The brain's diverse spatiotemporal activity patterns are fundamental to cognition and consciousness, yet how these macroscopic dynamics emerge from microscopic neural circuitry remains a critical challenge. We take a step in this direction by developing a spatially extended neural network model integrated with a spectral theory of its connectivity matrix. Our theory quantitatively demonstrates how local structural parameters, such as E/I neuron projection ranges, connection strengths, and density determine distinct features of the eigenvalue spectrum, specifically outlier eigenvalues and a bulk disk. These spectral signatures, in turn, precisely predict the network's emergent global dynamical regime, encompassing asynchronous states, synchronous states, oscillations, localized activity bumps, traveling waves, and chaos. Motivated by observations of shifting cortical dynamics in mice across arousal states, our framework not only provides a possible explanation for repertoire of behaviors but also offers a principled starting point for inferring underlying effective connectivity changes from macroscopic brain activity. By mechanistically linking neural structure to dynamics, this work advances a principled framework for dissecting how large-scale activity patterns—central to cognition and open questions in consciousness research—arise from, and constrain, local circuitry. The implementation code is available at https://github.com/huang-yh20/spatial-linear-project.

Recently, transformer-based image captioning models have achieved significant performance improvement. However, due to the limitations of region visual features and deterministic projections between image space and caption space, existing methods still suffer from disentangled visual features and rigid sentences. To address these issues, we first introduce panoptic segmentation to extract the segmentation region features, which can effectively alleviate the visual confusion caused by the widely-adopted region visual features. Then, we propose a panoptic segmentation based sequential conditional variational transformer (PS-SCVT) framework for diverse image captioning, which not only accurately extracts the image visual representations by fusing the segmentation region features and object detection features, but has the ability of learning one-to-many mappings from image space to caption space. The experimental results demonstrate that our approach achieves better interpretability and generalization performance compared with the state-of-the-art diverse image captioning models.

The phenomenon of a satellite thermal infrared (TIR) anomaly before earthquakes has been reported since the late 1980s. The reported increase of surface temperatures reaches 2-4 degC, occasionally higher. Usually, the anomaly appears one month to several days before the earthquake. Several mechanisms of hypothesis have been put forward to interpret the reported temperature increase. In this paper, the satellite TIR anomaly features of four earthquakes in the Taiwan region are first analyzed. To study the mechanisms of infrared anomaly a group of physical simulation experiments are carried out. The mechanism of the satellite Infrared anomaly before an earthquake in the Taiwan region is discussed based on the experimental results. Furthermore, a preliminary model for tectonic activity analysis and for short-term earthquake prediction based on the analysis of the satellite infrared anomaly before an earthquake in the Taiwan region is presented.