Searcharxiv⌕ Search

arXiv subjects

Peng Wu

Publications and source records attributed to Peng Wu.

At least 163 records · Page 9Linked to original sources
arXiv2019

Post-processing techniques of 4D flow MRI: velocity and wall shear stress

As the original velocity field obtained from four-dimensional (4D) flow magnetic resonance imaging (MRI) contains considerable amount of noises and errors, the available Divergence-free smoothing (DFS) method can be used to process the 4D flow MRI data for reducing noises, eliminating errors, fixing missing data and eventually providing the smoothed flow field. However, the traditional DFS does not have the ability to deal with the flow in the near wall region of vessel, especially for satisfying the no-slip boundary condition. In this study, therefore, an improved DFS method with specific near wall treatment is introduced for processing with 4D flow MRI inner flow with curved wall boundary as the blood flows. On the other hand, due to the coarse resolution of 4D flow MRI, velocity gradients in the near wall region are normally underestimated. As a result, a special wall function is required for accurately computing wall shear stress (WSS).

cs.CE↗
arXiv2019

Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering

Relation detection is a core step in many natural language process applications including knowledge base question answering. Previous efforts show that single-fact questions could be answered with high accuracy. However, one critical problem is that current approaches only get high accuracy for questions whose relations have been seen in the training data. But for unseen relations, the performance will drop rapidly. The main reason for this problem is that the representations for unseen relations are missing. In this paper, we propose a simple mapping method, named representation adapter, to learn the representation mapping for both seen and unseen relations based on previously learned relation embedding. We employ the adversarial objective and the reconstruction objective to improve the mapping performance. We re-organize the popular SimpleQuestion dataset to reveal and evaluate the problem of detecting unseen relations. Experiments show that our method can greatly improve the performance of unseen relations while the performance for those seen part is kept comparable to the state-of-the-art. Our code and data are available at https://github.com/wudapeng268/KBQA-Adapter.

cs.CL↗
arXiv2019

Curvature decompositions on Einstein four-manifolds

For Einstein four-manifolds with positive scalar curvature, we derive relations among various positivity conditions on the curvature tensor, some of which are of great importance in the study of the Ricci flow. These relations suggest possible new ideas to study the well-known rigidity conjecture for positively curved Einstein four-manifolds.

math.DG↗
arXiv2019

Multimodal Deep Network Embedding with Integrated Structure and Attribute Information

Network embedding is the process of learning low-dimensional representations for nodes in a network, while preserving node features. Existing studies only leverage network structure information and focus on preserving structural features. However, nodes in real-world networks often have a rich set of attributes providing extra semantic information. It has been demonstrated that both structural and attribute features are important for network analysis tasks. To preserve both features, we investigate the problem of integrating structure and attribute information to perform network embedding and propose a Multimodal Deep Network Embedding (MDNE) method. MDNE captures the non-linear network structures and the complex interactions among structures and attributes, using a deep model consisting of multiple layers of non-linear functions. Since structures and attributes are two different types of information, a multimodal learning method is adopted to pre-process them and help the model to better capture the correlations between node structure and attribute information. We employ both structural proximity and attribute proximity in the loss function to preserve the respective features and the representations are obtained by minimizing the loss function. Results of extensive experiments on four real-world datasets show that the proposed method performs significantly better than baselines on a variety of tasks, which demonstrate the effectiveness and generality of our method.

cs.LG↗
arXiv2019

Queue-reactive Hawkes models for the order flow

In this work we introduce two variants of multivariate Hawkes models with an explicit dependency on various queue sizes aimed at modeling the stochastic time evolution of a limit order book. The models we propose thus integrate the influence of both the current book state and the past order flow. The first variant considers the flow of order arrivals at a specific price level as independent from the other one and describes this flow by adding a Hawkes component to the arrival rates provided by the continuous time Markov "Queue Reactive" model of Huang et al. Empirical calibration using Level-I order book data from Eurex future assets (Bund and DAX) show that the Hawkes term dramatically improves the pure "Queue-Reactive" model not only for the description of the order flow properties (as e.g. the statistics of inter-event times) but also with respect to the shape of the queue distributions. The second variant we introduce describes the joint dynamics of all events occurring at best bid and ask sides of some order book during a trading day. This model can be considered as a queue dependent extension of the multivariate Hawkes order-book model of Bacry et al. We provide an explicit way to calibrate this model either with a Maximum-Likelihood method or with a Least-Square approach. Empirical estimation from Bund and DAX level-I order book data allow us to recover the main features of Hawkes interactions uncovered in Bacry et al. but also to unveil their joint dependence on bid and ask queue sizes. We notably find that while the market order or mid-price changes rates can mainly be functions on the volume imbalance this is not the case for the arrival rate of limit or cancel orders. Our findings also allows us to clearly bring to light various features that distinguish small and large tick assets.

q-fin.TR↗
arXiv2018

Localization length exponent in two models of quantum Hall plateau transitions

Motivated by the recent numerical studies on the Chalker-Coddington network model that found a larger-than-expected critical exponent of the localization length characterizing the integer quantum Hall plateau transitions, we revisited the exponent calculation in the continuum model and in the lattice model, both projected to the lowest Landau level or subband. Combining scaling results with or without the corrections of an irrelevant length scale, we obtain $ν= 2.48 \pm 0.02$, which is larger but still consistent with the earlier results in the two models, unlike what was found recently in the network model. The scaling of the total number of conducting states, as determined by the Chern number calculation, is accompanied by an effective irrelevant length scale exponent $y = 4.3$ in the lattice model, indicating that the irrelevant perturbations are insignificant in the topology number calculation.

cond-mat.dis-nn↗
arXiv2018

Dense Object Reconstruction from RGBD Images with Embedded Deep Shape Representations

Most problems involving simultaneous localization and mapping can nowadays be solved using one of two fundamentally different approaches. The traditional approach is given by a least-squares objective, which minimizes many local photometric or geometric residuals over explicitly parametrized structure and camera parameters. Unmodeled effects violating the lambertian surface assumption or geometric invariances of individual residuals are encountered through statistical averaging or the addition of robust kernels and smoothness terms. Aiming at more accurate measurement models and the inclusion of higher-order shape priors, the community more recently shifted its attention to deep end-to-end models for solving geometric localization and mapping problems. However, at test-time, these feed-forward models ignore the more traditional geometric or photometric consistency terms, thus leading to a low ability to recover fine details and potentially complete failure in corner case scenarios. With an application to dense object modeling from RGBD images, our work aims at taking the best of both worlds by embedding modern higher-order object shape priors into classical iterative residual minimization objectives. We demonstrate a general ability to improve mapping accuracy with respect to each modality alone, and present a successful application to real data.

cs.CV↗
arXiv2017

Understanding contact gating in Schottky barrier transistors from 2D channels

In this article, a novel two-path model is proposed to quantitatively explain sub-threshold characteristics of back-gated Schottky barrier FETs (SB-FETs) from 2D channel materials. The model integrates the 'conventional' model for SB-FETs with the phenomenon of contact gating - an effect that significantly affects the carrier injection from the source electrode in back-gated field effect transistors. The two-path model is validated by a careful comparison with experimental characteristics obtained from a large number of back-gated WSe2 devices with various channel thicknesses. Our findings are believed to be of critical importance for the quantitative analysis of many three-terminal devices with ultrathin body channels.

cond-mat.mes-hall↗
arXiv2017

Mining Target Attribute Subspace and Set of Target Communities in Large Attributed Networks

Community detection provides invaluable help for various applications, such as marketing and product recommendation. Traditional community detection methods designed for plain networks may not be able to detect communities with homogeneous attributes inside on attributed networks with attribute information. Most of recent attribute community detection methods may fail to capture the requirements of a specific application and not be able to mine the set of required communities for a specific application. In this paper, we aim to detect the set of target communities in the target subspace which has some focus attributes with large importance weights satisfying the requirements of a specific application. In order to improve the university of the problem, we address the problem in an extreme case where only two sample nodes in any potential target community are provided. A Target Subspace and Communities Mining (TSCM) method is proposed. In TSCM, a sample information extension method is designed to extend the two sample nodes to a set of exemplar nodes from which the target subspace is inferred. Then the set of target communities are located and mined based on the target subspace. Experiments on synthetic datasets demonstrate the effectiveness and efficiency of our method and applications on real-world datasets show its application values.

cs.SI↗
arXiv2017

Mining Application-aware Community Organization with Expanded Feature Subspaces from Concerned Attributes in Social Networks

Social networks are typical attributed networks with node attributes. Different from traditional attribute community detection problem aiming at obtaining the whole set of communities in the network, we study an application-oriented problem of mining an application-aware community organization with respect to specific concerned attributes. The concerned attributes are designated based on the requirements of any application by a user in advance. The application-aware community organization w.r.t. concerned attributes consists of the communities with feature subspaces containing these concerned attributes. Besides concerned attributes, feature subspace of each required community may contain some other relevant attributes. All relevant attributes of a feature subspace jointly describe and determine the community embedded in such subspace. Thus the problem includes two subproblems, i.e., how to expand the set of concerned attributes to complete feature subspaces and how to mine the communities embedded in the expanded subspaces. Two subproblems are jointly solved by optimizing a quality function called subspace fitness. An algorithm called ACM is proposed. In order to locate the communities potentially belonging to the application-aware community organization, cohesive parts of a network backbone composed of nodes with similar concerned attributes are detected and set as the community seeds. The set of concerned attributes is set as the initial subspace for all community seeds. Then each community seed and its attribute subspace are adjusted iteratively to optimize the subspace fitness. Extensive experiments on synthetic datasets demonstrate the effectiveness and efficiency of our method and applications on real-world networks show its application values.

cs.SI↗
arXiv2016

Modelling and Analysis of Network Security - an Algebraic Approach

Game theory has been applied to investigate network security. But different security scenarios were often modeled via different types of games and analyzed in an ad-hoc manner. In this paper, we propose an algebraic approach for modeling and analyzing uniformly several types of network security games. This approach is based on a probabilistic extension of the value-passing Calculus of Communicating Systems (CCS) which is regarded as a Generative model for Probabilistic Value-passing CCS (PVCCSG for short). Our approach gives a uniform framework, called PVCCSG based security model, for the security scenarios modeled via perfect and complete or incomplete information games. We present then a uniform algorithm for computing the Nash equilibria strategies of a network security game on its PVCCSG based security model. The algorithm first generates a transition system for each of the PVCCSG based security models, then simplifies this transition system through graph-theoretic abstraction and bisimulation minimization. Then, a backward induction method, which is only applicable to finite tree models, can be used to compute all the Nash equilibria strategies of the (possibly infinite) security games. This algorithm is implemented and can also be tuned smoothly for computing its social optimal strategies. The effectiveness and efficiency of this approach are further demonstrated with four detailed case studies from the field of network security.

cs.CR↗
arXiv2014

Heat Kernel on Smooth Metric Measure Spaces with Nonnegative Curvature

We derive a local Gaussian upper bound for the $f$-heat kernel on complete smooth metric measure space $(M,g,e^{-f}dv)$ with nonnegative Bakry-Émery Ricci curvature, which generalizes the classic Li-Yau estimate. As applications, we obtain a sharp $L_f^1$-Liouville theorem for $f$-subharmonic functions and an $L_f^1$-uniqueness property for nonnegative solutions of the $f$-heat equation, assuming $f$ is of at most quadratic growth. In particular, any $L_f^1$-integrable $f$-subharmonic function on gradient shrinking or steady Ricci solitons must be constant. We also provide explicit $f$-heat kernel for Gaussian solitons.

math.DG↗
arXiv2014

Berger curvature decomposition, Weitzenböck formula, and canonical metrics on four-manifolds

We first provide an alternative proof of the classical Weitzneböck formula for Einstein four-manifolds using Berger curvature decomposition, motivated by which we establish a unified framework for a Weitzenböck formula for a large class of canonical metrics on four-manifolds (or a Weitzenböck formula for "Einstein metrics" on four-dimensional smooth metric measure spaces). As applications, we classify Einstein four-manifolds of half two-nonnegative curvature operator, which in some sense provides a characterization of Kähler-Einstein metrics with positive scalar curvature on four-manifolds, we also discuss four-manifolds of half two-nonnegative curvature operator and half harmonic Weyl curvature.

math.DG↗
arXiv2014

Gradient shrinking Ricci solitons of half harmonic Weyl curvature

We prove that a four-dimensional gradient shrinking Ricci soliton with $δW^{\pm}=0$ is either Einstein, or a finite quotient of $S^3\times\mathbb{R}$, $S^2\times\mathbb{R}^2$ or $\mathbb{R}^4$. We also prove that a four-dimensional cscK gradient Ricci soliton is either Kähler-Einstein, or a finite quotient of $M\times\mathbb{C}$, where $M$ is a Riemann surface. The main arguments are curvature decompositions, the Weitzenböck formula for half Weyl curvature, and the maximum principle.

math.DG↗
arXiv2014

On $L^p$-Liouville property for smooth metric measure spaces

In this short paper we study $L_f^p$-Liouville property with $0<p<1$ for nonnegative $f$-subharmonic functions on a complete noncompact smooth metric measure space $(M,g,e^{-f}dv)$ with $\mathrm{Ric}_f^m$ bounded below for $0<m\leq\infty$. We prove a sharp $L_f^p$-Liouville theorem when $0<m<\infty$. We also prove an $L_f^p$-Liouville theorem when $\mathrm{Ric}_f\geq 0$ and $|f(x)|\leq δ(n) \ln r(x)$.

math.DG↗
arXiv2014

Heat kernel on smooth metric measure spaces and applications

We derive a Harnack inequality for positive solutions of the $f$-heat equation and Gaussian upper and lower bounds for the $f$-heat kernel on complete smooth metric measure spaces $(M, g, e^{-f}dv)$ with Bakry-Émery Ricci curvature bounded below. The lower bound is sharp. The main argument is the De Giorgi-Nash-Moser theory. As applications, we prove an $L^1_f$-Liouville theorem for $f$-subharmonic functions and an $L^1_f$-uniqueness theorem for $f$-heat equations when $f$ has at most linear growth. We also obtain eigenvalues estimates and $f$-Green's function estimates for the $f$-Laplace operator.

math.DG↗
arXiv2013

A generalization of the Cai--Galloway splitting theorem to smooth metric measure spaces

We generalize the splitting theorem of Cai-Galloway for complete Riemannian manifolds with $\Ric\geq-(n-1)$ admitting a family of compact hypersurfaces tending to infinity with mean curvatures tending to $n-1$ sufficiently fast to the setting of smooth metric measure spaces. This result complements and provides a new perspective on the splitting theorems recently proven by Munteanu-Wang and Su-Zhang. We show that the mean curvature assumption in our result is sharp, which also provides an example showing that the assumption $R\geq-(n-1)$ in the Munteanu-Wang splitting theorem for expanding gradient Ricci solitons cannot be relaxed to $R>-n$. We also use our result to study a certain class of conformally compact quasi-Einstein metrics, giving, as generalizations of respective results of Cai-Galloway and Lee, necessary conditions for the boundary to be connected and for the bottom of the spectrum of the weighted Laplacian to be maximal.

math.DG↗