Piecewise polynomial phase approximation approach for the analysis of reconstructed interference felds in digital holographic interferometry

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Holographic imaging of electromagnetic fields via electron-light quantum interference

Fabrizio Carbone, Ivan Madan, Enrico Pomarico, Tom Theodorus Antonius Lummen, Gabriele Berruto, Giovanni Maria Vanacore, Tatiana Latychevskaia

Holography relies on the interference between a known reference and a signal of interest to reconstruct both the amplitude and the phase of that signal. With electrons, the extension of holography to the ultrafast time domain remains a challenge, although ...

American Association for the Advancement of Science (AAAS)2019

Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler

Jean-Philippe Thiran, Gabriel Girard, Jonathan Rafael Patino Lopez, Erick Jorge Canales Rodriguez, Marco Pizzolato, Thomas Yu

Probabilistic parameter estimation in model fitting runs the gamut from maximum likelihood or maximum a posteriori point estimates from optimization to Markov Chain Monte Carlo (MCMC) sampling. The latter, while more computationally intensive, generally pr ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

Estimation of streamflow recession parameters: New insights from an analytic streamflow distribution model

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Streamflow recession analysis characterizes the storage-outflow relationship in catchments. This relationship, which typically follows a power law, summarizes all catchment-scale subsurface hydrological processes and has long been known to be a key descrip ...

2019

Slepian Guided Filtering of Graph Signals

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Joint localization of graph signals in vertex and spectral domain is achieved in Slepian vectors calculated by either maximizing energy concentration (mu) or minimizing modified embedded distance (xi) in the subgraph of interest. On the other hand, graph L ...

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Learning Hawkes Processes from a Handful of Events

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Learning the causal-interaction network of multivariate Hawkes processes is a useful task in many applications. Maximum-likelihood estimation is the most common approach to solve the problem in the presence of long observation sequences. However, when only ...

2019

Maximum-likelihood estimation of kinetic parameters via the extent-based incremental approach

Dominique Bonvin, Julien Léo Billeter, Diogo Filipe Mateus Rodrigues

For lumped homogeneous reaction systems, this paper presents a kinetic model identification scheme that provides maximum-likelihood parameter estimates and guarantees convergence to global optimality. The use of the extent-based incremental approach allows ...

PERGAMON-ELSEVIER SCIENCE LTD2019

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

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We present a scheme to obtain an inexpensive and reliable estimate of the uncertainty associated with the predictions of a machine-learning model of atomic and molecular properties. The scheme is based on resampling, with multiple models being generated ba ...

2019

Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning

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Many decision problems in science, engineering and economics are affected by uncertain parameters whose distribution is only indirectly observable through samples. The goal of data-driven decision-making is to learn a decision from finitely many training s ...

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Angular Accuracy of Steerable Feature Detectors

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The detection of landmarks or patterns is of interest for extracting features in biological images. Hence, algorithms for finding these keypoints have been extensively investigated in the literature, and their localization and detection properties are well ...

SIAM PUBLICATIONS2019

An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation

Volkan Cevher, Jonathan Mark Scarlett

Information theory plays an indispensable role in the development of algorithm-independent impossibility results, both for communication problems and for seemingly distinct areas such as statistics and machine learning. While numerous information-theoretic ...

Cambridge University Press2021

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