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The problem of scheduling with testing in the framework of explorable uncertainty models environments where some preliminary action can influence the duration of a task. In the model, each job has an unknown processing time that can be…

Data Structures and Algorithms · Computer Science 2021-08-20 Susanne Albers , Alexander Eckl

We study decidability of verification problems for timed automata extended with unbounded discrete data structures. More detailed, we extend timed automata with a pushdown stack. In this way, we obtain a strong model that may for instance…

Logic in Computer Science · Computer Science 2017-01-11 Karin Quaas

I consider the following generic scenario: an abstract model M of some 'real' system is only partially presented, or partially known to us, and we have to ensure that the actual system satisfies a given specification, formalised in some…

Logic in Computer Science · Computer Science 2020-12-24 Valentin Goranko

Computer-based systems have solved several domain problems, including industrial, military, education, and wearable. Nevertheless, such arrangements need high-quality software to guarantee security and safety as both are mandatory for…

We consider functional data which have only been observed on a subset of their domain. This paper aims to develop statistical tests to determine whether the function and the domain over which it is observed are independent. The assumption…

Methodology · Statistics 2025-12-04 Maximilian Ofner , Siegfried Hörmann , David Kraus , Dominik Liebl

We describe an algorithm to quantify dependence in a multivariate data set. The algorithm is able to identify any linear and non-linear dependence in the data set by performing a hypothesis test for two variables being independent. As a…

Data Analysis, Statistics and Probability · Physics 2012-11-06 Michael Feindt , Michael Prim

Quantitative properties of stochastic systems are usually specified in logics that allow one to compare the measure of executions satisfying certain temporal properties with thresholds. The model checking problem for stochastic systems with…

Logic in Computer Science · Computer Science 2010-05-11 Axel Legay , Benoit Delahaye

Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering.Such measurements can be viewed as…

We consider the problem of whether a given decision model, working with structured data, has individual fairness. Following the work of Dwork, a model is individually biased (or unfair) if there is a pair of valid inputs which are close to…

Machine Learning · Computer Science 2020-06-23 Philips George John , Deepak Vijaykeerthy , Diptikalyan Saha

We consider the model checking problem of infinite state systems given in the form of parameterized discrete timed networks with multiple clocks. We show that this problem is decidable with respect to specifications given by B- or…

Logic in Computer Science · Computer Science 2016-09-15 Benjamin Aminof , Sasha Rubin , Francesco Spegni , Florian Zuleger

We revisit the parameterized model checking problem for token-passing systems and specifications in indexed $\textsf{CTL}^\ast \backslash \textsf{X}$. Emerson and Namjoshi (1995, 2003) have shown that parameterized model checking of indexed…

Logic in Computer Science · Computer Science 2013-11-26 Benjamin Aminof , Swen Jacobs , Ayrat Khalimov , Sasha Rubin

We describe an approach to modelling and reasoning about data-centric business processes and present a form of general model checking. Our technique extends existing approaches, which explore systems only from concrete initial states.…

Logic in Computer Science · Computer Science 2012-07-12 Andreas Bauer , Peter Baumgartner , Michael Norrish

We study the problem of solving a linear sensing system when the observations are unlabeled. Specifically we seek a solution to a linear system of equations y = Ax when the order of the observations in the vector y is unknown. Focusing on…

Information Theory · Computer Science 2015-12-02 Jayakrishnan Unnikrishnan , Saeid Haghighatshoar , Martin Vetterli

The parameters of a linear compartment model are usually estimated from experimental input-output data. A problem arises when infinitely many parameter values can yield the same result; such a model is called unidentifiable. In this case,…

Combinatorics · Mathematics 2016-03-08 Jasmijn A. Baaijens , Jan Draisma

Graphical models provide a framework for exploration of multivariate dependence patterns. The connection between graph and statistical model is made by identifying the vertices of the graph with the observed variables and translating the…

Statistics Theory · Mathematics 2008-02-08 Mathias Drton , Michael D. Perlman

Identifying dependency in multivariate data is a common inference task that arises in numerous applications. However, existing nonparametric independence tests typically require computation that scales at least quadratically with the sample…

Methodology · Statistics 2021-07-08 Shai Gorsky , Li Ma

Identifying dependency between two random variables is a fundamental problem. The clear interpretability and ability of a procedure to provide information on the form of possible dependence is particularly important when exploring…

Methodology · Statistics 2026-04-27 Bogdan Ćmiel , Teresa Ledwina

Given a static vertex-selection problem (e.g. independent set, dominating set) on a graph, we can define a corresponding temporally satisfying reconfiguration problem on a temporal graph which asks for a sequence of solutions to the…

Data Structures and Algorithms · Computer Science 2025-09-22 Tom Davot , Jessica Enright , Laura Larios-Jones

We study the possibility of nullifying time-varying systems with memoryless output feedback. The systems we examine are linear single-input single-output finite-dimensional time-varying systems. For generic completely controllable and…

Optimization and Control · Mathematics 2007-05-23 Gera Weiss

Localization of unknown faults in industrial systems is a difficult task for data-driven diagnosis methods. The classification performance of many machine learning methods relies on the quality of training data. Unknown faults, for example…

Signal Processing · Electrical Eng. & Systems 2019-10-15 Daniel Jung