Flash-based solid-state drives (SSDs) have become increasingly popular in a variety of storage systems due to their impressive performance capabilities. However, due to the basic I/O unit asymmetry between the host systems and underlying storage devices, SSDs need to handle a large number of unaligned access which can lead to severe write amplification and page fragmentation, thereby degrading the I/O performance, space utilization and endurance of SSDs. For mitigating the negative effects caused by unaligned access, this paper proposes a request-based address mapping scheme for SSDs to map the request data to the minimum number of flash pages based on the request size, while a novel compression scheme considering the characteristics of SSDs is applied to further eliminate the page fragmentation that remains after mapping. Simulation tests on several realistic disk traces show that our proposal can improve the I/O performance by 31.7% and reduce page fragments by 25.4% on average, compared to state-of-the-art methods.

In flash-based solid-state drives (SSDs), certain small unaligned I/O requests span two logical pages though their size is not larger than the basic write/read unit of SSDs (i.e. an SSD page), and we term them as across-page requests. Servicing such across-page requests triggers two separated I/O operations on different SSD pages, and thus impacts the I/O performance and the endurance of SSDs. For mitigating negative effects caused by across-page requests, this paper proposes a novel flash translation layer (FTL) scheme for SSDs to separately re-align such requests via remapping them onto a single SSD page. Consequently, both read and write requests on the across-page data can be completed with one page-level I/O operation. Through a series of experiments based on the selected disk traces of real-world applications, we demonstrate that the proposed realigning method at FTL of SSD devices, can noticeably reduce the I/O latency by between 4.6% and 11.6%, and the erase number (i.e. the indicator of SSD endurance) by between 6.4% and 19.11%, compared to state-of-the-art methods.

Generative adversarial networks (GANs) can generate high-quality images from sampled latent codes. Recent works attempt to edit an image by manipulating its under-lying latent code, but rarely go beyond the basic task of at-tribute adjustment. We propose the first method that enables manipulation with multidimensional condition such as key-points and captions. Specifically, we design an algorithm that searches for a new latent code that satisfies the target condition based on the Surrogate Gradient Field (SGF) induced by an auxiliary mapping network. For quantitative comparison, we propose a metric to evaluate the disentanglement of manipulation methods. Thorough experimental analysis on the facial attribute adjustment task shows that our method outperforms state-of-the-art methods in disentanglement. We further apply our method to tasks of various condition modalities to demonstrate that our method can alter complex image properties such as keypoints and captions.

Modern high density SSDs commonly designate a part of their capacity as a cache using an Single-level Cell (SLC)-mode region. Partial programming is then adopted for reducing space fragmentation in the SLC-mode pages, but it exacerbates program disturb including both in-page disturb and neighbouring page disturb. This paper proposes a partial programming scheme (called intra-page update) by updating hot, small size data inside a given page to minimize the negative impact induced by program disturb. Moreover, we introduce a novel data movement principle to separate hot and cold write data in the SLC-mode cache when updating the data or carrying out garbage collection. As a result, the hot updated data can be kept in the SLC-mode cache and the cold data will be flushed onto the high density SSD region. Simulation tests on several realistic disk traces show that our proposal improves bit error rate by 9.2%, and I/O performance by 9.3% on average, compared to state-of-the-art methods, without a noticeable decrease in total endurance.

Face frontalization has always been an important field. Recently, with the introduction of generative adversarial networks (GANs), face frontalization has achieved remarkable success. A critical challenge during face frontalization is to ensure the features of the original profile image are retained. Even though some state-of-the-art methods can preserve identity features while rotating the face to the frontal view, they still have difficulty preserving facial expression features. Therefore, we propose the novel triangle cycle-consistent generative adversarial networks for the face frontalization task, termed TC-GAN. Our networks contain two generators and one discriminator. One of the generators generates the frontal contour, and the other generates the facial features. They work together to generate a photo-realistic frontal view of the face. We also introduce cycle-consistent loss to retain feature information effectively. To validate the advantages of TC-GAN, we apply it to the face frontalization task on two datasets. The experimental results demonstrate that our method can perform large-pose face frontalization while preserving the facial features (both identity and expression). To the best of our knowledge, TC-GAN outperforms the state-of-the-art methods in the preservation of facial identity and expression features during face frontalization.

Features extracted by deep networks have been popular in many visual search tasks. This article studies deep network structures and training schemes for mobile visual search. The goal is to learn an effective yet portable feature representation that is suitable for bridging the domain gap between mobile user photos and (mostly) professionally taken product images while keeping the computational cost acceptable for mobile-based applications. The technical contributions are twofold. First, we propose an alternative of the contrastive loss popularly used for training deep Siamese networks, namely robust contrastive loss, where we relax the penalty on some positive and negative pairs to alleviate overfitting. Second, a simple multitask fine-tuning scheme is leveraged to train the network, which not only utilizes knowledge from the provided training photo pairs but also harnesses additional information from the large ImageNet dataset to regularize the fine-tuning process. Extensive experiments on challenging real-world datasets demonstrate that both the robust contrastive loss and the multitask fine-tuning scheme are effective, leading to very promising results with a time cost suitable for mobile product search scenarios.

This paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video clip with multiple informative and diverse caption sentences. The proposed method is trained without explicit annotation of fine-grained sentence to video region-sequence correspondence, but is only based on weak video-level sentence annotations. It differs from existing video captioning systems in three technical aspects. First, we propose lexical fully convolutional neural networks (Lexical-FCN) with weakly supervised multi-instance multi-label learning to weakly link video regions with lexical labels. Second, we introduce a novel submodular maximization scheme to generate multiple informative and diverse region-sequences based on the Lexical-FCN outputs. A winner-takes-all scheme is adopted to weakly associate sentences to region-sequences in the training phase. Third, a sequence-to-sequence learning based language model is trained with the weakly supervised information obtained through the association process. We show that the proposed method can not only produce informative and diverse dense captions, but also outperform state-of-the-art single video captioning methods by a large margin.