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Partitioning of Eddy Covariance Footprint Evapotranspiration Using Field Data, UAS Observations and GeoAI in the U.S. Chihuahuan Desert
Habibur R. Howlider, Hernan A. Moreno, Marguerite E. Mauritz, Stephanie N. Marquez
https://arxiv.org/abs/2507.14829

Partitioning of Eddy Covariance Footprint Evapotranspiration Using Field Data, UAS Observations and GeoAI in the U.S. Chihuahuan Desert
This study proposes a new method for computing transpiration across an eddy covariance footprint using field observations of plant sap flow, phytomorphology sampling, uncrewed aerial system (UAS), deep learning-based digital image processing, and eddy covariance micrometeorological measurements. The method is applied to the Jornada Experimental Range, New Mexico, where we address three key questions: (1) What are the daily summer transpiration rates of Mesquite (Prosopis glandulosa) and Creosot…

TIL that the social.bbc (@…) instance is hosted in Ireland according to @…
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The non-fediverse part of the internet is NOT helping...but I need to study some Endless Sky videos so here I am in the non-fediverse!
But I'm saving you all FROM the non-Fediverse by only sharing my experiences and observations!
I don't recommend a blaster, you can hit your allies, so I'll switch to laser!
Money then: 150k
Money now: 544k
My cash: 10.6 M
Goal: 12 M
Cash now: 14.5 M

Adaptive and Regime-Aware RL for Portfolio Optimization
Gabriel Nixon Raj
https://arxiv.org/abs/2509.14385 https://arxiv.org/pdf/2509.14385

Adaptive and Regime-Aware RL for Portfolio Optimization
This study proposes a regime-aware reinforcement learning framework for long-horizon portfolio optimization. Moving beyond traditional feedforward and GARCH-based models, we design realistic environments where agents dynamically reallocate capital in response to latent macroeconomic regime shifts. Agents receive hybrid observations and are trained using constrained reward functions that incorporate volatility penalties, capital resets, and tail-risk shocks. We benchmark multiple architectures, …

Dark Energy Survey Year 6 Results: improved mitigation of spatially varying observational systematics with masking
M. Rodr\'iguez-Monroy, N. Weaverdyck, J. Elvin-Poole, I. Sevilla-Noarbe, A. Carnero Rosell, A. Drlica-Wagner, S. Avila, M. R. Becker, K. Bechtol, M. Crocce, M. Gatti, J. Mena-Fern\'andez, A. Porredon, D. Sanchez Cid, M. Yamamoto, M. Aguena, S. S. Allam, O. Alves, F. Andrade-Oliveira, D. Bacon, J. Blazek, S. Bocquet, D. Brooks, R. Camilleri, J. Carretero, R. Cawthon…

Dark Energy Survey Year 6 Results: improved mitigation of spatially varying observational systematics with masking
As photometric surveys reach unprecedented statistical precision, systematic uncertainties increasingly dominate large-scale structure probes relying on galaxy number density. Defining the final survey footprint is critical, as it excludes regions affected by artefacts or suboptimal observing conditions. For galaxy clustering, spatially varying observational systematics, such as seeing, are a leading source of bias. Template maps of contaminants are used to derive spatially dependent correction…

This news item almost flew under the radar:
This initial cohort of executives includes the chief technology officer from Palantir, Shyam Sankar, whose “Defense Reformation” website has become a talking point among defense tech community; Andrew Bosworth, the chief technology officer from Meta; Kevin Weil, OpenAI’s chief product officer and Bob McGrew who, until November, was chief re…

Local Observability of a Class of Feedforward Neural Networks
Yi Yang, Victor G. Lopez, Matthias A. M\"uller
https://arxiv.org/abs/2508.20544 https://…

Local Observability of a Class of Feedforward Neural Networks
Beyond the traditional neural network training methods based on gradient descent and its variants, state estimation techniques have been proposed to determine a set of ideal weights from a control-theoretic perspective. Hence, the concept of observability becomes relevant in neural network training. In this paper, we investigate local observability of a class of two-layer feedforward neural networks~(FNNs) with rectified linear unit~(ReLU) activation functions. We analyze local observability of…

In situ and remote observations of the ultraviolet footprint of the moon Callisto by the Juno spacecraft: #Juno Detected the Final Missing Auroral Signature from Jupiter’s Four Largest Moons: https://science.nasa.gov/missions/juno/juno-detected-the-final-missing-auroral-signature-from-jupiters-four-largest-moons/