Tootfinder

LibriQuote: A Speech Dataset of Fictional Character Utterances for Expressive Zero-Shot Speech Synthesis
Gaspard Michel, Elena V. Epure, Christophe Cerisara
https://arxiv.org/abs/2509.04072

LibriQuote: A Speech Dataset of Fictional Character Utterances for Expressive Zero-Shot Speech Synthesis
Text-to-speech (TTS) systems have recently achieved more expressive and natural speech synthesis by scaling to large speech datasets. However, the proportion of expressive speech in such large-scale corpora is often unclear. Besides, existing expressive speech corpora are typically smaller in scale and primarily used for benchmarking TTS systems. In this paper, we introduce the LibriQuote dataset, an English corpus derived from read audiobooks, designed for both fine-tuning and benchmarking exp…

Scaling GR(1) Synthesis via a Compositional Framework for LTL Discrete Event Control
Hern\'an Gagliardi, Victor Braberman, Sebastian Uchitel
https://arxiv.org/abs/2506.16557

Scaling GR(1) Synthesis via a Compositional Framework for LTL Discrete Event Control
We present a compositional approach to controller synthesis of discrete event system controllers with linear temporal logic (LTL) goals. We exploit the modular structure of the plant to be controlled, given as a set of labelled transition systems (LTS), to mitigate state explosion that monolithic approaches to synthesis are prone to. Maximally permissive safe controllers are iteratively built for subsets of the plant LTSs by solving weaker control problems. Observational synthesis equivalence i…

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…

MaXsive: High-Capacity and Robust Training-Free Generative Image Watermarking in Diffusion Models
Po-Yuan Mao, Cheng-Chang Tsai, Chun-Shien Lu
https://arxiv.org/abs/2507.21195 h…

MaXsive: High-Capacity and Robust Training-Free Generative Image Watermarking in Diffusion Models
The great success of the diffusion model in image synthesis led to the release of gigantic commercial models, raising the issue of copyright protection and inappropriate content generation. Training-free diffusion watermarking provides a low-cost solution for these issues. However, the prior works remain vulnerable to rotation, scaling, and translation (RST) attacks. Although some methods employ meticulously designed patterns to mitigate this issue, they often reduce watermark capacity, which c…

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…

Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
Piotr Gai\'nski, Oussama Boussif, Andrei Rekesh, Dmytro Shevchuk, Ali Parviz, Mike Tyers, Robert A. Batey, Micha{\l} Koziarski
https://arxiv.org/abs/2506.19865

Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
Template-based molecular generation offers a promising avenue for drug design by ensuring generated compounds are synthetically accessible through predefined reaction templates and building blocks. In this work, we tackle three core challenges in template-based GFlowNets: (1) minimizing synthesis cost, (2) scaling to large building block libraries, and (3) effectively utilizing small fragment sets. We propose \textbf{Recursive Cost Guidance}, a backward policy framework that employs auxiliary m…

Scaling Relations, Morphological Stability, and Asymptotic Freedom of Plasma-Surface Deposition Dynamics
Joel Saucedo, Uday Lamba, Hasitha Mahabaduge
https://arxiv.org/abs/2507.10645

Scaling Relations, Morphological Stability, and Asymptotic Freedom of Plasma-Surface Deposition Dynamics
Connecting plasma processing parameters to the resultant film microstructure remains a fundamental challenge in materials synthesis, one that has largely confined process design to empirical approaches. To bridge this gap, we develop a predictive analysis of coupling by applying a renormalization group (RG) analysis to an effective Hamiltonian for the stochastic dynamics of the plasma-surface interface, derived systematically from microscopic principles. The central result from this formalism i…

Yeast growth is controlled by the proportional scaling of mRNA and ribosome concentrations
Xin Gao, Michael Lanz, Rosslyn Grosely, Jonas Cremer, Joseph Puglisi, Jan M. Skotheim
https://arxiv.org/abs/2508.14997

Yeast growth is controlled by the proportional scaling of mRNA and ribosome concentrations
Despite growth being fundamental to all aspects of cell biology, we do not yet know its organizing principles in eukaryotic cells. Classic models derived from the bacteria E. coli posit that protein-synthesis rates are set by mass-action collisions between charged tRNAs produced by metabolic enzymes and mRNA-bound ribosomes. These models show that faster growth is achieved by simultaneously raising both ribosome content and peptide elongation speed. Here, we test if these models are valid for e…

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…