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What does it mean for a causal structure to be `unknown'? Can we even talk about `repetitions' of an experiment without prior knowledge of causal relations? And under what conditions can we say that a set of processes with arbitrary,…

Quantum Physics · Physics 2025-02-12 Fabio Costa , Jonathan Barrett , Sally Shrapnel

A new modeling framework for bipartite social networks arising from a sequence of partially time-ordered relational events is proposed. We directly model the joint distribution of the binary variables indicating if each single actor is…

Methodology · Statistics 2018-10-23 Francesco Bartolucci , Antonietta Mira , Stefano Peluso

Humans perceive the seemingly chaotic world in a structured and compositional way with the prerequisite of being able to segregate conceptual entities from the complex visual scenes. The mechanism of grouping basic visual elements of scenes…

Machine Learning · Computer Science 2019-04-30 Jinyang Yuan , Bin Li , Xiangyang Xue

We introduce novel estimators for quantile causal effects with high dimensional panel data (large $N$ and $T$), where only one or a few units are affected by the intervention or policy. Our method extends the generalized synthetic control…

Methodology · Statistics 2025-06-19 Yihong Xu , Li Zheng

Theories of natural language and concepts have been unable to model the flexibility, creativity, context-dependence, and emergence, exhibited by words, concepts and their combinations. The mathematical formalism of quantum theory has…

Artificial Intelligence · Computer Science 2016-09-09 Diederik Aerts , Jan Broekaert , Liane Gabora , Sandro Sozzo

In this work, we present a logical formalism for reasoning about quantum systems in finite dimension. Contrary to the usual approach in quantum logic, our formalism is based classical first-order logic, which allows us to use the tools of…

Quantum Physics · Physics 2026-02-19 Olivier Brunet

Programming languages with algebraic effects often track the computations' effects using type-and-effect systems. In this paper, we propose to view an algebraic effect theory of a computation as a variable context; consequently, we propose…

Programming Languages · Computer Science 2021-08-24 Nikita Zyuzin , Aleksandar Nanevski

We first show that the convex effect algebras (CEA) approach to quantum mechanics is more general than the general probabilistic theories approach. We then restrict our attention to finite-dimension CEA's. After an introductory Section~1,…

Quantum Physics · Physics 2019-12-12 Stan Gudder

We introduce the abstract notions of "monadic operational semantics", a small-step semantics where computational effects are modularly modeled by a monad, and "type-and-effect system", including "effect types" whose interpretation lifts…

Programming Languages · Computer Science 2025-04-15 Francesco Dagnino , Paola Giannini , Elena Zucca

Quantum field theory (QFT) in classical spacetime has revealed interesting and puzzling aspects about gravitational systems, in particular black hole thermodynamics and its information processing. Although quantum gravitational effects may…

Quantum Physics · Physics 2018-05-24 Ding Jia

Algebraic effects & handlers have become a standard approach for side-effects in functional programming. Their modular composition with other effects and clean separation of syntax and semantics make them attractive to a wide audience.…

Programming Languages · Computer Science 2024-11-27 Roger Bosman , Birthe van den Berg , Wenhao Tang , Tom Schrijvers

Kendall transformation is a conversion of an ordered feature into a vector of pairwise order relations between individual values. This way, it preserves ranking of observations and represents it in a categorical form. Such transformation…

Machine Learning · Computer Science 2023-08-15 Miron Bartosz Kursa

Existing guidelines for categorical color selection are heuristic, often grounded in intuition rather than empirical studies of readers' abilities. While design conventions recommend palettes maximize hue differences, more recent…

Human-Computer Interaction · Computer Science 2024-05-24 Chin Tseng , Arran Zeyu Wang , Ghulam Jilani Quadri , Danielle Albers Szafir

We present a general formalism with the aim of describing the situation of an entity, how it is, how it reacts to experiments, how we can make statistics with it, and how it changes under the influence of the rest of the universe. Therefore…

Quantum Physics · Physics 2007-05-23 Diederik Aerts

Case-control designs are an important tool in contrasting the effects of well-defined treatments. In this paper, we reconsider classical concepts, assumptions and principles and explore when the results of case-control studies can be…

Methodology · Statistics 2021-05-06 Bas B. L. Penning de Vries , Rolf H. H. Groenwold

This paper develops computationally feasible methods for estimating random effects models in the context of regression modelling of multiple independent time series of discrete valued counts in which there is serial dependence. Given…

Methodology · Statistics 2016-06-10 W. T. M. Dunsmuir , C. McKendry , R. T. Dean

In this report we present a new modelling framework for concepts based on quantum theory, and demonstrate how the conceptual representations can be learned automatically from data. A contribution of the work is a thorough category-theoretic…

Neurons and Cognition · Quantitative Biology 2023-03-01 Sean Tull , Razin A. Shaikh , Sara Sabrina Zemljic , Stephen Clark

Soundness and completeness with respect to equational theories for programming languages are fundamental properties in the study of categorical semantics. However, completeness results have not been established for programming languages…

Logic in Computer Science · Computer Science 2026-02-04 Satoshi Kura

In sequential causal inference, one estimates the causal net effect of treatment in treatment sequence on an outcome after last treatment in the presence of time-dependent covariates between treatments, improves the estimation by the…

Methodology · Statistics 2014-11-18 Li Yin , Xiaoqin Wang

Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system. While…

Machine Learning · Statistics 2021-08-30 Santtu Tikka , Antti Hyttinen , Juha Karvanen
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