arXiv subjects

Xinyue Zhang

Publications and source records attributed to Xinyue Zhang.

108 records · Page 6Linked to original sources

arXiv2021

A Stable FDTD Method with Embedded Reduced-Order Models

The computational efficiency of the Finite-Difference Time-Domain (FDTD) method can be significantly reduced by the presence of complex objects with fine features. Small geometrical details impose a fine mesh and a reduced time step, significantly increasing computational cost. Model order reduction has been proposed as a systematic way to generate compact models for complex objects, that one can then instantiate into a main FDTD mesh. However, the stability of FDTD with embedded reduced models remains an open problem. We propose a systematic method to generate reduced models for FDTD domains, and embed them into a main FDTD mesh with guaranteed stability up to the Courant-Friedrichs-Lewy (CFL) limit of the fine mesh. With a simple perturbation technique, the CFL of the whole scheme can be further extended beyond the fine grid's CFL limit. Reduced models can be created for arbitrary domains containing inhomogeneous and lossy materials. Numerical tests confirm the stability of the proposed method, and its potential to accelerate multiscale FDTD simulations.

cs.CE↗

arXiv2020

Diverse Melody Generation from Chinese Lyrics via Mutual Information Maximization

In this paper, we propose to adapt the method of mutual information maximization into the task of Chinese lyrics conditioned melody generation to improve the generation quality and diversity. We employ scheduled sampling and force decoding techniques to improve the alignment between lyrics and melodies. With our method, which we called Diverse Melody Generation (DMG), a sequence-to-sequence model learns to generate diverse melodies heavily depending on the input style ids, while keeping the tonality and improving the alignment. The experimental results of subjective tests show that DMG can generate more pleasing and coherent tunes than baseline methods.

cs.SD↗

arXiv2020

Revealing Secrets in SPARQL Session Level

Based on Semantic Web technologies, knowledge graphs help users to discover information of interest by using live SPARQL services. Answer-seekers often examine intermediate results iteratively and modify SPARQL queries repeatedly in a search session. In this context, understanding user behaviors is critical for effective intention prediction and query optimization. However, these behaviors have not yet been researched systematically at the SPARQL session level. This paper reveals secrets of session-level user search behaviors by conducting a comprehensive investigation over massive real-world SPARQL query logs. In particular, we thoroughly assess query changes made by users w.r.t. structural and data-driven features of SPARQL queries. To illustrate the potentiality of our findings, we employ an application example of how to use our findings, which might be valuable to devise efficient SPARQL caching, auto-completion, query suggestion, approximation, and relaxation techniques in the future.

cs.DB↗

arXiv2020

Evaluation of Inference Attack Models for Deep Learning on Medical Data

Deep learning has attracted broad interest in healthcare and medical communities. However, there has been little research into the privacy issues created by deep networks trained for medical applications. Recently developed inference attack algorithms indicate that images and text records can be reconstructed by malicious parties that have the ability to query deep networks. This gives rise to the concern that medical images and electronic health records containing sensitive patient information are vulnerable to these attacks. This paper aims to attract interest from researchers in the medical deep learning community to this important problem. We evaluate two prominent inference attack models, namely, attribute inference attack and model inversion attack. We show that they can reconstruct real-world medical images and clinical reports with high fidelity. We then investigate how to protect patients' privacy using defense mechanisms, such as label perturbation and model perturbation. We provide a comparison of attack results between the original and the medical deep learning models with defenses. The experimental evaluations show that our proposed defense approaches can effectively reduce the potential privacy leakage of medical deep learning from the inference attacks.

cs.LG↗

arXiv2020

Optical anapole mode in nanostructured lithium niobate for enhancing second harmonic generation

Second harmonic generation (SHG) with a material of large transparency is an attractive way of generating coherent light sources at exotic wavelength range such as VUV, UV and visible light. It is of critical importance to improve nonlinear conversion efficiency in order to find practical applications in quantum light source and high resolution nonlinear microscopy, etc. Here an enhanced SHG with conversion efficiency up to the order of 0.01% at SH wavelength of 282 nm under 11 GW/cm2 pump power via the excitation of anapole in lithium niobite (LiNbO3, or LN) nanodisk through the dominating d33 nonlinear coefficient is investigated. The anapole has advantages of strongly suppressing far-field scattering and well-confined internal field which helps to boost the nonlinear conversion. Anapoles in LN nanodisk is facilitated by high index contrast between LN and substrate with properties of near-zero-index via hyperbolic metamaterial structure design. By tailoring the multi-layers structure of hyperbolic metamaterials, the anapole excitation wavelength can be tuned at different wavelengths. It indicates that an enhanced SHG can be achieved at a wide range of pump light wavelengths via different design of the epsilon-near-zero (ENZ) hyperbolic metamaterials substrates. The proposed nanostructure in this work might hold significances for the enhanced light-matter interactions at the nanoscale such as integrated optics.

physics.optics↗

arXiv2020

Differentially Private and Fair Classification via Calibrated Functional Mechanism

Machine learning is increasingly becoming a powerful tool to make decisions in a wide variety of applications, such as medical diagnosis and autonomous driving. Privacy concerns related to the training data and unfair behaviors of some decisions with regard to certain attributes (e.g., sex, race) are becoming more critical. Thus, constructing a fair machine learning model while simultaneously providing privacy protection becomes a challenging problem. In this paper, we focus on the design of classification model with fairness and differential privacy guarantees by jointly combining functional mechanism and decision boundary fairness. In order to enforce $ε$-differential privacy and fairness, we leverage the functional mechanism to add different amounts of Laplace noise regarding different attributes to the polynomial coefficients of the objective function in consideration of fairness constraint. We further propose an utility-enhancement scheme, called relaxed functional mechanism by adding Gaussian noise instead of Laplace noise, hence achieving $(ε,δ)$-differential privacy. Based on the relaxed functional mechanism, we can design $(ε,δ)$-differentially private and fair classification model. Moreover, our theoretical analysis and empirical results demonstrate that our two approaches achieve both fairness and differential privacy while preserving good utility and outperform the state-of-the-art algorithms.

cs.CR↗

arXiv2019

From Dark Matter to Galaxies with Convolutional Neural Networks

Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations. However, to compare data with these simulations, the simulations in principle need to include gravity, magneto-hydrodyanmics, radiative transfer, etc. These ideal large-volume simulations (gravo-magneto-hydrodynamical) are incredibly computationally expensive which can cost tens of millions of CPU hours to run. In this paper, we propose a deep learning approach to map from the dark-matter-only simulation (computationally cheaper) to the galaxy distribution (from the much costlier cosmological simulation). The main challenge of this task is the high sparsity in the target galaxy distribution: space is mainly empty. We propose a cascade architecture composed of a classification filter followed by a regression procedure. We show that our result outperforms a state-of-the-art model used in the astronomical community, and provides a good trade-off between computational cost and prediction accuracy.

astro-ph.CO↗

arXiv2019

Stable halogen 2D Materials: the case of iodine and astatine

Two-dimensional (2D) materials have wide applications towards electronic devices, energy storages, and catalysis, et al. So far, most of the pure element 2D materials are composed of group IIIA,IVA, and VA elements. Beyond the scope, the orbit hybrid configuration becomes a key fact to influence 2D structure stably. Here we show a sp2d3 hybridization in the outmost electrons with O-shell for Iodine and P-shell for astatine element, builds up triangle configuration (beta-type) to form 2D structures beta-iodiene and beta-astatiene. Each atom is connected by pi bonds, and surrounded by 6 atoms. The pi bonds become possible, and band gap approaches zero because of interaction of unpaired single electron to each atom, depending on reducing bond length. By applying compression strain or spin orbit coupling (SOC), the Dirac points or topological nontrivial points can be available in the beta-iodiene and beta-astatiene. Our discovery has paved a new way to construction of 2D materials.

cond-mat.mtrl-sci↗

arXiv2019

Towards planar Iodine 2D crystal materials

Usually, the octet rule has limited the 2D materials by elements within groups IIIA_VA, whose outmost electrons can be considered to form hybridization orbit by s wave and p wave. The hybridization orbits can accommodate all the outmost electrons and form robust sigma bonds. Over VA group, the outmost electrons seem too abundant to be utilized in hybridization orbits. Here we show a spd2 hybridization rule, accommodating all the outmost electron of halogen elements. Each atom can be connected with contiguous atom by robust sigma bond, and carries one dangling lone-unpaired electron, implying formation of pi bond is possible. One atomic iodine layer can be robustly locked by the sigma bond, forming iodiene sheet by spd2 hybridization orbits. With application of compression strain, the pi bond forms, and the band inversion occurs simultaneously at the valence band and conductance band. Dirac points (line) and topological nontrivial points (arc or hoop) suggests the transformation of Dirac semimetal or topological semimetal happens.

cond-mat.mtrl-sci↗

arXiv2019

An Energy-Efficient Mixed-Signal Parallel Multiply-Accumulate (MAC) Engine Based on Stochastic Computing

Convolutional neural networks (CNN) have achieved excellent performance on various tasks, but deploying CNN to edge is constrained by the high energy consumption of convolution operation. Stochastic computing (SC) is an attractive paradigm which performs arithmetic operations with simple logic gates and low hardware cost. This paper presents an energy-efficient mixed-signal multiply-accumulate (MAC) engine based on SC. A parallel architecture is adopted in this work to solve the latency problem of SC. The simulation results show that the overall energy consumption of our design is 5.03pJ per 26-input MAC operation under 28nm CMOS technology.

eess.SP↗

arXiv2019

Memory System Designed for Multiply-Accumulate (MAC) Engine Based on Stochastic Computing

Convolutional neural network (CNN) achieves excellent performance on fascinating tasks such as image recognition and natural language processing at the cost of high power consumption. Stochastic computing (SC) is an attractive paradigm implemented in low power applications which performs arithmetic operations with simple logic and low hardware cost. However, conventional memory structure designed and optimized for binary computing leads to extra data conversion costs, which significantly decreases the energy efficiency. Therefore, a new memory system designed for SC-based multiply-accumulate (MAC) engine applied in CNN which is compatible with conventional memory system is proposed in this paper. As a result, the overall energy consumption of our new computing structure is 0.91pJ, which is reduced by 82.1% compared with the conventional structure, and the energy efficiency achieves 164.8 TOPS/W.

eess.SP↗

arXiv2019

A Parallel Bitstream Generator for Stochastic Computing

Stochastic computing (SC) presents high error tolerance and low hardware cost, and has great potential in applications such as neural networks and image processing. However, the bitstream generator, which converts a binary number to bitstreams, occupies a large area and energy consumption, thus weakening the superiority of SC. In this paper, we propose a novel technique for generating bitstreams in parallel, which needs only one clock for conversion and significantly reduces the hardware cost. Synthesis results demonstrate that the proposed parallel bitstream generator improves 2.5x area and 712x energy consumption.

cs.ET↗

arXiv2019

From Dark Matter to Galaxies with Convolutional Networks

Cosmological surveys aim at answering fundamental questions about our Universe, including the nature of dark matter or the reason of unexpected accelerated expansion of the Universe. In order to answer these questions, two important ingredients are needed: 1) data from observations and 2) a theoretical model that allows fast comparison between observation and theory. Most of the cosmological surveys observe galaxies, which are very difficult to model theoretically due to the complicated physics involved in their formation and evolution; modeling realistic galaxies over cosmological volumes requires running computationally expensive hydrodynamic simulations that can cost millions of CPU hours. In this paper, we propose to use deep learning to establish a mapping between the 3D galaxy distribution in hydrodynamic simulations and its underlying dark matter distribution. One of the major challenges in this pursuit is the very high sparsity in the predicted galaxy distribution. To this end, we develop a two-phase convolutional neural network architecture to generate fast galaxy catalogues, and compare our results against a standard cosmological technique. We find that our proposed approach either outperforms or is competitive with traditional cosmological techniques. Compared to the common methods used in cosmology, our approach also provides a nice trade-off between time-consumption (comparable to fastest benchmark in the literature) and the quality and accuracy of the predicted simulation. In combination with current and upcoming data from cosmological observations, our method has the potential to answer fundamental questions about our Universe with the highest accuracy.

astro-ph.CO↗

arXiv2018

Image Registration Based Flicker Solving in Video Face Replacement and Analysis Based Sub-pixel Image Registration

In this paper, a framework of video face replacement is proposed and it deals with the flicker of swapped face in video sequence. This framework contains two main innovations: 1) the technique of image registration is exploited to align the source and target video faces for eliminating the flicker or jitter of the segmented video face sequence; 2) a fast subpixel image registration method is proposed for farther accuracy and efficiency. Unlike the priori works, it minimizes the overlapping region and takes spatiotemporal coherence into account. Flicker in resulted videos is usually caused by the frequently changed bound of the blending target face and unregistered faces between and along video sequences. The subpixel image registration method is proposed to solve the flicker problem. During the alignment process, integer pixel registration is formulated by maximizing the similarity of images with down sampling strategy speeding up the process and sub-pixel image registration is a single-step image match via analytic method. Experimental results show the proposed algorithm reduces the computation time and gets a high accuracy when conducting experiments on different data sets.

cs.CV↗

arXiv2017

Making Faces: Universal Inverse Design of Surfaces with Thin Nematic Elastomer Sheets

Programmable shape-shifting materials can take different physical forms to achieve multifunctionality in a dynamic and controllable manner. Although morphing a shape from 2D to 3D via programmed inhomogeneous local deformations has been demonstrated in various ways, the inverse problem -- programming a sheet to take an arbitrary desired 3D shape -- is much harder yet critical to realize specific functions. Here, we address this inverse problem in thin liquid crystal elastomer (LCE) sheets, where the shape is preprogrammed by precise and local control of the molecular orientation of the liquid crystal monomers. We show how blueprints for arbitrary surface geometries as well as local extrinsic curvatures can be generated using approximate numerical methods. Backed by faithfully alignable and rapidly lockable LCE chemistry, we precisely embed our designs in LCE sheets using advanced top-down microfabrication techniques. We thus successfully produce flat sheets that, upon thermal activation, take an arbitrary desired shape, such as a face. The general design principles presented here for creating an arbitrary 3D shape will allow for exploration of unmet needs in flexible electronics, metamaterials, aerospace and medical devices, and more.

cond-mat.soft↗

arXiv2017

A Dissipation Theory for Three-Dimensional FDTD with Application to Stability Analysis and Subgridding

The finite-difference time-domain (FDTD) algorithm is a popular numerical method for solving electromagnetic problems. FDTD simulations can suffer from instability due to the explicit nature of the method. Stability enforcement can be particularly challenging in scenarios where a setup is composed of multiple components, such as grids of different resolution, advanced boundary conditions, reduced-order models, and lumped elements. We propose a dissipation theory for 3-D FDTD inspired by the principle of energy conservation. We view the FDTD update equations for a 3-D region as a dynamical system, and show under which conditions the system is dissipative. By requiring each component of an FDTD-like scheme to be dissipative, the stability of the overall coupled scheme follows by construction. The proposed framework enables the creation of provably stable schemes in an easy and modular fashion, since conditions are imposed on the individual components, rather than on the overall coupled scheme as in existing approaches. With the proposed framework, we derive a new subgridding scheme with guaranteed stability, low reflections, support for material traverse and arbitrary (odd) grid refinement ratio.

cs.CE↗

arXiv2016

A Dissipative Systems Theory for FDTD with Application to Stability Analysis and Subgridding

This paper establishes a far-reaching connection between the Finite-Difference Time-Domain method (FDTD) and the theory of dissipative systems. The FDTD equations for a rectangular region are written as a dynamical system having the magnetic and electric fields on the boundary as inputs and outputs. Suitable expressions for the energy stored in the region and the energy absorbed from the boundaries are introduced, and used to show that the FDTD system is dissipative under a generalized Courant-Friedrichs-Lewy condition. Based on the concept of dissipation, a powerful theoretical framework to investigate the stability of FDTD methods is devised. The new method makes FDTD stability proofs simpler, more intuitive, and modular. Stability conditions can indeed be given on the individual components (e.g. boundary conditions, meshes, embedded models) instead of the whole coupled setup. As an example of application, we derive a new subgridding method with material traverse, arbitrary grid refinement, and guaranteed stability. The method is easy to implement and has a straightforward stability proof. Numerical results confirm its stability, low reflections, and ability to handle material traverse.

cs.CE↗

arXiv2013

Production of very-high-$n$ strontium Rydberg atoms

The production of very-high-$n$, $n\sim300$-500, strontium Rydberg atoms is explored using a crossed laser-atom beam geometry. $n$$^{1}$S$_{0}$ and $n$$^{1}$D$_{2}$ states are created by two-photon excitation via the 5s5p $^{1}$P$_{1}$ intermediate state using radiation with wavelengths of $\sim$~461 and $\sim$ 413 nm. Rydberg atom densities as high as $\sim 3 \times 10^{5}$ cm$^{-3}$ have been achieved, sufficient that Rydberg-Rydberg interactions can become important. The isotope shifts in the Rydberg series limits are determined by tuning the 461 nm light to preferentially excite the different strontium isotopes. Photoexcitation in the presence of an applied electric field is examined. The initially quadratic Stark shift of the $n$$^{1}$P$_{1}$ and $n$$^{1}$D$_{2}$ states becomes near-linear at higher fields and the possible use of $n{}^{1}$D$_{2}$ states to create strongly-polarized, quasi-one-dimensional electronic states in strontium is discussed. The data are analyzed with the aid of a two-active-electron (TAE) approximation. The two-electron Hamiltonian, within which the Sr$^{2+}$ core is represented by a semi-empirical potential, is numerically diagonalized allowing calculation of the energies of high-$n$ Rydberg states and their photoexcitation probabilities.

physics.atom-ph↗