arXiv subjects

Xin He

Publications and source records attributed to Xin He.

At least 199 records · Page 11Linked to original sources

arXiv2019

Dielectric Behavior as a Screen in Rational Searches for Electronic Materials: Metal Pnictide Sulfosalts

Dielectric screening plays an important role in reducing the strength of carrier scattering and trapping by point defects for many semiconductors such as the halide perovskite solar materials. However, it was rarely considered as a screen to find new electronic semiconductors. We performed a material search study using the dielectric properties as a screen to identify potential electronic materials in the class of metal-pnictide ternary sulfosalts, containing Bi or Sb. These salts are basically ionic due to the electronegativity difference between the S and both the metal and pnictogen elements. However, we do find significant cross-gap hybridization between the S p-derived valence bands and pnictogen p-derived conduction bands in many of the materials. This leads to enhanced Born effective charges, and in several cases, highly enhanced dielectric constants. We find a chemical rule for high dielectric constants in terms of the bond connectivity of the pnictogen-chalcogen part of the crystal structure. We additionally find a series of compounds with low effective mass, high dielectric constant and other properties that suggest good performance as electronic materials and also several potential thermoelectric compounds. Experimental optical data and solar conversion efficiency are reported for Sn-Sb-S samples, and results in accord with predicted good performance are found. The results illustrate the utility of dielectric properties as a screen for identifying complex semiconductors.

cond-mat.mtrl-sci↗

arXiv2019

On-chip broadband plasmon-induced transparency based on plasmonic metal-insulator-metal waveguides

As an analogue of electromagnetically induced transparency (EIT), plasmon-induced transparency (PIT) has been realized both in plasmonic metamaterial and waveguide structures. Via near-field coupling within unit cells, PIT with broadband could be produced by plasmonic metamaterials, which, however, has not been realized in on-chip plasmonic waveguide structures. Here, we introduce broadband PIT based on a plasmonic metal-insulator-metal (MIM) waveguide system. Utilizing the direct coupling structure, PIT emerges based on an easy-fabricated structure without gap. By tuning coupling distance, the transparent window can be continuously tuned from narrow- to broadband. Such device is promising for on-chip applications on sensing, filtering and slow light over a broad frequency range.

physics.optics↗

arXiv2019

Hybridization-induced resonances with high quality factor in a plasmonic concentric ring-disk nanocavity

Plasmonic resonators have drawn more attention due to the ability to confine light into subwavelength scale. However, they always suffer from a low quality (Q) factor owing to the intrinsic loss of metal. Here, we numerically propose a plasmonic resonator with ultra-high Q factor based on plasmonic metal-insulator-metal (MIM) waveguide structures. The resonator consists of a disk cavity surrounded by a concentric ring cavity, possessing an ultra-small volume. Arising from the plasmon hybridization between plasmon modes in the disk and ring cavity, the induced bonding hybridized modes have ultra-narrow full wave at half maximum (FWHM) as well as ultra-high Q factors. The FWHM can be nearly 1 nm and Q factor can be more than 400. Furthermore, such device can act as a refractive index sensor with ultra-high figure of merit (FOM). This work provides a novel approach to design plasmonic high-Q-factor resonators, and has potential on-chip applications such as filters, sensors and nanolasers.

physics.optics↗

arXiv2019

Modelling of vorticity, sound and their interaction in two-dimensional superfluids

Vorticity in two-dimensional superfluids is subject to intense research efforts due to its role in quantum turbulence, dissipation and the BKT phase transition. Interaction of sound and vortices is of broad importance in Bose-Einstein condensates and superfluid helium [1-4]. However, both the modelling of the vortex flow field and of its interaction with sound are complicated hydrodynamic problems, with analytic solutions only available in special cases. In this work, we develop methods to compute both the vortex and sound flow fields in an arbitrary two-dimensional domain. Further, we analyse the dispersive interaction of vortices with sound modes in a two-dimensional superfluid and develop a model that quantifies this interaction for any vortex distribution on any two-dimensional bounded domain, possibly non-simply connected, exploiting analogies with fluid dynamics of an ideal gas and electrostatics. As an example application we use this technique to propose an experiment that should be able to unambiguously detect single circulation quanta in a helium thin film.

cond-mat.quant-gas↗

arXiv2019

Simple Physical Adversarial Examples against End-to-End Autonomous Driving Models

Recent advances in machine learning, especially techniques such as deep neural networks, are promoting a range of high-stakes applications, including autonomous driving, which often relies on deep learning for perception. While deep learning for perception has been shown to be vulnerable to a host of subtle adversarial manipulations of images, end-to-end demonstrations of successful attacks, which manipulate the physical environment and result in physical consequences, are scarce. Moreover, attacks typically involve carefully constructed adversarial examples at the level of pixels. We demonstrate the first end-to-end attacks on autonomous driving in simulation, using simple physically realizable attacks: the painting of black lines on the road. These attacks target deep neural network models for end-to-end autonomous driving control. A systematic investigation shows that such attacks are surprisingly easy to engineer, and we describe scenarios (e.g., right turns) in which they are highly effective, and others that are less vulnerable (e.g., driving straight). Further, we use network deconvolution to demonstrate that the attacks succeed by inducing activation patterns similar to entirely different scenarios used in training.

cs.RO↗

arXiv2019

Coherent vortex dynamics in a strongly-interacting superfluid on a silicon chip

Two-dimensional superfluidity and quantum turbulence are directly connected to the microscopic dynamics of quantized vortices. However, surface effects have prevented direct observations of coherent vortex dynamics in strongly-interacting two-dimensional systems. Here, we overcome this challenge by confining a two-dimensional droplet of superfluid helium at microscale on the atomically-smooth surface of a silicon chip. An on-chip optical microcavity allows laser-initiation of vortex clusters and nondestructive observation of their decay in a single shot. Coherent dynamics dominate, with thermal vortex diffusion suppressed by six orders-of-magnitude. This establishes a new on-chip platform to study emergent phenomena in strongly-interacting superfluids, test astrophysical dynamics such as those in the superfluid core of neutron stars in the laboratory, and construct quantum technologies such as precision inertial sensors.

cond-mat.other↗

arXiv2018

Experimental identification of critical condition for drastically enhancing thermoelectric power factor of two-dimensional layered materials

Nano-structuring is an extremely promising path to high performance thermoelectrics. Favorable improvements in thermal conductivity are attainable in many material systems, and theoretical work points to large improvements in electronic properties. However, realization of the electronic benefits in practical materials has been elusive experimentally. A key challenge is that experimental identification of the quantum confinement length, below which the thermoelectric power factor is significantly enhanced, remains elusive due to lack of simultaneous control of size and carrier density. Here we investigate gate tunable and temperature-dependent thermoelectric transport in $γ$ phase indium selenide ($γ$ InSe, n type semiconductor) samples with thickness varying from 7 to 29 nm. This allows us to properly map out dimension and doping space. Combining theoretical and experimental studies, we reveal that the sharper pre-edge of the conduction-band density of states arising from quantum confinement gives rise to an enhancement of the Seebeck coefficient and the power factor in the thinner InSe samples. Most importantly, we experimentally identify the role of the competition between quantum confinement length and thermal de Broglie wavelength in the enhancement of power factor. Our results provide an important and general experimental guideline for optimizing the power factor and improving the thermoelectric performance of two-dimensional layered semiconductors.

cond-mat.mes-hall↗

arXiv2018

Wi-Motion: A Robust Human Activity Recognition Using WiFi Signals

Recent research has shown that human motions and positions can be recognized through WiFi signals. The key intuition is that different motions and positions introduce different multi-path distortions in WiFi signals and generate different patterns in the time-series of channel state information (CSI). In this paper, we propose Wi-Motion, a WiFi-based human activities recognition system. Unlike existing systems, Wi-Motion adopts the amplitude and phase information extracted from the CSI sequence to construct the classifiers respectively, and combines the results using a combination strategy based on posterior probability. As the simulation results shows, Wi-Motion can recognize six human activities with the mean accuracy of 98:4%.

eess.SP↗

arXiv2018

On Measurement of the Spatio-Frequency Property of OFDM Backscattering

Orthogonal frequency-division multiplexing (OFDM) backscatter system, such as Wi-Fi backscatter, has recently been recognized as a promising technique for the IoT connectivity, due to its ubiquitous and low-cost property. This paper investigates the spatial-frequency property of the OFDM backscatter which takes the distance and the angle into account in different frequency bands. We deploy three typical scenarios for performing measurements to evaluate the received signal strength from the backscatter link. The impact of the distances among the transmitter, the tag and the receiver, as well as the angle between the transmitter and the tag is observed through the obtained measurement data. From the evaluation results, it is found that the best location of tag is either close to the receiver or the transmitter which depends on the frequency band, and the best angle is 90 degrees between the transmitter and the receiver. This work opens the shed light on the spatial deployment of the backscatter tag in different frequency band with the aim of improving the performance and reducing the interference.

cs.NI↗

arXiv2018

Active control of broadband plasmon-induced transparency in terahertz hybrid metal-graphene metamaterial

A hybrid metal-graphene metamaterial (MM) is reported to achieve the active control of the broadband plasmon-induced transparency (PIT) in THz region. The unit cell consists of one cut wire (CW), four U-shape resonators (USRs) and monolayer graphene sheets under the USRs. Via near-field coupling, broadband PIT can be produced through the interference between different modes. Based on different arrangements of graphene positions, not only can we achieve electrically switching the amplitude of broadband PIT, but also can realize modulating the bandwidth of the transparent window. Simultaneously, both the capability and region of slow light can be dynamically tunable. This work provides schemes to manipulate PIT with more degrees of freedom, which will find significant applications in multifunctional THz modulation.

physics.optics↗

arXiv2018

AxTrain: Hardware-Oriented Neural Network Training for Approximate Inference

The intrinsic error tolerance of neural network (NN) makes approximate computing a promising technique to improve the energy efficiency of NN inference. Conventional approximate computing focuses on balancing the efficiency-accuracy trade-off for existing pre-trained networks, which can lead to suboptimal solutions. In this paper, we propose AxTrain, a hardware-oriented training framework to facilitate approximate computing for NN inference. Specifically, AxTrain leverages the synergy between two orthogonal methods---one actively searches for a network parameters distribution with high error tolerance, and the other passively learns resilient weights by numerically incorporating the noise distributions of the approximate hardware in the forward pass during the training phase. Experimental results from various datasets with near-threshold computing and approximation multiplication strategies demonstrate AxTrain's ability to obtain resilient neural network parameters and system energy efficiency improvement.

cs.LG↗

arXiv2018

Multilayer Nonlinear Processing for Information Privacy in Sensor Networks

A sensor network wishes to transmit information to a fusion center to allow it to detect a public hypothesis, but at the same time prevent it from inferring a private hypothesis. We propose a multilayer nonlinear processing procedure at each sensor to distort the sensor's data before it is sent to the fusion center. In our proposed framework, sensors are grouped into clusters, and each sensor first applies a nonlinear fusion function on the information it receives from sensors in the same cluster and in a previous layer. A linear weighting matrix is then used to distort the information it sends to sensors in the next layer. We adopt a nonparametric approach and develop a modified mirror descent algorithm to optimize the weighting matrices so as to ensure that the regularized empirical risk of detecting the private hypothesis is above a given privacy threshold, while minimizing the regularized empirical risk of detecting the public hypothesis. Experiments on empirical datasets demonstrate that our approach is able to achieve a good trade-off between the error rates of the public and private hypothesis.

cs.CR↗

arXiv2017

Stellar Parameters of Main Sequence Turn-off Star Candidates Observed with the LAMOST and Kepler

Main sequence turn-off (MSTO) stars have advantages as indicators of Galactic evolution since their ages could be robustly estimated from atmospheric parameters. Hundreds of thousands of MSTO stars have been selected from the LAMOST Galactic sur- vey to study the evolution of the Galaxy, and it is vital to derive accurate stellar parameters. In this work, we select 150 MSTO star candidates from the MSTO stars sample of Xiang that have asteroseismic parameters and determine accurate stellar parameters for these stars combing the asteroseismic parameters deduced from the Kepler photometry and atmospheric parameters deduced from the LAMOST spectra.With this sample, we examine the age deter- mination as well as the contamination rate of the MSTO stars sample. A comparison of age between this work and Xiang shows a mean difference of 0.53 Gyr (7%) and a dispersion of 2.71 Gyr (28%). The results show that 79 of the candidates are MSTO stars, while the others are contaminations from either main sequence or sub-giant stars. The contamination rate for the oldest stars is much higher than that for the younger stars. The main cause for the high contamination rate is found to be the relatively large systematic bias in the LAMOST surface gravity estimates.

astro-ph.SR↗

arXiv2017

Towards Information Privacy for the Internet of Things

In an Internet of Things network, multiple sensors send information to a fusion center for it to infer a public hypothesis of interest. However, the same sensor information may be used by the fusion center to make inferences of a private nature that the sensors wish to protect. To model this, we adopt a decentralized hypothesis testing framework with binary public and private hypotheses. Each sensor makes a private observation and utilizes a local sensor decision rule or privacy mapping to summarize that observation independently of the other sensors. The local decision made by a sensor is then sent to the fusion center. Without assuming knowledge of the joint distribution of the sensor observations and hypotheses, we adopt a nonparametric learning approach to design local privacy mappings. We introduce the concept of an empirical normalized risk, which provides a theoretical guarantee for the network to achieve information privacy for the private hypothesis with high probability when the number of training samples is large. We develop iterative optimization algorithms to determine an appropriate privacy threshold and the best sensor privacy mappings, and show that they converge. Finally, we extend our approach to the case of a private multiple hypothesis. Numerical results on both synthetic and real data sets suggest that our proposed approach yields low error rates for inferring the public hypothesis, but high error rates for detecting the private hypothesis.

cs.IT↗

arXiv2017

Wilcoxon Rank-Based Tests for Clustered Data with R Package clusrank

Wilcoxon Rank-based tests are distribution-free alternatives to the popular two-sample and paired t-tests. For independent data, they are available in several R packages such as stats and coin. For clustered data, in spite of the recent methodological developments, there did not exist an R package that makes them available at one place. We present a package clusrank where the latest developments are implemented and wrapped under a unified user-friendly interface. With different methods dispatched based on the inputs, this package offers great flexibility in rank-based tests for various clustered data. Exact tests based on permutations are also provided for some methods. Details of the major schools of different methods are briefly reviewed. Usages of the package clusrank are illustrated with simulated data as well as a real dataset from an ophthalmological study. The package also enables convenient comparison between selected methods under settings that have not been studied before and the results are discussed.

stat.CO↗

arXiv2016

Thin film superfluid optomechanics

Excitations in superfluid helium represent attractive mechanical degrees of freedom for cavity optomechanics schemes. Here we numerically and analytically investigate the properties of optomechanical resonators formed by thin films of superfluid $^4$He covering micrometer-scale whispering gallery mode cavities. We predict that through proper optimization of the interaction between film and optical field, large optomechanical coupling rates $g_0>2π\times 100$ kHz and single photon cooperativities $C_0>10$ are achievable. Our analytical model reveals the unconventional behaviour of these thin films, such as thicker and heavier films exhibiting smaller effective mass and larger zero point motion. The optomechanical system outlined here provides access to unusual regimes such as $g_0>Ω_M$ and opens the prospect of laser cooling a liquid into its quantum ground state.

physics.optics↗

arXiv2016

Optimal Monotone Drawings of Trees

A monotone drawing of a graph G is a straight-line drawing of G such that, for every pair of vertices u,w in G, there exists abpath P_{uw} in G that is monotone in some direction l_{uw}. (Namely, the order of the orthogonal projections of the vertices of P_{uw} on l_{uw} is the same as the order they appear in P_{uw}.) The problem of finding monotone drawings for trees has been studied in several recent papers. The main focus is to reduce the size of the drawing. Currently, the smallest drawing size is O(n^{1.205}) x O(n^{1.205}). In this paper, we present an algorithm for constructing monotone drawings of trees on a grid of size at most 12n x 12n. The smaller drawing size is achieved by a new simple Path Draw algorithm, and a procedure that carefully assigns primitive vectors to the paths of the input tree T. We also show that there exists a tree T_0 such that any monotone drawing of T_0 must use a grid of size Omega(n) x Omega(n). So the size of our monotone drawing of trees is asymptotically optimal.

cs.DS↗

arXiv2015

Local convergence of critical random trees and continuous-state branching processes

We study the local convergence of critical Galton-Watson trees and Levy trees under various conditionings. Assuming a very general monotonicity property on the functional of random trees, we show that random trees conditioned to have large functional values always converge locally to immortal trees. We also derive a very general ratio limit property for functionals of random trees satisfying the monotonicity property. Then we move on to study the local convergence of critical continuous-state branching processes, and prove a similar result. Finally we give a definition of continuum condensation trees, which should be the correct local limits for certain subcritical Levy trees under suitable conditionings.

math.PR↗