Publications related to Mach (kernel)

Inferring free-energy barriers and kinetic rates from molecular dynamics via underdamped Langevin models

Sara Bonella, Fabio Pietrucci, David Daniel Girardier

Rare events include many of the most interesting transformation processes in condensed matter, from phase transitions to biomolecular conformational changes to chemical reactions. Access to the corresponding mechanisms, free-energy landscapes and kinetic r ...

Aip Publishing2023

Exploring brain-inspired multi-core heterogeneous hardware templates for low-power biomedical embedded systems

Benoît Walter Denkinger

The miniaturization of integrated circuits (ICs) and their higher performance and energy efficiency, combined with new machine learning algorithms and applications, have paved the way to intelligent, interconnected edge devices. In the medical domain, they ...

EPFL2023

Kernel Memory Networks: A Unifying Framework for Memory Modeling

Wulfram Gerstner, Johanni Michael Brea, Georgios Iatropoulos

We consider the problem of training a neural network to store a set of patterns with maximal noise robustness. A solution, in terms of optimal weights and state update rules, is derived by training each individual neuron to perform either kernel classifica ...

Curran Associates2022

Midas: Systematic Kernel TOCTTOU Protection

Mathias Josef Payer, Atri Bhattacharyya, Uros Tesic

Double-fetch bugs are a plague across all major operating system kernels. They occur when data is fetched twice across the user/kernel trust boundary while allowing concurrent modification. Such bugs enable an attacker to illegally access memory, cause den ...

2022

Learnable filter-banks for CNN-based audio applications

Nicolas Aspert, Benjamin Ricaud, Helena Peic Tukuljac, Laurent Colbois

We investigate the design of a convolutional layer where kernels are parameterized functions. This layer aims at being the input layer of convolutional neural networks for audio applications or applications involving time-series. The kernels are defined as ...

2022

NrOS: Effective Replication and Sharing in an Operating System

Sanidhya Kashyap, Ankit Bhardwaj

Writing a correct operating system kernel is notoriously hard. Kernel code requires manual memory management and type-unsafe code and must efficiently handle complex, asynchronous events. In addition, increasing CPU core counts further complicate kernel de ...

USENIX ASSOC2021

Motion Prediction Using Temporal Inception Module

Pascal Fua, Mathieu Salzmann, Wei Wang, Sena Kiciroglu

Human motion prediction is a necessary component for many applications in robotics and autonomous driving. Recent methods propose using sequence-to-sequence deep learning models to tackle this problem. However, they do not focus on exploiting different tem ...

Springer, Cham2020

Quantum Chemistry Meets Machine Learning

Anne-Clémence Corminboeuf, Alberto Fabrizio, Benjamin André René Meyer, Raimon Fabregat I De Aguilar-Amat

In this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the ...

SWISS CHEMICAL SOC2019

Evolving Takagi-Sugeno model based on online Gustafson-Kessel algorithm and kernel recursive least square method

Soroosh Shafieezadeh Abadeh

In this paper, we introduce an evolving system utilizing sparse weighted kernel least square as local models and online Gustafson-Kessel clustering algorithm for structure identification. Our proposed online clustering algorithm forms elliptical clusters w ...

2016

The IX Operating System: Combining Low Latency, High Throughput, and Efficiency in a Protected Dataplan

Edouard Bugnion, Christos Kozyrakis, Georgios Prekas, Mia Primorac, Ana Klimovic

The conventional wisdom is that aggressive networking requirements, such as high packet rates for small messages and μs-scale tail latency, are best addressed outside the kernel, in a user-level networking stack. We present ix, a dataplane operating system ...

Association for Computing Machinery2016

A Sparse Reformulation of the Green's Function Formalism Allows Efficient Simulations of Morphological Neuron Models

Marc-Oliver Gewaltig, Willem Anna Mark Wybo, Benjamin Torben-Nielsen, Daniele Boccalini

We prove that when a class of partial differential equations, generalized from the cable equation, is defined on tree graphs and the inputs are restricted to a spatially discrete, well chosen set of points, the Green's function (GF) formalism can be rewrit ...

Massachusetts Institute of Technology Press2015

The Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-based Hand Movement Classification

Barbara Caputo

There has been increasing interest in applying learning algorithms to improve the dexterity of myoelectric prostheses. In this work, we present a large-scale benchmark evaluation on the second iteration of the publicly released NinaPro database, which cont ...

2014

Rebasing I/O for Scientific Computing: Leveraging Storage Class Memory in an IBM BlueGene/Q Supercomputer

Miguel Angel Gila Arrondo, Felix Schürmann, Davide Tacchella, Pramod Shivaji Kumbhar, Alessandro Curioni, John Anthony Biddiscombe, Fabien Jonathan Delalondre, Peter Morjan, Blake Fitch, Lars Schneidenbach, Robert Germain, Bernard Metzler

Storage class memory is receiving increasing attention for use in HPC systems for the acceleration of intensive IO operations. We report a particular instance using SLC FLASH memory integrated with an IBM BlueGene/Q supercomputer at scale (Blue Gene Active ...

Springer2014

IX: A Protected Dataplane Operating System for High Throughput and Low Latency

Edouard Bugnion, Christos Kozyrakis, Georgios Prekas, Ana Klimovic

The conventional wisdom is that aggressive networking requirements, such as high packet rates for small messages and microsecond-scale tail latency, are best addressed outside the kernel, in a user-level networking stack. We present IX, a dataplane operati ...

USENIX2014

Fast O(1) Bilateral Filtering Using Trigonometric Range Kernels

Michaël Unser, Daniel Sage, Kunal Narayan Chaudhury

It is well known that spatial averaging can be realized (in space or frequency domain) using algorithms whose complexity does not scale with the size or shape of the filter. These fast algorithms are generally referred to as constant-time or O(1) algorithm ...

IEEE2011

A Method for Generalized Sampling and Reconstruction of Finite-Rate-of-Innovation Signals

Michaël Unser, Chandra Sekhar Seelamantula

We address the problem of generalized sampling and reconstruction of finite-rate-of-innovation signals. Specifically, we consider the problem of sampling streams of Dirac impulses and propose a two-channel method that enables fast, local reconstruction und ...

LATP2009

A Portable Kernel Abstraction for Low-Overhead Ephemeral Mapping Management

Willy Zwaenepoel

Modern operating systems create ephemeral virtual-to-physical mappings for a variety of purposes, ranging from the implementation of interprocess communication to the implementation of process tracing and debugging. With succeeding generations of processor ...

2005