Genome wide association study of seed and seedling root traits in sunflower

Cultivated sunflower (Helianthus annuus L.) is an internationally important crop harvested for seed oil and confectionary purposes. Sunflower unlike many other crops, has an extensive root system that can provide drought avoidance under water limiting conditions in the field. This genome wide association (GWA) study utilized 2D images of seeds and seedling roots from a diverse panel of sunflower genotypes (n = 288 lines) for QTL detection. The subsequent analyses of these images revealed vast phenotypic variation in total root length (mean = 169 cm, SD = 81 cm) and primary root length (mean = 29 cm, SD = 7 cm). ANOVA and PCA based on population information assigned by STRUCTURE indicated significant differences between restorer lines and maintainer lines for both seedling root and seed traits. In total, 29 unique markers associated with seed size and shape and seedling root traits were identified. This study provides the groundwork for future experiments with the final goal of identifying markers that breeders can use for marker assisted selection to improve sunflower seedling emergence and establishment

Step Optimal Implementations of Large Single-Writer Registers

We present two wait-free algorithms for simulating an l-bit single-writer register from k-bit single-writer registers, for any k >= 1. Our first algorithm has big-theta(l/k) step complexity for both Read and Write and uses big-theta (4^(l-k)) registers. An interesting feature of the algorithm is that Read operations do not write to shared variables. Our second algorithm has big-theta (l/k + (log n)/k) step complexity for both Read and Write, where n is the number of readers, but uses only big-theta (nl/k + n(log n)/k) registers. Combining both algorithms gives an implementation with big-theta (l/k) step complexity using big-theta (nl/k) space for any 1 <= k < l. We also prove that any implementation with big-O (l/k) step complexity for Read requires big-omega (l/k) step complexity for Write. Since reading l-bits requires at least ceiling(l/k) reads of k-bit registers, our lower bound shows that our implementation is step optimal

The Analysis of 2007 APEC News Coverage on the ABC, CNN and Xinhua Websites

This essay examines major issues of Australia, the United States and China concerned about in the 2007 APEC Summit held in Australia, and discusses the attitudes of these three countries towards to the 2007 APEC, by examining all the articles about 2007 APEC Summit from three major websites from these countries, ABC, CNN and Xinhua websites. Key words:  2007 APEC Summit; ABC; CNN; Xinhua Résumé:  Cet article examine les grands événements de l'Australie, des États-Unis et de la Chine au Sommet de l'APEC 2007, qui s'est tenue en Australie, et examine les attitudes de ces trois pays vis-à-vis de l'APEC 2007 en examinant tous les articles sur le Sommet de l'APEC 2007 dans les trois grands sites Web de ces pays, ABC, CNN et Xinhua.Mots-clés: sommet APEC 2007; ABC; CNN; Xinhu

Distributed human 3D pose estimation and action recognition.

In this paper, we propose a distributed solution for3D human pose estimation using a RGBD camera network. Thekey feature of our method is a dynamic hybrid consensus filter(DHCF) is introduced to fuse the multiple view informationof cameras. In contrast to the centralized fusion solution,the DHCF algorithm can be used in a distributed network,which requires no central information fusion center. Therefore,the DHCF based fusion algorithm can benefit from manyadvantages of distributed network. We also show that theproposed fusion algorithm can handle the occlusion problemseffectively, and achieve higher action recognition rate comparedto the ones using only single view information

Memories of the future: reception studies on Chinese science-fiction cinema, 1979-2016

This thesis explores the history of the reception of science-fiction film in China from 1979 to 2016. It asks how Chinese audience received this genre in film discourse and in the industry and aims to address the research gap between the complexity of the construction of genre as a multi-dimensional process and the historically specific nature of the construction of Chinese science fiction as a category. The project focuses on three different contexts of reception history: Hollywood science-fiction film, domestic science-fiction film and literature-as-canon reception strategy in China. These contexts are used to outline the different texts relevant to the ‘horizon of expectation’ from reception theory. In exploring how Chinese film discourse and the Chinese film industry received science fiction as a genre, this project aims to interpret the integrated circulation of reception history of science-fiction films in China in a specific Chinese social and historical context. Overall, it argues that the literature-as-canon strategy was a crucial solution for genrification in the Chinese science-fiction film field from 1979 to 2016

Visual SLAM based on dynamic object removal

Visual simultaneous localization and mapping (SLAM) is the core of intelligent robot navigation system. Many traditional SLAM algorithms assume that the scene is static. When a dynamic object appears in the environment, the accuracy of visual SLAM can degrade due to the interference of dynamic features of moving objects. This strong hypothesis limits the SLAM applications for service robot or driverless car in the real dynamic environment. In this paper, a dynamic object removal algorithm that combines object recognition and optical flow techniques is proposed in the visual SLAM framework for dynamic scenes. The experimental results show that our new method can detect moving object effectively and improve the SLAM performance compared to the state of the art methods

Simultaneous monocular visual odometry and depth reconstruction with scale recovery

In this paper, we propose a deep neural net-work that can estimate camera poses and reconstruct thefull resolution depths of the environment simultaneously usingonly monocular consecutive images. In contrast to traditionalmonocular visual odometry methods, which cannot estimatescaled depths, we here demonstrate the recovery of the scaleinformation using a sparse depth image as a supervision signalin the training step. In addition, based on the scaled depth,the relative poses between consecutive images can be estimatedusing the proposed deep neural network. Another novelty liesin the deployment of view synthesis, which can synthesize anew image of the scene from a different view (camera pose)given an input image. The view synthesis is the core techniqueused for constructing a loss function for the proposed neuralnetwork, which requires the knowledge of the predicted depthsand relative poses, such that the proposed method couples thevisual odometry and depth prediction together. In this way,both the estimated poses and the predicted depths from theneural network are scaled using the sparse depth image as thesupervision signal during training. The experimental results onthe KITTI dataset show competitive performance of our methodto handle challenging environments