Hi everyone, I recently finished my Ph.D. thesis on Differentiable Ray Tracing for Radio Propagation Modeling. Instead of just compiling my published papers, I tried to write it as an accessible, self-contained textbook for anyone interested in the intersection of radio propagation simulation, autodiff, and ML. Permanent handle: https://hdl.handle.net/2078.5/278727 Repo with TeX source files While my research focuses on wireless communications rather than pure ML, I think it fits right in here. A major part of the project revolves around automatic differentiation. By taking frameworks like JAX out of their traditional ML context and integrating differentiability into a ray tracing pipeline, we can compute exact gradients through complex physical environments. This allows us to solve inverse problems and directly train machine learning models, which is currently a hot topic in next-gen wireless design. To make the physics and the math easy to digest, the manuscript is split into three parts: Understanding: The physics fundamentals (electromagnetic theory, geometrical optics, and diffraction). Building: The algorithmic core, including GPU-accelerated path tracing and the discontinuity smoothing techniques you need to actually make differentiable simulations stable. Using: Practical applications like channel modeling, localization, material calibration, and ML-assisted generative path sampling. A major focus of my thesis is the link between scientific research and reproducible open-source software. On that note, I want to give a massive shoutout to Patrick Kidger (u/patrickkidger). His own thesis inspired me to go the "textbook way" for my manuscript, and I heavily relied on his fantastic JAX packages (jaxtyping, equinox, and optimistix) when developing my open-source libraries, such as DiffeRT. I hope you find it an interesting read! I'd be happy to answer any questions in the comments about differentiable simulation, ray tracing, or building ray tracing engines in JAX :-) If you are curious, you can watch the presentation slides and video teaser here submitted by /u/jeertmans [link] [Kommentare]
After Strategy sold BTC, many people suddenly seemed bearish. If your long-term view on Bitcoin hasn't changed, why does one big sale suddenly change everything? Is this genuine concern, or just short-term market noise? submitted by /u/KitchenPreference287 [link] [Kommentare]
The core idea in masked depth modeling is to treat the sensor's own missing regions as the masking signal rather than using random block dropout. Specular highlights, transparent surfaces, and textureless areas where RGB-D cameras return no valid depth become the natural training target. The model therefore learns on exactly the failure distribution it faces at inference. Robbyant, an embodied AI company under Ant Group, describes this framing in LingBot-Depth 2.0. Version 2.0 changes nothing in the training recipe except the encoder initialization and data scale. The encoder-init study is the clean experiment here: same MDM pipeline, same data curation, only the pretrained backbone swapped. Per the paper, the LingBot-Vision init wins on nearly every benchmark at ViT-L and on most benchmarks at ViT-g, with one concession: DINOv2 keeps an edge on the Hammer captures. The gap widens with data scale rather than washing out, per their scaling figure. They report best RMSE on 7 of 8 block-mask and sparse benchmarks and 6 of 8 real camera configurations across three capture suites (Hammer D435/L515/ToF, ClearGrasp D415/D435, and their own D415/D435/D455 set). They report the strongest numbers on the transparent-object ClearGrasp captures, with block-masked DIODE-Indoor RMSE roughly halving versus the 1.0 release. The attached images are screenshots from their paper (Tables 6, 7, 8 and a qualitative mirror/glass point-cloud figure); interactive point-cloud demos live on the project page. Depth 2.0 weights are not released, so none of these completion numbers can be independently rerun. Only the four Vision backbones are open under Apache-2.0 and checkable at https://github.com/robbyant/lingbot-vision, which hosts the paper and the open weights. The renders shown come from the vendor's comparison page. Does sensor-validity masking beat random masking for other sensing modalities, say lidar or thermal? That would test how general the framing really is. submitted by /u/Ok-Line2658 [link] [Kommentare]
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