Tootfinder

from my link log —
Evaluating TCP BBRv2 on the Dropbox edge network.
https://arxiv.org/abs/2008.07699
saved 2020-08-19 https://dotat.at/:/FHLM3.html

Evaluating BBRv2 on the Dropbox Edge Network
Nowadays, loss-based TCP congestion controls in general and CUBIC specifically became the de facto standard for the Internet. BBR congestion control challenges the loss-based approach by modeling the network based on estimated bandwidth and round-trip time. At Dropbox, we've been using BBRv1 since 2017 and are accustomed to its pros and cons. BBRv2 introduces a set of improvements to network modeling (explicit loss targets and inflight limits) and fairness (differential probing and headroom for…

R-Log: Incentivizing Log Analysis Capability in LLMs via Reasoning-based Reinforcement Learning
Yilun Liu, Ziang Chen, Song Xu, Minggui He, Shimin Tao, Weibin Meng, Yuming Xie, Tao Han, Chunguang Zhao, Jingzhou Du, Daimeng Wei, Shenglin Zhang, Yongqian Sun
https://arxiv.org/abs/2509.25987

R-Log: Incentivizing Log Analysis Capability in LLMs via Reasoning-based Reinforcement Learning
The growing complexity of log data in modern software systems has prompted the use of Large Language Models (LLMs) for automated log analysis. Current approaches typically rely on direct supervised fine-tuning (SFT) on log-label pairs. However, this exacerbates the domain discrepancy between general-purpose LLMs and specialized log data, causing overfitting. Furthermore, SFT's imbalanced loss computation often allows lengthy contexts to overwhelm critical, concise details in model answers, lead…

Environment-Aware Indoor LoRaWAN Path Loss: Parametric Regression Comparisons, Shadow Fading, and Calibrated Fade Margins
Nahshon Mokua Obiri, Kristof Van Laerhoven
https://arxiv.org/abs/2510.04346

Environment-Aware Indoor LoRaWAN Path Loss: Parametric Regression Comparisons, Shadow Fading, and Calibrated Fade Margins
Indoor LoRaWAN propagation is shaped by structural and time-varying context factors, which challenge log-distance models and the assumption of log-normal shadowing. We present an environment-aware, statistically disciplined path loss framework evaluated using leakage-safe cross-validation on a 12-month campaign in an eighth-floor office measuring 240 m^2. A log-distance multi-wall mean is augmented with environmental covariates (relative humidity, temperature, carbon dioxide, particulate matter…

Moments Matter: Posterior Recovery in Poisson Denoising via Log-Networks
Shirin Shoushtari, Edward P. Chandler, Ulugbek S. Kamilov
https://arxiv.org/abs/2510.07199 https://

Moments Matter: Posterior Recovery in Poisson Denoising via Log-Networks
Poisson denoising plays a central role in photon-limited imaging applications such as microscopy, astronomy, and medical imaging. It is common to train deep learning models for denoising using the mean-squared error (MSE) loss, which corresponds to computing the posterior mean $\mathbb{E}[x \mid y]$. When the noise is Gaussian, Tweedie's formula enables approximation of the posterior distribution through its higher-order moments. However, this connection no longer holds for Poisson denoising: w…

Log-free estimate for the resonant paraproduct in the 3D Navier-Stokes equations
Pylyp Cherevan
https://arxiv.org/abs/2510.06246 https://arxiv.org/pdf/2510…

Log-free estimate for the resonant paraproduct in the 3D Navier-Stokes equations
We consider the resonant paraproduct (high-high -> low regime) in the nonlinearity (u\cdot\nabla)u for the three-dimensional Navier-Stokes equations. For sufficiently smooth, divergence-free u, we establish the a priori estimate without logarithmic loss \|R_N(u)\|{\dot H^{-1}} \lesssim N^{-1}\,\|u\|{\dot H^{1/2}}\,\|u\|_{\dot H^{1}}, with a constant independent of the dyadic frequency N. The proof combines phase-geometric integration by parts along an adapted frame…

log scales like "number of nines" are fun when you start to get to poor levels of performance.
... in a room full of people who would have laughed at them if they had said "let's get up to three nines of reliability",
a team I work with took a KPI to "get loss rates down below 20%" and received wise nods from most of the engineers in the room.
...but that's the equivalent of "let's get up to 0.5 nines"
that's a …

Advantage Weighted Matching: Aligning RL with Pretraining in Diffusion Models
Shuchen Xue, Chongjian Ge, Shilong Zhang, Yichen Li, Zhi-Ming Ma
https://arxiv.org/abs/2509.25050 h…

Advantage Weighted Matching: Aligning RL with Pretraining in Diffusion Models
Reinforcement Learning (RL) has emerged as a central paradigm for advancing Large Language Models (LLMs), where pre-training and RL post-training share the same log-likelihood formulation. In contrast, recent RL approaches for diffusion models, most notably Denoising Diffusion Policy Optimization (DDPO), optimize an objective different from the pretraining objectives--score/flow matching loss. In this work, we establish a novel theoretical analysis: DDPO is an implicit form of score/flow matchi…

from my link log —
Saving Skylab.
https://www.airandspace.si.edu/stories/editorial/saving-skylab
saved 2025-11-30 https://

Saving Skylab
During launch, the micrometeoroid shield surrounding the Skylab Workshop ripped loose. Designed to protect the workshop from tiny space particles and the sun's scorching heat, its loss caused sunlight to raise internal temperatures to over 130° F, making the station uninhabitable and threatening foods, medicines, films, and experiments that were onboard. Astronaut Rusty Schweickart was responsible for testing a parasol on Earth and developing procedures for deploying it in space in an effort …