Composing music for video is essential yet challenging, leading to a growing interest in automating music generation for video applications. Existing approaches often struggle to achieve robust music-video correspondence and generative diversity, primarily due to inadequate feature alignment methods and insufficient datasets. In this study, we present General Video-to-Music Generation model (GVMGen), designed for generating high-related music to the video input. Our model employs hierarchical attentions to extract and align video features with music in both spatial and temporal dimensions, ensuring the preservation of pertinent features while minimizing redundancy. Remarkably, our method is versatile, capable of generating multi-style music from different video inputs, even in zero-shot scenarios. We also propose an evaluation model along with two novel objective metrics for assessing video-music alignment. Additionally, we have compiled a large-scale dataset comprising diverse types of video-music pairs. Experimental results demonstrate that GVMGen surpasses previous models in terms of music-video correspondence, music quality generative diversity, and application universality.

In seismic facies analysis, waveform clustering of self-organizing map (SOM) usually classifies waveforms truncated by horizon with a fixed time window. However, the fixed time window is not an appropriate method for the situation in which the thickness of the target layer varies in the horizontal direction. In order to adapt SOM to this situation and simultaneously preserve the advantages of SOM, we replace the vanilla SOM measure with dynamic time warp (DTW) distance. Compared with Euclidean distance, DTW distance requires more computational amount about path searching and weight parameter updating in SOM. Therefore, considering the redundancy of seismic data, we introduce a hierarchical clustering strategy, which uses cluster-based stratified sampling and hierarchical mapping to reduce the computational cost of training and prediction. In the visualization section, by combining with hue, saturation, value (HSV) coloring and hierarchical mapping, the method can quickly display the lateral distribution of stratigraphic. The proposed method is validated by a mound shape model in a simulation test. Finally, the method was successfully applied to the field data from a reef-bank reservoir. The experimental and application results show that, when the top and bottom interfaces of the destination layer can be clearly identified, the proposed method can effectively cluster the waveforms with variable length, and the computational cost is acceptable.

As a classical visualization tool for seismic facies analysis, the clustering process of self-organizing map (SOM) is generally divided into two stages: feature extraction and clustering. However, when the horizon pickings are ambiguous and waveforms are chaotic, the structural feature extraction cannot correspond well to the topological structure of SOM. To improve the performance of classification, we propose a one-stage method of deep embedded SOM (DESOM) for seismic facies visualization analysis, which means that the extracting feature representation and clustering are completed simultaneously. Moreover, the stability of the DESOM results can be improved by adding sparse constraints, and thus the visualization results can be displayed in more detail. In the experiment, by comparing with the external indexes of clustering and the hierarchical results of prototype categories, the superiorities of the DESOM and sparse DESOM (SDESOM) methods are verified based on a geophysical model. In the field data application, these methods are combined with the Hue, Saturation, Value (HSV) color mapping technology to display the geological structure information of the target horizon. According to the law of the correlation of facies and the internal cluster indexes, it approves that the DESOM method can improve the continuity of channels, and the SDESOM method can obtain more detailed information of seismic facies distribution.

Human epidermis, as the largest organ on body, has become a new design platform for wearable computing. The availability of miniature electronics makes more possibilities for on-skin designs. In this paper, we present AnimSkin, a thin-film interface, which will emit dynamic color animations directly on skin. This is done by using thermochromic material embedded with transparent electrode acting as a capacitive sensor. Moreover, an accessible and low-cost fabrication method is introduced. Individuals could also customize aesthetic graphic designs by following the detailed fabrication process to achieve personalized patterns. We propose four different dynamic types of color animation by applying certain voltage to the heating circuitry. With two examples, Email Reminder and Light Control System, we demonstrate how AnimSkin can be integrated into everyday life, and specifically, we show how AnimSkin can benefit areas such as on-skin design, thin-film interface and beauty technology.