Compute Express Link (CXL) is an open industry-standard interconnect protocol designed for communication between processors and devices, e.g., memory expansions. It brings new opportunities for enhancing system performance, particularly for workloads constrained by memory capacity. Consequently, CXL opens new avenues for the design of database management systems (DBMS), which is highly beneficial for managing complex data tasks. However, traditional techniques focused on addressing I/O bottlenecks are insufficient to meet the new challenges brought by CXL memory management. In this tutorial, we introduce the advantages and application scenarios of tiered CXL memory, pooled CXL memory, and shared CXL memory, as well as the new challenges that arise, including: (1) reducing data access, data exchange, and data transfer cost; (2) optimizing memory allocation and competition to improve memory utilization; (3) managing shared data for failure, series operators, and distributed transactions. We also review emerging techniques aimed at addressing these issues. Finally, we summarize new opportunities and highlight open research problems in this evolving area.

Despite the recent success of Neural Radiance Field (NeRF), it is still challenging to render large-scale driving scenes with long trajectories, particularly when the rendering quality and efficiency are in high demand. Existing methods for such scenes usually involve with spatial warping, geometric supervision from zero-shot normal or depth estimation, or scene division strategies, where the synthesized views are often blurry or fail to meet the requirement of efficient rendering. To address the above challenges, this paper presents a novel framework that learns a density space from the scenes to guide the construction of a point-based renderer, dubbed as DGNR (Density-Guided Neural Rendering). In DGNR, geometric priors are no longer needed, which can be intrinsically learned from the density space through volumetric rendering. Specifically, we make use of a differentiable renderer to synthesize images from the neural density features obtained from the learned density space. A density-based fusion module and geometric regularization are proposed to optimize the density space. By conducting experiments on a widely used autonomous driving dataset, we have validated the effectiveness of DGNR in synthesizing photorealistic driving scenes and achieving real-time capable rendering. Our project page is available athttps://github.com/JOP-Lee/DGNR-Rendering. Note to Practitioners—While Neural Radiance Field (NeRF) has been gaining attraction, it is still challenging to create highly detailed, efficient renderings of large driving scenes. Current methods often resort to spatial warping, geometric guidance from tools like zero-shot normal or depth estimates, or dividing the scene into smaller parts. Unfortunately, these techniques can result in blurred images or fail to meet efficiency needs. To solve these challenges, we introduce a learned density space to build a point-based renderer, termed Density-Guided Neural Rendering (DGNR). With DGNR, we no longer need geometric priors because the density space can inherently learn them through volume rendering. Specifically, we use a flexible renderer to create images from the neural density features derived from the learned density space. We have also proposed a density-based fusion module and geometric regularization to optimize the density space. We evaluated DGNR on a popular autonomous driving dataset and found it to be effective in creating realistic driving scenes and capable of real-time rendering. Project page:https://github.com/JOP-Lee/DGNR-Rendering.

With the development of advanced driver assistance systems~(ADAS) and autonomous vehicles, conducting experiments in various scenarios becomes an urgent need. Although having been capable of synthesizing photo-realistic street scenes, conventional image-to-image translation methods cannot produce coherent scenes due to the lack of 3D information. In this paper, a large-scale neural rendering method is proposed to synthesize the autonomous driving scene~(READ), which makes it possible to generate large-scale driving scenes in real time on a PC through a variety of sampling schemes. In order to effectively represent driving scenarios, we propose an ω-net rendering network to learn neural descriptors from sparse point clouds. Our model can not only synthesize photo-realistic driving scenes but also stitch and edit them. The promising experimental results show that our model performs well in large-scale driving scenarios.

The graphical user interface (GUI) is crucial for users to interact with mobile devices. However, accessibility issues in the GUI, such as undersized text and redundant information, lead to understanding and operating obstacles for billions of low vision users in our society. To alleviate this situation, academia and industry have proposed various accessibility-related methods. Still, their over-dependence on specific detection rules and their inability to automatically repair the GUI source code limit them in helping developers resolve these issues. In this paper, we propose a novel method, named AGAA, for capturing and repairing undersized text and redundant information issues in the GUI. The evaluation on 12 real-world apps and the user study on 36 low vision users demonstrate that AGAA is effective in resolving these issues and is useful in improving the mobile device experience for low vision users, respectively.

In this paper, we propose a portrait relighting method based on deep reinforcement learning (called PR-RL). Our PR-RL model could conduct portrait relighting by sequentially predicting local light editing strokes, and use strokes to conduct dodge and burn operations on the image lightness, simulating image editing by artists using brush strokes. Reinforcement learning with Deep Deterministic Policy Gradient is introduced to design our PR-RL model, defining the action (stroke parameters) in a continuous space, through which a reward can be designed to guide the agent to learn and relight a portrait image like an artist. To optimize the relighting effect, we further enable the reward to be location relevant and hence a coarse-to-fine strategy can be applied to select corresponding actions and maximize the performance of the proposed method. In comparison with the existing efforts, our proposed PR-RL method is locally effective, scale-invariant and interpretable. We apply the proposed method to tasks of portrait relighting based on both SH-lighting and reference images. The experiments show that our PR-RL method outperforms state-of-the-art methods in generating locally effective and interpretable high resolution relighting results for wild portrait images.

This paper proposes a collaborative deep reinforcement learning model for automatic image cropping (called CDRL-IC). By modeling image cropping as a decision-making process of reinforcement learning, our model could generate optimal cropping result in a few moving and zooming steps. An image with good composition is a comprehensive result by considering the relative importance of objects and also the spatial organization of visual elements. Therefore, emotion attention information which indicates the relationship and importance between objects is applied together with contextual information of color image for image cropping. In order to sufficiently use the emotion attention map and the color image, they are processed by two collaborative agents. The two agents make their primary learning separately and then share information through an information interaction module for making joint action prediction. In order to efficiently evaluate the cropping quality in the reward function, weighted Intersection Over Union (WIoU) is designed by integrating emotion attention map in the traditional IoU. Our CDRL-IC model is tested on a variety of datasets for both image cropping and thumbnail generation. The experiments show that our CDRL-IC model outperforms state-of-the-art methods on these benchmark datasets.

An automatic photo composition method based on collaborative deep reinforcement learning(called CDRL-RC) is proposed in this paper. Our method models photo composition as a markov decision-making process by reinforcement learning and generates cropping result through a series of moving and zooming actions. Emotional attention information is added to the composition task, which was trained by eye-tracking datasets to consider the relationship and importance between objects. In order to sufficiently use the emotional attention map and original image for image cropping, they are processed as inputs to two collaborative agents. For the collaborative composition of two agents, we design an information interaction module, which allows inter-agents to exchange information and give advice to each other, and finally predict the action together. In addition, we add attention weight to the traditional IoU to efficiently evaluate the cropping quantity in the reward function. Experiment results show that our CDRL-RC model achieved the state-of-the-art photo composition performance on a variety of datasets.

This paper studies the composition in portrait paintings and develops an algorithm to improve the composition of portrait photographs based on example portrait paintings. A study of portrait paintings shows that the placement of the face and the figure is pose-related. Based on this observation, this paper develops an algorithm to improve the composition of a portrait photograph by learning the placement of the face and the figure from an example portrait painting. This example portrait painting is selected based on the similarity of its figure pose to that of the input photograph. This similarity measure is modeled as a graph matching problem. Finally, space cropping is performed using an optimization function to assign a similar location for each body part of the figure in the photograph with that of the figure in the example portrait painting. The experimental results demonstrate the effectiveness of the proposed method. A user study shows that the proposed pose-based composition improvement is preferred more than rule-based methods and learning-based methods.