Xi Chen - Dalian University of Technology

Xi Chen
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Name
Xi Chen
Affiliation
Dalian University of Technology
City
Dalian Shi
Country
China

Pubs By Year

Pub Categories

 
Computer Science - Learning (10)
 
Statistics - Machine Learning (8)
 
Quantum Physics (6)
 
Astrophysics of Galaxies (6)
 
Physics - Materials Science (4)
 
Computer Science - Artificial Intelligence (3)
 
Physics - Chemical Physics (3)
 
Computer Science - Neural and Evolutionary Computing (3)
 
Computer Science - Computer Vision and Pattern Recognition (3)
 
Computer Science - Computational Complexity (3)
 
Physics - Strongly Correlated Electrons (3)
 
Mathematics - Algebraic Geometry (2)
 
Statistics - Methodology (2)
 
Physics - Atomic and Molecular Clusters (2)
 
Statistics - Theory (2)
 
Physics - Computational Physics (2)
 
Mathematics - Statistics (2)
 
Solar and Stellar Astrophysics (2)
 
Physics - Soft Condensed Matter (2)
 
Mathematics - Analysis of PDEs (1)
 
Physics - Geophysics (1)
 
Computer Science - Computational Geometry (1)
 
Physics - Fluid Dynamics (1)
 
Physics - Optics (1)
 
Computer Science - Distributed; Parallel; and Cluster Computing (1)
 
Cosmology and Nongalactic Astrophysics (1)
 
Computer Science - Computer Science and Game Theory (1)
 
Computer Science - Data Structures and Algorithms (1)
 
Physics - Mesoscopic Systems and Quantum Hall Effect (1)
 
Quantitative Biology - Tissues and Organs (1)
 
Physics - Medical Physics (1)

Publications Authored By Xi Chen

Shortcuts to adiabaticity have been proposed to speed up the "slow" adiabatic transport of an atom or a wave packet of atoms. However, the freedom of the inverse engineering approach with appropriate boundary conditions provides thousands of trap trajectories for different purposes, for example, time and energy minimizations. In this paper, we propose trigonometric protocols for fast and robust atomic transport, taking into account cubic or quartic anharmonicities. Read More

We investigate the ground state and finite-temperature properties of the spin-1/2 Heisenberg antiferromagnet on an infinite octa-kagome lattice by utilizing state-of-the-art tensor network-based numerical methods. It is shown that the ground state has a vanishing local magnetization and possesses a $1/2$-magnetization plateau with up-down-up-up spin configuration. A quantum phase transition at the critical coupling ratio $J_{d}/J_{t}=0. Read More

Compared with numerous X-ray dominant active galactic nuclei (AGNs) without emission-line signatures in their optical spectra, the X-ray selected AGNs with optical emission lines are probably still in the high-accretion phase of black hole growth. This paper presents an investigation on the fraction of these X-ray detected AGNs with optical emission-line spectra in 198 galaxy groups at $z<1$ in a rest frame 0.1-2. Read More

We put forward reverse engineering protocols to shape in time the components of the magnetic field to manipulate a single spin, two independent spins with different gyromagnetic factors, and two interacting spins in short amount of times. We also use these techniques to setup protocols robust against the exact knowledge of the gyromagnetic factors for the one spin problem, or to generate entangled states for two or more spins coupled by dipole-dipole interactions. Read More

A novel approach is presented for fast generation of synthetic seismograms due to microseismic events, using heterogeneous marine velocity models. The partial differential equations (PDEs) for the 3D elastic wave equation have been numerically solved using the Fourier domain pseudo-spectral method which is parallelizable on the graphics processing unit (GPU) cards, thus making it faster compared to traditional CPU based computing platforms. Due to computationally expensive forward simulation of large geological models, several combinations of individual synthetic seismic traces are used for specified microseismic event locations, in order to simulate the effect of realistic microseismic activity patterns in the subsurface. Read More

2017May
Affiliations: 1University of Tasmania, 2Shanghai Astronomical Observatory, 3University of Sydney, 4Shanghai Astronomical Observatory

We report the detection of maser emission from the $J=4-3$ transition of HC$_3$N at 36.4~GHz towards the nearby starburst galaxy NGC253. This is the first detection of maser emission from this transition in either a Galactic or extragalactic source. Read More

The Purple Mountain Observatory 13.7 m radio telescope has been used to search for 95 GHz (8$_0$--7$_1$A$^+$) class I methanol masers towards 1020 Bolocam Galactic Plane Survey (BGPS) sources, leading to 213 detections. We have compared the line width of the methanol and HCO$^+$ thermal emission in all of the methanol detections and on that basis we find 205 of the 213 detections are very likely to be masers. Read More

Two of the leading approaches for model-free reinforcement learning are policy gradient methods and $Q$-learning methods. $Q$-learning methods can be effective and sample-efficient when they work, however, it is not well-understood why they work, since empirically, the $Q$-values they estimate are very inaccurate. A partial explanation may be that $Q$-learning methods are secretly implementing policy gradient updates: we show that there is a precise equivalence between $Q$-learning and policy gradient methods in the setting of entropy-regularized reinforcement learning, that "soft" (entropy-regularized) $Q$-learning is exactly equivalent to a policy gradient method. Read More

We prove that any non-adaptive algorithm that tests whether an unknown Boolean function $f: \{0, 1\}^n\to \{0, 1\}$ is a $k$-junta or $\epsilon$-far from every $k$-junta must make $\widetilde{\Omega}(k^{3/2} / \epsilon)$ many queries for a wide range of parameters $k$ and $\epsilon$. Our result dramatically improves previous lower bounds from [BGSMdW13, STW15], and is essentially optimal given Blais's non-adaptive junta tester from [Blais08], which makes $\widetilde{O}(k^{3/2})/\epsilon$ queries. Combined with the adaptive tester of [Blais09] which makes $O(k\log k + k /\epsilon)$ queries, our result shows that adaptivity enables polynomial savings in query complexity for junta testing. Read More

The self-assembly mechanism of one-end-open carbon nanotubes (CNTs) suspended in an aqueous solution was studied by molecular dynamics simulations. It was shown that two one-end-open CNTs with different diameters can coaxially self-assemble into a nanocapsule. The nanocapsules formed were stable in aqueous solution under ambient conditions, and the pressure inside the nanocapsule was much higher than the ambient pressure due to the van der Waals interactions between two parts of the nanocapsule. Read More

A novel bilayer is introduced, consisting of a stiff film adhered to a soft substrate with patterned holes beneath the film and substrate interface. To uncover the transition of surface patterns, two dimensional plane strain simulations are performed on the defected bilayer subjected to uniaxial compression. Although the substrate is considered as the linear elastic material, the presence of defects can directly trigger the formation of locally ridged and then folding configurations from flat surface with a relatively small compressive strain. Read More

Pimple is one of the most common skin diseases for humans. The mechanical modeling of pimple growth is very limited. A finite element model is developed to quantify the deformation field with the expansion of follicle, and then the mechanical stimulus is related to the sensation of pain during the development of pimple. Read More

Testing independence among a number of (ultra) high-dimensional random samples is a fundamental and challenging problem. By arranging $n$ identically distributed $p$-dimensional random vectors into a $p \times n$ data matrix, we investigate the problem of testing independence among columns under the matrix-variate normal modeling of data. We propose a computationally simple and tuning-free test statistic, characterize its limiting null distribution, analyze the statistical power and prove its minimax optimality. Read More

We put forward a method for achieving fast and robust for magnetization reversal in a nanomagnet, by combining the inverse engineering and composite pulses. The magnetic fields, generated by microwave with time-dependent frequency, are first designed inversely within short operation time, and composite pulses are further incorporated to improve the fidelity through reducing the effect of magnetic anisotropy. The high-fidelity magnetization reversals are illustrated with numerical examples, and visualized on Bloch sphere. Read More

We explore the use of Evolution Strategies, a class of black box optimization algorithms, as an alternative to popular RL techniques such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show that ES is a viable solution strategy that scales extremely well with the number of CPUs available: By using hundreds to thousands of parallel workers, ES can solve 3D humanoid walking in 10 minutes and obtain competitive results on most Atari games after one hour of training time. In addition, we highlight several advantages of ES as a black box optimization technique: it is invariant to action frequency and delayed rewards, tolerant of extremely long horizons, and does not need temporal discounting or value function approximation. Read More

We study the problem of estimating an unknown vector $\theta$ from an observation $X$ drawn according to the normal distribution with mean $\theta$ and identity covariance matrix under the knowledge that $\theta$ belongs to a known closed convex set $\Theta$. In this general setting, Chatterjee (2014) proved that the natural constrained least squares estimator is "approximately admissible" for every $\Theta$. We extend this result by proving that the same property holds for all convex penalized estimators as well. Read More

Based on extensive evolutionary algorithm driven structural search, we propose a new diphosphorus trisulfide (P2S3) 2D crystal, which is dynamically, thermally and chemically stable as confirmed by the computed phonon spectrum and ab initio molecular dynamics simulations. This 2D crystalline phase of P2S3 corresponds to the global minimum in the Born-Oppenheimer surface of the phosphorus sulfide monolayers with 2:3 stoichiometries. It is a wide band gap (4. Read More

We prove a lower bound of $\tilde{\Omega}(n^{1/3})$ for the query complexity of any two-sided and adaptive algorithm that tests whether an unknown Boolean function $f:\{0,1\}^n\rightarrow \{0,1\}$ is monotone or far from monotone. This improves the recent bound of $\tilde{\Omega}(n^{1/4})$ for the same problem by Belovs and Blais [BB15]. Our result builds on a new family of random Boolean functions that can be viewed as a two-level extension of Talagrand's random DNFs. Read More

We show that the problem of finding an optimal bundle-pricing for a single additive buyer is #P-hard, even when the distributions have support size 2 for each item and the optimal solution is guaranteed to be a simple one: the seller picks a price for the grand bundle and a price for each individual item; the buyer can purchase either the grand bundle at the given price or any bundle of items at their total individual prices. We refer to this simple and natural family of pricing schemes as discounted item-pricings. In addition to the hardness result, we show that when the distributions are i. Read More

Ion hydrations are ubiquitous in natural and fundamental processes. A quantitative analysis of a novel CO2 sorbent driven by ion hydrations was presented by molecular dynamics (MD). We explored the humidity effect on the diffusion and structure of ion hydrations in CO2 sorbent, as well as the working mechanism of the moisture-swing CO2 sorbent. Read More

Segmenting human left ventricle (LV) in magnetic resonance imaging (MRI) images and calculating its volume are important for diagnosing cardiac diseases. In 2016, Kaggle organized a competition to estimate the volume of LV from MRI images. The dataset consisted of a large number of cases, but only provided systole and diastole volumes as labels. Read More

The hydration of ions in nanoscale hydrated clusters is ubiquitous and essential in many physical and chemical processes. Here we show that the hydrolysis reaction is strongly affected by relative humidity. The hydrolysis of CO32- with n = 1-8 water molecules is investigated by ab initio method. Read More

ReaxFF provides a method to model reactive chemical systems in large-scale molecular dynamics simulations. Here, we developed ReaxFF parameters for phosphorus and hydrogen to give a good description of the chemical and mechanical properties of pristine and defected black phosphorene. ReaxFF for P/H is transferable to a wide range of phosphorus and hydrogen containing systems including bulk black phosphorus, blue phosphorene, edge-hydrogenated phosphorene, phosphorus clusters and phosphorus hydride molecules. Read More

A novel system containing nanoporous materials and carbonate ions is proposed, which is capable to capture CO2 from ambient air simply by controlling the amount of water (humidity) in the system. The system absorbs CO2 from the air when the surrounding is dry, whereas desorbs CO2 when wet. A design of such a CO2 absorption/desorption system is investigated in this paper using molecular dynamics and quantum mechanics simulations, and also verified by experiments. Read More

The strong spin-spin exchange interaction in some low-dimensional magnetic materials can give rise to a high group velocity and thermal conductivity contribution from magnons. One example is the incommensurate layered compounds (Sr,Ca,La)14Cu24O41. The effects of grain boundaries and defects on quasi-one-dimensional magnon transport in these compounds are not well understood. Read More

PixelCNNs are a recently proposed class of powerful generative models with tractable likelihood. Here we discuss our implementation of PixelCNNs which we make available at https://github.com/openai/pixel-cnn. Read More

We present a Submillimeter Array (SMA) observation towards the young massive double-core system G350.69-0.49. Read More

Estimates of galaxy distances based on indicators that are independent of cosmological redshift are fundamental to astrophysics. Researchers use them to establish the extragalactic distance scale, to underpin estimates of the Hubble constant, and to study peculiar velocities induced by gravitational attractions that perturb the motions of galaxies with respect to the Hubble flow of universal expansion. In 2006 the NASA/IPAC Extragalactic Database (NED) began making available a comprehensive compilation of redshift-independent extragalactic distance estimates. Read More

Rank aggregation based on pairwise comparisons over a set of items has a wide range of applications. Although considerable research has been devoted to the development of rank aggregation algorithms, one basic question is how to efficiently collect a large amount of high-quality pairwise comparisons for the ranking purpose. Because of the advent of many crowdsourcing services, a crowd of workers are often hired to conduct pairwise comparisons with a small monetary reward for each pair they compare. Read More

Upper and lower bounds on the heat kernel on complete Riemannian manifolds were obtained in a series of pioneering works due to Cheng-Li-Yau, Cheeger-Yau and Li-Yau. However, these estimates do not give a complete picture of the heat kernel for all times and all pairs of points. Inspired by the work of Davies-Mandouvalos on $\mathbb{H}^{n + 1}$, we study heat kernel bounds on Cartan-Hadamard manifolds that are asymptotically hyperbolic in the sense of Mazzeo-Melrose. Read More

In nature, a variety of limbless locomotion patterns flourish from the small or basic life form (Escherichia coli, the amoeba, etc.) to the large or intelligent creatures (e.g. Read More

In this paper, we study the Severi variety $V_{L,g}$ of genus $g$ curves in $|L|$ on a general polarized K3 surface $(X,L)$. We show that the closure of every component of $V_{L,g}$ contains a component of $V_{L,g-1}$. As a consequence, we see that the general members of every component of $V_{L,g}$ are nodal. Read More

We propose a method for shortcut to adiabatic control of soliton matter waves in harmonic traps. The tunable interaction controlled by Feshbach resonance is inversely designed to achieve fast compression of soliton matter waves but within a short time, as compared to the conventional adiabatic compression. These results pave the way to control the nonlinear dynamics for matter waves and optical solitons by using shortcuts to adiabaticity. Read More

Count-based exploration algorithms are known to perform near-optimally when used in conjunction with tabular reinforcement learning (RL) methods for solving small discrete Markov decision processes (MDPs). It is generally thought that count-based methods cannot be applied in high-dimensional state spaces, since most states will only occur once. Recent deep RL exploration strategies are able to deal with high-dimensional continuous state spaces through complex heuristics, often relying on optimism in the face of uncertainty or intrinsic motivation. Read More

Shortcut to adiabaticity in various quantum systems has attracted much attention with the wide applications in quantum information processing and quantum control. In this paper, we concentrate on stimulated Raman shortcut-to-adiabatic passage in quantum three-level systems. To implement counter-diabatic driving but without additional coupling, we first reduce the quantum three-level systems to effective two-level problems at large intermediate-level detuning, or on resonance, apply counter-diabatic driving along with the unitary transformation, and eventually modify the pump and Stokes pulses for achieving fast and high-fidelity population transfer. Read More

Averaging diffeomorphisms is a challenging problem, and it has great applications in areas like medical image atlases. The simple Euclidean average can neither guarantee the averaged transformation is a diffeomorphism, nor get reasonable result when there is a local rotation. The goal of this paper is to propose a new approach to averaging diffeomorphisms based on the Jacobian determinant and the curl vector of the diffeomorphisms. Read More

Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture. In this paper, we present a simple but principled method to learn such global representations by combining Variational Autoencoder (VAE) with neural autoregressive models such as RNN, MADE and PixelRNN/CNN. Read More

Deep reinforcement learning (deep RL) has been successful in learning sophisticated behaviors automatically; however, the learning process requires a huge number of trials. In contrast, animals can learn new tasks in just a few trials, benefiting from their prior knowledge about the world. This paper seeks to bridge this gap. Read More

The stochastic gradient descent (SGD) algorithm has been widely used in statistical estimation for large-scale data due to its computational and memory efficiency. While most existing work focuses on the convergence of the objective function or the error of the obtained solution, we investigate the problem of statistical inference of the true model parameters based on SGD. To this end, we propose two consistent estimators of the asymptotic covariance of the average iterate from SGD: (1) an intuitive plug-in estimator and (2) a computationally more efficient batch-means estimator, which only uses the iterates from SGD. Read More

We generalize the results of Clemens, Ein, and Voisin regarding rational curves and zero cycles on generic projective complete intersections to the logarithmic setup. Read More

We investigated the physical properties of molecular clouds and star formation processes around infrared bubbles which are essentially expanding HII regions. We performed observations of 13 galactic infrared bubble fields containing 18 bubbles. Five molecular lines, 12CO (J=1-0), 13CO (J=1-0), C18O(J=1-0), HCN (J=1-0), and HCO+ (J=1-0), were observed, and several publicly available surveys, GLIMPSE, MIPSGAL, ATLASGAL, BGPS, VGPS, MAGPIS, and NVSS, were used for comparison. Read More

We investigate the reasons why context in object detection has limited utility by isolating and evaluating the predictive power of different context cues under ideal conditions in which context provided by an oracle. Based on this study, we propose a region-based context re-scoring method with dynamic context selection to remove noise and emphasize informative context. We introduce latent indicator variables to select (or ignore) potential contextual regions, and learn the selection strategy with latent-SVM. Read More

We study the temperature and doping evolution of the NMR Knight shift, spin relaxation rate, and spin echo decay time in the pseudogap regime of the two-dimensional Hubbard model for parameters believed to be relevant to cuprate superconductors using cluster dynamical mean field theory. We recover the suppression of the Knight shift seen in experiment upon entering the pseudogap regime and find agreement between single and two-particle measures of the pseudogap onset temperature. The simulated spin-echo decay time shows a linear in T behavior at high T which flattens off as T is lowered, and increases as doping is increased. Read More

Traffic flow count data in networks arise in many applications, such as automobile or aviation transportation, certain directed social network contexts, and Internet studies. Using an example of Internet browser traffic flow through site-segments of an international news website, we present Bayesian analyses of two linked classes of models which, in tandem, allow fast, scalable and interpretable Bayesian inference. We first develop flexible state-space models for streaming count data, able to adaptively characterize and quantify network dynamics efficiently in real-time. Read More

Chirality is an important concept that describes the asymmetry property of a system, which usually emerges spontaneously due to mirror symmetry breaking. Such spontaneous chirality manifests predominantly as parity breaking in modern physics, which has been studied extensively, for instance, in Higgs physics, double-well Bose-Einstein condensates, topological insulators and superconductors. In the optical domain, spontaneous chiral symmetry breaking has been elusive experimentally, especially for micro- and nano-photonics which demands multiple identical subsystems, such as photonic nanocavities, meta-molecules and other dual-core settings. Read More

The evolution of multi-mode instabilities in a hypersonic boundary layer and their effects on aerodynamic heating are investigated. Experiments are conducted in a Mach 6 wind tunnel using Rayleigh-scattering flow visualization, fast-response pressure sensors, fluorescent temperature-sensitive paint (TSP), and particle image velocimetry (PIV). Calculations are also performed based on both parabolized stability equations (PSE) and direct numerical simulations (DNS). Read More

The framework of normalizing flows provides a general strategy for flexible variational inference of posteriors over latent variables. We propose a new type of normalizing flow, inverse autoregressive flow (IAF), that, in contrast to earlier published flows, scales well to high-dimensional latent spaces. The proposed flow consists of a chain of invertible transformations, where each transformation is based on an autoregressive neural network. Read More

This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN is a generative adversarial network that also maximizes the mutual information between a small subset of the latent variables and the observation. We derive a lower bound to the mutual information objective that can be optimized efficiently, and show that our training procedure can be interpreted as a variation of the Wake-Sleep algorithm. Read More

We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Read More

The quantum phase transition, scaling behaviors, and thermodynamics in the spin-1/2 quantum Heisenberg model with antiferromagnetic coupling $J>0$ in armchair direction and ferromagnetic interaction $J'<0$ in zigzag direction on a honeycomb lattice are systematically studied using the continuous-time quantum Monte Carlo method. By calculating the Binder ratio $Q_{2}$ and spin stiffness $\rho$ in two directions for various coupling ratio $\alpha=J'/J$ under different lattice sizes, we found that a quantum phase transition from the dimerized phase to the stripe phase occurs at the quantum critical point $\alpha_c=-0.93$. Read More