Tootfinder

Die #Pflanzenkrankheit #Stolbur bedroht durch die Schilf-#Glasflügelzikade verstärkt Ernten in

Pflanzenkrankheit sorgt für massive Ernteausfälle
Erst die Zuckerrüben, nun auch Kartoffeln und anderes Gemüse: Eine Pflanzenkrankheit sorgt in Teilen Deutschlands für massive Probleme. Schuld an der Krankheit ist eine Zikaden-Art. Der Bauernverband ist in "großer Sorge".

"What Can We Learn From Open Access Journals in the Arab States?" @ Katina Magazine: https://katinamagazine.org/content/article/open-knowledge/2025/open-access-journals-arab-states
"In the 22 Arab states,

On Polynomial Stochastic Barrier Functions: Bernstein Versus Sum-of-Squares
Peter Amorese, Morteza Lahijanian
https://arxiv.org/abs/2506.09164 https://

On Polynomial Stochastic Barrier Functions: Bernstein Versus Sum-of-Squares
Stochastic Barrier Functions (SBFs) certify the safety of stochastic systems by formulating a functional optimization problem, which state-of-the-art methods solve using Sum-of-Squares (SoS) polynomials. This work focuses on polynomial SBFs and introduces a new formulation based on Bernstein polynomials and provides a comparative analysis of its theoretical and empirical performance against SoS methods. We show that the Bernstein formulation leads to a linear program (LP), in contrast to the se…

Development of a Photon-Counting Deadtime Noise Model that Extends Dynamic Range and Resolution in Atmospheric Lidar
Grant J. Kirchhoff, Matthew Hayman, Willem J. Marais, Jeffrey P. Thayer, Rory A. Barton-Grimley
https://arxiv.org/abs/2506.09253

Development of a Photon-Counting Deadtime Noise Model that Extends Dynamic Range and Resolution in Atmospheric Lidar
This work derives and validates a noise model that encapsulates deadtime of non-paralyzable detectors with random photon arrivals to enable advanced processing, like maximum-likelihood estimation, of high resolution atmospheric lidar profiles while accounting for deadtime bias. This estimator was validated across a wide dynamic range at high resolution (4 millimeters in range, 17 milliseconds in time). Experiments demonstrate that the noise model outperforms the current state-of-the-art for ver…

This https://arxiv.org/abs/2405.20771 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Towards Black-Box Membership Inference Attack for Diffusion Models
Given the rising popularity of AI-generated art and the associated copyright concerns, identifying whether an artwork was used to train a diffusion model is an important research topic. The work approaches this problem from the membership inference attack (MIA) perspective. We first identify the limitation of applying existing MIA methods for proprietary diffusion models: the required access of internal U-nets. To address the above problem, we introduce a novel membership inference attack metho…

Probability-One Optimization of Generalized Rayleigh Quotient Sum For Multi-Source Generalized Total Least-Squares
Dominik Friml, Pavel V\'aclavek
https://arxiv.org/abs/2506.09573

Probability-One Optimization of Generalized Rayleigh Quotient Sum For Multi-Source Generalized Total Least-Squares
This paper addresses the global optimization of the sum of the Rayleigh quotient and the generalized Rayleigh quotient on the unit sphere. While various methods have been proposed for this problem, they do not guarantee convergence to the global maximizer. To overcome this limitation, we introduce a probability-one homotopy optimization method that, under certain conditions, guarantees convergence to the global maximizer. The proposed method is analyzed alongside state-of-the-art approaches thr…

Übrigens, wer meine (soweit Neuerblühtes vorhanden täglichen) #blühklex Fotos ausm #klexenhaus mag: dieses Jahr versuche ich sie konsequent in einem eigenen nur dafür gedachten Instagram Account zu sammeln, wer mag, findet den unter https://www.instagram.com/bluehklex/profilecard/?igsh=MWg0d3QyNmFtbjBxdQ==.
(ja, Instagram böse, ich weiß, klei mi büdde anne fööt)

This https://arxiv.org/abs/2505.00546 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Directly Forecasting Belief for Reinforcement Learning with Delays
Reinforcement learning (RL) with delays is challenging as sensory perceptions lag behind the actual events: the RL agent needs to estimate the real state of its environment based on past observations. State-of-the-art (SOTA) methods typically employ recursive, step-by-step forecasting of states. This can cause the accumulation of compounding errors. To tackle this problem, our novel belief estimation method, named Directly Forecasting Belief Transformer (DFBT), directly forecasts states from ob…

BINGO! Simple Optimizers Win Big if Problems Collapse to a Few Buckets
Kishan Kumar Ganguly, Tim Menzies
https://arxiv.org/abs/2506.04509 https://

BINGO! Simple Optimizers Win Big if Problems Collapse to a Few Buckets
Traditional multi-objective optimization in software engineering (SE) can be slow and complex. This paper introduces the BINGO effect: a novel phenomenon where SE data surprisingly collapses into a tiny fraction of possible solution "buckets" (e.g., only 100 used from 4,096 expected). We show the BINGO effect's prevalence across 39 optimization in SE problems. Exploiting this, we optimize 10,000 times faster than state-of-the-art methods, with comparable effectiveness. Our new algorithms (LIT…

This https://arxiv.org/abs/2506.05376 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Red Teaming Roadmap Towards System-Level Safety
Large Language Model (LLM) safeguards, which implement request refusals, have become a widely adopted mitigation strategy against misuse. At the intersection of adversarial machine learning and AI safety, safeguard red teaming has effectively identified critical vulnerabilities in state-of-the-art refusal-trained LLMs. However, in our view the many conference submissions on LLM red teaming do not, in aggregate, prioritize the right research problems. First, testing against clear product safety …