Tootfinder

Leaked renders via Android Headlines show the Samsung Galaxy Z Flip 8, Z Fold 8, Watch 9, and Watch Ultra 2; the Galaxy Z Fold 8 is likely a new wide foldable (Lawrence Bonk/Engadget)
https://www.engadget.com/2210595/samsuing-z-flip-8-watch-9-wa…

Official Renders For Samsung's Galaxy Z Flip 8, Watch 9, Watch 2 Ultra And Z Fold 8 Leak
Renders of multiple new Samsung devices have arrived a few weeks before the company's next Unpacked event.

"Smoking Gun" Email Shows That Trump Summoned Multiple Agencies To Defund Colorado Over Tina Peters - Joe.My.God.
https://www.joemygod.com/2026/08/smoking-gun-email-shows-that-trump-summoned-multiple-agencies-to-defund-colorado-over-tina-peters/

If I were a scientist, this is the kind of sciency science I would spend my time sciencing.
#Coffee

Replaced article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Symmetry-Projected Weakly Compatible Multiparameter Quantum Sensing
G. R. Jin, Z. Y. Zhou, W. Yang

DefoEye: Python-Based Software for Facilitating Time-Series InSAR Analysis of Sentinel-1 Remote-Sensing Data
Alireza Taheri Dehkordi, Hossein Hashemi, Amir Naghibi
https://arxiv.org/abs/2608.04915 https://arxiv.org/pdf/2608.04915 https://arxiv.org/html/2608.04915
arXiv:2608.04915v1 Announce Type: new
Abstract: Many existing time-series Interferometric Synthetic Aperture Radar (TS-InSAR) software tools have limitations, including restricted geographic applicability, commercial licensing, and incomplete end-to-end processing support. Although GMTSAR avoids some of these constraints, it still requires substantial manual intervention and C-shell commands, lacks a user-friendly graphical interface, and omits important steps such as interferogram network pruning and anchoring of unwrapped interferograms. This paper introduces DefoEye (v1), an open-source Python-based software package that wraps GMTSAR and provides a unified, user-friendly TS-InSAR workflow for Sentinel-1 data. DefoEye supports parallel job execution, interferogram network pruning, and multiple anchoring options. Its performance was evaluated from 2020 to 2024 in four regions with different geological settings, deformation mechanisms, and atmospheric and climatic conditions. In Bologna, Italy; Gotland, Sweden; and Houston, USA, DefoEye results were compared with observations from 10 GNSS stations and showed strong agreement, with RMSE values of 4.3-11.9 mm and Pearson correlation coefficients of 0.63-0.95. In Karaj, Iran, where GNSS observations were unavailable, DefoEye was compared with other widely used processing tools and achieved similarly close agreement, with an RMSE of 4.8 mm/yr and a Pearson correlation coefficient of 0.98. These results demonstrate that DefoEye provides reliable TS-InSAR products for geological, hydrological, and environmental applications.
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LSR-Net: Long-Short-Range Operator Learning for Pattern Dynamics on Manifolds
Qian Serena Hou, Zecheng Gan
https://arxiv.org/abs/2607.00750 https://arxiv.org/pdf/2607.00750 https://arxiv.org/html/2607.00750
arXiv:2607.00750v1 Announce Type: new
Abstract: We propose the Long-Short-Range Neural Network (LSR-Net), an extensible operator-learning framework for predicting pattern dynamics on planar domains, spherical surfaces, and general manifolds. The method decomposes the forward evolution operator into a long-range component, represented by a compact Fourier multiplier constructed via the Sum-of-Exponentials (SOE) approximation, and a short-range component adapted to the underlying geometry and its intrinsic symmetries. For general manifolds represented by irregularly sampled point clouds, the long-range component is implemented by Gaussian gridding onto an auxiliary regular grid, where the Fourier multiplier is efficiently applied in k-space using FFT and the result is interpolated back to the original sample points. We evaluate LSR-Net on several benchmark systems, including the Allen-Cahn, Cahn-Hilliard, Schnakenberg, and Turing systems, over planar domains, spherical surfaces, and a blob-shaped manifold. Numerical results demonstrate that LSR-Net consistently achieves higher accuracy and improved stability compared with baseline operator-learning models. In particular, for Allen-Cahn dynamics on the sphere, the RMSE is reduced by approximately three orders of magnitude compared with the Spherical Fourier Neural Operator (SFNO). Rotation and reflection equivariance tests further confirm that the learned operator is consistent with these geometric transformations. These results indicate that LSR-Net provides an effective and robust approach for learning pattern dynamics on complex geometries.
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Replaced article(s) found for math.DS. https://arxiv.org/list/math.DS/new
[2/2]:
- Generic-case complexity of Whitehead's algorithm, revisited
Ilya Kapovich
https://arxiv.org/abs/1903.07040
- Exponential mixing for the randomly forced NLS equation
Yuxuan Chen, Shengquan Xiang, Zhifei Zhang, Jia-Cheng Zhao
https://arxiv.org/abs/2506.10318 https://mastoxiv.page/@arXiv_mathAP_bot/114675099940694476
- Machine-Precision Prediction of Low-Dimensional Chaotic Systems from Noise-Free Data
Christof Sch\"otz, Niklas Boers
https://arxiv.org/abs/2507.09652
- Limit theorems for inhomogeneous $\phi$-mixing Markov chains
Yeor Hafouta, Brenden Williams
https://arxiv.org/abs/2510.15323 https://mastoxiv.page/@arXiv_mathPR_bot/115405447591487390
- A kernel method for the learning of Wasserstein geometric flows
Jianyu Hu, Juan-Pablo Ortega, Daiying Yin
https://arxiv.org/abs/2511.06655 https://mastoxiv.page/@arXiv_mathNA_bot/115530572827554967
- Null-Validated Topological Signatures of Financial Market Dynamics
Samuel W. Akingbade
https://arxiv.org/abs/2602.00383 https://mastoxiv.page/@arXiv_qfinST_bot/116006072151481289
- Simple generators of rational function fields
Alexander Demin, Gleb Pogudin
https://arxiv.org/abs/2602.10878 https://mastoxiv.page/@arXiv_csSC_bot/116056681962763734
- Martin Boundary and Invariant Fields of Multiplicative SHE
Hongyi Chen
https://arxiv.org/abs/2602.16126 https://mastoxiv.page/@arXiv_mathPR_bot/116096386900973756
- Counting the number of $1_{m}$-preperiodic $\mathcal{O}_{K}$-points of a discrete dynamical syste...
Brian Kintu
https://arxiv.org/abs/2606.14468 https://mastoxiv.page/@arXiv_mathNT_bot/116753018953819097
- Shadowing and Hyperbolicity for Endomorphisms of Locally Compact Groups
Dekui Peng
https://arxiv.org/abs/2606.27647 https://mastoxiv.page/@arXiv_mathGR_bot/116832321784455128
- Uniform $L^{\infty}$-Boundedness of Global Attractors for Reaction-Diffusion Equations with Neuma...
Antonio L. Pereira
https://arxiv.org/abs/2607.08061 https://mastoxiv.page/@arXiv_mathAP_bot/116894588437564813
- Stability of closed characteristics and invariant sets on star-shaped hypersurfaces
Huagui Duan, Zihao Qi
https://arxiv.org/abs/2607.15546 https://mastoxiv.page/@arXiv_mathSG_bot/116951161229689295
- Vakonomic Fluids
Ritoban Roy-Chowdhury, Mohammad Sina Nabizadeh, Oliver Gross, Anthony Gruber, Albert Chern
https://arxiv.org/abs/2607.18312 https://mastoxiv.page/@arXiv_mathph_bot/116962502170736360
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