Big Tech's 'AI Climate Hoax': Study Shows 74% of Industry's Claims Unproven - Common Dreams
<a href="https://news.google.com/rss/articles/CBMib0FVX3lxTE1xaFpUVGFtNmtXcGVtUEdnbzJDYmlFeHU2TmxMZDVfZzY5OWRYc09yaG84dV9QcHNCUUxEMGJ1NldxQmU3ZWRJa05DbFB2MkdCanI3dDVxUHhBRTlmY2ltcUJTd3dTT2VVb2xJa2JBbw?oc=5" target="_blank">Big Tech's 'AI Climate Hoax': Study Shows 74% of Industry's Claims Unproven</a> <font color="#6f6f6f">Common Dreams</font>
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Learning Diagnostic Reasoning for Decision Support in Toxicology
arXiv:2603.29608v1 Announce Type: new Abstract: Acute poly-substance intoxication requires rapid, life-saving decisions under substantial uncertainty, as clinicians must rely on incomplete ingestion details and nonspecific symptoms. Effective diagnostic reasoning in this chaotic environment requires fusing unstructured, non-medical narratives (e.g. paramedic scene descriptions and unreliable patient self-reports or known histories), with structured medical data like vital signs. While Large Language Models (LLMs) show potential for processing such heterogeneous inputs, they struggle in this setting, often underperforming simple baselines that rely solely on patient histories. To address this, we present DeToxR (Decision-support for Toxicology with Reasoning), the first adaptation of Reinfo
Monocular Building Height Estimation from PhiSat-2 Imagery: Dataset and Method
arXiv:2603.29245v1 Announce Type: new Abstract: Monocular building height estimation from optical imagery is important for urban morphology characterization but remains challenging due to ambiguous height cues, large inter-city variations in building morphology, and the long-tailed distribution of building heights. PhiSat-2 is a promising open-access data source for this task because of its global coverage, 4.75 m spatial resolution, and seven-band spectral observations, yet its potential has not been systematically evaluated. To address this gap, we construct a PhiSat-2-Height dataset (PHDataset) and propose a Two-Stream Ordinal Network (TSONet). PHDataset contains 9,475 co-registered image-label patch pairs from 26 cities worldwide. TSONet jointly models footprint segmentation and height
Pseudo Label NCF for Sparse OHC Recommendation: Dual Representation Learning and the Separability Accuracy Trade off
arXiv:2603.24750v2 Announce Type: replace Abstract: Online Health Communities connect patients for peer support, but users face a discovery challenge when they have minimal prior interactions to guide personalization. We study recommendation under extreme interaction sparsity in a survey driven setting where each user provides a 16 dimensional intake vector and each support group has a structured feature profile. We extend Neural Collaborative Filtering architectures, including Matrix Factorization, Multi Layer Perceptron, and NeuMF, with an auxiliary pseudo label objective derived from survey group feature alignment using cosine similarity mapped to [0, 1]. The resulting Pseudo Label NCF learns dual embedding spaces: main embeddings for ranking and pseudo label embeddings for semantic ali
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