University of Trento

Sign language generation turns written sentences into continuous signing motion, which requires deciding which signs to express and then realizing handshape, trajectory and body coordination. Existing pose-based systems and reconstruction-only motion tokenizers entangle those two decisions, leaving the generator without a text-aligned intermediate target. SRVQ builds a hierarchy inside a single residual quantizer: the first codebook is supervised by text and gloss to encode sign content, later residual levels add kinematic detail, and a semantic generator followed by a kinematic generator predicts the codes. On CSL-Daily and Phoenix-2014T the full variant reaches retrieval R@1 of 82.31 and 70.25, versus 51.96 and 24.14 for SOKE, gains of 30.35 and 46.11 points.

Sign language generation turns written sentences into continuous signing motion, which requires deciding which signs to express and then realizing handshape, trajectory and body coordination. Existing pose-based systems and reconstruction-only motion tokenizers entangle those two decisions, leaving the generator without a text-aligned intermediate target. SRVQ builds a hierarchy inside a single residual quantizer: the first codebook is supervised by text and gloss to encode sign content, later residual levels add kinematic detail, and a semantic generator followed by a kinematic generator predicts the codes. On CSL-Daily and Phoenix-2014T the full variant reaches retrieval R@1 of 82.31 and 70.25, versus 51.96 and 24.14 for SOKE, gains of 30.35 and 46.11 points.

In high-barrier cis-trans isomerization the committor, the probability that a configuration reaches product before reactant, is nearly a step function with intermediate values only in a narrow band around the transition state. Combining AIMMD path sampling with symbolic regression, researchers learn the committor from unbiased molecular dynamics trajectories for N-retinylidene-lysine and the p-coumaric acid anion in vacuo and distill it into closed-form expressions. Four proper dihedrals around the reactive bond must couple nonlinearly to reproduce the S-shaped stepwise path seen in the transition-path ensemble, whereas the minimum free-energy path is a straight line.

In high-barrier cis-trans isomerization the committor, the probability that a configuration reaches product before reactant, is nearly a step function with intermediate values only in a narrow band around the transition state. Combining AIMMD path sampling with symbolic regression, researchers learn the committor from unbiased molecular dynamics trajectories for N-retinylidene-lysine and the p-coumaric acid anion in vacuo and distill it into closed-form expressions. Four proper dihedrals around the reactive bond must couple nonlinearly to reproduce the S-shaped stepwise path seen in the transition-path ensemble, whereas the minimum free-energy path is a straight line.

When sensor failures spread along graph edges and value propagation loses its footing, PEMix conditions the distribution of missing node features on global structural representations, raising TADPOLE macro-F1 curve area from 0.7469 to 0.8243.

When sensor failures spread along graph edges and value propagation loses its footing, PEMix conditions the distribution of missing node features on global structural representations, raising TADPOLE macro-F1 curve area from 0.7469 to 0.8243.

The work introduces a first statistical framework for epistemic uncertainty in node classification, comprising an information-growth experimental protocol and a consistency criterion, and uses projective graph data-generating processes to ensure nested graphs are coherent observations of the same process; the analysis shows that graph evidential deep learning (EDL) methods retain non-vanishing epistemic uncertainty at the population optimum and regulate epistemic and aleatoric uncertainty externally through hyperparameters, thus failing consistency, whereas graph bootstrap ensembles (GB-Ens) capture both data and procedural uncertainty through graph resampling and randomized training and exhibit epistemic uncertainty contraction beyond standard deep ensembles under the same protocol.

The work introduces a first statistical framework for epistemic uncertainty in node classification, comprising an information-growth experimental protocol and a consistency criterion, and uses projective graph data-generating processes to ensure nested graphs are coherent observations of the same process; the analysis shows that graph evidential deep learning (EDL) methods retain non-vanishing epistemic uncertainty at the population optimum and regulate epistemic and aleatoric uncertainty externally through hyperparameters, thus failing consistency, whereas graph bootstrap ensembles (GB-Ens) capture both data and procedural uncertainty through graph resampling and randomized training and exhibit epistemic uncertainty contraction beyond standard deep ensembles under the same protocol.

The work proposes a training-free method that uses fingertip and toe keypoints from a frozen Sapiens pose foundation model, combined with a per-frame proximity test against annotated holds, per-limb mutual exclusion, and a short temporal-persistence rule, to detect which holds a climber uses and when; on the The Way Up dataset (22 videos, 10 athletes, two routes) it reaches an event F1 of 90.2% on a held-out split (89.8% under leave-one-participant-out cross-validation) and 79.9% over all 22 videos at any temporal overlap, performs best on footholds (F1 89.8% overall, 96.

The work proposes a training-free method that uses fingertip and toe keypoints from a frozen Sapiens pose foundation model, combined with a per-frame proximity test against annotated holds, per-limb mutual exclusion, and a short temporal-persistence rule, to detect which holds a climber uses and when; on the The Way Up dataset (22 videos, 10 athletes, two routes) it reaches an event F1 of 90.2% on a held-out split (89.8% under leave-one-participant-out cross-validation) and 79.9% over all 22 videos at any temporal overlap, performs best on footholds (F1 89.8% overall, 96.