Cross-modal retrieval with 3D models has gained significant attention with the rapid growth of 3D assets. The core challenge lies in learning modality-invariant and discriminative features in a common space. Existing methods often rely on shared class centers in Euclidean space, overlooking directional relationships between samples and non-corresponding centers, while remaining sensitive to modality-specific scales, hindering the learning of discriminative cross-modal centers, especially for dispersed modalities like real-world images. To address these limitations, we propose the Discriminative Cosine Center Learning (DCCL) framework for 3D cross-modal retrieval. DCCL integrates the Adaptive Cosine Center Learning (ACCL) mechanism, optimizing cosine similarity on a shared hypersphere with adaptive penalties for challenging samples. Additionally, the Cross-Modal Affinity Learning (CMAL) mechanism reduces cross-modal discrepancies by pairwise matching data from different modalities. Extensive experiments on five benchmarks demonstrate that DCCL significantly outperforms baseline methods in both synthetic and real-world scenarios.

Virtual Reality (VR) creates a highly realistic and controllable simulation environment that can easily manipulate users' perception of space and time. However, while the sensation of “losing track of time” is often associated with enjoyable experiences, both the relationship between time perception and user experience in VR, and the underlying mechanisms of time perception itself, remain largely unexplored. In this study, we first investigated how different zeitgebers—such as light color, music tempo, and VR task—affect time perception. We then introduced the Relative Subjective Time Change (RSTC) method to explore the link between time perception and user experience quantitatively. Furthermore, to uncover the mechanisms underlying time perception in VR, we propose a computational model based on CNN and Transformer, named the Time Perception Modeling Network (TPM-Net), which leverages multimodal physiological data to infer users' time perception states in VR. In a between-subject experiment with 56 participants, our results indicate that the VR task factor significantly influences time perception, with red light and slow-tempo music contributing to an underestimation of time. The RSTC method effectively demonstrates that a relative underestimation of time in VR is strongly associated with enhanced user experience, presence, and engagement. Moreover, the TPM-Net shows great potential in modeling time perception, enabling further inference of relative changes in both time perception and user experience. Our study comprehensively elucidates the mechanisms of time perception in VR. It provides valuable insights and promising methodologies for exploring the relationship between time perception and user experience. Modeling time perception through physiological data marks a first step toward objectively assessing users' temporal perception states, offering a promising tool for VR-based therapy and training systems that require precise temporal awareness.

With the advancement of neural networks, neural network controlled systems (NNCSs) are increasingly deployed in safety-critical scenarios, making the safety verification of NNCSs imperative. Traditional verification methods based on overapproximated reachability analysis introduce precision loss during the verification process, which may lead to “Unknown” results. Moreover, standalone forward reachability analysis often fails to incorporate target safety properties into its verification process, while backward reachability analysis does not account for input constraints. To address these challenges, this paper proposes an iterative refinement approach by combining forward and backward reachability analysis. For a given safety property, our method guides state space partitioning and pruning on-the-fly by making use of the target safety constraints when verification results remain “Unknown”. By partitioning the state space into smaller subspaces, the precision loss due to overapproximation is significantly mitigated. Additionally, integrating forward and backward reachability analysis further counteracts these overapproximation effects through pruning, ultimately yielding more accurate verification outcomes. We demonstrate that our approach successfully verifies the system's safety properties with a 100% success rate across four benchmarks, whereas other verification tools for NNCSs such as Reach-LP-GSG, BReach-LP, and DRIP-Hpoly either return “Unknown” results or require up to$29 \%, 60 \%$, and 232% more time.

Deep neural network (DNN) has been increasingly deployed as a key component in safety-critical systems. However, the credibility of DNN components is uncertain due to the absence of formal specifications for their data preconditions, which are essential for ensuring trustworthy postconditions.In this paper, we propose a guess-and-check-based framework PreBoxes to automatically synthesize Boxes sufficient preconditions for DNN concerning rich safety and robustness postconditions.The framework operates in two phases: the guess phase generates potentially complex candidate preconditions through heuristic methods, while the check phase verifies these candidates with formal guarantees.The entire framework supports automatic and adaptive iterative running to obtain weaker preconditions as well.Such resulting preconditions can be leveraged to shield DNN for safety and enhance the interpretability of DNN in application.PreBoxes has been evaluated on over 20 models with 23 trustworthy properties of 4 benchmarks and compared with 3 existing typical schemes.The results show that not only does PreBoxes generally infer weaker non-trivial sufficient preconditions for DNN than others, but also it expands competitive capabilities to handle both complex properties and Non-ReLU complex structured networks.

Uninterpreted predicate solving is a fundamental problem in formal verification, including loop invariant and constrained horn clauses predicate solving. Existing approaches have been mostly in symbolic ways. While achieving sustainable progress, they still suffer from inefficiency and seem unable to leverage the ever-increasing computility, such as GPU. Recently, neural relaxation has been proposed to tackle this problem. They treat the uninterpreted predicate-solving task as an optimization problem by relaxing the discrete search process into a learning process of neural networks. However, two bottlenecks keep them from being valid. First, relaxed neural networks cannot match the original semantics of predicates rigorously; second, the neural networks are difficult to train to reach global optimization. Therefore, this article presents a novel discrete neural architecture with the Abstract Gradient Decent (AGD) algorithm to directly solve uninterpreted predicates in the discrete hypothesis space. The abstract gradient is for discrete neurons whose calculation rules are designed in an abstract domain. Our approach conforms to the original semantics of predicates, and the proposed AGD algorithm can achieve global optimization satisfactorily. We implement the tool Dasp in the Boxes abstract domain to solve uninterpreted predicates in the QF-NIA SMT theory. In the experiments, Dasp has outperformed seven state-of-the-art tools across three predicate synthesis tasks.