Publications related to Mechanism of thermally-activated prismatic slip in Mg

Related publications (17)

Eflop: a Sparsity-aware Metric for Evaluating Computational Cost in Spiking and Non-spiking Neural Networks

Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both artificial neural networks (ANNs) and spiking neural networks (SNNs) have been p ...

IOP Publishing Ltd2025

How Gradient Descent Balances Features: A Dynamical Analysis For Two-Layer Neural Networks

Volkan Cevher, Fanghui Liu, Zhenyu Zhu

This paper investigates the fundamental regression task of learning k neurons (a.k.a. teachers) from Gaussian input, using two-layer ReLU neural networks with width m (a.k.a. students) and m, k = O(1), trained via gradient descent under proper initializati ...

2025

Learning to Assemble with Alternative Plans

Gabriel Rémi Vallat, Maryam Kamgarpour, Stefana Parascho, Ziqi Wang, Jingwen Wang

We present a reinforcement learning framework for constructing assemblies composed of rigid parts, which are commonly seen in many historical masonry buildings and bridges. Traditional construction methods for such structures often depend on dense scaffold ...

Association for Computing Machinery (ACM)2025

Feature-based encoding of face identity by single neurons in the human amygdala and hippocampus

Emanuela De Falco

Neurons in the human amygdala and hippocampus are classically thought to encode a person’s identity invariant to visual features. However, it remains largely unknown how visual information from higher visual cortical areas is translated into such a semanti ...

2025

Fabrication of Multi-Material and Functionally Graded Metal Structures with Additive and Hybrid Manufacturing Methods

Antonios Nikolaos Baganis

Multi-material structures have gained significant attention for their ability to combine properties of different alloys, making them promising for technical applications and potential replacements for conventional single-alloy components. The advancement o ...

EPFL2025

On the number of regions of piecewise linear neural networks

Michaël Unser, Alexis Marie Frederic Goujon

Many feedforward neural networks (NNs) generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is affine. The number of these so-called linear regions offers a natural metric ...

2024

RAPNet: Resolution-Adaptive and Predictive Early Exit Network for Efficient Image Recognition

Yung-Cheng Chiang

Deploying compute-intensive deep neural networks (DNNs) on resource-constrained end devices has become a prominent trend, enabling localized intelligence. However, efficiently deploying these DNNs at scale poses challenges. To address this, extensive resea ...

2024

Quasi-operando Transmission Electron Microscopy Diagnostics for Electrocatalytic Processes in Liquids

Vasiliki Tileli

It is of great interest to the energy community to understand how the mechano-physico-chemical phenomena that eventually lead to device degradation are related to the startup, operation, and shutdown phases. For electrocatalytic systems operating in liquid ...

Swiss Chemical Soc2024

From event-based surprise to lifelong learning. A journey in the timescales of adaptation

Martin Louis Lucien Rémy Barry

Humans and animals constantly adapt to their environment over the course of their life. This thesis seeks to integrate various timescales of adaptation, ranging from the adaptation of synaptic connections between spiking neurons (milliseconds), rapid behav ...

EPFL2023

Thermally Promoted Cation Exchange at the Solid State in the Transmission Electron Microscope: How It Actually Works

Davide Deiana

Cation exchange offers a strong postsynthetic tool for nanoparticles that are unachievable via direct synthesis, but its velocity makes observing the onset of the reaction in the liquid state almost impossible. After successfully proving that cation exchan ...

AMER CHEMICAL SOC2023

Modeling of precipitate strengthening with near-chemical accuracy: case study of Al-6xxx alloys

William Curtin, Yi Hu

Many metal alloys are strengthened by controlling precipitation to achieve an optimal peak-aged condi-tion where the strength-limiting processes of precipitate shearing and Orowan looping are thought to be comparable. Qualitative models have long captured ...

PERGAMON-ELSEVIER SCIENCE LTD2022

A Practical Guide to Supervised Deep Learning for Bioimage Analysis: Challenges and good practices

Daniel Sage, Virginie Sophie Uhlmann, Laurène Donati

The variety of bioimage data and their quality have dramatically increased over the last decade. In parallel, the number of proposed deep learning (DL) models for their analysis grows by the day. Yet, the adequate reuse of published tools by practitioners ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation

Thomas Yu

Machine learning has become the state of the art for the solution of the diverse inverse problems arising from computer vision and medical imaging, e.g. denoising, super-resolution, de-blurring, reconstruction from scanner data, quantitative magnetic reson ...

EPFL2022

Weakly Supervised Deep Learning Methods for Biomicroscopy

Adrian Shajkofci

Optical microscopy, an invaluable tool in biology and medicine to observe and quantify cellular function, organ development, or disease mechanisms, requires constant trade-offs between spatial, temporal, and spectral resolution, invasiveness, acquisition t ...

EPFL2021

CryoGAN: A New Reconstruction Paradigm for Single-Particle Cryo-EM Via Deep Adversarial Learning

Michaël Unser, Michael Thompson McCann, Laurène Donati, Harshit Gupta

We present CryoGAN, a new paradigm for single-particle cryo-electron microscopy (cryo-EM) reconstruction based on unsupervised deep adversarial learning. In single-particle cryo-EM, the structure of a biomolecule needs to be reconstructed from a large set ...

2021

Biologically plausible unsupervised learning in shallow and deep neural networks

Bernd Albert Illing

The way our brain learns to disentangle complex signals into unambiguous concepts is fascinating but remains largely unknown. There is evidence, however, that hierarchical neural representations play a key role in the cortex. This thesis investigates biolo ...

EPFL2021

On the structure of (111) twist grain boundaries in diamond: atomistic simulations with Tersoff-type interatomic potentials

Carolina Baruffi

In nanocrystalline diamond thin films, the high grain boundary density and the associated grain boundary properties may dominate the overall material behavior. In the present work, we systematically investigate (111) twist grain boundaries in diamond carbo ...

PERGAMON-ELSEVIER SCIENCE LTD2021