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AssoCiAm: A Benchmark for Evaluating Association Thinking while Circumventing Ambiguity
Yifan Liu, Wenkuan Zhao, Shanshan Zhong, Jinghui Qin, Mingfu Liang, Zhongzhan Huang, Wushao Wen
https://arxiv.org/abs/2509.14171

AssoCiAm: A Benchmark for Evaluating Association Thinking while Circumventing Ambiguity
Recent advancements in multimodal large language models (MLLMs) have garnered significant attention, offering a promising pathway toward artificial general intelligence (AGI). Among the essential capabilities required for AGI, creativity has emerged as a critical trait for MLLMs, with association serving as its foundation. Association reflects a model' s ability to think creatively, making it vital to evaluate and understand. While several frameworks have been proposed to assess associative abi…

Sub-nanosecond structural dynamics of the martensitic transformation in Ni-Mn-Ga
Yuru Ge, Fabian Ganss, Daniel Schmidt, Daniel Hensel, Mike J. Bruckhoff, Sakshath Sadashivaiah, Bruno Neumann, Mariana Brede, Markus E. Gruner, Peter Gaal, Klara L\"unser, Sebastian F\"ahler
https://arxiv.org/abs/2509.06513

Sub-nanosecond structural dynamics of the martensitic transformation in Ni-Mn-Ga
Martensitic transformations drive a multitude of emerging applications, which range from high stroke actuation and, mechanocaloric refrigeration, to thermoelastic energy harvesting. All these applications benefit from faster transformations, as a high cycle frequency is essential for achieving high power density. However, systematic investigations of the fast dynamics and fundamental speed limits of martensitic transformations are scarce. Especially for ultrashort time transformations, the temp…

A neural network approach to kinetic Mie polarimetry for particle size diagnostics in nanodusty plasmas
Alexander Schmitz, Andreas Petersen, Franko Greiner
https://arxiv.org/abs/2510.02088

A neural network approach to kinetic Mie polarimetry for particle size diagnostics in nanodusty plasmas
The analysis of the size of nanoparticles is an essential task in plasma technology and dusty plasmas. Light scattering techniques, based on Mie theory, can be used as a non-invasive and in-situ diagnostic tool for this purpose. However, the standard back-calculation methods require expertise from the user. To address this, we introduce a neural network that performs the same task. We discuss how we set up and trained the network to analyze the size of plasma-grown amorphous carbon nanoparticle…

Bifrost Models of the Quiet Sun. I. Comparison with Solar Observations
Milan Go\v{s}i\'c, Viggo H. Hansteen, Alberto Sainz Dalda, Bart De Pontieu, Luc H. M. Rouppe van der Voort
https://arxiv.org/abs/2508.19355

Bifrost Models of the Quiet Sun. I. Comparison with Solar Observations
Studying the emergence of magnetic fields is essential for understanding the physical mechanisms behind various phenomena in the solar atmosphere. Most importantly, the emerging fields offer valuable insights into how energy and mass are transferred to the upper solar atmosphere. As a result, they have garnered significant attention from both observational and theoretical perspectives. In this article, we present two models of quiet-Sun-like magnetic fields generated by the Bifrost code. We com…

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QASTAnet: A DNN-based Quality Metric for Spatial Audio
Adrien Llave, Emma Granier, Gr\'egory Pallone
https://arxiv.org/abs/2509.16715 https://arxiv.org…

QASTAnet: A DNN-based Quality Metric for Spatial Audio
In the development of spatial audio technologies, reliable and shared methods for evaluating audio quality are essential. Listening tests are currently the standard but remain costly in terms of time and resources. Several models predicting subjective scores have been proposed, but they do not generalize well to real-world signals. In this paper, we propose QASTAnet (Quality Assessment for SpaTial Audio network), a new metric based on a deep neural network, specialized on spatial audio (ambison…