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arXiv 2021年

作者： Rocha, Luis M. Department of Systems Science and Industrial Engineering Binghamton University State University of New York BinghamtonNY United States Instituto Gulbenkian de Ciência Oeiras2780-156 Portugal Center for Social and Biomedical Complexity Luddy School of Informatics Computing & Engineering Indiana University BloomingtonIN United States

A recent article by Weidner et al. [2021] presents a method to extract graph properties that are predictive of the dynamical behavior of multivariate, discrete models of biochemical regulation. In other words, a method that uses only features from the structure of network interactions to predict which nodes are most involved in automata network dynamics. However, the authors claim that dynamical analysis of large automata network models is "not even feasible." To make sure that others are not discouraged from working on this problem, it is important to clarify that effective dynamical analysis of automata network models, to the contrary, is feasible. Unlike what is suggested in the article, graph-based analysis of static features is not the only analytical avenue for large systems biology models of regulation and signaling dynamics because there are dynamical methods that are, indeed, scalable. © 2021, CC BY-SA.

关键词： Graphic methods

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Detecting optimal gaze behavior of successful basketball free throwing via machine learning system

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Human Movement Science 2025年 102卷

作者： Ayoub Asadi Afkham Daneshfar Mohammad R. Saeedpour-Parizi Christopher A. Aiken Ann Smiley Department of Motor Behavior Faculty of Sport Sciences Alzahra University Tehran Iran Department of Kinesiology Iowa State University Ames IA USA Department of Intelligent Systems Engineering Luddy School of Informatics Computing and Engineering Indiana University Bloomington Bloomington IN USA Department of Kinesiology New Mexico State University USA

Eye tracking in sport is an emerging field that explores the relationships between visual function and motor performance. However, research has shown that visual behaviors are distinct enough to detect superior performance; and serve as a suitable input for designing machine learning systems, few study has been tested yet the eye tracking machine learning in sport tasks. The current research investigated the eye movement behaviors for detecting successful performance using machine learning. The gaze behavior of 25 student basketball players during the hit and miss free- throwing's trials was collected and analyzed by statistical (JMP pro) and machine learning (Python) approaches. Results showed significant differences between saccade duration in hit and miss trials. In previous studies of free throwing, fixations were used as a measure of visual information processing, but our results showed that the metrics related to saccades were more important for successful performance than those related to fixations. These findings highlight the importance of eye tracking machine learning in sport domain and suggest that successful performance can be reliably predicted from performers' eye movement data. Our results provide primary insights as well as inspiration for future research focusing on developing eye-tracking machine learning systems to detect proficiency in motor skills.

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Rapidly-exploring Random Trees for Testing Automated Vehicles

Rapidly-exploring Random Trees for Testing Automated Vehicle...

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International Conference on Intelligent Transportation

作者： Cumhur Erkan Tuncali Georgios Fainekos School of Computing Informatics and Decision Systems Arizona State University Tempe AZ USA

ISBN: (纸本)9781538670255

One of the expectations from fully or partially automated vehicles is to never cause an accident and actively avoid dangerous situations. However, an automated vehicle may not be able to avoid all collisions, e.g., collisions caused by other vehicles. Hence, it is important for the system developers to understand the boundary case scenarios where an autonomous vehicle can no longer avoid a collision. In this paper, an automated test generation approach that utilizes Rapidly-exploring Random Trees is presented to explore these boundary scenarios. An important advantage of the approach is the openness of the test scenarios: one can set the road geometry and the number of adversarial objects and let the system search for interesting trajectories and environment parameters. A cost function is proposed which guides the test generation toward almost-avoidable collisions or near-misses.

关键词： Roads Test pattern generators Geometry Trajectory Cost function Safety

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Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers

Endocrine oncology (Bristol, England)

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Endocrine oncology (Bristol, England) 2025年第1期5卷 e240025页

作者： John Metzcar Rachael Guenter Yafei Wang Kimberly M Baker Kate E Lines Department of Intelligent Systems Engineering Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana USA. Department of Informatics Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana USA. Therapy Modeling and Development Center University of Minnesota-Twin Cities Minneapolis Minnesota USA. Department of Surgery Heersink School of Medicine University of Alabama at Birmingham Birmingham Alabama USA. Department of Biology Shaheen College of Arts and Sciences University of Indianapolis Indianapolis Indiana USA. OCDEM Radcliffe Department of Medicine University of Oxford Churchill Hospital Oxford UK. Department of Medical and Biological Sciences Oxford Brookes University Oxford UK.

Neuroendocrine tumors (NETs) occur sporadically or as part of rare endocrine tumor syndromes (RETSs) such as multiple endocrine neoplasia 1 and von Hippel-Lindau syndromes. Due to their relative rarity and lack of model systems, NETs and RETSs are difficult to study, hindering advancements in therapeutic development. Causal or mechanistic mathematical modeling is widely deployed in disease areas such as breast and prostate cancers, aiding the understanding of observations and streamlining and modeling efforts. Mathematical modeling, while not yet widely utilized in NET research, offers an opportunity to accelerate NET research and therapy development. To illustrate this, we highlight examples of how mathematical modeling associated with more common endocrine cancers has been successfully used in the preclinical, translational and clinical settings. We also provide a scope of the limited work that has been done in NETs and map how these techniques can be utilized in NET research to address specific outstanding challenges in the field. Finally, we include practical details such as hardware and data requirements, present advantages and disadvantages of various mathematical modeling approaches and discuss challenges of using mathematical modeling. Through a cross-disciplinary approach, we believe that many currently difficult problems can be made more tractable by applying mathematical modeling and that the field of rare diseases in endocrine oncology is well poised to take advantage of these techniques.

关键词： clinical mathematical oncology modeling multiple neuroendocrine neoplasia neuroendocrine tumors systems biology virtual clinical trials

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Visual programming language environment for different IoT and robotics platforms in computer science education

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CAAI Transactions on Intelligence Technology 2018年第2期3卷 119-130页

作者： Gennaro De Luca Zhongtao Li Sami Mian Yinong Chen School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85287-8809 USA School of Information Science and Engineering University of Jinan Shandong People's Republic of China

This study presents the authors＇ recent research and application of a new visual programming language and its development environment： VIPLE （Visual IoT/Robotics Programming Language Environment） at Arizona State University （ASU）. ASU VIPLE supports a variety of loT devices and robots based on an open architecture. Based on computational thinking, VIPLE supports the integration of engineering design process, workflow, fundamental programming concepts, control flow, parallel computing, event-driven programming, and service-oriented computing seamlessly into a wide range of curricula, such as introduction to computing, introduction to engineering, service-oriented computing, and software integration. It is actively used at ASU in several sections of FSE 100： Introduction to engineering and in CSE 446： Software Integration and engineering, as well as in several other universities worldwide.

关键词：工作流程编程概念计算机技术人工智能

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Learning Interactive Behaviors for Musculoskeletal Robots Using Bayesian Interaction Primitives

Learning Interactive Behaviors for Musculoskeletal Robots Us...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Joseph Campbell Arne Hitzmann Simon Stepputtis Shuhei Ikemoto Koh Hosoda Heni Ben Amor school of Computing Informatics and Decision Systems Engineering Arizona State University Graduate School of Engineering Science Osaka University Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology

Musculoskeletal robots that are based on pneumatic actuation have a variety of properties, such as compliance and back-drivability, that render them particularly appealing for human-robot collaboration. However, programming interactive and responsive behaviors for such systems is extremely challenging due to the nonlinearity and uncertainty inherent to their control. In this paper, we propose an approach for learning Bayesian Interaction Primitives for musculoskeletal robots given a limited set of example demonstrations. We show that this approach is capable of real-time state estimation and response generation for interaction with a robot for which no analytical model exists. Human-robot interaction experiments on a 'handshake' task show that the approach generalizes to new positions, interaction partners, and movement velocities.

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An Adaptive Robustness Evolution Algorithm with Self-Competition for Scale-Free Internet of Things

An Adaptive Robustness Evolution Algorithm with Self-Competi...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Tie Qiu Zilong Lu Keqiu Li Guoliang Xue Dapeng Oliver Wu College of Intelligence and Computing Tianjin University Tianjin China School of Software Dalian University of Technology Dalian China School of Computing Information and Decision Systems Engineering Arizona State University Tempe AZ USA Department of Electrical and Computer Engineering University of Florida Gainesville FL USA

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

Internet of Things (IoT) includes numerous sensing nodes that constitute a large scale-free network. Optimizing the network topology for increased resistance against malicious attacks is an NP-hard problem. Heuristic algorithms, particularly genetic algorithms, can effectively cope with such problems. However, conventional genetic algorithms are prone to falling into premature convergence owing to the lack of global search ability caused by the loss of population diversity during evolution. Although this can be alleviated by increasing population size, additional computational overhead will be incurred. Moreover, after crossover and mutation operations, individual changes in the population are mixed, and loss of optimal individuals may occur, which will slow down the evolution of the population. Therefore, we combine the population state with the evolutionary process and propose an Adaptive Robustness Evolution Algorithm (AREA) with self-competition for scale-free IoT topologies. In AREA, the crossover and mutation operations are dynamically adjusted according to population diversity to ensure global search ability. Moreover, a self-competitive mechanism is used to ensure convergence. The simulation results demonstrate that AREA is more effective in improving the robustness of scale-free IoT networks than several existing methods.

关键词： Sociology Statistics Robustness Network topology Topology Convergence Measurement

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Assessing the validity of saliency maps for abnormality localization in medical imaging

arXiv

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arXiv 2020年

作者： Thumbavanam Arun, Nishanth Gaw, Nathan Singh, Praveer Chang, Ken Hoebel, Katharina Viktoria Patel, Jay Gidwani, Mishka Kalpathy-Cramer, Jayashree Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital BostonMA United States Arizona State University Mayo Clinic Center for Innovative Imaging School of Computing Informatics and Decision Systems Engineering TempeAZ United States

Saliency maps have become a widely used method to assess which areas of the input image are most pertinent to the prediction of a trained neural network. However, in the context of medical imaging, there is no study to our knowledge that has examined the efficacy of these techniques and quantified them using overlap with ground truth bounding boxes. In this work, we explored the credibility of the various existing saliency map methods on the RSNA Pneumonia dataset. We found that GradCAM was the most sensitive to model parameter and label randomization, and was highly agnostic to model architecture. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

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Learning interactive behaviors for musculoskeletal robots using bayesian interaction primitives

arXiv

引用

arXiv 2019年

作者： Campbell, Joseph Hitzmann, Arne Stepputtis, Simon Ikemoto, Shuhei Hosoda, Koh Amor, Heni Ben School of Computing Informatics Decision Systems Engineering Arizona State University Graduate School of Engineering Science Osaka University Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology

关键词： Human robot interaction

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Residual2Vec: Debiasing Graph embedding with random graphs

arXiv

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arXiv 2021年

作者： Kojaku, Sadamori Yoon, Jisung Constantino, Isabel Ahn, Yong-Yeol Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University United States Department of Industrial Management and Engineering Pohang University of Science and Technology Korea Republic of Indiana University Network Science Institute Indiana University United States Connection Science Massachusetts Institute of Technology United States

Graph embedding maps a graph into a convenient vector-space representation for graph analysis and machine learning applications. Many graph embedding methods hinge on a sampling of context nodes based on random walks. However, random walks can be a biased sampler due to the structural properties of graphs. Most notably, random walks are biased by the degree of each node, where a node is sampled proportionally to its degree. The implication of such biases has not been clear, particularly in the context of graph representation learning. Here, we investigate the impact of the random walks' bias on graph embedding and propose residual2vec, a general graph embedding method that can debias various structural biases in graphs by using random graphs. We demonstrate that this debiasing not only improves link prediction and clustering performance but also allows us to explicitly model salient structural properties in graph embedding. © 2021, CC BY.

关键词： Random processes

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