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UniMo: Universal Motion Correction For Medical Images without Network Retraining

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arXiv 2024年

作者： Wang, Jian Faghihpirayesh, Razieh Joca, Danny Golland, Polina Gholipour, Ali Boston Children’s Hospital Harvard Medical School BostonMA United States Department of Electrical and Computer Engineering Northeastern University United States Department of Radiology Boston Children’s Hospital Harvard Medical School BostonMA United States CSAIL [Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology [MIT CambridgeMA United States Department of Radiological Sciences the Department of Electrical Engineering and Computer Science The University of California IrvineCA United States Department of Radiology Boston Children’s Hospital BostonMA United States

In this paper, we introduce a Universal Motion Correction (UniMo) framework, leveraging deep neural networks to tackle the challenges of motion correction across diverse imaging modalities. Our approach employs advanced neural network architectures with equivariant filters, overcoming the limitations of current models that require iterative inference or retraining for new image modalities. UniMo enables onetime training on a single modality while maintaining high stability and adaptability for inference across multiple unseen image modalities. We developed a joint learning framework that integrates multimodal knowledge from both shape and images that faithfully improve motion correction accuracy despite image appearance variations. UniMo features a geometric deformation augmenter that enhances the robustness of global motion correction by addressing any local deformations whether they are caused by object deformations or geometric distortions, and also generates augmented data to improve the training process. Our experimental results, conducted on various datasets with four different image modalities, demonstrate that UniMo surpasses existing motion correction methods in terms of accuracy. By offering a comprehensive solution to motion correction, UniMo marks a significant advancement in medical imaging, especially in challenging applications with wide ranges of motion, such as fetal imaging. The code for this work is available online 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

Centralized vs Decentralized Targeted Brute-Force Attacks: Guessing with Side-Information

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Centralized vs Decentralized Targeted Brute-Force Attacks: G...

作者： Salamatian, Salman Huleihel, Wasim Beirami, Ahmad Cohen, Asaf Medard, Muriel Department of Electrical Engineering and Computer Science Mit CambridgeMA United States Department of Electrical Engineering-Systems Tel-Aviv University Tel-Aviv Israel

According to recent empirical studies, a majority of users have the same, or very similar, passwords across multiple password-secured online services. This practice can have disastrous consequences, as one password being compromised puts all the other accounts at much higher risk. Generally, an adversary may use any side-information he/she possesses about the user, be it demographic information, password reuse on a previously compromised account, or any other relevant information to devise a better brute-force strategy (so called targeted attack). In this work, we consider a distributed brute-force attack scenario in which m adversaries, each observing some side information, attempt breaching a password secured system. We compare two strategies: an uncoordinated attack in which the adversaries query the system based on their own side-information until they find the correct password, and a fully coordinated attack in which the adversaries pool their side-information and query the system together. For passwords X of length n, generated independently and identically from a distribution PX, we establish an asymptotic closed-form expression for the uncoordinated and coordinated strategies when the side-information Y(m) are generated independently from passing X through a memoryless channel PY|X, as the length of the password n goes to infinity. We illustrate our results for binary symmetric channels and binary erasure channels, two families of side-information channels which model password reuse. We demonstrate that two coordinated agents perform asymptotically better than any finite number of uncoordinated agents for these channels, meaning that sharing side-information is very valuable in distributed attacks. © 2005-2012 IEEE.

关键词： Search engines

Differentially private federated Bayesian optimization with distributed exploration

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arXiv 2021年

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

Bayesian optimization (BO) has recently been extended to the federated learning (FL) setting by the federated Thompson sampling (FTS) algorithm, which has promising applications such as federated hyperparameter tuning. However, FTS is not equipped with a rigorous privacy guarantee which is an important consideration in FL. Recent works have incorporated differential privacy (DP) into the training of deep neural networks through a general framework for adding DP to iterative algorithms. Following this general DP framework, our work here integrates DP into FTS to preserve user-level privacy. We also leverage the ability of this general DP framework to handle different parameter vectors, as well as the technique of local modeling for BO, to further improve the utility of our algorithm through distributed exploration (DE). The resulting differentially private FTS with DE (DP-FTS-DE) algorithm is endowed with theoretical guarantees for both the privacy and utility and is amenable to interesting theoretical insights about the privacy-utility trade-off. We also use real-world experiments to show that DP-FTS-DE achieves high utility (competitive performance) with a strong privacy guarantee (small privacy loss) and induces a trade-off between privacy and utility. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

Augmenting Control over Exploration Space in Molecular Dynamics Simulators to Streamline De Novo Analysis through Generative Control Policies

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arXiv 2023年

作者： Gonzalez-Rojas, Paloma Emmel, Andrew Martinez, Louis Malur, Neil Rutledge, Gregory Department of Chemical Engineering MIT Climate and Sustainability Consortium Massachusetts Institute of Technology Cambridge United States Department of Biological Engineering Massachusetts Institute of Technology United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Department of Chemical Engineering Massachusetts Institute of Technology United States

This study introduces the P5 model - a foundational method that utilizes reinforcement learning (RL) to augment control, effectiveness, and scalability in molecular dynamics simulations (MD). Our innovative strategy optimizes the sampling of target polymer chain conformations, marking an efficiency improvement of over 37.1%. The RL-induced control policies function as an inductive bias, modulating Brownian forces to steer the system towards the preferred state, thereby expanding the exploration of the configuration space beyond what traditional MD allows. This broadened exploration generates a more varied set of conformations and targets specific properties, a feature pivotal for progress in polymer development, drug discovery, and material design. Our technique offers significant advantages when investigating new systems with limited prior knowledge, opening up new methodologies for tackling complex simulation problems with generative techniques. © 2023, CC BY-NC-SA.

关键词： Molecular dynamics

Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes

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arXiv 2023年

作者： Nayebi, Aran Rajalingham, Rishi Jazayeri, Mehrdad Yang, Guangyu Robert McGovern Institute for Brain Research MIT CambridgeMA02139 United States Department of Brain and Cognitive Sciences MIT CambridgeMA02139 United States Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States Reality Labs Meta 390 9th Ave New YorkNY10001 United States

Humans and animals have a rich and flexible understanding of the physical world, which enables them to infer the underlying dynamical trajectories of objects and events, plausible future states, and use that to plan and anticipate the consequences of actions. However, the neural mechanisms underlying these computations are unclear. We combine a goal-driven modeling approach with dense neurophysiological data and high-throughput human behavioral readouts that contain thousands of comparisons to directly impinge on this question. Specifically, we construct and evaluate several classes of sensory-cognitive networks to predict the future state of rich, ethologically-relevant environments, ranging from self-supervised end-to-end models with pixel-wise or object-slot objectives, to models that future predict in the latent space of purely static image-pretrained or dynamic video-pretrained foundation models. We find that "scale is not all you need", and that many state-of-the-art machine learning models fail to perform well on our neural and behavioral benchmarks for future prediction. In fact, only one class of models matches these data well overall. We find that neural responses are currently best predicted by models trained to predict the future state of their environment in the latent space of pretrained foundation models optimized for dynamic scenes in a self-supervised manner. These models also approach the neurons' ability to predict the environmental state variables that are visually hidden from view, despite not being explicitly trained to do so. Finally, we find that not all foundation model latents are equal. Notably, models that future predict in the latent space of video foundation models that are optimized to support a diverse range of egocentric sensorimotor tasks, reasonably match both human behavioral error patterns and neural dynamics across all environmental scenarios that we were able to test. Overall, these findings suggest that the neural mechanisms a

关键词： Forecasting

Model Predictive Control for Magnetically-Actuated Cellbots

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arXiv 2024年

作者： Kermanshah, Mehdi Beaver, Logan E. Sokolich, Max Kirmizitas, Fatma Ceren Das, Sambeeta Tron, Roberto Weiss, Ron Belta, Calin Department of Mechanical Engineering Boston University BostonMA United States Department of Mechanical and Aerospace Engineering Old Dominion University NorfolkVA United States Department of Mechanical Engineering University of Delaware NewarkDE United States Department of Animal and Food Sciences University of Delaware NewarkDE United States Department of Biological Engineering MIT BostonMA United States Department of Electrical and Computer Engineering Department of Computer Science University of Maryland College ParkMD United States

This paper presents a control framework for magnetically actuated cellbots, which combines Model Predictive Control (MPC) with Gaussian Processes (GPs) as a disturbance estimator for precise trajectory tracking. To address the challenges posed by unmodeled dynamics, we integrate data-driven modeling with model-based control to accurately track desired trajectories using relatively small data. To the best of our knowledge, this is the first work to integrate data-driven modeling with model-based control for the magnetic actuation of cellbots. The GP effectively learns and predicts unmodeled disturbances, providing uncertainty bounds as well. We validate our method through experiments with cellbots, demonstrating improved trajectory tracking accuracy. © 2024, CC BY-NC-ND.

关键词： Predictive control systems

A Deep Generative Learning Approach to Two-stage Adaptive Robust Optimization

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arXiv 2024年

作者： Brenner, Aron Khorramfar, Rahman Sun, Jennifer Amin, Saurabh Laboratory for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA United States MIT Energy Initiative Massachusetts Institute of Technology CambridgeMA United States Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA United States Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States

Two-stage adaptive robust optimization (ARO) is a powerful approach for planning under uncertainty, balancing first-stage decisions with recourse decisions made after uncertainty is realized. To account for uncertainty, modelers typically define a simple uncertainty set over which potential outcomes are considered. However, classical methods for defining these sets unintentionally capture a wide range of unrealistic outcomes, resulting in overly-conservative and costly planning in anticipation of unlikely contingencies. In this work, we introduce AGRO, a solution algorithm that performs adversarial generation for two-stage adaptive robust optimization using a variational autoencoder. AGRO generates high-dimensional contingencies that are simultaneously adversarial and realistic, improving the robustness of first-stage decisions at a lower planning cost than standard methods. To ensure generated contingencies lie in high-density regions of the uncertainty distribution, AGRO defines a tight uncertainty set as the image of "latent" uncertainty sets under the VAE decoding transformation. Projected gradient ascent is then used to maximize recourse costs over the latent uncertainty sets by leveraging differentiable optimization methods. We demonstrate the cost-efficiency of AGRO by applying it to both a synthetic production-distribution problem and a real-world power system expansion setting. We show that AGRO outperforms the standard column-and-constraint algorithm by up to 1.8% in production-distribution planning and up to 11.6% in power system expansion. © 2024, CC BY.

关键词： Generative adversarial networks

Evaluation of human-AI teams for learned and rule-based agents in Hanabi 21

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Evaluation of human-AI teams for learned and rule-based agen...

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Ho Chit Siu Jaime D. Peña Yutai Zhou Edenna Chen Victor J. Lopez Kyle Palko Kimberlee C. Chang Ross E. Allen MIT Lincoln Laboratory MIT Department of Electrical Engineering and Computer Science U.S. Air Force Artificial Intelligence Accelerator

ISBN: (纸本)9781713845393

Deep reinforcement learning has generated superhuman AI in competitive games such as Go and StarCraft. Can similar learning techniques create a superior AI teammate for human-machine collaborative games? Will humans prefer AI teammates that improve objective team performance or those that improve subjective metrics of trust? In this study, we perform a single-blind evaluation of teams of humans and AI agents in the cooperative card game Hanabi, with both rule-based and learning-based agents. In addition to the game score, used as an objective metric of the human-AI team performance, we also quantify subjective measures of the human's perceived performance, teamwork, interpretability, trust, and overall preference of AI teammate. We find that humans have a clear preference toward a rule-based AI teammate (SmartBot) over a state-of-the-art learning-based AI teammate (Other-Play) across nearly all subjective metrics, and generally view the learning-based agent negatively, despite no statistical difference in the game score. This result has implications for future AI design and reinforcement learning benchmarking, highlighting the need to incorporate subjective metrics of human-AI teaming rather than a singular focus on objective task performance.

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SCORE: A Second-Order Conic Initialization for Range-Aided SLAM

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SCORE: A Second-Order Conic Initialization for Range-Aided S...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Alan Papalia Joseph Morales Kevin J. Doherty David M. Rosen John J. Leonard Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology (MIT) Cambridge MA USA Department of Applied Ocean Physics & Engineering Woods Hole Oceanographic Institution Woods Hole MA USA Department of Electrical & Computer Engineering Northeastern University Boston MA USA Department of Math Northeastern University Boston MA USA

We present a novel initialization technique for the range-aided simultaneous localization and mapping (RA-SLAM) problem. In RA-SLAM we consider measurements of point-to-point distances in addition to measurements of rigid transformations to landmark or pose variables. Standard formulations of RA-SLAM approach the problem as non-convex optimization, which requires a good initialization to obtain quality results. The initialization technique proposed here relaxes the RA-SLAM problem to a convex problem which is then solved to determine an initialization for the original, non-convex problem. The relaxation is a second-order cone program (SOCP), which is derived from a quadratically constrained quadratic program (QCQP) formulation of the RA-SLAM problem. As a SOCP, the method is highly scalable. We name this relaxation Second-order COnic RElaxation for RA-SLAM (SCORE). To our knowledge, this work represents the first convex relaxation for RA-SLAM. We present real-world and simulated experiments which show SCORE initialization permits the efficient recovery of quality solutions for a variety of challenging single- and multi-robot RA-SLAM problems with thousands of poses and range measurements.

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