Tootfinder

STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis
Jiatao Gu, Tianrong Chen, David Berthelot, Huangjie Zheng, Yuyang Wang, Ruixiang Zhang, Laurent Dinh, Miguel Angel Bautista, Josh Susskind, Shuangfei Zhai
https://arxiv.org/abs/2506.06276

STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis
We present STARFlow, a scalable generative model based on normalizing flows that achieves strong performance in high-resolution image synthesis. The core of STARFlow is Transformer Autoregressive Flow (TARFlow), which combines the expressive power of normalizing flows with the structured modeling capabilities of Autoregressive Transformers. We first establish the theoretical universality of TARFlow for modeling continuous distributions. Building on this foundation, we introduce several key arch…

hansom swag fellowes

Synthesis of discrete-continuous quantum circuits with multimodal diffusion models
Florian F\"urrutter, Zohim Chandani, Ikko Hamamura, Hans J. Briegel, Gorka Mu\~noz-Gil
https://arxiv.org/abs/2506.01666

Synthesis of discrete-continuous quantum circuits with multimodal diffusion models
Efficiently compiling quantum operations remains a major bottleneck in scaling quantum computing. Today's state-of-the-art methods achieve low compilation error by combining search algorithms with gradient-based parameter optimization, but they incur long runtimes and require multiple calls to quantum hardware or expensive classical simulations, making their scaling prohibitive. Recently, machine-learning models have emerged as an alternative, though they are currently restricted to discrete ga…

Assessing the Alignment of Audio Representations with Timbre Similarity Ratings
Haokun Tian, Stefan Lattner, Charalampos Saitis
https://arxiv.org/abs/2507.07764

Assessing the Alignment of Audio Representations with Timbre Similarity Ratings
Psychoacoustical so-called "timbre spaces" map perceptual similarity ratings of instrument sounds onto low-dimensional embeddings via multidimensional scaling, but suffer from scalability issues and are incapable of generalization. Recent results from audio (music and speech) quality assessment as well as image similarity have shown that deep learning is able to produce embeddings that align well with human perception while being largely free from these constraints. Although the existing human-…

Compression, simulation, and synthesis of turbulent flows with tensor trains
Stefano Pisoni, Raghavendra Dheeraj Peddinti, Egor Tiunov, Siddhartha E. Guzman, Leandro Aolita
https://arxiv.org/abs/2506.05477

Compression, simulation, and synthesis of turbulent flows with tensor trains
Numerical simulations of turbulent fluids are paramount to real-life applications, from predicting and modeling flows to diagnostic purposes in engineering. However, they are also computationally challenging due to their intrinsically non-linear dynamics, which requires a very high spatial resolution to accurately describe them. A promising idea is to represent flows on a discrete mesh using tensor trains (TTs), featuring a convenient scaling of the number of parameters with the mesh size. Howe…

Enhanced plasma heating via interaction with high-contrast laser and cone-shaped target
Yuga Karaki, Yoshitaka Mori, Eigo Ebisawa, Yuichi Inubushi, Sadaoki Kojima, Kohei Yamanoi, Yuki Abe, Takumi Tsuido, Hiroki Matsubara, Rinya Akematsu, Ryo Omura, Ryunosuke Takizawa, King Fai Farley Law, Eisuke Miura, Yasunobu Arikawa, Keisuke Shigemori, Akifumi Iwamoto, Katsuhiro Ishii, Ryohei Hanayama, Yoneyoshi Kitagawa, Hiroshi Sawada, Takayoshi Sano, Natsumi Iwata, Yasuhiko Sentoku, Atsushi Sunah…

Enhanced plasma heating via interaction with high-contrast laser and cone-shaped target
We investigated plasma heating enhancement using a high-intensity, high-contrast laser and a cone-attached target. Fast electron spectra and X-ray emission were measured with an electron spectrometer and a Bragg crystal spectrometer. The results were analyzed using PrismSPECT simulations with a two-component electron distribution model and empirical scaling laws. X-ray pinhole images showed that the cone effectively focused multi-spot laser light near its tip, enhancing local emission. While hi…

Scaling, Fractal Dynamics and Critical Exponents{: Application} in a non-integer dimensional Ising model
Henrique A. de Lima, Ismael S. S. Carrasco, Marcio Santos, Fernando A. Oliveira
https://arxiv.org/abs/2507.00956

Scaling, Fractal Dynamics and Critical Exponents{: Application} in a non-integer dimensional Ising model
Moving beyond simple associations, researchers need tools to quantify how variables influence each other in space and time. Correlation functions provide a mathematical framework for characterizing these essential dependencies, revealing insights into causality, structure, and hidden patterns within complex systems. In physical systems with many degrees of freedom, such as gases, liquids, and solids, a statistical analysis of these correlations is essential. For a field $Ψ(\vec{x},t)$ that dep…

A LoD of Gaussians: Unified Training and Rendering for Ultra-Large Scale Reconstruction with External Memory
Felix Windisch, Lukas Radl, Thomas K\"ohler, Michael Steiner, Dieter Schmalstieg, Markus Steinberger
https://arxiv.org/abs/2507.01110

A LoD of Gaussians: Unified Training and Rendering for Ultra-Large Scale Reconstruction with External Memory
Gaussian Splatting has emerged as a high-performance technique for novel view synthesis, enabling real-time rendering and high-quality reconstruction of small scenes. However, scaling to larger environments has so far relied on partitioning the scene into chunks -- a strategy that introduces artifacts at chunk boundaries, complicates training across varying scales, and is poorly suited to unstructured scenarios such as city-scale flyovers combined with street-level views. Moreover, rendering re…

Dynamical detection of extended nonergodic states in many-body quantum systems
David A. Zarate-Herrada, Isa\'ias Vallejo-Fabila, Lea F. Santos, E. Jonathan Torres-Herrera
https://arxiv.org/abs/2505.23910

Dynamical detection of extended nonergodic states in many-body quantum systems
Fractal dimensions are tools for probing the structure of quantum states and identifying whether they are localized or delocalized in a given basis. These quantities are commonly extracted through finite-size scaling, which limits the analysis to relatively small system sizes. In this work, we demonstrate that the correlation fractal dimension $D_2$ can be directly obtained from the long-time dynamics of interacting many-body quantum systems. Specifically, we show that it coincides with the exp…