Hole burning in a nanomechanical resonator coupled to a Cooper pair box

We propose a scheme to create holes in the statistical distribution of excitations of a nanomechanical resonator. It employs a controllable coupling between this system and a Cooper pair box. The success probability and the fidelity are calculated and compared with those obtained in the atom-field system via distinct schemes. As an application we show how to use the hole-burning scheme to prepare (low excited) Fock states.Comment: 7 pages, 10 figure

Geochemistry and petrogenesis of volcanic rocks from Daimao Seamount (South China Sea) and their tectonic implications

The South China Sea (SCS) experienced three episodes of seafloor spreading and left three fossil spreading centers presently located at 18°N, 17°N and 15.5°N. Spreading ceased at these three locations during magnetic anomaly 10, 8, and 5c, respectively. Daimao Seamount (16.6. Ma) was formed 10. my after the cessation of the 17°N spreading center. Volcaniclastic rocks and shallow-water carbonate facies near the summit of Daimao Seamount provide key information on the seamount's geologic history. New major and trace element and Sr-Nd-Pb isotopic compositions of basaltic breccia clasts in the volcaniclastics suggest that Daimao and other SCS seamounts have typical ocean island basalt-like composition and possess a 'Dupal' isotopic signature. Our new analyses, combined with available data, indicate that the basaltic foundation of Daimao Seamount was formed through subaqueous explosive volcanic eruptions at 16.6. Ma. The seamount subsided rapidly (>. 0.12. mm/y) at first, allowing the deposition of shallow-water, coral-bearing carbonates around its summit and, then, at a slower rate (<. 0.12. mm/y). We propose that the parental magmas of SCS seamount lavas originated from the Hainan mantle plume. In contrast, lavas from contemporaneous seamounts in other marginal basins in the western Pacific are subduction-related

Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM

Hourglass Face Detector for Hard Face

Face detection is an upstream task of facial image analysis. In many real-world scenarios, we need to detect small, occluded or dense faces that are hard to detect, but hard face detection is a challenging task in particular considering the balance between accuracy and inference speed for real-world applications. This paper proposes an Hourglass Face Detector (HFD) for hard face by developing a deep one-stage fully-convolutional hourglass network, which achieves an excellent balance between accuracy and inference speed. To this end, the HFD firstly shrinks a feature map by a series of stridden convolutional layers rather than pooling layers, so that useful subtle information is preserved better. Secondly, it exploits context information by merging fine-grained shallow feature maps with deep ones full of semantic information, making a better fusion of detailed information and semantic information to achieve a better detection of small faces. Moreover, the HFD exploits prior and multiscale information from the training data to enhance its scale-invariance and adaptability of anchor scales. Compared with the SSH and S3FD methods, the HFD can achieve a better performance in average precision on detecting hard faces as well as a quicker inference. Experiments on the WIDER FACE and FDDB datasets demonstrate the superior performance of our proposed method

An Equalized Margin Loss for Face Recognition

In this paper, we propose a new loss function, termed the equalized margin (EqM) loss, which is designed to make both intra-class scopes and inter-class margins similar over all classes, such that all the classes can be evenly distributed on the hypersphere of the feature space. The EqM loss controls both the lower limit of intra-class similarity by exploiting hard sample mining and the upper limit of inter-class similarity by assuring equalized margins. Therefore, using the EqM loss, we can not only obtain more discriminative features, but also overcome the negative impacts from the data imbalance on the inter-class margins. We also observe that the EqM loss is stable with the variation of the scale in normalized Softmax. Furthermore, by conducting extensive experiments on LFW, YTF, CFP, MegaFace and IJB-B, we are able to verify the effectiveness and superiority of the EqM loss, compared with other state-of-the- art loss functions for face recogniti

RGB Guided Depth Map Super-Resolution with Coupled U-Net

The depth maps captured by RGB-D cameras usually are of low resolution, entailing recent efforts to develop depth super-resolution (DSR) methods. However, several problems remain in existing DSR methods. First, conventional DSR methods often suffer from unexpected artifacts. Secondly, high-resolution (HR) RGB features and low-resolution (LR) depth features are often fused in shallow layers only. Thirdly, only the last layer of features is used for reconstruction. To address the above problems, we propose Coupled U-Net (CU-Net), a new color image guided DSR method built on two U-Net branches for HR color images and LR depth maps, respectively. The CU-Net embeds a dual skip connection structure to leverage the feature interaction of the two branches, and a multi-scale fusion to fuse the deeper and multi-scale features of two branch decoders for more effective feature reconstruction. Moreover, a channel attention module is proposed to eliminate artifacts. Extensive experiments show that the proposed CU-Net outperforms state-of-the-art methods

Better Stereo Matching From Simple Yet Effective Wrangling of Deep Features

Cost volume plays a pivotal role in stereo matching. Most recent works focused on deep feature extraction and cost refinement for a more accurate cost volume. Unlike them, we probe from a different perspective: feature wrangling. We find that simple wrangling of deep features can effectively improve the construction of cost volume and thus the performance of stereo matching. Specifically, we develop two simple yet effective wrangling techniques of deep features, spatially a differentiable feature transformation and channel-wise a memory-economical feature expansion, for better cost construction. Exploiting the local ordering information provided by a differentiable rank transform, we achieve an enhancement of the search for correspondence; with the help of disparity division, our feature expansion allows for more features into the cost volume with no extra memory required. Equipped with these two feature wrangling techniques, our simple network can perform outstandingly on the widely used KITTI and Sceneflow datasets

Parallax Contextual Representations For Stereo Matching

In this work, we study the context aggregation in stereo matching from a new parallax perspective. Unlike previous works, we propose to characterize and augment a pixel with its parallax contextual representation (PCR), which has not been explored before. We also propose a new concept called disparity prototype to describe the overall representation of a disparity plane. Our proposed PCR module consists of three steps: 1) divide disparity planes for a rough estimation of disparity; 2) estimate the disparity prototypes for each disparity plane; 3) derive PCR-augmented representations with disparity prototypes. Extensive experiments on various datasets using different networks validate the effectiveness of our proposal