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Reasoning under uncertainty is a fundamental challenge in Artificial Intelligence. As with most of these challenges, there is a harsh dilemma between the expressive power of the language used, and the tractability of the computational…

Artificial Intelligence · Computer Science 2025-05-08 Luise Ge , Brendan Juba , Kris Nilsson

Continuing our investigation into the Hierarchical Reference Theory of fluids for thermodynamic states of infinite isothermal compressibility kappa[T] we now turn to the available numerical evidence to elucidate the character of the partial…

Statistical Mechanics · Physics 2007-05-23 Albert Reiner , Gerhard Kahl

Mechanistic interpretability aims to understand model behaviors in terms of specific, interpretable features, often hypothesized to manifest as low-dimensional subspaces of activations. Specifically, recent studies have explored subspace…

Machine Learning · Computer Science 2023-12-07 Aleksandar Makelov , Georg Lange , Neel Nanda

Explainable AI (XAI) is frequently positioned as a technical problem of revealing the inner workings of an AI model. This position is affected by unexamined onto-epistemological assumptions: meaning is treated as immanent to the model, the…

Artificial Intelligence · Computer Science 2026-01-26 Fabio Morreale , Joan Serrà , Yuki Mitsufuji

Hardy's paradox (equivalently, Hardy's non-locality or Hardy's test) [\href{https://link.aps.org/doi/10.1103/PhysRevLett.68.2981}{L. Hardy, Phys. Rev. Lett. \textbf{68}, 2981 (1992)}] is used to show non-locality without inequalities and it…

Quantum Physics · Physics 2021-02-25 Soumya Das , Goutam Paul

We give new proofs of Hardy space estimates for fractional and singular integral operators on weighted and variable exponent Hardy spaces. Our proofs consist of several interlocking ideas: finite atomic decompositions in terms of $L^\infty$…

Classical Analysis and ODEs · Mathematics 2019-02-12 David Cruz-Uribe , Kabe Moen , Hanh Nguyen

Mechanistic interpretability is the program of explaining what AI systems are doing in terms of their internal mechanisms. I analyze some aspects of the program, along with setting out some concrete challenges and assessing progress to…

Artificial Intelligence · Computer Science 2025-01-28 David J. Chalmers

We introduce topological differential testing (TDT), an approach to extracting the consensus behavior of a set of programs on a corpus of inputs. TDT uses the topological notion of a simplicial complex (and implicitly draws on richer…

Software Engineering · Computer Science 2020-03-06 Kristopher Ambrose , Steve Huntsman , Michael Robinson , Matvey Yutin

We develop a unified view of topological phase transitions (TPTs) in solids by revising the classical band theory with the inclusion of topology. Re-evaluating the band evolution from an "atomic crystal" [a normal insulator (NI)] to a solid…

Materials Science · Physics 2020-05-11 Huaqing Huang , Feng Liu

The quantum smoothing theory [Tsang, Phys. Rev. Lett. 102, 250403 (2009); Phys. Rev. A, in press (e-print arXiv:0906.4133)] is extended to account for discrete jumps in the classical random process to be estimated, discrete variables in the…

Quantum Physics · Physics 2010-01-26 Mankei Tsang

Given two distinct subsets $A,B$ in the state space of some dynamical system, Transition Path Theory (TPT) was successfully used to describe the statistical behavior of transitions from $A$ to $B$ in the ergodic limit of the stationary…

Dynamical Systems · Mathematics 2020-11-03 Luzie Helfmann , Enric Ribera Borrell , Christof Schütte , Péter Koltai

We demonstrate that, in the tripartite scenario with all parties' local events being space-like separated, Hardy-type nonlocality constitutes a stronger manifestation of nonlocal correlations than those captured by Mermin-type inequalities,…

Quantum Physics · Physics 2025-04-29 Ranendu Adhikary , Souradeep Sasmal , Arup Roy

A discrete time crystal is a phase unique to nonequilibrium systems, where discrete time translation symmetry is spontaneously broken. Most of conventional time crystals proposed so far rely on spontaneous breaking of on-site symmetries and…

Mesoscale and Nanoscale Physics · Physics 2018-08-30 Kaoru Mizuta , Kazuaki Takasan , Masaya Nakagawa , Norio Kawakami

We give an overview of parts of the theory of Hardy spaces from the viewpoint of signals and systems theory. There are books on this topic, which dates back to Bode, Nyquist, and Wiener, and that eventually led to the developement of…

Complex Variables · Mathematics 2020-09-29 Nicola Arcozzi , Richard Rochberg

We study how to understand the complex coordinates involved in the non-Hermitian but PT-symmetric systems. We explore a PT-symmetric oscillator model to show that the entire information on the complex position is attainable. Its real part…

Quantum Physics · Physics 2015-09-21 Jin-Ho Cho

The formalism of generalized probabilistic theories (GPTs) was originally developed as a way to characterize the landscape of conceivable physical theories. Thus, the GPT describing a given physical theory necessarily includes all…

Quantum Physics · Physics 2024-04-05 John H. Selby , David Schmid , Elie Wolfe , Ana Belén Sainz , Ravi Kunjwal , Robert W. Spekkens

We apply persistent homology to the task of discovering and characterizing phase transitions, using lattice spin models from statistical physics for working examples. Persistence images provide a useful representation of the homological…

Statistical Mechanics · Physics 2020-12-03 Alex Cole , Gregory J. Loges , Gary Shiu

In this study, we study weak values from a quantum-logical viewpoint. In addition, we examine the validity of the counterfactual statements of Hardy's paradox, which are based on weak values, and we show that these statements have not been…

Quantum Physics · Physics 2013-07-23 Riuji Mochizuki

Several arguments have been proposed some years ago, attempting to prove the impossibility of defining Lorentz-invariant elements of reality. Here I revisit that question, and bring a number of additional considerations to it. I will first…

Quantum Physics · Physics 2015-06-12 Louis Marchildon

In recent years, machine learning methods have been used to assist scientists in scientific research. Human scientific theories are based on a series of concepts. How machine learns the concepts from experimental data will be an important…

Machine Learning · Computer Science 2022-05-12 Ruyu Yang
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