Pseudo labeling (PL) is a wide-applied strategy to enlarge the labeled dataset by self-annotating the potential samples during the training process.

Our ablation studies (Table3in the main paper) havealready proved the advantage of mask prior. From Figure 2, we can see that the coarse masks indicate the rough locations of objects which can help the object detection network predicttheboundingboxes. Tovalidate the transferability ofour similarity transfer,we evaluate our similarity network trained on COCO-60 trainval set. Wetreat the similarity prediction task as abinary classification task, in which the binary label 1 (resp., 0) means that two bounding boxes belong to the same category (resp.,different The precision, recall and F1 scores are summarized in Table 1. We observe that the gap between the performance of similarity network on base categories and novel categories is negligible (e.g., F1 Scores 84.9% v.s.

Neural prediction offers a promising approach to forecasting the individual variability of neurocognitive functions and disorders and providing prognostic indicators for personalized invention. However, it is challenging to translate neural predictive models into medical artificial intelligent applications due to the limitations of domain shift and label scarcity. Here, we propose the directed evolution model (DEM), a novel computational model that mimics the trial-and-error processes of biological directed evolution to approximate optimal solutions for predictive modeling tasks. We demonstrated that the directed evolution algorithm is an effective strategy for uncertainty exploration, enhancing generalization in reinforcement learning. Furthermore, by incorporating replay buffer and continual backpropagate methods into DEM, we provide evidence of achieving better trade-off between exploitation and exploration in continuous learning settings. We conducted experiments on four different datasets for children with cochlear implants whose spoken language developmental outcomes vary considerably on the individual-child level. Preoperative neural MRI data has shown to accurately predict the post-operative outcome of these children within but not across datasets. Our results show that DEM can efficiently improve the performance of cross-domain pre-implantation neural predictions while addressing the challenge of label scarcity in target domain.

Uncertain knowledge graphs (UKGs) associate each triple with a confidence score to provide more precise knowledge representations. Recently, since real-world UKGs suffer from the incompleteness, uncertain knowledge graph (UKG) completion attracts more attention, aiming to complete missing triples and confidences. Current studies attempt to learn UKG embeddings to solve this problem, but they neglect the extremely imbalanced distributions of triple confidences. This causes that the learnt embeddings are insufficient to high-quality UKG completion. Thus, in this paper, to address the above issue, we propose a new semi-supervised Confidence Distribution Learning (ssCDL) method for UKG completion, where each triple confidence is transformed into a confidence distribution to introduce more supervision information of different confidences to reinforce the embedding learning process. ssCDL iteratively learns UKG embedding by relational learning on labeled data (i.e., existing triples with confidences) and unlabeled data with pseudo labels (i.e., unseen triples with the generated confidences), which are predicted by meta-learning to augment the training data and rebalance the distribution of triple confidences. Experiments on two UKG datasets demonstrate that ssCDL consistently outperforms state-of-the-art baselines in different evaluation metrics.

Pseudo - haptics exploit carefully crafted visual or auditory cues to trick the brain into "feeling" forces that are never physically applied, offering a low - cost alternative to traditional haptic hardware. Here, we present a comparative psychophysical study that quantifies how visual and auditory stimuli combine to evoke pseudo - haptic pressure sensations on a commodity tablet. Using a Unity - based Rollball game, participants (n = 4) guided a virtual ball across three textured terrains while their fi nger forces were captured in real time with a Robotous RFT40 force - torque sensor. Each terrain was paired with a distinct rolling - sound profile spanning 440 Hz - 4.7 kHz, 440 Hz - 13.1 kHz, or 440 Hz - 8.9 kHz; crevice collisions triggered additional "knoc king" bursts to heighten realism. Average tactile forces increased systematically with cue intensity: 0.40 N, 0.79 N and 0.88 N for visual - only trials and 0.41 N, 0.81 N and 0.90 N for audio - only trials on Terrains 1 - 3, respectively. Higher audio frequenci es and denser visual textures both elicited stronger muscle activation, and their combination further reduced the force needed to perceive surface changes, confirming multisensory integration. These results demonstrate that consumer - grade isometric devices can reliably induce, and measure graded pseudo - haptic feedback without specialized actuators, opening a path toward affordable rehabilitation tools, training simulators and assistive interfaces .