastructures for two types of range search. Discrete & Computational Geometry 2, 2 (1987), 113-126). We show that after preprocessing a set of homothetic polygons with constant number of vertices, the queries can be answered in O(log n+k) optimal time, where k is the output size. the preprocessing takes O(n log n) space and time. We also study the problem in dynamic setting where insertion and deletion operations are also allowed.
Healthcare is a human right and in this complex technology driven world, healthcare industry is equipped with modern technology for the solution of disease but struggles when it conies to prevent them beforehand. Mach...
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ISBN:
(纸本)9781728127910
Healthcare is a human right and in this complex technology driven world, healthcare industry is equipped with modern technology for the solution of disease but struggles when it conies to prevent them beforehand. Machine learning can transform healthcare industry. Machine Learning provides a wide scope of apparatuses, strategies and structures to address difficulties like electronic record the executives, information combination, PC supported judgments and disease expectation. this research paper aims to predict disease accurately according to the symptoms of patients and helps doctor in better diagnosis, further reducing the cost of treatment and improving quality of life. It includes the comparative study of the outcomes and time required for analysis and prediction of disease by various machine learning algorithms and contribute towards research in healthcare department.
In this work, we propose a combined approach of model-based and machine learning techniques for damage identification in bridge structures. First, a finite element model is calibrated with real data from experimental ...
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AUTODOCK is a molecular docking software widely used in computational drug design. Its time-consuming executions have motivated the development of AUTODOCK-GPU, an OpenCL-accelerated version that can run on GPUs and C...
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ISBN:
(纸本)9781665415576
AUTODOCK is a molecular docking software widely used in computational drug design. Its time-consuming executions have motivated the development of AUTODOCK-GPU, an OpenCL-accelerated version that can run on GPUs and CPUs. this work discusses the development of AUTODOCK-GPU from a programming perspective, detailing how our design addresses the irregularity of AuToDocK while pushing towards higher performance. Details on required data transformations, re-structuring of complex functionality, as well as the performance impact of different configurations are also discussed. While AUTODOCK-GPU reaches speedup factors of 341x on a Titan V GPU and 51x on a 48-core Xeon Platinum 8175M CPU, experiments show that performance gains are highly dependent on the molecular complexity under analysis. Finally, we summarize our preliminary experiences when porting AUTODOCK onto FPGAs.
Geological evaluation and groundwater assessment, especially in arid areas, are considerable targets for constructing recent and sustainable development communities. the current work aims to apply an integrated approa...
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Geological evaluation and groundwater assessment, especially in arid areas, are considerable targets for constructing recent and sustainable development communities. the current work aims to apply an integrated approach to acquire geologic structures and groundwater potentiality at highly deformed area. As a case study, remote sensing (RS), aeromagnetic, and geoelectrical data are conducted to delineate the subsurface structures and hydrogeological regime at Aswan City. Initially, remote sensing data with GIS software are utilized to delineate the surface structures and watershed configuration. Moreover, the reduced to magnetic pole (RTP) aeromagnetic data is processed and interpreted using appropriate filters. In an attempt to demonstrate the subsurface structures and basement relief maps, the RTP map was analyzed considering the RS data which was stated in previous stage. In the light of RTP aeromagnetic results and well logging data, the direct current resistivity (DCR) sounding is executed particularly along paleochannel and flood plain portion. Due to the inversion process problem of DCR field data, advanced solutions and algorithms are applied to improve the property of the results. Based upon overall results mentioned above, the correlation between subsurface structures and aquifer formation can be monitored. the present approach can be applied for groundwater exploration in this and other similar geological and hydrogeological environments around the world.
Vibration measured data in civil engineering structures, is a physical quantity varying with time, contaminated from varied sources and affected by uncertainties not accounted for during the data acquisition. It can b...
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