arXiv subjects

Yong Zhao

Publications and source records attributed to Yong Zhao.

At least 235 records · Page 13Linked to original sources

arXiv2017

More On Large-Momentum Effective Theory Approach to Parton Physics

Large-Momentum Effective Theory (or LaMET) advocated by the present authors provides a direct approach to simulate parton physics in Eulidean lattice QCD theory. Recently, there has been much interest in this theory in the literature, with some questioning its validity and effectiveness. Here we provide some discussions aiming at a further exposition of this approach. In particular, we explain why it does not have the usual power divergence problem in lattice QCD calculations for the moments of parton distributions. We show that although the Ioffe-time distribution provides an alternative way to extract the parton distribution from the same lattice observable, it also requires the same large momentum limit as in LaMET to obtain a precision calculation. With a proper quantification of errors, both extraction methods should be compared with the same lattice data.

hep-ph↗

arXiv2017

Kernalised Multi-resolution Convnet for Visual Tracking

Visual tracking is intrinsically a temporal problem. Discriminative Correlation Filters (DCF) have demonstrated excellent performance for high-speed generic visual object tracking. Built upon their seminal work, there has been a plethora of recent improvements relying on convolutional neural network (CNN) pretrained on ImageNet as a feature extractor for visual tracking. However, most of their works relying on ad hoc analysis to design the weights for different layers either using boosting or hedging techniques as an ensemble tracker. In this paper, we go beyond the conventional DCF framework and propose a Kernalised Multi-resolution Convnet (KMC) formulation that utilises hierarchical response maps to directly output the target movement. When directly deployed the learnt network to predict the unseen challenging UAV tracking dataset without any weight adjustment, the proposed model consistently achieves excellent tracking performance. Moreover, the transfered multi-reslution CNN renders it possible to be integrated into the RNN temporal learning framework, therefore opening the door on the end-to-end temporal deep learning (TDL) for visual tracking.

cs.CV↗

arXiv2017

Renormalization in Large Momentum Effective Theory of Parton Physics

In the large-momentum effective field theory approach to parton physics, the matrix elements of non-local operators of quark and gluon fields, linked by straight Wilson lines in a spatial direction, are calculated in lattice quantum chromodynamics as a function of hadron momentum. Using the heavy-quark effective theory formalism, we show a multiplicative renormalization of these operators at all orders in perturbation theory, both in dimensional and lattice regularizations. The result provides a theoretical basis for extracting parton properties through properly renormalized observables in Monte Carlo simulations.

hep-ph↗

arXiv2017

Gluon orbital angular momentum at small-$x$

We present a general analysis of the orbital angular momentum (OAM) distribution of gluons $L_g(x)$ inside the nucleon with particular emphasis on the small-$x$ region. We derive a novel operator representation of $L_g(x)$ in terms of Wilson lines and argue that it is approximately proportional to the gluon helicity distribution $L_g(x) \approx -2ΔG(x)$ at small-$x$. We also compute longitudinal single spin asymmetry in exclusive diffractive dijet production in lepton-nucleon scattering in the next-to-eikonal approximation and show that the asymmetry is a direct probe of the gluon helicity/OAM distribution as well as the QCD odderon exchange.

hep-ph↗

arXiv2017

Glue spin and helicity in proton from lattice QCD

We report the first lattice QCD calculation of the glue spin in the nucleon. The lattice calculation is carried out with valence overlap fermions on 2+1 flavor DWF gauge configurations on four lattice spacings and four volumes including an ensemble with physical values for the quark masses. The glue spin $S_{G}$ in the Coulomb gauge in the $\overline{\text{MS}}$ scheme is obtained with the 1-loop perturbative matching. We find the results fairly insensitive to lattice spacing and quark masses. We also find that the proton momentum dependence of $S_G$ in the range $0\leq |\vec{p}| < 1.5$ GeV is very mild, and we determine it in the large momentum limit to be $S_{G}=0.251(47)(16)$ at the physical pion mass in the $\overline{\text{MS}}$ scheme at $μ^2=10$ GeV$^2$. If the matching procedure in large momentum effective theory is neglected, $S_G$ is equal to the glue helicity measured in high-energy scattering experiments.

hep-ph↗

arXiv2017

End-to-End Attention based Text-Dependent Speaker Verification

A new type of End-to-End system for text-dependent speaker verification is presented in this paper. Previously, using the phonetically discriminative/speaker discriminative DNNs as feature extractors for speaker verification has shown promising results. The extracted frame-level (DNN bottleneck, posterior or d-vector) features are equally weighted and aggregated to compute an utterance-level speaker representation (d-vector or i-vector). In this work we use speaker discriminative CNNs to extract the noise-robust frame-level features. These features are smartly combined to form an utterance-level speaker vector through an attention mechanism. The proposed attention model takes the speaker discriminative information and the phonetic information to learn the weights. The whole system, including the CNN and attention model, is joint optimized using an end-to-end criterion. The training algorithm imitates exactly the evaluation process --- directly mapping a test utterance and a few target speaker utterances into a single verification score. The algorithm can automatically select the most similar impostor for each target speaker to train the network. We demonstrated the effectiveness of the proposed end-to-end system on Windows $10$ "Hey Cortana" speaker verification task.

cs.CL↗

arXiv2016

Performance Analysis for Training-Based Multi-Pair Two-Way Full-Duplex Relaying with Massive Antennas

This paper considers a multi-pair two-way amplify-and-forward relaying system, where multiple pairs of full-duplex users are served via a full-duplex relay with massive antennas, and the relay adopts maximum-ratio combining/maximum-ratio transmission (MRC/MRT) processing. The orthogonal pilot scheme and the least square method are firstly exploited to estimate the channel state information (CSI). When the number of relay antennas is finite, we derive an approximate sum rate expression which is shown to be a good predictor of the ergodic sum rate, especially in large number of antennas. Then the corresponding achievable rate expression is obtained by adopting another pilot scheme which estimates the composite CSI for each user pair to reduce the pilot overhead of channel estimation. We analyze the achievable rates of the two pilot schemes and then show the relative merits of the two methods. Furthermore, power allocation strategies for users and the relay are proposed based on sum rate maximization and max-min fairness criterion, respectively. Finally, numerical results verify the accuracy of the analytical results and show the performance gains achieved by the proposed power allocation.

cs.IT↗

arXiv2016

Cache-enabled Uplink Transmission in Wireless Small Cell Networks

It is starting to become a big trend in the era of social networking that people produce and upload user-generated contents to Internet via wireless networks, bringing a significant burden on wireless uplink networks. In this paper, we contribute to designing and theoretical understanding of wireless cache-enabled upload transmission in a delay-tolerant small cell network to relieve the burden, and then propose the corresponding scheduling policies for the small base station (SBS) under the infinite and finite cache sizes. Specifically, the cache ability introduced by SBS enables SBS to eliminate the redundancy among the upload contents from users. This strategy not only alleviates the wireless backhual traffic congestion from SBS to a macro base station (MBS), but also improves the transmission efficiency of SBS. We then investigate the scheduling schemes of SBS to offload more data traffic under caching size constraint. Moreover, two operational regions for the wireless cache-enabled upload network, namely, the delay-limited region and the cache-limited region, are established to reveal the fundamental tradeoff between the delay tolerance and the cache ability. Finally, numerical results are provided to demonstrate the significant performance gains of the proposed wireless cache-enabled upload network.

cs.IT↗

arXiv2016

The 1-loop correction of the QCD energy momentum tensor with the overlap fermion and HYP smeared Iwasaki gluon

We present the 1-loop renormalization of the energy momentum tensor using the overlap fermion and a HYP-smeared Iwasaki gauge action. We also calculate the 1-loop matching coefficient that convert the lattice simulation results renormalized in the RI/MOM scheme to the $\overline{\textrm{MS}}$ scheme. The dependence of the renormalization on the gauge action and the number of HYP smearing steps are also investigated.

nucl-th↗

arXiv2016

Hunting the Gluon Orbital Angular Momentum at the Electron-Ion Collider

Applying the connection between the parton Wigner distribution and orbital angular momentum (OAM), we investigate the probe of the gluon OAM in hard scattering processes at the planned electron-ion collider. We show that the single longitudinal target-spin asymmetry in the hard diffractive dijet production is very sensitive to the gluon OAM distribution. The associated spin asymmetry leads to a characteristic azimuthal angular correlation of $\sin(ϕ_q -ϕ_Δ)$, where $ϕ_Δ$ and $ϕ_q$ are the azimuthal angles of the proton momentum transfer and the relative transverse momentum between the quark-antiquark pair. This study will enable a first measurement of the gluon OAM in the proton spin sum rule.

hep-ph↗

arXiv2016

Efficient Convolutional Auto-Encoding via Random Convexification and Frequency-Domain Minimization

The omnipresence of deep learning architectures such as deep convolutional neural networks (CNN)s is fueled by the synergistic combination of ever-increasing labeled datasets and specialized hardware. Despite the indisputable success, the reliance on huge amounts of labeled data and specialized hardware can be a limiting factor when approaching new applications. To help alleviating these limitations, we propose an efficient learning strategy for layer-wise unsupervised training of deep CNNs on conventional hardware in acceptable time. Our proposed strategy consists of randomly convexifying the reconstruction contractive auto-encoding (RCAE) learning objective and solving the resulting large-scale convex minimization problem in the frequency domain via coordinate descent (CD). The main advantages of our proposed learning strategy are: (1) single tunable optimization parameter; (2) fast and guaranteed convergence; (3) possibilities for full parallelization. Numerical experiments show that our proposed learning strategy scales (in the worst case) linearly with image size, number of filters and filter size.

stat.ML↗

arXiv2016

Binarized Neural Networks on the ImageNet Classification Task

We trained Binarized Neural Networks (BNNs) on the high resolution ImageNet ILSVRC-2102 dataset classification task and achieved a good performance. With a moderate size network of 13 layers, we obtained top-5 classification accuracy rate of 84.1 % on validation set through network distillation, much better than previous published results of 73.2% on XNOR network and 69.1% on binarized GoogleNET. We expect networks of better performance can be obtained by following our current strategies. We provide a detailed discussion and preliminary analysis on strategies used in the network training.

cs.CV↗

arXiv2016

Opportunistic Channel Sharing in Stochastic Networks with Dynamic Traffic

In this paper, we consider the stochastic network with dynamic traffic. The spatial distribution of access points (APs) and users are first modeled as mutually independent Poisson point processes (PPPs). Different from most previous literatures which assume all the APs are fully loaded, we consider the fact that APs having no data to transmit do not generate interference to users. The APs opportunistically share the channel according to the existence of the packet to be transmitted and the proposed interference suppression strategy. In the interference suppression region, only one AP can be active at a time to transmit the packet on the channel and the other adjacent APs keep silent to reduce serious interference. The idle probability of any AP, influenced by the traffic load and availability of the channels, is analyzed. The density of simultaneously active APs in the network is obtained and the packet loss rate is further elaborated. We reveal the impacts of network features (e.g., AP density, user density and channel state) and service features (e.g., user request, packet size) on the network performance. Simulation results validate our proposed model.

cs.NI↗

arXiv2016

Interference Cancellation at Receivers in Cache-Enabled Wireless Networks

In this paper, we propose to exploit the limited cache packets as side information to cancel incoming interference at the receiver side. We consider a stochastic network where the random locations of base stations and users are modeled using Poisson point processes. Caching schemes to reap both the local caching gain and the interference cancellation gain for the users are developed based on two factors: the density of different user subsets and the packets cached in the corresponding subsets. The packet loss rate (PLR) is analyzed, which depends on both the cached packets and the channel state information (CSI) available at the receiver. Theoretical results reveal the tradeoff between caching resource and wireless resource. The performance for different caching schemes are analyzed and the minimum achievable PLR for the distributed caching is derived.

cs.IT↗

arXiv2016

Long distance co-propagation of quantum key distribution and terabit classical optical data channels

Quantum key distribution (QKD) generates symmetric keys between two remote parties, and guarantees the keys not accessible to any third party. Wavelength division multiplexing (WDM) between QKD and classical optical communications by sharing the existing fibre optics infrastructure is highly desired in order to reduce the cost of QKD applications. However, quantum signals are extremely weak and thus easily affected by the spontaneous Raman scattering effect from intensive classical light. Here, by means of wavelength selecting and spectral and temporal filtering, we realize the multiplexing and long distance co-propagation of QKD and Terabit classical coherent optical communication system up to 80km. The data capacity is two orders of magnitude larger than the previous results. Our demonstration verifies the feasibility of QKD and classical communication to share the resources of backbone fibre links, and thus taking the utility of QKD a great step forward.

quant-ph↗

arXiv2015

Spin Decomposition of Electron in QED

We perform a systematic study on the spin decomposition of an electron in QED at one-loop order. It is found that the electron orbital angular momentum defined in Jaffe-Manohar and Ji spin sum rules agrees with each other, and the so-called potential angular momentum vanishes at this order. The calculations are performed in both dimensional regularization and Pauli-Villars regularization for the ultraviolet divergences, and they lead to consistent results. We further investigate the calculations in terms of light-front wave functions, and find a missing contribution from the instantaneous interaction in light-front quantization. This clarifies the confusing issues raised recently in the literature on the spin decomposition of an electron, and will help to consolidate the spin physics program for nucleons in QCD.

hep-ph↗

arXiv2015

New Results on Stability of Singular Stochastic Markov Jump Systems with State-Dependent Noise

This paper aims to develop the stability theory for singular stochastic Markov jump systems with state-dependent noise, including both continuous- and discrete-time cases. The sufficient conditions for the existence and uniqueness of a solution to the system equation are provided. Some new and fundamental concepts such as non-impulsiveness and mean square admissibility are introduced, which are different from those of other existing works. By making use of the $\mathcal{H}$-representation technique and the pseudo inverse $E^+$ of a singular matrix $E$, sufficient conditions ensuring the system to be mean square admissible are established in terms of strict linear matrix inequalities, which can be regarded as extensions of the corresponding results of deterministic singular systems and normal stochastic systems. Practical examples are given to demonstrate the effectiveness of the proposed approaches.

math.OC↗

arXiv2015

Orbital Angular Momentum and Generalized Transverse Momentum Distribution

We show that, when boosted to the infinite momentum frame, the quark and gluon orbital angular momentum operators defined in the nucleon spin sum rule of X. S. Chen et al. are the same as those derived from generalized transverse momentum distributions. This completes the connection between the infinite momentum limit of each term in that sum rule and experimentally measurable observables. We also show that these orbital angular momentum operators can be defined locally, and discuss the strategies of calculating them in lattice QCD.

hep-ph↗