Tootfinder

Anomalous slow-down of the bound state dynamics in a non-locally coupled quantum circuit
Biswajit Paul, Suman Mondal, Tapan Mishra
https://arxiv.org/abs/2506.09818

Anomalous slow-down of the bound state dynamics in a non-locally coupled quantum circuit
Additional hopping channels in a tight-binding lattice is known to introduce faster dynamics of a quantum mechanical particle. However, we show that in the case of a repulsively bound state, the dynamics becomes abnormally slow when next-nearest neighbor (NNN) hopping is allowed for the particles. We show that such slowing down occurs for some magic strength of the NNN hopping at which the bound state band exhibits a quasi-flatband feature. We reveal this anomalous dynamical behavior by analyzi…

Prediction of the SVOM MXT camera end of life spectral performance based onproton irradiation results
Clara Plasse, Diego G\"otz, Aline Meuris, Miguel Fernandez Moita, Philippe Ferrando, Leo Favier, Francesco Ceraudo
#toXiv_bot_toot

A General Approach to the Shape Transition of Run-and-Tumble Particles: The 1D PDMP Framework for Invariant Measure Regularity
Leo Hahn
https://arxiv.org/abs/2506.02274

A General Approach to the Shape Transition of Run-and-Tumble Particles: The 1D PDMP Framework for Invariant Measure Regularity
Run-and-tumble particles (RTPs) have emerged as a paradigmatic example for studying nonequilibrium phenomena in statistical mechanics. The invariant measure of a wide class of RTPs subjected to a potential possesses a density, which is continuous when tumble rates are high and discontinuous when they are low. This key feature is known as shape transition. By comparison with the Boltzmann distribution characteristic of thermodynamic equilibrium, this constitutes a qualitative indicator of the re…

This https://arxiv.org/abs/2502.00168 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Supervised Quadratic Feature Analysis: Information Geometry Approach for Dimensionality Reduction
Supervised dimensionality reduction aims to map labeled data to a low-dimensional feature space while maximizing class discriminability. Directly computing discriminability is often impractical, so an alternative approach is to learn features that maximize a distance or dissimilarity measure between classes. The Fisher-Rao distance is an important information geometry distance in statistical manifolds. It is induced by the Fisher information metric, a tool widely used for understanding neural r…

Join Nikolay Sivko at this year's Berlin Buzzwords for his talk, in which he will delve into delay accounting — a frequently overlooked feature that provides valuable insights into CPU time shortages and application latency. Discover how to use these kernel metrics to improve performance analysis and system optimisation.
Learn more:

New set of #Gentoo #Linux Distribution Kernels (6.1.143, 6.6.96, 6.12.36, 6.15.5) is out. This set brings some major changes:
• I've backported a bunch of changes from sys-kernel/gentoo-kernel to sys-kernel/vanilla-kernel that were missing — notably wider architecture support.
• I've added default #RISCV configs to 6.12 (in addition to 6.15), since Fedora had them.
• All three packages are based off the baseline kernel tarball upstream patch (vanilla-kernel used to fetch patch-level tarball every time, and gentoo-kernel* used genpatches for patch versions). This should reduce disk space and bandwidth use.
• All three packages now support verify-sig. Rather than verifying the uncompressed tarball signature, we now use upstream `sha256sums.asc` file to verify the compressed tarball and patch.
• sys-kernel/gentoo-kernel* now repackages genpatches. This means patchset that's much leaner and faster to apply (since we just fetch and use the combined upstream patch rather than including point patches). This also means that we are able to release Distribution Kernels before gentoo-sources are done.
The changes still need to be done to 5.15 and 5.10 branches — we're going to do for the next upstream releases of these.
#kernel

Forecasting Nigerian Equity Stock Returns Using Long Short-Term Memory Technique
Adebola K. Ojo, Ifechukwude Jude Okafor
https://arxiv.org/abs/2507.01964 h…

Forecasting Nigerian Equity Stock Returns Using Long Short-Term Memory Technique
Investors and stock market analysts face major challenges in predicting stock returns and making wise investment decisions. The predictability of equity stock returns can boost investor confidence, but it remains a difficult task. To address this issue, a study was conducted using a Long Short-term Memory (LSTM) model to predict future stock market movements. The study used a historical dataset from the Nigerian Stock Exchange (NSE), which was cleaned and normalized to design the LSTM model. Th…

Sparse Imagination for Efficient Visual World Model Planning
Junha Chun, Youngjoon Jeong, Taesup Kim
https://arxiv.org/abs/2506.01392 https://

Sparse Imagination for Efficient Visual World Model Planning
World model based planning has significantly improved decision-making in complex environments by enabling agents to simulate future states and make informed choices. However, ensuring the prediction accuracy of world models often demands substantial computational resources, posing a major challenge for real-time applications. This computational burden is particularly restrictive in robotics, where resources are severely constrained. To address this limitation, we propose a Sparse Imagination fo…