On Determining Deep Holes of Generalized Reed-Solomon Codes

For a linear code, deep holes are defined to be vectors that are further away from codewords than all other vectors. The problem of deciding whether a received word is a deep hole for generalized Reed-Solomon codes is proved to be co-NP-complete. For the extended Reed-Solomon codes RS_q(\F_q,k), a conjecture was made to classify deep holes by Cheng and Murray in 2007. Since then a lot of effort has been made to prove the conjecture, or its various forms. In this paper, we classify deep holes completely for generalized Reed-Solomon codes $RS_p (D,k)$, where $p$ is a prime, $|D| > k \geqslant \frac{p-1}{2}$. Our techniques are built on the idea of deep hole trees, and several results concerning the Erd{\"o}s-Heilbronn conjecture

Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-sequence Cardiac MR Images Segmentation

Analysis and modeling of the ventricles and myocardium are important in the diagnostic and treatment of heart diseases. Manual delineation of those tissues in cardiac MR (CMR) scans is laborious and time-consuming. The ambiguity of the boundaries makes the segmentation task rather challenging. Furthermore, the annotations on some modalities such as Late Gadolinium Enhancement (LGE) MRI, are often not available. We propose an end-to-end segmentation framework based on convolutional neural network (CNN) and adversarial learning. A dilated residual U-shape network is used as a segmentor to generate the prediction mask; meanwhile, a CNN is utilized as a discriminator model to judge the segmentation quality. To leverage the available annotations across modalities per patient, a new loss function named weak domain-transfer loss is introduced to the pipeline. The proposed model is evaluated on the public dataset released by the challenge organizer in MICCAI 2019, which consists of 45 sets of multi-sequence CMR images. We demonstrate that the proposed adversarial pipeline outperforms baseline deep-learning methods.Comment: 9 pages, 4 figures, conferenc

Deeply-Learned Part-Aligned Representations for Person Re-Identification

In this paper, we address the problem of person re-identification, which refers to associating the persons captured from different cameras. We propose a simple yet effective human part-aligned representation for handling the body part misalignment problem. Our approach decomposes the human body into regions (parts) which are discriminative for person matching, accordingly computes the representations over the regions, and aggregates the similarities computed between the corresponding regions of a pair of probe and gallery images as the overall matching score. Our formulation, inspired by attention models, is a deep neural network modeling the three steps together, which is learnt through minimizing the triplet loss function without requiring body part labeling information. Unlike most existing deep learning algorithms that learn a global or spatial partition-based local representation, our approach performs human body partition, and thus is more robust to pose changes and various human spatial distributions in the person bounding box. Our approach shows state-of-the-art results over standard datasets, Market-$1501$, CUHK$03$, CUHK$01$ and VIPeR.Comment: Accepted by ICCV 201

Uncertainties in the calibrations of star formation rate

The calibrations of star formation rate (SFR) are prone to be affected by many factors, such as metallicity, initial mass function (IMF), evolutionary population synthesis (EPS) models and so on. In this paper we will discuss the effects of binary interactions, metallicity, EPS models and IMF on several widely used SFR calibrations based on the EPS models of Yunnan with and without binary interactions, BC03, SB99, PEGASE and POPSTAR. The inclusion of binary interactions makes these SFR conversion coefficients smaller (less than 0.2dex), and these differences increase with metallicity. The differences in the calibration coefficient between SFR and the luminosity of $\rm H\alpha$ recombination line (C$_{\rm H\alpha}$) and that between SFR and the ultraviolet (UV) fluxes at 1500 and 2800\,$\rm \AA$ (C$_{i, {\rm UV}}$), caused by IMF, are independent of metallicity (0.03-0.33\,dex) except $\Delta$C$_{\rm H\alpha, IMF}$ when using the POPSTAR and $\Delta$C$_{i, {\rm UV, IMF}}$ when using the PEGASE models. Moreover, we find that $L_{\rm 2800}$ is not suitable to the linear calibration of SFR at low metallicities. At last, we compare the effects of these several factors on the SFR calibrations considered in this paper. The effects of metallicity/IMF and EPS models on the C$_{\rm H\alpha}$ and C$_{\rm FIR}$ (the conversion coefficient between SFR and the far-infrared flux) are the largest among these factors, respectively. For the calibration between SFR and C$_{i, {\rm UV}}$, the effects of these several factors are comparable.Comment: 17 pages, 7 figures, 9 tables, accepted by MNRA

Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths

Zero-shot recognition aims to accurately recognize objects of unseen classes by using a shared visual-semantic mapping between the image feature space and the semantic embedding space. This mapping is learned on training data of seen classes and is expected to have transfer ability to unseen classes. In this paper, we tackle this problem by exploiting the intrinsic relationship between the semantic space manifold and the transfer ability of visual-semantic mapping. We formalize their connection and cast zero-shot recognition as a joint optimization problem. Motivated by this, we propose a novel framework for zero-shot recognition, which contains dual visual-semantic mapping paths. Our analysis shows this framework can not only apply prior semantic knowledge to infer underlying semantic manifold in the image feature space, but also generate optimized semantic embedding space, which can enhance the transfer ability of the visual-semantic mapping to unseen classes. The proposed method is evaluated for zero-shot recognition on four benchmark datasets, achieving outstanding results.Comment: Accepted as a full paper in IEEE Computer Vision and Pattern Recognition (CVPR) 201

Online Metric-Weighted Linear Representations for Robust Visual Tracking

In this paper, we propose a visual tracker based on a metric-weighted linear representation of appearance. In order to capture the interdependence of different feature dimensions, we develop two online distance metric learning methods using proximity comparison information and structured output learning. The learned metric is then incorporated into a linear representation of appearance. We show that online distance metric learning significantly improves the robustness of the tracker, especially on those sequences exhibiting drastic appearance changes. In order to bound growth in the number of training samples, we design a time-weighted reservoir sampling method. Moreover, we enable our tracker to automatically perform object identification during the process of object tracking, by introducing a collection of static template samples belonging to several object classes of interest. Object identification results for an entire video sequence are achieved by systematically combining the tracking information and visual recognition at each frame. Experimental results on challenging video sequences demonstrate the effectiveness of the method for both inter-frame tracking and object identification.Comment: 51 pages. Appearing in IEEE Transactions on Pattern Analysis and Machine Intelligenc