Publications related to Analyse I

Residual-based attention in physics-informed neural networks

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Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

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Climbing into their Skin to Understand Contextual Protein-Protein Associations and Localizations: Functional Investigations in Transgenic Live Model Organisms

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Borrowing some quotes from Harper Lee's novel "To Kill A Mockingbird" to help frame our manuscript, we discuss methods to profile local proteomes. We initially focus on chemical biology regimens that function in live organisms and use reactive biotin speci ...

Wiley-V C H Verlag Gmbh2024

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

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2024

We study applications of spectral positivity and the averaged null energy condition (ANEC) to renormalization group (RG) flows in two-dimensional quantum field theory. We find a succinct new proof of the Zamolodchikov c-theorem, and derive further independ ...

New York2024

In Medio Stat Virtus: Combining Boolean and Pattern Matching

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Technology mapping transforms a technology-independent representation into a technology-dependent one given a library of cells. This process is performed by means of local replacements that are extracted by matching sections of the subject graph to library ...

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BPS invariants from p-adic integrals

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We define p-adic BPS or pBPS invariants for moduli spaces M-beta,M-chi of one-dimensional sheaves on del Pezzo and K3 surfaces by means of integration over a non-archimedean local field F. Our definition relies on a canonical measure mu can on the F-analyt ...

Cambridge Univ Press2024

Graph generative deep learning models with an application to circuit topologies

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Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison.Managing their complexity and facilitating their design drove part of the co-evolution of moder ...

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Hitting with Probability One for Stochastic Heat Equations with Additive Noise

Robert Dalang, Fei Pu

We study the hitting probabilities of the solution to a system of d stochastic heat equations with additive noise subject to Dirichlet boundary conditions. We show that for any bounded Borel set with positive (d-6)\documentclass[12pt]{minimal} \usepackage{ ...

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Network-based kinetic models: Emergence of a statistical description of the graph topology

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In this paper, we propose a novel approach that employs kinetic equations to describe the collective dynamics emerging from graph-mediated pairwise interactions in multi-agent systems. We formally show that for large graphs and specific classes of interact ...

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Morphological processes and potential for lacustrine delta restoration

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Most lacustrine deltas are nowadays highly anthropized systems. River training works during past centuries considered rivers mainly as vectors of water and sediments from a point source to a sink. However, numerous problems have been identified by resident ...

2024

Analyse I

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Residual-based attention in physics-informed neural networks

Climbing into their Skin to Understand Contextual Protein-Protein Associations and Localizations: Functional Investigations in Transgenic Live Model Organisms

Efficient Continual Finite-Sum Minimization

In Medio Stat Virtus: Combining Boolean and Pattern Matching

BPS invariants from p-adic integrals

Graph generative deep learning models with an application to circuit topologies

Hitting with Probability One for Stochastic Heat Equations with Additive Noise

Network-based kinetic models: Emergence of a statistical description of the graph topology

Morphological processes and potential for lacustrine delta restoration

Residual-based attention in physics-informed neural networks

Climbing into their Skin to Understand Contextual Protein-Protein Associations and Localizations: Functional Investigations in Transgenic Live Model Organisms

In Medio Stat Virtus: Combining Boolean and Pattern Matching

BPS invariants from p-adic integrals

Hitting with Probability One for Stochastic Heat Equations with Additive Noise

Graph generative deep learning models with an application to circuit topologies

Network-based kinetic models: Emergence of a statistical description of the graph topology

Efficient Continual Finite-Sum Minimization

Morphological processes and potential for lacustrine delta restoration