A Sampling Theory for Non-Decaying Signals

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Chaos sets a fundamental limit to quantum-information processing schemes. We study the onset of chaos in spatially extended quantum many-body systems that are relevant to quantum optical devices. We consider an extended version of the Tavis-Cummings model ...

The recent geopolitical conflicts in Europe have underscored the vulnerability of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy systems ensuring sustainable energy supply in the future, the ...

This paper studies kernel ridge regression in high dimensions under covariate shifts and analyzes the role of importance re-weighting. We first derive the asymptotic expansion of high dimensional kernels under covariate shifts. By a bias-variance decomposi ...

In this work, we analyze space-time reduced basis methods for the efficient numerical simulation of haemodynamics in arteries. The classical formulation of the reduced basis (RB) method features dimensionality reduction in space, while finite difference sc ...

Computer systems rely heavily on abstraction to manage the exponential growth of complexity across hardware and software. Due to practical considerations of compatibility between components of these complex systems across generations, developers have favou ...

Situational awareness strategies are essential for the reliable and secure operation of the electric power grid which represents critical infrastructure in modern society. With the rise of converter-interfaced renewable generation and the consequent shift ...

Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the first-stage process, learn-ing an optimal feature map is often formulated as a ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

We study the problem of estimating an unknown function from noisy data using shallow ReLU neural networks. The estimators we study minimize the sum of squared data-fitting errors plus a regularization term proportional to the squared Euclidean norm of the ...

A kernel method for estimating a probability density function from an independent and identically distributed sample drawn from such density is presented. Our estimator is a linear combination of kernel functions, the coefficients of which are determined b ...

The vibrational response of solid materials and structural components is substantially governed by their mechanical and geometrical properties. Low-frequency vibrations and modal frequencies are sensitive to global geometrical deviations, while high-freque ...

In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, and sends model upd ...

Electronic analog to digital converters (ADCs) are run-ning up against the well-known bit depth versus bandwidth trade off. Towards this end, radio frequency (RF) photonic-enhanced ADCs have been the subject of interest for some time. Optical frequency com ...

Nonlinear model predictive control (NMPC) has been widely adopted to manipulate bilinear systems with dynamics that include products of the inputs and the states. These systems are ubiquitous in chemical processes, mechanical systems, and quantum physics, ...

The goal of this thesis is the development and the analysis of numerical methods for problems where the unknown is a curve on a smooth manifold. In particular, the thesis is structured around the three following problems: homotopy continuation, curve inter ...

The recent geopolitical conflicts in Europe highlighted the sensibility of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy system configurations to ensure energy supply stability, it is necess ...

We discuss the effects of image scanning microscopy using doughnut beam illumination on the properties of signal strength and integrated intensity. Doughnut beam illumination can give better optical sectioning and background rejection than Airy disk illumi ...

Epilepsy is a common chronic neurological disorder that causes recurring seizures and affects more than 50 million people worldwide. Implantable medical devices (IMDs) are regarded as effective tools to cure patients who suffer from refractory epilepsy. Se ...

Rare events include many of the most interesting transformation processes in condensed matter, from phase transitions to biomolecular conformational changes to chemical reactions. Access to the corresponding mechanisms, free-energy landscapes and kinetic r ...

We present LISA, a proof system and proof assistant for constructing proofs in schematic first-order logic and axiomatic set theory. The logical kernel of the system is a proof checker for first-order logic with equality and schematic predicate and fun ...

A Sampling Theory for Non-Decaying Signals

Chat with Graph Search

Transition to chaos in extended systems and their quantum impurity models

Identification of typical district configurations: A two-step global sensitivity analysis framework

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

SPACE-TIME REDUCED BASIS METHODS FOR PARAMETRIZED UNSTEADY STOKES EQUATIONS

Secure Interface Design Leveraging Hardware/Software Support

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

End-to-end kernel learning via generative random Fourier features

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

Density estimation in RKHS with application to Korobov spaces in high dimensions

Vibrational NDT with Under-sampled Data through Physics-informed Neural Networks

Federated Linear Bandit Learning via Over-the-air Computation

Architecture for integrated RF photonic downconversion of electronic signals

A Proximal-Point Lagrangian-Based Parallelizable Nonconvex Solver for Bilinear Model Predictive Control

Retraction-based numerical methods for continuation, interpolation and time integration on manifolds

Identification of typical district configurations: A two-step global sensitivity analysis framework

Image scanning microscopy with a doughnut beam: signal strength and integrated intensity

Hardware Implementation of Digital Signal Processing Algorithms for Programmable Epilepsy Control Systems

Inferring free-energy barriers and kinetic rates from molecular dynamics via underdamped Langevin models

LISA – A Modern Proof System