Publications related to Inverse function

Efficient Continual Finite-Sum Minimization

Volkan Cevher, Efstratios Panteleimon Skoulakis, Leello Tadesse Dadi

Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

2024

Experimental characterization and stochastic models for time-dependent rupture of thin-ply composite laminates

Thin-laminate composites with thicknesses below 200 mu m hold significant promise for future, larger, and lighter deployable structures. This paper presents a study of the time-dependent failure behavior of thin carbon-fiber laminates under bending, focusi ...

2024

Noncollinear DFT plus U and Hubbard parameters with fully relativistic ultrasoft pseudopotentials

Nicola Marzari, Luca Binci

The magnetic, noncollinear parametrization of Dudarev's DFT + U method is generalized to fully relativistic ultrasoft pseudopotentials. We present the definition of the DFT + U total energy functional and the calculation of forces and stresses in the case ...

College Pk2023

A Majority Lemma for Randomised Query Complexity

Mika Tapani Göös, Gilbert Théodore Maystre

We show that computing the majority of n copies of a boolean function g has randomised query complexity R(Maj∘gⁿ) = Θ(n⋅R ̅_{1/n}(g)). In fact, we show that to obtain a similar result for any composed function f∘gⁿ, it suffices to prove a sufficiently stro ...

Schloss Dagstuhl - Leibniz-Zentrum für Informatik2021

Pulay forces in density-functional theory with extended Hubbard functionals: From nonorthogonalized to orthogonalized manifolds

Nicola Marzari, Iurii Timrov, Matteo Cococcioni, Francesco Aquilante, Luca Binci

We present a derivation of the exact expression for Pulay forces in density-functional theory calculations augmented with extended Hubbard functionals and arising from the use of orthogonalized atomic orbitals as projectors for the Hubbard manifold. The de ...

AMER PHYSICAL SOC2020

Inverse Modelling and Predictive Inference in Continuum Mechanics: a Data-Driven Approach

Claire Marianne Charlotte Capelo

The explosive growth of machine learning in the age of data has led to a new probabilistic and data-driven approach to solving very different types of problems. In this paper we study the feasibility of using such data-driven algorithms to solve classic ph ...

2020

The Almost-Sure Asymptotic Behavior Of The Solution To The Stochastic Heat Equation With Levy Noise

Carsten Hao Ye Chong

We examine the almost-sure asymptotics of the solution to the stochastic heat equation driven by a Levy space-time white noise. When a spatial point is fixed and time tends to infinity, we show that the solution develops unusually high peaks over short tim ...

INST MATHEMATICAL STATISTICS2020

The Power of Many Samples in Query Complexity

Mika Tapani Göös

The randomized query complexity 𝖱(f) of a boolean function f: {0,1}ⁿ → {0,1} is famously characterized (via Yao’s minimax) by the least number of queries needed to distinguish a distribution 𝒟₀ over 0-inputs from a distribution 𝒟₁ over 1-inputs, maximized ...

Schloss Dagstuhl - Leibniz-Zentrum für Informatik2020

When Is Amplification Necessary for Composition in Randomized Query Complexity?

Mika Tapani Göös

Suppose we have randomized decision trees for an outer function f and an inner function g. The natural approach for obtaining a randomized decision tree for the composed function (f∘ gⁿ)(x¹,…,xⁿ) = f(g(x¹),…,g(xⁿ)) involves amplifying the success probabili ...

Schloss Dagstuhl - Leibniz-Zentrum für Informatik2020

The dynamics of motor learning through the formation of internal models

Camilla Pierella

A medical student learning to perform a laparoscopic procedure or a recently paralyzed user of a powered wheelchair must learn to operate machinery via interfaces that translate their actions into commands for an external device. Since the user’s actions a ...

2019

Inverse function

Graph Chatbot

Chat with Graph Search

Efficient Continual Finite-Sum Minimization

Experimental characterization and stochastic models for time-dependent rupture of thin-ply composite laminates

Noncollinear DFT plus U and Hubbard parameters with fully relativistic ultrasoft pseudopotentials

A Majority Lemma for Randomised Query Complexity

Pulay forces in density-functional theory with extended Hubbard functionals: From nonorthogonalized to orthogonalized manifolds

Inverse Modelling and Predictive Inference in Continuum Mechanics: a Data-Driven Approach

The Almost-Sure Asymptotic Behavior Of The Solution To The Stochastic Heat Equation With Levy Noise

The Power of Many Samples in Query Complexity

When Is Amplification Necessary for Composition in Randomized Query Complexity?

The dynamics of motor learning through the formation of internal models

Experimental characterization and stochastic models for time-dependent rupture of thin-ply composite laminates

Noncollinear DFT plus U and Hubbard parameters with fully relativistic ultrasoft pseudopotentials

Inverse Modelling and Predictive Inference in Continuum Mechanics: a Data-Driven Approach

A Majority Lemma for Randomised Query Complexity

Efficient Continual Finite-Sum Minimization

The Almost-Sure Asymptotic Behavior Of The Solution To The Stochastic Heat Equation With Levy Noise

When Is Amplification Necessary for Composition in Randomized Query Complexity?

Pulay forces in density-functional theory with extended Hubbard functionals: From nonorthogonalized to orthogonalized manifolds

The Power of Many Samples in Query Complexity

The dynamics of motor learning through the formation of internal models