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The notion of ordinal concavity of utility functions has recently been considered by Hafalir, Kojima, Yenmez, and Yokote in economics while there exist earlier related works in discrete optimization and operations research. In the present…

Combinatorics · Mathematics 2024-11-14 Satoru Fujishige , Fuhito Kojima , Koji Yokote

We give a new characterization of the class of rational string functions from formal language theory using order-preserving interpretations with respect to a very weak monadic programming language. This refines the known characterization of…

Logic in Computer Science · Computer Science 2023-02-08 Siddharth Bhaskar , Jane Chandlee , Adam Jardine

We present a symbolic learning framework inspired by cognitive-like memory functionalities (i.e., storing, retrieving, consolidating and forgetting) to generate task representations to support high-level task planning and knowledge…

Robotics · Computer Science 2024-04-22 Luca Buoncompagni , Fulvio Mastrogiovanni

Gutzwiller wavefunction is a physically well motivated trial wavefunction for describing correlated electron systems. In this work, a new approximation is introduced to facilitate evaluation of the expectation value of any operator within…

Strongly Correlated Electrons · Physics 2015-08-25 Jun Liu , Yongxin Yao , Cai-Zhuang Wang , Kai-Ming Ho

There continues to be widespread interest in 1/f^(alpha) behavior in baseline power spectral densities (PSD's) but its origins remain controversial. Zwanzig-Mori projection operators provide a rigorous, common starting place for building a…

Adaptation and Self-Organizing Systems · Physics 2009-08-07 David Hsu , Murielle Hsu , John M. Beggs

This paper studies the problem of learning computable functions in the limit by extending Gold's inductive inference framework to incorporate \textit{computational observations} and \textit{restricted input sources}. Complimentary to the…

Machine Learning · Computer Science 2025-07-11 Hristo Papazov , Nicolas Flammarion

Learning probabilistic surrogates for partial differential equations remains challenging in data-scarce regimes: neural operators require large amounts of high-fidelity data, while generative approaches typically sacrifice resolution…

Computation · Statistics 2025-12-18 Sahil Bhola , Karthik Duraisamy

This study presents a fractional-order continuum mechanics approach that allows combining selected characteristics of nonlocal elasticity, typical of classical integral and gradient formulations, under a single frame-invariant framework.…

Numerical Analysis · Mathematics 2020-05-21 Sansit Patnaik , Sai Sidhardh , Fabio Semperlotti

Functional depth is used for ranking functional observations from most outlying to most typical. The ranks produced by functional depth have been proposed as the basis for functional classifiers, rank tests, and data visualization…

Methodology · Statistics 2016-11-02 James P. Long , Jianhua Z. Huang

In current practice a formal analysis of hybrid system models is assertion-based. The work presented here is based on features that look beyond functional correctness toward a quantitative evaluation of behavioral attributes. A feature…

Logic in Computer Science · Computer Science 2019-02-25 Antonio Anastasio Bruto da Costa , Goran Frehse , Pallab Dasgupta

We obtain a general characterization of discrete-time all-pass rational matrix functions from state-space representations. It can be employed to address model reduction problems in the same vein of the theory developed by Glover in the…

Optimization and Control · Mathematics 2015-09-22 Augusto Ferrante , Giorgio Picci

The paper presents an interesting mathematical feedback between the formalism of coherent states and the field of integrals and integral representations involving special functions. This materializes through an easy and fast method to…

Quantum Physics · Physics 2024-08-21 Dušan Popov

A growing body of work has established the modelling of stochastic processes as a promising area of application for quantum techologies; it has been shown that quantum models are able to replicate the future statistics of a stochastic…

Quantum Physics · Physics 2020-03-25 Matthew Ho , Mile Gu , Thomas J. Elliott

We study the thermodynamic formalism of locally compact Markov shifts with transient potential functions. In particular, we show that the Ruelle operator admits positive continuous eigenfunctions and positive Radon eigenmeasures in forms of…

Dynamical Systems · Mathematics 2018-11-13 Ofer Shwartz

The brain is often studied from a network perspective, where functional activity is assessed using functional Magnetic Resonance Imaging (fMRI) to estimate connectivity between predefined neuronal regions. Functional connectivity can be…

Applications · Statistics 2025-07-22 Olivier Bisson , Yanis Aeschlimann , Samuel Deslauriers-Gauthier , Xavier Pennec

We introduce formulas for the logarithms of Drinfeld modules using a framework recently developed by the second author. We write the logarithm function as the evaluation under a motivic map of a product of rigid analytic trivializations of…

Number Theory · Mathematics 2025-10-31 Oğuz Gezmiş , Nathan Green

A theory of recursive definitions has been mechanized in Isabelle's Zermelo-Fraenkel (ZF) set theory. The objective is to support the formalization of particular recursive definitions for use in verification, semantics proofs and other…

Logic in Computer Science · Computer Science 2008-02-03 Lawrence C. Paulson

The standard reinforcement learning (RL) formulation considers the expectation of the (discounted) cumulative reward. This is limiting in applications where we are concerned with not only the expected performance, but also the distribution…

Machine Learning · Computer Science 2019-06-13 Changjian Li , Krzysztof Czarnecki

Functionals that explicitly depend on occupied, unoccupied, or fractionally-occupied orbitals are rigorously formalized using Clifford algebras, and a variational principle is established that facilitates orbital (and occupation)…

Quantum Physics · Physics 2024-04-26 Neil Qiang Su

Memory effects in open quantum dynamics are often incorporated in the equation of motion through a superoperator known as the memory kernel, which encodes how past states affect future dynamics. However, the usual prescription for…

Quantum Physics · Physics 2018-07-13 Felix A. Pollock , Kavan Modi