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Dynamic Mapping of Temperature Using Phase-Change Materials

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Dynamic Mapping of Temperature Using Phase-Change Materials

Conference on Lasers and Electro-Optics (CLEO)

作者： Nicholas Nobile John Erickson Carlos Ríos Yifei Zhang Juejun Hu Feng Xiong Nathan Youngblood Dept. of Electrical and Computer Engineering Univ. of Pittsburgh Pittsburgh PA USA Department of Materials Science & Engineering University of Maryland College Park MD USA Institute for Research in Electronics and Applied Physics University of Maryland College Park MD USA Department of Materials Science & Engineering MIT Cambridge MA USA

GST pixels are fabricated on metallic heaters where the optical reflection is observed and used to extract dynamic temperature changes. Our results provide a new way to measure the thermal response of microsystems in ... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Temperature measurement Radio frequency Phase change materials Phase measurement Optical switches Optical variables measurement Real-time systems

3D Shape Perception Integrates Intuitive Physics and Analysis-by-Synthesis

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arXiv 2023年

作者： Yildirim, Ilker Siegel, Max H. Soltani, Amir A. Chaudhari, Shraman Ray Tenenbaum, Joshua B. Department of Psychology Yale University New HavenCT United States Department of Statistics & Data Science Yale University New HavenCT United States Wu-Tsai Institute Yale University New HavenCT United States Department of Brain & Cognitive Sciences MIT CambridgeMA United States The Center for Brains Minds and Machines MIT CambridgeMA United States Department of Psychology Boston College Chestnut HillMA United States Department of Electrical Engineering & Computer Science MIT CambridgeMA United States

Many surface cues support three-dimensional shape perception, but people can sometimes still see shape when these features are missing – in extreme cases, even when an object is completely occluded, as when covered with a draped cloth. We propose a framework for 3D shape perception that explains perception in both typical and atypical cases as analysis-by-synthesis, or inference in a generative model of image formation: the model integrates intuitive physics to explain how shape can be inferred from deformations it causes to other objects, as in cloth-draping. Behavioral and computational studies comparing this account with several alternatives show that it best matches human observers in both accuracy and response times, and is the only model that correlates significantly with human performance on difficult discriminations. Our results suggest that bottom-up deep neural network models are not fully adequate accounts of human shape perception, and point to how machine vision systems might achieve more human-like robustness. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI

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arXiv 2022年

作者： Xu, Junshen Moyer, Daniel Grant, P. Ellen Golland, Polina Iglesias, Juan Eugenio Adalsteinsson, Elfar Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA United States Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children’s Hospital BostonMA United States Harvard Medical School BostonMA United States Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering University College London United Kingdom Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School Boston United States Institute for Medical Engineering and Science MIT CambridgeMA United States

Volumetric reconstruction of fetal brains from multiple stacks of MR slices, acquired in the presence of almost unpredictable and often severe subject motion, is a challenging task that is highly sensitive to the initialization of slice-to-volume transformations. We propose a novel slice-to-volume registration method using Transformers trained on synthetically transformed data, which model multiple stacks of MR slices as a sequence. With the attention mechanism, our model automatically detects the relevance between slices and predicts the transformation of one slice using information from other slices. We also estimate the underlying 3D volume to assist slice-to-volume registration and update the volume and transformations alternately to improve accuracy. Results on synthetic data show that our method achieves lower registration error and better reconstruction quality compared with existing state-of-the-art methods. Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion. Copyright © 2022, The Authors. All rights reserved.

关键词： Brain

Implications of Delayed Reopening in Controlling the COVID-19 Surge in Southern and West-Central USA

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Health Data science 2021年第1期2021卷 78-87页

作者： Raj Dandekar Emma Wang George Barbastathis Chris Rackauckas Department of Computational Science and Engineering Massachusetts Institute of TechnologyCambridgeUSA MA 02139 Department of Electrical Engineering and Computer Sciences Massachusetts Institute of TechnologyCambridgeMA 02139USA Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Singapore-MIT Alliance for Research and Technology(SMART)Centre Singapore 138602 Department of Applied Mathematics Massachusetts Institute of TechnologyCambridgeMA 02139USA

In the wake of the rapid surge in the COVID-19-infected cases seen in Southern and West-Central USA in the period of June-July 2020,there is an urgent need to develop robust,data-driven models to quantify the effect which early reopening had on the infected case count *** particular,it is imperative to address the question:How many infected cases could have been prevented,had the worst affected states not reopened early?To address this question,we have developed a novel COVID-19 model by augmenting the classical SIR epidemiological model with a neural network *** model decomposes the contribution of quarantine strength to the infection time series,allowing us to quantify the role of quarantine control and the associated reopening policies in the US states which showed a major surge in *** show that the upsurge in the infected cases seen in these states is strongly corelated with a drop in the quarantine/lockdown strength diagnosed by our ***,our results demonstrate that in the event of a stricter lockdown without early reopening,the number of active infected cases recorded on 14 July could have been reduced by more than 40%in all states considered,with the actual number of infections reduced being more than 100,000 for the states of Florida and *** we continue our fight against COVID-19,our proposed model can be used as a valuable asset to simulate the effect of several reopening strategies on the infected count evolution,for any region under consideration.

关键词： COV neural Central

Demonstration of Density Matrix Exponentiation Using a Superconducting Quantum Processor

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Physical Review X 2022年第1期12卷 011005-011005页

作者： M. Kjaergaard M. E. Schwartz A. Greene G. O. Samach A. Bengtsson M. O’Keeffe C. M. McNally J. Braumüller D. K. Kim P. Krantz M. Marvian A. Melville B. M. Niedzielski Y. Sung R. Winik J. Yoder D. Rosenberg K. Obenland S. Lloyd T. P. Orlando I. Marvian S. Gustavsson W. D. Oliver Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA MIT Lincoln Laboratory Lexington Massachusetts 02421 USA Department of Electrical Engineering & Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Microtechnology and Nanoscience Chalmers University of Technology Göteborg 412 96 Sweden Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Departments of Physics & Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

Density matrix exponentiation (DME) is a general technique for using a quantum state ρ to enact the quantum operation e−iρθ on a target system. It was first proposed in the context of quantum machine learning, but has since been shown to have broad applications in quantum metrology and computation. No experimental demonstration of DME has been performed thus far due to its demanding circuit depths and the need to efficiently generate multiple identical copies of ρ during the finite lifetime of the target system. In this work, we describe the first demonstration of the DME algorithm, which we accomplish using a superconducting quantum processor. Our demonstration relies on a 99.7% fidelity controlled-phase gate implemented using two tunable superconducting transmon qubits. We achieve a fidelity surpassing 90% at circuit depths exceeding 70 when comparing the output of the circuit executed on our quantum processor to a simulation assuming perfect operations and measurements.

关键词： Quantum algorithms Quantum computation Quantum information processing

Kinetic Monte Carlo Simulations of Sodium Ion Transport in NaSICON Electrodes

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arXiv 2023年

作者： Wang, Ziliang Mishra, Tara P. Xie, Weihang Deng, Zeyu Gautam, Gopalakrishnan Sai Cheetham, Anthony K. Canepa, Pieremanuele Department of Materials Science and Engineering National University of Singapore 9 Engineering Drive 1 117575 Singapore Department of Materials Engineering Indian Institute of Science Karnataka Bengaluru560012 India Singapore-MIT Alliance for Research and Technology 1 CREATE Way 10-01 CREATE Tower Singapore138602 Singapore Materials Department and Materials Research Laboratory University of California Santa BarbaraCA93106 United States Department of Electrical & Computer Engineering University of Houston HoustonTX77204 United States

The development of high-performance sodium (Na) ion batteries requires improved electrode materials. The energy and power densities of Na superionic conductor (NaSICON) electrode materials are promising for large-scale energy storage applications. However, several practical issues limit the full utilization of the theoretical energy densities of NaSICON electrodes. A pressing challenge lies in the limited sodium extraction in low Na content NaSICONs, e.g., Na1VIVVIV(PO4)3 ↔ VVVIV(PO4)3 + 1e− + 1Na+. Hence, it is important to quantify the Na-ion mobility in a broad range of NaSICON electrodes. Using a kinetic Monte Carlo approach bearing the accuracy of first-principles calculations, we elucidate the variability of Na-ion transport vs. Na content in three important NaSICON electrodes, NaxTi2(PO4)3, NaxV2(PO4)3, and NaxCr2(PO4)3. Our study suggests that Na+ transport in NaSICON electrodes is almost entirely determined by the local electrostatic and chemical environment set by the transition metal and the polyanionic scaffold. The competition with the ordering-disordering phenomena of Na-vacancies also plays a role in influencing Na-transport. We identify the Na content providing the highest room-temperature diffusivities in these electrodes, i.e., Na2.7Ti2(PO4)3, Na2.9V2(PO4)3, and Na2.6Cr2(PO4)3. We link the variations in the Na+ kinetic properties by analyzing the competition of ligand field stabilization transition metal ions and their ionic radii. We interpret the limited Na-extraction at x = 1 observed experimentally by gaining insights into the local Na-vacancy interplay. We propose that targeted chemical substitutions of transition metals disrupting local charge arrangements will be critical to reducing the occurrence of strong Na+-vacancy orderings at low Na concentrations, thus, expanding the accessible capacities of these electrode materials. Copyright © 2023, The Authors. All rights reserved.

关键词： Metal ions

Toward information-driven and risk-bounded autonomy for adaptive science and exploration

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Toward information-driven and risk-bounded autonomy for adap...

Accelerating Space Commerce, Exploration, and New Discovery Conference, ASCEND 2020

作者： Ayton, Benjamin Reeves, Marlyse Timmons, Eric Williams, Brian C. Ingham, Michel D. MIT Computer Science and Artificial Intelligence Laboratory Department of Aeronautics and Astronautics 32 Vassar St CambridgeMA02139 United States MIT Computer Science and Artificial Intelligence Laboratory Department of Electrical Engineering and Computer Science 32 Vassar St CambridgeMA02139 United States 4800 Oak Grove Dr PasadenaCA91109 United States

ISBN: (数字)9781624106088

ISBN: (纸本)9781624106088

While the primary purpose of robotic space exploration systems is to gather scientific data, it is equally important that engineering operations are performed and engineering constraints are respected in order to prolong the mission life and ensure the integrity of the observations taken. However, science and engineering operations are often at odds with each other as attempting to obtain the "best" data may violate engineering operations constraints and place the mission at risk. Historically, mission systems engineering has separated the process of planning for science from engineering operations, with the engineering operations constrained to support the science measurement plan with acceptable risk. This task division leads to multiple design iterations between the science and engineering operations which results in compromised, conservative operations that reduce science return and are more brittle than desired. To overcome these limitations, we present an approach for autonomous mission planning that explicitly models and reasons about the coupling between science and engineering operations, resulting in higher science return, while maintaining acceptable levels of risk. Our approach is to develop an information-driven, risk-bounded plan executive that is capable of producing missions satisfying the goals and constraints expressed in these programs. In this paper, we describe in detail the risk-bounded, information-driven execution problem and lay out the architecture used in our information-directed plan executive ‘Enterprise’. We then show the performance of the current version of Enterprise on two space exploration scenarios. Finally, we conclude with thoughts on future work, including on the design of a proposed information-theoretic language that will allow operators and scientists to specify their objectives in terms of questions about scientific phenomena or the configuration of the space system. © 2020 The mitRE Corporation. All Rights Reserved.

关键词： Space Exploration Systems engineering Space Systems Asteroids Spacecraft Missions Motion Planning Europa Gaussian Process Mixed Integer Linear Programming Markov Decision Process

Degradation of Ta2O5/SiO2 Dielectric Cavity Mirrors in Ultra-High Vacuum

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arXiv 2023年

作者： Rudelis, Alyssa Hu, Beili Sinclair, Josiah Bytyqi, Edita Schwartzman, Alan Brenes, Roberto Zhitomirsky, Tamar Kadosh Schleier-Smith, Monika Vuletić, Vladan Department of Physics MIT-Harvard Center for Ultracold Atoms Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Electrical Engineering and Computer Science Department Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Stanford University StanfordCA94305 United States

In order for optical cavities to enable strong light-matter interactions for quantum metrology, networking, and scalability in quantum computing systems, their mirrors must have minimal losses. However, high-finesse dielectric cavity mirrors can degrade in ultra-high vacuum (UHV), increasing the challenges of upgrading to cavity-coupled quantum systems. We observe the optical degradation of high-finesse dielectric optical cavity mirrors after high-temperature UHV bake in the form of a substantial increase in surface roughness. We provide an explanation of the degradation through atomic force microscopy (AFM), X-ray fluorescence (XRF), selective wet etching, and optical measurements. We find the degradation is explained by oxygen reduction in Ta2O5 followed by growth of tantalum sub-oxide defects with height to width aspect ratios near ten. We discuss the dependence of mirror loss on surface roughness and finally give recommendations to avoid degradation to allow for quick adoption of cavity-coupled systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Aspect ratio

Learning-Based Calibration of Flux Crosstalk in Transmon Qubit Arrays

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Physical Review Applied 2023年第2期20卷 024070-024070页

作者： Cora N. Barrett Amir H. Karamlou Sarah E. Muschinske Ilan T. Rosen Jochen Braumüller Rabindra Das David K. Kim Bethany M. Niedzielski Meghan Schuldt Kyle Serniak Mollie E. Schwartz Jonilyn L. Yoder Terry P. Orlando Simon Gustavsson Jeffrey A. Grover William D. Oliver Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Wellesley College Wellesley Massachusetts 02481 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA MIT Lincoln Laboratory Lexington Massachusetts 02421 USA Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

Superconducting quantum processors comprising flux-tunable data and coupler qubits are a promising platform for quantum computation. However, magnetic flux crosstalk between the flux-control lines and the constituent qubits impedes precision control of qubit frequencies, presenting a challenge to scaling this platform. In order to implement high-fidelity digital and analog quantum operations, one must characterize the flux crosstalk and compensate for it. In this work, we introduce a learning-based calibration protocol and demonstrate its experimental performance by calibrating an array of 16 flux-tunable transmon qubits. To demonstrate the extensibility of our protocol, we simulate the crosstalk matrix learning procedure for larger arrays of transmon qubits. We observe an empirically linear scaling in calibration time with system size while maintaining a median qubit frequency error below 300 kHz.

关键词： Machine learning Optoelectronics Quantum control Quantum gates Quantum information with solid state qubits Superconducting qubits Microwave techniques