Tootfinder

»WLAN-Wellen reichen, um jeden auch ohne Handy zu identifizieren:
Forschende des Karlsruher Instituts für Technologie (@…) haben gezeigt, dass sich aus gewöhnlicher WLAN-Kommunikation Bilder der Umgebung re­kon­stru­ieren lassen - und dass so Personen zuverlässig identifiziert werden können.«
Nicht die erste Meldung in dieser Richtung und is…

WLAN-Wellen reichen, um jeden auch ohne Handy zu identifizieren
Forschende des Karlsruher Instituts für Technologie (KIT) haben gezeigt, dass sich aus gewöhnlicher WLAN-Kommunikation Bilder der Umgebung rekonstruieren lassen - und dass so Personen zuverlässig identifiziert werden können.

Nächste Woche dann mal dieses Heidelberg. (Wir haben da die große Fraktionsklausur Grüne Landtag BW, u.a. mit Bürgerdialog im Karlstorbahnhof am Dienstagabend.)

A systematic search for orbital periods of polars with TESS. Methods, detection limits, and results
Santiago Hern\'andez-D\'iaz (Institut f\"ur Astronomie und Astrophysik, Eberhard Karls Universit\"at T\"ubingen), Beate Stelzer (Institut f\"ur Astronomie und Astrophysik, Eberhard Karls Universit\"at T\"ubingen), Axel Schwope (Leibniz Institut f\"ur Astrophysik Potsdam), Daniela Mu\~noz-Giraldo (Institut f\"ur Astronomie und Astrophysik, E…

A systematic search for orbital periods of polars with TESS. Methods, detection limits, and results
Context. Determining the orbital periods of cataclysmic variable stars (CVs) is essential for confirming candidates and for the understanding of their evolutionary state. The Transiting Exoplanet Survey Satellite (TESS) provides month-long photometric data across nearly the entire sky that can be used to search for periodic variability in such systems. Aims. This study aims to identify and confirm the orbital periods for members of a recent compilation of magnetic CVs (known as polars) using TE…

#NorskTut #Hippie

The Gravitational Wave Memory from Binary Neutron Star Mergers
Jamie Bamber, Antonios Tsokaros, Milton Ruiz, Stuart L. Shapiro, Marc Favata, Matthew Karlson, Fabrizio Venturi Pi\~nas
https://arxiv.org/abs/2510.09742

The Gravitational Wave Memory from Binary Neutron Star Mergers
The gravitational wave signal produced by the merger of two compact objects includes both an oscillatory transient and a non-oscillatory part, the so-called memory effect. This produces a permanent displacement of test masses and has not yet been measured. We use general relativistic magnetohydrodynamic simulations, including neutrinos, with several representative viable equations of state, to quantify--for the first time--the effects of the neutron star magnetic field, neutrino emission, and t…

Large scale statistically validated comorbidity networks
Paride Crisafulli, Tobias Galla, Antti Karlsson, Salvatore Miccich\`e, Jyrki Piilo, Rosario N. Mantegna
https://arxiv.org/abs/2510.09737

Large scale statistically validated comorbidity networks
We obtain comorbidity networks starting from medical information stored in electronic health records collected by the Wellbeing Services County of Southwest Finland (Varha). Based on the data, we associate each patient to one or more diseases and construct complex comorbidity networks associated with large patient cohorts characterized by an age interval and sex. The information about diseases in electronic health records is coded using the highest granularity present in the international class…

Data Driven Discovery of Emergent Dynamics in Reaction Diffusion Systems from Sparse and Noisy Observations
Saumitra Dwivedi, Ricardo da Silva Torres, Ibrahim A. Hameed, Gunnar Tufte, Anniken Susanne T. Karlsen
https://arxiv.org/abs/2509.09278

Data Driven Discovery of Emergent Dynamics in Reaction Diffusion Systems from Sparse and Noisy Observations
Data-driven discovery of emergent dynamics is gaining popularity, particularly in the context of reaction-diffusion systems. These systems are widely studied across various fields, including neuroscience, ecology, epidemiology, and several other subject areas that deal with emergent dynamics. A current challenge in the discovery process relates to system identification when there is no prior knowledge of the underlying physics. We attempt to address this challenge by learning Soft Artificial Li…