Thought, the LLM can verbalize that the unknown city is Paris and use this fact to answer downstream questions.

These features are then fed into a V alue Classification Network (VCN) which directly generates classification solutions.

Until now, no tabular method has consistently outperformed classical supervised learning, which ignores these shifts.

To address the above issues, we propose CrossGNN, a linear complexity GNN model to refine the cross-scale and cross-variable interaction for MTS. To deal with the unexpected noise in time dimension, an adaptive multi-scale identifier (AMSI) is leveraged to construct multi-scale time series with reduced noise.

This intervention significantly improves the performance of LLaMA models on the TruthfulQA benchmark.

Rulers once closed cafés, burned beans, and even executed someone--all for a cup of coffee. A photograph taken in the 1920s shows a group of men gather at a small roadside coffee stall in Cairo, Egypt. Breakthroughs, discoveries, and DIY tips sent six days a week. Bach wrote a cantata about it . Scholars, philosophers, and lawyers have argued over it.

Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' A Russian drone attack killed two women and a man in Vilniansk in Ukraine's front-line Zaporizhia region, the head of the regional military administration, Ivan Fedorov, said on the Telegram messaging app. The attack also destroyed houses after fires broke out, Fedorov said.

Infertility is a major global health issue, and while in-vitro fertilization has improved treatment outcomes, embryo selection remains a critical bottleneck. Time-lapse imaging enables continuous, non-invasive monitoring of embryo development, yet most automated assessment methods rely solely on conventional morphokinetic features and overlook emerging biomarkers. Cytoplasmic Strings, thin filamentous structures connecting the inner cell mass and trophectoderm in expanded blastocysts, have been associated with faster blastocyst formation, higher blastocyst grades, and improved viability. However, CS assessment currently depends on manual visual inspection, which is labor-intensive, subjective, and severely affected by detection and subtle visual appearance. In this work, we present, to the best of our knowledge, the first computational framework for CS analysis in human IVF embryos. We first design a human-in-the-loop annotation pipeline to curate a biologically validated CS dataset from TLI videos, comprising 13,568 frames with highly sparse CS-positive instances. Building on this dataset, we propose a two-stage deep learning framework that (i) classifies CS presence at the frame level and (ii) localizes CS regions in positive cases. To address severe imbalance and feature uncertainty, we introduce the Novel Uncertainty-aware Contractive Embedding (NUCE) loss, which couples confidence-aware reweighting with an embedding contraction term to form compact, well-separated class clusters. NUCE consistently improves F1-score across five transformer backbones, while RF-DETR-based localization achieves state-of-the-art (SOTA) detection performance for thin, low-contrast CS structures. The source code will be made publicly available at: https://github.com/HamadYA/CS_Detection.