arXiv subjects

Peng Wang

Publications and source records attributed to Peng Wang.

At least 1,009 records · Page 56Linked to original sources

arXiv2016

New Insights on Stacking Fault Behavior in Twin Induced Plasticity from Meta-Atom Molecular Dynamics Simulations

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. In this work, we report several new insights on the stacking fault behavior in twin induced plasticity from our meta-atom molecular dynamics simulation: The stacking fault interactions are dominated by dislocation reactions taking place spontaneously, different from the proposed mechanism in literatures; The competition among generating a single stacking fault, a twinning partial and a trailing partial dislocation is dependent on a unique parameter, i.e. stacking fault energy, which in turn determines deformation twinning behaviors. The complex twin-slip and twin-dislocation interactions demonstrate the dual role of deformation twins as both dislocation barrier and storage, potentially contributing to the high strength and ductility of advanced materials like TWIP steels where deformation twinning dominated plasticity accounts for the superb strength-ductility combination.

cond-mat.mtrl-sci↗

arXiv2016

Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net

Parsing articulated objects, e.g. humans and animals, into semantic parts (e.g. body, head and arms, etc.) from natural images is a challenging and fundamental problem for computer vision. A big difficulty is the large variability of scale and location for objects and their corresponding parts. Even limited mistakes in estimating scale and location will degrade the parsing output and cause errors in boundary details. To tackle these difficulties, we propose a "Hierarchical Auto-Zoom Net" (HAZN) for object part parsing which adapts to the local scales of objects and parts. HAZN is a sequence of two "Auto-Zoom Net" (AZNs), each employing fully convolutional networks that perform two tasks: (1) predict the locations and scales of object instances (the first AZN) or their parts (the second AZN); (2) estimate the part scores for predicted object instance or part regions. Our model can adaptively "zoom" (resize) predicted image regions into their proper scales to refine the parsing. We conduct extensive experiments over the PASCAL part datasets on humans, horses, and cows. For humans, our approach significantly outperforms the state-of-the-arts by 5% mIOU and is especially better at segmenting small instances and small parts. We obtain similar improvements for parsing cows and horses over alternative methods. In summary, our strategy of first zooming into objects and then zooming into parts is very effective. It also enables us to process different regions of the image at different scales adaptively so that, for example, we do not need to waste computational resources scaling the entire image.

cs.CV↗

arXiv2016

Towards Understanding What Contributes to Forming an Opinion

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. In the former case, the participants can have their altitudes changed in the process of competition between the self-driving force and environment-driving force. In the latter case, all of the participants are pulled by the environment. Some regularity behind the dynamics of opinion is also revealed, for instance, the information entropy decays with time in a special way, etc. the results may help us to get some deep understandings to formation of a public opinion.

physics.soc-ph↗

arXiv2016

Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution

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. To model this distribution, we propose to use Gaussian Processes (GP) to construct two separate generative models for the case of the "regular object" and the "other objects". More specifically, we design a new covariance function to simultaneously model the detection score at a single region and the score dependencies at multiple regions. We finally demonstrate the superior performance of our method on a large dataset newly proposed in this paper.

cs.CV↗

arXiv2016

Order-aware Convolutional Pooling for Video Based Action Recognition

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. Inspired by the capacity of Convolutional Neural Networks (CNN) in making use of the internal structure of images for information abstraction, we propose to apply the temporal convolution operation to the frame-level representations to extract the dynamic information. However, directly implementing this idea on the original high-dimensional feature would inevitably result in parameter explosion. To tackle this problem, we view the temporal evolution of the feature value at each feature dimension as a 1D signal and learn a unique convolutional filter bank for each of these 1D signals. We conduct experiments on two challenging video-based action recognition datasets, HMDB51 and UCF101; and demonstrate that the proposed method is superior to the conventional pooling methods.

cs.CV↗

arXiv2016

Distributed Subgradient-based Multi-agent Optimization with More General Step Sizes

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. It is proved that in both unconstrained and constrained optimization problems, the agents' estimates reach consensus and converge to the optimal solution with the more general choice of step sizes. The idea is to show that a weighted average of the agents' estimates approaches the optimal solution, but with different approaches. In the unconstrained case, the optimal convergence of the weighted average of the agents' estimates is proved by analyzing the distance change from the weighted average to the optimal solution and showing that the weighted average is arbitrarily close to the optimal solution. In the constrained case, this is achieved by analyzing the distance change from the agents' estimates to the optimal solution and utilizing the boundedness of the constraints. Then the optimal convergence of the agents' estimates follows because consensus is reached in both cases. These results are valid for both a strongly connected time-invariant graph and time-varying balanced graphs that are jointly strongly connected.

math.OC↗

arXiv2016

Character of frustration on magnetic correlation in doped Hubbard model

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 ($π,π$)$\rightarrow$($π,\frac{5π}{6}$)$\rightarrow$($\frac{5π}{6},\frac{5π}{6}$)$\rightarrow$ ($\frac{2π}{3},\frac{2π}{3}$), indicating the frustration's remarkable effect on the magnetism. In the studied filling region =1.0-1.3, the doping behaves like some kinds of {\it{frustration}}, which destroys the $(π,π)$ AFM correlation quickly and push the magnetic order to a wide range of the $(\frac{2π}{3},\frac{2π}{3})$ $120^{\circ}$ order when the $t'/t$ is large enough. Our non-perturbative calculations reveal a rich magnetic phase diagram over both the frustration and electron doping.

cond-mat.str-el↗

arXiv2016

Repairing atomic vacancies in single-layer MoSe2 field-effect transistor and its defect dynamics

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. Single/double Se vacancies are revealed originally, which cause some mid-gap impurity states and localize the device carriers. They are found repaired with the result of improved electronic transport. Such a picture is confirmed by a 1.5cm-1 red shift in the Raman spectra.

cond-mat.mes-hall↗

arXiv2016

Comparison of the LBE and DUGKS methods for DNS of decaying homogeneous isotropic turbulence

The main objective of this work is to perform a detailed comparison of the lattice Boltzmann equation (LBE) and the recently developed discrete unified gas-kinetic scheme (DUGKS) methods for direct numerical simulation (DNS) of the decaying homogeneous isotropic turbulence (DHIT) in a periodic box. The flow fields and key statistical quantities computed by both methods are compared with those from pseudo-spectral (PS) method. The results show that the LBE and DUGKS have almost the same accuracy when the flow field is well-resolved, and that the LBE is less dissipative and is slightly more efficient than the DUGKS, but the latter has a superior numerical stability, particularly for high Reynolds number flows. Therefore, the DUGKS method can be viewed as a viable tool for DNS of turbulent flows. It should be emphasized that the main advantage of the DUGKS when compared with the LBE method is its feasibility in adopting nonuniform meshes, which is critical for wall-bounded turbulent flows. The present work provides a basis for further applications of DUGKS in studying the physics of the turbulent flows.

physics.comp-ph↗

arXiv2016

Wafer-scale arrayed p-n junctions based on few-layer epitaxial GaTe

Two-dimensional (2D) materials have attracted substantial attention in electronic and optoelectronic applications with superior advantages of being flexible, transparent and highly tunable. Gapless graphene exhibits ultra-broadband and fast photoresponse while the 2D semiconducting MoS2 and GaTe unveil high sensitivity and tunable responsivity to visible light. However, the device yield and the repeatability call for a further improvement of the 2D materials to render large-scale uniformity. Here we report a layer-by-layer growth of wafer-scale GaTe with a hole mobility of 28.4 cm2/Vs by molecular beam epitaxy. The arrayed p-n junctions were developed by growing few-layer GaTe directly on three-inch Si wafers. The resultant diodes reveal good rectifying characteristics, photoresponse with a maximum photoresponsivity of 2.74 A/W and a high photovoltaic external quantum efficiency up to 62%. The photocurrent reaches saturation fast enough to capture a time constant of 22 μs and shows no sign of device degradation after 1.37 million cycles of operation. Most strikingly, such high performance has been achieved across the entire wafer, making the volume production of devices accessible. Finally, several photo-images were acquired by the GaTe/Si photodiodes with a reasonable contrast and spatial resolution, demonstrating for the first time the potential of integrating the 2D materials with the silicon technology for novel optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2016

Arrayed van der Waals Vertical Heterostructures based on 2D GaSe Grown by Molecular Beam Epitaxy

Vertically stacking two dimensional (2D) materials can enable the design of novel electronic and optoelectronic devices and realize complex functionality. However, the fabrication of such artificial heterostructures in wafer scale with an atomically-sharp interface poses an unprecedented challenge. Here, we demonstrate a convenient and controllable approach for the production of wafer-scale 2D GaSe thin films by molecular beam epitaxy. In-situ reflection high-energy electron diffraction oscillations and Raman spectroscopy reveal a layer-by-layer van der Waals epitaxial growth mode. Highly-efficient photodetector arrays were fabricated based on few-layer GaSe on Si. These photodiodes show steady rectifying characteristics and a relatively high external quantum efficiency of 23.6%. The resultant photoresponse is super-fast and robust with a response time of 60 us. Importantly, the device shows no sign of degradation after 1 million cycles of operation. Our study establishes a new approach to produce controllable, robust and large-area 2D heterostructures and presents a crucial step for further practical applications.

cond-mat.mes-hall↗

arXiv2016

Broadband Photovoltaic Detectors based on an Atomically Thin Heterostructure

Van der Waals junctions of two-dimensional materials with an atomically sharp interface open up unprecedented opportunities to design and study functional heterostructures. Semiconducting transition metal dichalcogenides have shown tremendous potential for future applications due to their unique electronic properties and strong light-matter interaction. However, many important optoelectronic applications, such as broadband photodetection, are severely hindered by their limited spectral range and reduced light absorption. Here, we present a p-g-n heterostructure formed by sandwiching graphene with a gapless bandstructure and wide absorption spectrum in an atomically thin p-n junction to overcome these major limitations. We have successfully demonstrated a MoS2-graphene-WSe2 heterostructure for broadband photodetection in the visible to short-wavelength infrared range at room temperature that exhibits competitive device performance, including a specific detectivity of up to 1011 Jones in the near-infrared region. Our results pave the way toward the implementation of atomically thin heterostructures for broadband and sensitive optoelectronic applications.

cond-mat.mtrl-sci↗

arXiv2016

Probing Carrier Transport and Structure-property Relationship of Highly Ordered Organic Semiconductors at Two-dimensional Limit

One of the basic assumptions in organic field-effect transistors, the most fundamental device unit in organic electronics, is that charge transport occurs two-dimensionally in the first few molecular layers near the dielectric interface. Although the mobility of bulk organic semiconductors has increased dramatically, direct probing of intrinsic charge transport in the two-dimensional limit has not been possible due to excessive disorders and traps in ultrathin organic thin films. Here, highly ordered mono- to tetra-layer pentacene crystals are realized by van der Waals (vdW) epitaxy on hexagonal BN. We find that the charge transport is dominated by hopping in the first conductive layer, but transforms to band-like in subsequent layers. Such abrupt phase transition is attributed to strong modulation of the molecular packing by interfacial vdW interactions, as corroborated by quantitative structural characterization and density functional theory calculations. The structural modulation becomes negligible beyond the second conductive layer, leading to a mobility saturation thickness of only ~3nm. Highly ordered organic ultrathin films provide a platform for new physics and device structures (such as heterostructures and quantum wells) that are not possible in conventional bulk crystals.

cond-mat.mes-hall↗

arXiv2015

Performance Impact of LoS and NLoS Transmissions in Dense Cellular Networks

In this paper, we introduce a sophisticated path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions to study their impact on the performance of dense small cell networks (SCNs). Analytical results are obtained for the coverage probability and the area spectral efficiency (ASE), assuming both a general path loss model and a special case with a linear LoS probability function. The performance impact of LoS and NLoS transmissions in dense SCNs in terms of the coverage probability and the ASE is significant, both quantitatively and qualitatively, compared with the previous work that does not differentiate LoS and NLoS transmissions. Our analysis demonstrates that the network coverage probability first increases with the increase of the base station (BS) density, and then decreases as the SCN becomes denser. This decrease further makes the ASE suffer from a slow growth or even a decrease with network densification. The ASE will grow almost linearly as the BS density goes ultra dense. For practical regime of the BS density, the performance results derived from our analysis are distinctively different from previous results, and thus shed new insights on the design and deployment of future dense SCNs.

cs.IT↗

arXiv2015

Homogeneous Field and WKB Approximation In Deformed Quantum Mechanics with Minimal Length

In the framework of the deformed quantum mechanics with minimal length, we consider the motion of a non-relativistic particle in a homogeneous external field. We find the integral representation for the physically acceptable wave function in the position representation. Using the method of steepest descent, we obtain the asymptotic expansions of the wave function at large positive and negative arguments. We then employ the leading asymptotic expressions to derive the WKB connection formula, which proceeds from classically forbidden region to classically allowed one through a turning point. By the WKB connection formula, we prove the Bohr-Sommerfeld quantization rule up to $\mathcal{O}(β)$. We also show that, if the slope of the potential at a turning point is too steep, the WKB connection formula fall apart around the turning point.

hep-th↗

arXiv2015

The Classical Limit of Minimal Length Uncertainty Relation:Revisit with the Hamilton-Jacobi Method

The existence of a minimum measurable length could deform not only the standard quantum mechanics but also classical physics. The effects of the minimal length on classical orbits of particles in a gravitation field have been investigated before, using the deformed Poisson bracket or Schwarzschild metric. In this paper, we use the Hamilton-Jacobi method to study motions of particles in the context of deformed Newtonian mechanics and general relativity. Specifically, the precession of planetary orbits, deflection of light, and time delay in radar propagation are considered in this paper. We also set limits on the deformation parameter by comparing our results with the observational measurements. Finally, comparison with results from previous papers is given at the end of this paper.

gr-qc↗

arXiv2015

Pose-Guided Human Parsing with Deep Learned Features

Parsing human body into semantic regions is crucial to human-centric analysis. In this paper, we propose a segment-based parsing pipeline that explores human pose information, i.e. the joint location of a human model, which improves the part proposal, accelerates the inference and regularizes the parsing process at the same time. Specifically, we first generate part segment proposals with respect to human joints predicted by a deep model, then part- specific ranking models are trained for segment selection using both pose-based features and deep-learned part potential features. Finally, the best ensemble of the proposed part segments are inferred though an And-Or Graph. We evaluate our approach on the popular Penn-Fudan pedestrian parsing dataset, and demonstrate the effectiveness of using the pose information for each stage of the parsing pipeline. Finally, we show that our approach yields superior part segmentation accuracy comparing to the state-of-the-art methods.

cs.CV↗

arXiv2015

Explicit Knowledge-based Reasoning for Visual Question Answering

We describe a method for visual question answering which is capable of reasoning about contents of an image on the basis of information extracted from a large-scale knowledge base. The method not only answers natural language questions using concepts not contained in the image, but can provide an explanation of the reasoning by which it developed its answer. The method is capable of answering far more complex questions than the predominant long short-term memory-based approach, and outperforms it significantly in the testing. We also provide a dataset and a protocol by which to evaluate such methods, thus addressing one of the key issues in general visual ques- tion answering.

cs.CV↗