arXiv subjects

Peng Wang

Publications and source records attributed to Peng Wang.

At least 955 records · Page 53Linked to original sources

arXiv2017

Will the Area Spectral Efficiency Monotonically Grow as Small Cells Go Dense?

In this paper, we introduce a sophisticated path loss model into the stochastic geometry analysis incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions to study their performance impact in small cell networks (SCNs). Analytical results are obtained on the coverage probability and the area spectral efficiency (ASE) assuming both a general path loss model and a special case of path loss model recommended by the 3rd Generation Partnership Project (3GPP) standards. The performance impact of LoS and NLoS transmissions in SCNs in terms of the coverage probability and the ASE is shown to be significant both quantitatively and qualitatively, compared with previous work that does not differentiate LoS and NLoS transmissions. Particularly, our analysis demonstrates that when the density of small cells is larger than a threshold, the network coverage probability will decrease as small cells become denser, which in turn makes the ASE suffer from a slow growth or even a notable decrease. For practical regime of small cell density, the performance results derived from our analysis are distinctively different from previous results, and shed new insights on the design and deployment of future dense/ultra-dense SCNs.

cs.NI↗

arXiv2017

The build up of the correlation between halo spin and the large scale structure

Both simulations and observations have confirmed that the spin of haloes/galaxies is correlated with the large scale structure (LSS) with a mass dependence such that the spin of low-mass haloes/galaxies tend to be parallel with the LSS, while that of massive haloes/galaxies tend to be perpendicular with the LSS. It is still unclear how this mass dependence is built up over time. We use N-body simulations to trace the evolution of the halo spin-LSS correlation and find that at early times the spin of all halo progenitors is parallel with the LSS. As time goes on, mass collapsing around massive halo is more isotropic, especially the recent mass accretion along the slowest collapsing direction is significant and it brings the halo spin to be perpendicular with the LSS. Adopting the $fractional$ $anisotropy$ (FA) parameter to describe the degree of anisotropy of the large-scale environment, we find that the spin-LSS correlation is a strong function of the environment such that a higher FA (more anisotropic environment) leads to an aligned signal, and a lower anisotropy leads to a misaligned signal. In general, our results show that the spin-LSS correlation is a combined consequence of mass flow and halo growth within the cosmic web. Our predicted environmental dependence between spin and large-scale structure can be further tested using galaxy surveys.

astro-ph.CO↗

arXiv2017

Halo intrinsic alignment: dependence on mass, formation time and environment

In this paper we use high-resolution cosmological simulations to study halo intrinsic alignment and its dependence on mass, formation time and large-scale environment. In agreement with previous studies using N-body simulations, it is found that massive halos have stronger alignment. For given mass, older halos have stronger alignment than younger ones. By identifying the cosmic environment of halo using Hessian matrix, we find that for given mass, halos in cluster regions also have stronger alignment than those in filament. The existing theory has not addressed these dependencies explicitly. In this work we extend the linear alignment model with inclusion of halo bias and find that the halo alignment with its mass and formation time dependence can be explained by halo bias. However, the model can not account for the environment dependence, as it is found that halo bias is lower in cluster and higher in filament. Our results suggest that halo bias and environment are independent factors in determining halo alignment. We also study the halo alignment correlation function and find that halos are strongly clustered along their major axes and less clustered along the minor axes. The correlated halo alignment can extend to scale as large as $100h^{-1}$Mpc where its feature is mainly driven by the baryon acoustic oscillation effect.

astro-ph.CO↗

arXiv2017

Classification of symmetry properties of waveguide modes in presence of gain/losses, anisotropy/bianisotropy, or continuous/discrete rotational symmetry

We study the symmetric properties of waveguide modes in presence of gain/losses, anisotropy/bianisotropy, or continuous/discrete rotational symmetry. We provide a comprehensive approach to identity the modal symmetry by constructing a $4\times4$ waveguide Hamiltonian and searching the symmetric operation in association with the corresponding waveguides. We classify the chiral/time reversal/parity/parity time/rotational symmetry for different waveguides, and provide the criterion for the aforementioned symmetry operations. Lastly, we provide examples to illustrate how the symmetry operations can be used to classify the modal properties from the symmetric relation between modal profiles of several different waveguides.

physics.optics↗

arXiv2017

Joint Multi-Person Pose Estimation and Semantic Part Segmentation

Human pose estimation and semantic part segmentation are two complementary tasks in computer vision. In this paper, we propose to solve the two tasks jointly for natural multi-person images, in which the estimated pose provides object-level shape prior to regularize part segments while the part-level segments constrain the variation of pose locations. Specifically, we first train two fully convolutional neural networks (FCNs), namely Pose FCN and Part FCN, to provide initial estimation of pose joint potential and semantic part potential. Then, to refine pose joint location, the two types of potentials are fused with a fully-connected conditional random field (FCRF), where a novel segment-joint smoothness term is used to encourage semantic and spatial consistency between parts and joints. To refine part segments, the refined pose and the original part potential are integrated through a Part FCN, where the skeleton feature from pose serves as additional regularization cues for part segments. Finally, to reduce the complexity of the FCRF, we induce human detection boxes and infer the graph inside each box, making the inference forty times faster. Since there's no dataset that contains both part segments and pose labels, we extend the PASCAL VOC part dataset with human pose joints and perform extensive experiments to compare our method against several most recent strategies. We show that on this dataset our algorithm surpasses competing methods by a large margin in both tasks.

cs.CV↗

arXiv2017

FVQA: Fact-based Visual Question Answering

Visual Question Answering (VQA) has attracted a lot of attention in both Computer Vision and Natural Language Processing communities, not least because it offers insight into the relationships between two important sources of information. Current datasets, and the models built upon them, have focused on questions which are answerable by direct analysis of the question and image alone. The set of such questions that require no external information to answer is interesting, but very limited. It excludes questions which require common sense, or basic factual knowledge to answer, for example. Here we introduce FVQA, a VQA dataset which requires, and supports, much deeper reasoning. FVQA only contains questions which require external information to answer. We thus extend a conventional visual question answering dataset, which contains image-question-answerg triplets, through additional image-question-answer-supporting fact tuples. The supporting fact is represented as a structural triplet, such as . We evaluate several baseline models on the FVQA dataset, and describe a novel model which is capable of reasoning about an image on the basis of supporting facts.

cs.CV↗

arXiv2017

Sequential data assimilation with multiple nonlinear models and applications to subsurface flow

Complex systems are often described with competing models. Such divergence of interpretation on the system may stem from model fidelity, mathematical simplicity, and more generally, our limited knowledge of the underlying processes. Meanwhile, available but limited observations of system state could further complicates one's prediction choices. Over the years, data assimilation techniques, such as the Kalman filter, have become essential tools for improved system estimation by incorporating both models forecast and measurement; but its potential to mitigate the impacts of aforementioned model-form uncertainty has yet to be developed. Based on an earlier study of Multi-model Kalman filter, we propose a novel framework to assimilate multiple models with observation data for nonlinear systems, using extended Kalman filter, ensemble Kalman filter and particle filter, respectively. Through numerical examples of subsurface flow, we demonstrate that the new assimilation framework provides an effective and improved forecast of system behaviour.

math.NA↗

arXiv2017

Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks

In this work, we jointly address the problem of text detection and recognition in natural scene images based on convolutional recurrent neural networks. We propose a unified network that simultaneously localizes and recognizes text with a single forward pass, avoiding intermediate processes like image cropping and feature re-calculation, word separation, or character grouping. In contrast to existing approaches that consider text detection and recognition as two distinct tasks and tackle them one by one, the proposed framework settles these two tasks concurrently. The whole framework can be trained end-to-end, requiring only images, the ground-truth bounding boxes and text labels. Through end-to-end training, the learned features can be more informative, which improves the overall performance. The convolutional features are calculated only once and shared by both detection and recognition, which saves processing time. Our proposed method has achieved competitive performance on several benchmark datasets.

cs.CV↗

arXiv2017

Room-temperature high detectivity mid-infrared photodetectors based on black arsenic phosphorus

The mid-infrared (MIR) spectral range, pertaining to important applications such as molecular 'fingerprint' imaging, remote sensing, free space telecommunication and optical radar, is of particular scientific interest and technological importance. However, state-of-the-art materials for MIR detection are limited by intrinsic noise and inconvenient fabrication processes, resulting in high cost photodetectors requiring cryogenic operation. We report black arsenic-phosphorus-based long wavelength infrared photodetectors with room temperature operation up to 8.2 um, entering the second MIR atmospheric transmission window. Combined with a van der Waals heterojunction, room temperature specific detectivity higher than 4.9*10^9 Jones was obtained in the 3-5 um range. The photodetector works in a zero-bias photovoltaic mode, enabling fast photoresponse and low dark noise. Our van der Waals heterojunction photodector not only exemplify black arsenic-phosphorus as a promising candidate for MIR opto-electronic applications, but also pave the way for a general strategy to suppress 1/f noise in photonic devices.

cond-mat.mtrl-sci↗

arXiv2017

Failsafe Mechanism Design of Multicopters Based on Supervisory Control Theory

In order to handle undesirable failures of a multicopter which occur in either the pre-flight process or the in-flight process, a failsafe mechanism design method based on supervisory control theory is proposed for the semi-autonomous control mode. Failsafe mechanism is a control logic that guides what subsequent actions the multicopter should take, by taking account of real-time information from guidance, attitude control, diagnosis, and other low-level subsystems. In order to design a failsafe mechanism for multicopters, safety issues of multicopters are introduced. Then, user requirements including functional requirements and safety requirements are textually described, where function requirements determine a general multicopter plant, and safety requirements cover the failsafe measures dealing with the presented safety issues. In order to model the user requirements by discrete-event systems, several multicopter modes and events are defined. On this basis, the multicopter plant and control specifications are modeled by automata. Then, a supervisor is synthesized by monolithic supervisory control theory. In addition, we present three examples to demonstrate the potential blocking phenomenon due to inappropriate design of control specifications. Also, we discuss the meaning of correctness and the properties of the obtained supervisor. This makes the failsafe mechanism convincingly correct and effective. Finally, based on the obtained supervisory controller generated by TCT software, an implementation method suitable for multicopters is presented, in which the supervisory controller is transformed into decision-making codes.

eess.SY↗

arXiv2017

Local geometry from entanglement entropy

Constructing the corresponding geometries from given entanglement entropies of a boundary QFT is a big challenge and leads to the grand project \emph{ it from Qubit}. Based on the observation that the AdS metric in the Riemann Normal Coordinates (RNC) can be summed into a closed form, we find that the AdS$_3$ metric in RNC can be straightforwardly read off from the entanglement entropy of CFT$_2$. We use the finite length or finite temperature CFT$_2$ as examples to demonstrate the identification.

hep-th↗

arXiv2017

Semantic Instance Segmentation via Deep Metric Learning

We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together. Our similarity metric is based on a deep, fully convolutional embedding model. Our grouping method is based on selecting all points that are sufficiently similar to a set of "seed points", chosen from a deep, fully convolutional scoring model. We show competitive results on the Pascal VOC instance segmentation benchmark.

cs.CV↗

arXiv2017

Action Growth in $f\left(R\right)$ Gravity

Inspired by the recent "Complexity = Action" conjecture, we use the approach proposed by Lehner et al. to calculate the rate of the action of the WheelerDeWitt patch at late times for static uncharged and charged black holes in $f\left( R\right) $ gravity. Our results have the same expressions in terms of the mass, charge, and electrical potentials at the horizons of black holes as in Einstein's gravity. In the context of $f\left( R\right) $ gravity, the Lloyd bound is saturated for uncharged black holes but violated for charged black holes near extremality. For charged black holes far away from the ground states, the Lloyd bound is violated in four dimensions but satisfied in higher dimensions.

hep-th↗

arXiv2017

Testing Holographic Conjectures of Complexity with Born-Infeld Black Holes

In this paper, we use Born-Infeld black holes to test two recent holographic conjectures of complexity, the "Complexity = Action" (CA) duality and "Complexity = Volume 2.0" (CV) duality. The complexity of a boundary state is identified with the action of the Wheeler-deWitt patch in CA duality, while this complexity is identified with the spacetime volume of the WdW patch in CV duality. In particular, we check whether the Born-Infeld black holes violate the Lloyd bound: $\mathcal{\dot{C}\leq}\frac{2}{π\hbar}\left[ \left( M-QΦ\right) -\left( M-QΦ\right) _{\text{gs}}\right] $, where gs stands for the ground state for a given electrostatic potential. We find that the ground states are either some extremal black hole or regular spacetime with nonvanishing charges. Near extremality, the Lloyd bound is violated in both dualities. Near the charged regular spacetime, this bound is satisfied in CV duality but violated in CA duality. When moving away from the ground state on a constant potential curve, the Lloyd bound tend to be saturated from below in CA duality, while $\mathcal{\dot{C}}$ is $π/2$ times as large as the Lloyd bound in CV duality.

hep-th↗

arXiv2017

Correspondence between genus expansion and $α^{\prime}$ expansion in string theory

In this paper, we demonstrate that locally, the $α^{\prime}$ expansion of a string propagating in AdS can be summed into a closed expression, where the $α'$ dependence is manifested. The T-dual of this sum exactly matches the expression controlling all genus expansion in the Goparkumar-Vafa formula, which in turn also matches the loop expansion of the Chern-Simons gauge theory. We therefore find an exact correspondence between the $α^{\prime}$ expansion for a string moving in AdS and the genus expansion of a string propagating in four dimensional flat spacetime. We are then able to give a closed form of the $α'$ expansion for all values of $\sqrt{α'}/R_{AdS}$. Moreover, the correspondence makes it possible to conjecture the exact $g_s$ dependence of the strongly coupled theories.

hep-th↗

arXiv2017

A general explanation on the correlation of dark matter halo spin with the large scale environment

Both simulations and observations have found that the spin of halo/galaxy is correlated with the large scale environment, and particularly the spin of halo flips in filament. A consistent picture of halo spin evolution in different environments is still lacked. Using N-body simulation we find that halo spin with its environment evolves continuously from sheet to cluster, and the flip of halo spin happens both in filament and nodes. For the flip in filament can be explained by halo formation time and the migrating time when its environment changes from sheet to filament. For low-mass haloes, they form first in sheets and migrate into filaments later, so their mass and spin growth inside filament are lower, and the original spin is still parallel to filament. For massive haloes, they migrate into filaments first, and most of their mass and spin growth are obtained in filaments, so the resulted spin is perpendicular to filament. Our results well explain the overall evolution of cosmic web in the cold dark matter model and can be tested using high-redshift data. The scenario can also be tested against alternative models of dark matter, such as warm/hot dark matter, where the structure formation will proceed in a different way.

astro-ph.CO↗

arXiv2017

A Fast and Compact Saliency Score Regression Network Based on Fully Convolutional Network

Visual saliency detection aims at identifying the most visually distinctive parts in an image, and serves as a pre-processing step for a variety of computer vision and image processing tasks. To this end, the saliency detection procedure must be as fast and compact as possible and optimally processes input images in a real time manner. It is an essential application requirement for the saliency detection task. However, contemporary detection methods often utilize some complicated procedures to pursue feeble improvements on the detection precession, which always take hundreds of milliseconds and make them not easy to be applied practically. In this paper, we tackle this problem by proposing a fast and compact saliency score regression network which employs fully convolutional network, a special deep convolutional neural network, to estimate the saliency of objects in images. It is an extremely simplified end-to-end deep neural network without any pre-processings and post-processings. When given an image, the network can directly predict a dense full-resolution saliency map (image-to-image prediction). It works like a compact pipeline which effectively simplifies the detection procedure. Our method is evaluated on six public datasets, and experimental results show that it can achieve comparable or better precision performance than the state-of-the-art methods while get a significant improvement in detection speed (35 FPS, processing in real time).

cs.CV↗

arXiv2017

WKB Approximation for a Deformed Schrodinger-like Equation and its Applications to Quasinormal Modes of Black Holes and Quantum Cosmology

In this paper, we use the WKB approximation method to approximately solve a deformed Schrodinger-like differential equation: $\left[ -\hbar^{2} \partial_ξ^{2}g^{2}\left( -i\hbarα\partial_ξ\right) -p^{2}\left( ξ\right) \right] ψ\left( ξ\right) =0$, which are frequently dealt with in various effective models of quantum gravity, where the parameter $α$ characterizes effects of quantum gravity. For an arbitrary function $g\left( x\right) $ satisfying several properties proposed in the paper, we find the WKB solutions, the WKB connection formulas through a turning point, the deformed Bohr--Sommerfeld quantization rule, and the deformed tunneling rate formula through a potential barrier. Several examples of applying the WKB approximation to the deformed quantum mechanics are investigated. In particular, we calculate the bound states of the Pöschl-Teller potential and estimate the effects of quantum gravity on the quasinormal modes of a Schwarzschild black hole. Moreover, the area quantum of the black hole is considered via Bohr's correspondence principle. Finally, the WKB solutions of the deformed Wheeler--DeWitt equation for a closed Friedmann universe with a scalar field are obtained, and the effects of quantum gravity on the probability of sufficient inflation is discussed in the context of the tunneling proposal.

gr-qc↗