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Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent Space

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arXiv 2023年

作者： Zhang, Molin Xu, Junshen Arefeen, Yamin Adalsteinsson, Elfar Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States

Fast spin-echo (FSE) pulse sequences for Magnetic Resonance Imaging (MRI) offer important imaging contrast in clinically feasible scan times. T2-shuffling is widely used to resolve temporal signal dynamics in FSE acquisitions by exploiting temporal correlations via linear latent space and a predefined regularizer. However, predefined regularizers fail to exploit the incoherence especially for 2D *** self-supervised learning methods achieve high-fidelity reconstructions by learning a regularizer from undersampled data without a standard supervised training data set. In this work, we propose a novel approach that utilizes a self supervised learning framework to learn a regularizer constrained on a linear latent space which improves time-resolved FSE images reconstruction quality. Additionally, in regimes without groundtruth sensitivity maps, we propose joint estimation of coil-sensitivity maps using an iterative reconstruction technique. Our technique functions is in a zero-shot fashion, as it only utilizes data from a single scan of highly undersampled time series images. We perform experiments on simulated and retrospective in-vivo data to evaluate the performance of the proposed zero-shot learning method for temporal FSE reconstruction. The results demonstrate the success of our proposed method where NMSE and SSIM are significantly increased and the artifacts are reduced. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

A Study on the functionality of PID and Fuzzy Logic Controllers for Quadratic Boost Converter (Qbc) 3

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A Study on the functionality of PID and Fuzzy Logic Controll...

3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Arigela, Sri Harsha Krishnan, T. Santhana Krishnakumar, S.K. Mohanraj, A. Mahajan, Mangesh R. Revathi, R. Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Mechanical Engineering Andhra Pradesh Guntur522302 India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai602105 India Academy of Maritime Education and Training Department of Mechanical Engineering Tamil Nadu Chennai600119 India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore641202 India Dr. Vishwanath Karad MIT World Peace University Department of Mechanical Engineering Maharashtra Pune411038 India Karpagam Academy of Higher Education Department of Computer Science Tamil Nadu Coimbatore641021 India

ISBN: (纸本)9781665462754

A large voltage gain design of a QBC - quadratic boost converter is shown in this work, along with a comparison of the functions of a fuzzy logic controller(FLC) along with traditional P,I,D controller when used with a quadratic boost converter. MATLAB/SIMULINK modeling is used to create the simulation circuit, and the relevant features are examined. The findings from comparing a number of time domain parameters demonstrate that FLCs are not only much better than other controllers in terms of rise time, response speed, and peak overshoot, also significantly excellent in terms of controller output signal, which is much interesting in terms of hardware. © 2022 IEEE.

关键词： Controllers

Effective Dimension in Bandit Problems under Censorship

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arXiv 2023年

作者： Guinet, Gauthier Amin, Saurabh Jaillet, Patrick AWS AI Labs United States MIT United States Dept. of Civil and Environmental Engineering Laboratory for Information and Decision Systems United States Dept. of Electrical Engineering and Computer Science & Laboratory for Information and Decision Systems United States

In this paper, we study both multi-armed and contextual bandit problems in censored environments. Our goal is to estimate the performance loss due to censorship in the context of classical algorithms designed for uncensored environments. Our main contributions include the introduction of a broad class of censorship models and their analysis in terms of the effective dimension of the problem – a natural measure of its underlying statistical complexity and main driver of the regret bound. In particular, the effective dimension allows us to maintain the structure of the original problem at first order, while embedding it in a bigger space, and thus naturally leads to results analogous to uncensored settings. Our analysis involves a continuous generalization of the Elliptical Potential Inequality, which we believe is of independent interest. We also discover an interesting property of decision-making under censorship: a transient phase during which initial misspecification of censorship is self-corrected at an extra cost;followed by a stationary phase that reflects the inherent slowdown of learning governed by the effective dimension. Our results are useful for applications of sequential decision-making models where the feedback received depends on strategic uncertainty (e.g., agents’ willingness to follow a recommendation) and/or random uncertainty (e.g., loss or delay in arrival of information). © 2023, CC BY.

关键词： Decision making

A wasserstein minimax framework for mixed linear regression

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arXiv 2021年

作者： Diamandis, Theo Eldar, Yonina C. Fallah, Alireza Farnia, Farzan Ozdaglar, Asuman Department of Electrical Engineering & Computer Science MIT United States Faculty of Math and Computer Science Weizmann Institute of Science Israel

Multi-modal distributions are commonly used to model clustered data in statistical learning tasks. In this paper, we consider the Mixed Linear Regression (MLR) problem. We propose an optimal transport-based framework for MLR problems, Wasserstein Mixed Linear Regression (WMLR), which minimizes the Wasserstein distance between the learned and target mixture regression models. Through a model-based duality analysis, WMLR reduces the underlying MLR task to a nonconvex-concave minimax optimization problem, which can be provably solved to find a minimax stationary point by the Gradient Descent Ascent (GDA) algorithm. In the special case of mixtures of two linear regression models, we show that WMLR enjoys global convergence and generalization guarantees. We prove that WMLR’s sample complexity grows linearly with the dimension of data. Finally, we discuss the application of WMLR to the federated learning task where the training samples are collected by multiple agents in a network. Unlike the Expectation Maximization algorithm, WMLR directly extends to the distributed, federated learning setting. We support our theoretical results through several numerical experiments, which highlight our framework’s ability to handle the federated learning setting with mixture models. Copyright © 2021, The Authors. All rights reserved.

关键词： Maximum principle

Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling

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arXiv 2023年

作者： Ghosh, Susobhan Kim, Raphael Chhabria, Prasidh Dwivedi, Raaz Klasnja, Predrag Liao, Peng Zhang, Kelly Murphy, Susan Department of Computer Science Harvard University United States Department of Biostatistics Harvard University United States Department of Statistics Harvard University United States Department of Electrical Engineering and Computer Science MIT United States School of Information University of Michigan United States Harvard University United States

There is a growing interest in using reinforcement learning (RL) to personalize sequences of treatments in digital health to support users in adopting healthier behaviors. Such sequential decision-making problems involve decisions about when to treat and how to treat based on the user's context (e.g., prior activity level, location, etc.). Online RL is a promising data-driven approach for this problem as it learns based on each user's historical responses and uses that knowledge to personalize these decisions. However, to decide whether the RL algorithm should be included in an "optimized" intervention for real-world deployment, we must assess the data evidence indicating that the RL algorithm is actually personalizing the treatments to its users. Due to the stochasticity in the RL algorithm, one may get a false impression that it is learning in certain states and using this learning to provide specific treatments. We use a working definition of personalization and introduce a resampling-based methodology for investigating whether the personalization exhibited by the RL algorithm is an artifact of the RL algorithm stochasticity. We illustrate our methodology with a case study by analyzing the data from a physical activity clinical trial called HeartSteps, which included the use of an online RL algorithm. We demonstrate how our approach enhances data-driven truth-in-advertising of algorithm personalization both across all users as well as within specific users in the study. © 2023, CC BY.

关键词： Reinforcement learning

Federated Bayesian optimization via Thompson sampling 34

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Federated Bayesian optimization via Thompson sampling

34th Conference on Neural Information Processing Systems, NeurIPS 2020

作者： Dai, Zhongxiang Low, Bryan Kian Hsiang Jaillet, Patrick Dept. of Computer Science National University of Singapore Singapore Singapore Dept. of Electrical Engineering and Computer Science MIT United States

Bayesian optimization (BO) is a prominent approach to optimizing expensive-to-evaluate black-box functions. The massive computational capability of edge devices such as mobile phones, coupled with privacy concerns, has led to a surging interest in federated learning (FL) which focuses on collaborative training of deep neural networks (DNNs) via first-order optimization techniques. However, some common machine learning tasks such as hyperparameter tuning of DNNs lack access to gradients and thus require zeroth-order/black-box optimization. This hints at the possibility of extending BO to the FL setting (FBO) for agents to collaborate in these black-box optimization tasks. This paper presents federated Thompson sampling (FTS) which overcomes a number of key challenges of FBO and FL in a principled way: We (a) use random Fourier features to approximate the Gaussian process surrogate model used in BO, which naturally produces the parameters to be exchanged between agents, (b) design FTS based on Thompson sampling, which significantly reduces the number of parameters to be exchanged, and (c) provide a theoretical convergence guarantee that is robust against heterogeneous agents, which is a major challenge in FL and FBO. We empirically demonstrate the effectiveness of FTS in terms of communication efficiency, computational efficiency, and practical performance. © 2020 Neural information processing systems foundation. All rights reserved.

关键词： Deep neural networks

Variational Bayesian unlearning 34

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Variational Bayesian unlearning

34th Conference on Neural Information Processing Systems, NeurIPS 2020

作者： Nguyen, Quoc Phong Hsiang Low, Bryan Kian Jaillet, Patrick Dept. of Computer Science National University of Singapore Singapore Singapore Dept. of Electrical Engineering and Computer Science MIT United States

This paper studies the problem of approximately unlearning a Bayesian model from a small subset of the training data to be erased. We frame this problem as one of minimizing the Kullback-Leibler divergence between the approximate posterior belief of model parameters after directly unlearning from erased data vs. the exact posterior belief from retraining with remaining data. Using the variational inference (VI) framework, we show that it is equivalent to minimizing an evidence upper bound which trades off between fully unlearning from erased data vs. not entirely forgetting the posterior belief given the full data (i.e., including the remaining data);the latter prevents catastrophic unlearning that can render the model useless. In model training with VI, only an approximate (instead of exact) posterior belief given the full data can be obtained, which makes unlearning even more challenging. We propose two novel tricks to tackle this challenge. We empirically demonstrate our unlearning methods on Bayesian models such as sparse Gaussian process and logistic regression using synthetic and real-world datasets. © 2020 Neural information processing systems foundation. All rights reserved.

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LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuits

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arXiv 2024年

作者： Kochar, Dimple Vijay Wang, Hanrui Chandrakasan, Anantha P. Zhang, Xin Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States MIT-IBM Watson AI Lab CambridgeMA United States IBM T. J. Watson Research Center Yorktown HeightsNY United States

Traditional approaches for designing analog circuits are time-consuming and require significant human expertise. Existing automation efforts using methods like Bayesian Optimization (BO) and Reinforcement Learning (RL) are suboptimal and costly to generalize across different topologies and technology nodes. In our work, we introduce a novel approach, LEDRO, utilizing Large Language Models (LLMs) in conjunction with optimization techniques to iteratively refine the design space for analog circuit sizing. LEDRO is highly generalizable compared to other RL and BO baselines, eliminating the need for design annotation or model training for different topologies or technology nodes. We conduct a comprehensive evaluation of our proposed framework and baseline on 22 different Op-Amp topologies across four FinFET technology nodes. Results demonstrate the superior performance of LEDRO as it outperforms our best baseline by an average of 13% FoM improvement with 2.15× speed-up on low-complexity Op-Amps and 48% FoM improvement with 1.7× speed-up on high-complexity Op-Amps. This highlights LEDRO’s effective performance, efficiency, and generalizability. Copyright © 2024, The Authors. All rights reserved.

关键词： Operational amplifiers

Assessing Affective Engagement with Narratives of Invisible Disability

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Assessing Affective Engagement with Narratives of Invisible ...

International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII

作者： Daniel T. Kessler David Y.J. Kim Grace S. Ahn Neska Elhaouij Rosalind Picard MIT Center for Constructive Communication Massachusetts Institute of Technology Cambridge USA Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge USA MIT System Design and Management Massachusetts Institute of Technology Cambridge USA Affective Computing Group Massachusetts Institute of Technology Cambridge USA

Narratives about invisible disabilities are poorly represented in public discourse and often go undisclosed [1], leading to false assumptions, discrimination, and stigma [2] against those who experience these conditions. To address these issues, recent studies have suggested that disclosure of first-person narratives of invisible disabilities should be increased [3]. To understand the mechanisms affecting recipients of such narratives, the present study evaluates how social media users (N = 124) engage affectively with this content in a digitally mediated narrative-form intervention designed to reduce harmful assumptions against persons who experience invisible disabilities. Results of this study indicate that such an intervention may prove effective at reducing harmful assumptions on the basis of visual cues, and in line with past research, finds that affect may play an important role in assumption-making processes [4]. Findings from this study may be used to inform novel digital interventions capable of counteracting harmful assumptions that drive prejudicial behaviors against a wide range of populations and communities.

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