World Wire

Information extraction teams face a recurring choice. Small encoder models are cheap but rigid, and large language models are flexible but expensive per document. Fastino released GLiNER2.5 to narrow that gap. The release replaces span enumeration with boundary prediction: the model scores where an entity starts and ends instead of scoring every candidate span against a width grid. That single change removes the maximum entity width, allows a 4,096-word context, and keeps computation linear in sequence length for a fixed schema. It also unlocks joint entity-relation decoding, cross-task label constraints, and per-span attributes. Across 16 zero-shot benchmarks, the multilingual checkpoint reaches 56.17 overall macro F1 against 56.09 for GLiNER2, with a 24.75-point gain on XNLI. Three checkpoints ship on Hugging Face under Apache 2.0 at 74M, 194M, and 287M parameters. Is it deployable? Yes, Fastino released three GLiNER2.5 checkpoints on Hugging Face under Apache 2.0, with l...

A team from Google Research and USC has released Mobility-Embedded POIs (ME-POIs) , a framework that folds aggregate human movement into text-based place embeddings. The premise is that language models describe what a place is , but not how it is used . Two coffee shops can share a category, an address block, and a text vector, while one runs commuter turnover and the other holds customers for ninety minutes. ME-POIs encodes each visit as a contextualized vector, then uses contrastive learning to align those visits with one learnable prototype per POI. Across five map-enrichment tasks on Los Angeles and Houston mobility data, adding ME-POIs to strong text encoders improved 34 of 35 model-task pairings in Los Angeles, with relative gains up to 81.9% F1 on visit intent and a 24.7% MAE reduction on busyness. Notably, a variant trained on mobility alone beat Gemini embeddings on price-level classification. Is it deployable? Partially, it is a framework you rebuild, not a checkpo...

In this tutorial, we explore a LabPlot -inspired scientific data analysis workflow in Python while preserving the structure and terminology of LabPlot’s aspect tree, analysis kernels, plotting system, and project model. We build reusable components to import tabular data, compute descriptive statistics, smooth and differentiate signals, perform Fourier analysis and filtering, detect peaks, integrate curves, reduce data, and fit nonlinear models with detailed statistical diagnostics. We then apply these tools to a realistic spectroscopy example: removing periodic interference, identifying overlapping peaks, fitting a multi-Gaussian model, inspecting residuals, visualizing results through themed worksheets, exporting figures, and saving project data in LabPlot-compatible .lml-style files. Finally, we extend the same workflow to batch processing so we can analyze multiple temperature-dependent spectra and fit secondary trends across the resulting measurements. Copy Code Copied Use a ...