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This paper presents efficient algorithms for testing the finite, polynomial, and exponential ambiguity of finite automata with $\epsilon$-transitions. It gives an algorithm for testing the exponential ambiguity of an automaton $A$ in time…

Computational Complexity · Computer Science 2008-02-25 Cyril Allauzen , Mehryar Mohri , Ashish Rastogi

For many real time applications, it is important to validate the information received from the sensors before entering higher levels of reasoning. This paper presents an any time probabilistic algorithm for validating the information…

Artificial Intelligence · Computer Science 2013-02-01 Pablo H. Ibarguengoytia , Luis Enrique Sucar , Sunil Vadera

Interior-point methods for linear programming problems require the repeated solution of a linear system of equations. Solving these linear systems is non-trivial due to the severe ill-conditioning of the matrices towards convergence. This…

Optimization and Control · Mathematics 2021-05-05 Jeffrey Cornelis , Wim Vanroose

We verify the correctness of a variety of mutual exclusion algorithms through model checking. We look at algorithms where communication is via shared read/write registers, where those registers can be atomic or non-atomic. For the…

Logic in Computer Science · Computer Science 2025-07-18 Rob van Glabbeek , Bas Luttik , Myrthe Spronck

Proof-labeling schemes are known mechanisms providing nodes of networks with certificates that can be verified locally by distributed algorithms. Given a boolean predicate on network states, such schemes enable to check whether the…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-04-26 Laurent Feuilloley , Pierre Fraigniaud

Classification systems typically act in isolation, meaning they are required to implicitly memorize the characteristics of all candidate classes in order to classify. The cost of this is increased memory usage and poor sample efficiency. We…

Machine Learning · Computer Science 2018-09-14 Harris Chan , Atef Chaudhury , Kevin Shen

Inverse problems exist in a wide variety of physical domains from aerospace engineering to medical imaging. The goal is to infer the underlying state from a set of observations. When the forward model that produced the observations is…

Machine Learning · Computer Science 2023-01-06 Chelsea Sidrane , Sydney Katz , Anthony Corso , Mykel J. Kochenderfer

We investigate the approximation for computing the sum $a_1+...+a_n$ with an input of a list of nonnegative elements $a_1,..., a_n$. If all elements are in the range $[0,1]$, there is a randomized algorithm that can compute an…

Data Structures and Algorithms · Computer Science 2012-03-01 Bin Fu , Wenfeng Li , Zhiyong Peng

This paper presents a {theoretical study} of the problem of verifying linearizability at runtime, where one seeks for a concurrent algorithm for verifying that the current execution of a given concurrent shared object implementation is…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-11-14 Armando Castañeda , Gilde Valeria Rodríguez

In this paper, we study the strong convergence of an algorithm to solve the variational inequality problem which extends(Thong et al, Numerical Algorithms. 78, 1045-1060 (2018)). We have reduced and refined some of their algorithm's…

Numerical Analysis · Mathematics 2021-05-11 Mostafa Ghadampour , Donal O'Regan , Ebrahim Soori , Ravi. p. Agarwal

We present algorithms to evaluate two types of multiple sums, which appear in higher-order loop computations. We consider expansions of a generalized hypergeometric-type sums, $\sum_{n_1,...,n_N} [Gamma(a1.n+c1) Gamma(a2.n}+c2) ...…

High Energy Physics - Theory · Physics 2015-06-12 C. Anzai , Y. Sumino

Methods to certify the robustness of neural networks in the presence of input uncertainty are vital in safety-critical settings. Most certification methods in the literature are designed for adversarial input uncertainty, but researchers…

Machine Learning · Computer Science 2023-01-26 Brendon G. Anderson , Somayeh Sojoudi

Understanding properties of deep neural networks is an important challenge in deep learning. In this paper, we take a step in this direction by proposing a rigorous way of verifying properties of a popular class of neural networks,…

Machine Learning · Statistics 2018-06-04 Nina Narodytska , Shiva Prasad Kasiviswanathan , Leonid Ryzhyk , Mooly Sagiv , Toby Walsh

Relational verification encompasses research directions such as reasoning about data abstraction, reasoning about security and privacy, secure compilation, and functional specificaton of tensor programs, among others. Several relational…

Logic in Computer Science · Computer Science 2025-09-08 Ramana Nagasamudram , Anindya Banerjee , David A. Naumann

We consider comparisons of statistical learning algorithms using multiple data sets, via leave-one-in cross-study validation: each of the algorithms is trained on one data set; the resulting model is then validated on each remaining data…

Applications · Statistics 2015-06-02 Lorenzo Trippa , Levi Waldron , Curtis Huttenhower , Giovanni Parmigiani

Failures in satellite components are costly and challenging to address, often requiring significant human and material resources. Embedding a hybrid AI-based system for fault detection directly in the satellite can greatly reduce this…

Machine Learning · Computer Science 2025-11-19 Delphine Longuet , Amira Elouazzani , Alejandro Penacho Riveiros , Nicola Bastianello

We obtain a complete characterization of the norm attainment set of a bounded linear functional on a normed space, in terms of a semi-inner-product defined on the space. Motivated by this result, we further apply the concept of…

Functional Analysis · Mathematics 2018-03-19 Debmalya Sain

We consider the task of certifying that a random $d$-dimensional subspace $X$ in $\mathbb{R}^n$ is well-spread - every vector $x \in X$ satisfies $c\sqrt{n} \|x\|_2 \leq \|x\|_1 \leq \sqrt{n}\|x\|_2$. In a seminal work, Barak et. al. showed…

Data Structures and Algorithms · Computer Science 2024-05-10 Venkatesan Guruswami , Jun-Ting Hsieh , Prasad Raghavendra

As neural networks (NNs) are increasingly introduced into safety-critical domains, there is a growing need to formally verify NNs before deployment. In this work we focus on the formal verification problem of NN equivalence which aims to…

Machine Learning · Computer Science 2021-12-14 Samuel Teuber , Marko Kleine Büning , Philipp Kern , Carsten Sinz

Oblique images are aerial photographs taken at oblique angles to the earth's surface. Projections of vector and other geospatial data in these images depend on camera parameters, positions of the geospatial entities, surface terrain,…

Computer Vision and Pattern Recognition · Computer Science 2022-06-22 Pragyana Mishra , Eyal Ofek , Gur Kimchi
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