While spatial reasoning has made progress in object localization relationships, it often overlooks object orientation—a key factor in 6-DoF fine-grained manipulation. Traditional pose representations rely on pre-defined frames or templates, limiting generalization and semantic grounding. In this paper, we introduce the concept of semantic orientation, which defines object orientations using natural language in a reference-frame-free manner (e.g., the ''plug-in'' direction of a USB or the ''handle'' direction of a cup). To support this, we construct OrienText300K, a large-scale dataset of 3D objects annotated with semantic orientations, and develop PointSO, a general model for zero-shot semantic orientation prediction. By integrating semantic orientation into VLM agents, our SoFar framework enables 6-DoF spatial reasoning and generates robotic actions. Extensive experiments demonstrated the effectiveness and generalization of our SoFar, e.g., zero-shot 48.7\% successful rate on Open6DOR and zero-shot 74.9\% successful rate on SIMPLER-Env.

In steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI), improving the recognition performance for new subjects without calibration data is the key challenge for practical application. Unsupervised transfer learning is an effective way to overcome it. However, existing studies focus solely on what to transfer, rather than how to effectively transfer, resulting in unsatisfactory transfer effectiveness or even negative transfer. In this study, an innovative unsupervised cross-subject transfer learning method for SSVEP-BCI was proposed, named SUTL. It involves that subject transferability estimation (STE) and a multi-domain alignment method were proposed to alleviate the potential interference of differences in SSVEP signal distribution among subjects. STE screens appropriate transferable subjects from the source subject pool, while domain alignment directly makes all subjects more similar. Then, SUTL sufficiently exploits the information of the selected source subjects, learning and transferring both generalization knowledge and subject-specific knowledge to boost the recognition performance for the new subject. The performance of SUTL was evaluated on two public SSVEP datasets (benchmark dataset and BETA dataset) with 40 classes, the extensive experimental results reveal that SUTL markedly boosts the effectiveness of SSVEP cross-subject transfer and dramatically outperforms the state-of-art methods. SUTL significantly enhances the recognition performance of SSVEP-BCI for new subjects and facilitates its practical application.

Estimation of seismic structural correlation can provide useful information for structural interpretation, events correlation enhancement, signal-to-noise ratio (SNR) improvement, subsurface modeling, and geophysical inversion. In this letter, we propose a Shannon entropy-based method to measure seismic local correlation and further enhance the correlation of seismic events. The proposed method is derived from the local plane-wave model and formulated as a Shannon entropy form. The probabilities are calculated along with the coordinate direction of local slope and estimated by solving two least-squares problems in local areas. Application to both synthetic and field data sets demonstrates the superior performance of the Shannon entropy-based method over traditional alternatives.