Predicting drug–target interactions (DTIs) is a fundamental task in computational drug discovery, yet it remains challenging under distribution shifts and limited training data. Existing approaches often suffer from poor generalization, weak cross-modal alignment between molecular and protein representations, and vulnerability to noisy supervision.We propose ESP-DTI, a unified framework designed to enhance generalization by integrating large-scale protein language models with curriculum learning and cross-modal contrastive alignment. Specifically, we leverage ESM-2 to encode context-aware protein representations and adopt a CLIP-style contrastive objective to align drug and protein embeddings in a shared latent space. To further improve learning robustness, we introduce a progressive curriculum sampling strategy that dynamically schedules training instances based on model confidence, enabling a gradual shift from easy to hard examples.Experimental results on four benchmark datasets demonstrate that ESP-DTI consistently outperforms state-of-the-art baselines, achieving a +3.1% improvement in average accuracy. Ablation studies confirm the complementary benefits of each component, validating their collective contribution to robust and generalizable DTI prediction.Our work underscores the effectiveness of combining pretrained protein language models with structured training curricula and cross-modal contrastive learning for reliable DTI prediction under real-world, distribution-shifted conditions.

This paper addresses the challenging task of generating dynamic Human-Object Interactions from textual descriptions, named Text2HOI. While most existing works assume interactions with limited body parts or static objects, our task involves addressing the variation in human motion, the diversity of object shapes, and the semantic vagueness of object motion simultaneously. To tackle this, we propose a novel Text-guided Human-Object Interaction diffusion model with Relation Intervention (THOR). THOR is a cohesive diffusion model equipped with a relation intervention mechanism. In each diffusion step, we initiate text-guided human and object motion and then leverage human-object relations to intervene in object motion. This intervention enhances the spatial-temporal relations between humans and objects, with human-centric motion providing additional guidance for synthesizing consistent motion from text. To achieve more reasonable and realistic results, relation intervention loss is introduced at different levels of motion granularity.

Humans constantly interact with objects in daily life tasks. Capturing such processes and subsequently conducting visual inferences from a fixed viewpoint suffers from occlusions, shape and texture ambiguities, motions, etc. To mitigate the problem, it is essential to build a training dataset that captures free-viewpoint interactions. We construct a dense multi-view dome to acquire a complex human object interaction dataset, named HODome, that consists of ~71 M frames on 10 subjects interacting with 23 objects. To process the HODome dataset, we develop NeuralDome, a layer-wise neural processing pipeline tailored for multi-view video inputs to conduct accurate tracking, geometry reconstruction and free-view rendering, for both human subjects and objects. Extensive experiments on the HODome dataset demonstrate the effectiveness of NeuralDome on a variety of inference, modeling, and rendering tasks. Both the dataset and the NeuralDome tools will be disseminated to the community for further development, which can be found at https://juzezhang.github.io/NeuralDome

The active noise control (ANC) is a complementary technique to the passive noise control (PNC) to reduce the low frequency noise. The ANC controller can be implemented by pre-trained filters or adaptive filters. The adaptive ANC controller is advantageous in its adaptation to environmental changes. However, the algorithm complexity of the adaptive ANC controller increases with the scale of ANC applications, making it difficult to be carried out on low-cost processors. To resolve this problem, cloud computing should be utilized in ANC systems, and thus the wireless networked ANC system is proposed. Since it is crucial for ANC controllers to generate the anti-noise wave in real time, this paper formulates a digital twin architecture that implements the control filter adaptation in the cloud and the anti-noise signal generation on the local controller, respectively. A digital twin filtered-reference least mean squares (DT-FxLMS) algorithm is proposed to coordinate the digital twin with the local controller. Simulation and experiment results demonstrate the effectiveness and efficiency of the wireless networked ANC system based on the digital twin architecture.