It's not the worst solution to the issue, but the images are definitely AI. Breakthroughs, discoveries, and DIY tips sent six days a week. It's increasingly difficult to keep track of all the generative AI slop currently fooling unsuspecting internetgoers on any given day. Earlier this month, a (fake) image went viral after enough people genuinely believed North Carolina conservationists kept wild horses warm during winter storms by wrapping them in fiberglass insulation . Now, another hoax is tugging on the heartstrings of animal lovers.

In this section, we detail the model architectures examined in the experiments and list all hyperpa-rameters used in the experiments. Both architectures consist of five stages, each consisting of a combination of convolutional layers with ReLU activation and max pooling layers. The base number of channels in consecutive stages for VGG architectures equals 64, 128, 256, 512, and 512. The subsequent stages are composed of residual blocks. In the case of ResNets, we report the results for the'conv2' layers.

Inside the Colosseum's Passage of Commodus, where emperors once walked One theory suggests the infamous Roman emperor survived an assassination attempt in the tunnel now open to the public. From October 2024 to September 2025, a team of experts restored part of the tunnel that's open to visitors for the first time. Breakthroughs, discoveries, and DIY tips sent six days a week. They say all roads lead to Rome . But in the Eternal City, all of the major roads were thought to lead somewhere very specific--a single column called the Milliarium Auereum, or the golden milestone.

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Deep neural networks are widely known for their remarkable effectiveness across various tasks, with the consensus that deeper networks implicitly learn more complex data representations. This paper shows that sufficiently deep networks trained for supervised image classification split into two distinct parts that contribute to the resulting data representations differently. The initial layers create linearly-separable representations, while the subsequent layers, which we refer to as \textit{the tunnel}, compress these representations and have a minimal impact on the overall performance. We explore the tunnel's behavior through comprehensive empirical studies, highlighting that it emerges early in the training process. Its depth depends on the relation between the network's capacity and task complexity. Furthermore, we show that the tunnel degrades out-of-distribution generalization and discuss its implications for continual learning.

--Legged locomotion in constrained spaces (called crawl spaces) is challenging. In crawl spaces, current proprioceptive locomotion learning methods are difficult to achieve traverse because only ground features are inferred. In this study, a point cloud supervis ed RL framework for proprioceptive locomotion in crawl spaces is proposed . A state estimation network is designed to estimate the robot's collision states as well as ground and spatial features for locomotion . A point cloud feature extraction method is proposed to supervise the state estimation network . The method uses representation of the point cloud in polar coordinate frame and MLP s for efficient feature extracti on. Experiments demonstrate that, compared with existing methods, our method exhibits faster iteration time in the training and more agile locomotion in crawl spaces. This study enhances the ability of leg ged robots to traverse constrained spaces w ithout requiring exteroceptive sensors. N recent years, legged robots have demonstrated remarkable terrain traversal capabilities, exhibiting significant application value.

The MARWIN robot operates at the European XFEL to perform autonomous radiation monitoring in long, monotonous accelerator tunnels where conventional localization approaches struggle. Its current navigation concept combines lidar-based edge detection, wheel/lidar odometry with periodic QR-code referencing, and fuzzy control of wall distance, rotation, and longitudinal position. While robust in predefined sections, this design lacks flexibility for unknown geometries and obstacles. This paper explores deep visual stereo odometry (DVSO) with 3D-geometric constraints as a focused alternative. DVSO is purely vision-based, leveraging stereo disparity, optical flow, and self-supervised learning to jointly estimate depth and ego-motion without labeled data. For global consistency, DVSO can subsequently be fused with absolute references (e.g., landmarks) or other sensors. We provide a conceptual evaluation for accelerator tunnel environments, using the European XFEL as a case study. Expected benefits include reduced scale drift via stereo, low-cost sensing, and scalable data collection, while challenges remain in low-texture surfaces, lighting variability, computational load, and robustness under radiation. The paper defines a research agenda toward enabling MARWIN to navigate more autonomously in constrained, safety-critical infrastructures.

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