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Estado del clima en Jalisco: temporada de lluvias y comportamiento extremo de la temperatura en 2024
Mauricio L\'opez-Reyes, Julio Eduardo Zamora-Salvador, Alma Delia Ort\'iz-Ba\~nuelos, Stephany Paulina Arellano-Ram\'irez, Carlos Rom\'an-Casta\~neda, Armando Gonz\'alez-Figueroa, Hector Hugo Ulloa-God\'inez, Mario E. Garc\'ia Guadalupe

Estado del clima en Jalisco: temporada de lluvias y comportamiento extremo de la temperatura en 2024
This article analyzes the characteristics of the rainy season and extreme temperatures recorded in the state of Jalisco, Mexico, during 2024. Using ERA5 and CHIRPS reanalysis data and observations from thirteen meteorological stations, patterns of precipitation and temperature anomalies were identified. The results show a late start and early end of the rainy season, with positive anomalies in the central and southern regions, and significant deficits in the coastal strip. Furthermore, 2024 sto…

The near infrared airglow continuum conundrum. Constraints for ground-based faint object spectroscopy
J. K. M. Viuho (Cosmic Dawn Center, Niels Bohr Institute, University of Copenhagen, Jagtvej 155A, DK-2200, Copenhagen N, Denmark, Nordic Optical Telescope, Rambla Jose Ana Fernandez Perez 7, ES-38711 Brena Baja, Spain, Department of Physics and Astronomy, Aarhus University, Munkegade 120, DK-8000 Aarhus C, Denmark), J. P. U. Fynbo (Cosmic Dawn Center, Niels Bohr Institute, University of Copenhagen, Jagtvej 155A, DK-2200, Copenhagen N, Denmark), M. I. Andersen (Cosmic Dawn Center, Niels Bohr Institute, University of Copenhagen, Jagtvej 155A, DK-2200, Copenhagen N, Denmark)
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How Not to Detect Prompt Injections with an LLM
Sarthak Choudhary, Divyam Anshumaan, Nils Palumbo, Somesh Jha
https://arxiv.org/abs/2507.05630 https://

How Not to Detect Prompt Injections with an LLM
LLM-integrated applications and agents are vulnerable to prompt injection attacks, in which adversaries embed malicious instructions within seemingly benign user inputs to manipulate the LLM's intended behavior. Recent defenses based on $\textit{known-answer detection}$ (KAD) have achieved near-perfect performance by using an LLM to classify inputs as clean or contaminated. In this work, we formally characterize the KAD framework and uncover a structural vulnerability in its design that invalid…

🇺🇦 #NowPlaying on BBCRadio3's #NightTracks
Douglas J. Cuomo, Nels Cline & Aizuri Quartet:
🎵 Dedication - Part II
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