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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qian, William Zavatone-Veth, Jacob A. Ruben, Benjamin S. Pehlevan, Cengiz Biophysics Graduate Program Harvard University CambridgeMA02138 United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Center for Brain Science Harvard University CambridgeMA02138 United States Department of Physics Harvard University CambridgeMA02138 United States

One of the central goals of neuroscience is to gain a mechanistic understanding of how the dynamics of neural circuits give rise to their observed function. A popular approach towards this end is to train recurrent neural networks (RNNs) to reproduce experimental recordings of neural activity. These trained RNNs are then treated as surrogate models of biological neural circuits, whose properties can be dissected via dynamical systems analysis. How reliable are the mechanistic insights derived from this procedure? While recent advances in population-level recording technologies have allowed simultaneous recording of up to tens of thousands of neurons, this represents only a tiny fraction of most cortical circuits. Here we show that observing only a subset of neurons in a circuit can create mechanistic mismatches between a simulated teacher network and a data-constrained student, even when the two networks have matching single-unit dynamics. In particular, partial observation of models of low-dimensional cortical dynamics based on functionally feedforward or low-rank connectivity can lead to surrogate models with spurious attractor structure. Our results illustrate the challenges inherent in accurately uncovering neural mechanisms from single-trial data, and suggest the need for new methods of validating data-constrained models for neural dynamics. © 2024 Neural information processing systems foundation. All rights reserved.

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Quantum frequency mixing using an N-V diamond microscope

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Physical Review Applied 2024年第6期22卷 064051-064051页

作者： Samuel J. Karlson Pauli Kehayias Jennifer M. Schloss Andrew C. Maccabe Adam Libson David F. Phillips Guoqing Wang Paola Cappellaro Danielle A. Braje Massachusetts Institute of Technology (MIT) Lincoln Laboratory Lexington Massachusetts 02421 USA Department of Nuclear Science and Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Current affiliation: Quantum Science and Engineering Program Harvard University Cambridge Massachusetts 02138 USA. Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

Wide-field magnetic microscopy using nitrogen-vacancy (N-V) centers in diamond can yield high-quality magnetic images of dc and ac magnetic fields. The unique combination of micron-scale spatial resolution of scalar or vector fields at room temperature and parallel camera readout make this an appealing technique for applications in biology, geology, condensed-matter physics, and electronics. However, while N-V magnetic microscopy has achieved great success in these areas, historically the accessible frequency range has been limited. In this paper, we overcome this limitation by implementing the recently developed technique of quantum frequency mixing. With this approach, we generate wide-field magnetic images of test structures driven by alternating currents up to 70 MHz, well outside the reach of dc and Rabi magnetometry methods. With further improvements, this approach could find utility in hyperspectral imaging for electronics power-spectrum analysis, electronics diagnostics and troubleshooting, and quantum computing hardware validation.

关键词： Quantum computers

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Deep potentials for materials science

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Materials Futures 2022年第2期1卷 89-115页

作者： Tongqi Wen Linfeng Zhang Han Wang Weinan E David J Srolovitz Department of Mechanical Engineering The University of Hong KongHong KongHong Kong Special Administrative Region of China DP Technology BeijingPeople’s Republic of China AI for Science Institute BeijingPeople’s Republic of China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijingPeople’s Republic of China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingPeople’s Republic of China School of Mathematical Sciences Peking UniversityBeijingPeople’s Republic of China Department of Mathematics and Program in Applied and Computational Mathematics Princeton UniversityPrincetonNJUnited States of America International Digital Economy Academy(IDEA) ShenzhenPeople’s Republic of China

To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;*** learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)*** this review,we provide an introduction to DP methods in computational materials *** theory underlying the DP method is presented along with a step-by-step introduction to their development and *** also review materials applications of DPs in a wide range of materials *** DP Library provides a platform for the development of DPs and a database of extant *** discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

关键词： deep potential atomistic simulation machine learning potential neural network

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The Radial Dynamics and Acoustic Emissions of Phase-Shift Droplets are Impacted by Mechanical Properties of Tissue-Mimicking Hydrogels

SSRN

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SSRN 2024年

作者： Kaushik, Anuj Abeid, Bachir A. Estrada, Jonathan B. Fowlkes, Jeffry Brian Fabiilli, Mario L. Aliabouzar, Mitra Department of Radiology University of Michigan Ann ArborMI United States Department of Mechanical Engineering University of Michigan Ann ArborMI United States Department of Biomedical Engineering University of Michigan Ann ArborMI United States Applied Physics Program University of Michigan Ann ArborMI United States

Acoustic droplet vaporization (ADV) offers a dynamic approach for generating bubbles on demand, presenting new possibilities in biomedical applications. Although ADV has been investigated in various biomedical applications, its potential in tissue characterization remains unexplored. Here, we investigated the effects of surrounding media on the radial dynamics and acoustic emissions of ADV bubbles using theoretical and experimental methodologies. For theoretical studies, bubble dynamics were combined with the Kelvin-Voight material constitutive model, accounting for viscoelasticity of the media. The radial dynamics and acoustic emissions of the ADV-bubbles were recorded via ultra-high-speed microscopy and passive cavitation detection, respectively. Perfluoropentane phase-shift droplets were embedded in tissue-mimicking hydrogels of varying fibrin concentrations, representing different elastic moduli. Radial dynamics and the acoustic emissions, both temporal and spectral, of the ADV-bubbles depended significantly on fibrin elasticity. For example, an increase in fibrin elastic modulus from ≈0.2 kPa to ≈6 kPa reduced the maximum expansion radius of the ADV-bubbles by 50%. A similar increase in medium elasticity significantly impacted both linear (e.g., fundamental) and nonlinear (e.g., subharmonic) acoustic responses of the ADV-bubbles, by up to 10 dB. The sensitivity of ADV to the surrounding media was dependent on acoustic parameters such as driving pressure and the droplets concentration. Further analysis of the acoustic emissions revealed distinct ADV signal characteristics, which were significantly influenced by the surrounding media. © 2024, The Authors. All rights reserved.

关键词： Drops

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GPU Implementation of a Gas-Phase Chemistry Solver in the CMAQ Chemical Transport Model

ACS ES&T air

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ACS ES&T air 2025年第2期2卷 226-235页

作者： Duncan Quevedo Khanh Do George Delic José Rodríguez-Borbón Bryan M Wong Cesunica E Ivey Department of Civil and Environmental Engineering University of California Berkeley California 94720 United States. Department of Chemical and Environmental Engineering University of California Riverside California 92557 United States. Center for Environmental Research and Technology Riverside California 92507 United States. Hiperism Consulting LLC Chapel Hill North Carolina 27514 United States. Department of Chemistry Department of Physics & Astronomy and Material Science & Engineering Program University of California Riverside California 92557 United States.

The Community Multiscale Air Quality (CMAQ) model simulates atmospheric phenomena, including advection, diffusion, gas-phase chemistry, aerosol physics and chemistry, and cloud processes. Gas-phase chemistry is often a major computational bottleneck due to its representation as large systems of coupled nonlinear stiff differential equations. We leverage the parallel computational performance of graphics processing unit (GPU) hardware to accelerate the numerical integration of these systems in CMAQ's CHEM module. Our implementation, dubbed CMAQ-CUDA, in reference to its use in the Compute Unified Device Architecture (CUDA) general purpose GPU (GPGPU) computing solution, migrates CMAQ's Rosenbrock solver from Fortran to CUDA Fortran. CMAQ-CUDA accelerates the Rosenbrock solver such that simulations using the chemical mechanisms RACM2, CB6R5, and SAPRC07 require only 51%, 50%, or 35% as much time, respectively, as CMAQv5.4 to complete a chemistry time step. Our results demonstrate that CMAQ is amenable to GPU acceleration and highlight a novel Rosenbrock solver implementation for reducing the computational burden imposed by the CHEM module.

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Machine-learned design principles for enhanced red emission from nitride quantum wells

arXiv

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arXiv 2024年

作者： Pant, Nick Armitage, Rob Kioupakis, Emmanouil Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Materials Science & Engineering University of Michigan Ann ArborMI48109 United States Lumileds LLC San JoseCA95131 United States

Significant effort has been devoted toward mitigating polarization fields in nitride LEDs, as they are viewed as detrimental to light emission, albeit with limited success for red emission. Here, we show that such fields can unusually enhance the optical oscillator strength of red-emitting nitride quantum wells. This insight is enabled by machine-learned models trained on multi-scale quantum-mechanical simulations, which allow for a global exploration of the quantum-well design space at greatly reduced computational cost. Larger polarization fields correlate with higher wave-function overlaps for red emission, a consequence of the quantum-confined Stark effect enabling thinner quantum wells. This finding has implications for designing efficient red nitride LEDs, which are crucial for miniaturizing polychromatic LED pixels to the micron scale for extended-reality and biomedical applications. More broadly, this work highlights the power of machine learning in accelerating the discovery of unconventional paradigms for semiconductor design. Copyright © 2024, The Authors. All rights reserved.

关键词： Semiconductor quantum wells

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Adaptive multi-altitude search and sampling of sparsely distributed natural phenomena

Adaptive multi-altitude search and sampling of sparsely dist...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jessica E. Todd Seth McCammon Yogesh Girdhar Nicholas Roy Dana Yoerger Department of Aeronautics and Astronautics MIT-WHOI Joint Program in Applied Ocean Science and Engineering at the Woods Hole Oceanographic Institution (WHOI) Massachusetts Institute of Technology (MIT) Applied Ocean Physics and Engineering Department Woods Hole Oceanographic Institution Woods Hole MA USA Department of Aeronautics and Astronautics Massachusetts Institute of Technology

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this paper, we propose a novel method for autonomously seeking out sparsely distributed targets in an unknown underwater environment. Our Sparse Adaptive Search and Sample (SASS) algorithm mixes low-altitude observations of discrete targets with high-altitude observations of the surrounding substrates. By using prior information about the distribution of targets across substrate types in combination with belief modelling over these substrates in the environment, high-altitude observations provide information that allows SASS to quickly guide the robot to areas with high target densities. A maximally informative path is autonomously constructed online using Monte Carlo Tree Search with a novel acquisition function to guide the search to maximise observations of unique targets. We demonstrate our approach in a set of simulated trials using a novel generative species model. SASS consistently outperforms the canonical boustrophedon planner by up to 36% in seeking out unique targets in the first 75-90% of time it takes for a boustrophedon survey. Additionally, we verify the performance of SASS on two real world coral reef datasets.

关键词： Surveys Adaptation models Monte Carlo methods Adaptive systems Marine vegetation Substrates Intelligent robots

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Experimental & Numerical Investigations of Ultra-High-Speed Dynamics of Optically Induced Droplet Vaporization in Soft Materials

SSRN

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SSRN 2024年

作者： Abeid, Bachir A. Fabiilli, Mario L. Aliabouzar, Mitra Estrada, Jon B. Department of Mechanical Engineering University of Michigan Ann ArborMI United States Department of Radiology University of Michigan Ann ArborMI United States Department of Biomedical Engineering University of Michigan Ann ArborMI United States Applied Physics Program University of Michigan Ann ArborMI United States

Perfluorocarbon (PFC) droplets represent a novel class of phase-shift contrast agent with a promise in applications in biomedical and bioengineering fields. PFC droplets undergo a fast liquid-gas transition upon exposure to acoustic or optical triggering, offering a potential adaptable and versatile tool as contrast agent in diagnostics imaging and localized drug delivery vehicles in therapeutics systems. In this paper, we utilize advanced imaging techniques to investigate ultra-high-speed inertial dynamics and rectified quasi-static (low-speed) diffusion evolution of optically induced PFC droplet vaporization within three different hydrogels, each of different concentrations, examining effects such as droplet size and PFC core on bubble dynamics and material viscoelastic properties. Gelatin hydrogels reveal concentration-dependent impacts on bubble expansion and material elasticity. Embedding PFC droplets in gelatin increases internal pressure, resulting in higher equilibrium radius and continuous bubble growth during quasi-static evolution. Similar trends are observed in fibrin and polyacrylamide matrices, with differences in bubble behavior attributed to matrix properties and droplet presence. Interestingly, droplet size exhibits minimal impact on bubble expansion during inertial dynamics but influences quasi-static evolution, with larger droplets leading to continuous growth beyond 60 seconds. Furthermore, the core composition of PFC droplets significantly affects bubble behavior, with higher boiling point droplets exhibiting higher maximum expansion and slower quasi-static dissolution rates. Overall, the study sheds light on the intricate interplay between droplet characteristics, matrix properties, and multi-timescale bubble dynamics, offering valuable insights into their behavior within biomimetic hydrogels. © 2024, The Authors. All rights reserved.

关键词： Viscoelasticity

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Development of Low-cost and Robust IoT Field Station for Coffee Plantation

Development of Low-cost and Robust IoT Field Station for Cof...

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International Conference on Knowledge and Smart Technology (KST)

作者： Satetha Siyang Teerakiat Kerdcharoen Materials Science and Engineering Program Faculty of Science Mahidol University Bangkok 10400 Thailand Department of Physics Faculty of Science and Research Network of NANOTEC Mahidol University National Nanotechnology Center Bangkok 10400 Thailand

Thailand has a growing demand in coffee consumption every year. Currently, the nation’s capacity for producing coffee is limited, necessitating a significant amount of imports from overseas. The Arabica coffee in the north of Thailand, where the scent and taste of coffee have a distinct identity based on varied cultivation locations, has reasonably good quality. Because of the impact of the environment, it is still challenging to maintain the quality of coffee beans from season to season. Moreover, fierce competition with nearby nations like Laos and Vietnam leads to lower prices of coffee beans. This forces Thai farmers and entrepreneurs to embrace more advanced technologies in order to comprehend the environment of each plantation region and boost their level of competition. This project aims to develop a trustworthy and cost-effective IoT field monitoring solution to assist farmers in understanding the climate conditions in various plantation areas. In order to examine the relationship between the environments, the data collected by the devices can be transferred back to the public cloud server. Then, it can be used as knowledge to raise Thai coffee beans’ quality to a premium level so they can continue to compete with coffee beans from other countries.

关键词： Cloud computing Agriculture Servers Monitoring Farming Meteorology

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Multi-wavelength edge detection based on nonlocal multilayer GaAs–AlAs thin films

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Optics Letters 2025年第12期50卷 3856-3859页

作者： Sina Mohammadi Matthew Markowitz Francesco Monticone Mohammad Ali Miri Maria Tamargo Ph.D. Program in Physics The Graduate Center of the City University of New York New York New York 10016 USA Department of Physics Queens College of the City University of New York Queens New York 11367 USA School of Electrical and Computer Engineering Cornell University Ithaca New York 14853 USA Department of Chemistry The City College of New York New York New York 10031 USA

We demonstrate a compact multilayer GaAs–AlAs structure for passive optical edge detection at multiple wavelengths. Through the inverse design of the layer thicknesses, this structure manipulates spatial frequency components of an incoming wavefront, selectively reflecting high-frequency features while suppressing low-frequency intensity variations. Simulations reveal a reflectance transition from minimal to near-total as a function of numerical aperture, a property leveraged for enhancing edge contrast in optical imaging. For the first time, to our knowledge, we utilize molecular beam epitaxy (MBE) to fabricate edge detection devices, ensuring structural fidelity. Material characterization confirms high-quality interfaces, precise thickness control, and excellent uniformity, validating the suitability of MBE for this application. Experimental angle-resolved reflectance measurements closely align with theoretical predictions, demonstrating the feasibility of this approach for real-time, hardware-based optical image processing. The proposed design automatically works for at least two wavelengths and can be readily extended to operate at multiple wavelengths simultaneously. This work opens new possibilities for employing multilayer interference structures in high-performance optical imaging and real-time signal processing.

关键词： Digital image processing Edge detection Inverse design Optical imaging Signal processing Thin films

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