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arXiv 2024年

作者： Xie, Pinchen Chen, Yixiao Weinan, E. Car, Roberto Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States AI for Science Institute Beijing China Center for Machine Learning Research School of Mathematical Sciences Peking University Beijing China Department of Chemistry Department of Physics Program in Applied and Computational Mathematics Princeton Materials Institute Princeton University PrincetonNJ08544 United States

We report an extensive molecular dynamics study of ab-initio quality of the ferroelectric phase transition in crystalline PbTiO3. We model anharmonicity accurately in terms of potential energy and polarization surfaces trained on density functional theory data with modern machine learning techniques. Our simulations demonstrate that the transition has a strong order-disorder character, in agreement with diffraction experiments, and provide fresh insight into the approach to equilibrium upon crossing the transition temperature. We find that the macroscopic polarization appears/disappears due to dipolar switching at the nanometer scale. We also compute the infrared optical absorption spectra in both the ferroelectric and the paraelectric phases, finding good agreement with the experimental Raman frequencies. For a long time, the almost ideal displacive character of the soft mode detected by Raman scattering in the paraelectric phase has been contrasted with the order-disorder character of the transition suggested by diffraction. We settle this issue by showing that the soft mode coexists with a strong Debye relaxation due to the thermal disordering of the dipoles. The Debye relaxation feature is centered at zero frequency and appears near the transition temperature in both the ferroelectric and the paraelectric phases. © 2024, CC BY.

关键词： Ferroelectricity

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Direct current generation by dielectric loss in ferroelectrics

arXiv

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arXiv 2024年

作者： Morimoto, Takahiro Nagaosa, Naoto Department of Applied Physics The University of Tokyo Hongo Tokyo113-8656 Japan Wako Saitama351-0198 Japan Fundamental Quantum Science Program TRIP Headquarters RIKEN Wako351-0198 Japan

We study direct current (DC) generation induced by microwave irradiation to ferroelectric materials. The DC generation originates from microwave absorption called dielectric loss due to the delay of dielectric response. Such current generation can be formulated as the low-frequency limit of the phonon shift current which arises from an increase of electric polarization accompanying photoexcitation of phonons due to the electron-phonon coupling. To study the DC generation by the dielectric loss, we apply the diagrammatic treatment of nonlinear optical responses to photoexcitations of phonons and derive the general formula for phonon shift current. We then study the DC generation in the low-frequency region and find that the current scales as ∝ ω2 for the linearly polarized light and time reversal symmetric systems. We estimate the order of magnitude of the DC generation by dielectric loss, indicating its feasibility for experimental detection in the GHz region. Copyright © 2024, The Authors. All rights reserved.

关键词： Microwave irradiation

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Dark Current and Noise Characterization of InGaAs/GaAsSb T2SL Photodetectors 22

Dark Current and Noise Characterization of InGaAs/GaAsSb T2S...

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Optical Components and Materials XXII 2025

作者： Gil, Armando Phillips, Jamie Ettenberg, Martin H. Kurdak, Cagliyan Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States Princeton Infrared Technologies Inc. 7 Deer Park Dr. Ste. E Monmouth JunctionNJ08852 United States Department of Physics University of Michigan Ann ArborMI48109 United States

ISBN: (纸本)9781510684720

We investigate the dark current and dark current noise spectra of a short-wavelength infrared based blind photodiode as a function of temperature. We designed and built a custom cryogenic system with a slow temperature relaxation time to perform measurements. The measured device uses a type-II superlattice (2.9 nm InGaAs / 2.9 nm GaAsSb) absorption layer with an effective bandgap of 580 meV that is lattice-matched to InP. At temperatures above 150 K the dark current activation energy was found to be 140 meV, which rules out diffusion- or GR-limited currents and explains that large magnitudes of dark current when compared to similar devices. When normalized to current, we find that 1/f noise grows with temperature, reaches a maximum at about 200 K, then reduces as the temperature goes to 300 K. © 2025 SPIE.

关键词： Indium arsenide

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DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning Methods

DeepSeeColor: Realtime Adaptive Color Correction for Autonom...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Stewart Jamieson Jonathan P. How Yogesh Girdhar MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering Department of Aeronautics and Astronautics Massachusetts Institute of Technology (MIT) Applied Ocean Physics and Engineering Department Woods Hole Oceanographic Institution (WHOI)

Successful applications of complex vision-based behaviours underwater have lagged behind progress in terrestrial and aerial domains. This is largely due to the degraded image quality resulting from the physical phenomena involved in underwater image formation. Spectrally-selective light attenuation drains some colors from underwater images while backscattering adds others, making it challenging to perform vision-based tasks underwater. State-of-the-art methods for underwater color correction optimize the parameters of image formation models to restore the full spectrum of color to underwater imagery. However, these methods have high computational complexity that is unfavourable for realtime use by autonomous underwater vehicles (AUVs), as a result of having been primarily designed for offline color correction. Here, we present DeepSeeColor, a novel algorithm that combines a state-of-the-art underwater image formation model with the computational efficiency of deep learning frameworks. In our experiments, we show that DeepSeeColor offers comparable performance to the popular “Sea-Thru” algorithm [1] while being able to rapidly process images at up to 60Hz, thus making it suitable for use onboard AUVs as a preprocessing step to enable more robust vision-based behaviours.

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A Low-Cost Entangling Drifter for Sargassum Tracking: Design and Experimental Evaluation

A Low-Cost Entangling Drifter for Sargassum Tracking: Design...

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OCEANS

作者： Chase Pixa Kevin Manganini Alyson Myers Anna PM Michel MIT-WHOI Joint Program in Applied Ocean Science and Engineering Cambridge USA Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution USA Fearless Fund Washington D.C. USA

Since 2011, Sargassum, a free-floating macroalgae, has been causing significant ecological and economic damage to coastal regions in the Northern Equatorial Atlantic due to its voluminous blooms. To better understand the fate and effects of Sargassum, approaches are needed to track its transport. Here we developed a low-cost drifter, designed to entangle with Sargassum, to aid in its tracking providing in situ movement data to ground-truth models and supplementing gaps in satellite imaging. The results of 27 drifter designs and five days of field trials are presented. The successful entanglement and tracking with the Sargassum demonstrated here can guide future studies to further our understanding of the movement of Sargassum in the great Atlantic Sargassum belt (GASB).

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Diffusion curvature for estimating local curvature in high dimensional data 22

Diffusion curvature for estimating local curvature in high d...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Dhananjay Bhaskar Kincaid MacDonald Oluwadamilola Fasina Dawson Thomas Bastian Rieck Ian Adelstein Smita Krishnaswamy Department of Genetics Yale University Department of Mathematics Yale University Applied Mathematics Program Yale University Department of Mathematics Department of Physics Yale University Institute of AI for Health Helmholtz Pioneer Campus Helmholtz Munich Department of Computer Science Department of Genetics Applied Mathematics Program Program for Computational Biology and Bioinformatics Yale University

ISBN: (纸本)9781713871088

We introduce a new intrinsic measure of local curvature on point-cloud data called diffusion curvature. Our measure uses the framework of diffusion maps, including the data diffusion operator, to structure point cloud data and define local curvature based on the laziness of a random walk starting at a point or region of the data. We show that this laziness directly relates to volume comparison results from Riemannian geometry. We then extend this scalar curvature notion to an entire quadratic form using neural network estimations based on the diffusion map of point-cloud data. We show applications of both estimations on toy data, single-cell data and on estimating local Hessian matrices of neural network loss landscapes.

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Electronic-photonic quantum systems on-chip

Electronic-photonic quantum systems on-chip

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Quantum 2.0, QUANTUM 2022

作者： Wang, Imbert Ramesh, Anirudh Kramnik, Danielius Fargas Cabanillas, Josep M. Stojanović, Vladimir Kumar, Prem Popović, Miloš A. Department of Electrical and Computer Engineering Photonics Center Boston University BostonMA02215 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Electrical Engineering and Computer Science UC Berkeley BerkeleyCA94720 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

We present progress towards realizing electronic-photonic quantum systems on-chip;particularly, entangled photon-pair sources, placing them in the context of previous work, and outlining our vision for mass-producible... 详细信息

ISBN: (纸本)9781957171111

关键词： Quantum entanglement

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Spectroscopic Properties and Judd-Ofelt Analysis of Eu3+doped Ba-Na-B Glasses for Photonic Applications 7

Spectroscopic Properties and Judd-Ofelt Analysis of Eu3+dope...

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7th Annual International Seminar on Trends in science and science Education, AISTSSE 2020

作者： Luewarasirikul, N. Kaewkhao, J. Applied Physics Program Faculty of Sciences and Technology Suan Sunandha Rajabhat University Bangkok10300 Thailand Physics Program Faculty of Science and Technology Nakhon Pathom Rajabhat University Nakhon Pathom73000 Thailand Nakhon Pathom Rajabhat University Nakhon Pathom73000 Thailand

In this work, barium sodium borate glasses doped with europium ions were fabricated by melt-quenching technique with the compositions of 10BaO:25Na2O:(65-x)B2O3:xEu2O3(where x = 0.0, 0.1, 0.5, 1.0, 1.5 and 2.0 mol%). The density, molar volume and refractive index were carried out at room temperature. The densities of the Eu3+-doped glass samples are around 2.6663 to 2.7850 g/cm3, the molar volumes are around 28.5337 to 29.3455 cm3/mol and the refractive indexes are around 1.5379 to 1.5624. The absorption spectra, recorded in the UV-Vis-NIR region from 350 to 2500 nm, showed the highest peak at 393 nm (corresponding to the 7F0?5L6 transition) in Vis region. The emission spectra, observed with 394 nm excitation wavelength, showed the peaks centered at 590, 613, 652 and 700 nm (corresponding to the 5D0?7F1, 7F2, 7F3 and 7F4 transitions, respectively). The luminescence lifetimes were found to be 1.948, 1.938, 1.924, 1.913 and 1.901 ms for 0.1, 0.5, 1.0, 1.5 and 2.0 mol% of Eu2O3 doped in the glass samples, respectively. The color coordinates of the emitted light for all glass samples were found to be (0.65, 0.35) in the CIE 1931 chromaticity diagram (located in reddish orange region). The emission spectra showed that the glass with 1.5 mol% of Eu2O3 provided the highest peak for all glass samples because of the concentration quenching effect. Judd-Ofelt analysis was also employed in this work to investigate the radiative properties of the glass samples. © Published under licence by IOP Publishing Ltd.

关键词： Glass

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Using 4D-STEM to Map 3D Morphologies of 2D Materials

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 262-263页

作者： Mireles, Adan Shi, Chuqiao Park, Jeongwon Shin, Bongki Sung, Suk Hyun Ophus, Colin Hovden, Robert Kang, Kibum Han, Yimo Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX United States Department of Materials Science and NanoEngineering Rice University Houston TX United States Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea Department of Materials Science and Engineering University of Michigan Ann Arbor MI United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley CA United States

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Apache Spark Powered: Enhancing Network Intrusion Detection System Using Random Forest

Apache Spark Powered: Enhancing Network Intrusion Detection ...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Hesham Mamdouh Mohamed Tarek Abdelrahman Radwan Adel Saeed Ahmed Abdeen Monier Ashraf Khaled M. Fouad Ibrahim Abdelbaky School of Engineering and Applied Science (EAS) Nile University Giza Egypt CS program School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Faculty of Computer Science and Engineering New Mansoura University Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

The increasing sophistication of cyber attacks necessitates effective intrusion detection systems. We propose a novel intrusion detection method integrating deep learning with big data management using Apache Spark. Leveraging the comprehensive CSE-CIC-IDS2018 dataset, we apply extensive data preprocessing, including handling missing and unreliable values, duplicates, and redundant columns. In addition, implementation of a Random Forest based feature importance approach is derived to prioritize the most impactful Features. Furthermore, stratified k-fold cross-validation is used for a model selection process on a class-imbalanced dataset. Our weighted Random Forest classifier achieves a remarkable weighted average F1-score of 0.999 and a test inference time of 0.673 seconds using only the top 34 features, outperforming previous studies without sampling techniques. The proposed architecture offers a scalable and accurate solution for intrusion detection in cloud architectures, demonstrating the effectiveness of combining deep learning and big data technologies for cybersecurity.

关键词： Deep learning Cloud computing Data preprocessing Network intrusion detection Cluster computing Forestry Big Data Random forests Cyberattack Testing

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