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HyperAI Super Neural
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HyperAI Super Neural

Deconstructing the sophistication and universality of technology, covering cutting-edge AI for Science case studies.

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HyperAI Super Neural
HyperAI Super Neural
Mar 18, 2026 · Artificial Intelligence

How Google’s Gemini Extracted 2.6 Million Flood Events from 150 Countries’ News

Google Research released the open‑source Groundsource flood dataset, built by automatically processing more than 5 million news articles from over 150 countries with the Gemini large‑language model, yielding over 2.6 million verified flood event records that are evaluated against GDACS and DFO for precision, recall, and spatial resolution.

AI extractionGoogleGroundsource
0 likes · 13 min read
How Google’s Gemini Extracted 2.6 Million Flood Events from 150 Countries’ News
HyperAI Super Neural
HyperAI Super Neural
Mar 16, 2026 · Artificial Intelligence

AI‑Driven Quantum Refinement: AQuaRef Enables Full‑Atom Protein Model Optimization with Quantum Constraints

A collaborative team from Carnegie Mellon, Wrocław, and Florida universities introduced AQuaRef, an AI‑powered quantum refinement method that leverages the AIMNet2 machine‑learning potential to achieve near‑classical‑force‑field speed while closely approximating quantum‑mechanical results for full‑atom protein models, outperforming traditional restraints on low‑resolution structures.

AIAIMNet2Machine Learning Potentials
0 likes · 16 min read
AI‑Driven Quantum Refinement: AQuaRef Enables Full‑Atom Protein Model Optimization with Quantum Constraints
HyperAI Super Neural
HyperAI Super Neural
Mar 12, 2026 · Artificial Intelligence

Stanford’s Merlin: Single‑GPU 3D Abdominal CT Vision‑Language Model Leads 752 Tasks

Stanford researchers introduced Merlin, the first native 3D abdominal CT vision‑language foundation model trained on a single NVIDIA A6000 GPU using a 25,494‑scan dataset, and demonstrated its superiority across 752 benchmark tasks—including zero‑shot classification, phenotype prediction, cross‑modal retrieval, disease forecasting, report generation, and 3D segmentation—outperforming existing baselines.

3D CTDisease PredictionMedical Imaging AI
0 likes · 18 min read
Stanford’s Merlin: Single‑GPU 3D Abdominal CT Vision‑Language Model Leads 752 Tasks
HyperAI Super Neural
HyperAI Super Neural
Mar 11, 2026 · Artificial Intelligence

Bi-TEAM Raises Hemolysis Prediction Accuracy 350% with Unified Biological‑Semantic and Chemical‑Precision Framework

Bi-TEAM, a cross‑scale representation learning framework that injects local chemical variations into a global protein context, outperforms state‑of‑the‑art baselines on ten biochemical datasets, achieving a 350% boost in hemolysis prediction accuracy and a 66% MCC increase under strict scaffold splits.

Bi-TEAMchemical modificationhemolysis prediction
0 likes · 15 min read
Bi-TEAM Raises Hemolysis Prediction Accuracy 350% with Unified Biological‑Semantic and Chemical‑Precision Framework
HyperAI Super Neural
HyperAI Super Neural
Mar 9, 2026 · Artificial Intelligence

Physics‑Informed GNN Breakthrough for Accurate, Real‑Time Multi‑Body Dynamics

Researchers from EPFL introduce DYNAMI‑CAL GraphNet, a graph neural network that embeds linear and angular momentum conservation, delivering highly accurate, interpretable and real‑time predictions for complex multi‑body systems across robotics, aerospace and materials science, and outperforming existing baselines on four diverse benchmark datasets.

DYNAMI‑CAL GraphNetembodied AIgraph neural networks
0 likes · 16 min read
Physics‑Informed GNN Breakthrough for Accurate, Real‑Time Multi‑Body Dynamics
HyperAI Super Neural
HyperAI Super Neural
Mar 5, 2026 · Artificial Intelligence

ML Predicts Dual Mortality Risk for HCC Liver Transplant Candidates (11,647 Cases)

Using a dataset of 11,647 hepatocellular carcinoma patients, a French research team combined ensemble learning, SHAP explainability, UMAP dimensionality reduction and K‑medoids clustering to build an interpretable model that outperforms traditional scores in predicting three‑month wait‑list mortality and defines seven clinically distinct risk sub‑groups.

Ensemble LearningHepatocellular CarcinomaK-Medoids
0 likes · 14 min read
ML Predicts Dual Mortality Risk for HCC Liver Transplant Candidates (11,647 Cases)
HyperAI Super Neural
HyperAI Super Neural
Mar 4, 2026 · Artificial Intelligence

MIT’s APOLLO Framework Breaks Limits, Separating Shared and Modality‑Specific Cell Signals

MIT and ETH Zurich introduce APOLLO, a deep‑learning autoencoder that learns a partially overlapping latent space to explicitly disentangle shared and modality‑specific information in multimodal single‑cell datasets, demonstrating superior cell‑type classification, cross‑modal prediction, and protein localization insights across sequencing and imaging data.

autoencoderbioinformaticsdeep learning
0 likes · 14 min read
MIT’s APOLLO Framework Breaks Limits, Separating Shared and Modality‑Specific Cell Signals