DISC: Deep Image Saliency Computing via Progressive Representation Learning

Salient object detection increasingly receives attention as an important component or step in several pattern recognition and image processing tasks. Although a variety of powerful saliency models have been intensively proposed, they usually involve heavy feature (or model) engineering based on priors (or assumptions) about the properties of objects and backgrounds. Inspired by the effectiveness of recently developed feature learning, we provide a novel Deep Image Saliency Computing (DISC) framework for fine-grained image saliency computing. In particular, we model the image saliency from both the coarse- and fine-level observations, and utilize the deep convolutional neural network (CNN) to learn the saliency representation in a progressive manner. Specifically, our saliency model is built upon two stacked CNNs. The first CNN generates a coarse-level saliency map by taking the overall image as the input, roughly identifying saliency regions in the global context. Furthermore, we integrate superpixel-based local context information in the first CNN to refine the coarse-level saliency map. Guided by the coarse saliency map, the second CNN focuses on the local context to produce fine-grained and accurate saliency map while preserving object details. For a testing image, the two CNNs collaboratively conduct the saliency computing in one shot. Our DISC framework is capable of uniformly highlighting the objects-of-interest from complex background while preserving well object details. Extensive experiments on several standard benchmarks suggest that DISC outperforms other state-of-the-art methods and it also generalizes well across datasets without additional training. The executable version of DISC is available online: http://vision.sysu.edu.cn/projects/DISC.Comment: This manuscript is the accepted version for IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), 201

Spectral broadening effects in optical communication networks : impact and security issue

The impact of spectral broadening effects on the performance of nonlinear compensation applied to both modern dispersion-unmanaged and legacy dispersion-managed optical communication systems has been analysed and quantified. It is found that including the full broadened spectrum at the receiver enables a substantial improvement in the performance of nonlinear compensation

Nonparametric Bayesian Models for Joint Analysis of Imagery and Text

It has been increasingly important to develop statistical models to manage large-scale high-dimensional image data. This thesis presents novel hierarchical nonparametric Bayesian models for joint analysis of imagery and text. This thesis consists two main parts.The first part is based on single image processing. We first present a spatially dependent model for simultaneous image segmentation and interpretation. Given a corrupted image, by imposing spatial inter-relationships within imagery, the model not only improves reconstruction performance but also yields smooth segmentation. Then we develop online variational Bayesian algorithm for dictionary learning to process large-scale datasets, based on online stochastic optimization with a natu- ral gradient step. We show that dictionary is learned simultaneously with image reconstruction on large natural images containing tens of millions of pixels.The second part applies dictionary learning for joint analysis of multiple image and text to infer relationship among images. We show that feature extraction and image organization with annotation (when available) can be integrated by unifying dictionary learning and hierarchical topic modeling. We present image organization in both "flat" and hierarchical constructions. Compared with traditional algorithms feature extraction is separated from model learning, our algorithms not only better fits the datasets, but also provides richer and more interpretable structures of image</p

Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey

Data Augmentation (DA) has emerged as an indispensable strategy in Time Series Classification (TSC), primarily due to its capacity to amplify training samples, thereby bolstering model robustness, diversifying datasets, and curtailing overfitting. However, the current landscape of DA in TSC is plagued with fragmented literature reviews, nebulous methodological taxonomies, inadequate evaluative measures, and a dearth of accessible, user-oriented tools. In light of these challenges, this study embarks on an exhaustive dissection of DA methodologies within the TSC realm. Our initial approach involved an extensive literature review spanning a decade, revealing that contemporary surveys scarcely capture the breadth of advancements in DA for TSC, prompting us to meticulously analyze over 100 scholarly articles to distill more than 60 unique DA techniques. This rigorous analysis precipitated the formulation of a novel taxonomy, purpose-built for the intricacies of DA in TSC, categorizing techniques into five principal echelons: Transformation-Based, Pattern-Based, Generative, Decomposition-Based, and Automated Data Augmentation. Our taxonomy promises to serve as a robust navigational aid for scholars, offering clarity and direction in method selection. Addressing the conspicuous absence of holistic evaluations for prevalent DA techniques, we executed an all-encompassing empirical assessment, wherein upwards of 15 DA strategies were subjected to scrutiny across 8 UCR time-series datasets, employing ResNet and a multi-faceted evaluation paradigm encompassing Accuracy, Method Ranking, and Residual Analysis, yielding a benchmark accuracy of 88.94 +- 11.83%. Our investigation underscored the inconsistent efficacies of DA techniques, with..

Microsatellite Markers in the Mud Crab (Scylla paramamosain) and their Application in Population Genetics and Marker- Assisted Selection

The mud crab (Scylla paramamosain) is a commercially important species for aquaculture and fisheries in China. In this study, a total of 302 polymorphic microsatellite markers have been isolated and characterized. The observed and expected heterozygosity ranged from 0.04 to 1.00 and from 0.04 to 0.96 per locus. The wild populations distributed along South-eastern China coasts showed high genetic diversity (HO ranged from 0.62 to 0.77) and low genetic differentiation (FST = 0.018). Meanwhile, a significant association (r2 = 0.11) was identified between genetic and geographic distance of 11 locations. Furthermore, a PCR-based parentage assignment method was successfully developed using seven polymorphic microsatellite loci that could correctly assign 95% of the progeny to their parents. Moreover, three polymorphic microsatellite loci were identified to be significantly associated with 12 growth traits of S. paramamosain, and four genotypes were considered to be great potential for marker-assisted selection. Finally, a first preliminary genetic linkage map with 65 linkage groups and 212 molecular markers was constructed using microsatellite and AFLP markers for S. paramamosain. This map was 2746 cM in length, and covered approximately 50% of the estimated genome. This study provides novel insights into genome biology and molecular marker-assisted selection for S. paramamosain