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Multivariate polynomials arise in many different disciplines. Representing such a polynomial as a vector of univariate polynomials can offer useful insight, as well as more intuitive understanding. For this, techniques based on tensor…

Optimization and Control · Mathematics 2016-01-29 Gabriel Hollander , Philippe Dreesen , Mariya Ishteva , Johan Schoukens

We prove that, on bounded expansion classes, every first-order formula with modulo counting is equivalent, in a linear-time computable monadic expansion, to an existential first-order formula. As a consequence, we derive, on bounded…

Logic in Computer Science · Computer Science 2023-03-24 J. Nesetril , P. Ossona de Mendez , S. Siebertz

We introduce a reduction order called the weighted path order (WPO) that subsumes many existing reduction orders. WPO compares weights of terms as in the Knuth-Bendix order (KBO), while WPO allows weights to be computed by a wide class of…

Logic in Computer Science · Computer Science 2014-04-25 Akihisa Yamada , Keiichirou Kusakari , Toshiki Sakabe

The application of binary matrices are numerous. Representing a matrix as a mixture of a small collection of latent vectors via low-rank decomposition is often seen as an advantageous method to interpret and analyze data. In this work, we…

Numerical Analysis · Mathematics 2021-11-03 Derek DeSantis , Erik Skau , Duc P. Truong , Boian Alexandrov

Reasoning on large and complex real-world models is a computationally difficult task, yet one that is required for effective use of many AI applications. A plethora of inference algorithms have been developed that work well on specific…

Artificial Intelligence · Computer Science 2016-06-13 Avi Pfeffer , Brian Ruttenberg , William Kretschmer

This paper presents a new Monte Carlo (MC) algorithm for time-dependent particle transport problems with global variance reduction based on automatic weight windows (WWs). The centers of WWs at a time step are defined by the solution of an…

Numerical Analysis · Mathematics 2026-03-18 Caleb A. Shaw , Dmitriy Y. Anistratov

Low-rank model compression is a widely used technique for reducing the computational load when training machine learning models. However, existing methods often rely on relaxing the low-rank constraint of the model weights using a…

Signal Processing · Electrical Eng. & Systems 2023-07-05 Ye Xue , Vincent Lau

Recent developments in termination analysis for declarative programs emphasize the use of appropriate models for the logical theory representing the program at stake as a generic approach to prove termination of declarative programs. In…

Programming Languages · Computer Science 2015-12-23 Salvador Lucas

Let $\mathfrak{g}$ be a semisimple Lie algebra over $\mathbb{C}$ having rank $l$ and let $V=L(\lambda)$ be an irreducible finite-dimensional $\mathfrak{g}$-module having highest weight $\lambda.$ Computations of weight multiplicities in…

Representation Theory · Mathematics 2016-04-06 Mikaël Cavallin

Recommendation from implicit feedback is a highly challenging task due to the lack of the reliable observed negative data. A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight…

Information Retrieval · Computer Science 2020-03-05 Jiawei Chen , Can Wang , Sheng Zhou , Qihao Shi , Jingbang Chen , Yan Feng , Chun Chen

Interpretation methods and their restrictions to polynomials have been deeply used to control the termination and complexity of first-order term rewrite systems. This paper extends interpretation methods to a pure higher order functional…

Logic in Computer Science · Computer Science 2023-06-22 Emmanuel Hainry , Romain Péchoux

We characterize the inclusions of weighted classes of entire functions in terms of the defining weights resp. weight systems. First we treat weights defined in terms of a so-called associated weight function where the weight(system) is…

Complex Variables · Mathematics 2024-01-24 Gerhard Schindl

There has been a great deal of recent interest in methods for performing lifted inference; however, most of this work assumes that the first-order model is given as input to the system. Here, we describe lifted inference algorithms that…

Artificial Intelligence · Computer Science 2012-05-14 Prithviraj Sen , Amol Deshpande , Lise Getoor

The central open question in Descriptive Complexity is whether there is a logic that characterizes deterministic polynomial time (PTIME) on relational structures. Towards this goal, we define a logic that is obtained from first-order logic…

Logic in Computer Science · Computer Science 2021-11-16 Eugenia Ternovska

While the Ising model remains essential to understand physical phenomena, its natural connection to combinatorial reasoning makes it also one of the best models to probe complex systems in science and engineering. We bring a computational…

Computational Physics · Physics 2022-12-27 Shaan A. Nagy , Roger Paredes , Jeffrey M. Dudek , Leonardo Dueñas-Osorio , Moshe Y. Vardi

Since the 1970s with the work of McNaughton, Papert and Sch\"utzenberger, a regular language is known to be definable in the first-order logic if and only if its syntactic monoid is aperiodic. This algebraic characterisation of a…

Logic in Computer Science · Computer Science 2023-07-28 Dhruv Nevatia , Benjamin Monmege

We study extensions of expressive decidable fragments of first-order logic with circumscription, in particular the two-variable fragment FO$^2$, its extension C$^2$ with counting quantifiers, and the guarded fragment GF. We prove that if…

Artificial Intelligence · Computer Science 2024-08-23 Carsten Lutz , Quentin Manière

This dissertation is a contribution to the project of second-order set theory, which has seen a revival in recent years. The approach is to understand second-order set theory by studying the structure of models of second-order set theories.…

Logic · Mathematics 2018-04-26 Kameryn J Williams

We study computational aspects of relational marginal polytopes which are statistical relational learning counterparts of marginal polytopes, well-known from probabilistic graphical models. Here, given some first-order logic formula, we can…

Artificial Intelligence · Computer Science 2020-01-16 Ondrej Kuzelka , Yuyi Wang

We introduce a new framework for the analysis of preprocessing routines for parameterized counting problems. Existing frameworks that encapsulate parameterized counting problems permit the usage of exponential (rather than polynomial) time…

Data Structures and Algorithms · Computer Science 2023-08-07 Daniel Lokshtanov , Pranabendu Misra , Saket Saurabh , Meirav Zehavi