A Universal Update-pacing Framework For Visual Tracking

This paper proposes a novel framework to alleviate the model drift problem in visual tracking, which is based on paced updates and trajectory selection. Given a base tracker, an ensemble of trackers is generated, in which each tracker's update behavior will be paced and then traces the target object forward and backward to generate a pair of trajectories in an interval. Then, we implicitly perform self-examination based on trajectory pair of each tracker and select the most robust tracker. The proposed framework can effectively leverage temporal context of sequential frames and avoid to learn corrupted information. Extensive experiments on the standard benchmark suggest that the proposed framework achieves superior performance against state-of-the-art trackers.Comment: Submitted to ICIP 201

Vector dark matter from split SU(2) gauge bosons

We propose a vector dark matter model with an exotic dark SU(2) gauge group. Two Higgs triplets are introduced to spontaneously break the symmetry. All of the dark gauge bosons become massive, and the lightest one is a viable vector DM candidate. Its stability is guaranteed by a remaining Z_2 symmetry. We study the parameter space constrained by the Higgs measurement data, the dark matter relic density, and direct and indirect detection experiments. We find numerous parameter points satisfying all the constraints, and they could be further tested in future experiments. Similar methodology can be used to construct vector dark matter models from an arbitrary SO(N) gauge group.Comment: 25 pages, 5 figure

Axenic in vitro cultivation and genome diploidization of the moss Vesicularia montagnei for horticulture utilization

Mosses are widely used in the establishment of greenery. However, little research has been conducted to choose a suitable species or improve their performance for this application. In our study, we examined Vesicularia montagnei (V. montagnei), a robust moss that is widely distributed in temperate, subtropical, and tropical Asia with varying environmental conditions. Axenic cultivation system of V. montagnei was developed on modified BCD medium, which enabled its propagation and multiplication in vitro. In this axenic cultivation environment, several diploid V. montagnei lines with enhancement of rhizoid system were generated through artificial induction of diploidization. Transcriptomic analysis revealed that several genes responsible for jasmonic acid (JA) biosynthesis and signaling showed significant higher expression levels in the diploid lines compared to the wild type. These results are consistent with the increasement of JA content in the diploid lines. Our establishment of the axenic cultivation method may provide useful information for further study of other Vesicularia species. The diploid V. montagnei lines with improved rhizoid system may hold promising potential for greenery applications. Additionally, our study sheds light on the biosynthesis and functions of JA in the early landed plants

A compendium of genetic regulatory effects across pig tissues

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.</p

Light Field Image Reconstruction and Super-resolution

Light field images provide richer visual information with multiple light rays to capture the appearance of objects from different directions. However, light field cameras have suffered from the inherent trade-off between angular and spatial resolution. Two tasks are focusing on alleviating this limitation, namely light field reconstruction and light field super-resolution. In this research, we study these problems and propose novel methods for the two light field tasks. The thesis falls into four parts: Firstly, to obtain deep features but avoid solely stacking convolution layers to build a very deep network, we propose U-SAS-Net for light field reconstruction which combines the merits of U-Net and separable angular and spatial convolutions (SAS). Secondly, we take advantage of the attention mechanism to propose two attention modules, namely channel-wise and SAI-wise attention modules, and embed them into U-SAS-Net to form a Channel-wise and SAI-wise Attention Network (CSANet). The attention modules can help the baseline obtain visible performance improvement at small extra costs of memory and computation. Thirdly, we study the domain asymmetry existing in the light field images and refine SAS with extra spatial convolutions. To further improve the spatio-angular feature representation, we adopt dense connections for both spatial and angular domains and propose the novel Spatio-Angular Dense Network (SADenseNet) for light field reconstruction. Finally, we investigate the previous decomposition methods' deficiency and propose unified decomposition kernels with intra-domain and inter-domain connections embedded in the novel Decomposition Kernel Network (DKNet) for light field super-resolution. Furthermore, we generalize the feature loss, which is originally for single images, to guide the network to generate visually pleasing results with more textures

Underground Defects Detection Based on GPR by Fusing Simple Linear Iterative Clustering Phash (SLIC-Phash) and Convolutional Block Attention Module (CBAM)-YOLOv8

Ground Penetrating Radar (GPR) is an effective non-destructive detection method, that is frequently utilized in the detection of urban underground defects because of its quick speed, convenient and flexible operation, and high resolution. However, there are some limitations to defect detection using GPR, such as less data, poor data quality, and complexity of data interpretation. In this study, an underground defect detection system based on GPR was established. First, a Simple Linear Iterative Clustering (SLIC)-PHash, a Data Augmentation (DA) optimization algorithm, was created to obtain high-quality datasets. Second, the Convolutional Block Attention Module (CBAM)-YOLOv8, a detection model, was produced for the recognition of defects. This model uses GhostConv and CBAM to create a lighter design that better focuses on target detection and increases efficiency. Finally, a one-click detection system was formed by fusing SLIC-Phsh and CBAM-YOLOv8, which were used for one-click GPR dataset optimization and defect detection. The developed system has the best detection mAP and F1 scores of 90.8&#x0025; and 88.3&#x0025;, respectively, compared to several well-known Deep Learning (DL)-based techniques. The results demonstrated that the system proposed in this paper can greatly improve detection efficiency and reduce detection time by achieving a good balance between detection speed and accuracy