arXiv subjects

Gang Chen

Publications and source records attributed to Gang Chen.

At least 415 records · Page 23Linked to original sources

arXiv2020

A new method for driven-dissipative problems: Keldysh-Heisenberg equations

Driven-dissipative systems have recently attracted great attention due to the existence of novel physical phenomena with no analog in the equilibrium case. The Keldysh path-integral theory is a powerful tool to investigate these systems. However, it has still been challenge to study strong nonlinear effects implemented by recent experiments, since in this case the photon number is few and quantum fluctuations play a crucial role in dynamics of system. Here we develop a new approach for deriving exact steady states of driven-dissipative systems by introducing the Keldysh partition function in the Fock-state basis and then mapping the standard saddle-point equations into KeldyshHeisenberg equations. We take the strong Kerr nonlinear resonators with/without the nonlinear driving as two examples to illustrate our method. It is found that in the absence of the nonlinear driving, the exact steady state obtained does not exhibit bistability and agree well with the complex P-representation solution. While in the presence of the nonlinear driving, the multiphoton resonance effects are revealed and are consistent with the qualitative analysis. Our method provides an intuitive way to explore a variety of driven-dissipative systems especially with strong correlations.

quant-ph↗

arXiv2020

Effective and Efficient Dropout for Deep Convolutional Neural Networks

Convolutional Neural networks (CNNs) based applications have become ubiquitous, where proper regularization is greatly needed. To prevent large neural network models from overfitting, dropout has been widely used as an efficient regularization technique in practice. However, many recent works show that the standard dropout is ineffective or even detrimental to the training of CNNs. In this paper, we revisit this issue and examine various dropout variants in an attempt to improve existing dropout-based regularization techniques for CNNs. We attribute the failure of standard dropout to the conflict between the stochasticity of dropout and its following Batch Normalization (BN), and propose to reduce the conflict by placing dropout operations right before the convolutional operation instead of BN, or totally address this issue by replacing BN with Group Normalization (GN). We further introduce a structurally more suited dropout variant Drop-Conv2d, which provides more efficient and effective regularization for deep CNNs. These dropout variants can be readily integrated into the building blocks of CNNs and implemented in existing deep learning platforms. Extensive experiments on benchmark datasets including CIFAR, SVHN and ImageNet are conducted to compare the existing building blocks and the proposed ones with dropout training. Results show that our building blocks improve over state-of-the-art CNNs significantly, which is mainly due to the better regularization and implicit model ensemble effect.

cs.LG↗

arXiv2020

Dynamical Zeeman resonance in spin-orbit-coupled spin-1 Bose gases

We predict a dynamical resonant effect, which is driven by externally applied linear and quadratic Zeeman fields, in a spin-orbit-coupled spin-1 Bose-Einstein condensate. The Bose-Einstein condensate is assumed to be initialized in some superposed state of Zeeman sublevels and subject to a sudden shift of the trapping potential. It is shown that the time-averaged center-of-mass oscillation and the spin polarizations of the Bose-Einstein condensate exhibit remarkable resonant peaks when the Zeeman fields are tuned to certain strengths. The underlying physics behind this resonance can be traced back to the out-of-phase interference of the dynamical phases carried by different spinorbit states. By analyzing the single particle spectrum, the resonant condition is summarized as a simple algebraic relation, connecting the strengths of the linear and quadratic Zeeman fields. This property is potentially applicable in quantum information and quantum precision measurement.

cond-mat.quant-gas↗

arXiv2020

Study on the $Ω_{c}^{0}$ States Decaying to $Ξ_{c}^{+}K^{-}$ in $pp$ collisions at $\sqrt{s}=7$, 13 TeV

The production of strange particles $Ξ_{c}^{+}, K^{-}$ is simulated in mid-rapidity $pp$ collisions at $\sqrt{s}=7$ TeV with $0.2 \leq pt \leq 6$~GeV/c using the {\footnotesize PACIAE} model. The results are consistent with LHCb experimental data on $Ξ_{c}^{+}$ and $K^{-}$ yield. Then, a dynamically constrained phase-space coalescence ({\footnotesize DCPC}) model plus {\footnotesize PACIAE} model was used to produce the $Ξ_{c}^{+}K^{-}$ bound states and study the narrow excited $Ω_{c}^{0}$ states through $Ω_{c}^{0} \to Ξ_{c}^{+}K^{-}$ in $pp$ collisions at $\sqrt{s}=7$ and 13 TeV. The yield, transverse momentum distribution, and rapidity distribution of the five new excited $Ω_{c}^{0}$ states of $Ω_{c}(3000)^{0}$, $Ω_{c}(3050)^{0}$, $Ω_{c}(3066)^{0}$,$Ω_{c}(3090)^{0}$and $Ω_{c}(3119)^{0}$ were predicted.

hep-ph↗

arXiv2020

Robust Federated Recommendation System

Federated recommendation systems can provide good performance without collecting users' private data, making them attractive. However, they are susceptible to low-cost poisoning attacks that can degrade their performance. In this paper, we develop a novel federated recommendation technique that is robust against the poisoning attack where Byzantine clients prevail. We argue that the key to Byzantine detection is monitoring of gradients of the model parameters of clients. We then propose a robust learning strategy where instead of using model parameters, the central server computes and utilizes the gradients to filter out Byzantine clients. Theoretically, we justify our robust learning strategy by our proposed definition of Byzantine resilience. Empirically, we confirm the efficacy of our robust learning strategy employing four datasets in a federated recommendation system.

cs.LG↗

arXiv2020

Impact of single string structure and multiple string interaction on strangeness production in Pb+Pb collisions at $\sqrt{S_{NN}} = 2.76$ TeV

We present a systematic study on the strange and multi-strange particles production in Pb+Pb collisions at $\sqrt{S_{NN}} = 2.76$ TeV based on the PACIAE model simulations. Two different mechanisms, namely the single string structure variations and multiple string interactions, are implemented in the simulations. These modifications give rise to an enhancement of the string tension value involved in the Lund string fragmentation framework and generate more strange particles in the hadronic final state. By comparing the simulation results with the ALICE experimental data, it turns out that the inclusion of the variable effective string tension in the PACIAE model is capable to reach an improved agreement between theory and experiment on the strangeness production in Pb+Pb collisions.

nucl-th↗

arXiv2020

Decorrelated Double Q-learning

Q-learning with value function approximation may have the poor performance because of overestimation bias and imprecise estimate. Specifically, overestimation bias is from the maximum operator over noise estimate, which is exaggerated using the estimate of a subsequent state. Inspired by the recent advance of deep reinforcement learning and Double Q-learning, we introduce the decorrelated double Q-learning (D2Q). Specifically, we introduce the decorrelated regularization item to reduce the correlation between value function approximators, which can lead to less biased estimation and low variance. The experimental results on a suite of MuJoCo continuous control tasks demonstrate that our decorrelated double Q-learning can effectively improve the performance.

cs.LG↗

arXiv2020

Atomic self-organization emerging from tunable quadrature coupling

The recent experimental observation of dissipation-induced structural instability provides new opportunities for exploring the competition mechanism between stationary and nonstationary dynamics [Science 366, 1496 (2019)]. In that study, two orthogonal quadratures of cavity field are coupled to two different Zeeman states of a spinor Bose-Einstein condensate (BEC). Here we propose a novel scheme to couple two density-wave degrees of freedom of a BEC to two quadratures of the cavity field. Being drastically different from previous studies, the light-matter quadratures coupling in our model is endowed with a tunable coupling angle. Apart from the uniform and self-organized phases, we unravel a dynamically unstable state induced by the cavity dissipation. Interestingly, the dissipation defines a particular coupling angle, across which the instabilities disappear. Moreover, at this critical coupling angle, one of the two atomic density waves can be independently excited without affecting one another. It is also found that our system can be mapped into a reduced three-level model under the commonly used low-excitation-mode approximation. However, the effectiveness of this approximation is shown to be broken by the dissipation nature for some special system parameters, hinting that the low-excitation-mode approximation is insufficient in capturing some dissipation-sensitive physics. Our work enriches the quantum simulation toolbox in the cavity-quantum-electrodynamics system and broadens the frontiers of light-matter interaction.

quant-ph↗

arXiv2020

A Néel-type antiferromagnetic order in the spin 1/2 rare-earth honeycomb YbCl$_3$

Most of the searches for Kitaev materials deal with $4d/5d$ magnets with spin-orbit-coupled ${J=1/2}$ local moments such as iridates and $α$-RuCl$_3$. Here we propose the monoclinic YbCl$_3$ with a Yb$^{3+}$ honeycomb lattice for the exploration of Kiteav physics. We perform thermodynamic, $ac$ susceptibility, angle-dependent magnetic torque and neutron diffraction measurements on YbCl$_3$ single crystal. We find that the Yb$^{3+}$ ion exhibits a Kramers doublet ground state that gives rise to an effective spin ${J_{\text{eff}}=1/2}$ local moment. The compound exhibits short-range magnetic order below 1.20 K, followed by a long-range Néel-type antiferromagnetic order at 0.60 K, below which the ordered Yb$^{3+}$ spins lie in the $ac$ plane with an angle of 16(11)$^{\circ}$ away from the $a$ axis. These orders can be suppressed by in-plane and out-of-plane magnetic fields at around 6 and 10 T, respectively. Moreover, the Néel temperature varies non-monotonically under the out-of-plane magnetic fields. The in-plane magnetic anisotropy and the reduced order moment 0.8(1) $μ_B$ at 0.25 K indicate that YbCl$_3$ could be a two-dimensional spin system to proximate the Kitaev physics.

cond-mat.str-el↗

arXiv2020

Hybrid deep neural network based prediction method for unsteady flows with moving boundaries

A novel hybrid deep neural network architecture is designed to capture the spatial-temporal features of unsteady flows around moving boundaries directly from high-dimensional unsteady flow fields data. The hybrid deep neural network is constituted by the convolutional neural network (CNN), improved convolutional Long-Short Term Memory neural network (ConvLSTM) and deconvolutional neural network (DeCNN). Flow fields at future time step can be predicted through flow fields by previous time steps and boundary positions at those steps by the novel hybrid deep neural network. Unsteady wake flows around a forced oscillation cylinder with various amplitudes are calculated to establish the datasets as training samples for training the hybrid deep neural networks. The trained hybrid deep neural networks are then tested by predicting the unsteady flow fields around a forced oscillation cylinder with new amplitude. The effect of neural network structure parameters on prediction accuracy was analyzed. The hybrid deep neural network, constituted by the best parameter combination, is used to predict the flow fields in the future time. The predicted flow fields are in good agreement with those calculated directly by computational fluid dynamic solver, which means that this kind of deep neural network can capture accurate spatial-temporal information from the spatial-temporal series of unsteady flows around moving boundaries. The result shows the potential capability of this kind novel hybrid deep neural network in flow control for vibrating cylinder, where the fast calculation of high-dimensional nonlinear unsteady flow around moving boundaries is needed.

physics.comp-ph↗

arXiv2020

$L^\infty$ norm error estimates for HDG methods applied to the Poisson equation with an application to the Dirichlet boundary control problem

We prove quasi-optimal $L^\infty$ norm error estimates (up to logarithmic factors) for the solution of Poisson's problem by the standard Hybridizable Discontinuous Galerkin (HDG) method. Although such estimates are available for conforming and mixed finite element methods, this is the first proof for HDG. The method of proof is motivated by known $L^\infty$ norm estimates for mixed finite elements. We show two applications: the first is to prove optimal convergence rates for boundary flux estimates, and the second is to prove that numerically observed convergence rates for the solution of a Dirichlet boundary control problem are to be expected theoretically. Numerical examples show that the predicted rates are seen in practice.

math.NA↗

arXiv2020

Quantum spiral spin-tensor magnetism

The characterization of quantum magnetism in a large spin ($\geq 1$) system naturally involves both spin-vectors and -tensors. While certain types of spin-vector (e.g., ferromagnetic, spiral) and spin-tensor (e.g., nematic in frustrated lattices) orders have been investigated separately, the coexistence and correlation between them have not been well explored. Here we propose a novel quantum spiral spin-tensor order on a spin-1 Heisenberg chain subject to a spiral spin-tensor Zeeman field, which can be experimentally realized using a Raman-dressed cold atom optical lattice. We develop a method to fully characterize quantum phases of such spiral tensor magnetism with the coexistence of spin-vector and spin-tensor orders as well as their correlations using eight geometric parameters. Our method provides a powerful tool for characterizing spin-1 quantum magnetism and opens an avenue for exploring novel magnetic orders and spin-tensor electronics/atomtronics in large-spin systems.

cond-mat.quant-gas↗

arXiv2020

Merging Deterministic Policy Gradient Estimations with Varied Bias-Variance Tradeoff for Effective Deep Reinforcement Learning

Deep reinforcement learning (DRL) on Markov decision processes (MDPs) with continuous action spaces is often approached by directly training parametric policies along the direction of estimated policy gradients (PGs). Previous research revealed that the performance of these PG algorithms depends heavily on the bias-variance tradeoffs involved in estimating and using PGs. A notable approach towards balancing this tradeoff is to merge both on-policy and off-policy gradient estimations. However existing PG merging methods can be sample inefficient and are not suitable to train deterministic policies directly. To address these issues, this paper introduces elite PGs and strengthens their variance reduction effect by adopting elitism and policy consolidation techniques to regularize policy training based on policy behavioral knowledge extracted from elite trajectories. Meanwhile, we propose a two-step method to merge elite PGs and conventional PGs as a new extension of the conventional interpolation merging method. At both the theoretical and experimental levels, we show that both two-step merging and interpolation merging can induce varied bias-variance tradeoffs during policy training. They enable us to effectively use elite PGs and mitigate their performance impact on trained policies. Our experiments also show that two-step merging can outperform interpolation merging and several state-of-the-art algorithms on six benchmark control tasks.

cs.LG↗

arXiv2020

Large nonreciprocal absorption and emission of radiation in type-I Weyl semimetals with time reversal symmetry breaking

The equality between the spectral, directional emittance and absorptance of an object under local thermal equilibrium is known as Kirchhoff's law of radiation. The breakdown of Kirchhoff's law of radiation is physically allowed by breaking time reversal symmetry and can open opportunities for nonreciprocal light emitters and absorbers. Large anomalous Hall conductivity and angle recently observed in topological Weyl semimetals, particularly type-I magnetic Weyl semimetals and type-II Weyl semimetals, are expected to create large nonreciprocal electromagnetic wave propagation. In this work, we focus on type-I magnetic Weyl semimetals and show via modeling and simulation that nonreciprocal surface plasmons polaritons can result in pronounced nonreciprocity without an external magnetic field. The modeling in this work begins with a single pair of Weyl nodes, followed by a more realistic model with multiple paired Weyl nodes. Fermi-arc surface states are also taken into account through the surface conductivity. This work points to the promising applicability of topological Weyl semimetals for magneto-optical and energy applications.

cond-mat.mes-hall↗

arXiv2020

Detection of multipolar orders in the spin-orbit-entangled 5d Mott insulator Ba2MgReO6

In electronic solids with strong spin-orbit interactions (SOIs), the spin and orbital degrees of freedom of an electron are quantum mechanically entangled, which may result in an exotic multipolar order instead of a conventional dipolar order such as a magnetic order. Such a higher-degree order is called "hidden order" because of difficulties in experimental detection. Moreover, the number of candidate compounds is limited, especially rare in d electron systems, in which an interplay between SOIs and Coulomb interactions is expected to cause rich physics. Here, we employ state-of-the-art synchrotron X-ray diffraction techniques on a high-quality single crystal to probe subtle symmetry breaking induced by a multipolar order. We unequivocally demonstrate that the double-perovskite Ba2MgReO6 exhibits successive transitions to quadrupolar and then dipolar orders upon cooling, which is consistent with a theory considering SOIs. Our findings are a significant step towards understanding the intriguing physics of multipoles realized by spin-orbit-entangled 5d electrons.

cond-mat.str-el↗

arXiv2020

Featureless quantum paramagnet with frustrated criticality and competing spiral magnetism on spin-1 honeycomb lattice magnet

We study the spin-1 honeycomb lattice magnets with frustrated exchange interactions. The proposed microscopic spin model contains first and second neighbor Heisenberg interactions as well as the single-ion anisotropy. We establish a rich phase diagram that includes a featureless quantum paramagnet and various spin spiral states induced by the mechanism of order by quantum disorder. Although the quantum paramagnet is dubbed featureless, it is shown that, the magnetic excitations develop a contour degeneracy in the reciprocal space at the band minima. These contour degenerate excitations are responsible for the frustrated criticality from the quantum paramagnet to the ordered phases. This work illustrates the effects of magnetic frustration on both magnetic orderings and the magnetic excitations. We discuss the experimental relevance to various Ni-based honeycomb lattice magnets.

cond-mat.str-el↗

arXiv2020

ForkBase: Immutable, Tamper-evident Storage Substrate for Branchable Applications

Data collaboration activities typically require systematic or protocol-based coordination to be scalable. Git, an effective enabler for collaborative coding, has been attested for its success in countless projects around the world. Hence, applying the Git philosophy to general data collaboration beyond coding is motivating. We call it Git for data. However, the original Git design handles data at the file granule, which is considered too coarse-grained for many database applications. We argue that Git for data should be co-designed with database systems. To this end, we developed ForkBase to make Git for data practical. ForkBase is a distributed, immutable storage system designed for data version management and data collaborative operation. In this demonstration, we show how ForkBase can greatly facilitate collaborative data management and how its novel data deduplication technique can improve storage efficiency for archiving massive data versions.

cs.DB↗

arXiv2020

Topological phase transition and nontrivial thermal Hall signatures in honeycomb lattice magnets

We investigate spinon band topology and engineering from the interplay between long-ranged magnetic order and fractionalized spinons, as well as Zeeman coupling under external magnetic fields, in honeycomb lattice magnets. The synergism of Néel order and magnetic fields could reconstruct the spinon bands and drive a topological phase transition from the coexisting phase of long-ranged order and chiral spin liquid with semion topological order to the conventional magnetic order. Our prediction can be immediately tested through thermal Hall transport measurements among the honeycomb lattice magnets that are tuned to be proximate to the quantum critical point. Our theory should also shed light on the critical behavior of honeycomb Kitaev materials with emergent Majorana fermion bands. We suggest a possible relevance to the spin-1/2 honeycomb spin liquid candidate material In$_3$Cu$_2$VO$_9$.

cond-mat.str-el↗