arXiv subjects

Yue Tan

Publications and source records attributed to Yue Tan.

75 records · Page 5Linked to original sources

arXiv2019

X(3872) in an unquenched quark model

In this paper, we calculate mass and probability fractions of meson-meson components of $X(3872)$ in an unquenched quark model. Different from most of other unquenched quark models, the quark pairs creation operator from $^3P_0$ is modified by considering effects of the created quark pair energy and separation between the created quark pair and the valence quark-pair. In the calculation, all the wavefunctions of mesons and the relative motion between two mesons are obtained by solving the corresponding Schrödinger equation with the help of gaussian expansion method. The multi-channel coupling of quark-antiquark state with possible meson-meson states are performed. The results show that the $X(3872)$ can be described as a mixing state of dominant charmonium state (70\%) and meson-meson components (30\%).

hep-ph↗

arXiv2019

RDMA Performance Isolation With Justitia

Despite its increasing popularity, most of RDMA's benefits such as ultra-low latency can be achieved only when running an application in isolation. Using microbenchmarks and real open-source RDMA applications, we identify a series of performance anomalies when multiple applications coexist and show that such anomalies are pervasive across InfiniBand, RoCEv2, and iWARP. They arise due to a fundamental tradeoff between performance isolation and work conservation, which the state-of-the-art RDMA congestion control protocols such as DCQCN cannot resolve. We present Justitia to address these performance anomalies. Justitia is a software-only, host-based, and easy-to-deploy solution that maximizes RNIC utilization while guaranteeing performance isolation via shaping, rate limiting, and pacing at senders. Our evaluation of Justitia on multiple RDMA implementations show that Justitia effectively isolates different types of traffic and significantly improves latency (by up to 56.9x) and throughput (by up to 9.7x) of real-world RDMA-based applications without compromising low CPU usage or modifying the applications.

cs.NI↗

arXiv2019

An In-Vehicle KWS System with Multi-Source Fusion for Vehicle Applications

In order to maximize detection precision rate as well as the recall rate, this paper proposes an in-vehicle multi-source fusion scheme in Keyword Spotting (KWS) System for vehicle applications. Vehicle information, as a new source for the original system, is collected by an in-vehicle data acquisition platform while the user is driving. A Deep Neural Network (DNN) is trained to extract acoustic features and make a speech classification. Based on the posterior probabilities obtained from DNN, the vehicle information including the speed and direction of vehicle is applied to choose the suitable parameter from a pair of sensitivity values for the KWS system. The experimental results show that the KWS system with the proposed multi-source fusion scheme can achieve better performances in term of precision rate, recall rate, and mean square error compared to the system without it.

cs.LG↗