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arXiv 2021年

作者： Soni, Utkarsh Sreedharan, Sarath Kambhampati, Subbarao School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

There is a growing interest in designing autonomous agents that can work alongside humans. Such agents will undoubtedly be expected to explain their behavior and decisions. While generating explanations is an actively researched topic, most works tend to focus on methods that generate explanations that are one size fits all. As in the specifics of the usermodel are completely ignored. The handful of works that look at tailoring their explanation to the user's background rely on having specific models of the users (either analytic models or learned labeling models). The goal of this work is thus to propose an end-to-end adaptive explanation generation system that begins by learning the different types of users that the agent could interact with. Then during the interaction with the target user, it is tasked with identifying the type on the fly and adjust its explanations accordingly. The former is achieved by a data-driven clustering approach while for the latter, we compile our explanation generation problem into a POMDP. We demonstrate the usefulness of our system on two domains using state-of-the-art POMDP solvers. We also report the results of a user study that investigates the benefits of providing personalized explanations in a human-robot interaction setting. Copyright © 2021, The Authors. All rights reserved.

关键词： Human robot interaction

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Learning causal models of autonomous agents using interventions

arXiv

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arXiv 2021年

作者： Verma, Pulkit Srivastava, Siddharth School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

One of the several obstacles in the widespread use of AI systems is the lack of requirements of interpretability that can enable a layperson to ensure the safe and reliable behavior of such systems. We extend the analysis of an agent assessment module that lets an AI system execute high-level instruction sequences in simulators and answer the user queries about its execution of sequences of actions. We show that such a primitive queryresponse capability is sufficient to efficiently derive a user-interpretable causal model of the system in stationary, fully observable, and deterministic settings. We also introduce dynamic causal decision networks (DCDNs) that capture the causal structure of STRIPS-like domains. A comparative analysis of different classes of queries is also presented in terms of the computational requirements needed to answer them and the efforts required to evaluate their responses to learn the correct model. Copyright © 2021, The Authors. All rights reserved.

关键词： Autonomous agents

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KartalOl: a new deep neural network framework based on transfer learning for iris segmentation and localization task—new dataset for iris segmentation

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Iran Journal of Computer Science 2023年第4期6卷 307-319页

作者： Khiarak, Jalil Nourmohammadi Oskouei, Amin Golzari Nasab, Samaneh Salehi Jaryani, Farhang Moafinejad, Seyed Naeim Pourmohamad, Rana Amini, Yasin Noshad, Morteza Institute of Control and Computation Engineering Warsaw University of Technology Ul. Nowowiejska 15/19 Warsaw 00-665 Poland Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran School of Engineering Lorestan University Aleshtar Campus Aleshtar Iran Faculty of Engineering Arak University Arak Iran Department of Physics Shahid Beheshti University Tehran Iran School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Faculty of Electrical and Computer Engineering University of Kharazmi Tehran Iran Stanford University Stanford United States

Iris segmentation and localization in unconstrained environments are challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literature have somehow mitigated some of the above-mentioned issues. In this paper, motivated by these weaknesses, we propose a framework that employs a deep neural network-based approach to iris segmentation and localization. The proposed framework is based on a U-Net architecture initialized with a pre-trained MobileNetV2 model. In addition, to better study the detectors in iris recognition scenarios, we have collected 1000 images. The provided dataset (KartalOl) is made publicly available for the research community. In the proposed framework, to have better generalization, we fine-tuned the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. Likewise, data augmentation techniques are applied on images. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed framework is trained and tested in CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa, along with the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The implementation source code of KartalOl is made publicly available at https://***/Jalilnkh/KartalOl-NIR-ISL2021031301. © The Author(s) 2023.

关键词： Iris segmentation Localization MobileNetV2 model Unconstrained environment

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Reconstructing Retinal Visual Images from 3T fMRI Data Enhanced by Unsupervised Learning

Reconstructing Retinal Visual Images from 3T fMRI Data Enhan...

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IEEE International Symposium on Biomedical Imaging

作者： Yujian Xiong Wenhui Zhu Zhong-Lin Lu Yalin Wang School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA Division of Arts and Sciences NYU Shanghai Shanghai China Center for Neural Science and Department of Psychology New York University New York USA NYU Shanghai NYU-ECNU Institute of Brain and Cognitive Science Shanghai China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The reconstruction of human visual inputs from brain activity, particularly through functional Magnetic Resonance Imaging (fMRI), holds promising avenues for unraveling the mechanisms of the human visual system. Despite the significant strides made by deep learning methods in improving the quality and interpretability of visual reconstruction, there remains a substantial demand for high-quality, long-duration, subject-specific 7-Tesla fMRI experiments. The challenge arises in integrating diverse smaller 3-Tesla datasets or accommodating new subjects with brief and low-quality fMRI scans. In response to these constraints, we propose a novel framework that generates enhanced 3T fMRI data through an unsupervised Generative Adversarial Network (GAN), leveraging unpaired training across two distinct fMRI datasets in 7T and 3T, respectively. This approach aims to overcome the limitations of the scarcity of high-quality 7-Tesla data and the challenges associated with brief and low-quality scans in 3-Tesla experiments. In this paper, we demonstrate the reconstruction capabilities of the enhanced 3T fMRI data, highlighting its proficiency in generating superior input visual images compared to data-intensive methods trained and tested on a single subject.

关键词： Training Visualization Functional magnetic resonance imaging Visual systems Generative adversarial networks Data models Task analysis

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Enhancing 3T Retinotopic Maps Using Diffeomorphic Registration

Enhancing 3T Retinotopic Maps Using Diffeomorphic Registrati...

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IEEE International Symposium on Biomedical Imaging

作者： Negar Jalili-Mallak Yanshuai Tu Zhong-Lin Lu Yalin Wang School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA Division of Arts and Sciences NYU Shanghai Shanghai China Center for Neural Science and Department of Psychology New York University New York USA NYU-ECNU Institute of Brain and Cognitive Science NYU Shanghai Shanghai China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Retinotopic mapping aims to uncover the relationship between visual stimuli on the retina and neural responses on the visual cortical surface. This study advances retinotopic mapping by applying diffeomorphic registration to the 3T NYU retinotopy dataset, encompassing analyze-PRF and mrVista data. Diffeomorphic Registration for Retinotopic Maps (DRRM) quantifies the diffeomorphic condition, ensuring accurate alignment of retinotopic maps without topological violations. Leveraging the Beltrami coefficient and topological condition, DRRM significantly enhances retinotopic map accuracy. Evaluation against existing methods demonstrates DRRM’s superiority on various datasets, including 3T and 7T retinotopy data. The application of diffeomorphic registration improves the interpretability of low-quality retinotopic maps, holding promise for clinical applications.

关键词： Visualization Accuracy Oxygen Time series analysis Functional magnetic resonance imaging Retina Iterative methods

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On designing experiments for a dynamic response modeled by regression splines

On designing experiments for a dynamic response modeled by r...

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作者： Pan, Rong Saleh, Moein School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States PayPal San Jose United States

Dynamic response systems are often found in science, engineering, and medical applications, but the discussion on experimental design for such a system is relatively rare in literature. For an experimenter, designing such experiments requires making decisions on (1) when or where to take response measurements along the dynamic variable and (2) how to choose the combination of experimental factors and their levels. The first consideration is unique for such experiments, especially when the measurement cost is high. In this paper, we present a design approach through the mixed-effect linear model, which is based on a hierarchical B-spline function for the dynamic response. We develop several theorems that can assist in finding a statistically efficient sampling plan and propose an algorithm for searching the D-optimal design of a dynamic response system. © 2019 John Wiley & Sons, Ltd.

关键词： Dynamic response

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Coupling Weap and Leap Models Using Interaction Modeling

Coupling Weap and Leap Models Using Interaction Modeling

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2020 Spring Simulation Conference, SpringSim 2020

作者： Fard, Mostafa D. Sarjoughian, Hessam S. Arizona State University Arizona Center for Integrative Modeling Simulation School of Computing Informatics and Decision Systems Engineering TempeAZ United States

ISBN: (纸本)9781565553705

It is useful to model the interactions between the water and energy models separately. A coupled water and energy model using an interaction model lends itself to developing flexible, yet rigorous predictive simulation studies. A WEAP-LEAP interaction model is developed based on the Knowledge Interchange Broker modeling approach. A RESTful simulation framework is developed for coupling the Componentized-WEAP and Componentized-LEAP systems. Every interaction model is defined to have a set of modules where each module has its input and output ports with data transformation schemes. The input and output ports of the Componentized-WEAP and Componentized-LEAP models are coupled with those of the interaction model. A cyclic, synchronous execution protocol is defined for concurrent, bidirectional data transformations. The computational efficiency of this loosely coupled water-energy framework is acceptable compared with the tightly WEAP-LEAP system. This proposed framework shows understanding the water-energy system nexus is advantageous using interaction modeling. © 2020 SCS.

关键词： Computational efficiency

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A Stable Analytical Solution Method for Car-Like Robot Trajectory Tracking and Optimization

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IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 39-47页

作者： Keyvan Majd Mohammad Razeghi-Jahromi Abdollah Homaifar School of Computing Informaticsand Decision Systems EngineeringArizona State UniversityTempeAZ 85281 USA ABB Corporate Research Center RaleighNC 27606 USA Department of Electrical and Computer Engineering North Carolina A&T State UniversityGreensboroNC 27403 USA

In this paper, the car-like robot kinematic model trajectory tracking and control problem is revisited by exploring an optimal analytical solution which guarantees the global exponential stability of the tracking error. The problem is formulated in the form of tracking error optimization in which the quadratic errors of the position, velocity, and acceleration are minimized subject to the rear-wheel car-like robot kinematic model. The input-output linearization technique is employed to transform the nonlinear problem into a linear formulation. By using the variational approach, the analytical solution is obtained, which is guaranteed to be globally exponentially stable and is also appropriate for real-time applications. The simulation results demonstrate the validity of the proposed mechanism in generating an optimal trajectory and control inputs by evaluating the proposed method in an eight-shape tracking scenario.

关键词： Global asymptotic stability input-output linearization optimal control trajectory tracking

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Impact of Cardiac Dose on Overall Survival in Lung Stereotactic Body Radiotherapy (SBRT) Compared to Conventionally Fractionated Radiotherapy for Locally Advanced Non-Small Cell Lung Cancer (LA-NSCLC)

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Journal of Cancer Therapy 2021年第7期12卷 409-423页

作者： Justin D. Anderson Jiuyun Hu Jing Li Steven E. Schild Mirek Fatyga Department of Radiation Oncology Mayo Clinic Phoenix Arizona USA School of Computing Informatics and Decision Systems Engineering Tempe Arizona USA School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta Georgia USA

Purpose: To examine possible association between heart irradiation and Overall Survival (OS) in lung SBRT patients and to compare observed associations with cardiac toxicity models previously derived in LA-NSCLC patie... 详细信息

Purpose: To examine possible association between heart irradiation and Overall Survival (OS) in lung SBRT patients and to compare observed associations with cardiac toxicity models previously derived in LA-NSCLC patient studies. Materials and Methods: 197 Patients treated with lung SBRT at Mayo Clinic Arizona were selected for this IRB-approved study. Multivariate Cox model with Akaike Information Criterion (AIC) was used to select patient specific covariates associated with OS. Heart dosimetry was represented by V_D indices, which is a percentage of volume exposed to dose D or greater. Multivariate Cox models with patient specific cova

关键词： Lung Cancer Lung SBRT Cardiac Toxicity Lung Radiation Therapy Non-Small Cell Lung Cancer

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ENHANCING 3T RETINOTOPIC MAPS USING DIFFEOMORPHIC REGISTRATION

arXiv

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arXiv 2024年

作者： Jalili-Mallak, Negar Tu, Yanshuai Lu, Zhong-Lin Wang, Yalin School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Division of Arts and Sciences NYU Shanghai Shanghai China Center for Neural Science Department of Psychology New York University New York United States NYU-ECNU Institute of Brain and Cognitive Science NYU Shanghai Shanghai China

关键词： Mapping

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