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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Acosta, Francisco Dinc, Fatih Redman, William T. Madhav, Manu Klindt, David Miolane, Nina Department of Physics UC Santa Barbara United States CNC Program Stanford University United States Applied Physics Laboratory Johns Hopkins University United States University of British Columbia Canada NeuroAI CSHL United States UC Santa Barbara United States

Grid cells in the mammalian brain are fundamental to spatial navigation, and therefore crucial to how animals perceive and interact with their environment. Traditionally, grid cells are thought support path integration through highly symmetric hexagonal lattice firing patterns. However, recent findings show that their firing patterns become distorted in the presence of significant spatial landmarks such as rewarded locations. This introduces a novel perspective of dynamic, subjective, and action-relevant interactions between spatial representations and environmental cues. Here, we propose a practical and theoretical framework to quantify and explain these interactions. To this end, we train path-integrating recurrent neural networks (piRNNs) on a spatial navigation task, whose goal is to predict the agent's position with a special focus on rewarded locations. Grid-like neurons naturally emerge from the training of piRNNs, which allows us to investigate how the two aspects of the task, space and reward, are integrated in their firing patterns. We find that geometry, but not topology, of the grid cell population code becomes distorted. Surprisingly, these distortions are global in the firing patterns of the grid cells despite local changes in the reward. Our results indicate that after training with location-specific reward information, the preserved representational topology supports successful path integration, whereas the emergent heterogeneity in individual responses due to global distortions may encode dynamically changing environmental cues. By bridging the gap between computational models and the biological reality of spatial navigation under reward information, we offer new insights into how neural systems prioritize environmental landmarks in their spatial navigation code. © 2024 Neural information processing systems foundation. All rights reserved.

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AI Aided GPR Data Multipath Summation Using x-t Stacking Weights 47

AI Aided GPR Data Multipath Summation Using x-t Stacking Wei...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Nikos, Economou Sobhi, Nasir Said, Al Abri Al Shaqsi, Bader Hamdan, Hamdan School of Mineral Resources Engineering Technical University of Crete Applied Geophysics Lab Chania Greece Sultan Qaboos University College of Science Department of Earth Sciences Muscat Oman Sharjah University Petroleum Geosciences and Remote Sensing Program Department of Applied Physics and Astronomy United Arab Emirates

ISBN: (纸本)9798350365597

Migration velocity of Ground Penetrating Radar (GPR) data is a low wavenumber attribute crucial for migration. Obtaining a migration velocity model, which is considered closer to a Root Mean Square (RMS) model, from zero-offset (ZO) data requires analysis of the available diffractions whose density and (x, t) coverage is random. Thus, accuracy and efficiency of such a velocity model either for migration or its usage for interval velocity model estimation is not guaranteed. Here, with the aid of an Artificiaal Intelligence (AI) algorithm, which detects diffractions and usage of their kinematic information, we generate a diffraction velocity model used for the assignment of 2D weights for weighted multipath summation. The scope is to efficiently focus the scattered energy within a GPR section. We describe this methodology and demonstrate its application in enhancing the lateral continuity of reflections. We compare it with the multipath summation using lD weights with simulated data and present its application on marble assessment GPR data for imaging cracks in the subsurface structure. © 2024 IEEE.

关键词： Diffraction

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Tungsten Nitride (W5N6): An Ultraresilient 2D Semimetal

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Nano Letters 2024年第1期24卷 67-73页

作者： Chin, Hao-Ting Wang, Deng-Chi Gulo, Desman Perdamaian Yao, Yu-Chi Yeh, Hao-Chen Muthu, Jeyavelan Chen, Ding-Rui Kao, Tzu-Chun Kalbáč, Martin Lin, Ping-Hui Cheng, Cheng-Maw Hofmann, Mario Liang, Chi-Te Liu, Hsiang-Lin Chuang, Feng-Chuan Hsieh, Ya-Ping Molecular Science and Technology Program Taiwan International Graduate Program Academia Sinica Taipei10617 Taiwan International Graduate Program of Molecular Science and Technology National Taiwan University Taipei10617 Taiwan Institute of Atomic and Molecular Sciences Academia Sinica Taipei10617 Taiwan Department of Physics National Sun Yat-Sen University Kaohsiung80424 Taiwan Department of Physics National Taiwan Normal University Taipei11677 Taiwan Department of Physics National Taiwan University Taipei10617 Taiwan Institute of Physics Academia Sinica Taipei115201 Taiwan International Graduate Program of Nano Science and Technology National Taiwan University Taipei10617 Taiwan Graduate Institute of Applied Physics National Taiwan University Taipei10617 Taiwan J. Heyrovský Institute of Physical Chemistry Czech Academy of Sciences Dolejškova 2155/3 Prague182 23 Czech Republic Hsinchu300092 Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan Center for Theoretical and Computational Physics National Sun Yat-sen University Kaohsiung80424 Taiwan

Two-dimensional transition metal nitrides offer intriguing possibilities for achieving novel electronic and mechanical functionality owing to their distinctive and tunable bonding characteristics compared to other 2D materials. We demonstrate here the enabling effects of strong bonding on the morphology and functionality of 2D tungsten nitrides. The employed bottom-up synthesis experienced a unique substrate stabilization effect beyond van-der-Waals epitaxy that favored W5N6 over lower metal nitrides. Comprehensive structural and electronic characterization reveals that monolayer W5N6 can be synthesized at large scale and shows semimetallic behavior with an intriguing indirect band structure. Moreover, the material exhibits exceptional resilience against mechanical damage and chemical reactions. Leveraging these electronic properties and robustness, we demonstrate the application of W5N6 as atomic-scale dry etch stops that allow the integration of high-performance 2D materials contacts. These findings highlight the potential of 2D transition metal nitrides for realizing advanced electronic devices and functional interfaces. © 2023 American Chemical Society

关键词： Tungsten compounds

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Sensing beyond the exceptional point for high detectivity

Sensing beyond the exceptional point for high detectivity

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Li, Zhichao Prasad, Ciril S. Wang, Xielin Zhang, Ding Naik, Gururaj V. Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States

Exceptional point (EP)-based optical sensors exhibit exceptional sensitivity but poor detectivity. Slightly off EP operation boosts detectivity without much loss in sensitivity. We experimentally demonstrate a high-detectivity-off-EP plasmonic-photonic hybrid sensor for anti-mouse IgG protein. © Optica Publishing Group 2024

关键词： Hybrid sensors

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Sensing beyond the exceptional point for high detectivity

Sensing beyond the exceptional point for high detectivity

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Laser beams

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Matrix confinement modulates 3D spheroid sorting and burst-like collective migration

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Acta Biomaterialia 2024年 179卷 192-206页

作者： Cai, Grace Li, Xinzhi Lin, Shan-Shan Chen, Samuel J. Rodgers, Nicole C. Koning, Katherine M. Bi, Dapeng Liu, Allen P. Applied Physics Program University of Michigan Ann ArborMI United States Department of Physics Carnegie Mellon University PittsburghPA United States Institute of Molecular Medicine College of Medicine National Taiwan University Taipei Taiwan Department of Mechanical Engineering University of Michigan Ann ArborMI United States Cellular and Molecular Biology Program University of Michigan Ann ArborMI United States Department of Physics Northeastern University BostonMA United States Department of Biomedical Engineering University of Michigan Ann ArborMI United States Department of Biophysics University of Michigan Ann ArborMI United States

While it is known that cells with differential adhesion tend to segregate and preferentially sort, the physical forces governing sorting and invasion in heterogeneous tumors remain poorly understood. To investigate this, we tune matrix confinement, mimicking changes in the stiffness and confinement of the tumor microenvironment, to explore how physical confinement influences individual and collective cell migration in 3D spheroids. High levels of confinement lead to cell sorting while reducing matrix confinement triggers the collective fluidization of cell motion. Cell sorting, which depends on cell-cell adhesion, is crucial to this phenomenon. Burst-like migration does not occur for spheroids that have not undergone sorting, regardless of the degree of matrix confinement. Using computational Self-Propelled Voronoi modeling, we show that spheroid sorting and invasion into the matrix depend on the balance between cell-generated forces and matrix resistance. The findings support a model where matrix confinement modulates 3D spheroid sorting and unjamming in an adhesion-dependent manner, providing insights into the mechanisms of cell sorting and migration in the primary tumor and toward distant metastatic sites. Statement of significance: The mechanical properties of the tumor microenvironment significantly influence cancer cell migration within the primary tumor, yet how these properties affect intercellular interactions in heterogeneous tumors is not well understood. By utilizing calcium and calcium chelators, we dynamically alter collagen-alginate hydrogel stiffness and investigate tumor cell behavior within co-culture spheroids in response to varying degrees of matrix confinement. High confinement is found to trigger cell sorting while reducing confinement for sorted spheroids facilitates collective cell invasion. Notably, without prior sorting, spheroids do not exhibit burst-like migration, regardless of confinement levels. This work establishes that matrix confin

关键词： Tumors

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Time-dependent growth of nuclear spin polarization under periodic optical pumping

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Physical Review B 2023年第11期108卷 115203-115203页

作者： Michael J. Dominguez Hua-Wei Hsu Vanessa Sih Applied Physics Program University of Michigan Ann Arbor Michigan 48109 USA Department of Physics University of Michigan Ann Arbor Michigan 48109 USA

Consecutive circularly polarized optical pulses generate and rotate electron spin polarization through optical orientation and the optical Stark effect. We perform time- and magnetic-field-dependent optical pump-probe measurements on gallium arsenide and observe a variable Overhauser field growth that depends on the external magnetic field and laser wavelength. We show that the time dependence of the nuclear spin polarization can be attributed to the time-averaged electron spin polarization produced along the external magnetic field direction.

关键词： Spin coherence Spin polarization Spin pumping Dynamic nuclear polarization Magneto-optical Kerr effect Ultrafast pump-probe spectroscopy

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Ordered and disordered stealthy hyperuniform point patterns across spatial dimensions

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Physical Review Research 2024年第3期6卷 033260页

作者： Peter K. Morse Paul J. Steinhardt Salvatore Torquato Department of Chemistry Department of Physics Princeton Institute of Materials Princeton Center for Theoretical Science Program in Applied and Computational Mathematics

In previous work [Phys. Rev. X 5, 021020 (2015)] it was shown that stealthy hyperuniform systems can be regarded as hard spheres in Fourier space in the sense that the structure factor is exactly zero in a spherical region around the origin in analogy with the pair-correlation function of real-space hard spheres. While this earlier work focused on spatial dimensions d=1–4, here we extend the analysis to higher dimensions in order to make connections to high-dimensional sphere packings and the mean-field theory of glasses. We exploit this correspondence to confirm that the densest Fourier-space hard-sphere system is that of a Bravais lattice in contrast to real-space hard spheres, whose densest configuration is conjectured to be disordered. In passing, we give a concise form for the position of the first Bragg peak. We also extend the virial series previously suggested for disordered stealthy hyperuniform systems to higher dimensions in order to predict spatial decorrelation as a function of dimension. This prediction is then borne out by numerical simulations of disordered stealthy hyperuniform ground states in dimensions d=2–8, which have only recently been made possible due to a highly parallelized algorithm.

关键词： Classical statistical mechanics Disordered systems

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DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

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Journal of Physical Chemistry A 2022年第49期126卷 9154-9164页

作者： Li, Wenfei Ou, Qi Chen, Yixiao Cao, Yu Liu, Renxi Zhang, Chunyi Zheng, Daye Cai, Chun Wu, Xifan Wang, Han Chen, Mohan Zhang, Linfeng AI for Science Institute Beijing100080 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States HEDPS CAPT College of Engineering School of Physics Peking University Beijing100871 China Department of Physics Temple University PhiladelphiaPA19122 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China DP Technology Beijing100080 China

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for different levels of QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training an ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), an ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model and then use the DeePKS model to label a much larger amount of configurations to train an ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open source and ready for use in various applications. © 2022 The Journal of Physical Chemistry A. All right reserved.

关键词： Density functional theory

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Modulating Functions Analysis For Wireless Data Transmission Because Of Advanced Sensors In Energy-Saving Process Of Washing

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2023年第6期21卷 695-700页

作者： Calderón Ch., J. Alan Sotelo, Julio C. Tafur Gamarra, Benjamín Barriga Alencastre, Jorge Álex, Quispe M. Ccarita, F. Alan Applied Nanophysics Institute for Physics Technical University of Ilmenau Ilmenau98693 Germany Mechatronic Engineering Master Program Engineering Department Pontificia Universidad Católica del Perú Peru Aplicaciones Avanzadas en Sistema Mecatrónicos JACH S.A.C Peru

The vibration measurement of flows in transient or steady-state is a very complicated task. Moreover, to correlate it with a temperature process is quite important according to get information regarding molecular kinetic energy of thermal sources (such as flow measurement used in cleaning tasks) according to achieve heating transfer information of a thermal process. However, there is a trouble concerning the transduction stage in the measurement while it is not a transducer designed algorithm as a consequence of a mathematical model, which correlates the calibration data with the theoretical model of the heating/vibration transfer. For this reason, in this research is proposed intelligent sensors/transducers, which are based on Anodic Aluminium Oxide (AAO) and a mathematical procedure of the measurement instrumentation according to adaptive coefficients in the Modulating Functions strategies analysis and getting optimal measurements. In this research is explained and analysed the temperature measurement process and the transduction process as the strict correlation of the calibration of the temperature/vibration sensor. For this reason, there were evaluated different transducers and the temperature/vibration reference for the calibration. The based on nanostructures temperature sensors are designed by specific and complex procedures according to achieve quite operation range, robustness and precision. Moreover, the transduction can be obtained through different electrical answer variables such as voltage, electrical current or capacitance and possible to send by wireless mechanisms and protocols to the main control system. Therefore, the evaluation of the designed advanced sensor performance was achieved by the vibration and temperature measurement of the water surface of a ultrasound washing machine, for which the designed sensor enhances energy-saving of the washing process. © 2023, European Association for the Development of Renewable Energy, Environment and Powe

关键词： Kinetics

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