To achieve efficient file access to the file system backend, file system clients employ various intricate optimization techniques, such as local data/metadata caching and direct data access. However, these techniques impose a significant load on the host CPU, posing substantial challenges to the valuable CPU resources.

Hand-drawn sketches and sketch colourization are the most laborious but necessary steps for fashion designers to design exquisite clothes, especially when the fashion design requires distinctive and personal characteristics from designer style. This paper presents an artificial intelligent aided fashion design system, namely StyleMe, to support the automatic generation of clothing sketches with designer style. Given the clothing pictures specified by the designer, StyleMe can use deep learning based generative model to generate clothing sketches that are consistent with the designer style. The system also supports intelligent colourization on clothing sketch by style transfer, according to specified styles from the real fashion images. Through a series of performance evaluations and user studies, we found that our system can generate effective clothing sketches as good as fashion designers’ human work, and significantly improve the efficiency of fashion design with its sketch colourization method.

Persistent memory (PM) file systems have been developed to achieve high performance by exploiting the advanced features of PMs, including nonvolatility, byte addressability, and dynamic random access memory (DRAM) like performance. Unfortunately, these PMs suffer from limited write endurance. Existing space management strategies of PM file systems can induce a severely unbalanced wear problem, which can damage the underlying PMs quickly. In this paper, we propose a Wear-leveling-aware Multi-grained Allocator, called WMAlloc, to achieve the wear leveling of PMs while improving the performance of file systems. WMAlloc adopts multiple min-heaps to manage the unused space of PMs. Each heap represents an allocation granularity. Then, WMAlloc allocates less-worn blocks from the corresponding min-heap for allocation requests. Moreover, to avoid recursive split and inefficient heap locations in WMAlloc, we further propose a bitmap-based multi-heap tree (BMT) to enhance WMAlloc, namely, WMAlloc-BMT. We implement WMAlloc and WMAlloc-BMT in the Linux kernel based on NOVA, a typical PM file system. Experimental results show that, compared with the original NOVA and dynamic wear-aware range management (DWARM), which is the state-of-the-art wear-leveling-aware allocator of PM file systems, WMAlloc can, respectively, achieve 4.11× and 1.81× maximum write number reduction and 1.02× and 1.64× performance with four workloads on average. Furthermore, WMAlloc-BMT outperforms WMAlloc with 1.08× performance and achieves 1.17× maximum write number reduction with four workloads on average.

As a natural extension of image-based cross-modal recipe retrieval, retrieving a specific video given a recipe as the query is seldom explored. There are various temporal and spatial elements hidden in cooking videos. In addition, current image-based cross-modal recipe retrieval approaches mostly emphasize the understanding of textual and visual content independently. Such methods overlook the interaction between textual and visual content. In this work, we innovatively propose a new problem of video-based cross-modal recipe retrieval and thoroughly investigate this issue under the attention paradigm. In particular, we firstly exploit a parallel-attention network to independently learn the representations of videos and recipes. Next, a co-attention network is proposed to explicitly emphasize the cross-modal interactive features between videos and recipes. Meanwhile, a cross-modal fusion sub-network is proposed to learn both the independent and collaborative dynamics, which can enhance the associated representation of videos and recipes. Last but not the least, the embedding vectors of videos and recipes stemming from joint network are optimized with a pairwise ranking loss. Extensive experiments on a self-collected dataset have verified the effectiveness and rationality of our proposed solution.