Publications related to Loadable kernel module

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 ...

Melville2023

End-to-end kernel learning via generative random Fourier features

Fanghui Liu, Jie Yang

Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the first-stage process, learn-ing an optimal feature map is often formulated as a ...

ELSEVIER SCI LTD2023

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

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Matthieu Wyart, Umberto Maria Tomasini, Antonio Sclocchi

Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

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

What can linearized neural networks actually say about generalization?

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Guillermo Ortiz Jimenez

For certain infinitely-wide neural networks, the neural tangent kernel (NTK) theory fully characterizes generalization, but for the networks used in practice, the empirical NTK only provides a rough first-order approximation. Still, a growing body of work ...

2021

USBFuzz: A Framework for Fuzzing USB Drivers by Device Emulation

Mathias Josef Payer, Hui Peng

The Universal Serial Bus (USB) connects external devices to a host. This interface exposes the OS kernels and device drivers to attacks by malicious devices. Unfortunately, kernels and drivers were developed under a security model that implicitly trusts co ...

USENIX ASSOC2020

Snap-On User-Space Manager for Dynamically Reconfigurable System-on-Chips

Paolo Ienne, Andrea Guerrieri, Sahand Kashani

Due to increased embedded processing requirements, modern SoCs are becoming heterogeneous computing platforms by combining traditional processing units with custom reconfigurable hardware accelerators (HAs) on an FPGA fabric. However, efficiently managing ...

2019

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Loadable kernel module

ACTOR: Action-Guided Kernel Fuzzing

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

End-to-end kernel learning via generative random Fourier features

Midas: Systematic Kernel TOCTTOU Protection

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

NrOS: Effective Replication and Sharing in an Operating System

Multikernel Regression with Sparsity Constraint

What can linearized neural networks actually say about generalization?

USBFuzz: A Framework for Fuzzing USB Drivers by Device Emulation

Snap-On User-Space Manager for Dynamically Reconfigurable System-on-Chips

Graph Chatbot

Chat with Graph Search

NrOS: Effective Replication and Sharing in an Operating System

Midas: Systematic Kernel TOCTTOU Protection

What can linearized neural networks actually say about generalization?

Multikernel Regression with Sparsity Constraint

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

End-to-end kernel learning via generative random Fourier features

Snap-On User-Space Manager for Dynamically Reconfigurable System-on-Chips