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This paper establishes the separation of complexity classes $\mathbf{P}$ and $\mathbf{NP}$ through a novel homological algebraic approach grounded in category theory. We construct the computational category $\mathbf{Comp}$, embedding…

Computational Complexity · Computer Science 2025-12-22 Jian-Gang Tang

This work, shows how propositional resolution can be generalized to obtain a resolution proof system for constrained pseudo-propositional logic (CPPL), which is an extension resulted from inserting the natural numbers with few constraints…

Logic · Mathematics 2023-06-13 Ahmad-Saher Azizi-Sultan

Causal inconsistency arises when the underlying causal graphs captured by generative models like \textit{Normalizing Flows} (NFs) are inconsistent with those specified in causal models like \textit{Struct Causal Models} (SCMs). This…

Machine Learning · Computer Science 2024-12-18 Qingyang Zhou , Kangjie Lu , Meng Xu

Second-order Recurrent Neural Networks (2RNNs) extend RNNs by leveraging second-order interactions for sequence modelling. These models are provably more expressive than their first-order counterparts and have connections to well-studied…

Machine Learning · Computer Science 2024-06-10 Maude Lizaire , Michael Rizvi-Martel , Marawan Gamal Abdel Hameed , Guillaume Rabusseau

A value of a CSP instance is typically defined as a fraction of constraints that can be simultaneously met. We propose an alternative definition of a value of an instance and show that, for purely combinatorial reasons, a value of an…

Computational Complexity · Computer Science 2021-07-21 Libor Barto , Marcin Kozik

Many real world problems naturally appear as constraints satisfaction problems (CSP), for which very efficient algorithms are known. Most of these involve the combination of two techniques: some direct propagation of constraints between…

Artificial Intelligence · Computer Science 2013-04-12 Denis Berthier

What functions can Neural Processes represent? We analyze the representational capacity of popular NP architectures: Conditional Neural Processes (CNPs), Attentive Neural Processes (ANPs), Transformer Neural Processes (TNPs), and their…

Machine Learning · Computer Science 2026-05-26 Robin Young

We introduce trace definability, a weak notion of interpretability, and trace equivalence, a weak notion of equivalence for first order structures and theories. In particular we get an interesting weak equivalence notion for $\mathrm{NIP}$…

Logic · Mathematics 2022-04-07 Erik Walsberg

This paper talk about the influence of Connection and Dispersion on Computational Complexity. And talk about the HornCNF's connection and CNF's dispersion, and show the difference between CNFSAT and HornSAT. First, I talk the relation…

Logic in Computer Science · Computer Science 2011-08-10 Koji Kobayashi

The convex feasibility problem (CFP) is to find a feasible point in the intersection of finitely many convex and closed sets. If the intersection is empty then the CFP is inconsistent and a feasible point does not exist. However,…

Optimization and Control · Mathematics 2018-04-27 Yair Censor , Maroun Zaknoon

We describe simple algebraic and combinatorial characterisations of finite relational core structures admitting finitely many obstructions. As a consequence, we show that it is decidable to determine whether a constraint satisfaction…

Logic in Computer Science · Computer Science 2015-07-01 Benoit Larose , Cynthia Loten , Claude Tardif

Conditional Neural Processes (CNP; Garnelo et al., 2018) are an attractive family of meta-learning models which produce well-calibrated predictions, enable fast inference at test time, and are trainable via a simple maximum likelihood…

Machine Learning · Computer Science 2021-10-19 Stratis Markou , James Requeima , Wessel Bruinsma , Richard Turner

We investigate the data exchange from relational databases to RDF graphs inspired by R2RML with the addition of target shape schemas. We study the problems of consistency i.e., checking that every source instance admits a solution, and…

Databases · Computer Science 2020-04-01 Iovka Boneva , Jose Lozano , Sławek Staworko

In this article, we discuss the question of whether P equals NP, we do not follow the line of research of many researchers, which is to try to find such a problem Q, and the problem Q belongs to the class of NP-complete, if the problem Q is…

Computational Complexity · Computer Science 2024-03-26 Jian-Gang Tang

We extend the convergence law for sparse random graphs proven by Lynch to arbitrary relational languages. We consider a finite relational vocabulary $\sigma$ and a first order theory $T$ for $\sigma$ composed of symmetry and…

Combinatorics · Mathematics 2020-06-15 Lázaro Alberto Larrauri

The objective of this article is to formalize the definition of NP problems. We construct a mathematical model of discrete problems as independence systems with weighted elements. We introduce two auxiliary sets that characterize the…

Data Structures and Algorithms · Computer Science 2007-05-23 Anatoly D. Plotnikov

The CSP of a first-order theory $T$ is the problem of deciding for a given finite set $S$ of atomic formulas whether $T \cup S$ is satisfiable. Let $T_1$ and $T_2$ be two theories with countably infinite models and disjoint signatures.…

Logic · Mathematics 2023-06-22 Manuel Bodirsky , Johannes Greiner

This paper considers the length of resolution proofs when using Krishnamurthy's classic symmetry rules. We show that inconsistent linear equation systems of bounded width over a fixed finite field $\mathbb{F}_p$ with $p$ a prime have, in…

Computational Complexity · Computer Science 2021-01-14 Pascal Schweitzer , Constantin Seebach

We study the Constraint Satisfaction Problem CSP(A), where A is first-order definable in (Z;+,1) and contains +. We prove such problems are either in P or NP-complete.

Computational Complexity · Computer Science 2018-07-04 Manuel Bodirsky , Barnaby Martin , Marcello Mamino , Antoine Mottet

In this work, we propose a novel link prediction model and further boost it by studying graph incompleteness. First, we introduce MPNN-then-SF, an innovative architecture leveraging structural feature (SF) to guide MPNN's representation…

Machine Learning · Computer Science 2024-06-05 Xiyuan Wang , Haotong Yang , Muhan Zhang