arXiv subjects

Bo Xiao

Publications and source records attributed to Bo Xiao.

At least 55 records · Page 3Linked to original sources

arXiv2020

Matter flow method for alleviating checkerboard oscillations in triangular mesh SGH Lagrangian simulation

When the SGH Lagrangian based on triangle mesh is used to simulate compressible hydrodynamics, because of the stiffness of triangular mesh, the problem of physical quantity cell-to-cell spatial oscillation (also called "checkerboard oscillation") is easy to occur. A matter flow method is proposed to alleviate the oscillation of physical quantities caused by triangular stiffness. The basic idea of this method is to attribute the stiffness of triangle to the fact that the edges of triangle mesh can not do bending motion, and to compensate the effect of triangle edge bending motion by means of matter flow. Three effects are considered in our matter flow method: (1) transport of the mass, momentum and energy carried by the moving matter; (2) the work done on the element, since the flow of matter changes the specific volume of the grid element; (3) the effect of matter flow on the strain rate in the element. Numerical experiments show that the proposed matter flow method can effectively alleviate the spatial oscillation of physical quantities.

physics.comp-ph↗

arXiv2020

Constrained-Space Optimization and Reinforcement Learning for Complex Tasks

Learning from Demonstration is increasingly used for transferring operator manipulation skills to robots. In practice, it is important to cater for limited data and imperfect human demonstrations, as well as underlying safety constraints. This paper presents a constrained-space optimization and reinforcement learning scheme for managing complex tasks. Through interactions within the constrained space, the reinforcement learning agent is trained to optimize the manipulation skills according to a defined reward function. After learning, the optimal policy is derived from the well-trained reinforcement learning agent, which is then implemented to guide the robot to conduct tasks that are similar to the experts' demonstrations. The effectiveness of the proposed method is verified with a robotic suturing task, demonstrating that the learned policy outperformed the experts' demonstrations in terms of the smoothness of the joint motion and end-effector trajectories, as well as the overall task completion time.

cs.RO↗

arXiv2019

Wave scattering properties of multiple weakly-coupled complex systems

The statistics of scattering of waves inside single ray-chaotic enclosures have been successfully described by the Random Coupling Model (RCM). We expand the RCM to systems consisting of multiple complex ray-chaotic enclosures with variable coupling scenarios. The statistical properties of the model-generated quantities are tested against measured data of electrically large multi-cavity systems of various designs. The statistics of model-generated trans-impedance and induced voltages on a load impedance agree well with the experimental results. The RCM coupled chaotic enclosure model is general and can be applied to other physical systems including coupled quantum dots, disordered nanowires, and short-wavelength electromagnetic propagation through rooms in buildings, aircraft and ships.

physics.class-ph↗

arXiv2019

Topologically Protected Photonic Modes in Composite Quantum Hall/Quantum Spin Hall Waveguides

Photonic topological systems, the electromagnetic analog of the topological materials in condensed matter physics, create many opportunities to design optical devices with novel properties. We present an experimental realization of the bi-anisotropic meta waveguide photonic system replicating both quantum Hall (QH) and quantum spin-Hall (QSH) topological insulating phases. With careful design, a composite QH-QSH photonic topological material is created and experimentally shown to support reflection-free edgemodes, a heterogeneous topological structure that is unprecedented in condensed matter physics. The effective spin degree of freedom of such topologically protected modes determines their unique pathways through these systems, free from backscattering and able to travel around sharp corners. {As an example of their novel properties, we experimentally demonstrate reflection-less photonic devices including a 2-port isolator, a unique 3-port topological device, and a full 4-port circulator based on composite QH and QSH structures

cond-mat.mes-hall↗

arXiv2019

Classification and prediction of wave chaotic systems with machine learning techniques

The wave properties of complex scattering systems that are large compared to the wavelength, and show chaos in the classical limit, are extremely sensitive to system details. A solution to the wave equation for a specific configuration can change substantially under small perturbations. Due to this extreme sensitivity, it is difficult to discern basic information about a complex system simply from scattering data as a function of energy or frequency, at least by eye. In this work, we employ supervised machine learning algorithms to reveal and classify hidden information about the complex scattering system presented in the data. As an example we are able to distinguish the total number of connected cavities in a linear chain of weakly coupled lossy enclosures from measured reflection data. A predictive machine learning algorithm for the future states of a perturbed complex scattering system is also trained with a recurrent neural network. Given a finite training data series, the reflection/transmission properties can be forecast by the proposed algorithm.

cond-mat.dis-nn↗

arXiv2019

Competition between Phase Separation and Spin Density Wave or Charge Density Wave Order: Role of Long-Range Interactions

Recent studies of pairing and charge order in materials such as FeSe, SrTiO$_3$, and 2H-NbSe$_2$ have suggested that momentum dependence of the electron-phonon coupling plays an important role in their properties. Initial attempts to study Hamiltonians which either do not include or else truncate the range of Coulomb repulsion have noted that the resulting spatial non-locality of the electron-phonon interaction leads to a dominant tendency to phase separation. Here we present Quantum Monte Carlo results for such models in which we incorporate both on-site and intersite electron-electron interactions. We show that these can stabilize phases in which the density is homogeneous and determine the associated phase boundaries. As a consequence, the physics of momentum dependent electron-phonon coupling can be determined outside of the trivial phase separated regime.

cond-mat.str-el↗

arXiv2019

One-dimensional Hubbard-Holstein model with finite range electron-phonon coupling

The Hubbard-Holstein model describes fermions on a discrete lattice, with on-site repulsion between fermions and a coupling to phonons that are localized on sites. Generally, at half-filling, increasing the coupling $g$ to the phonons drives the system towards a Peierls charge density wave state whereas increasing the electron-electron interaction $U$ drives the fermions into a Mott antiferromagnet. At low $g$ and $U$, or when doped, the system is metallic. In one-dimension, using quantum Monte Carlo simulations, we study the case where fermions have a long range coupling to phonons, with characteristic range $ξ$, interpolating between the Holstein and Fröhlich limits. Without electron-electron interaction, the fermions adopt a Peierls state when the coupling to the phonons is strong enough. This state is destabilized by a small coupling range $ξ$, and leads to a collapse of the fermions, and, consequently, phase separation. Increasing interaction $U$ will drive any of these three phases (metallic, Peierls, phase separation) into a Mott insulator phase. The phase separation region is once again present in the $U \ne 0$ case, even for small values of the coupling range.

cond-mat.str-el↗

arXiv2018

Magneto-Rayleigh-Taylor instability driven by a rotating magnetic field: Cylindrical liner configuration

We propose using a directional time-varying (rotating) driving magnetic field to suppress magneto-Rayleigh-Taylor (MRT) instability in dynamic Z-pinches. A rotational drive magnetic field is equivalent to two magnetic-field components, Θ and Z, that alternate in time, referred to as an alternate Theta-Z-pinch configuration. We consider the finitely thick cylindrical liner configuration in this paper. We numerically integrate the perturbation equation to stagnation time based on the optimal background unperturbed trajectories. We assess the cumulative growth of the dominant mode selected by some mechanism at the beginning of an implosion. The maximum e-folding number at stagnation of the dominant mode of an optimized alternate Theta-Z-pinch is significantly lower than that of the standard Theta- or Z-pinch. The directional rotation of the magnetic field contributes to suppress the instabilities, independent of the finite thickness. The finite thickness effect plays a role only when the orientation of the magnetic field varies in time whereas it does not appear in the standard Theta- or Z-pinch. The rotating frequency of the magnetic field and the thickness of liner are both having a monotonic effect on suppression. Their synergistic effect can enhance the suppression on MRT instability. Because the MRT instability can be well suppressed in this way, the alternate Theta-Z-pinch configuration has potential applications in liner inertial fusion. This work is supported by the NSFC (Grant Nos. 11405167, 51407171, 11571293, 11605188, and 11605189) and the Foundation of the China Academy of Engineering Physics (No. 2015B0201023).

physics.plasm-ph↗

arXiv2018

Play Duration based User-Entity Affinity Modeling in Spoken Dialog System

Multimedia streaming services over spoken dialog systems have become ubiquitous. User-entity affinity modeling is critical for the system to understand and disambiguate user intents and personalize user experiences. However, fully voice-based interaction demands quantification of novel behavioral cues to determine user affinities. In this work, we propose using play duration cues to learn a matrix factorization based collaborative filtering model. We first binarize play durations to obtain implicit positive and negative affinity labels. The Bayesian Personalized Ranking objective and learning algorithm are employed in our low-rank matrix factorization approach. To cope with uncertainties in the implicit affinity labels, we propose to apply a weighting function that emphasizes the importance of high confidence samples. Based on a large-scale database of Alexa music service records, we evaluate the affinity models by computing Spearman correlation between play durations and predicted affinities. Comparing different data utilizations and weighting functions, we find that employing both positive and negative affinity samples with a convex weighting function yields the best performance. Further analysis demonstrates the model's effectiveness on individual entity level and provides insights on the temporal dynamics of observed affinities.

cs.IR↗

arXiv2018

Uncovering Universal Wave Fluctuations In a Scaled Ray-Chaotic Cavity With Remote Injection

The Random Coupling Model (RCM), introduced by Zheng, Antonsen and Ott, predicts the statistical properties of waves inside a ray-chaotic enclosure in the semi-classical regime by using Random Matrix Theory, combined with system-specific information. Experiments on single cavities are in general agreement with the predictions of the RCM. It is now desired to test the RCM on more complex structures, such as a cascade or network of coupled cavities, that represent realistic situations, but which are difficult to test due to the large size of the structures of interest. This paper presents a novel experimental setup that replaces a cubic-meter-scale microwave cavity with a miniaturized cavity, scaled down by a factor of 20 in each dimension, operated at a frequency scaled up by a factor of 20 and having wall conductivity appropriately scaled up by a factor of 20. We demonstrate experimentally that the miniaturized cavity maintains the statistical wave properties of the larger cavity. This scaled setup opens the opportunity to study wave properties in large structures such as the floor of an office building, a ship, or an aircraft, in a controlled laboratory setting.

physics.class-ph↗

arXiv2017

Encoding Multifunctional Nanostructured Metasurfaces

Metasurfaces offer unconventional control of light to shape optical wavefronts within two-dimensional nanoscale structures. A nanostructured metallic thin film can establish an exclusive relationship between its surface structure and optical spectrum. The unique structural features, continuous metal coverage and smooth surface profile, can help obtain highly coherent plasmonic resonances that strongly depend on the structural parameters of nanostructured surfaces. It allows us to encode optical responses and information into a structural format on a variety of materials. We show experimentally that the nanostructured metasurfaces can be encoded to detect and display chemical and biomolecular interactions, show colorful graphic images, and create multichannel holograms.

physics.optics↗

arXiv2017

Improving the staggered grid Lagrangian hydrodynamics for modeling multi-material flows

In this work, we make two improvements on the staggered grid hydrodynamics (SGH) Lagrangian scheme for modeling 2-dimensional compressible multi-material flows on triangular mesh. The first improvement is the construction of a dynamic local remeshing scheme for preventing mesh distortion. The remeshing scheme is similar to many published algorithms except that it introduces some special operations for treating grids around multi-material interfaces. This makes the simulation of extremely deforming and topology-variable multi-material processes possible, such as the complete process of a heavy fluid dipping into a light fluid. The second improvement is the construction of an Euler-like flow on each edge of the mesh to count for the "edge-bending" effect, so as to mitigate the "checkerboard" oscillation that commonly exists in Lagrangian simulations, especially the triangular mesh based simulations. Several typical hydrodynamic problems are simulated by the improved staggered grid Lagrangian hydrodynamic method to test its performance.

physics.comp-ph↗

arXiv2016

Exciting Reflectionless Unidirectional Edge Modes in a Reciprocal Photonic Topological Insulator Medium

Photonic topological insulators are an interesting class of materials whose photonic band structure can have a bandgap in the bulk while supporting topologically protected unidirectional edge modes. Recent studies [1-6] on bianisotropic metamaterials that emulate the electronic quantum spin Hall effect using its electromagnetic analog are examples of such systems with relatively simple and elegant design. In this paper, we present a rotating magnetic dipole antenna, composed of two perpendicularly oriented coils, that can efficiently excite the unidirectional topologically protected surface waves in the bianisotropic metawaveguide (BMW) structure recently realized by Ma, et al. [1], despite the fact that the BMW medium does not break time-reversal invariance. In addition to achieving high directivity, the antenna can be tuned continuously to excite reflectionless edge modes to the two opposite directions with various amplitude ratios. We demonstrate its performance through experiment and compare to simulation results.

cond-mat.mes-hall↗

arXiv2016

Focusing Waves at Arbitrary Locations in a Ray-Chaotic Enclosure Using Time-Reversed Synthetic Sonas

Time reversal methods are widely used to achieve wave focusing in acoustics and electromagnetics. A typical time reversal experiment requires that a transmitter be initially present at the target focusing point, which limits the application of this technique. In this paper, we propose a method to focus waves at an arbitary location inside a complex enclosure using a numerically calculated wave excitation signal. We use a semi-classical ray algorithm to calculate the signal that would be received at a transceiver port resulting from the injection of a short pulse at the desired target location. The time-reversed version of this signal is then injected into the transceiver port and an approximate reconstruction of the short pulse is created at the target. The quaility of the pulse reconstruction is quantified in three different ways and the values of these metrics are predicted by the statistics of the scattering-parameter $|S_{21}|^2$ between the transceiver and target points in the enclosure. We experimentally demonstrate the method using a flat microwave billiard and quantify the reconstruction quality as a function of enclosure loss, port coupling and other considerations.

nlin.CD↗

arXiv2016

A `fast-burning' mechanism for magnetic diffusion

Fast-burning mechanism describes the rapid penetration, with a sharp-shaped wave-front, of a strong magnetic field into a conductive metal whose electric resistance poses an abrupt rise at some critical temperature. With its wave-front sweeping over a solid metal, the fast-burning can melt or vaporize the metal very rapidly. This paper derives formulas for the existence conditions and wave-front velocity of a fast-burning.

physics.plasm-ph↗

arXiv2016

Inv-ASKIT: A Parallel Fast Diret Solver for Kernel Matrices

We present a parallel algorithm for computing the approximate factorization of an $N$-by-$N$ kernel matrix. Once this factorization has been constructed (with $N \log^2 N $ work), we can solve linear systems with this matrix with $N \log N $ work. Kernel matrices represent pairwise interactions of points in metric spaces. They appear in machine learning, approximation theory, and computational physics. Kernel matrices are typically dense (matrix multiplication scales quadratically with $N$) and ill-conditioned (solves can require 100s of Krylov iterations). Thus, fast algorithms for matrix multiplication and factorization are critical for scalability. Recently we introduced ASKIT, a new method for approximating a kernel matrix that resembles N-body methods. Here we introduce INV-ASKIT, a factorization scheme based on ASKIT. We describe the new method, derive complexity estimates, and conduct an empirical study of its accuracy and scalability. We report results on real-world datasets including "COVTYPE" ($0.5$M points in 54 dimensions), "SUSY" ($4.5$M points in 8 dimensions) and "MNIST" (2M points in 784 dimensions) using shared and distributed memory parallelism. In our largest run we approximately factorize a dense matrix of size 32M $\times$ 32M (generated from points in 64 dimensions) on 4,096 Sandy-Bridge cores. To our knowledge these results improve the state of the art by several orders of magnitude.

math.NA↗

arXiv2016

Forward model with space-variant of source size for reconstruction on x-ray radiographic image

Forward imaging technique is the base of combined method on density reconstruction with the forward calculation and inverse problem solution. In the paper, we introduced the projection equation for the radiographic system with areal source blur and detector blur, gained the projecting matrix from any point source to any detector pixel with x-ray trace technique, proposed the ideal on gridding the areal source as many point sources with different weights, and used the blurring window as the effect of the detector blur. We used the forward projection equation to gain the same deviation information about the object edge as the experimental image. Our forward projection equation is combined with Constrained Conjugate Gradient method to form a new method for density reconstruction, XTRACE-CCG. The new method worked on the simulated image of French Test Object and experimental image. The same results have been concluded the affecting range of the blur is decreased and can be controlled to one or two pixels. The method is also suitable for reconstruction of density-variant object. The capability of our method to handle blur effect is useful for all radiographic systems with larger source size comparing to pixel size.

physics.med-ph↗

arXiv2015

Deep Speech 2: End-to-End Speech Recognition in English and Mandarin

We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages. Key to our approach is our application of HPC techniques, resulting in a 7x speedup over our previous system. Because of this efficiency, experiments that previously took weeks now run in days. This enables us to iterate more quickly to identify superior architectures and algorithms. As a result, in several cases, our system is competitive with the transcription of human workers when benchmarked on standard datasets. Finally, using a technique called Batch Dispatch with GPUs in the data center, we show that our system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.

cs.CL↗