SymTrack is the first systematic detection-free framework for scene text tracking that constructs benchmarks from video text spotting datasets and reports up to 11.97% AUC gains over prior trackers.
Wormholes in Maximal Supergravity
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
abstract
In this brief note, we reconsider the problem of finding Euclidean wormhole solutions to maximal supergravity in d dimensions. We find that such solutions exists for all d less than or equal to 9. However, we argue that, in toroidally-compactified string theories, these saddle points never contribute to the path integral because of a tension with U-duality.
years
2026 9verdicts
UNVERDICTED 9representative citing papers
Gaussian process regression enables implicit multi-camera calibration by learning 2D-to-3D mappings with built-in uncertainty and active learning for efficient data use.
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A new tail dependence measure for linear processes with regularly varying distributions is introduced, incorporating persistence effects and validated via simulations and cryptocurrency data analysis.
MCBP detects boundaries by computing discrete mean curvature from k-nearest neighbor patches on the data manifold, then decomposes data into low-curvature smooth and high-curvature boundary subsets to improve clustering.
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ProMoTA integrates process modeling with automated end-to-end traceability generation and analysis for model transformation chains in MDE, demonstrated on a wireless sensor network IoT application.
Hybrid feature fusion of API calls and n-grams with voting-based classifier fusion achieves 99.72% accuracy and 0.989 AUC for malware family classification on Microsoft dataset.
The paper identifies key unresolved questions in giant planet formation, interiors, and their role in planetary systems.
citing papers explorer
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A Hybrid Approach For Malware Classification Using Secondary Features Fusion
Hybrid feature fusion of API calls and n-grams with voting-based classifier fusion achieves 99.72% accuracy and 0.989 AUC for malware family classification on Microsoft dataset.