A hybrid clustering and graph based algorithm for tagSNP selection

TagSNP selection, which aims to select a small subset of informative single nucleotide polymorphisms (SNPs) to represent the whole large SNP set, has played an important role in current genomic research. Not only can this cut down the cost of genotyping by filtering a large number of redundant SNPs, but also it can accelerate the study of genome-wide disease association. In this paper, we propose a new hybrid method called CMDStagger that combines the ideas of the clustering and the graph algorithm, to find the minimum set of tagSNPs. The proposed algorithm uses the information of the linkage disequilibrium association and the haplotype diversity to reduce the information loss in tagSNP selection, and has no limit of block partition. The approach is tested on eight benchmark datasets from Hapmap and chromosome 5q31. Experimental results show that the algorithm in this paper can reduce the selection time and obtain less tagSNPs with high prediction accuracy. It indicates that this method has better performance than previous ones.     

Control of frictional dynamics of a one-dimensional particle array

Control of frictional forces is required in many applications of tribology. While the problem is approached by chemical means traditionally, a recent approach was proposed to control the system mechanically to tune frictional responses. We design feedback control laws for a one-dimensional particle array sliding on a surface subject to friction. The Frenkel-Kontorova model describing the dynamics is a nonlinear interconnected system and the accessible control elements are average quantities only. We prove local stability of equilibrium points of the un-controlled system in the presence of linear and nonlinear particle interactions, respectively. We then formulate a tracking control problem, whose control objective is for the average system to reach a designated targeted velocity using accessible elements. Sufficient stabilization conditions are explicitly derived for the closed-loop error systems using the Lyapunov theory based methods. Simulation results show satisfactory performances. The results can be applied to other physical systems whose dynamics is described by the Frenkel-Kontorova model.     

Symptomatic subclavian steal syndrome four decades after operation for dysphagia lusoria

Congenital abnormalities of the aortic arch may lead to signs and symptoms of tracheal and esophageal obstruction secondary to a restrictive vascular ring. There are many case reports and monographs concerning the surgical management of dysphagia lusoria. This case provides the first example of long-term follow-up of surgical intervention for relief of dysphagia lusoria. A 45-year-old laborer presented with a several year history of episodic bilateral blindness and a more recent onset of "drop attacks." Notably this patient had presented at the age of 18 months with difficulty breathing and eating since birth. The patient also had frequent upper respiratory infections and episodes of pneumonia. Workup revealed a right-sided aortic arch with a left ligamentum arteriosum. When he was first seen in our clinic, history and physical examination revealed claudication and diminished pulses in the left upper extremity. Arteriography and duplex studies confirmed reversal of flow in the patient's left vertebral artery. The arteriogram demonstrated the presence of a right-sided aortic arch and descending aorta along with the proximal stump of the previously ligated left subclavian artery. He underwent left carotid to left axillary artery bypass for the treatment of symptomatic subclavian steal syndrome. His symptoms have resolved with return of antegrade vertebral flow and the presence of normal pulses in the left arm. Congenital aortic abnormalities that lead to tracheal and esophageal compromise are numerous and varied. Surgical management requires a thorough understanding of the person's anatomy and preoperative planning. The life expectancy of patients with dysphagia lusoria necessitates consideration of the long-term consequences of surgical intervention.     

A preliminary attempt to use radiomic features in the diagnosis of extra-articular long head biceps tendinitis

Magnetic Resonance Materials in Physics, Biology and Medicine - This study aims to present a radiomic application in diagnosing the long head of biceps (LHB) tendinitis. Moreover, we evaluated...     

Relational attention-based Markov logic network for visual navigation

The Journal of Supercomputing - We argue the agent’s low generalization problem for searching target object in challenging visual navigation could be solved by "how" and...     

Syntax–Aware graph convolutional network for the recognition of chinese implicit inter-sentence relations

The Journal of Supercomputing - In the literature, most previous studies on English implicit inter-sentence relation recognition only focused on semantic interactions, which could not exploit the...     

Joint Classification and Regression for Visual Tracking with Fully Convolutional Siamese Networks

International Journal of Computer Vision - Visual tracking of generic objects is one of the fundamental but challenging problems in computer vision. Here, we propose a novel fully convolutional...     

A Survey on Long-Tailed Visual Recognition

The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the performance of deep learning models is often dominated by the head classes while the learning of the tail classes is severely underdeveloped. In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. In this survey, we focus on the problems caused by long-tailed data distribution, sort out the representative long-tailed visual recognition datasets and summarize some mainstream long-tailed studies. Specifically, we summarize these studies into ten categories from the perspective of representation learning, and outline the highlights and limitations of each category. Besides, we have studied four quantitative metrics for evaluating the imbalance, and suggest using the Gini coefficient to evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade, and find that the long-tailed phenomenon is widespread and has not been fully studied. Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers.

     

Relation network based on multi-granular hypergraphs for person re-identification

Applied Intelligence - In recent years, person re-identification (re-ID) has become a widespread research topic that focuses on retrieving target pedestrians from a set of images, typically taken...