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OpenAI Killed Three Products in One Week. Anthropic Shipped an Operating System - thetechpencil.com

<a href="https://news.google.com/rss/articles/CBMisgFBVV95cUxNNExXSklUcjh3amEtdGtUTlFJQllyeUN6X0JXVDVwNUhqRldpVkxNeFhTVWtIRmdDR3hkdWU0alBhc1lYZ0l0d043MGJqTTNWMm95WWtIbXpmb3ZZOGV6aE82d210Q3hGNG5jbEVXY1BIOFZKa29iRTBHQnRTTUN1VkduS042S1VZM0JTblZhRkZBR19jUFE2dFJWQWhQdUFIYkxFRFA1Qi1qQWI5QkZzLU9n?oc=5" target="_blank">OpenAI Killed Three Products in One Week. Anthropic Shipped an Operating System</a> <font color="#6f6f6f">thetechpencil.com</font>

Google News: OpenAI

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MaskAdapt: Learning Flexible Motion Adaptation via Mask-Invariant Prior for Physics-Based Characters

arXiv:2603.29272v1 Announce Type: new Abstract: We present MaskAdapt, a framework for flexible motion adaptation in physics-based humanoid control. The framework follows a two-stage residual learning paradigm. In the first stage, we train a mask-invariant base policy using stochastic body-part masking and a regularization term that enforces consistent action distributions across masking conditions. This yields a robust motion prior that remains stable under missing observations, anticipating later adaptation in those regions. In the second stage, a residual policy is trained atop the frozen base controller to modify only the targeted body parts while preserving the original behaviors elsewhere. We demonstrate the versatility of this design through two applications: (i) motion composition,

arXiv cs.CV

1mabout 3 hours ago

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mtslearn: Machine Learning in Python for Medical Time Series

arXiv:2603.29432v1 Announce Type: new Abstract: Medical time-series data captures the dynamic progression of patient conditions, playing a vital role in modern clinical decision support systems. However, real-world clinical data is highly heterogeneous and inconsistently formatted. Furthermore, existing machine learning tools often have steep learning curves and fragmented workflows. Consequently, a significant gap remains between cutting-edge AI technologies and clinical application. To address this, we introduce mtslearn, an end-to-end integrated toolkit specifically designed for medical time-series data. First, the framework provides a unified data interface that automates the parsing and alignment of wide, long, and flat data formats. This design significantly reduces data cleaning ove

arXiv cs.LG

1mabout 3 hours ago

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Unbiased Model Prediction Without Using Protected Attribute Information

arXiv:2603.29270v1 Announce Type: new Abstract: The problem of bias persists in the deep learning community as models continue to provide disparate performance across different demographic subgroups. Therefore, several algorithms have been proposed to improve the fairness of deep models. However, a majority of these algorithms utilize the protected attribute information for bias mitigation, which severely limits their application in real-world scenarios. To address this concern, we have proposed a novel algorithm, termed as \textbf{Non-Protected Attribute-based Debiasing (NPAD)} algorithm for bias mitigation, that does not require the protected attribute information. The proposed NPAD algorithm utilizes the auxiliary information provided by the non-protected attributes to optimize the mode

arXiv cs.CV

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