Tootfinder

Greenland is shrinking slightly and drifting slowly toward the northwest, GNSS stations reveal https://phys.org/news/2025-10-greenland-slightly-drifting-slowly-northwest.html

Greenland is shrinking slightly and drifting slowly toward the northwest, GNSS stations reveal
Greenland is being twisted, compressed, and stretched. This happens due to plate tectonics and movements in the bedrock, caused by the large ice sheets on top melting and reducing pressure on the subsurface.

🪧 When Values Eat Their Young: How Ideal-Driven Groups Drift into Their Own Shadow
#politics

A windswept snow drift
Settled firm against the door
Helps us stay inside.
#dailyhaikuprompt - snow drift
#haiku
#poem

DriftBench: Defining and Generating Data and Query Workload Drift for Benchmarking
Guanli Liu, Renata Borovica-Gajic
https://arxiv.org/abs/2510.10858 https://

DriftBench: Defining and Generating Data and Query Workload Drift for Benchmarking
Data and workload drift are key to evaluating database components such as caching, cardinality estimation, indexing, and query optimization. Yet, existing benchmarks are static, offering little to no support for modeling drift. This limitation stems from the lack of clear definitions and tools for generating data and workload drift. Motivated by this gap, we propose a unified taxonomy for data and workload drift, grounded in observations from both academia and industry. Building on this foundat…

@… https://lisajanepersky.substack.com/

Riffs and Glitter, Drifters and Litter | Lisa Jane Persky | Substack
Experiments, Ephemera, Ectoplasm. Click to read Riffs and Glitter, Drifters and Litter, by Lisa Jane Persky, a Substack publication. Launched 7 days ago.

Capturing System Drift with Time Series Calibration for Global 21-cm Cosmology Experiments
Christian J. Kirkham, Dominic J. Anstey, Eloy de Lera Acedo
https://arxiv.org/abs/2509.13010

Capturing System Drift with Time Series Calibration for Global 21-cm Cosmology Experiments
To achieve the sensitivity required to detect signals from neutral hydrogen from the Cosmic Dawn and Epoch of Reionisation it is critical to have a well-calibrated instrument which has a stable calibration over the course of the observation. Previous calibration methods do not explicitly use the time information available and make assumptions on the impedance matching of the reference sources. Here we present a new calibration method based on noise wave parameters which fits a calibration solut…

$L^2$-solutions to stochastic reaction-diffusion equations with superlinear drifts driven by space-time white noise^
Shijie Shang, Pengyu Wang, Tusheng Zhang
https://arxiv.org/abs/2509.12744

$L^2$-solutions to stochastic reaction-diffusion equations with superlinear drifts driven by space-time white noise^
Consider the following stochastic reaction-diffusion equation with logarithmic superlinear coefficient b, driven by space-time white noise W: $$ u_t(t,x) = (1/2)u_{xx}(t,x) + b(u(t,x)) + σ(u(t,x))W(dt,dx) $$ for $t > 0$ and $x \in [0,1]$, with initial condition $$ u(0,x) = u_0(x) $$ for $x \in [0,1]$, where $u_0 \in L^2[0,1]$. In this paper, we establish existence and uniqueness of probabilistically strong solutions in $C(R_+, L^2[0,1])$. Our result resolves a problem from [Ann. Probab. 47 (…

An efficient iteration method to reconstruct the drift term from the final measurement
Dakang Cen, Wenlong Zhang, Zhidong Zhang
https://arxiv.org/abs/2510.10940 https://

An efficient iteration method to reconstruct the drift term from the final measurement
This work investigates the inverse drift problem in the one-dimensional parabolic equation with the final time data. The authors construct an operator first, whose fixed points are the unknown drift, and then apply it to prove the uniqueness. The proof of uniqueness contains an iteration converging to the drift, which inspires the numerical algorithm. To handle the ill-posedness of the inverse problem, the authors add the mollification on the data first in the iterative algorithm, and then prov…

Mitigating Model Drift in Developing Economies Using Synthetic Data and Outliers
Ilyas Varshavskiy, Bonu Boboeva, Shuhrat Khalilbekov, Azizjon Azimi, Sergey Shulgin, Akhlitdin Nizamitdinov, Haitz Saez de Ocariz Borde
https://arxiv.org/abs/2510.09294

Mitigating Model Drift in Developing Economies Using Synthetic Data and Outliers
Machine Learning models in finance are highly susceptible to model drift, where predictive performance declines as data distributions shift. This issue is especially acute in developing economies such as those in Central Asia and the Caucasus - including Tajikistan, Uzbekistan, Kazakhstan, and Azerbaijan - where frequent and unpredictable macroeconomics shocks destabilize financial data. To the best of our knowledge, this is among the first studies to examine drift mitigation methods on financi…

Physics-Informed Neural Networks vs. Physics Models for Non-Invasive Glucose Monitoring: A Comparative Study Under Realistic Synthetic Conditions
Riyaadh Gani
https://arxiv.org/abs/2509.12253

Physics-Informed Neural Networks vs. Physics Models for Non-Invasive Glucose Monitoring: A Comparative Study Under Realistic Synthetic Conditions
Non-invasive glucose monitors often fail outside the lab because existing datasets ignore hardware noise, environmental drift, and person-to-person physiology. We introduce the first ultra-realistic near-infrared (NIR) simulator that injects 12-bit ADC quantisation, +/-0.1% LED ageing, photodiode dark noise, 15-45 C temperature, 30-90% relative humidity, contact-pressure variation, Fitzpatrick I-VI melanin, and diurnal glucose excursions (dawn phenomenon). Using this platform (rho glucose-NIR =…