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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Pontula, Sahil Sloan, Jamison Rivera, Nicholas Soljačić, Marin Department of Physics MIT CambridgeMA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Department ofPhysics Harvard CambridgeMA United States

We predict that a combination of frequency-dependent outcoupling and optical non-linearity in semiconductor lasers can create sharply intensity-dependent loss that squeezes inten-sity noise far below the shot noise li... 详细信息

ISBN: (纸本)9781957171258

关键词： Semiconductor lasers

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Multi-variable control to mitigate loads in CRISPRa networks

Multi-variable control to mitigate loads in CRISPRa networks

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IEEE Conference on Decision and Control

作者： Krishna Manoj Theodore W. Grunberg Domitilla Del Vecchio Department of Mechanical Engineering MIT MA USA Department of Electrical Engineering and Computer Science MIT MA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The discovery of CRISPR-mediated gene activation (CRISPRa) has transformed the way in which we perform genetic screening, bioproduction and therapeutics through its ability to scale and multiplex. However, the emergence of loads on the key molecular resources constituting CRISPRa by the orthogonal short RNA that guide such resources to gene targets, couple theoretically independent CRISPRa modules. This coupling negates the ability of CRISPRa systems to concurrently regulate multiple genes independent of one another. In this paper, we propose to reduce this coupling by mitigating the loads on the molecular resources that constitute CRISPRa. In particular, we design a multi-variable controller that makes the concentration of these molecular resources robust to variations in the level of the short RNA loads. This work serves as a foundation to design and implement CRISPRa controllers for practical applications.

关键词： Couplings Multiplexing Hands Degradation Sensitivity RNA Genetics Adaptive control

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Controlling quantum fluctuations of macroscopic light with sharply nonlinear gain

Controlling quantum fluctuations of macroscopic light with s...

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CLEO: science and Innovations, S and I 2022

作者： Nguyen, Linh Sloan, Jamison Rivera, Nicholas Soljačić, Marin Department of Physics MIT CambridgeMA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States

ISBN: (纸本)9781557528209

Here, we introduce a mechanism of "sharp nonlinear gain" that strongly suppresses quantum fluctuations of lasers. This effect can lead to orders-of-magnitude suppression of intensity fluctuations in real lasers, as well as macroscopic sub-Poissonian light. © Optica Publishing Group 2022, © 2022 The Author(s)

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Image Schema Decompositions of the Conceptual Dependency INGEST Primitive: A Study of Paraphrases 7

Image Schema Decompositions of the Conceptual Dependency ING...

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7th Image Schema Day, ISD7 2023

作者： Macbeth, Jamie C. Kilayko, Alexis Zhao, Zoie Song, Sophie Zheng, Winnie X. Department of Computer Science Smith College 10 Elm St NorthamptonMA01063 United States Department of Electrical Engineering and Computer Science MIT 77 Massachusetts Ave CambridgeMA02139 United States

One of the hallmarks of the Schank-Minsky Conceptual Dependency Trans-Frames meaning representation system is that it attempts to express complex meanings by building large and complex conceptual structures using a relatively small number of primitives. Recently comparisons of image schemas with Conceptual Dependency primitives revealed ways of possibly reducing the number of primitives while maintaining the expressiveness of the set—an important research goal because it increases the flexibility and richness of the primitive-decomposed structures in a way that better approximates human cognition. Inspired by this prior work, we employ a paraphrase generation system to explore the replacement of the Conceptual Dependency INGEST primitive by PTRANS and CONTAIN, which are the analogs of Source_Path_Goal and Containment image schemas. The results of the study bring us a step closer to a possible unification of image schemas with Conceptual Dependency. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Natural language processing systems

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Analytic Design of Flat-Wire Inductors for High-Current and Compact DC-DC Converters

arXiv

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arXiv 2024年

作者： Mohammadi, Sajjad Kirtley, James L. Namadmalan, Alireza Electrical Engineering and Computer Science MIT Cambridge United States Cork Ireland

This paper presents analytic study and design considerations of flat wire inductors with distributed gaps for high-power and compact DC-DC Converters. The focus is eddy-current loss components within the conductors due to fringing and leakage fluxes. A magnetic equivalent circuit (MEC) is proposed in which eddy currents are modeled by MMFs opposing the primary flux as well as frequency dependent reluctances, which finally leads to a frequency dependent inductance describing the behavior of the inductor at high frequencies. Three formulations for DC resistance depending on the required accuracy are developed. Calculations of the AC resistance based on vector potential obtained from FEM are provided. To provide an insight into the optimized design of such inductors, components of the magnetic flux and induced eddy currents along with sensitivity of the main inductor quantities such as DCR, ESR, loss components and inductance values to the design parameters are investigated. Finally, an inductor is prototyped and experimentally tested to verify the design. Copyright © 2024, The Authors. All rights reserved.

关键词： Eddy current testing

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Developing a Series of AI Challenges for the United States Department of the Air Force∗

Developing a Series of AI Challenges for the United States D...

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2022 IEEE High Performance Extreme Computing Conference, HPEC 2022

作者： Gadepally, Vijay Angelides, Gregory Barbu, Andrei Bowne, Andrew Brattain, Laura J. Broderick, Tamara Cabrera, Armando Carl, Glenn Carter, Ronisha Cha, Miriam Cowen, Emilie Cummings, Jesse Freeman, Bill Glass, James Goldberg, Sam Hamilton, Mark Heldt, Thomas Huang, Kuan Wei Isola, Phillip Katz, Boris Koerner, Jamie Lin, Yen-Chen Mayo, David Mcalpin, Kyle Perron, Taylor Piou, Jean Rao, Hrishikesh M. Reynolds, Hayley Samuel, Kaira Samsi, Siddharth Schmidt, Morgan Shing, Leslie Simek, Olga Swenson, Brandon Sze, Vivienne Taylor, Jonathan Tylkin, Paul Veillette, Mark Weiss, Matthew L Wollaber, Allan Yuditskaya, Sophia Kepner, Jeremy MIT Lincoln Laboratory United States MIT Computer Science and Artificial Intelligence Laboratory United States United States Department of the Air Force United States MIT Electrical Engineering and Computer Science United States Atmospheric and Plenetary Science MIT Department of Earth United States MIT Institute for Medical Engineering & Science United States MIT Connection Science United States

ISBN: (数字)9781665497862

ISBN: (纸本)9781665497862

American leadership in AI. These broad strategy documents have influenced organizations such as the United States Department of the Air Force (DAF). The DAF-mit AI Accelerator is an initiative between the DAF and mit to bridge the gap between AI researchers and DAF mission requirements. Several projects supported by the DAF-mit AI Accelerator are developing public challenge problems that address numerous Federal AI research priorities. These challenges target priorities by making large, AI-ready datasets publicly available, incentivizing open-source solutions, and creating a demand signal for dual use technologies that can stimulate further research. In this article, we describe these public challenges being developed and how their application contributes to scientific advances. © 2022 IEEE.

关键词： Large dataset

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An Artificial-Intelligence-Based SLAM Processor With Scene-Adaptive Sampling and Hybrid NeRF Model Training Acceleration

IEEE Transactions on Circuits and Systems for Artificial Int...

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IEEE Transactions on Circuits and Systems for Artificial Intelligence 2024年第1期1卷 3-14页

作者： Gwangtae Park Seokchan Song Haoyang Sang Dongseok Im Donghyeon Han Sangyeob Kim Hongseok Lee Hoi-Jun Yoo School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea School of Electrical Engineering and Computer Science Massachusetts Institute of Technology (MIT) Cambridge MA USA

This paper presents an energy-efficient artificial intelligence (AI) based SLAM processor to provide camera pose together with red, green, blue, and depth (RGB-D) reconstruction of the environment near the user. It acquires such information in real-time through the tracking and mapping process that performs deep neural network (DNN) training on neural radiance fields (NeRF) model by receiving RGB-D camera input as the ground truth, without relying on any prior data in indoor and unknown user environments. However, the realization of AI-based SLAM on performance- and battery-constrained devices is extremely challenging due to the massive computation amount and heterogeneous characteristics at different DNN training stages and layers. To satisfy the hardware requirements of SLAM in mobile devices, this article proposed three key features. A Scene-adaptive Sampling (SAS) algorithm is proposed and implemented in the sampling core to detect and eliminate energy consumption caused by redundant workloads at both the tracking and mapping procedures. After preprocessing at the sampling core, training the hybrid NeRF model which consists of 3D embedding and sparse mixture-of-experts (SMoE) layers is accelerated for high throughput. 3D embedding unit (3D-EU) provides the data-path that is shareable among feed-forward (FF), back-propagation (BP), and weight-gradient (WG) stages of the 3D embedding layer. Moreover, SMoE acceleration architecture schedules the execution order of SMoE layer to maximize the reuse amount of expert parameters. Finally, the proposed real-time SLAM processor achieves 0.14 $\boldsymbol{\mu}$ J/point energy consumption, which is 2.86 $\boldsymbol{\times}$ smaller than the state-of-the-art.

关键词： Simultaneous localization and mapping Neural radiance field Three-dimensional displays Training Cameras Hardware Artificial intelligence

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A Novel Radial-Flux IPM Eddy-Current Coupler for Slip-Synchronous PM Wind Generators

A Novel Radial-Flux IPM Eddy-Current Coupler for Slip-Synchr...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Sajjad Mohammadi Gholamreza Davarpanah James L. Kirtley Electrical Engineering and Computer Science MIT Cambridge USA Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

A novel radial-flux eddy-current coupler with interior permanent magnets (IPM) is proposed, providing higher demagnetization tolerance, making it well-suited for applications with limited accessibility, such as offshore slip-synchronous PM wind generators for which a new compact topology is offered by unifying coupler and generator. FEM is employed in design and derivation of coupler quantities. Finally, the coupler is prototyped to experimentally validate the design and the simulation results.

关键词： Torque Simulation Couplers Generators Topology Finite element analysis Eddy currents Transient analysis Demagnetization Turbines

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High-accuracy sampling from constrained spaces with the Metropolis-adjusted Preconditioned Langevin Algorithm

arXiv

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arXiv 2024年

作者： Srinivasan, Vishwak Wibisono, Andre Wilson, Ashia Department of Electrical Engineering and Computer Science MIT United States Department of Computer Science Yale University United States

In this work, we propose a first-order sampling method called the Metropolis-adjusted Preconditioned Langevin Algorithm for approximate sampling from a target distribution whose support is a proper convex subset of Rd. Our proposed method is the result of applying a Metropolis-Hastings filter to the Markov chain formed by a single step of the preconditioned Langevin algorithm with a metric G, and is motivated by the natural gradient descent algorithm for optimisation. We derive non-asymptotic upper bounds for the mixing time of this method for sampling from target distributions whose potentials are bounded relative to G, and for exponential distributions restricted to the support. Our analysis suggests that if G satisfies stronger notions of self-concordance introduced in (Kook and Vempala, 2024), then these mixing time upper bounds have a strictly better dependence on the dimension than when is merely self-concordant. We also provide numerical experiments that demonstrates the practicality of our proposed method. Our method is a high-accuracy sampler due to the polylogarithmic dependence on the error tolerance in our mixing time upper bounds. © 2024, CC BY.

关键词： Markov chains

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Consistent diffusion meets tweedie: training exact ambient diffusion models with noisy data 24

Consistent diffusion meets tweedie: training exact ambient d...

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Proceedings of the 41st International Conference on Machine Learning

作者： Giannis Daras Alexandros G. Dimakis Constantinos Daskalakis Department of Computer Science University of Texas at Austin and Archimedes AI Department of Electrical and Computer Engineering University of Texas at Austin Department of Electrical Engineering and Computer Science MIT and Archimedes AI

Ambient diffusion is a recently proposed framework for training diffusion models using corrupted data. Both Ambient Diffusion and alternative SURE-based approaches for learning diffusion models from corrupted data resort to approximations which deteriorate performance. We present the first framework for training diffusion models that provably sample from the uncorrupted distribution given only noisy training data, solving an open problem in Ambient diffusion. Our key technical contribution is a method that uses a double application of Tweedie's formula and a consistency loss function that allows us to extend sampling at noise levels below the observed data noise. We also provide further evidence that diffusion models memorize from their training sets by identifying extremely corrupted images that are almost perfectly reconstructed, raising copyright and privacy concerns. Our method for training using corrupted samples can be used to mitigate this problem. We demonstrate this by fine-tuning Stable Diffusion XL to generate samples from a distribution using only noisy samples. Our framework reduces the amount of memorization of the fine-tuning dataset, while maintaining competitive performance.

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