Tootfinder

I'm pretty happy with the top side Samtec ARF6 launch simulations now.
S11:
* Red: No cutout, -12 dB at 10 GHz, -7 at 20 GHz, -4.8 at 30 GHz
* Blue: Rectangular ground cutout the size of the pad, -22.5 at 10 GHz, -16.4 at 20 GHz, -11.7 at 30 GHz
* Green: 30um larger than the pad on all sides (1.06 x 0.41 mm), -30.5 at 10 GHz, -22 at 20, -15 at 30.
With the oversized cutout it's more than good enough for QSGMII and 25Gbase-R.
TDR:
* Red: No cutou…

Mechanical Automation with Vision: A Design for Rubik's Cube Solver
Abhinav Chalise, Nimesh Gopal Pradhan, Nishan Khanal, Prashant Raj Bista, Dinesh Baniya Kshatri
https://arxiv.org/abs/2508.12469 …

Mechanical Automation with Vision: A Design for Rubik's Cube Solver
The core mechanical system is built around three stepper motors for physical manipulation, a microcontroller for hardware control, a camera and YOLO detection model for real-time cube state detection. A significant software component is the development of a user-friendly graphical user interface (GUI) designed in Unity. The initial state after detection from real-time YOLOv8 model (Precision 0.98443, Recall 0.98419, Box Loss 0.42051, Class Loss 0.2611) is virtualized on GUI. To get the solution…

Tory Horton, Jaxson Dart, Adonai Mitchell among deep sleepers for 2025 https://www.espn.com/fantasy/football/story/_/id/45968597/2025-fantasy-football-sleepers-nfl-deep-leagues

First EM simulations on the switch logic board after sorting out some licensing issues with the new Sonnet version.
V19 is a lot faster than v18, it doubles the thread cap from 8 to 16 and also replaces the legacy SSE based matrix solver with an AVX-based version (geee, i wonder where they might have got that from...).
First test is the BGA launch for LC0_PHY0_LANE1_TX, a 5 Gbps QSGMII link but also representative of some of the 25G SERDES.
Return loss is better than -13 dB…

Bridging Pattern-Aware Complexity with NP-Hard Optimization: A Unifying Framework and Empirical Study
Olivier Saidi
https://arxiv.org/abs/2506.13810 https:…

Bridging Pattern-Aware Complexity with NP-Hard Optimization: A Unifying Framework and Empirical Study
NP hard optimization problems like the Traveling Salesman Problem (TSP) defy efficient solutions in the worst case, yet real-world instances often exhibit exploitable patterns. We propose a novel patternaware complexity framework that quantifies and leverages structural regularities e.g., clustering, symmetry to reduce effective computational complexity across domains, including financial forecasting and LLM optimization. With rigorous definitions, theorems, and a meta learning driven solver pi…

Deep Learning-Based Prediction of High Explosive Induced Fluid Dynamics
Francis G. VanGessel, Mitul Pandya
https://arxiv.org/abs/2506.14710 https://…

Deep Learning-Based Prediction of High Explosive Induced Fluid Dynamics
Underwater explosions produce complex fluid phenomena relevant to diverse applications including maritime engineering, medical therapeutics, and inertial confinement fusion. These systems exhibit multiphase flows, chemical kinetics, and highly compressible dynamics that challenge traditional computational approaches. Current hydrodynamic solvers, while accurate, are computationally expensive and non-differentiable, limiting their use in design optimization and real-time applications. Here we sh…

Sonnet 133 - CXXXIII
Beshrew that heart that makes my heart to groan
For that deep wound it gives my friend and me!
Is't not enough to torture me alone,
But slave to slavery my sweet'st friend must be?
Me from myself thy cruel eye hath taken,
And my next self thou harder hast engrossed:
Of him, myself, and thee I am forsaken;
A torment thrice three-fold thus to be crossed.
Prison my heart in thy steel bosom's ward,
But t…

Raiders Training Camp Report No. 14: Deep Dive on the New Depth Chart https://www.si.com/nfl/raiders/las-vegas-pete-carroll-training-camp-san-francisco-49ers-tom-brady

Accurate Depth-Resolved Temperature Profiling via Thermal-Radiation Spectroscopy: Numerical Methods vs Machine Learning
Dmitrii Shymkiv, Zhongyuan Wang, Brigham Thornock, Aiden Karpf, Camila Nunez, Yuzhe Xiao
https://arxiv.org/abs/2506.14554

Accurate Depth-Resolved Temperature Profiling via Thermal-Radiation Spectroscopy: Numerical Methods vs Machine Learning
We present and compare three approaches for accurately retrieving depth-resolved temperature distributions within materials from their thermal-radiation spectra, based on: (1) a nonlinear equation solver implemented in commercial software, (2) a custom-built nonlinear equation solver, and (3) a deep neural network (DNN) model. These methods are first validated using synthetic datasets comprising randomly generated temperature profiles and corresponding noisy thermal-radiation spectra for three …

Parton distribution and fragmentation functions with massive gluons
Gustavo B. Bopsin, Bruno El-Bennich, Gast\~ao Krein, Fernando E. Serna, Roberto C. da Silveira
https://arxiv.org/abs/2507.12544

Parton distribution and fragmentation functions with massive gluons
The correct description of the hadron's structure requires understanding how quarks and gluons form the observable hadrons and how they are distributed within them. Two key nonperturbative quantities encapsulate this information: parton distribution functions (PDFs) and fragmentation functions (FFs). The former define a probabilistic light-front momentum distribution of partons within a hadron, whereas the latter describe the hadronization process of high-energy partons. Computing these functio…