Features
Self-supervised contrastive learning for time-series
Supervised embedding using auxiliary behavioral labels
Hybrid hypothesis- and discovery-driven modes
Consistency metrics for comparing latent spaces
k-nearest neighbor decoding from embeddings
Supports calcium imaging and electrophysiology data
Integration with DeepLabCut for pose embeddings
scikit-learn-compatible API
Built-in plotting with matplotlib and plotly
Time-series attribution maps (AISTATS 2025)
Multi-session and multi-animal data support
GPU-accelerated training via PyTorch
Docker container for reproducible analysis
Multi-sensor core scanning: RGB, XRF, hyperspectral, LiDAR
LIBS-based detection of REEs and light elements (via Lumo Analytics)
AI-powered core logging on Digital Core Table
Resource modeling with RMSP integration
Drill Hole Optimizer for mine planning
Sub-48-hour turnaround time
4x faster than manual core logging
Over 400% project acceleration
End-to-end workflow from scanning to modeling
Domain expertise throughout mining cycle
High-fidelity data capture and analytics
Consistent logging with fewer errors
Professional consulting and training services
Decision engineering for critical mineral exploration
Cloud-based digital core collaboration
