Tootfinder

An S-shaped filament formed due to Cloud-Cloud Collision in molecular cloud G178.28-00.61
Tianwei Zhang, Tie Liu, Yuefang Wu, Linjing Feng, Sihan Jiao, Derek Ward-Thompson, Alessio Traficante, Helen J Fraser, James Di Francesco, Doug Johnstone, Paul F. Goldsmith, Yasuo Doi, Xunchuan Liu, Chang Won Lee, Fengwei Xu, Ram K. Yadav, Glenn J White, Leonardo Bronfman, Yi-Jehng Kuan, Kee-Tae Kim, Donghui Quan

An S-shaped filament formed due to Cloud-Cloud Collision in molecular cloud G178.28-00.61
We present compelling observational evidence supporting G178.28-00.61 as an early-stage candidate for Cloud-Cloud Collision (CCC), with indications of the formation of an S-shaped filament, evenly-separated dense cores, and young star clusters. The observations of CO molecular line emission demonstrate the existence of two interacting molecular clouds with systemic velocities of 0.8 km/s and -1.2 km/s, respectively. The convergence zone of these two clouds reveals an S-shaped filament in the JC…

An Elementary Characterization of Bargmann Invariants
Sagar Silva Pratapsi, Jo\~ao Gouveia, Leonardo Novo, Ernesto F. Galv\~ao
https://arxiv.org/abs/2506.17132

An Elementary Characterization of Bargmann Invariants
Bargmann invariants, also known as multivariate traces of quantum states $\operatorname{Tr}(ρ_1 ρ_2 \cdots ρ_n)$, are unitary invariant quantities used to characterize weak values, Kirkwood-Dirac quasiprobabilities, out-of-time-order correlators (OTOCs), and geometric phases. Here we give a complete characterization of the set $B_n$ of complex values that $n$-th order invariants can take, resolving some recently proposed conjectures. We show that $B_n$ is equal to the range of invariants ari…

Minimal Per-Flow Backlog Bounds at an Aggregate FIFO Server under Piecewise-Linear Arrival Curves
Lukas Wildberger, Anja Hamscher, Jens B. Schmitt
https://arxiv.org/abs/2506.16914

Minimal Per-Flow Backlog Bounds at an Aggregate FIFO Server under Piecewise-Linear Arrival Curves
Network Calculus (NC) is a versatile methodology based on min-plus algebra to derive worst-case per-flow performance bounds in networked systems with many concurrent flows. In particular, NC can analyze many scheduling disciplines; yet, somewhat surprisingly, an aggregate FIFO server is a notoriously hard case due to its min-plus non-linearity. A resort is to represent the FIFO residual service by a family of functions with a free parameter instead of just a single curve. For simple token-bucke…

laplax -- Laplace Approximations with JAX
Tobias Weber, B\'alint Mucs\'anyi, Lenard Rommel, Thomas Christie, Lars Kas\"uschke, Marvin Pf\"ortner, Philipp Hennig
https://arxiv.org/abs/2507.17013

laplax -- Laplace Approximations with JAX
The Laplace approximation provides a scalable and efficient means of quantifying weight-space uncertainty in deep neural networks, enabling the application of Bayesian tools such as predictive uncertainty and model selection via Occam's razor. In this work, we introduce laplax, a new open-source Python package for performing Laplace approximations with jax. Designed with a modular and purely functional architecture and minimal external dependencies, laplax offers a flexible and researcher-frien…

Symmetry-breaking motility of an active hinge in a crowded channel
Leonardo Garibaldi Rigon, Yongjoo Baek
https://arxiv.org/abs/2506.16722 https://

Symmetry-breaking motility of an active hinge in a crowded channel
A recent experiment [Son et al., Soft Matter, 2024, 20,2777-2788] showed that self-propelled particles confined within a circular boundary filled with granular medium spontaneously form a motile cluster that stays on the boundary. This cluster exhibits persistent (counter)clockwise motion driven by symmetry breaking, which arises from a positive feedback between the asymmetry of the cluster and those of the surrounding granular medium. To investigate this symmetry-breaking mechanism in broader …

Numerical solution of the Lindblad master equation using the Runge-Kutta method implemented in Python
Leonardi Hern\'andez S\'anchez, Iv\'an Alejandro Bocanegra Garay, Ariel Flores Rosas, Ir\'an Ramos Prieto, Francisco Soto Eguibar, H\'ector Manuel Moya Cessa
https://arxiv.org/abs/2506.19238

Numerical solution of the Lindblad master equation using the Runge-Kutta method implemented in Python
The dynamics of open quantum systems is governed by the Lindblad master equation, which provides a consistent framework for incorporating environmental effects into the evolution of the system. Since exact solutions are rarely available, numerical methods become essential tools for analyzing such systems. This article presents a step-by-step implementation of the fourth-order Runge-Kutta method in Python to solve the Lindblad equation for a single quantized field mode subject to decay. A cohere…

Talking to...uh...um...Machines: The Impact of Disfluent Speech Agents on Partner Models and Perspective Taking
Rhys Jacka, Paola R. Pe\~na, Sophie Leonard, \'Eva Sz\'ekely, Benjamin R. Cowan
https://arxiv.org/abs/2507.18315

Talking to...uh...um...Machines: The Impact of Disfluent Speech Agents on Partner Models and Perspective Taking
Speech disfluencies play a role in perspective-taking and audience design in human-human communication (HHC), but little is known about their impact in human-machine dialogue (HMD). In an online Namer-Matcher task, sixty-one participants interacted with a speech agent using either fluent or disfluent speech. Participants completed a partner-modelling questionnaire (PMQ) both before and after the task. Post-interaction evaluations indicated that participants perceived the disfluent agent as more…

Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data
Arif Pathan
https://arxiv.org/abs/2506.17244

Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data
Short-term sentiment forecasting in financial markets (e.g., stocks, indices) is challenging due to volatility, non-linearity, and noise in OHLC (Open, High, Low, Close) data. This paper introduces a novel CMG (Chaos-Markov-Gaussian) framework that integrates chaos theory, Markov property, and Gaussian processes to improve prediction accuracy. Chaos theory captures nonlinear dynamics; the Markov chain models regime shifts; Gaussian processes add probabilistic reasoning. We enhance the framework…

A Quantum Superposition of Black Hole Evaporation Histories: Recovering Unitarity
Ali Akil, Lorenzo Giannelli, Leonardo Modesto, Oscar Dahlsten, Giulio Chiribella, Caslav Brukner
https://arxiv.org/abs/2507.17031

A Quantum Superposition of Black Hole Evaporation Histories: Recovering Unitarity
Black hole evaporation is one of the most striking phenomena at the interface between gravity and quantum physics. In Hawking's semi-classical treatment, where matter is quantum mechanical and the spacetime is definite and classical, evaporation leads to an apparent loss of unitarity of the overall evolution, and to the so-called black hole information paradox. Here, we go beyond this semi-classical treatment and formulate a toy quantum model of black hole evaporation that allows the black hole…

Comparison of Optimised Geometric Deep Learning Architectures, over Varying Toxicological Assay Data Environments
Alexander D. Kalian, Lennart Otte, Jaewook Lee, Emilio Benfenati, Jean-Lou C. M. Dorne, Claire Potter, Olivia J. Osborne, Miao Guo, Christer Hogstrand
https://arxiv.org/abs/2507.17775

Comparison of Optimised Geometric Deep Learning Architectures, over Varying Toxicological Assay Data Environments
Geometric deep learning is an emerging technique in Artificial Intelligence (AI) driven cheminformatics, however the unique implications of different Graph Neural Network (GNN) architectures are poorly explored, for this space. This study compared performances of Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs) and Graph Isomorphism Networks (GINs), applied to 7 different toxicological assay datasets of varying data abundance and endpoint, to perform binary classification of…