arXiv subjects

Liang Yang

Publications and source records attributed to Liang Yang.

At least 127 records · Page 7Linked to original sources

arXiv2020

The TianQin project: current progress on science and technology

TianQin is a planned space-based gravitational wave (GW) observatory consisting of three earth orbiting satellites with an orbital radius of about $10^5~{\rm km}$. The satellites will form a equilateral triangle constellation the plane of which is nearly perpendicular to the ecliptic plane. TianQin aims to detect GWs between $10^{-4}~{\rm Hz}$ and $1~{\rm Hz}$ that can be generated by a wide variety of important astrophysical and cosmological sources, including the inspiral of Galactic ultra-compact binaries, the inspiral of stellar-mass black hole binaries, extreme mass ratio inspirals, the merger of massive black hole binaries, and possibly the energetic processes in the very early universe or exotic sources such as cosmic strings. In order to start science operations around 2035, a roadmap called the 0123 plan is being used to bring the key technologies of TianQin to maturity, supported by the construction of a series of research facilities on the ground. Two major projects of the 0123 plan are being carried out. In this process, the team has created a new generation $17~{\rm cm}$ single-body hollow corner-cube retro-reflector which has been launched with the QueQiao satellite on 21 May 2018; a new laser ranging station equipped with a $1.2~{\rm m}$ telescope has been constructed and the station has successfully ranged to all the five retro-reflectors on the Moon; and the TianQin-1 experimental satellite has been launched on 20 December 2019 and the first round result shows that the satellite has exceeded all of its mission requirements.

gr-qc↗

arXiv2020

The First Round Result from the TianQin-1 Satellite

The TianQin-1 satellite (TQ-1), which is the first technology demonstration satellite for the TianQin project, was launched on 20 December 2019. The first round of experiment had been carried out from 21 December 2019 until 1 April 2020. The residual acceleration of the satellite is found to be about $1\times10^{-10}~{\rm m}/{\rm s}^{2}/{\rm Hz}^{1/2}$ at $0.1~{\rm Hz}\,$ and about $5\times10^{-11}~{\rm m}/{\rm s}^{2}/{\rm Hz}^{1/2}$ at $0.05~{\rm Hz}\,$, measured by an inertial sensor with a sensitivity of $5\times10^{-12}~{\rm m}/{\rm s}^{2}/{\rm Hz}^{1/2}$ at $0.1~{\rm Hz}\,$. The micro-Newton thrusters has demonstrated a thrust resolution of $0.1~μ{\rm N}$ and a thrust noise of $0.3~μ{\rm N}/{\rm Hz}^{1/2}$ at $0.1~{\rm Hz}$. The residual noise of the satellite with drag-free control is $3\times10^{-9}~{\rm m}/{\rm s}^{2}/{\rm Hz}^{1/2}$ at $0.1~{\rm Hz}\,$. The noise level of the optical readout system is about $30~{\rm pm}/{\rm Hz}^{1/2}$ at $0.1~{\rm Hz}\,$. The temperature stability at temperature monitoring position is controlled to be about $\pm3~{\rm mK}$ per orbit, and the mismatch between the center-of-mass of the satellite and that of the test mass is measured with a precision of better than $0.1~{\rm mm}$.

physics.ins-det↗

arXiv2020

GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet

Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) devices to detect the subsurface objects (i.e. rebars, utility pipes) and reveal the underground scene. One of the biggest challenges in GPR based inspection is the subsurface targets reconstruction. In order to address this issue, this paper presents a 3D GPR migration and dielectric prediction system to detect and reconstruct underground targets. This system is composed of three modules: 1) visual inertial fusion (VIF) module to generate the pose information of GPR device, 2) deep neural network module (i.e., DepthNet) which detects B-scan of GPR image, extracts hyperbola features to remove the noise in B-scan data and predicts dielectric to determine the depth of the objects, 3) 3D GPR migration module which synchronizes the pose information with GPR scan data processed by DepthNet to reconstruct and visualize the 3D underground targets. Our proposed DepthNet processes the GPR data by removing the noise in B-scan image as well as predicting depth of subsurface objects. For DepthNet model training and testing, we collect the real GPR data in the concrete test pit at Geophysical Survey System Inc. (GSSI) and create the synthetic GPR data by using gprMax3.0 simulator. The dataset we create includes 350 labeled GPR images. The DepthNet achieves an average accuracy of 92.64% for B-scan feature detection and an 0.112 average error for underground target depth prediction. In addition, the experimental results verify that our proposed method improve the migration accuracy and performance in generating 3D GPR image compared with the traditional migration methods.

eess.SP↗

arXiv2020

Channel Estimation via Direct Calculation and Deep Learning for RIS-Aided mmWave Systems

This paper proposes a novel reconfigurable intelligent surface (RIS) architecture which enables channel estimation of RIS-assisted millimeter wave (mmWave) systems. More specifically, two channel estimation methods, namely, direct calculation (DC) and deep learning (DL) methods, are proposed to skillfully convert the overall channel estimation into two tasks: the channel estimation and the angle parameter estimation of a small number of active elements. In particular, the direct calculation method calculates the angle parameters directly through the channel estimates of adjacent active elements and, based on it, the DL method reduces the angle offset rate and further improves the accuracy of angle parameter estimation. Compared with the traditional methods, the proposed schemes reduce the complexity of the RIS channel estimation while outperforming the beam training method in terms of minimum square error, achievable rate, and outage probability.

cs.IT↗

arXiv2020

Judging a Book by Its Cover: The Effect of Facial Perception on Centrality in Social Networks

Facial appearance matters in social networks. Individuals frequently make trait judgments from facial clues. Although these face-based impressions lack the evidence to determine validity, they are of vital importance, because they may relate to human network-based social behavior, such as seeking certain individuals for help, advice, dating, and cooperation, and thus they may relate to centrality in social networks. However, little to no work has investigated the apparent facial traits that influence network centrality, despite the large amount of research on attributions of the central position including personality and behavior. In this paper, we examine whether perceived traits based on facial appearance affect network centrality by exploring the initial stage of social network formation in a first-year college residential area. We took face photos of participants who are freshmen living in the same residential area, and we asked them to nominate community members linking to different networks. We then collected facial perception data by requiring other participants to rate facial images for three main attributions: dominance, trustworthiness, and attractiveness. Meanwhile, we proposed a framework to discover how facial appearance affects social networks. Our results revealed that perceived facial traits were correlated with the network centrality and that they were indicative to predict the centrality of people in different networks. Our findings provide psychological evidence regarding the interaction between faces and network centrality. Our findings also offer insights in to a combination of psychological and social network techniques, and they highlight the function of facial bias in cuing and signaling social traits. To the best of our knowledge, we are the first to explore the influence of facial perception on centrality in social networks.

cs.SI↗

arXiv2020

Secrecy Outage Probability Analysis for RIS-Assisted NOMA Systems

In this paper, the physical layer security (PLS) for a novel reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) system in a multi-user scenario is investigated, where we consider the worst case that the eavesdropper also utilizes the advantage of the RISs. More specifically, we derive analytical results for the secrecy outage probability (SOP). From the numerical results, we observe that the use of RISs can improve the secrecy performance compared to traditional NOMA systems. However, for the worst case that the received signals at the eavesdropper comes from the RISs and source, increasing the number of intelligent elements on the RIS has a negative impact on the secrecy performance. At high SNRs, the system's SOP tends to a constant. Finally, the secrecy performance can be improved through the group selection.

eess.SP↗

arXiv2020

Outage Probability and Capacity Scaling Law of Multiple RIS-Aided Cooperative Networks

In this letter, we consider a dual-hop cooperative network assisted by multiple reconfigurable intelligent surfaces (RISs). Assuming that the RIS with the highest instantaneous end-to-end signal-to-noise ratio (SNR) is selected to aid the communication, the outage probability (OP) and average sum-rate are investigated. Specifically, an exact analysis for the OP is developed. In addition, relying on the extreme value theory, closed-form expressions for the asymptotic OP and asymptotic sum-rate are derived, based on which the capacity scaling law is established. Our results are corroborated through simulations and insightful discussions are provided. In particular, our analysis shows that the number of RISs as well as the number of reflecting elements play a crucial role in the capacity scaling law of multiple RIS-aided cooperative networks. Also, comparisons with relay-aided systems are carried out to demonstrate that the proposed system setup outperforms relaying schemes both in terms of the OP and average sum-rate.

cs.IT↗

arXiv2020

Accurate Closed-Form Approximations to Channel Distributions of RIS-Aided Wireless Systems

This paper proposes highly accurate closed-form approximations to channel distributions of two different reconfigurable intelligent surface (RIS)-based wireless system setups, namely, dual-hop RIS-aided (RIS-DH) scheme and RIS-aided transmit (RIS-T) scheme. Differently from previous works, the proposed approximations reveal to be very tight for arbitrary number $N$ of reflecting metasurface's elements. Our findings are then applied to the performance analysis of the considered systems, in which the outage probability, bit error rate, and average channel capacity are derived. Results show that the achievable diversity orders $G_d$ for RIS-DH and RIS-T schemes are $N-1<G_d<N$ and $N$, respectively. Furthermore, it is revealed that both schemes can not provide the multiplexing gain and only diversity gains are achieved. For the RIS-DH scheme, the channels are similar to the keyhole multiple-input multiple-output (MIMO) channels with only one degree of freedom, while the RIS-T scheme is like the transmit diversity structure.

cs.IT↗

arXiv2020

Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes

The deficiency of 3D segmentation labels is one of the main obstacles to effective point cloud segmentation, especially for scenes in the wild with varieties of different objects. To alleviate this issue, we propose a novel deep graph convolutional network-based framework for large-scale semantic scene segmentation in point clouds with sole 2D supervision. Different with numerous preceding multi-view supervised approaches focusing on single object point clouds, we argue that 2D supervision is capable of providing sufficient guidance information for training 3D semantic segmentation models of natural scene point clouds while not explicitly capturing their inherent structures, even with only single view per training sample. Specifically, a Graph-based Pyramid Feature Network (GPFN) is designed to implicitly infer both global and local features of point sets and an Observability Network (OBSNet) is introduced to further solve object occlusion problem caused by complicated spatial relations of objects in 3D scenes. During the projection process, perspective rendering and semantic fusion modules are proposed to provide refined 2D supervision signals for training along with a 2D-3D joint optimization strategy. Extensive experimental results demonstrate the effectiveness of our 2D supervised framework, which achieves comparable results with the state-of-the-art approaches trained with full 3D labels, for semantic point cloud segmentation on the popular SUNCG synthetic dataset and S3DIS real-world dataset.

cs.CV↗

arXiv2020

Dirac series for some real exceptional Lie groups

Up to equivalence, this paper classifies all the irreducible unitary representations with non-zero Dirac cohomology for the following simple real exceptional Lie groups: ${\rm EI}=E_{6(6)}, {\rm EIV}=E_{6(-26)}, {\rm FI}=F_{4(4)}, {\rm FII}=F_{4(-20)}$. Along the way, we find an irreducible unitary representation of $F_{4(4)}$ whose Dirac index vanishes, while its Dirac cohomology is non-zero. This disproves a conjecture raised in 2015 asserting that there should be no cancellation between the even part and the odd part of the Dirac cohomology.

math.RT↗

arXiv2020

On the Performance of Dual-Hop Systems over Mixed FSO/mmWave Fading Channels

Free-space optical (FSO) links are considered as a cost-efficient way to fill the backhaul/fronthaul connectivity gap between millimeter wave (mmWave) access networks and optical fiber based central networks. In this paper, we investigate the end-to-end performance of dual-hop mixed FSO/mmWave systems to address this combined use. The FSO link is modeled as a Gamma-Gamma fading channel using both heterodyne detection and indirect modulation/direct detection with pointing error impairments, while the mmWave link experiences the fluctuating two-ray fading. Under the assumption of both amplify-and-forward and decode-and-forward relaying, we derive novel closed-form expressions for the outage probability, average bit error probability (BER), ergodic capacity, effective capacity in terms of bivariate Fox's $H$-functions. Additionally, we discuss the diversity gain and provide other important engineering insights based on the high signal-to-noise-ratio analysis of the outage probability and the average BER. Finally, all our analytical results are verified using Monte Carlo simulations.

cs.IT↗

arXiv2019

Statistical characterization of the yield stress of nanoparticles

Atomistic simulations are performed to study the statistical mechanical property of gold nanoparticles. It is demonstrated that the yielding behavior of gold nanoparticles is governed by dislocation nucleation around surface steps. Since the nucleation of dislocations is an activated process with the aid of thermal fluctuation, the yield stress at a specific temperature should exhibit a statistical distribution rather than a definite constant value. Molecular dynamics simulations reveal that the yield stress follows a Gaussian distribution at a specific temperature. As the temperature increases, the mean value of yield stress decreases while the width of distribution becomes larger. Based on numerical analysis, the dependence of the mean yield stress on temperature can be well described by a parabolic function. Present study illuminates the statistical features of the yielding behavior of nanostructured elements.

cond-mat.mtrl-sci↗

arXiv2019

Multi-Label Learning with Deep Forest

In multi-label learning, each instance is associated with multiple labels and the crucial task is how to leverage label correlations in building models. Deep neural network methods usually jointly embed the feature and label information into a latent space to exploit label correlations. However, the success of these methods highly depends on the precise choice of model depth. Deep forest is a recent deep learning framework based on tree model ensembles, which does not rely on backpropagation. We consider the advantages of deep forest models are very appropriate for solving multi-label problems. Therefore we design the Multi-Label Deep Forest (MLDF) method with two mechanisms: measure-aware feature reuse and measure-aware layer growth. The measure-aware feature reuse mechanism reuses the good representation in the previous layer guided by confidence. The measure-aware layer growth mechanism ensures MLDF gradually increase the model complexity by performance measure. MLDF handles two challenging problems at the same time: one is restricting the model complexity to ease the overfitting issue; another is optimizing the performance measure on user's demand since there are many different measures in the multi-label evaluation. Experiments show that our proposal not only beats the compared methods over six measures on benchmark datasets but also enjoys label correlation discovery and other desired properties in multi-label learning.

cs.LG↗

arXiv2019

An experiment to search for dark matter interactions using sodium iodide detectors

Observations of galaxies and primordial radiation suggest that the Universe is made mostly of non-luminous dark matter. Several types of new fundamental particles have been proposed as candidates for dark matter such as weakly interacting massive particles (WIMPs) but no definitive signal has been seen despite concerted efforts by many collaborations. One exception is the much-debated claim by the DAMA collaboration of a statistically significant annual modulation in the event rate of their experiment with a period and phase consistent with that expected from WIMP dark matter. Several groups have been working to develop experiments with the aim of reproducing DAMA's results using the same target medium. Here we report results from the initial operation of the COSINE-100 experiment. COSINE-100 uses sodium iodide as the target medium-the same medium as DAMA-and is designed to carry out a model-independent test of DAMA's claim. Initial data based on the first 59.5 days indicate that there is no excess of events over the expected background, confirming that DAMA's annual modulation signal is in severe tension with results from other experiments under the assumption of dark matter having spin independent interactions and the Standard Halo Model. COSINE-100 is now taking data to study the presence of dark matter-induced annual modulation in the event rate of the sodium iodide detectors.

astro-ph.IM↗

arXiv2019

Secrecy Outage Analysis Over Fluctuating Two-Ray Fading Channels

In this letter, we analyze the secrecy outage probability (SOP) over fluctuating two-ray fading channels but with a different definition from the one adopted in [5]. Following the new defined SOP, we derive an analytical closed-form expression for our proposed SOP, as well as an asymptotic formula valid in the high signal-to-noise ratio region of the source to destination link. In the numerical results section, we perform some Monte-Carlo simulations to validate the accuracy of our derived expressions, and also present the probability gap between our proposed SOP and the SOP in [5].

eess.SP↗

arXiv2019

Secure Analysis Over Generalized-K Channels

In this letter, we adopt the SOP definition in [4] and the simplified model of [8], and derive a closed-form expression for the proposed SOP over GK fading channels. To simplify this expression and obtain additional insights, we also perform an asymptotic analysis of the main link in the high SNR region.

eess.SP↗

arXiv2019

Forest Representation Learning Guided by Margin Distribution

In this paper, we reformulate the forest representation learning approach as an additive model which boosts the augmented feature instead of the prediction. We substantially improve the upper bound of generalization gap from $\mathcal{O}(\sqrt\frac{\ln m}{m})$ to $\mathcal{O}(\frac{\ln m}{m})$, while $λ$ - the margin ratio between the margin standard deviation and the margin mean is small enough. This tighter upper bound inspires us to optimize the margin distribution ratio $λ$. Therefore, we design the margin distribution reweighting approach (mdDF) to achieve small ratio $λ$ by boosting the augmented feature. Experiments and visualizations confirm the effectiveness of the approach in terms of performance and representation learning ability. This study offers a novel understanding of the cascaded deep forest from the margin-theory perspective and further uses the mdDF approach to guide the layer-by-layer forest representation learning.

cs.LG↗

arXiv2019

Secure mmWave Communications in Cognitive Radio Networks

In this letter, the secrecy performance in cognitive radio networks (CRNs) over fluctuating two-ray (FTR) channels, which is used to model the millimetre wave channel, is investigated in terms of the secrecy outage probability (SOP). Specifically, we consider the case where a source (S) transmits confidential messages to a destination (D), and an eavesdropper wants to wiretap the information from S to D. In a CRN framework, we assume that the primary user shares its spectrum with S, where S adopts the underlay strategy to control its transmit power without impairing the quality of service of the primary user. After some mathematical manipulations, an exact analytical expression for the SOP is derived. In order to get physical and technical insights into the effect of the channel parameters on the SOP, we derive an asymptotic formula for the SOP in the high signal-to-noise ratio region of the S--D link. We finally show some selected Monte-Carlo simulation results to validate the correctness of our derived analytical expressions.

eess.SP↗