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We propose a new algorithm to compute a shifted proper orthogonal decomposition (sPOD) for systems dominated by multiple transport velocities. The sPOD is a recently proposed mode decomposition technique which overcomes the poor performance…

Numerical Analysis · Mathematics 2018-03-06 Philipp Schulze , Julius Reiss , Volker Mehrmann

Computing the probability of a formula given the probabilities or weights associated with other formulas is a natural extension of logical inference to the probabilistic setting. Surprisingly, this problem has received little attention in…

Artificial Intelligence · Computer Science 2012-03-19 Vibhav Gogate , Pedro Domingos

We study which standard operators of probabilistic process calculi allow for compositional reasoning with respect to bisimulation metric semantics. We argue that uniform continuity (generalizing the earlier proposed property of…

Logic in Computer Science · Computer Science 2019-03-14 Daniel Gebler , Kim G. Larsen , Simone Tini

Off-policy evaluation (OPE) aims to estimate the benefit of following a counterfactual sequence of actions, given data collected from executed sequences. However, existing OPE estimators often exhibit high bias and high variance in problems…

Machine Learning · Computer Science 2023-07-17 Aaman Rebello , Shengpu Tang , Jenna Wiens , Sonali Parbhoo

This volume contains the proceedings of the Combined 25th International Workshop on Expressiveness in Concurrency and the 15th Workshop on Structural Operational Semantics (EXPRESS/SOS 2018), which was held on September 3, 2018, in Beijing,…

Logic in Computer Science · Computer Science 2018-08-27 Jorge A. Pérez , Simone Tini

Probabilistic conditioning is concerned with the identification of a distribution of a random variable $X$ given a random variable $Y$. It is a cornerstone of scientific and engineering applications where modeling uncertainty is key. This…

Machine Learning · Statistics 2026-05-13 Panos Tsimpos , Edoardo Calvello , Ayoub Belhadji , Nicholas H. Nelsen

Boolean tensor decomposition approximates data of multi-way binary relationships as product of interpretable low-rank binary factors, following the rules of Boolean algebra. Here, we present its first probabilistic treatment. We facilitate…

Machine Learning · Statistics 2018-05-15 Tammo Rukat , Chris C. Holmes , Christopher Yau

Exponential operator decompositions are an important tool in many fields of physics, for example, in quantum control, quantum computation, or condensed matter physics. In this work, we present a method for obtaining such decompositions,…

Quantum Physics · Physics 2011-10-19 Seckin Sefi , Peter van Loock

In this paper, we introduce a method for approximating the solution to inference and optimization tasks in uncertain and deterministic reasoning. Such tasks are in general intractable for exact algorithms because of the large number of…

Artificial Intelligence · Computer Science 2012-12-12 David Ephraim Larkin

Modal synthesis methods are a long-standing approach for modelling distributed musical systems. In some cases extensions are possible in order to handle geometric nonlinearities. One such case is the high-amplitude vibration of a string,…

Sound · Computer Science 2025-05-16 Victor Zheleznov , Stefan Bilbao , Alec Wright , Simon King

We present a new soundness proof of Concurrent Separation Logic (CSL) based on a structural operational semantics (SOS). We build on two previous proofs and develop new auxiliary notions to achieve the goal. One uses a denotational…

Logic in Computer Science · Computer Science 2017-12-06 Pedro Soares , António Ravara , Simão Melo de Sousa

This paper shows how the use of Structural Operational Semantics (SOS) in the style popularized by the process-algebra community can lead to a more succinct and useful construction for building finite automata from regular expressions. Such…

Formal Languages and Automata Theory · Computer Science 2020-02-19 Rance Cleaveland

Two of the most studied extensions of trace and testing equivalences to nondeterministic and probabilistic processes induce distinctions that have been questioned and lack properties that are desirable. Probabilistic trace-distribution…

Logic in Computer Science · Computer Science 2015-07-01 Marco Bernardo , Rocco De Nicola , Michele Loreti

Global polynomial optimization is an important tool across applied mathematics, with many applications in operations research, engineering, and physical sciences. In various settings, the polynomials depend on external parameters that may…

Optimization and Control · Mathematics 2024-06-14 Richard L. Zhu , Mathias Oster , Yuehaw Khoo

We develop fast spectral algorithms for tensor decomposition that match the robustness guarantees of the best known polynomial-time algorithms for this problem based on the sum-of-squares (SOS) semidefinite programming hierarchy. Our…

Machine Learning · Computer Science 2017-06-28 Tselil Schramm , David Steurer

This paper develops a novel data-driven technique to compute the participation factors for nonlinear systems based on the Koopman mode decomposition. Provided that certain conditions are satisfied, it is shown that the proposed technique…

Systems and Control · Computer Science 2021-06-01 Marcos Netto , Yoshihiko Susuki , Lamine Mili

Harnessing the predictive capability of Markov process models requires propagating probability density functions (beliefs) through the model. For many existing models however, belief propagation is analytically infeasible, requiring…

Machine Learning · Computer Science 2026-04-10 Peter Amorese , Morteza Lahijanian

This position paper on the (meta-)theory of Structural Operational Semantic (SOS) is motivated by the following two questions: (1) Is the (meta-)theory of SOS dying out as a research field? (2) If so, is it possible to rejuvenate this field…

Logic in Computer Science · Computer Science 2023-09-15 Luca Aceto , Pierluigi Crescenzi , Anna Ingólfsdóttir , Mohammad Reza Mousavi

The recent proliferation of richly structured probabilistic models raises the question of how to automatically determine an appropriate model for a dataset. We investigate this question for a space of matrix decomposition models which can…

Machine Learning · Computer Science 2012-10-19 Roger Grosse , Ruslan R Salakhutdinov , William T. Freeman , Joshua B. Tenenbaum

Higher-order processes with parameterization are capable of abstraction and application (migrated from the lambda-calculus), and thus are computationally more expressive. For the minimal higher-order concurrency, it is well-known that the…

Logic in Computer Science · Computer Science 2021-08-25 Xian Xu , Wenbo Zhang