Midas: Systematic Kernel TOCTTOU Protection

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Computer systems rely heavily on abstraction to manage the exponential growth of complexity across hardware and software. Due to practical considerations of compatibility between components of these complex systems across generations, developers have favou ...

Dehydration is a frequent condition in the elderly and can lead to serious health complications if not compensated timely. Early diagnosis can be problematic, as medical examinations in the hospital would be needed. Fully wearable low-cost multisensing dev ...

Most memory corruptions occur on the heap. To harden userspace applications and prevent heap-based exploitation, Google has developed Scudo. Since Android 11, Scudo has replaced jemalloc as the default heap implementation for all native code on Android. Sc ...

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 ...

Fuzzing reliably and efficiently finds bugs in software, including operating system kernels. In general, higher code coverage leads to the discovery of more bugs. This is why most existing kernel fuzzers adopt strategies to generate a series of inputs that ...

In this work, we propose an open-hardware low-power coarse-grained reconfigurable array connected to a lightweight microcontroller and enclosed in an application mapping framework. The latter provides complete support to configure kernels in the reconfigur ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

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 ...

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 ...

In confidential computing, the view of the system software is Manichean: the host operating system is untrusted and the TEE runtime system is fully trusted. However, the runtime system is often as complex as a full operating system, and thus is not free fr ...

A kernel method for estimating a probability density function from an independent and identically distributed sample drawn from such density is presented. Our estimator is a linear combination of kernel functions, the coefficients of which are determined b ...

We present LISA, a proof system and proof assistant for constructing proofs in schematic first-order logic and axiomatic set theory. The logical kernel of the system is a proof checker for first-order logic with equality and schematic predicate and fun ...

Virtual Memory (VM) is a critical programming abstraction that is widely used in various modern computing platforms. With the rise of datacenter computing and birth of planet-scale online services, the semantic and capacity requirements from memory have ev ...

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 ...

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 ...

Developing and fine-tuning software programs for heterogeneous hardware such as CPU/GPU processing platforms comprise a highly complex endeavor that demands considerable time and effort of software engineers and requires evaluating various fundamental comp ...

In this thesis, we propose model order reduction techniques for high-dimensional PDEs that preserve structures of the original problems and develop a closure modeling framework leveraging the Mori-Zwanzig formalism and recurrent neural networks. Since high ...

Dr.Jit is a new just-in-time compiler for physically based rendering and its derivative. Dr.Jit expedites research on these topics in two ways: first, it traces high-level simulation code (e.g., written in Python) and aggressively simplifies and specialize ...

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 ...

In this paper, we provide a Banach-space formulation of supervised learning with generalized total-variation (gTV) regularization. We identify the class of kernel functions that are admissible in this framework. Then, we propose a variation of supervised l ...

Midas: Systematic Kernel TOCTTOU Protection

Chattez avec Graph Search

Secure Interface Design Leveraging Hardware/Software Support

Design and in vitro Characterization of a Wearable Multisensing System for Hydration Monitoring

Exploiting android’s hardened memory allocator

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

ACTOR: Action-Guided Kernel Fuzzing

An Open-Hardware Coarse-Grained Reconfigurable Array for Edge Computing

Bayes-optimal Learning of Deep Random Networks of Extensive-width

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

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

A RISC-V Extension to Minimize Privileges of Enclave Runtimes

Density estimation in RKHS with application to Korobov spaces in high dimensions

LISA – A Modern Proof System

Rebooting Virtual Memory with Midgard

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

Kernel Memory Networks: A Unifying Framework for Memory Modeling

Dynamic SIMD Parallel Execution on GPU from High-Level Dataflow Synthesis

Structure-preserving approaches and data-driven closure modeling for model order reduction

Dr.Jit: A Just-In-Time Compiler for Differentiable Rendering

NrOS: Effective Replication and Sharing in an Operating System

Multikernel Regression with Sparsity Constraint