This happened a few months ago when I was working on an analysis project that dealt with time-series data. The dataset was large (10 years of data). I was using a standard profiling tool to check the pipeline. Everything looked fine because the tool reported 3% missing data rate for volume columns. I didn't think much about it because I thought it was noise, as this was my first time working with time-series data, but the downstream models weren't acting right. That's when I thought something was off, and I actually looked at the data and found the 3% missing data was not noise; in fact, it was a 6-day worth of missing data. It didn't stop here, though, as the data also had leakage, and the model hit 99% accuracy. The rolling windows and lag features were also messed up, as the chronological sequence was broken. Looking back, if I had done proper EDA, this would not have happened. But I decided to make a small validation tool called tsauditor that catches chronological breaks, leakage, and sudden sequential spikes present in global boundaries. It also adds a description along with evidence on why the data point is faulty and suggests fixes It's open source, lightweight, and on PyPI. I also added an example notebook, which has a side-by-side comparison of tsauditor with a standard profiling tool. You can also check out the comparison notebook on NBViewer. I wanted to simplify the EDA process and reduce the number of custom scripts for a dataset. Link in comments submitted by /u/severecaseofsarcarsm [link] [Kommentare]
I've been experimenting with converting ordinary third-person videos into humanoid motion data. This demo includes several motion categories: • Acting • Sports • Combat • Dance The motivation is not animation alone. Recent humanoid robotics work increasingly relies on large-scale motion datasets and motion priors to improve movement quality, robustness, and generalization. Projects such as NVIDIA KIMODO also show the value of scaling high-quality motion data for downstream humanoid motion generation and control. This made me wonder whether ordinary videos could become a low-cost source of motion data for humanoid systems. There is already a massive amount of human motion available in online videos. If useful motion can be extracted reliably, it may help expand humanoid motion datasets beyond traditional mocap pipelines. For this experiment, I focused on: • Foot contact stability • Reduced foot sliding • Natural balance and movement dynamics • Consistency across different motion styles The long-term idea is: Video → Motion Data → Motion Models → Humanoid Control For anyone interested in testing their own clips, I made a public demo available here: huggingface demo I'd love to hear thoughts from people working on humanoid robotics, motion generation, imitation learning, or robot locomotion. submitted by /u/AIMoCap [link] [Kommentare]
As soon as we started doing any operations above Europe, we noticed that there was really something going on there.
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