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Light-induced tunable optical properties of 1T-TaS2 at different temperatures

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Light-induced tunable optical properties of 1T-TaS2 at diffe...

Conference on Lasers and Electro-Optics (CLEO)

作者： Weijian Li Gururaj V. Naik Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Electrical & Computer Engineering Rice University Houston TX USA

The c-axis permittivity of 1T-TaS 2 – a quasi-2D charge-density-wave material – changes upon illumination due to light-induced reorganization of CDW stacking. Here we probe the mechanism of this reorganization and ... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Laser theory Temperature Stacking Lighting Lasers and electrooptics Probes Permittivity

Unbiased Estimation of the Vanilla and Deterministic Ensemble Kalman–Bucy Filters

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arXiv 2022年

作者： Álvarez Ballesteros, Miguel Ángel Chada, Neil K. Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this article we consider the development of an unbiased estimator for the ensemble Kalman–Bucy filter (EnKBF). The EnKBF is a continuous-time filtering methodology which can be viewed as a continuous-time analogue of the famous discrete-time ensemble Kalman filter. Our unbiased estimators will be motivated from recent work [Rhee & Glynn 2010, [31]] which introduces randomization as a means to produce unbiased and finite variance estimators. The randomization enters through both the level of discretization, and through the number of samples at each level. Our estimator will be specific to linear and Gaussian settings, where we know that the EnKBF is consistent, in the particle limit N → ∞, with the KBF. We highlight this for two particular variants of the EnKBF, i.e. the deterministic and vanilla variants, and demonstrate this on a linear Ornstein–Uhlenbeck process. We compare this with the EnKBF and the multilevel (MLEnKBF), for experiments with varying dimension size. We also provide a proof of the multilevel deterministic EnKBF, which provides a guideline for some of the unbiased methods. © 2022, CC BY-NC-SA.

关键词： Continuous time systems

TiO2-based Flexible Integrated Photonics for High-sensitivity Temperature Sensing

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TiO2-based Flexible Integrated Photonics for High-sensitivit...

Conference on Lasers and Electro-Optics (CLEO)

作者： Xuan Zhao Bo Fan Ashok Veeraraghavan Jacob Robinson Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Electrical & Computer Engineering Rice University Houston TX USA

We report a flexible temperature sensor based on TiO 2 photonics that shows double the sensitivity compared to silicon photonics. This high sensitivity and biocompatibility pave the way towards point-of-care temperat... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Temperature sensors Sensitivity Point of care Silicon photonics Sensors Titanium dioxide Electrooptical waveguides

Unbiased and Multilevel Methods for a Class of Diffusions Partially Observed via Marked Point Processes

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arXiv 2023年

作者： Alvarez, Miguel Jasra, Ajay Ruzayqat, Hamza Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this article we consider the filtering problem associated to partially observed diffusions, with observations following a marked point process. In the model, the data form a point process with observation times that have its intensity driven by a diffusion, with the associated marks also depending upon the diffusion process. We assume that one must resort to time-discretizing the diffusion process and develop particle and multilevel particle filters to recursively approximate the filter. In particular, we prove that our multilevel particle filter can achieve a mean square error (MSE) of O(∈2) (∈ > 0 and arbitrary) with a cost of O(∈-2.5) versus using a particle filter which has a cost of O(∈-3) to achieve the same MSE. We then show how this methodology can be extended to give unbiased (that is with no time-discretization error) estimators of the filter, which are proved to have finite variance and with high-probability have finite cost. Finally, we extend our methodology to the problem of online static-parameter estimation. Key words: Unbiased Methods, Multilevel Monte Carlo, Non-Linear Filtering, Point Processes, Parameter *** Codes 60G55, 60G35, 62M20, 62F30 © 2023, CC BY.

关键词： Diffusion

Bayesian Parameter Inference for Partially Observed Stochastic Volterra Equations

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arXiv 2023年

作者： Jasra, Ajay Ruzayqat, Hamza Wu, Amin Applied Mathematics & Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this article we consider Bayesian parameter inference for a type of partially observed stochastic Volterra equation (SVE). SVEs are found in many areas such as physics and mathematical finance. In the latter field they can be used to represent long memory in unobserved volatility processes. In many cases of practical interest, SVEs must be time-discretized and then parameter inference is based upon the posterior associated to this time-discretized process. Based upon recent studies on time-discretization of SVEs (e.g. [24]) we use Euler-Maruyama methods for the afore-mentioned discretization. We then show how multilevel Markov chain Monte Carlo (MCMC) methods [19] can be applied in this context. In the examples we study, we give a proof that shows that the cost to achieve a 4 mean square error (MSE) of O(ϵ2), ϵ > 0, is O(ϵ− 4/2H+1 ), where H is the Hurst parameter. If one uses a single level MCMC method then the cost is O(ϵ−2(2H+3)/ 2H+1 ) to achieve the same MSE. We illustrate these results in the context of state-space and stochastic volatility models, with the latter applied to real *** Codes 62F15, 62M20, 60J10, 60J22, 65C40 © 2023, CC BY.

关键词： Mean square error

Low light quantum phase transition in 1T-TaS2 at room temperature

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Low light quantum phase transition in 1T-TaS2 at room temper...

Conference on Lasers and Electro-Optics (CLEO)

作者： Weijian Li Gururaj V. Naik Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Electrical & Computer Engineering Rice University Houston TX USA

Light-matter interaction in quantum materials presents opportunities for discovery. We observe a low-intensity light-induced phase transition in 1T-TaS 2 , a quasi-2D material supporting charge-density-waves (CDW). We... 详细信息

ISBN: (纸本)9781943580910

关键词： Stacking Lighting Lasers and electrooptics Optical materials Laser tuning Laser transitions Laser applications

Carbon nanotube growth from Cu-Co alloys for field emission applications

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Carbon nanotube growth from Cu-Co alloys for field emission ...

IEEE International Conference on Vacuum Microelectronics (IVMC)

作者： W. Hofmeister W.P. Kang Y.M. Wong J.L. Davidson Interdisciplinary Graduate Program in Material Science Vanderbilt University Nashville TN USA Department of Electrical Engineering and Computer Science Vanderbilt University Nashville TN USA

We have used Cu-Co alloy to generate uniform distribution of Co precipitates, and successfully grown CNTs by CVD on the Cu support.

关键词： Carbon nanotubes Conducting materials Transistors Application software Cobalt alloys Copper alloys Materials science and technology Hydrocarbons Chemical vapor deposition Nanocrystals

Implicit Neural Representations for Deconvolving SAS Images

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Implicit Neural Representations for Deconvolving SAS Images

OCEANS

作者： Albert Reed Thomas Blanford Daniel C. Brown Suren Jayasuriya School of Electrical Computer and Energy Engineering Arizona State University The Applied Research Laboratory Pennsylvania State University Graduate Program in Acoustics Pennsylvania State University

ISBN: (纸本)9781665427883

Synthetic aperture sonar (SAS) image resolution is constrained by waveform bandwidth and array geometry. Specifically, the waveform bandwidth determines a point spread function (PSF) that blurs the locations of point scatterers in the scene. In theory, deconvolving the reconstructed SAS image with the scene PSF restores the original distribution of scatterers and yields sharper reconstructions. However, deconvolution is an ill-posed operation that is highly sensitive to noise. In this work, we leverage implicit neural representations (INRs), shown to be strong priors for the natural image space, to deconvolve SAS images. Importantly, our method does not require training data, as we perform our deconvolution through an analysis-by-synthesis optimization in a self-supervised fashion. We validate our method on simulated SAS data created with a point scattering model and real data captured with an in-air circular SAS. This work is an important first step towards applying neural networks for SAS image deconvolution.

关键词： Deconvolution Oceans Neural networks Training data Scattering Bandwidth Image restoration

Bayesian Parameter Inference for Partially Observed SDEs driven by Fractional Brownian Motion

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arXiv 2022年

作者： Maama, Mohamed Jasra, Ajay Ombao, Hernando Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this paper we consider Bayesian parameter inference for partially observed fractional Brownian motion (fBM) models. The approach we follow is to time-discretize the hidden process and then to design Markov chain Monte Carlo (MCMC) algorithms to sample from the posterior density on the parameters given data. We rely on a novel representation of the time discretization, which seeks to sample from an approximation of the posterior and then corrects via importance sampling;the approximation reduces the time (in terms of total observation time T) by O(T). This method is extended by using a multilevel MCMC method which can reduce the computational cost to achieve a given mean square error (MSE) versus using a single time discretization. Our methods are illustrated on simulated and real data. Copyright © 2022, The Authors. All rights reserved.

关键词： Mean square error