Supersymmetries in nonequilibrium Langevin dynamics

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A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

Jean-Philippe Thiran, Roser Vinals Terres

Ultrafast ultrasound imaging, characterized by high frame rates, generates low-quality images. Convolutional neural networks (CNNs) have demonstrated great potential to enhance image quality without compromising the frame rate. However, CNNs have been most ...

2023

Arbitrary Decisions are a Hidden Cost of Differentially Private Training

Carmela González Troncoso, Bogdan Kulynych

Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Ga ...

Assoc Computing Machinery2023

Thermodynamic bounds on time-reversal asymmetry

Shiling Liang

Quantifying irreversibility of a system using finite information constitutes a major challenge in stochastic thermodynamics. We introduce an observable that measures the time-reversal asymmetry between two states after a given time lag. Our central result ...

Amer Physical Soc2023

Low-Rank Tensor Methods for High-Dimensional Problems

Christoph Max Strössner

In this thesis, we propose and analyze novel numerical algorithms for solving three different high-dimensional problems involving tensors. The commonality of these problems is that the tensors can potentially be well approximated in low-rank formats. Ident ...

EPFL2023

Improving time-resolution and sensitivity of in situ X-ray photoelectron spectroscopy of a powder catalyst by modulated excitation

Oliver Kröcher, Davide Ferri, Filippo Buttignol

Ambient pressure X-ray photoelectron spectroscopy (APXPS) is a powerful tool to characterize the surface structure of heterogeneous catalysts in situ. In order to improve the time resolution and the signal-to-noise (S/N) ratio of photoemission spectra, we ...

ROYAL SOC CHEMISTRY2023

Spatially multiplexed single-molecule translocations through a nanopore at controlled speeds

Gordanna Pistoletti Blanchet, Aleksandra Radenovic, Georg Fantner, Sanjin Marion, Vytautas Navikas, Samuel Mendes Leitão, Barney Frederick Drake, Simon Finn Mayer, Helena Miljkovic

In current nanopore-based label-free single-molecule sensing technologies, stochastic processes influence the selection of translocating molecule, translocation rate and translocation velocity. As a result, single-molecule translocations are challenging to ...

2023

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Emilio Maddalena

This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

EPFL2023

Millimeter-wave to near-terahertz sensors based on reversible insulator-to-metal transition in VO2

Mihai Adrian Ionescu, Teodor Rosca, Fatemeh Qaderi Rahaqi

In the quest for low power bio-inspired spiking sensors, functional oxides like vanadium dioxide are expected to enable future energy efficient sensing. Here, we report uncooled millimeter-wave spiking detectors based on the sensitivity of insulator-to-met ...

SPRINGERNATURE2023

Quantization for Decentralized Learning Under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif

In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained fo ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Federated Linear Bandit Learning via Over-the-air Computation

Yuning Jiang, Xin Liu

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 ...

IEEE2023

On a Finite-Size Neuronal Population Equation

Tilo Schwalger, Valentin Marc Schmutz

Population equations for infinitely large networks of spiking neurons have a long tradition in theoret-ical neuroscience. In this work, we analyze a recent generalization of these equations to populations of finite size, which takes the form of a nonlinear ...

SIAM PUBLICATIONS2023

Optimizing Sales Forecasting, Inventory, Pricing and Sourcing Decisions

Yara Kayyali El Alem

In this thesis we address various factors that contribute both theoretically and practically to mitigating supply demand mismatches. The thesis is composed of three chapters, where each chapter is an independent scientific paper. In the first paper, we dev ...

EPFL2023