Rapid mineral characterization is essential for applications ranging from mineral exploration to industrial ore processing. To this end, Hyperspectral Imaging (HSI) has emerged as a promising sensing modality thanks to its fine spectral resolution, enabling mineral discrimination in both close-range and remote sensing settings. However, the scarcity of publicly available datasets with reliable ground-truth labels hinders the development and evaluation of HSI-based mineral identification methods. We release Minerals in the Wild, a multi-purpose dataset comprising 1,132 rock specimens collected across Europe. For each specimen, we provide an HSI acquisition together with an elemental characterization obtained via an XRF sensor. We define the task of elemental characterization on our dataset and propose a pruning mechanism that removes distant signatures from the USGS dictionary prior to a convex optimization approach for matching HSI pixels with USGS spectral signatures. Finally, we empirically show that our approach outperforms simpler baselines.
Eleftheria Tetoula-Tsonga, George Arvanitakis, Theodoros Giannakas Institute of Communication et al.· 0 citations
Simulation results demonstrate that the proposed sub-6 GHz-assisted GNN-based beamformer achieves competitive and often superior sum-rate performance compared to classical baselines that rely on full mmWave CSI.
Sina Tavakolian, Abolfazl Zakeri, Ahmed Alkhateeb et al.· 0 citations
The TSExplorer tool enables users to inspect high-dimensional datasets through multiple complementary 2D visualizations derived from high-dimensional feature representations to support a wide range of workflows.
Einari Vaaras, Manu Airaksinen, O. Räsänen· 0 citations
This work analyzes 6,531 speeches over 200 years of UK parliamentary debate by using large language models to classify a speaker's perspective towards women's suffrage and political representation, as well as analyse sexist speech in parliament from the lens of the Ambivalent Sexism Inventory.
Mohammad Omar Khursheed, Mandira Sawkar, Ashiqur R. KhudaBukhsh· 0 citations
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VisER is proposed, a training-free two-sided metric for object-level hallucination detection that improves AUROC and AUPR over a range of baselines and measures whether object-context compatibility is backed by object-specific evidence from image tokens.
Afsaneh Hasanebrahimi, Hanxun Huang, Christopher Leckie et al.· 0 citations
Improvements are consistent across realized risk, risk-adjusted performance, and drawdown control, remain after the modeled execution frictions, and are supported by a 99.9\% Model Confidence Set that retains only the neural estimator.
Christian Bongiorno, Lorenzo Villassero· 0 citations
This work introduces a method that induces sparse neural activity in heavily quantized linear-attention models with minimal performance loss, and positions sparse, quantized linear-attention models as a natural fit for deploying LLMs on event-driven multi-core platforms.
Simon Richter, Ruhai Lin, Jason Yik et al.· 0 citations
This work provides a system-theoretic interpretation of generalization in learning-enabled dynamical systems arising in data-driven optimization and feedback control approximation, and establishes a matrix inequality-based certificate and a uniform stability bound that separates the one-sample sensitivity of the learned operator, and an algorithm-dependent dynamical gain.
MineAmongUs is introduced, a 3D multimodal Among Us sandbox where imposter agents must deceive crewmates through joint verbal and non-verbal action, and ARIA is proposed, a configurable VLM-agent harness that exposes five cognitive-component ablation axes and opens a new path for embodied VLM-agent alignment research.
Jaewoo Ahn, Junseo Kim, Hyunseo Kim et al.· 0 citations
DBloom speeds up generation with an efficient draft model (drafter) that proposes tokens for a target model to verify in one pass, preserving the target's output distribution and recommending the histogram as a preflight check before spending training compute.
This work proposes a decompose-before-reconstruct approach, which significantly improves mesh fidelity and novel-view synthesis and novel-view synthesis, while supporting object-wise modifiability and interactivity.
Minhas Kamal, Hiranya Garbha Kumar, Mahedi Kamal et al.· 0 citations
A reproducible age-prediction benchmark across six rs-fMRI datasets is introduced and provides common inputs, model settings, data splits, and analysis scripts so that future SPD matrix learning methods can be evaluated under the same external-validation protocol.
Ce Ju, A. Collas, Florent Bouchard et al.· 0 citations
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
Adaptive AI agents can help make BIM data more machine-readable by navigating IFC models, interpreting inconsistent information, and mapping it to defined standards. In this blog, Alok Rawat shares findings from a real-world pilot in construction workflows. The post Adaptive AI Agents in Construction Workflows appeared first on GPT-Lab.
