1,721,018 research outputs found
Physics-aware machine learning surrogates for real-time manufacturing digital twin
Full text linkIn this Manufacturing Blue Sky idea, we envision a Cyber Adaptive Manufacturing Intelligent System (CyAMIS, pronounced Siamese) that integrates concepts from the emerging area of physics-aware machine learning (ML) to formulate, develop, and deploy a near-real-time Siamese (digital) twin to reliably and efficiently achieve exceptional part quality and desired material properties in additive manufacturing processes. We believe such a real-time digital twin framework to be the future of modern additive manufacturing systems, ultimately leading to Manufacturing 5.0 systems.This is a manuscript of the article published as Balu, Aditya, Soumik Sarkar, Baskar Ganapathysubramanian, and Adarsh Krishnamurthy. "Physics-aware machine learning surrogates for real-time manufacturing digital twin." Manufacturing Letters 34 (2022): 71-74. doi: https://doi.org/10.1016/j.mfglet.2022.08.013
Decentralized Deep Learning Using Momentum-Accelerated Consensus
Full text linkWe consider the problem of decentralized deep learning where multiple agents collaborate to learn from a distributed dataset. While there exist several decentralized deep learning approaches, the majority consider a central parameter-server topology for aggregating the model parameters from the agents. However, such a topology may be inapplicable in networked systems such as ad-hoc mobile networks, field robotics, and power network systems where direct communication with the central parameter server may be inefficient. In this context, we propose and analyze a novel decentralized deep learning algorithm where the agents interact over a fixed communication topology (without a central server). Our algorithm is based on the heavy-ball acceleration method used in gradient-based optimization. We propose a novel consensus protocol where each agent shares with its neighbors its model parameters as well as gradient-momentum values during the optimization process. We consider both strongly convex and non-convex objective functions and theoretically analyze our algorithm's performance. We present several empirical comparisons with competing decentralized learning methods to demonstrate the efficacy of our approach under different communication topologies.This is a preprint from Balu, Aditya, Zhanhong Jiang, Sin Yong Tan, Chinmay Hedge, Young M. Lee, and Soumik Sarkar. "Decentralized Deep Learning using Momentum-Accelerated Consensus." arXiv preprint arXiv:2010.11166 (2020). doi: https://doi.org/10.48550/arXiv.2010.11166. Published as Balu, Aditya, Zhanhong Jiang, Sin Yong Tan, Chinmay Hedge, Young M. Lee, and Soumik Sarkar. "Decentralized deep learning using momentum-accelerated consensus." In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3675-3679. IEEE, 2021. doi: https://doi.org/10.1109/ICASSP39728.2021.9414564
A Deep Learning Framework for Design and Analysis of Surgical Bioprosthetic Heart Valves
Full text linkBioprosthetic heart valves (BHVs) are commonly used as heart valve replacements but they are prone to fatigue failure; estimating their remaining life directly from medical images is difficult. Analyzing the valve performance can provide better guidance for personalized valve design. However, such analyses are often computationally intensive. In this work, we introduce the concept of deep learning (DL) based finite element analysis (DLFEA) to learn the deformation biomechanics of bioprosthetic aortic valves directly from simulations. The proposed DL framework can eliminate the time-consuming biomechanics simulations, while predicting valve deformations with the same fidelity. We present statistical results that demonstrate the high performance of the DLFEA framework and the applicability of the framework to predict bioprosthetic aortic valve deformations. With further development, such a tool can provide fast decision support for designing surgical bioprosthetic aortic valves. Ultimately, this framework could be extended to other BHVs and improve patient care.This article is published as Balu, Aditya, Sahiti Nallagonda, Fei Xu, Adarsh Krishnamurthy, Ming-Chen Hsu, and Soumik Sarkar. "A Deep Learning Framework for Design and Analysis of Surgical Bioprosthetic Heart Valves." Scientific Reports 9 (2019): 18560. DOI: 10.1038/s41598-019-54707-9. Posted with permission.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Distributed Multigrid Neural Solvers on Megavoxel Domains
Full text linkWe consider the distributed training of large-scale neural networks that serve as PDE solvers producing full field outputs. We specifically consider neural solvers for the generalized 3D Poisson equation over megavoxel domains. A scalable framework is presented that integrates two distinct advances. First, we accelerate training a large model via a method analogous to the multigrid technique used in numerical linear algebra. Here, the network is trained using a hierarchy of increasing resolution inputs in sequence, analogous to the 'V', 'W', 'F', and 'Half-V' cycles used in multigrid approaches. In conjunction with the multi-grid approach, we implement a distributed deep learning framework which significantly reduces the time to solve. We show the scalability of this approach on both GPU (Azure VMs on Cloud) and CPU clusters (PSC Bridges2). This approach is deployed to train a generalized 3D Poisson solver that scales well to predict output full-field solutions up to the resolution of 512x512x512 for a high dimensional family of inputs.This is a pre-print of the article Balu, Aditya, Sergio Botelho, Biswajit Khara, Vinay Rao, Chinmay Hegde, Soumik Sarkar, Santi Adavani, Adarsh Krishnamurthy, and Baskar Ganapathysubramanian. "Distributed Multigrid Neural Solvers on Megavoxel Domains." arXiv preprint arXiv:2104.14538 (2021). Posted with permission.</p
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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