Accurate visual localization from aerial views is a fundamental problem with applications in mapping, large-area inspection, and search-and-rescue operations. In many scenarios, these systems require high-precision localization while operating with limited resources (e.g., no internet connection or GNSS/GPS support), making large image databases or heavy 3D models impractical. Surprisingly, little attention has been given to leveraging orthographic geodata as an alternative paradigm, which is lightweight and increasingly available through free releases by governmental authorities (e.g., the European Union). To fill this gap, we propose OrthoLoC, the first large-scale dataset comprising 16,425 UAV images from Germany and the United States with multiple modalities.

The tall container was almost certainly used for distillation experiments. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The ceramic container is over 1.5 feet tall. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Hosted by Eleanor Drage and Kerry McInerney, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. How can communities take back control of the digital infrastructure that powers everyday life? In this episode, Eleanor Drage speaks with Tara Merk about how community-owned data centers could transform digital ownership and challenge the dominance of Big Tech. The conversation explores alternative models of internet infrastructure that prioritize local empowerment, sustainability, and cooperative governance over corporate control. Drawing on examples from Germany's renewable energy sector and community-led initiatives, Merk reflects on how decentralized ownership models can create fairer and more environmentally responsible technological systems.

Bereich How DER SPIEGEL processed the data from the NSDAP membership card file aufklappen The NSDAP membership card file was recently made available by the US National Archives on its website in digitized form. DER SPIEGEL downloaded all of these documents and extracted the content with the help of artificial intelligence. To minimize errors when reading the old files, the dataset was first classified with the help of machine learning and pre-sorted into groups. The handwriting on the index cards is in some cases difficult to read, on some the text has faded, many are written in old German script (Sütterlin). Other cards, meanwhile, were filled out with a typewriter.

A former member of the German militant group Red Army Faction (RAF) has been jailed for 13 years for carrying out a string of armed robberies between 1999 and 2016. Daniela Klette, 67, was finally caught in a flat in Berlin in 2024 after more than 30 years on the run. She went on trial last year. Her defence had called for her acquittal but the court in Verden in Lower Saxony found her guilty on Wednesday of aggravated robbery, violating weapons laws and other offences over a 17-year period. Klette's RAF group, also known as the Baader-Meinhof gang, was eventually disbanded after a campaign of murder, kidnapping and bombing from the early 1970s to the early 1990s.

Score matching is an alternative to maximum likelihood estimation when the normalizing constant is unknown or too costly to evaluate. However, vanilla score matching has shown to be inefficient relative to maximum likelihood estimation for multimodal distributions with well-separated modes, which are commonly encountered in practical applications. We compare a novel diffusion-based denoising score matching estimator (DDSME) to the vanilla score matching estimator (SME) in this scenario. In particular, we prove statistical guarantees for both estimators, showing that the error bound for the vanilla SME worsens when the separation between the modes increases, which can be avoided in case of the DDSME with suitable hyperparameter tuning. This provides a novel theoretical explanation for the superior behavior of diffusion-based score matching over the vanilla version.

Uncertainty quantification (UQ) is critical for safety-critical domains like healthcare, yet it is rarely evaluated under realistic out-of-distribution (OOD) conditions. Here, we assessed predictive performance and uncertainty reliability for deep learning-based blood pressure (BP) estimation from photoplethysmography (PPG) signals under both in-distribution (ID) and OOD settings. Using an XResNet1D-50 trained on PulseDB and tested on four external datasets, we compared deep ensembles (DE) and Monte Carlo dropout (MCD) with Gaussian negative log-likelihood (GNLL) and mean squared error (MSE) losses, optionally followed by post-hoc recalibration via conformal prediction (CP), temperature scaling (TS), and isotonic regression (IR). The key findings of our study are as follows: (1) DE provides stronger predictive robustness under domain shift than MCD, an advantage that becomes clear primarily under external shift. (2) Recalibrated GNLL-based methods yield the best uncertainty calibration (e.g., GNLL+DE+CP for systolic blood pressure (SBP), GNLL+DE+TS for diastolic blood pressure (DBP)), while MSE-based uncertainty requires recalibration to become practically useful. (3) Across settings, CP and TS offer the most consistent gains, with IR remaining competitive in several cases. Overall, our results identify DE-based methods as most robust for predictive performance under domain shift, GNLL as strongest for native UQ, and recalibration as essential for making MSE-based uncertainty practical. These findings highlight the need to jointly assess predictive accuracy and calibration on external data for trustworthy cuffless BP estimation

An escaped tiger believed to be owned by Germany's Tiger Queen has been shot dead by police after attacking one of its keepers, according to local media reports. Police say a 73-year-old man was seriously injured after being attacked on Sunday while he was inside the animal's enclosure, located in a privately-owned facility on the outskirts of the German city of Leipzig. The tiger escaped the enclosure and was found shortly after by armed police, who shot and killed the animal. The site of the enclosure is believed to be owned by controversial trainer and private owner Carmen Zander, who describes herself as Germany's Tiger Queen. The animal was one of eight big cats kept at the industrial site near the German town of Schkeuditz, according to local media.

We establish the first population risk bounds for Kolmogorov-Arnold Networks (KANs) trained by mini-batch SGD with gradient clipping, covering non-private SGD as well as differentially private SGD (DP-SGD) with Gaussian perturbations that interpolate between independent and temporally correlated noise. This setting is substantially closer to practice than prior KAN theory along two axes: training is by mini-batch SGD, the standard recipe for modern networks, rather than full-batch gradient descent (GD); and correlated-noise mechanisms have empirically shown a more favorable privacy-utility tradeoff than independent-noise mechanisms. Our results cover the corresponding full-batch GD and independent-noise DP-GD results for KANs by Wang et al. (2026), while yielding sharper fixed-second-layer specializations. The technical core is a new analysis route for correlated-noise DP training in the non-convex regime. Temporal dependence breaks the conditional-centering structure underlying standard one-step SGD arguments, and the projection step obstructs the exact cancellation structure of correlated perturbations. We address these difficulties through an auxiliary unprojected dynamics, a shifted iterate that absorbs the current noise perturbation, and a high-probability bootstrap certifying projection inactivity. Combining this optimization analysis with a stability-based generalization argument yields the stated population risk bounds. To the best of our knowledge, this is the first optimization and population risk analysis of a correlated-noise mechanism for DP training beyond convex learning, in particular for neural networks.

Uta Frith seems remarkably cheerful and content for someone who's spent six decades trying and failing to get to grips with her life's obsession. "Very little has stood the test of time," she tells me as we sit down in her living room in a leafy estate in Harrow-on-the-Hill, London. Around us, high-ceilinged walls papered in a luxurious red print are barely visible between rammed bookshelves, several model brains and a collection of abstract art. Frith has been searching for the mechanisms that underpin the enigmatic condition of autism ever since she first met profoundly autistic children in the late 1960s. "We could identify them intuitively, but not really scientifically - and I have to say that this is, unfortunately, still the case." Still, Frith's influence on our ever-shifting understanding of autism has been monumental.