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Fundamental diagram constrained dynamic optimal transport via proximal splitting methods
Anqi Dong, Karl Henrik Johansson, Johan Karlsson
https://arxiv.org/abs/2507.20717 https:…

Fundamental diagram constrained dynamic optimal transport via proximal splitting methods
Optimal transport has recently been brought forward as a tool for modeling and efficiently solving a variety of flow problems, such as origin-destination problems and multi-commodity flow problems. Although the framework has shown to be effective for many large scale flow problems, the formulations typically lack dynamic properties used in common traffic models, such as the Lighthill-Whitham-Richards model. In this work, we propose an optimal transport framework that includes dynamic constraint…

Multiscale geometrical and topological learning in the analysis of soft matter collective dynamics
Tetiana Orlova, Amaranta Membrillo Solis, Hayley R. O. Sohn, Tristan Madeleine, Giampaolo D'Alessandro, Ivan I. Smalyukh, Malgosia Kaczmarek, Jacek Brodzki
https://arxiv.org/abs/2507.21265

Multiscale geometrical and topological learning in the analysis of soft matter collective dynamics
Understanding the behavior and evolution of a dynamical many-body system by analyzing patterns in their experimentally captured images is a promising method relevant for a variety of living and non-living self-assembled systems. The arrays of moving liquid crystal skyrmions studied here are a representative example of hierarchically organized materials that exhibit complex spatiotemporal dynamics driven by multiscale processes. Joint geometric and topological data analysis (TDA) offers a powerf…

This https://arxiv.org/abs/2405.14979 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

CraftsMan3D: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner
We present a novel generative 3D modeling system, coined CraftsMan, which can generate high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed surfaces, and, notably, allows for refining the geometry in an interactive manner. Despite the significant advancements in 3D generation, existing methods still struggle with lengthy optimization processes, irregular mesh topologies, noisy surfaces, and difficulties in accommodating user edits, consequently impeding t…

Bayesian Modeling for Aggregated Relational Data: A Unified Perspective
Owen G. Ward, Anna L. Smith, Tian Zheng
https://arxiv.org/abs/2506.21353 https://…

Bayesian Modeling for Aggregated Relational Data: A Unified Perspective
Aggregated relational data is widely collected to study social network theory. It has been used to address a variety of key problems in fields such as sociology, public health and economics. ARD models enable researchers to estimate the size of hidden populations, estimate personal network sizes, understand global network structures and fit complex latent variable models to massive network data. Many of the successes of ARD models have been driven by the utilisation of Bayesian modeling, which …

Assessing the Sensitivities of Input-Output Methods for Natural Hazard-Induced Power Outage Macroeconomic Impacts
Matthew Sprintson, Edward Oughton
https://arxiv.org/abs/2507.19989

Assessing the Sensitivities of Input-Output Methods for Natural Hazard-Induced Power Outage Macroeconomic Impacts
It is estimated that over one-fourth of US households experienced a power outage in 2023, costing on average US $\$150$ Bn annually, with $87\%$ of outages caused by natural hazards. Indeed, numerous studies have examined the macroeconomic impact of power network interruptions, employing a wide variety of modeling methods and data parameterization techniques, which warrants further investigation. In this paper, we quantify the macroeconomic effects of three significant natural hazard-induced US…

Modeling Excess Mortality and Interest Rates using Mixed Fractional Brownian Motions
Kenneth Q. Zhou, Hongjuan Zhou
https://arxiv.org/abs/2507.19445 https://

Modeling Excess Mortality and Interest Rates using Mixed Fractional Brownian Motions
Recent studies have identified long-range dependence as a key feature in the dynamics of both mortality and interest rates. Building on this insight, we develop a novel bi-variate stochastic framework based on mixed fractional Brownian motions to jointly model their long-memory behavior and instantaneous correlation. Analytical solutions are derived under the risk-neutral measure for explicitly pricing zero-coupon bonds and extreme mortality bonds, while capturing the impact of persistent and c…

SCORE-SET: A dataset of GuitarPro files for Music Phrase Generation and Sequence Learning
Vishakh Begari
https://arxiv.org/abs/2507.18723 https://arxiv.org…

SCORE-SET: A dataset of GuitarPro files for Music Phrase Generation and Sequence Learning
A curated dataset of Guitar Pro tablature files (.gp5 format), tailored for tasks involving guitar music generation, sequence modeling, and performance-aware learning is provided. The dataset is derived from MIDI notes in MAESTRO and GiantMIDI which have been adapted into rhythm guitar tracks. These tracks are further processed to include a variety of expression settings typical of guitar performance, such as bends, slides, vibrato, and palm muting, to better reflect the nuances of real-world g…

Neural network ensemble for computing cross sections for rotational transitions in H$_{2}$O H$_{2}$O collisions
Bikramaditya Mandal, Dmitri Babikov, Phillip C. Stancil, Robert C. Forrey, Roman V. Krems, Naduvalath Balakrishnan
https://arxiv.org/abs/2507.18974

Neural network ensemble for computing cross sections for rotational transitions in H$_{2}$O + H$_{2}$O collisions
Water (H$_2$O) is one of the most abundant molecules in the universe and is found in a wide variety of astrophysical environments. Rotational transitions in H$_2$O + H$_2$O collisions are important in modeling environments rich in water molecules but they are computationally intractable using quantum mechanical methods. Here, we present a machine learning (ML) tool using an ensemble of neural networks (NNs) to predict cross sections to construct a database of rate coefficients for rotationally …

Generating real-time detailed ground visualisations from sparse aerial point clouds
Aidan Murray, Eddie Waite, Caleb Ross, Scarlet Mitchell, Alexander Bradley, Joanna Jamrozy, Kenny Mitchell
https://arxiv.org/abs/2507.18664

Generating real-time detailed ground visualisations from sparse aerial point clouds
Building realistic wide scale outdoor 3D content with sufficient visual quality to observe at walking eye level or from driven vehicles is often carried out by large teams of artists skilled in modelling, texturing, material shading and lighting, which typically leads to both prohibitive costs and reduced accuracy honoring the variety of real world ground truth landscapes. In our proposed method, we define a process to automatically amplify real-world scanned data and render real-time in animat…

Regression approaches for modelling genotype-environment interaction and making predictions into unseen environments
Maksym Hrachov, Hans-Peter Piepho, Niaz Md. Farhat Rahman, Waqas Ahmed Malik
https://arxiv.org/abs/2507.18125

Regression approaches for modelling genotype-environment interaction and making predictions into unseen environments
In plant breeding and variety testing, there is an increasing interest in making use of environmental information to enhance predictions for new environments. Here, we will review linear mixed models that have been proposed for this purpose. The emphasis will be on predictions and on methods to assess the uncertainty of predictions for new environments. Our point of departure is straight-line regression, which may be extended to multiple environmental covariates and genotype-specific responses.…