Tootfinder

Best practices for nonadiabatic molecular dynamics simulations
Antonio Prlj, Jack T. Taylor, Ji\v{r}\'i Jano\v{s}, Petr Slav\'i\v{c}ek, Federica Agostini, Basile F. E. Curchod
https://arxiv.org/abs/2508.05263

Best practices for nonadiabatic molecular dynamics simulations
Nonadiabatic molecular dynamics simulations aim to describe the coupled electron-nuclear dynamics of molecules in excited electronic states. These simulations have been applied to understand a plethora of photochemical and photophysical processes, and, as a result, the number of nonadiabatic dynamics simulations has been growing significantly over the past decade. Yet, the field remains in its infancy, and a potential user may find it difficult to approach this type of simulation, given their c…

Artificially intelligent agents in the social and behavioral sciences: A history and outlook
Petter Holme, Milena Tsvetkova
https://arxiv.org/abs/2510.05743 https://

Artificially intelligent agents in the social and behavioral sciences: A history and outlook
We review the historical development and current trends of artificially intelligent agents (agentic AI) in the social and behavioral sciences: from the first programmable computers, and social simulations soon thereafter, to today's experiments with large language models. This overview emphasizes the role of AI in the scientific process and the changes brought about, both through technological advancements and the broader evolution of science from around 1950 to the present. Some of the specifi…

Toward Co-adapting Machine Learning Job Shape and Cluster Topology
Shawn Shuoshuo Chen, Daiyaan Arfeen, Minlan Yu, Peter Steenkiste, Srinivasan Seshan
https://arxiv.org/abs/2510.03891

Toward Co-adapting Machine Learning Job Shape and Cluster Topology
Allocating resources to distributed machine learning jobs in multi-tenant torus-topology clusters must meet each job's specific placement and communication requirements, which are typically described using shapes. There is an inherent tension between minimizing network contention and maximizing cluster utilization when placing various-shaped jobs. While existing schedulers typically optimize for one objective at the expense of the other, we demonstrate that both can be achieved simultaneously. …

UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation
Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Yihao Liu, Savannah P. Hays, Dzung L. Pham, Ellen M. Mowry, Scott D. Newsome, Peter A. Calabresi, Aaron Carass, Jerry L. Prince
https://arxiv.org/a…

UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation
Automated segmentation of multiple sclerosis (MS) lesions using multicontrast magnetic resonance (MR) images improves efficiency and reproducibility compared to manual delineation, with deep learning (DL) methods achieving state-of-the-art performance. However, these DL-based methods have yet to simultaneously optimize in-domain accuracy and out-of-domain generalization when trained on a single source with limited data, or their performance has been unsatisfactory. To fill this gap, we propose …

Medium Access for Multi-Cell ISAC through Scheduling of Radar and Communication Tasks
Jo\~ao Henrique Inacio de Souza, Fabio Saggese, Kun Chen-Hu, Petar Popovski
https://arxiv.org/abs/2510.05821

Medium Access for Multi-Cell ISAC through Scheduling of Radar and Communication Tasks
This paper focuses on communication, radar search, and tracking task scheduling in multi-cell integrated sensing and communication (ISAC) networks under quality of service (QoS) constraints. We propose a medium access control framework multiplexing the tasks while optimizing radar scan patterns through an interference-aware assignment formulation. Simulations show that our solution guarantees target QoS with improved resource efficiency over baseline schemes, highlighting the benefits of coordi…

LEMURS dataset: Large-scale multi-detector ElectroMagnetic Universal Representation of Showers
Peter McKeown (CERN), Piyush Raikwar (CERN), Anna Zaborowska (CERN)
https://arxiv.org/abs/2509.05108

LEMURS dataset: Large-scale multi-detector ElectroMagnetic Universal Representation of Showers
We present LEMURS: an extensive dataset of simulated calorimeter showers designed to support the development and benchmarking of fast simulation methods in high-energy physics, most notably providing a step towards the development of foundation models. This new dataset is more robust than the well-established CaloChallenge dataset 2, featuring substantially greater statistics, a wider range of incident angles in the detector, and most crucially multiple detector geometries (including more reali…

Can We Fix Social Media? Testing Prosocial Interventions using Generative Social Simulation
Maik Larooij, Petter T\"ornberg
https://arxiv.org/abs/2508.03385 https://…

Can We Fix Social Media? Testing Prosocial Interventions using Generative Social Simulation
Social media platforms have been widely linked to societal harms, including rising polarization and the erosion of constructive debate. Can these problems be mitigated through prosocial interventions? We address this question using a novel method - generative social simulation - that embeds Large Language Models within Agent-Based Models to create socially rich synthetic platforms. We create a minimal platform where agents can post, repost, and follow others. We find that the resulting followin…

Alex Karp: Watching you - Die Welt von Palantir und Alex Karp
Erstmals schaut ein Dokumentarfilm auf den US-Milliardär Alex Karp und seine so mysteriöse wie abgeschottete Firma Palantir. Neben Elon Musk, seinem Mitgründer Peter Thiel und #SamAltman gilt der CEO von #Palantir als einer der bedeute…

Watching you – Die Welt von Palantir und Alex Karp - hier anschauen
Erstmals schaut ein Dokumentarfilm auf den US-Milliardär Alex Karp und seine so mysteriöse wie abgeschottete Firma Palantir. Neben Elon Musk, seinem Mitgründer Peter Thiel und Sam Altman gilt der CEO von Palantir als einer der bedeutendsten Digital Leader. Wie sie profitiert Alex Karp von der Wahl Donald Trumps zum Präsidenten der USA. Doch hierzulande ist er noch recht unbekannt – und das obwohl auch deutsche Polizeibehörden mit der Palantir-Software “Gotham” arbeiten: Das erfolgrei…

Point cloud segmentation for 3D Clothed Human Layering
Davide Garavaso, Federico Masi, Pietro Musoni, Umberto Castellani
https://arxiv.org/abs/2508.05531 https://

Point cloud segmentation for 3D Clothed Human Layering
3D Cloth modeling and simulation is essential for avatars creation in several fields, such as fashion, entertainment, and animation. Achieving high-quality results is challenging due to the large variability of clothed body especially in the generation of realistic wrinkles. 3D scan acquisitions provide more accuracy in the representation of real-world objects but lack semantic information that can be inferred with a reliable semantic reconstruction pipeline. To this aim, shape segmentation pla…

Agentic Additive Manufacturing Alloy Discovery
Peter Pak, Achuth Chandrasekhar, Amir Barati Farimani
https://arxiv.org/abs/2510.02567 https://arxiv.org/pdf…

Agentic Additive Manufacturing Alloy Discovery
Agentic systems enable the intelligent use of research tooling, augmenting a researcher's ability to investigate and propose novel solutions to existing problems. Within Additive Manufacturing (AM), alloy discovery remains a complex challenge, often requiring expertise in the various domains of materials science, thermodynamic simulations, and experimental analysis. Large Language Model (LLM) enabled agents can facilitate this endeavor by utilizing their extensive knowledge base to dispatch too…