CDF
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Toward Multi-Domain and Long-Tailed Quantization via Feature Alignment and Scaling
Announce Type: new Abstract: Quantizing deep neural networks is essential for efficient inference on resource-constrained devices. However, most existing methods are designed for single-domain and class-balanced data, leaving practical settings with domain shifts or severe class imbalance underexplored. We address these challenges with Efficient Multi-Domain Alignment Quantization (EmaQ), which aligns domain distributions through a CDF-based projection and uses sensitivity-aware weight...
Toward Multi-Domain and Long-Tailed Quantization via Feature Alignment and Scaling
arXiv:2606.04920v2 Announce Type: replace Abstract: Quantizing deep neural networks is essential for efficient inference on resource-constrained devices. However, most existing methods are designed for single-domain and class-balanced data, leaving practical settings with domain shifts or severe class imbalance underexplored. We address these challenges with Efficient Multi-Domain Alignment Quantization (EmaQ), which aligns domain distributions through a CDF-based projection and uses...
SIRT7 regulates dosage compensation and safeguards the female X chromosome
Abstract Sirtuins are deacetylases implicated in stress responses and longevity in mammals1,2. Although their differential impact on disease for the two sexes has been noted3,4,5,6,7, the underlying reasons are unclear. Here, using Sirt7 as a model in mice, we examine the mechanisms leading to sex differences and find that Sirt7−/− female mice have decreased fitness throughout their lifespan.
Dav2d
Let dav2d be dav2d A codec does not really exist until everyone can decode it. Today, we announce dav2d, a fast decoder for the new AV2 codec, developed by members of the VideoLAN community. A few weeks ago, we opened the repository and started development in public.
HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series
arXiv:2605.11130v4 Announce Type: replace Abstract: Critical events in multivariate time series, from turbine failures to cardiac arrhythmias, demand accurate prediction, yet labeled data is scarce because such events are rare and costly to annotate. We introduce HEPA (Horizon-conditioned Event Predictive Architecture), built on two key principles. First, a causal Transformer encoder is pretrained via a Joint-Embedding Predictive Architecture (JEPA): a horizon-conditioned predictor learns to...