Publications de Benjamin Bejar Haro

Publications associées (14)

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Ntorina Thanou, Nicola Parolini, Benjamin Bejar Haro, Daniel Rodrigo Lopez, Ekaterina Krymova, Andrea Kraus, David Kraus

Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive p ...

eLIFE SCIENCES PUBL LTD2023

Sampling at unknown locations: Uniqueness and reconstruction under constraints

Martin Vetterli, Adam James Scholefield, Benjamin Bejar Haro, Golnooshsadat Elhami, Michalina Wanda Pacholska

Traditional sampling results assume that the sample locations are known. Motivated by simultaneous localization and mapping (SLAM) and structure from motion (SfM), we investigate sampling at unknown locations. Without further constraints, the problem is of ...

2018

Sampling at unknown locations, with an application in surface retrieval

Martin Vetterli, Adam James Scholefield, Benjamin Bejar Haro, Michalina Wanda Pacholska

We consider the problem of sampling at unknown locations. We prove that, in this setting, if we take arbitrarily many samples of a polynomial or real bandlimited signal, it is possible to find another function in the same class, arbitrarily far away from t ...

2017

Topology Optimization For Energy-Efficient Communications In Consensus Wireless Networks

Martin Vetterli, Benjamin Bejar Haro

Over the past years there has been an increasing interest in developing distributed computation methods over wireless networks. A new communication paradigm has emerged where distributed algorithms such as consensus have played a key role in the developmen ...

IEEE2014

The regularized tau estimator: A robust and efficient solution to ill-posed linear inverse problems

Martin Vetterli, Benjamin Bejar Haro, Marta Martinez-Camara

Linear inverse problems are ubiquitous. Often the measurements do not follow a Gaussian distribution. Additionally, a model matrix with a large condition number can complicate the problem further by making it ill-posed. In this case, the performance of pop ...

2017

Unlabeled Sensing: Reconstruction Algorithm and Theoretical Guarantees

Martin Vetterli, Adam James Scholefield, Benjamin Bejar Haro, Golnooshsadat Elhami

It often happens that we are interested in reconstructing an unknown signal from partial measurements. Also, it is typically assumed that the location (temporal or spatial) of the samples is known and that the only distortion present in the observations is ...

Ieee2017

Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide

Ntorina Thanou, Christine Choirat, Benjamin Bejar Haro, Guillaume Romain Obozinski, Ekaterina Krymova, Gavin Keenyan Lee

Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor its evolution, inform the public, and assist governments in decision-making. Here, we present a globally applicable method, integrated in a daily updated ...

National Academy of Sciences2022

Fully data-driven and modular building thermal control with physically consistent modeling

Benjamin Bejar Haro, Carl Pierre Jacques Remlinger

Machine learning has experienced significant growth in the smart building sector, whether for building modeling or energy management. Data-driven approaches leverage available measurements to bypass the slow and costly calibration of physics-based models, ...

2025

Shape from bandwidth: the 2-D orthogonal projection case

Martin Vetterli, Adam James Scholefield, Benjamin Bejar Haro

Could bandwidth—one of the most classic concepts in signal processing—have a new purpose? In this paper, we investigate the feasibility of using bandwidth to infer shape from a single image. As a first analysis, we limit our attention to orthographic proje ...

Ieee2017

Sampling Continuous-time Sparse Signals: A Frequency-domain Perspective

Martin Vetterli, Benjamin Bejar Haro

We address the problem of sampling and reconstruction of sparse signals with finite rate of innovation. We derive general conditions under which perfect reconstruction is possible for sampling kernels satisfying Strang-Fix conditions. Previous results on t ...

2018

Enhancing spatial resolution in MÖNCH for electron microscopy via deep learning

Benjamin Bejar Haro, Luis Felipe Barba Flores, Carl Pierre Jacques Remlinger

However, their use in imaging applications has been limited by their relatively large pixel size (≥ 55 μm) and high-energy (>80 keV) electrons scattering over multiple pixels in the sensor layer. To realize the full potential of fast, radiation-hard HPDs a ...

2024

Enhancing local-Transmitting less-Improving global

Martin Vetterli, Benjamin Bejar Haro

Super-resolving a natural image is an ill-posed problem. The classical approach is based on the registration and subsequent interpolation of a given set of low-resolution images. However, achieving satisfactory results typically requires the combination of ...

IEEE2015

Shuffled multi-channel sparse signal recovery

Benjamin Bejar Haro

Mismatches between samples and their respective channel or target commonly arise in several real-world applications. For instance, whole-brain calcium imaging of freely moving organisms, multiple-target tracking or multi-person contactless vital sign monit ...

2024

A robust method for inverse transport modelling of atmospheric emissions using blind outlier detection

Martin Vetterli, Benjamin Bejar Haro, Marta Martinez-Camara

Emissions of harmful substances into the atmosphere are a serious environmental concern. In order to understand and predict their effects, it is necessary to estimate the exact quantity and timing of the emissions, from sensor measurements taken at differe ...

2014