# Peng Wang - Nankai University

## Contact Details

NamePeng Wang |
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AffiliationNankai University |
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CityTianjin |
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CountryChina |
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## Pubs By Year |
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## External Links |
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## Pub CategoriesComputer Science - Computer Vision and Pattern Recognition (12) General Relativity and Quantum Cosmology (7) High Energy Physics - Theory (6) Physics - Optics (4) Physics - Physics and Society (4) Mathematics - Information Theory (3) Computer Science - Information Theory (3) Physics - Materials Science (2) Physics - Mesoscopic Systems and Quantum Hall Effect (2) Computer Science - Multimedia (1) Statistics - Methodology (1) Computer Science - Networking and Internet Architecture (1) Physics - Strongly Correlated Electrons (1) Mathematics - Optimization and Control (1) Mathematics - Differential Geometry (1) Nonlinear Sciences - Adaptation and Self-Organizing Systems (1) Statistics - Applications (1) Physics - Soft Condensed Matter (1) Computer Science - Information Retrieval (1) Computer Science - Programming Languages (1) Cosmology and Nongalactic Astrophysics (1) Computer Science - Computation and Language (1) Physics - Fluid Dynamics (1) Physics - Instrumentation and Detectors (1) Statistics - Machine Learning (1) Computer Science - Learning (1) Physics - Statistical Mechanics (1) |

## Publications Authored By Peng Wang

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. Read More

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. Read More

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. Read More

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. Read More

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. Read More

In this paper, we demonstrate that locally, the $\alpha^{\prime}$ expansion of a string propagating in AdS can be summed into a closed expression, where the $\alpha'$ 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 $\alpha^{\prime}$ expansion for a string moving in AdS and the genus expansion of a string propagating in four dimensional flat spacetime. Read More

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. Read More

We present Low*, a language for low-level programming and verification, and its application to high-assurance optimized cryptographic libraries. Low* is a shallow embedding of a small, sequential, well-behaved subset of C in F*, a dependently- typed variant of ML aimed at program verification. Departing from ML, Low* does not involve any garbage collection or implicit heap allocation; instead, it has a structured memory model \`a la CompCert, and it provides the control required for writing efficient low-level security-critical code. Read More

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. Read More

In this paper, we use the WKB approximation method to approximately solve a deformed Schrodinger-like differential equation: $\left[ -\hbar^{2} \partial_{\xi}^{2}g^{2}\left( -i\hbar\alpha\partial_{\xi}\right) -p^{2}\left( \xi\right) \right] \psi\left( \xi\right) =0$, which are frequently dealt with in various effective models of quantum gravity, where the parameter $\alpha$ 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. Read More

Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC^2), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw text features are firstly embedded into compact binary codes by using one existing unsupervised dimensionality reduction methods. Read More

In the study of rarefied gas dynamics, the discrete velocity method (DVM) has been widely employed to solve the gas kinetic equations. Although various versions of DVM have been developed, their performance, in terms of accuracy and computational efficiency, is yet to be compreheively studied in the whole flow regime. Here, the traditional third-order time-implicit Godunov DVM (GDVM) and the recently developed discrete unified gas-kinetic scheme (DUGKS) are analysed in finding steady-state solutions of the force-driven Poiseuille and lid-driven cavity flows. Read More

One of the most intriguing features of the Visual Question Answering (VQA) challenge is the unpredictability of the questions. Extracting the information required to answer them demands a variety of image operations from detection and counting, to segmentation and reconstruction. To train a method to perform even one of these operations accurately from {image,question,answer} tuples would be challenging, but to aim to achieve them all with a limited set of such training data seems ambitious at best. Read More

Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining in popularity due to the widespread deployment of low-cost sensors and their connection to the Internet. Meanwhile, deep learning provides useful tools for processing and analyzing these big machinery data. Read More

We demonstrate electro-optic tuning of an on-chip lithium niobate microresonator with integrated in-plane microelectrodes. First two metallic microelectrodes on the substrate were formed via femtosecond laser process. Then a high-Q lithium niobate microresonator located between the microelectrodes was fabricated by femtosecond laser direct writing accompanied by focused ion beam milling. Read More

Since the first successful synthesis of graphene just over a decade ago, a variety of two-dimensional (2D) materials (e.g., transition metal-dichalcogenides, hexagonal boron-nitride, etc. Read More

Writing optical waveguides with femtosecond laser pulses provides the capability of forming three-dimensional photonic circuits for manipulating light fields in both linear and nonlinear manners. To fully explore this potential, large depths of the buried waveguides in transparent substrates are often desirable to facilitate achieving vertical integration of waveguides in a multi-layer configuration, which, however, is hampered by rapidly degraded axial resolution caused by optical aberration. Here, we show that with the correction of the spherical aberration, polarization-independent waveguides can be inscribed in a nonlinear optical crystal lithium niobate (LN) at depths up to 1. Read More

To study quantum effects on the bulk tachyon dynamics, we replace $R$ with $f(R)$ in the low-energy effective action that couples gravity, the dilaton, and the bulk closed string tachyon of bosonic closed string theory and study properties of their classical solutions. The $\alpha^{\prime}$ corrections of the graviton-dilaton-tachyon system are implemented in the $f(R)$. We obtain the tachyon-induced rolling solutions and show that the string metric does not need to remain fixed in some cases. Read More

The self-referenced spectral interferometry (SRSI) technique, which is usually used for microjoule-level femtosecond pulses characterization, is improved to characterize weak femtosecond pulses with nanojoule based on the transient-grating effect. Both femtosecond pulses from an amplifier with 3 nJ per pulse at 1 kHz repetition rates and femtosecond pulses from an oscillator with less than 0.5 nJ per pulse at 84 MHz repetition rates are successfully characterized. Read More

In the deformed quantum mechanics with a minimal length, one WKB connection formula through a turning point is derived. We then use it to calculate tunnelling rates through potential barriers under the WKB approximation. Finally, the minimal length effects on two examples of quantum tunneling in nuclear and atomic physics are discussed Read More

A liquid subjected to negative pressure is thermodynamically metastable. Confined within a small volume, negative pressure can build up until cavities form spontaneously. The critical negative pressure for cavitation in water has been theoretically predicted to be in the range of -100 to -200 MPa at room temperature, whereas values around -30 MPa have been obtained by many experiments. Read More

A practical statistical analysis on the regional populations and GDPs of China is conducted. The result shows that the distribution of the populations and that of the GDPs obeys the shifted power law, respectively. To understand these characteristics, a generalized Langevin equation describing variation of population is proposed based on the correlation between population and GDP as well as the random fluctuations of the related factors. Read More

In this paper, we investigate the optimization of non-uniform linear antenna arrays (NULAs) for millimeter wave (mmWave) line-of-sight (LoS) multiple-input multiple-output (MIMO) channels. Our focus is on the maximization of the system effective multiplexing gain (EMG), by optimizing the individual antenna positions in the transmit/receive NULAs. Here the EMG is defined as the number of signal streams that are practically supported by the channel at a finite SNR. Read More

Fine-grained classification is a relatively new field that has concentrated on using information from a single image, while ignoring the enormous potential of using video data to improve classification. In this work we present the novel task of video-based fine-grained object classification, propose a corresponding new video dataset, and perform a systematic study of several recent deep convolutional neural network (DCNN) based approaches, which we specifically adapt to the task. We evaluate three-dimensional DCNNs, two-stream DCNNs, and bilinear DCNNs. Read More

Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. Given an image and a question in natural language, it requires reasoning over visual elements of the image and general knowledge to infer the correct answer. In the first part of this survey, we examine the state of the art by comparing modern approaches to the problem. Read More

This paper discusses EFSM for SDL and transforms EFSM into a novel control model of discrete event systems. We firstly propose a control model of discrete event systems, where the event set is made up of several conflicting pairs and control is implemented to select one event of the pair. Then we transform EFSM for SDL to the control model to clarify the control mechanism functioning in SDL flow graphs. Read More

The performances of penalized likelihood approaches profoundly depend on the selection of the tuning parameter; however there has not been a common agreement on the criterion for choosing the tuning parameter. Moreover, penalized likelihood estimation based on a single value of the tuning parameter would suffer from several drawbacks. This article introduces a novel approach for feature selection based on the whole solution paths rather than choosing one single tuning parameter, which significantly improves the selection accuracy. Read More

Instance retrieval requires one to search for images that contain a particular object within a large corpus. Recent studies show that using image features generated by pooling convolutional layer feature maps (CFMs) of a pretrained convolutional neural network (CNN) leads to promising performance for this task. However, due to the global pooling strategy adopted in those works, the generated image feature is less robust to image clutter and tends to be contaminated by the irrelevant image patterns. Read More

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. Read More

InGaAs/GaAsBi/InGaAs quantum wells (QWs) were grown on GaAs substrates by gas source molecular beam epitaxy for realizing the type II band-edge line-up. Both type I and type II transitions were observed in the Bi containing W QWs and the photoluminescence intensity was enhanced in the sample with a high Bi content, which is mainly due to the improvement of carrier confinement. Blue-shift of type II transitions at high excitation power density was observed and ascribed to the band-bending effect. Read More

In this letter, we develop a low-complexity transceiver design, referred to as semi-random beam pairing (SRBP), for sparse multipath massive MIMO channels. By exploring a sparse representation of the MIMO channel in the virtual angular domain, we generate a set of transmit-receive beam pairs in a semi-random way to support the simultaneous transmission of multiple data streams. These data streams can be easily separated at the receiver via a successive interference cancelation (SIC) technique, and the power allocation among them are optimized based on the classical waterfilling principle. Read More

We report on fabrication of depressed cladding optical waveguides buried in lithium niobate crystal with shaped femtosecond laser pulses. Depressed cladding waveguides of variable mode-field sizes are fabricated by forming the four sides of the cladding using a slit-beam shaping technique. We show that the waveguides fabricated by our technique allows single-mode propagation of the light polarized in both vertical and horizontal directions. Read More

A key enabling technology of NFV is software dataplane, which has attracted much attention in both academia and industry recently. Yet, till now there is little understanding about its performance in practice. In this paper, we make a benchmark measurement study of NFV software dataplanes in terms of packet processing capability, one of the most fundamental and critical performance metrics. Read More

To well understand crowd behavior, microscopic models have been developed in recent decades, in which an individual's behavioral/psychological status can be modeled and simulated. A well-known model is the social-force model innovated by physical scientists. This model has been widely accepted and mainly used in simulation of crowd evacuation in the past decade. Read More

The severe bandwidth shortage in conventional microwave bands has spurred the exploration of the millimeter wave (MMW) spectrum for the next revolution in wireless communications. However, there is still lack of proper channel modeling for the MMW wireless propagation, especially in the case of outdoor environments. In this paper, we develop a geometry-based stochastic channel model to statistically characterize the effect of all the first-order reflection paths between the transmitter and receiver. Read More

A physical description of an opinion evolution is conducted based on the Hamilton-Jacobi equation derived from a generalized potential and the corresponding Langevin equation. The investigation mainly focuses on the heterogeneities such as age, connection circle and overall quality of the participants involved in the opinion exchange process. The evolutionary patterns of opinion can be described by solution of the Hamilton-Jacobi equation, information entropy. Read More

This paper aims to provide a description of totally isotropic Willmore two-spheres and their adjoint transforms. We first recall the isotropic harmonic maps which are introduced by H\'elein, Xia-Shen and Ma for the study of Willmore surfaces. Then we derive a description of the normalized potential (some Lie algebra valued meromorphic 1-forms) of totally isotropic Willmore two-spheres in terms of the isotropic harmonic maps. Read More

There is growing interest in promoting deformation twinning for plasticity in advanced materials, as highly organized twin boundaries are beneficial to better strength-ductility combination in contrast to disordered grain boundaries. Twinning deformation typically involves the kinetics of stacking faults, its interaction with dislocations, and dislocation - twin boundary interactions. While the latter has been intensively investigated, the dynamics of stacking faults has been less known. Read More

Compared to other applications in computer vision, convolutional neural networks have under-performed on pedestrian detection. A breakthrough was made very recently by using sophisticated deep CNN models, with a number of hand-crafted features, or explicit occlusion handling mechanism. In this work, we show that by re-using the convolutional feature maps (CFMs) of a deep convolutional neural network (DCNN) model as image features to train an ensemble of boosted decision models, we are able to achieve the best reported accuracy without using specially designed learning algorithms. Read More

Much recent progress in Vision-to-Language problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image features to text. In this paper we first propose a method of incorporating high-level concepts into the successful CNN-RNN approach, and show that it achieves a significant improvement on the state-of-the-art in both image captioning and visual question answering. Read More

Opinion evolution mechanism can be captured by physics modeling approaches. In this context, a kinetic equation is established by defining a generalized displace (cognitive-level), a driving force and the related generalized potential, information quantity, altitude. It has been shown that the details of opinion evolution depends the type of the driving force, self-dominated driving or environment- dominated driving. Read More

This paper is concerned with the channel estimation problem in Millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent sparse nature of the mmWave channel, we first propose a fast channel estimation (FCE) algorithm based on a novel overlapped beam pattern design, which can increase the amount of information carried by each channel measurement and thus reduce the required channel estimation time compared to the existing non-overlapped designs. We develop a maximum likelihood (ML) estimator to optimally extract the path information from the channel measurements. Read More

Doubly special relativity (DSR) is an effective model for encoding quantum gravity in flat spacetime. To incorporate DSR into general relativity, one could use "Gravity's rainbow", where the spacetime background felt by a test particle would depend on its energy. In this scenario, one could rewrite the rainbow metric $g_{\mu\nu}\left( E\right) $ in terms of some orthonormal frame fields and use the modified equivalence principle to determine the energy dependence of $g_{\mu\nu}\left( E\right) $. Read More

In this work, we study the challenging problem of identifying the irregular status of objects from images in an "open world" setting, that is, distinguishing the irregular status of an object category from its regular status as well as objects from other categories in the absence of "irregular object" training data. To address this problem, we propose a novel approach by inspecting the distribution of the detection scores at multiple image regions based on the detector trained from the "regular object" and "other objects". The key observation motivating our approach is that for "regular object" images as well as "other objects" images, the region-level scores follow their own essential patterns in terms of both the score values and the spatial distributions while the detection scores obtained from an "irregular object" image tend to break these patterns. Read More

We report on fabrication of tubular optical waveguides buried in ZBLAN glass based on transverse femtosecond laser direct writing. Irradiation in ZBLAN with focused femtosecond laser pulses leads to decrease of refractive index in the modified region. Tubular optical waveguides of variable mode areas are fabricated by forming the four sides of the cladding with slit-shaped femtosecond laser pulses, ensuring single mode waveguiding with a mode field dimension as small as ~ 4 {\mu}m. Read More

Most video based action recognition approaches create the video-level representation by temporally pooling the features extracted at each frame. The pooling methods that they adopt, however, usually completely or partially neglect the dynamic information contained in the temporal domain, which may undermine the discriminative power of the resulting video representation since the video sequence order could unveil the evolution of a specific event or action. To overcome this drawback and explore the importance of incorporating the temporal order information, in this paper we propose a novel temporal pooling approach to aggregate the frame-level features. Read More

A wider selection of step sizes is explored for the distributed subgradient algorithm for multi-agent optimization problems, for both time-invariant and time-varying communication topologies. The square summable requirement of the step sizes commonly adopted in the literature is removed. The step sizes are only required to be positive, vanishing and non-summable. Read More

The magnetic correlation in the Hubbard model on a two-dimensional
anisotropic triangular lattice is studied by using the determinant quantum
Monte Carlo method. Around half filling, it is found that the increasing
frustration $t'/t$ could change the wave vector of maximum spin correlation
along
($\pi,\pi$)$\rightarrow$($\pi,\frac{5\pi}{6}$)$\rightarrow$($\frac{5\pi}{6},\frac{5\pi}{6}$)$\rightarrow$
($\frac{2\pi}{3},\frac{2\pi}{3}$), indicating the frustration's remarkable
effect on the magnetism. In the studied filling region

**Authors:**Yuze Meng, Chongyi Ling, Run Xin, Peng Wang, You Song, Haijun Bu, Si Gao, Xuefeng Wang, Fengqi Song, Jinlan Wang, Xinran Wang, Baigeng Wang, Guanghou Wang

Here we repair the single-layer MoSe2 field-effect transistors by the EDTA processing, after which the devices' room-temperature carrier mobility increases from 0.1 to over 70cm2/Vs. The atomic dynamics is constructed by the combined study of the first-principle calculation, aberration-corrected transmission electron microscopy and Raman spectroscopy. Read More

Deriving from the gradient vector of a generative model of local features, Fisher vector coding (FVC) has been identified as an effective coding method for image classification. Most, if not all, FVC implementations employ the Gaussian mixture model (GMM) to depict the generation process of local features. However, the representative power of the GMM could be limited because it essentially assumes that local features can be characterized by a fixed number of feature prototypes and the number of prototypes is usually small in FVC. Read More