A Simpler and Faster NIC Driver Model for Network Functions

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Formally verifying the correctness of software is necessary to merit the trust people put in software systems. Currently, formal verification requires human effort to prove that a piece of code matches its specification and code changes to improve verifiab ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

Formal verification of real-world software systems remains challenging for a number of reasons, including lack of automation, friction in specifying properties, and limited support for the diverse programming paradigms used in industry. In this thesis we m ...

Modern programmers routinely use third-party code, and infrastructure operators deploy software they did not write. This would not be possible without semantic interfaces---documentation, header files, specifications---that succinctly describe what that th ...

Post-quantum cryptography is a branch of cryptography which deals with cryptographic algorithms whose hardness assumptions are not based on problems known to be solvable by a quantum computer, such as the RSA problem, factoring or discrete logarithms. This ...

Model-checking intends to verify whether a property is satisfied by a model, or not. Model-checking of high-level models, e.g. SysML models, usually first requires a model transformation to a low level formal specification. The present papers proposes a ne ...

This paper describes the first practical single-trace side-channel power analysis of SIKE. While SIKE is a post-quantum key exchange, the scheme still relies on a secret elliptic curve scalar multiplication which involves a loop of a double-and-add procedu ...

Software network functions (NFs), such as a network address translator, load balancer, or proxy, promise to bring flexibility and rapid innovation to computer networks and to reduce operational costs. However, continuous updates and flexibility typically c ...

p>We study the dynamics of optimization and the generalization properties of one-hidden layer neural networks with quadratic activation function in the overparametrized regime where the layer width m is larger than the input dimension d. We conside ...

Two distinct limits for deep learning have been derived as the network width h -> infinity, depending on how the weights of the last layer scale with h. In the neural tangent Kernel (NTK) limit, the dynamics becomes linear in the weights and is described b ...

We present the design and implementation of Vigor, a software stack and toolchain for building and running software network middleboxes that are guaranteed to be correct, while preserving competitive performance and developer productivity. Developers write ...

Program synthesis was first proposed a few decades ago, but in the last decade it has gained increased momentum in the research community. The increasing complexity of software has dictated the urgent need for improved supporting tools that verify the soft ...

While most models of randomly connected neural networks assume single-neuron models with simple dynamics, neurons in the brain exhibit complex intrinsic dynamics over multiple timescales. We analyze how the dynamical properties of single neurons and recurr ...

Software network functions promise to simplify the deployment of network services and reduce network operation cost. However, they face the challenge of unpredictable performance. Given this performance variability, it is imperative that during deployment, ...

In our daily lives, people or devices frequently need to learn their location for many reasons as some services depend on the absolute location or the proximity. The outcomes of positioning systems can have critical effects e.g., on military, emergency. Th ...

At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit [12, 9], thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: ...

We present a Network Address Translator (NAT) written in C and proven to be semantically correct according to RFC 3022, as well as crash-free and memory-safe. There exists a lot of recent work on network verification, but it mostly assumes models of networ ...

Operating systems have been shown to waste machine resources by leaving cores idle while work is ready to be scheduled. This results in suboptimal performance for user applications, and wasted power. Recent progress in formal verification methods have led ...

Computer networks are an important part of our life. Yet, they are traditionally hard to manage and operate. The recent shift to Software-Defined Networking (SDN) is promised to change the way in which the networks are run by their operators. However, SDN ...

Approximate computing has gained high attention in various applications that can benefit from a reduction in costs by lowering the accuracy. In this paper we present an optimization approach for functional approximation of Binary Decision Diagrams (BDDs) w ...

A Simpler and Faster NIC Driver Model for Network Functions

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Automated Formal Verification of Software Network Functions

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Scaling Language Features for Program Verification

Performance Interfaces for Network Functions

Attacks on some post-quantum cryptographic protocols: The case of the Legendre PRF and SIKE

Direct Model-checking of SysML Models

Full key recovery side-channel attack against ephemeral SIKE on the Cortex-M4

Verification of Software Network Functions with No Verification Expertise

Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions

Disentangling feature and lazy training in deep neural networks

Verifying Software Network Functions with No Verification Expertise

Scaling Functional Synthesis and Repair

How single neuron properties shape chaotic dynamics and signal transmission in random neural networks

Automated Synthesis of Adversarial Workloads for Network Functions

Implications of Position in Cryptography

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

A Formally Verified NAT

Towards Proving Optimistic Multicore Schedulers

Simplifying Development and Management of Software-Defined Networks

Approximate BDD Optimization with Prioritized epsilon-Preferred Evolutionary Algorithm