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Although the latent spaces learned by distinct neural networks are not generally directly comparable, recent work in machine learning has shown that it is possible to use the similarities and differences among latent space vectors to derive…

Neurons and Cognition · Quantitative Biology 2023-09-12 Alex B. Kiefer , Christopher L. Buckley

Humans use causality and hypothetical retrospection in their daily decision-making, planning, and understanding of life events. The human mind, while retrospecting a given situation, think about questions such as "What was the cause of the…

Artificial Intelligence · Computer Science 2022-01-12 Utkarshani Jaimini , Amit Sheth

In this paper, we revisit the notion of quantum entanglement induced by the deformation of phase-space through noncommutative space (NC) parameters. The geometric structure of the state space for Gaussian states in NC-space is illustrated…

Quantum Physics · Physics 2025-02-11 Shilpa Nandi , Pinaki Patra

Non-classical probability (along with its underlying logic) is a defining feature of quantum mechanics. A formulation that incorporates them, inherently and directly, would promise a unified description of seemingly different prescriptions…

Quantum Physics · Physics 2019-05-21 Soumik Adhikary , Sooryansh Asthana , V. Ravishankar

While correlation measures are used to discern statistical relationships between observed variables in almost all branches of data-driven scientific inquiry, what we are really interested in is the existence of causal dependence. Designing…

Machine Learning · Computer Science 2014-06-26 Ishanu Chattopadhyay

In previous two papers, we defined fractional Brauer configuration algebras and developed their covering theory. In this paper, we study the representation theory of fractional Brauer graph algebras of type MS, a special class of fractional…

Representation Theory · Mathematics 2026-04-24 Nengqun Li , Yuming Liu

Graph states form a rich class of entangled states that exhibit important aspects of multi-partite entanglement. At the same time, they can be described by a number of parameters that grows only moderately with the system size. They have a…

Quantum Physics · Physics 2007-05-23 M. Hein , W. Dür , J. Eisert , R. Raussendorf , M. Van den Nest , H. -J. Briegel

Granger causality is a commonly used method for uncovering information flow and dependencies in a time series. Here we introduce JGC (Jacobian Granger Causality), a neural network-based approach to Granger causality using the Jacobian as a…

Machine Learning · Computer Science 2022-05-20 Suryadi , Yew-Soon Ong , Lock Yue Chew

Graph states provide a powerful framework for describing multipartite entanglement in quantum information science. In their standard formulation, graph states are generated by controlled-$Z$ interactions and naturally encode symmetric…

Quantum Physics · Physics 2026-05-05 Matheus R. de Jesus , Eduardo O. C. Hoefel , Renato M. Angelo

The coupling of gravity to a scalar field raises a number of interesting questions of principle since the usual minimal coupling obtained by replacing ordinary derivatives with covariant derivatives is not available -- they are the same…

General Relativity and Quantum Cosmology · Physics 2011-10-26 Y. N. Srivastava , J. Swain , A. Widom

Qualitative causal relationships compactly express the direction, dependency, temporal constraints, and monotonicity constraints of discrete or continuous interactions in the world. In everyday or academic language, we may express…

Artificial Intelligence · Computer Science 2022-02-25 Scott Friedman , Ian Magnusson , Vasanth Sarathy , Sonja Schmer-Galunder

Quantum non-Gaussianity is a key resource for quantum advantage in continuous-variable systems. We introduce a general framework to quantify non-Gaussianity based on correlation generation: two copies of a state become correlated at a…

Quantum Physics · Physics 2025-08-28 Oliver Hahn , Ryuji Takagi

We investigate the existence of ground states with prescribed mass for the NLS energy with combined $L^2$-critical and subcritical nonlinearities, on a general non-compact metric graph $\mathcal{G}$. The interplay between the different…

Analysis of PDEs · Mathematics 2020-11-04 Dario Pierotti , Nicola Soave

Previous work has axiomatised the cardinality operation in relation algebras, which counts the number of edges of an unweighted graph. We generalise the cardinality axioms to Stone relation algebras, which model weighted graphs, and study…

Logic in Computer Science · Computer Science 2026-03-11 Hitoshi Furusawa , Walter Guttmann

In this article we show that there are branching systems (which induce representations of the graph algebra $C^*(E)$) associated to each row-countable graph $E$. For row-countable graphs, we characterize the condition $(L)$ via branching…

Operator Algebras · Mathematics 2019-10-30 Ben hur Eidt , Danilo Royer

A factor-graph representation of quantum-mechanical probabilities is proposed. Unlike standard statistical models, the proposed representation uses auxiliary variables (state variables) that are not random variables.

Information Theory · Computer Science 2016-11-15 Hans-Andrea Loeliger , Pascal O. Vontobel

The present paper deals with the identification of nonlinear mechanical vibrations. A grey-box, or semi-physical, nonlinear state-space representation is introduced, expressing the nonlinear basis functions using a limited number of…

Systems and Control · Computer Science 2018-05-01 Jean-Philippe Noël , Johan Schoukens

We consider coherent sublinear expectations on a measurable space, without assuming the existence of a dominating probability measure. By considering a decomposition of the space in terms of the supports of the measures representing our…

Probability · Mathematics 2011-10-27 Samuel N. Cohen

We present simple models which exhibit some of the remarkable features expected to hold for the as yet unknown non-perturbative formulation of string theories. Among these are: (a) the absence of a background or embedding space for the full…

High Energy Physics - Theory · Physics 2008-11-26 T. Filk

Causality has traditionally been a scientific way to generate knowledge by relating causes to effects. From an imaginery point of view, causal graphs are a helpful tool for representing and infering new causal information. In previous…

Artificial Intelligence · Computer Science 2020-02-07 Eduardo C. Garrido-Merchán , C. Puente , A. Sobrino , J. A. Olivas