LDA
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Related Articles from SNS
LDA-1B: Scaling Latent Dynamics Action Model via Universal Embodied Data Ingestion
Announce Type: replace Abstract: Recent robot foundation models largely rely on large-scale behavior cloning, which imitates expert actions but discards transferable dynamics knowledge embedded in heterogeneous embodied data. While the Unified World Model (UWM) formulation has the potential to leverage such diverse data, existing instantiations struggle to scale to foundation-level due to coarse data usage and fragmented datasets.
Topic modeling reveals thermally partitioned and taxonomically distinct microbial subcommunities across prokaryotes and phytoplankton in the Laurentian Great Lakes
Identifying discrete microbial assemblages and their environmental drivers across multiple biological fractions simultaneously remains a central challenge in aquatic microbial ecology. We applied an integrated analytical pipeline built around Latent Dirichlet Allocation (LDA) to an eight-year 16S rRNA amplicon time series from the Laurentian Great Lakes, spanning four size-fractionated biological blocks free-living prokaryotes, particle-associated prokaryotes, and small and large...
Disentangling Similarity and Relatedness in Topic Models
Announce Type: replace Abstract: The recent success of large pre-trained language models (PLMs) has motivated their integration into topic modeling. However, PLM-augmented topic models differ from classical co-occurrence models such as Latent Dirichlet Allocation (LDA) not only in performance, but also in the type of semantic structure they capture. We formalize this distinction along two psycholinguistic axes: thematic relatedness (dog/bone) and taxonomic similarity (dog/wolf).
DFT calculations of magnetocrystalline anisotropy energy with fixed spin moment
arXiv:2603.09502v2 Announce Type: replace-cross Abstract: The development of new-generation permanent magnets is based on experimental efforts and innovative theoretical tools for modeling magnetic properties. Magnetocrystalline anisotropy energy (MAE) - one of the main intrinsic properties of permanent magnets - can be calculated using density functional theory (DFT). However, MAEs determined with different exchange-correlation potentials can vary widely.
The Lie We Tell: Correcting the Euclidean Fallacy in Vision Language Action Policies via Score Matching on Tangent Space
Announce Type: new Abstract: Diffusion-based Vision-Language-Action policies achieve remarkable success in robotic manipulation, yet commit a fundamental geometric error we term the $\textbf{Euclidean Fallacy}$: representing SE(3) poses as flat $\mathbb{R}^{12}$ vectors. This approximation induces (1) manifold drift violating SO(3) constraints, (2) broken equivariance under coordinate transformations, and (3) non-geodesic trajectories with excessive kinematic cost. We introduce $\textbf{Lie...
REStack: A Large-Scale Dataset of Reverse Engineering Discussions from Stack Exchange
arXiv:2606.05493v1 Announce Type: new Abstract: Reverse engineering (RE) is a critical activity in software engineering and cybersecurity, supporting tasks such as malware analysis, vulnerability discovery, legacy system maintenance, and firmware inspection. Despite its importance, there is limited empirical understanding of the challenges, topics, and knowledge gaps faced by RE practitioners in real-world settings, and no publicly available dataset has systematically captured RE discussions...
How Much Capacity Does EEG Denoising Need? Ultra-Compact Networks reveal Benchmark Saturation and Metric-Utility Gap
arXiv:2606.08594v1 Announce Type: new Abstract: Deep learning EEG denoising architectures have scaled from tens of thousands to tens of millions of parameters, yet no prior study has isolated model capacity as the experimental variable or tested whether reconstruction metrics predict downstream neural-signal utility. We address both gaps by fixing architecture, loss, data split, and training recipe while sweeping only channel width from 1.05K to 40.26K parameters in a minimal...
A Systematic Evaluation of Current Architectures in Wind Power Forecasting
arXiv:2606.02849v1 Announce Type: new Abstract: Interval wind speed forecasting is essential for the efficient integration of wind energy into power systems, as it accounts for the inherent uncertainty of wind resources. This study presents a systematic literature review focused on hybrid approaches to interval forecasting of wind generation, exploring the combination of deep learning, modal decomposition, and statistical methods. To guide the paper selection, Latent Dirichlet Allocation...