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Optical Fibers and Sensors for Medical Diagnostics, Treatment, and Environmental Applications XXV 2025

作者： Alexis, Al Anderson, London Khan, Mohammad A. Division of Physics Engineering Mathematics and Computer Science Optical Science Center for Applied Research Delaware State University 1200 N. Dupont Hwy DoverDE19901 United States

ISBN: (纸本)9781510683686

Mid-infrared laser-based sensing is widely used for the detection and quantification of trace gases for environmental monitoring, industrial process control, defense, and security applications. However, such laser spectroscopy-based field instruments require frequent calibration to correct for long-term drifts and precision. In many situations, such calibrations may be redundant and may not be effective in monitoring instrument errors or discriminating "real"environmental effects with instrument drifts. This paper shows that physics and diffusion-based models can be developed to predict gradual or rapid changes in density at given temperatures and appropriate source characterizations. We used a 3.3μm, laser-based instrument to demonstrate an adaptive and auto-calibration methodology to account for drifts during field deployments and an adaptive method to validate environmental changes, e.g., plume detection, diurnal variations, and flux emissions. We field operation and implementation of adaptive auto-calibration. The instruments are used for the detection of methane and water vapor, but an adaptive auto-calibration technique can be employed for any laser sensing instrument. © 2025 SPIE.

关键词： Laser spectroscopy

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Unbiased Approximations for Stationary Distributions of McKean-Vlasov SDEs

arXiv

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arXiv 2024年

作者： Awadelkarim, Elsiddig 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 Thuwal23955-6900 Saudi Arabia Department of Mathematics City University of Hong Kong Hong Kong School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China

We consider the development of unbiased estimators, to approximate the stationary distribution of Mckean-Vlasov stochastic differential equations (MVSDEs). These are an important class of processes, which frequently appear in applications such as mathematical finance, biology and opinion dynamics. Typically the stationary distribution is unknown and indeed one cannot simulate such processes exactly. As a result one commonly requires a time-discretization scheme which results in a discretization bias and a bias from not being able to simulate the associated stationary distribution. To overcome this bias, we present a new unbiased estimator taking motivation from the literature on unbiased Monte Carlo. We prove the unbiasedness of our estimator, under assumptions. In order to prove this we require developing ergodicity results of various discrete time processes, through an appropriate discretization scheme, towards the invariant measure. Numerous numerical experiments are provided, on a range of MVSDEs, to demonstrate the effectiveness of our unbiased estimator. Such examples include the Currie-Weiss model, a 3D neuroscience model and a parameter estimation problem. © 2024, CC BY-NC-SA.

关键词： Stochastic systems

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Control of Magnetic Skyrmions in an Exchange Biased van der Waals Ferromagnet

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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 1710-1711页

作者： McCray, Arthur R C Lebedev, Dmitry Arpaci, Sevdenur Te Velthuis, Suzanne G E Lopez-Dominguez, Victor Amiri, Pedram Khalili Hersam, Mark C. Petford-Long, Amanda K. Phatak, Charudatta Materials Science Division Argonne National Laboratory Lemont IL United States Applied Physics Program Northwestern University Evanston IL United States Department of Materials Science and Engineering Northwestern University Evanston IL United States Department of Electrical and Computer Engineering Northwestern University Evanston IL United States

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Temperature-anisotropy conjugate magnon squeezing in antiferromagnets

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Physical Review B 2023年第14期108卷 144302-144302页

作者： Mahroo Shiranzaei Jonas Fransson Vahid Azimi Mousolou Division of Materials Theory Department of Physics and Astronomy Uppsala University Box 516 SE-75120 Uppsala Sweden Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan 81746-73441 Iran

Quantum squeezing is an essential asset in the field of quantum science and technology. In this paper, we investigate the impact of temperature and anisotropy on squeezing of quantum fluctuations in two-mode magnon states within uniaxial antiferromagnetic materials. Through our analysis, we discover that the inherent nonlinearity in these bipartite magnon systems gives rise to a conjugate magnon squeezing effect across all energy eigenbasis states, driven by temperature and anisotropy. We show that temperature induces amplitude squeezing, whereas anisotropy leads to phase squeezing. In addition, we observe that the two-mode squeezing characteristic of magnon eigenenergy states is associated with amplitude squeezing. This highlights the constructive impact of temperature and the destructive impact of anisotropy on two-mode magnon squeezing. Nonetheless, our analysis shows that the destructive effect of anisotropy is bounded. We demonstrate this by showing that, at a given temperature, the squeezing of the momentum (phase) quadrature [or equivalently, the stretching of the position (amplitude) quadrature] approaches a constant function of anisotropy after a finite value of anisotropy. Moreover, our paper demonstrates that higher magnon squeeze factors can be achieved at higher temperatures, smaller levels of anisotropy, and closer to the Brillouin zone center. All these characteristics are specific to low-energy magnons in the uniaxial antiferromagnetic materials that we examine here.

关键词： Magnons Quantum correlations in quantum information Quantum information processing with continuous variables Quantum information theory Quantum metrology Spin dynamics Spintronics Temperature

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Physics-informed machine learning with smoothed particle hydrodynamics: Hierarchy of reduced Lagrangian models of turbulence

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Physical Review Fluids 2023年第5期8卷 054602-054602页

作者： Michael Woodward Yifeng Tian Criston Hyett Chris Fryer Mikhail Stepanov Daniel Livescu Michael Chertkov Graduate Interdisciplinary Program in Applied Mathematics University of Arizona Tucson Arizona 85721 USA Department of Mathematics University of Arizona Tucson Arizona 85721 USA Computer Computational and Statistical Sciences Division LANL Los Alamos New Mexico 87544 USA

Building efficient, accurate, and generalizable reduced-order models of developed turbulence remains a major challenge. This manuscript approaches this problem by developing a hierarchy of parameterized reduced Lagrangian models for turbulent flows, and it investigates the effects of enforcing physical structure through smoothed particle hydrodynamics (SPH) versus relying on neural networks (NNs) as universal function approximators. Starting from NN parametrizations of a Lagrangian acceleration operator, this hierarchy of models gradually incorporates a weakly compressible and parameterized SPH framework, which enforces physical symmetries, such as Galilean, rotational, and translational invariances. Within this hierarchy, two new parameterized smoothing kernels are developed to increase the flexibility of the learn-able SPH simulators. For each model we experiment with different loss functions which are minimized using gradient based optimization, where efficient computations of gradients are obtained by using automatic differentiation and sensitivity analysis. Each model within the hierarchy is trained on two data sets associated with weakly compressible homogeneous isotropic turbulence: (1) a validation set using weakly compressible SPH; and (2) a high-fidelity set from direct numerical simulations. Numerical evidence shows that encoding more SPH structure improves generalizability to different turbulent Mach numbers and time shifts, and that including the novel parameterized smoothing kernels improves the accuracy of SPH at the resolved scales.

关键词： Sensitivity analysis

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Instilling Inductive Biases with Subnetworks

arXiv

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arXiv 2023年

作者： Zhang, Enyan Lepori, Michael A. Pavlick, Ellie Department of Computer Science Brown University RI United States Division of Applied Mathematics Brown University RI United States

Despite the recent success of artificial neural networks on a variety of tasks, we have little knowledge or control over the exact solutions these models implement. Instilling inductive biases — preferences for some solutions over others — into these models is one promising path toward understanding and controlling their behavior. Much work has been done to study the inherent inductive biases of models and instill different inductive biases through hand-designed architectures or carefully curated training regimens. In this work, we explore a more mechanistic approach: Subtask Induction. Our method discovers a functional subnetwork that implements a particular subtask within a trained model and uses it to instill inductive biases towards solutions utilizing that subtask. Subtask Induction is flexible and efficient, and we demonstrate its effectiveness with two experiments. First, we show that Subtask Induction significantly reduces the amount of training data required for a model to adopt a specific, generalizable solution to a modular arithmetic task. Second, we demonstrate that Subtask Induction successfully induces a human-like shape bias while increasing data efficiency for convolutional and transformer-based image classification models. Our code is available at the associated github repository. © 2023, CC BY-NC-SA.

关键词： Neural networks

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Seeking optimal Human/Machine collaborative practice in antisemitic terminology detection

Seeking optimal Human/Machine collaborative practice in anti...

引用

2024 IEEE Digital Platforms and Societal Harms, DPSH 2024

作者： Melillo, Wendy Emami, Jessica Guarinos, Solene Kikkisetti, Dhanush Klein, Melanie Liubovich, Lisa Mustafa, Raza Ul Japkowicz, Nathalie American University Journalism Division School of Communication United States American University College of Arts and Science Department of Sociology United States American University College of Arts and Science Department of Mathematics United States American University Justice Law and Criminology School of Public Affairs United States American University Data Science for Applied Public Affairs School of Public Affairs United States American University College of Arts and Science Department of Computer Science United States

ISBN: (纸本)9798350351309

This study is concerned with the use of antisemitic language on loosely moderated extremist alt-right social media. It compares, contrasts, and combines human-and machinebased approaches for discovering antisemitic coded or noncoded terminology in such media. Coded terminology refers to the language used in closed-knit communities to communicate beliefs and attitudes without expressing them explicitly. In noncoded discourse, the views of the writer are expressed more explicitly. The detection and analysis of antisemitic terminology in social media is an important problem that can help uncover and monitor the evolution of societal attitudes towards the Jews, and eventually, towards other minority groups once we expand our study. Given the growing prevalence of antisemitic rhetoric in online public spaces, there is a greater need for academic studies to determine its presence and identify strategies to curb its influence. Scholars have noted how intolerant expressions can threaten democratic values. Antisemitic content, like other forms of hate speech, has the potential to radicalize people who may commit violence based on their prejudicial viewpoints. The purpose of this paper is to discuss three different approaches for discovering antisemitic terminology on social media. In particular, it describes an experiment in which three groups-human, software, and mixed-"competed' on the task of retrieving antisemitic terminology over a two-month period (January-February, 2024). The results were manually evaluated for relevance and frequency of occurrence in the next month period (March 2024). The study shows that each group discovered valuable terminology and that neither the human nor the machine group could replace the other. Instead, we advocate that both groups work together using their respective knowledge to optimize the discovery of new terminology on social media. © 2024 IEEE.

关键词： Terminology

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Parameter Estimation for Partially Observed McKean-Vlasov Diffusions

arXiv

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arXiv 2024年

作者： Jasra, Ajay Maama, Mohamed Tempone, Raul School of Data Science The Chinese University of Hong Kong CN Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Chair of Mathematics for Uncertainty Quantification RWTH Aachen University Aachen52062 Germany

In this article we consider likelihood-based estimation of static parameters for a class of partially observed McKean-Vlasov (POMV) diffusion process with discrete-time observations over a fixed time interval. In particular, using the framework of [5] we develop a new randomized multilevel Monte Carlo method for estimating the parameters, based upon Markovian stochastic approximation methodology. New Markov chain Monte Carlo algorithms for the POMV model are introduced facilitating the application of [5]. We prove, under assumptions, that the expectation of our estimator is biased, but with expected small and controllable bias. Our approach is implemented on several examples. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Design of an Autonomous Cyber Defence Agent using Hybrid AI models

Design of an Autonomous Cyber Defence Agent using Hybrid AI ...

引用

2024 International Conference on Military Communication and Information Systems, ICMCIS 2024

作者： Loevenich, Johannes F. Adler, Erik Mercier, Remi Velazquez, Alexander Lopes, Roberto Rigolin F. Thales Deutschland Ditzingen Germany University of Osnabrück Department of Mathematics/Computer Science Osnabrück Germany Medical Informatics Heilbronn University of Applied Sciences Heilbronn Germany Télécom Paris Paris France Information Technology Division U.S. Naval Research Laboratory WashingtonDC United States

ISBN: (纸本)9798350373196

This paper extends the design of an autonomous cyber defence (ACD) agent to monitor and actuate within a protected core network segment. The goal is to take advantage of recent developments in AI models to define a hybrid architecture that combines deep reinforcement learning (DRL), large language models (LLMs), and rule-based models. The motivation comes from the fact that modern network segments within colored clouds are using software-defined controllers with the means to host ACD agents and other cybersecurity tools implementing hybrid AI models. For example, our ACD agent uses a DRL model and the chatbot uses an LLM to create an interface with human cybersecurity experts. The ACD agent was evaluated against two red agent strategies in a gym environment using a set of actions to defend services in the network (monitor, analyse, decoy, remove, and restore). Our chatbot was developed using retrieval augmented generation and a prompting agent to augment a pre-trained LLM with data from cybersecurity knowledge graphs. We performed a comparative analysis between a baseline implementation and our chatbot using generation/retrieval metrics. The results suggest that both ACD agent and chatbot can potentially enhance the defence of critical networks connected to untrusted infrastructure. © 2024 IEEE.

关键词： Reinforcement learning

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Autonomous Electron Tomography Reconstruction Using Bayesian Optimization

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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 1915-1916页

作者： Millsaps, William Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Hovden, Robert Department of Nuclear Engineering Radiological Sciences University of Michigan Ann Arbor MI United States Department of Materials Science and Engineering University of Michigan Ann Arbor MI United States Mathematics and Computer Science Division Argonne National Laboratory Lemont IL United States Advanced Photon Source Facility Argonne National Laboratory Lemont IL United States Applied Physics Program University of Michigan Ann Arbor MI United States

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