Tissue segmentation in pathological images plays a crucial role for the diagnosis and prognosis of human cancers. However, due to the complexity of tumor micro-environment, it is difficult to annotate all tissue types especially for the categories with small tissue proportions, which limits the ability of the traditional tissue segmentation models to these tissue types with zero training samples. To address the above issues, we present a novel architecture, ZSPMLG, that relies on pathology vision-language foundation model (i.e., CONCH) to learn pixel-wise classifiers for both seen and unseen tissue types based on their text descriptions. Specifically, we firstly apply large language model (LLM) to generate the descriptions for both seen and unseen tissue categories, followed by feeding them to the CONCH text encoder to acquire their corresponding prototypes that are shared by both vision and semantic space. By considering that the textual descriptions of specific tissue categories can be observed from the pathological images at different scales of magnification, our ZSPMLG consists of Mixture of Local Experts (MoLE) and Mixture of Global Experts (MoGE) modules, where MoLE performs the specialized decoding that can map individual scale patch-level representation to dense pixel-level representation, while MoGE aims at fusing the multi-scale representations together. Finally, a convolutional layer is designed to map the pixel-level representation to the category prototype for tissue segmentation on both seen and unseen categories. We evaluate our method on three datasets and the experimental results demonstrate the superiority of our method on both seen and unseen tissue categories.

ABSTRACT As an important spiritual driving force to promote the development of public welfare undertakings, charity culture is currently facing challenges such as lack of professional talents and inefficient resource management. The purpose of this research is to build a digital platform of charity cultural resources that integrates virtual reality technology and bidirectional gated circulation unit network, so as to solve the problems of low efficiency of charity cultural information processing, insufficient interaction, and weak public cognition. By introducing a sentence‐level attention mechanism and a relationship perception enhancement module, the bidirectional gated cyclic unit network model is improved, and the entity extraction and relationship mining capabilities of the charity culture knowledge map are optimized. Combine virtual reality technology to realize dynamic visual display of cultural resources to improve platform performance. In the initial stage of the pattern, the growth rate of F1 was very fast; then the growth rate slowed down, and finally stabilized, with good generalization performance. At 80% CPU occupancy, the response time of the system did not change by more than 0.18 s. With 500 and 1000 parallel users respectively, the communication efficiency of the platform reached 98.37%, demonstrating good signal transmission and communication performance. However, the hardware environment of the digital platform constructed in this study may limit its popularity, and the real‐time update and maintenance of the knowledge graph are faced with challenges.

Real-time operating systems employ spatial and temporal isolation to guarantee predictability and schedulability of real-time systems on multi-core processors. Any unbounded and uncontrolled cross-core performance interference poses a significant threat to system time safety. However, the current Linux kernel has a number of interference issues and represents a primary source of interference. Unfortunately, existing research does not systematically and deeply explore the cross-core performance interference issue within the OS itself. This paper presents our industry practice for mitigating crosscore performance interference in Linux over the past 6 years. We have fixed dozens of interference issues in different Linux subsystems. Compared to the version without our improvements, our enhancements reduce the worst-case jitter by a factor of 8.7, resulting in a maximum $11.5 x$ improvement over system schedulability. For the worst-case latency in the Core Flight System and the Robot Operating System 2, we achieve a 1.6x and $1.64 x$ reduction over RT-Linux. Based on our development experience, we summarize the lessons we learned and offer our suggestions to system developers for systematically eliminating cross-core interference from the following aspects: task management, resource management, and concurrency management. Most of our modifications have been merged into Linux upstream and released in commercial distributions.

Abstract To help the new media presentation and brand building of Dongguan City memory, a virtual reality modeling model based on multi‐perspective and deep learning is proposed. First, in order to address the issue of imbalanced input and output information in depth map prediction tasks, as well as poor accuracy of predicted depth map boundaries, a depth map prediction model based on multiple perspectives and deep learning is built. Then, a single perspective modeling framework is proposed to address the scarcity of perspectives in practical situations, and a dynamic fusion model is built for single‐view virtual reality scenes based on multi‐view generation networks. The results indicated that the mean square error, average relative error, and average logarithmic error were minimized at 0.52, 0.138, and 0.068, respectively. The multi‐threshold accuracy index demonstrated peak values at 0.772, 0.8821, and 0.947, respectively. The grid simplification algorithm exhibited the shortest running times at 1.57, 2.52, 3.91, and 6.53 s, respectively. Moreover, the single‐view modeling frame displayed the smallest angle distance at 0.1449, while the overlap degree of the point cloud scene reached the highest levels at 77.47, 79.49, 83.5, and 84.47, respectively. To sum up, the model has a good application effect in virtual reality modeling and positively affects virtual reality technology development.