arXiv subjects

Yan Wang

Publications and source records attributed to Yan Wang.

At least 937 records · Page 52Linked to original sources

arXiv2019

A novel metric for community detection

Research into detection of dense communities has recently attracted increasing attention within network science, various metrics for detection of such communities have been proposed. The most popular metric -- Modularity -- is based on the so-called rule that the links within communities are denser than external links among communities, has become the default. However, this default metric suffers from ambiguity, and worse, all augmentations of modularity and based on a narrow intuition of what it means to form a "community". We argue that in specific, but quite common systems, links within a community are not necessarily more common than links between communities. Instead we propose that the defining characteristic of a community is that links are more predictable within a community rather than between communities. In this paper, based on the effect of communities on link prediction, we propose a novel metric for the community detection based directly on this feature. We find that our metric is more robustness than traditional modularity. Consequently, we can achieve an evaluation of algorithm stability for the same detection algorithm in different networks. Our metric also can directly uncover the false community detection, and infer more statistical characteristics for detection algorithms.

physics.soc-ph↗

arXiv2019

An Empirical Study on the Characteristics of Question-Answering Process on Developer Forums

Developer forums are one of the most popular and useful Q&A websites on API usages. The analysis of API forums can be a critical resource for the automated question and answer approaches. In this paper, we empirically study three API forums including Twitter, eBay, and AdWords, to investigate the characteristics of question-answering process. We observe that +60% of the posts on all three forums were answered by providing API method names or documentation. +85% of the questions were answered by API development teams and the answers from API development teams drew fewer follow-up questions. Our results provide empirical evidences for us in a future work to build automated solutions to answer developer questions on API forums.

cs.SE↗

arXiv2019

Dynamics of a Reaction-Diffusion Benthic-Drift Model with Strong Allee Effect Growth

The dynamics of a reaction-diffusion-advection benthic-drift population model that links changes in the flow regime and habitat availability with population dynamics is studied. In the model, the stream is divided into drift zone and benthic zone, and the population is divided into two interacting compartments, individuals residing in the benthic zone and individuals dispersing in the drift zone. The benthic population growth is assumed to be of strong Allee effect type. The influence of flow speed and individual transfer rates between zones on the population persistence and extinction is considered, and the criteria of population persistence or extinction are formulated and proved.

math.AP↗

arXiv2019

Multi-Scale Attentional Network for Multi-Focal Segmentation of Active Bleed after Pelvic Fractures

Trauma is the worldwide leading cause of death and disability in those younger than 45 years, and pelvic fractures are a major source of morbidity and mortality. Automated segmentation of multiple foci of arterial bleeding from abdominopelvic trauma CT could provide rapid objective measurements of the total extent of active bleeding, potentially augmenting outcome prediction at the point of care, while improving patient triage, allocation of appropriate resources, and time to definitive intervention. In spite of the importance of active bleeding in the quick tempo of trauma care, the task is still quite challenging due to the variable contrast, intensity, location, size, shape, and multiplicity of bleeding foci. Existing work [4] presents a heuristic rule-based segmentation technique which requires multiple stages and cannot be efficiently optimized end-to-end. To this end, we present, Multi-Scale Attentional Network (MSAN), the first yet reliable end-to-end network, for automated segmentation of active hemorrhage from contrast-enhanced trauma CT scans. MSAN consists of the following components: 1) an encoder which fully integrates the global contextual information from holistic 2D slices; 2) a multi-scale strategy applied both in the training stage and the inference stage to handle the challenges induced by variation of target sizes; 3) an attentional module to further refine the deep features, leading to better segmentation quality; and 4) a multi-view mechanism to fully leverage the 3D information. Our MSAN reports a significant improvement of more than 7% compared to prior arts in terms of DSC.

eess.IV↗

arXiv2019

Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%. Due to subtle texture changes of PDAC, pancreatic dual-phase imaging is recommended for better diagnosis of pancreatic disease. In this study, we aim at enhancing PDAC automatic segmentation by integrating multi-phase information (i.e., arterial phase and venous phase). To this end, we present Hyper-Pairing Network (HPN), a 3D fully convolution neural network which effectively integrates information from different phases. The proposed approach consists of a dual path network where the two parallel streams are interconnected with hyper-connections for intensive information exchange. Additionally, a pairing loss is added to encourage the commonality between high-level feature representations of different phases. Compared to prior arts which use single phase data, HPN reports a significant improvement up to 7.73% (from 56.21% to 63.94%) in terms of DSC.

eess.IV↗

arXiv2019

Deep Differentiable Random Forests for Age Estimation

Age estimation from facial images is typically cast as a label distribution learning or regression problem, since aging is a gradual progress. Its main challenge is the facial feature space w.r.t. ages is inhomogeneous, due to the large variation in facial appearance across different persons of the same age and the non-stationary property of aging. In this paper, we propose two Deep Differentiable Random Forests methods, Deep Label Distribution Learning Forest (DLDLF) and Deep Regression Forest (DRF), for age estimation. Both of them connect split nodes to the top layer of convolutional neural networks (CNNs) and deal with inhomogeneous data by jointly learning input-dependent data partitions at the split nodes and age distributions at the leaf nodes. This joint learning follows an alternating strategy: (1) Fixing the leaf nodes and optimizing the split nodes and the CNN parameters by Back-propagation; (2) Fixing the split nodes and optimizing the leaf nodes by Variational Bounding. Two Deterministic Annealing processes are introduced into the learning of the split and leaf nodes, respectively, to avoid poor local optima and obtain better estimates of tree parameters free of initial values. Experimental results show that DLDLF and DRF achieve state-of-the-art performance on three age estimation datasets.

cs.CV↗

arXiv2019

Science with the TianQin observatory: Preliminary results on massive black hole binaries

We investigate the prospects of detecting gravitational waves from coalescing massive black hole binaries in the Universe with the TianQin observatory, a space-based gravitational wave interferometer proposed to be launched in the 2030s. To frame the scientific scope of the mission, in this paper, we carry out a preliminary estimation of the signal-to-noise ratio, detection rate and parameter estimation precision of massive black hole binaries detectable by TianQin. In order to make our results as robust as possible, we consider several models of the growth history of massive black holes, exploring the effect of some key astrophysical prescriptions as well the impact of the employed computational methods. In the most optimistic model, TianQin can detect as many as approximately 60 mergers per year. If TianQin detects a merger at redshift of 15, it will be capable of estimating its luminosity distance to within an accuracy of 10%; for a nearby event at redshift approximately 2, TianQin can issue early warnings 24 hours before coalescence, with a timing accuracy of around three hours and a sky localization ability of approximately 80 deg$^2$, thus enabling multi-messenger observations.

astro-ph.HE↗

arXiv2019

Semi-Supervised Adversarial Monocular Depth Estimation

In this paper, we address the problem of monocular depth estimation when only a limited number of training image-depth pairs are available. To achieve a high regression accuracy, the state-of-the-art estimation methods rely on CNNs trained with a large number of image-depth pairs, which are prohibitively costly or even infeasible to acquire. Aiming to break the curse of such expensive data collections, we propose a semi-supervised adversarial learning framework that only utilizes a small number of image-depth pairs in conjunction with a large number of easily-available monocular images to achieve high performance. In particular, we use one generator to regress the depth and two discriminators to evaluate the predicted depth , i.e., one inspects the image-depth pair while the other inspects the depth channel alone. These two discriminators provide their feedbacks to the generator as the loss to generate more realistic and accurate depth predictions. Experiments show that the proposed approach can (1) improve most state-of-the-art models on the NYUD v2 dataset by effectively leveraging additional unlabeled data sources; (2) reach state-of-the-art accuracy when the training set is small, e.g., on the Make3D dataset; (3) adapt well to an unseen new dataset (Make3D in our case) after training on an annotated dataset (KITTI in our case).

cs.CV↗

arXiv2019

A systematic review of fuzzing based on machine learning techniques

Security vulnerabilities play a vital role in network security system. Fuzzing technology is widely used as a vulnerability discovery technology to reduce damage in advance. However, traditional fuzzing techniques have many challenges, such as how to mutate input seed files, how to increase code coverage, and how to effectively bypass verification. Machine learning technology has been introduced as a new method into fuzzing test to alleviate these challenges. This paper reviews the research progress of using machine learning technology for fuzzing test in recent years, analyzes how machine learning improve the fuzz process and results, and sheds light on future work in fuzzing. Firstly, this paper discusses the reasons why machine learning techniques can be used for fuzzing scenarios and identifies six different stages in which machine learning have been used. Then this paper systematically study the machine learning based fuzzing models from selection of machine learning algorithm, pre-processing methods, datasets, evaluation metrics, and hyperparameters setting. Next, this paper assesses the performance of the machine learning models based on the frequently used evaluation metrics. The results of the evaluation prove that machine learning technology has an acceptable capability of categorize predictive for fuzzing. Finally, the comparison on capability of discovering vulnerabilities between traditional fuzzing tools and machine learning based fuzzing tools is analyzed. The results depict that the introduction of machine learning technology can improve the performance of fuzzing. However, there are still some limitations, such as unbalanced training samples and difficult to extract the characteristics related to vulnerabilities.

cs.CR↗

arXiv2019

Squeezing enhanced atom-cavity interaction in coupled cavities with high dissipation rates

The realization of the strong coupling regime is requisite for implementing quantum information tasks. Here, a method for enhancing the atom-field coupling in highly dissipative coupled cavities is proposed. By introducing parametric squeezing into the primary cavity which is only virtually excited under specific parametric conditions, coupling enhancement between atom and auxiliary cavity is realized for proper squeezing parameters. This enables the system to be robust against large cavity decay and atomic spontaneous emission. The observation of vacuum Rabi oscillations show that the originally weak-coupled system can be enhanced into effective strong coupling regime.

quant-ph↗

arXiv2019

A Convex Optimization-based Dynamic Model Identification Package for the da Vinci Research Kit

The da Vinci Research Kit (dVRK) is a teleoperated surgical robotic system. For dynamic simulations and model-based control, the dynamic model of the dVRK is required. We present an open-source dynamic model identification package for the dVRK, capable of modeling the parallelograms, springs, counterweight, and tendon couplings, which are inherent to the dVRK. A convex optimization-based method is used to identify the dynamic parameters of the dVRK subject to physical consistency. Experimental results show the effectiveness of the modeling and the robustness of the package. Although this software package is originally developed for the dVRK, it is feasible to apply it on other similar robots.

cs.RO↗

arXiv2019

An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging

Underwater images play a key role in ocean exploration, but often suffer from severe quality degradation due to light absorption and scattering in water medium. Although major breakthroughs have been made recently in the general area of image enhancement and restoration, the applicability of new methods for improving the quality of underwater images has not specifically been captured. In this paper, we review the image enhancement and restoration methods that tackle typical underwater image impairments, including some extreme degradations and distortions. Firstly, we introduce the key causes of quality reduction in underwater images, in terms of the underwater image formation model (IFM). Then, we review underwater restoration methods, considering both the IFM-free and the IFM-based approaches. Next, we present an experimental-based comparative evaluation of state-of-the-art IFM-free and IFM-based methods, considering also the prior-based parameter estimation algorithms of the IFM-based methods, using both subjective and objective analysis (the used code is freely available at https://github.com/wangyanckxx/Single-Underwater-Image-Enhancement-and-Color-Restoration). Starting from this study, we pinpoint the key shortcomings of existing methods, drawing recommendations for future research in this area. Our review of underwater image enhancement and restoration provides researchers with the necessary background to appreciate challenges and opportunities in this important field.

eess.IV↗

arXiv2019

Low-regularity integrators for nonlinear Dirac equations

In this work, we consider the numerical integration of the nonlinear Dirac equation and the Dirac-Poisson system (NDEs) under rough initial data. We propose a ultra low-regularity integrator (ULI) for solving the NDEs which enables optimal first-order time convergence in $H^r$ for solutions in $H^{r}$, i.e., without requiring any additional regularity on the solution. In contrast to classical methods, ULI overcomes the numerical loss of derivatives and is therefore more efficient and accurate for approximating low regular solutions. Convergence theorems and the extension of ULI to second order are established. Numerical experiments confirm the theoretical results and underline the favourable error behaviour of the new method at low regularity compared to classical integration schemes.

math.NA↗

arXiv2019

Linearized modified gravity theories and gravitational waves physics in the GBD theory

The generalized Brans-Dicke (abbreviated as GBD) theory is obtained by replacing the Ricci scalar $R$ in the original Brans-Dicke (BD) action with an arbitrary function $f(R)$. Comparing with other theories, some interesting properties have been found or some problems existing in other theories could be solved in the GBD theory. For example, (1) the state parameter of geometrical dark energy in the GBD model can cross over the phantom boundary $w=-1$, without bearing the problems existing in the quintom model (e.g. the problem of negative kinetic term and the fine-tuning problem, etc); (2) $f(R)$ theory is equivalent to the BD theory with a potential (BDV) with the couple parameter $ω=0$, where the kinetic term of field in the equivalent BDV theory is absent. While the scalar fields in the GBD own the non-disappeared kinetic term, when one compares the GBD theory with the $f(R)$ theory. In this paper, we continue to investigate the GBD theory. Using the method of the weak-field approximation, we explore the linearized physics in the GBD theory. The linearized equations of the gravitational field and two scalar fields are given. We investigate their solutions in the linearized theory for a point mass. It is shown that the problem of the $γ$ (parametrized post-Newtonian parameter) value in the $f(R)$ theory could be solved in the GBD theory, where the theoretical $γ$ value in the GBD theory can be consistent with the observational results. At last, we study the gravitational waves physics in the vacuum for the GBD theory. It is found that the gravitational radiation in the GBD theory has new freedoms beyond the two standard modes in the general relativity theory.

gr-qc↗

arXiv2019

Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map

Underwater images often have severe quality degradation and distortion due to light absorption and scattering in the water medium. A hazed image formation model is widely used to restore the image quality. It depends on two optical parameters: the background light and the transmission map. Underwater images can also be enhanced by color and contrast correction from the perspective of image processing. In this paper, we propose an effective underwater image enhancement method for underwater images in composition of underwater image restoration and color correction. Firstly, a manually annotated background lights (MABLs) database is developed. With reference to the relationship between MABLs and the histogram distributions of various underwater images, robust statistical models of BLs estimation are provided. Next, the TM of R channel is roughly estimated based on the new underwater dark channel prior via the statistic of clear and high resolution underwater images, then a scene depth map based on the underwater light attenuation prior and an adjusted reversed saturation map are applied to compensate and modify the coarse TM of R channel. Next, TMs of G-B channels are estimated based on the difference of attenuation ratios between R channel and G-B channels. Finally, to improve the color and contrast of the restored image with a natural appearance, a variation of white balance is introduced as post-processing. In order to guide the priority of underwater image enhancement, sufficient evaluations are conducted to discuss the impacts of the key parameters including BL and TM, and the importance of the color correction. Comparisons with other state-of-the-art methods demonstrate that our proposed underwater image enhancement method can achieve higher accuracy of estimated BLs, less computation time, more superior performance, and more valuable information retention.

eess.IV↗

arXiv2019

7-Connected Graphs are 4-Ordered

A graph $G$ is $k$-ordered if for any distinct vertices $v_1, v_2, \ldots, v_k \in V(G)$, it has a cycle through $v_1, v_2, \ldots, v_k$ in order. Let $f(k)$ denote the minimum integer so that every $f(k)$-connected graph is $k$-ordered. The first non-trivial case of determining $f(k)$ is when $k=4$, where the previously best known bounds are $7 \leq f(4) \leq 40$. We prove that in fact $f(4)=7$.

math.CO↗

arXiv2019

The key to the weak-ties phenomenon

The study of the weak-ties phenomenon has a long and well documented history, research into the application of this social phenomenon has recently attracted increasing attention. However, further exploration of the reasons behind the weak-ties phenomenon is still challenging. Fortunately, data-driven network science provides a novel way with substantial explanatory power to analyze the causal mechanism behind social phenomenon. Inspired by this perspective, we propose an approach to further explore the driving factors behind the temporal weak-ties phenomenon. We find that the obvious intuition underlying the weak-ties phenomenon is incorrect, and often large numbers of unknown mutual friends associated with these weak ties is one of the key reason for the emergence of the weak-ties phenomenon. In particular, for example scientific collaborators with weak ties prefer to be involved in direct collaboration rather than share ideas with mutual colleagues -- there is a natural tendency to collapse short strong chains of connection.

physics.soc-ph↗

arXiv2019

Preliminary study on parameter estimation accuracy of supermassive black hole binary inspirals for TianQin

We use the Fisher information matrix method to calculate the parameter estimation accuracy of inspiraling supermassive black holes binaries for TianQin, a space-borne laser interferometric detector aimed at detecting gravitational waves in the millihertz frequency band. The `restricted' post-Newtonian waveform in which third order post-Newtonian (3PN) phase including spin effects (spin-orbit $β$ and spin-spin $σ$) and first-order eccentricity contribution is employed. Monte Carlo simulations using $10^3$ binaries for mass pairs with component masses in the range of $({10^5},{10^7}){M_ \odot }$ and cosmological redshift $z=0.5$ show that the medians of the root-mean-square error distributions for the chirp mass $M_c$ and symmetric mass ratio $η$ are in the range of $\sim 0.02\% - 0.7\% $ and $\sim 4\% - 8\% $, respectively. The luminosity distance $D_L$ can be determined to be $\sim 1\% - 3\% $, and the angular resolution of source $ΔΩ$ is better than 12 deg$^2$. The corresponding results for $z=1.0$ and $2.0$, which are deteriorated with the decreasing of the signal-to-noise ratio, have also been given. We show that adding spin parameters degrades measurement accuracy of the mass parameters (${M_c}$, $η$), and the time and the orbital phase of coalescence ($t_c$, $ϕ_c$); the inclusion of the first-order eccentricity correction to the phase worsens the estimation accuracy comparing with the circular cases. We also show the effects of post-Newtonian order on parameter estimation accuracy by comparing the results based on second order and third order post-Newtonian phases. Moreover, we calculate the horizon distance of supermassive black hole binaries for TianQin.

astro-ph.IM↗