Ensembits is the first tokenizer of protein conformational ensembles that outperforms static tokenizers on RMSF prediction and matches them on function and mutation tasks while using less pretraining data.
The Hungarian method for the assignment problem,
39 Pith papers cite this work, alongside 9,789 external citations. Polarity classification is still indexing.
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A systematic analysis of evaluation practices in multimedia event extraction reveals that minor methodological choices cause large performance swings and overestimation of cross-modal grounding ability.
A Hungarian-algorithm solver for the signed assignment problem disambiguates randomly wired and polarized ITER magnetic sensors down to SNR 50 with C>0.97 on regular first-wall arrays.
BridgeVLM internalizes causal supervision in VLMs via causal graph induction, Causal Tokens, and RAMP layers with M3S training, raising intervention accuracy on CausalVLBench from 33.2% to 54.4% and structure learning F1 from 33.4% to 75.1%.
Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
Bounded fitting can be extended to expressive description logics while retaining generalization guarantees and implemented practically via SAT solvers.
ProtoSSL discovers generalizable prototypes from unlabeled time-series via self-supervision and assigns them to new tasks for interpretable predictions, outperforming supervised baselines in low-data regimes on ECG datasets.
An intrinsic effective sample size for manifold MCMC is defined via kernel discrepancy as the number of independent draws yielding equivalent expected squared discrepancy to the target.
The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.
Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
An HSMM integrated with discrete-time survival analysis is applied to four years of Shanghai metro smart card data to identify five mobility states, directional transitions, and state-dependent exit/re-entry hazards.
Manticore-Deep uses tiled Bayesian field-level inference on SDSS and BOSS data to produce posterior ensembles of 3D cosmic fields that are consistent with LCDM and validated by 7.4σ CMB lensing and 3.5σ kSZ detections.
Develops RBS and MSLS heuristics exploiting follower optimality properties for bilevel uniform parallel machine scheduling with up to 500 jobs.
Joint location-scale minimization for geometric medians on product manifolds degenerates to marginal medians, and three new scale-selection methods restore identifiability with asymptotic guarantees.
Grounded Correspondence maintains temporal consistency via deterministic bipartite matching on frozen backbone features instead of learned predictors, achieving competitive results on MOVi and YouTube-VIS with zero learnable temporal parameters.
PACMAB is a perception-aware two-sided learning framework for multi-platform mobile crowdsensing that models the setting as a dynamic hypergame and achieves at least 41% more completed tasks than benchmarks in simulations without assuming complete information.
Success bias in collective theory-building leads to systematic overestimation of theory quality, narrower search, and paradoxically lower performance when agents optimize for apparent success.
GNNs with Gumbel-Sinkhorn layers sample stoichiometry-compliant crystal structures unsupervisedly and outperform heuristics while matching commercial solvers.
WISE unifies representation via BEP, feature weighting via LOFO, two-stage clustering, and intrinsic explanations via DFI for mixed-type tabular data, outperforming baselines on six datasets.
Continual learning robots form a significantly more stable invariant subnetwork than constant-task controls, and preserving it improves adaptation while damaging it hurts performance.
A new compiler for surface codes on QCCD trapped-ion hardware shows that 2-ion traps outperform larger traps in logical clock speed and hardware efficiency, beating prior compilers by 3.8X on average.
Evolving hexacopter morphologies together with learnable controllers produces unconventional drones that outperform standard designs on complex tasks while introducing new metrics for evolution-learning interactions.
Introduces the XAMI benchmark dataset of 1000 annotated XMM-Newton images for artefact detection together with a hybrid CNN-transformer instance segmentation demonstration.
SAT-RTS introduces a pipeline that abstracts high-dimensional RTS sequences into discrete tactical labels and hierarchical visualizations to improve interpretability of AI micromanagement.
citing papers explorer
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Dynamic Hypergame for Task Assignment in Multi-platform Mobile Crowdsensing Under Incomplete Information
PACMAB is a perception-aware two-sided learning framework for multi-platform mobile crowdsensing that models the setting as a dynamic hypergame and achieves at least 41% more completed tasks than benchmarks in simulations without assuming complete information.