On the computation of directional scale-discretized wavelet transforms on the sphere

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Electrical stimulation of the visual nervous system could improve the quality of life of patients affected by acquired blindness by restoring some visual sensations, but requires careful optimization of stimulation parameters to produce useful perceptions. ...

In this paper, we present a new parameterization and optimization procedure for minimizing the weight of ribbed plates. The primary goal is to reduce embodied CO2 in concrete floors as part of the effort to diminish the carbon footprint of the construction ...

We introduce an algorithm to reconstruct a mesh from discrete samples of a shape's Signed Distance Function (SDF). A simple geometric reinterpretation of the SDF lets us formulate the problem through a point cloud, from which a surface can be extracted wit ...

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 ...

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

Advances in computational capabilities and large volumes of experimental data have established computer simulations of brain tissue models as an important pillar in modern neuroscience. Alongside, a variety of domain specific languages (DSLs) have been dev ...

In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In the decentralized setting, in which workers commu ...

Compilers assure that any produced optimized code is semantically equivalent to the original code. However, even "correct" compilers may introduce security bugs as security properties go beyond translation correctness. Security bugs introduced by such corr ...

In Process Systems Engineering, computationally-demanding models are frequent and plentiful. Handling such complexity in an optimization framework in a fast and reliable way is essential, not only for generating meaningful solutions but also for providing ...

There are various possibilities to realize coil winding designs for an inductive power transfer system. In order to achieve high power transfer efficiency and power density and explore trade-offs between the two, design optimization around the coil link is ...

This paper presents a theoretical analysis of linear interpolation as a principled method for stabilizing (large-scale) neural network training. We argue that instabilities in the optimization process are often caused by the nonmonotonicity of the loss lan ...

Code-reuse attacks are dangerous threats that attracted the attention of the security community for years. These attacks aim at corrupting important control-flow transfers for taking control of a process without injecting code. Nowadays, the combinations o ...

Artificial Intelligence (AI) techniques are considered the most advanced approaches for diagnosing faults in power transformers. Dissolved Gas Analysis (DGA) is the conventional approach widely adopted for diagnosing incipient faults in power transformers. ...

The problem of learning graphons has attracted considerable attention across several scientific communities, with significant progress over the re-cent years in sparser regimes. Yet, the current techniques still require diverg-ing degrees in order to succe ...

Analytical workloads are evolving as the number of users surges and applications that submit queries in batches become popular. However, traditional analytical databases that optimize-then-execute each query individually struggle to provide timely response ...

Designing turbocompressors is a complex and challenging task, as it involves balancing conflicting objectives such as efficiency, stability, and robustness against manufacturing deviations. This paper proposes an integrated design methodology for turbocomp ...

Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault ...

A Mach-Zehnder Interferometer (MZI) based optical fibre sensing technique, developed and patented by EPFL, is an efficient and economical way to detect hotspots in High Temperature Superconductor (HTS) applications. Due to the MZI sensitivity being a compo ...

Spectral-based graph neural networks (SGNNs) have been attracting increasing attention in graph representation learning. However, existing SGNNs are limited in implementing graph filters with rigid transforms and cannot adapt to signals residing on graphs ...

In this paper, the recommended implementation of the post-quantum key exchange SIKE for Cortex-M4 is attacked through power analysis with a single trace by clustering with the k-means algorithm the power samples of all the invocations of the elliptic curve ...

On the computation of directional scale-discretized wavelet transforms on the sphere

Chattez avec Graph Search

Decoding electroencephalographic responses to visual stimuli compatible with electrical stimulation

Mesh-based topology, shape and sizing optimization of ribbed plates

Reach For the Arcs: Reconstructing Surfaces from SDFs via Tangent Points

Shuffled multi-channel sparse signal recovery

Scalable constrained optimization

MOD2IR: High-Performance Code Generation for a Biophysically Detailed Neuronal Simulation DSL

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

Silent Bugs Matter: A Study of Compiler-Introduced Security Bugs

A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering

Modeling and Design Optimization of Loosely Coupled PCB Spiral Coils in Inductive Power Transfer Systems

Stable Nonconvex-Nonconcave Training via Linear Interpolation

WarpAttack: Bypassing CFI through Compiler-Introduced Double-Fetches

Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

Learning Sparse Graphons And The Generalized Kesten-Stigum Threshold

Efficient Concurrent Analytical Query Processing using Data and Workload-conscious Sharing

An Integrated Approach to Designing Robust Turbocompressors on Gas Bearings Through Surrogate Modeling and Constrained Multi-Objective Optimization

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Optical fibre based quench detection in HTS applications using machine learning classifiers

Graph Neural Networks With Lifting-Based Adaptive Graph Wavelets

Single-Trace Clustering Power Analysis of the Point-Swapping Procedure in the Three Point Ladder of Cortex-M4 SIKE