Distributed Sensing of Signals Linked by Sparse Filtering

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Years of a fierce competition have naturally selected the fittest deep learning algorithms. Yet, although these models work well in practice, we still lack a proper characterization of why they do so. This poses serious questions about the robustness, trus ...

Ever-increasing demands from the industry require better performance out of the same system to achieve a competitive advantage. In systems functioning at different operating points, a fundamental trade-off between robustness and performance can limit the a ...

The paper considers the problem of estimating the parameters of the compound admittance matrix of three-phase untransposed low-and medium-voltage electrical distribution networks using synchrophasor measurements from phasor measurement units (PMUs). The wo ...

Recently, a new adaptive path interpolation method has been developed as a simple and versatile scheme to calculate exactly the asymptotic mutual information of Bayesian inference problems defined on dense factor graphs. These include random linear and gen ...

In this work we consider an online learning problem, called Online k-Clustering with Moving Costs, at which a learner maintains a set of k facilities over T rounds so as to minimize the connection cost of an adversarially selected sequence of clients. The ...

BACKGROUND:The indication for mechanical thrombectomy (MT) in stroke patients with large vessel occlusion has been constantly expanded over the past years. Despite remarkable treatment effects at the group level in clinical trials, many patients remain sev ...

In this paper, we provide a Banach-space formulation of supervised learning with generalized total-variation (gTV) regularization. We identify the class of kernel functions that are admissible in this framework. Then, we propose a variation of supervised l ...

Intrinsic neutron noise experiments offer a non-invasive manner to measure the prompt decay constant or reactivity of fissile systems. Using the fluctuations in the density of fission chains, one can infer the kinetics parameters via correlation analysis s ...

paper studies exploiting action-level learning (imitation) in the optimal control problem context. Cost functions defined by the optimal control methods are similar to the goal-level learning (emulation) in animals. However, imitating the robot's or others ...

Motivated by the widespread use of large gridded data sets in the atmospheric sciences, we propose a new model for extremes of areal data that is inspired by the simultaneous autoregressive (SAR) model in classical spatial statistics. Our extreme SAR model ...

In this paper, the new mathematical correlation of two quantum systems that were initially allowed to interact and then separated is being formulated and analyzed. These correlations are illustrated by many examples and are also connected with fractals at ...

We study the dynamics and interactions of the critical fluctuations of the Verwey transition in magnetite (Fe3O4) in the pretransition region by means of inelastic neutron scattering experiments on a natural single crystal. We find that a Delta(5) mode int ...

Novel forms of light beams carrying orbital angular momentum (OAM) have recently gained interest, especially due to some of their intriguing propagation features. Here, we experimentally demonstrate the generation of near-diffraction-free two-dimensional ( ...

The discrete time analysis of the phase detector linear range extension in the first and the second order sub sampling phase-locked loop (SSPLL) is presented. The aim is to understand how much the stability and the pull-in range are affected by the linear ...

BackgroundTinnitus is a heterogeneous condition which may be associated with moderate to severe disability, but the reasons why only a subset of individuals is burdened by the condition are not fully clear. Ecological momentary assessment (EMA) allows a be ...

Deep neural networks often have millions of parameters. This can hinder their deployment to low-end devices, not only due to high memory requirements but also because of increased latency at inference. We propose a novel model compression method that gener ...

Distributed Sensing of Signals Linked by Sparse Filtering

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The inductive bias of deep learning: Connecting weights and functions

Frequency domain LPV controller synthesis for a positioning system with uncertain scheduling parameters

Compound Admittance Matrix Estimation of Three-phase Untransposed Power Distribution Grids using Synchrophasor Measurements

Adaptive Path Interpolation Method for Sparse Systems: Application to a Censored Block Model

Efficient Online Clustering with Moving Costs

Preserved Corticospinal Tract Revealed by Acute Perfusion Imaging Relates to Better Outcome After Thrombectomy in Stroke

Multikernel Regression with Sparsity Constraint

Towards the Validation of Noise Experiments in the CROCUS Reactor Using the TRIPOLI-4 Monte Carlo Code in Analog Mode

Optimal Control Combining Emulation and Imitation to Acquire Physical Assistance Skills

Simultaneous autoregressive models for spatial extremes

Mathematical Approach to Distant Correlations of Physical Observables and Its Fractal Generalisation

Energy domain versus time domain precursor fluctuations above the Verwey transition in magnetite

Synthesis of near-diffraction-free orbital-angular-momentum space-time wave packets having a controllable group velocity using a frequency comb

Discrete Time Analysis of Phase Detector Linear Range Extension in Sub-Sampling PLL

Daily Contributors of Tinnitus Loudness and Distress: An Ecological Momentary Assessment Study

Dynamic Model Pruning with Feedback