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SSRN 2024年

作者： Shaik, Anjum Larsen, Kristoffer Lane, Nancy E. Zhao, Chen Su, Kuan-Jui Keyak, Joyce H. Tian, Qing Sha, Qiuying Shen, Hui Deng, Hong-Wen Zhou, Weihua Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Department of Mathematical Sciences Michigan Technological University HoughtonMI United States Department of Internal Medicine Division of Rheumatology UC Davis Health SacramentoCA95817 United States Department of Computer Science Kennesaw State University 680 Arntson Dr MariettaGA30060 United States Division of Biomedical Informatics and Genomics Tulane Center of Biomedical Informatics and Genomics Deming Department of Medicine Tulane University New OrleansLA70112 United States Department of Radiological Sciences Department of Biomedical Engineering Department of Mechanical and Aerospace Engineering University of California IrvineCA United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI49931 United States

Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant *** study cohort included 547 patients, with 94 experiencing hip fracture. To assess the risk of hip fracture, clinical variables and clinical variables combined with hip DXA imaging features were evaluated as predictors, followed by a novel staged approach. Hip DXA imaging features included those extracted by convolutional neural networks (CNNs), shape measurements, and texture features. Two ensemble machine learning models were evaluated: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and imaging features) using the logistic regression as the base classifier and bootstrapping for ensemble learning. The staged approach was developed using uncertainty quantification from Ensemble 1 which was used to decide if hip DXA imaging features were necessary to improve prediction for each subject. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (Auc) of 0.9541, an accuracy of 0.9195, a sensitivity of 0.8078, and a specificity of 0.9427. The staged model also performed well, with an Auc of 0.8486, an accuracy of 0.8611, a sensitivity of 0.5578, and a specificity of 0.9249, outperforming Ensemble 1, which had an Auc of 0.5549, an accuracy of 0.7239, a sensitivity of 0.1956, and a specificity of 0.8343. Furthermore, the staged model suggested that 54.49% of patients did not require DXA scanning, effectively balancing accuracy and specificity, while offering a robust solution when DXA data acquisition is not f

关键词： Machine learning

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A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture

arXiv

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arXiv 2024年

Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. We propose a novel staged model that combines advanced imaging and clinical data to improve predictive performance. By using convolutional neural networks (CNNs) to extract features from hip DXA images, along with clinical variables, shape measurements, and texture features, our method provides a comprehensive framework for assessing fracture risk. The study cohort included 547 patients, with 94 experiencing hip fracture. A staged machine learning-based model was developed using two ensemble models: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and DXA imaging features). This staged approach used uncertainty quantification from Ensemble 1 to decide if DXA features are necessary for further prediction. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (Auc) of 0.9541, an accuracy of 0.9195, a sensitivity of 0.8078, and a specificity of 0.9427. The staged model also performed well, with an Auc of 0.8486, an accuracy of 0.8611, a sensitivity of 0.5578, and a specificity of 0.9249, outperforming Ensemble 1, which had an Auc of 0.5549, an accuracy of 0.7239, a sensitivity of 0.1956, and a specificity of 0.8343. Furthermore, the staged model suggested that 54.49% of patients did not require DXA scanning. It effectively balanced accuracy and specificity, offering a robust solution when DXA data acquisition is not always feasible. Statistical tests confirmed significant differences between the models, hi

关键词： Machine learning

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Quantifying the global support network for non-state armed groups (NAGs)

arXiv

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arXiv 2021年

作者： Cai, Weiran San-Akca, Belgin Snyder, Jordan Gordon, Grayson Maoz, Zeev D’Souza, Raissa M. Department of Computer Science University of California at Davis 1 Shields Ave. DavisCA95616 United States Complexity Sciences Center UC Davis CA95616 United States Department of International Relations Koç University Istanbul34450 Turkey Department of Mathematics UC Davis CA95616 United States Department of Applied Mathematics University of Washington SeattleWA98103 United States Department of Political Sciences UC Davis 1 Shields Ave. DavisCA95616 United States Department of Mechanical and Aerospace Engineering UC Davis 1 Shields Ave. DavisCA95616 United States Santa Fe Institute 1399 Hyde Park Rd Santa FeNM87501 United States

Human history has been shaped by armed conflicts. Rather than large-scale interstate wars, low-intensity attacks have been more prevalent in the post-World War era. These attacks are often carried out by non-state armed groups (NAGs), which are supported by host states (HSs). We analyze the global bipartite network of NAG-HS support and its evolution over the period of 1945-2010. We find striking parallels to ecological networks such as mutualistic and parasitic forms of support, and a nested and modular network architecture. The nestedness emerges from preferential behaviors: highly connected players are more likely to both gain and lose connections. Long-persisting major modules are identified, reflecting both regional and trans-regional interests, which show significant turnover in their membership, contrary to the transitory ones. Revealing this architecture further enables the identification of actor’s roles and provide insights for effective intervention strategies. © 2021, CC BY.

关键词： Network architecture

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Diffusion and consensus in a weakly coupled network of networks

arXiv

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arXiv 2020年

作者： Yi, Yuhao Das, Anirban Patterson, Stacy Bamieh, Bassam Zhang, Zhongzhi Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Department of Mechanical Engineering UC Santa Barbara Santa BarbaraCA93106 United States Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China

We study diffusion and consensus dynamics in a Network of Networks model. In this model, there is a collection of sub-networks, connected to one another using a small number of links. We consider a setting where the links between networks have small weights, or are used less frequently than links within each sub-network. Using spectral perturbation theory, we analyze the diffusion rate and convergence rate of the investigated systems. Our analysis shows that the first order approximation of the diffusion and convergence rates is independent of the topologies of the individual graphs;the rates depend only on the number of nodes in each graph and the topology of the connecting edges. The second order analysis shows a relationship between the diffusion and convergence rates and the information centrality of the connecting nodes within each sub-network. We further highlight these theoretical results through numerical examples. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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Rethinking road surface 3D reconstruction and pothole detection: From perspective transformation to disparity map segmentation

arXiv

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arXiv 2020年

作者： Fan, Rui Ozgunalp, Umar Wang, Yuan Liu, Ming Pitas, Ioannis UC San Diego United States Department of Electrical and Electronics Engineering Cyprus International University Mersin Turkey Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Department of Informatics University of Thessaloniki Greece

Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety and vehicle condition. Pothole detection is typically performed by either structural engineers or certified inspectors. This task is, however, not only hazardous for the personnel but also extremely time-consuming. This paper presents an efficient pothole detection algorithm based on road disparity map estimation and segmentation. We first generalize the perspective transformation by incorporating the stereo rig roll angle. The road disparities are then estimated using semi-global matching. A disparity map transformation algorithm is then performed to better distinguish the damaged road areas. Finally, we utilize simple linear iterative clustering to group the transformed disparities into a collection of superpixels. The potholes are then detected by finding the superpixels, whose values are lower than an adaptively determined threshold. The proposed algorithm is implemented on an NVIDIA RTX 2080 Ti GPU in CUDA. The experiments demonstrate the accuracy and efficiency of our proposed road pothole detection algorithm, where an accuracy of 99.6% and an F-score of 89.4% are achieved. Copyright © 2020, The Authors. All rights reserved.

关键词： Landforms

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Towards automated pain detection in children using facial and electrodermal activity 1st

Towards automated pain detection in children using facial an...

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1st International Workshop on Artificial Intelligence in Health, AIH 2018

作者： Xu, Xiaojing Susam, Büşra Tuğçe Nezamfar, Hooman Diaz, Damaris Craig, Kenneth D. Goodwin, Matthew S. Akcakaya, Murat Huang, Jeannie S. de Sa, Virginia R. Department of Electrical and Computer Engineering UC San Diego La Jolla CA United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering Northeastern University BostonMA United States Rady Childrens Hospital and Department of Pediatrics UC San Diego La Jolla CA United States Department of Psychology University of British Columbia VancouverBC Canada Department of Health Sciences Northeastern University BostonMA United States Department of Cognitive Science UC San Diego La Jolla CA United States

ISBN: (纸本)9783030127374

Accurately determining pain levels in children is difficult, even for trained professionals and parents. Facial activity and electrodermal activity (EDA) provide rich information about pain, and both have been used in automated pain detection. In this paper, we discuss preliminary steps towards fusing models trained on video and EDA features respectively. We compare fusion models using original video features and those using transferred video features which are less sensitive to environmental changes. We demonstrate the benefit of the fusion and the transferred video features with a special test case involving domain adaptation and improved performance relative to using EDA and video features alone. © Springer Nature Switzerland AG 2019.

关键词： Electrodes

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Object Proposal Algorithms in the Wild: Are they Generalizable to Robot Perception?

Object Proposal Algorithms in the Wild: Are they Generalizab...

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

作者： Darren M. Chan Laurel D. Riek Department of Computer Science and Engineering UC San Diego La Jolla CA USA

The recent emergence of object proposal algorithms in the computer vision community shows great promise to addressing difficult problems in robotic such as object discovery and salient object detection. However, it is difficult to determine how these algorithms actually perform for real-world robot vision applications, because the standard evaluation protocol uses datasets which do not adequately account for real-world noise (motion blur, occlusion, etc.). We evaluated several state-of-the-art object proposal algorithms using naturalistic datasets from the robotics community, and found a substantial performance drop across all algorithms. This suggests that many object proposal algorithms are not as generalizable as the computer vision literature purports, which can have a significant impact on how they are applied to robotics. We also conducted a study on how each algorithm is influenced by specific kinds of real-world robot vision challenges, including variable brightness, gamma correction, Gaussian blur, and Gaussian noise. Our results provide insight into certain weaknesses of object proposal algorithms, which can be used to gauge how they might be suitable for different robotics applications. It is our intent that this work will motivate future research about how to design more flexible and robust object proposal algorithms for the robotics community.

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InFix: Automatically Repairing Novice Program Inputs

InFix: Automatically Repairing Novice Program Inputs

引用

IEEE International Conference on Automated Software engineering (ASE)

作者： Madeline Endres Georgios Sakkas Benjamin Cosman Ranjit Jhala Westley Weimer Computer Science and Engineering University of Michigan Ann Arbor MI USA Computer Science and Engineering UC San Diego La Jolla CA USA

This paper presents InFix, a technique for automatically fixing erroneous program inputs for novice programmers. Unlike comparable existing approaches for automatic debugging and maintenance tasks, InFix repairs input data rather than source code, does not require test cases, and does not require special annotations. Instead, we take advantage of patterns commonly used by novice programmers to automatically create helpful, high quality input repairs. InFix iteratively applies error-message based templates and random mutations based on insights about the debugging behavior of novices. This paper presents an implementation of InFix for Python. We evaluate on 29,995 unique scenarios with input-related errors collected from four years of data from Python Tutor, a free online programming tutoring environment. Our results generalize and scale; compared to previous work, we consider an order of magnitude more unique programs. Overall, InFix is able to repair 94.5% of deterministic input errors. We also present the results of a human study with 97 participants. Surprisingly, this simple approach produces high quality repairs; humans judged the output of InFix to be equally helpful and within 4% of the quality of human-generated repairs.

关键词： Maintenance engineering Python Debugging Syntactics Semantics Open area test sites

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A dynamical systems perspective on nesterov acceleration

arXiv

引用

arXiv 2019年

作者： Muehlebach, Michael Jordan, Michael I. Electrical Engineering and Computer Science Department UC Berkeley BerkeleyCA United States

We present a dynamical system framework for understanding Nesterov’s accelerated gradient method. In contrast to earlier work, our derivation does not rely on a vanishing step size argument. We show that Nesterov acceleration arises from discretizing an ordinary differential equation with a semi-implicit Euler integration scheme. We analyze both the underlying differential equation as well as the discretization to obtain insights into the phenomenon of acceleration. The analysis suggests that a curvature-dependent damping term lies at the heart of the phenomenon. We further establish connections between the discretized and the continuous-time dynamics. Copyright © 2019, The Authors. All rights reserved.

关键词： Dynamical systems

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Wearable activity recognition for robust human-robot teaming in safety-critical environments via hybrid neural networks

Wearable activity recognition for robust human-robot teaming...

引用

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

作者： Andrea E. Frank Alyssa Kubota Laurel D. Riek Department of Computer Science and Engineering UC San Diego La Jolla CA USA

In this work, we present a novel non-visual HAR system that achieves state-of-the-art performance on realistic SCE tasks via a single wearable sensor. We leverage surface electromyography and inertial data from a low-profile wearable sensor to attain performant robot perception while remaining unobtrusive and user-friendly. By capturing both convolutional and temporal features with a hybrid CNN-LSTM classifier, our system is able to robustly and effectively classify complex, full-body human activities with only this single sensor. We perform a rigorous analysis of our method on two datasets representative of SCE tasks, and compare performance with several prominent HAR algorithms. Results show our system substantially outperforms rival algorithms in identifying complex human tasks from minimal sensing hardware, achieving F1-scores up to 84% over 31 strenuous activity classes. To our knowledge, we are the first to robustly identify complex full-body tasks using a single, unobtrusive sensor feasible for real-world use in SCEs. Using our approach, robots will be able to more reliably understand human activity, enabling them to safely navigate sensitive, crowded spaces.

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