arXiv:2602.14981v2 Announce Type: replace-cross Abstract: Generalized partially linear single-index models (GPLSIMs) provide a flexible and interpretable semiparametric framework for longitudinal outcomes by combining a low-dimensional parametric component with a nonparametric index component. For repeated measurements, valid inference is challenging because within-subject correlation induces nuisance parameters and variance estimation can be unstable in semiparametric settings. We propose a profile estimating-equation approach based on spline approximation of the unknown link function and construct a subject-level block empirical likelihood (BEL) for […]

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:::info Astounding Stories of Super-Science February, 2026, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Moors and the Fens, volume 1 (of 3) – Chapter IX: Advances the Story but little Astounding Stories of Super-Science February 2026: The Moors and the Fens, volume 1 (of 3) – Chapter IX Advances the Story but little By J. H. Riddell ::: Mr. John Merapie never got any […]

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Traditional machine learning systems are typically designed for static data distributions, which suffer from catastrophic forgetting when learning from evolving data streams. Class-Incremental Learning (CIL) addresses this challenge by enabling learning systems to continuously learn new classes while preserving prior knowledge. With the rise of pre-trained models (PTMs) such as CLIP, leveraging their strong generalization and semantic alignment capabilities has become a promising direction in CIL. However, existing CLIP-based CIL methods are often scattered across disparate codebases, rely […]

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arXiv:2602.15082v1 Announce Type: new Abstract: Neural audio compression models have recently achieved extreme compression rates, enabling efficient latent generative modeling. Conversely, latent generative models have been applied to compression, pushing the limits of continuous and discrete approaches. However, existing methods remain constrained to low-resolution audio and degrade substantially at very low bitrates, where audible artifacts are prominent. In this paper, we present S-PRESSO, a 48kHz sound effect compression model that produces both continuous and discrete embeddings at ultra-low […]

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arXiv:2601.09039v1 Announce Type: new Abstract: Large language model (LLM) tokenizers act as structured compressors: by mapping text to discrete token sequences, they determine token count (and thus compute and context usage) and the statistical structure seen by downstream models. Despite their central role in LLM pipelines, the link between tokenization, compression efficiency and induced structure is not well understood. We empirically demonstrate that tokenizer training scale redistributes entropy: as training data grows, the token stream becomes more diverse […]

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arXiv:2511.19797v3 Announce Type: replace-cross Abstract: We propose Terminal Velocity Matching (TVM), a generalization of flow matching that enables high-fidelity one- and few-step generative modeling. TVM models the transition between any two diffusion timesteps and regularizes its behavior at its terminal time rather than at the initial time. We prove that TVM provides an upper bound on the $2$-Wasserstein distance between data and model distributions when the model is Lipschitz continuous. However, since Diffusion Transformers lack this property, we […]

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You didn’t become an educator to spend your Sunday nights fighting with bullet points and formatting margins. You did it for that specific moment—the “click”—when a student’s eyes light up because a complex concept finally makes sense. But somewhere between administrative burdens and the pressure to publish, that joy often gets buried under mountains of administrative friction. We spend 80% of our energy structuring content and only 20% delivering it with passion. This imbalance is the silent killer […]

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Note: I posted this on r/androiddev but thought the deployment side might interest this sub. One of the biggest pains in mobile ML deployment is that your trained model usually sits unencrypted in the APK. If you spent $50k fine-tuning a model, that’s a liability. I open-sourced a tool called TensorSeal that handles the encryption/decryption pipeline for Android. It ensures the model is only decrypted in memory (RAM) right before inference, keeping the disk footprint encrypted. It uses […]

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