Nowadays, while most machine learning research on customer journey optimization has focused on short-term success metrics such as click-through rates and optimal ad placement, there has been little consideration given to developing a coherent system for end-to-end customer journey optimization. Such a system would encompass all aspects of the customer experience, from presenting the right product value to the right users, to understanding a user's likelihood of conversion and long-term value to the platform, as well as their propensity for cross-selling and risk of churning. Currently, models and algorithms for customer journey optimization are often developed in isolation, leading to inefficiencies in modeling and data pipelines. Furthermore, the customer is often viewed as a collection of different entities by different organizational departments (such as marketing, sales, and finance), which can lead to additional friction in the customer experience. This workshop seeks to bridge the gap between academic researchers and industrial practitioners who are interested in building holistic solutions for end-to-end customer journey optimization. In addition, with the rising popularity of generative AI and LLM, we want to use this venue to exchange ideas regarding their applications in different stages of customer journey, and how the new technologies could help businesses achieve their objectives.

Nowadays, while most machine learning research on customer journey optimization has focused on short-term success metrics such as click-through rates and optimal ad placement, there has been little consideration given to developing a coherent system for end-to-end customer journey optimization. Such a system would encompass all aspects of the customer experience, from presenting the right product value to the right users, to understanding a user's likelihood of conversion and long-term value to the platform, as well as their propensity for cross-selling and risk of churning. Currently, models and algorithms for customer journey optimization are often developed in isolation, leading to inefficiencies in modeling and data pipelines. Furthermore, the customer is often viewed as a collection of different entities by different organizational departments (such as marketing, sales, and finance), which can lead to additional friction in the customer experience. This workshop seeks to bridge the gap between academic researchers and industrial practitioners who are interested in building holistic solutions for end-to-end customer journey optimization. By fostering collaboration and cross-disciplinary discussion, the workshop aims to accelerate progress in this rapidly evolving field.

Long noncoding RNAs (lncRNAs) are emerging as a class of important regulators participating in various biological functions and disease processes. With the widespread application of next-generation sequencing technologies, large numbers of lncRNAs have been identified, producing plenty of lncRNA annotation resources in different contexts. However, at present, we lack a comprehensive overview of these lncRNA annotation resources. In this study, we reviewed 24 currently available lncRNA annotation resources referring to > 205 000 lncRNAs in over 50 tissues and cell lines. We characterized these annotation resources from different aspects, including exon structure, expression, histone modification and function. We found many distinct properties among these annotation resources. Especially, these resources showed diverse chromatin signatures, remarkable tissue and cell type dependence and functional specificity. Our results suggested the incompleteness and complementarity of current lncRNA annotations and the necessity of integration of multiple resources to comprehensively characterize lncRNAs. Finally, we developed 'LNCat' (lncRNA atlas, freely available at http://biocc.hrbmu.edu.cn/LNCat/), a user-friendly database that provides a genome browser of lncRNA structures, visualization of different resources from multiple angles and download of different combinations of lncRNA annotations, and supports rapid exploration, comparison and integration of lncRNA annotation resources. Overall, our study provides a comprehensive comparison of numerous lncRNA annotations, and can facilitate understanding of lncRNAs in human disease.

Image matching is between two or more images find matching points of the same name. Image dense matching is an important guarantee for three-dimensional reconstruction, extraction precision DEM, DSM. Since the UAV has flexible features, and can work in a complex environment, which makes UAV used is widely. UAVs can obtain a high degree overlapping images, which has a very important role for urban reconstruction and the map data update. UAV video image data acquired high degree of overlap is very large, feature extraction and feature generator consumes much time, how to effectively reduce the feature extraction and feature generates the amount of time? This paper presents a high degree of overlap for UAV video imaging characterization methods. The method uses Harris corner detection operator, then adopts feature descriptor simplified-DASIY (abbreviation: S-DASIY) to characterize detecting corners and generate the 25-dimensional feature descriptor for the corners; and in accordance with the appropriate matching criteria to match the feature points of the images, to get the match points between images. Characterized by experiments herein described method can effectively reduce the amount of time characterization.

Today, there are an increasing number of Unmanned Aerial Vehicle (UAV) platforms being equipped with video for real-time observation. Unmanned Aerial Vehicle Systems (UAVs) have been generally used in a great variety of missions because of its inexpensiveness and flexibility. However, UAV often experience unintended translation and rotation due to atmospheric turbulence. And it leads the compensation image to losing lots of information when the large drift occurs. In order to get rid of the unwanted jitter and retain the most information, an estimate of the intentional motion is necessary. This paper presents a novel approach to estimating the intentional motion by adaptive compensation algorithm. And it can retain image information and remove image jitter when the pre-path is deemed to be a straight line. The method has been demonstrated through a series of experiments on real aviation video data.

As a new platform for remote sensing, UAV (unmanned aerial vehicle) is getting more and more attention in recent years. In UAV remote sensing system, control algorithm is a key technique. First, structure of UAV remote sensing system is given. Then, three shooting control algorithms are discussed in details. The experiments and collected images show the algorithms effective.

One method is presented for moisacing the remote sensing image in this paper from the UAV.The method includes two parts:the Gauss Pyramid and the gray projection .This method should reduce the calculation amount and promote the speed. The experimentation result shows the method is effective for the UAV images.