arXiv subjects

Yi Zhang

Publications and source records attributed to Yi Zhang.

At least 1,207 records · Page 67Linked to original sources

arXiv2017

On-Line Cluster Reconstruction Of GEM Detector Based On FPGA Technology

In this work, a serial on-line cluster reconstruction technique based on FPGA technology was developed to compress experiment data and reduce the dead time of data transmission and storage. At the same time, X-ray imaging experiment based on a two-dimensional positive sensitive triple GEM detector with an effective readout area of 10 cm*10 cm was done to demonstrate this technique with FPGA development board. The result showed that the reconstruction technology was practicality and efficient. It provides a new idea for data compression of large spectrometers.

physics.ins-det↗

arXiv2017

Deep Embedding Forest: Forest-based Serving with Deep Embedding Features

Deep Neural Networks (DNN) have demonstrated superior ability to extract high level embedding vectors from low level features. Despite the success, the serving time is still the bottleneck due to expensive run-time computation of multiple layers of dense matrices. GPGPU, FPGA, or ASIC-based serving systems require additional hardware that are not in the mainstream design of most commercial applications. In contrast, tree or forest-based models are widely adopted because of low serving cost, but heavily depend on carefully engineered features. This work proposes a Deep Embedding Forest model that benefits from the best of both worlds. The model consists of a number of embedding layers and a forest/tree layer. The former maps high dimensional (hundreds of thousands to millions) and heterogeneous low-level features to the lower dimensional (thousands) vectors, and the latter ensures fast serving. Built on top of a representative DNN model called Deep Crossing, and two forest/tree-based models including XGBoost and LightGBM, a two-step Deep Embedding Forest algorithm is demonstrated to achieve on-par or slightly better performance as compared with the DNN counterpart, with only a fraction of serving time on conventional hardware. After comparing with a joint optimization algorithm called partial fuzzification, also proposed in this paper, it is concluded that the two-step Deep Embedding Forest has achieved near optimal performance. Experiments based on large scale data sets (up to 1 billion samples) from a major sponsored search engine proves the efficacy of the proposed model.

cs.LG↗

arXiv2017

Hitting Times in Markov Chains with Restart and their Application to Network Centrality

Motivated by applications in telecommunications, computer scienceand physics, we consider a discrete-time Markov process withrestart. At each step the process eitherwith a positive probability restarts from a given distribution, orwith the complementary probability continues according to a Markovtransition kernel. The main contribution of the present work is thatwe obtain an explicit expression for the expectation of the hittingtime (to a given target set) of the process with restart.The formula is convenient when considering the problem of optimizationof the expected hitting time with respect to the restart probability.We illustrate our results with two examplesin uncountable and countable state spaces andwith an application to network centrality.

cs.PF↗

arXiv2017

Magnetic-induced condensate, vortices and vortons in color-flavor-locked-type matter

By considering Higgs modes within the Ginzburg-Landau framework, we study influences of a rotated magnetic field on the color-flavor-locked-type matter of dense QCD. We demonstrate, in a model-independent way, that a diquark condensate may be triggered by the magnetic response of rotated-charged Higgs modes, in addition to the known color-flavor-locked condensate. Moreover, the condensate is applied to explore formations of vortices in the presence of external magnetic fields. The superfluid-like vortices are constructed for the magnetic-induced condensate. In the situation including both kinds of condensates, the theoretical possibility of vortons is suggested and the formation condition and the energy stability are investigated semi-classically.

hep-ph↗

arXiv2017

The Hall number across a van Hove singularity

In the context of the relaxation time approximation to Boltzmann transport theory, we examine the behavior of the Hall number, $n_H$, of a metal in the neighborhood of a Lifshitz transition from a closed Fermi surface to open sheets. We find a universal non-analytic dependence of $n_H$ on the electron density in the high field limit, but a non-singular dependence at low fields. The existence of an assumed nematic transition produces a doping dependent $n_H$ similar to that observed in recent experiments in the high temperature superconductor YBa$_2$Cu$_3$O$_{7-x}$.

cond-mat.str-el↗

arXiv2017

Quantum Loop Topography for Machine Learning

Despite rapidly growing interest in harnessing machine learning in the study of quantum many-body systems, training neural networks to identify quantum phases is a nontrivial challenge. The key challenge is in efficiently extracting essential information from the many-body Hamiltonian or wave function and turning the information into an image that can be fed into a neural network. When targeting topological phases, this task becomes particularly challenging as topological phases are defined in terms of non-local properties. Here we introduce quantum loop topography (QLT): a procedure of constructing a multi-dimensional image from the "sample" Hamiltonian or wave function by evaluating two-point operators that form loops at independent Monte Carlo steps. The loop configuration is guided by characteristic response for defining the phase, which is Hall conductivity for the cases at hand. Feeding QLT to a fully-connected neural network with a single hidden layer, we demonstrate that the architecture can be effectively trained to distinguish Chern insulator and fractional Chern insulator from trivial insulators with high fidelity. In addition to establishing the first case of obtaining a phase diagram with topological quantum phase transition with machine learning, the perspective of bridging traditional condensed matter theory with machine learning will be broadly valuable.

cond-mat.str-el↗

arXiv2017

Effective face landmark localization via single deep network

In this paper, we propose a novel face alignment method using single deep network (SDN) on existing limited training data. Rather than using a max-pooling layer followed one convolutional layer in typical convolutional neural networks (CNN), SDN adopts a stack of 3 layer groups instead. Each group layer contains two convolutional layers and a max-pooling layer, which can extract the features hierarchically. Moreover, an effective data augmentation strategy and corresponding training skills are also proposed to over-come the lack of training images on COFW and 300-W da-tasets. The experiment results show that our method outper-forms state-of-the-art methods in both detection accuracy and speed.

cs.CV↗

arXiv2017

The Giant Radio Array for Neutrino Detection

The Giant Radio Array for Neutrino Detection (GRAND) is a planned array of ~200 000 radio antennas deployed over ~200 000 km2 in a mountainous site. It aims primarly at detecting high-energy neutrinos via the observation of extensive air showers induced by the decay in the atmosphere of taus produced by the interaction of cosmic neutrinos under the Earth surface. GRAND aims at reaching a neutrino sensitivity of 5.10$^{11}$ E$^{-2}$ GeV$^{-1}$cm$^{-2}$s$^{-1}$sr$^{-1}$ above 3.10$^{16}$ eV. This ensures the detection of cosmogenic neutrinos in the most pessimistic source models, and ~50 events per year are expected for the standard models. The instrument will also detect UHECRs and possibly FRBs. Here we show how our preliminary design should enable us to reach our sensitivity goals, and discuss the steps to be taken to achieve GRAND.

astro-ph.IM↗

arXiv2016

A Fusion Method Based on Decision Reliability Ratio for Finger Vein Verification

Finger vein verification has developed a lot since its first proposal, but there is still not a perfect algorithm. It is proved that algorithms with the same overall accuracy may have different misclassified patterns. We could make use of this complementation to fuse individual algorithms together for more precise result. According to our observation, algorithm has different confidence on its decisions but it is seldom considered in fusion methods. Our work is first to define decision reliability ratio to quantify this confidence, and then propose the Maximum Decision Reliability Ratio (MDRR) fusion method incorporating Weighted Voting. Experiment conducted on a data set of 1000 fingers and 5 images per finger proves the effectiveness of the method. The classifier obtained by MDRR method gets an accuracy of 99.42% while the maximum accuracy of the original individual classifiers is 97.77%. The experiment results also show the MDRR outperforms the traditional fusion methods as Voting, Weighted Voting, Sum and Weighted Sum.

cs.CV↗

arXiv2016

Generalized Multiband Typical Medium Dynamical Cluster Approximation: Application to (Ga,Mn)N

We generalize the multiband typical medium dynamical cluster approximation and the formalism introduced by Blackman, Esterling and Berk so that it can deal with localization in multiband disordered systems with both diagonal and off-diagonal disorder with complicated potentials. We also introduce a new ansatz for the angle resolved typical density of states that greatly improves the numerical stability of the method while preserving the independence of scattering events at different frequencies. Starting from the first-principles effective Hamiltonian, we apply this method to the diluted magnetic semiconductor Ga$_{1-x}$Mn$_x$N, and find the impurity band is completely localized for Mn concentrations $x<0.03$ while for $0.03 <x<0.10$ the impurity band has delocalized states but the chemical potential resides at or above the mobility edge. So, the system is always insulating within the experimental compositional limit ($x\approx 0.10$) due to Anderson localization. However, for $0.03 <x<0.10$ hole doping could make the system metallic allowing double exchange mediated, or enhanced, ferromagnetism. The developed method is expected to have a large impact on first-principles studies of Anderson localization.

cond-mat.dis-nn↗

arXiv2016

The Strong Decays of X(3940) and X(4160)

The new mesons $X(3940)$ and $X(4160)$ have been found by Belle Collaboration in the processes $e^+e^-\to J/ψD^{(*)}\bar D^{(*)}$. Considering $X(3940)$ and $X(4160)$ as $η_c(3S)$ and $η_c(4S)$ states, the two-body open charm OZI-allowed strong decay of $η_c(3S)$ and $η_c(4S)$ are studied by the improved Bethe-Salpeter method combine with the $^3P_0$ model. The strong decay width of $η_c(3S)$ is $Γ_{η_c(3S)}=(33.5^{+18.4}_{-15.3})$ MeV, which is closed to the result of $X(3940)$, therefore, $η_c(3S)$ is a good candidate of $X(3940)$. The strong decay width of $η_c(4S)$ is $Γ_{η_c(4S)}=(69.9^{+22.4}_{-21.1})$ MeV, considering the errors of the results, it's closed to the lower limit of $X(4160)$. But the ratio of the decay width $\frac{Γ(D\bar D^*)}{Γ(D^*\bar D^*)}$ of $η_c(4S)$ is larger than the experimental data of $X(4160)$. According to the above analysis, $η_c(4S)$ is not the candidate of $X(4160)$, and more investigations of $X(4160)$ is needed.

hep-ph↗

arXiv2016

Note on discounted continuous-time Markov decision processes with a lower bounding function

In this paper, we consider the discounted continuous-time Markov decision process (CTMDP) with a lower bounding function. In this model, the negative part of each cost rate is bounded by the drift function, say $w$, whereas the positive part is allowed to be arbitrarily unbounded. Our focus is on the existence of a stationary optimal policy for the discounted CTMDP problems out of the more general class. Both constrained and unconstrained problems are considered. Our investigations are based on a useful transformation for nonhomogeneous Markov pure jump processes that has not yet been widely applied to the study of CTMDPs. This technique was not employed in previous literature, but it clarifies the roles of the imposed conditions in a rather transparent way. As a consequence, we withdraw and weaken several conditions commonly imposed in the literature.

math.OC↗

arXiv2016

Continuous-time Markov decision processes with exponential utility

In this paper, we consider a continuous-time Markov decision process (CTMDP) in Borel spaces, where the certainty equivalent with respect to the exponential utility of the total undiscounted cost is to be minimized. The cost rate is nonnegative. We establish the optimality equation. Under the compactness-continuity condition, we show the existence of a deterministic stationary optimal policy. We reduce the risk-sensitive CTMDP problem to an equivalent risk-sensitive discrete-time Markov decision process, which is with the same state and action spaces as the original CTMDP. In particular, the value iteration algorithm for the CTMDP problem follows from this reduction. We do not need impose any condition on the growth of the transition and cost rate in the state, and the controlled process could be explosive.

math.OC↗

arXiv2016

On the nonexplosion and explosion for nonhomogeneous Markov pure jump processes

In this paper, we obtain new drift-type conditions for nonexplosion and explosion for nonhomogeneous Markov pure jump processes in Borel state spaces. The conditions are sharp; e.g., the one for nonexplosion is necessary if the state space is in addition locally compact and the $Q$-function satisfies weak Feller-type and local boundedness conditions. We comment on the relations of our conditions with the existing ones in the literature, and demonstrate some possible applications.

math.PR↗

arXiv2016

ARPES study of the epitaxially grown topological crystalline insulator SnTe(111)

SnTe is a prototypical topological crystalline insulator, in which the gapless surface state is protected by a crystal symmetry. The hallmark of the topological properties in SnTe is the Dirac cones projected to the surfaces with mirror symmetry, stemming from the band inversion near the L points of its bulk Brillouin zone, which can be measured by angle-resolved photoemission. We have obtained the (111) surface of SnTe film by molecular beam epitaxy on BaF2(111) substrate. Photon-energy-dependence of in situ angle-resolved photoemission, covering multiple Brillouin zones in the direction perpendicular tothe (111) surface, demonstrate the projected Dirac cones at the Gamma_bar and M_bar points of the surface Brillouinzone. In addition, we observe a Dirac-cone-like band structure at the Gamma point of the bulk Brillouin zone,whose Dirac energy is largely different from those at the Gamma_bar and M_bar points.

cond-mat.mtrl-sci↗

arXiv2016

Prediction of Manipulation Actions

Looking at a person's hands one often can tell what the person is going to do next, how his/her hands are moving and where they will be, because an actor's intentions shape his/her movement kinematics during action execution. Similarly, active systems with real-time constraints must not simply rely on passive video-segment classification, but they have to continuously update their estimates and predict future actions. In this paper, we study the prediction of dexterous actions. We recorded from subjects performing different manipulation actions on the same object, such as "squeezing", "flipping", "washing", "wiping" and "scratching" with a sponge. In psychophysical experiments, we evaluated human observers' skills in predicting actions from video sequences of different length, depicting the hand movement in the preparation and execution of actions before and after contact with the object. We then developed a recurrent neural network based method for action prediction using as input patches around the hand. We also used the same formalism to predict the forces on the finger tips using for training synchronized video and force data streams. Evaluations on two new datasets showed that our system closely matches human performance in the recognition task, and demonstrate the ability of our algorithm to predict what and how a dexterous action is performed.

cs.CV↗

arXiv2016

Low-dose CT denoising with convolutional neural network

To reduce the potential radiation risk, low-dose CT has attracted much attention. However, simply lowering the radiation dose will lead to significant deterioration of the image quality. In this paper, we propose a noise reduction method for low-dose CT via deep neural network without accessing original projection data. A deep convolutional neural network is trained to transform low-dose CT images towards normal-dose CT images, patch by patch. Visual and quantitative evaluation demonstrates a competing performance of the proposed method.

physics.med-ph↗

arXiv2016

Low-Dose CT via Deep Neural Network

In order to reduce the potential radiation risk, low-dose CT has attracted more and more attention. However, simply lowering the radiation dose will significantly degrade the imaging quality. In this paper, we propose a noise reduction method for low-dose CT via deep learning without accessing the original projection data. An architecture of deep convolutional neural network was considered to map the low-dose CT images into its corresponding normal-dose CT images patch by patch. Qualitative and quantitative evaluations demonstrate a state-the-art performance of the proposed method.

physics.med-ph↗