6,013 research outputs found
Analogue event horizons in dielectric medium
In this thesis I numerically study an optical pulse travelling in a dielectric medium as an analogue event horizon. A novel numerical method is developed to study the scattering properties of this optical system. Numerical solutions of scattering problems often exhibit instabilities. The staircase approximation, in addition, can cause slow convergence. We present a differential equation for the scattering matrix which solves both of these problems. The new algorithm inherits the numerical stability of the S matrix algorithm and converges faster for a smoothly varying potential than the S matrix algorithm with the staircase approximation. We apply our equation to solve a 1D stationary scattering of plane waves from a non-periodic smoothly varying pulse/scatterer travelling with a constant velocity in a lossless medium. The properties of stability and the convergence of the Riccati matrix equation are demonstrated. Furthermore, we include a relative velocity between the scatterer and the wave medium to generalise the algorithm further where the number of right and left going modes are not equal. The algorithm is applicable for stationary scattering process from arbitrarily shaped smooth scatterers, periodic or non-periodic, even when the scatterer is varying at the scale of wavelengths. This method is used to present numerical results for a sub-femtoseconds optical pulse travelling in bulk silica. We calculate the analogue hawking radiation from the analogue system. The temperature of the hawking radiation is studied systematically with many different profiles of pulses. We find out steepness, intensity and duration of the pulse are most important in producing analogue hawking radiation in these systems. A better numerical and theoretical understanding will make the experiments better suited to detect hawking radiation
Assessing the advancement of artificial intelligence and drones’ integration in agriculture through a bibliometric study
Integrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field’s transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era
Life on a scale:Deep brain stimulation in anorexia nervosa
Anorexia nervosa (AN) is a severe psychiatric disorder marked by low body weight, body image abnormalities, and anxiety and shows elevated rates of morbidity, comorbidity and mortality. Given the limited availability of evidence-based treatments, there is an urgent need to investigate new therapeutic options that are informed by the disorder’s underlying neurobiological mechanisms. This thesis represents the first study in the Netherlands and one of a limited number globally to evaluate the efficacy, safety, and tolerability of deep brain stimulation (DBS) in the treatment of AN. DBS has the advantage of being both reversible and adjustable. Beyond assessing the primary impact of DBS on body weight, psychological parameters, and quality of life, this research is novel in its comprehensive approach. We integrated evaluations of efficacy with critical examinations of the functional impact of DBS in AN, including fMRI, electroencephalography EEG, as well as endocrinological and metabolic assessments. Furthermore, this work situates AN within a broader theoretical framework, specifically focusing on its manifestation as a form of self-destructive behavior. Finally, we reflect on the practical, ethical and philosophical aspects of conducting an experimental, invasive procedure in a vulnerable patient group. This thesis deepens our understanding of the neurobiological underpinnings of AN and paves the way for future research and potential clinical applications of DBS in the management of severe and enduring AN
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
A Quasi-Newton Subspace Trust Region Algorithm for Least-square Problems in Min-max Optimization
The first-order optimality conditions of convexly constrained
nonconvex-nonconcave min-max optimization problems formulate variational
inequality problems, which are equivalent to a system of nonsmooth equations.
In this paper, we propose a quasi-Newton subspace trust region (QNSTR)
algorithm for the least-square problem defined by the smoothing approximation
of the nonsmooth equation. Based on the structure of the least-square problem,
we use an adaptive quasi-Newton formula to approximate the Hessian matrix and
solve a low-dimensional strongly convex quadratic program with ellipse
constraints in a subspace at each step of QNSTR algorithm. According to the
structure of the adaptive quasi-Newton formula and the subspace technique, the
strongly convex quadratic program at each step can be solved efficiently. We
prove the global convergence of QNSTR algorithm to an -first-order
stationary point of the min-max optimization problem. Moreover, we present
numerical results of QNSTR algorithm with different subspaces for the mixed
generative adversarial networks in eye image segmentation using real data to
show the efficiency and effectiveness of QNSTR algorithm for solving large
scale min-max optimization problems
Curvature-Aware Derivative-Free Optimization
The paper discusses derivative-free optimization (DFO), which involves
minimizing a function without access to gradients or directional derivatives,
only function evaluations. Classical DFO methods, which mimic gradient-based
methods, such as Nelder-Mead and direct search have limited scalability for
high-dimensional problems. Zeroth-order methods have been gaining popularity
due to the demands of large-scale machine learning applications, and the paper
focuses on the selection of the step size in these methods. The
proposed approach, called Curvature-Aware Random Search (CARS), uses first- and
second-order finite difference approximations to compute a candidate
. We prove that for strongly convex objective functions, CARS
converges linearly provided that the search direction is drawn from a
distribution satisfying very mild conditions. We also present a Cubic
Regularized variant of CARS, named CARS-CR, which converges in a rate of
without the assumption of strong convexity. Numerical
experiments show that CARS and CARS-CR match or exceed the state-of-the-arts on
benchmark problem sets.Comment: 31 pages, 9 figure
Simulations of idealised 3D atmospheric flows on terrestrial planets using LFRic-Atmosphere
We demonstrate that LFRic-Atmosphere, a model built using the Met Office's
GungHo dynamical core, is able to reproduce idealised large-scale atmospheric
circulation patterns specified by several widely-used benchmark recipes. This
is motivated by the rapid rate of exoplanet discovery and the ever-growing need
for numerical modelling and characterisation of their atmospheres. Here we
present LFRic-Atmosphere's results for the idealised tests imitating
circulation regimes commonly used in the exoplanet modelling community. The
benchmarks include three analytic forcing cases: the standard Held-Suarez test,
the Menou-Rauscher Earth-like test, and the Merlis-Schneider Tidally Locked
Earth test. Qualitatively, LFRic-Atmosphere agrees well with other numerical
models and shows excellent conservation properties in terms of total mass,
angular momentum and kinetic energy. We then use LFRic-Atmosphere with a more
realistic representation of physical processes (radiation, subgrid-scale
mixing, convection, clouds) by configuring it for the four TRAPPIST-1 Habitable
Atmosphere Intercomparison (THAI) scenarios. This is the first application of
LFRic-Atmosphere to a possible climate of a confirmed terrestrial exoplanet.
LFRic-Atmosphere reproduces the THAI scenarios within the spread of the
existing models across a range of key climatic variables. Our work shows that
LFRic-Atmosphere performs well in the seven benchmark tests for terrestrial
atmospheres, justifying its use in future exoplanet climate studies.Comment: 34 pages, 9(12) figures; Submitted to Geoscientific Model
Development; Comments are welcome (see Discussion tab on the journal's
website: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-647
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators
We propose a novel machine learning framework for solving optimization
problems governed by large-scale partial differential equations (PDEs) with
high-dimensional random parameters. Such optimization under uncertainty (OUU)
problems may be computational prohibitive using classical methods, particularly
when a large number of samples is needed to evaluate risk measures at every
iteration of an optimization algorithm, where each sample requires the solution
of an expensive-to-solve PDE. To address this challenge, we propose a new
neural operator approximation of the PDE solution operator that has the
combined merits of (1) accurate approximation of not only the map from the
joint inputs of random parameters and optimization variables to the PDE state,
but also its derivative with respect to the optimization variables, (2)
efficient construction of the neural network using reduced basis architectures
that are scalable to high-dimensional OUU problems, and (3) requiring only a
limited number of training data to achieve high accuracy for both the PDE
solution and the OUU solution. We refer to such neural operators as multi-input
reduced basis derivative informed neural operators (MR-DINOs). We demonstrate
the accuracy and efficiency our approach through several numerical experiments,
i.e. the risk-averse control of a semilinear elliptic PDE and the steady state
Navier--Stokes equations in two and three spatial dimensions, each involving
random field inputs. Across the examples, MR-DINOs offer -- reductions in execution time, and are able to produce OUU solutions of
comparable accuracies to those from standard PDE based solutions while being
over more cost-efficient after factoring in the cost of
construction
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