arXiv subjects
Publications and source records attributed to Kai Wang.
As a partial answer to a question of Rao, a deterministic and customizable efficient algorithm is presented to test whether an arbitrary graphical degree sequence has a bipartite realization. The algorithm can be configured to run in polynomial time, at the expense of possibly producing an erroneous output on some "yes" instances but with very low error rate.
In this paper, we propose a deep learning based assistive system to improve the environment perception experience of visually impaired (VI). The system is composed of a wearable terminal equipped with an RGBD camera and an earphone, a powerful processor mainly for deep learning inferences and a smart phone for touch-based interaction. A data-driven learning approach is proposed to predict safe and reliable walkable instructions using RGBD data and the established semantic map. This map is also used to help VI understand their 3D surrounding objects and layout through well-designed touchscreen interactions. The quantitative and qualitative experimental results show that our learning based obstacle avoidance approach achieves excellent results in both indoor and outdoor datasets with low-lying obstacles. Meanwhile, user studies have also been carried out in various scenarios and showed the improvement of VI's environment perception experience with our system.
We have investigated collective breathing modes of a unitary Fermi gas in deformed harmonic traps. The ground state is studied by the Superfluid Local Density Approximation (SLDA) and small-amplitude collective modes are studied by the iterative Quasiparticle Random Phase Approximation (QRPA). The results illustrate the evolutions of collective modes of a small system in traps from spherical to elongated or pancake deformations. For small spherical systems, the influences of different SLDA parameters are significant, and, in particular, a large pairing strength can shift up the oscillation frequency of collective mode. The transition currents from QRPA show that the compressional flow patterns are nontrivial and dependent on the deformation. Finally, the finite size effects are demonstrated to be reasonable when progressing towards larger systems. Our studies indicate that experiments on small and medium systems are valuable for understanding effective interactions in systems with varying sizes and trap deformations.
Employing two state-of-the-art methods, second-order many-body perturbation theory and multiconfiguration Dirac-Fock, highly accurate calculations are performed for the lowest 318 fine-structure levels arising from the $2s^{2} 2p^{4}$, $2s 2p^{5}$, $2p^{6}$, $2s^{2} 2p^{3} 3l$, $2s 2p^{4} 3l$, $2p^{5} 3l$, and $2s^{2} 2p^{3} 4l$ configurations in O-like \mbox{Mo XXXV}. Complete and consistent atomic data, including excitation energies, lifetimes, wavelengths, and E1, E2, M1 line strengths, oscillator strengths, and transition rates among these 318 levels are provided. Comparisons are made between the present two data sets, as well as with other available experimental and theoretical values. The present data are accurate enough for identification and deblending of emission lines involving the $n=3,4$ levels and are also useful for modeling and diagnosing fusion plasmas\kw{. These data can} be considered as a benchmark for other calculations.
Magnetic Resonance Imaging(MRI) has been widely used in clinical application and pathology research by helping doctors make more accurate diagnoses. On the other hand, accurate diagnosis by MRI remains a great challenge as images obtained via present MRI techniques usually have low resolutions. Improving MRI image quality and resolution thus becomes a critically important task. This paper presents an innovative Coupled-Projection Residual Network (CPRN) for MRI super-resolution. The CPRN consists of two complementary sub-networks: a shallow network and a deep network that keep the content consistency while learning high frequency differences between low-resolution and high-resolution images. The shallow sub-network employs coupled-projection for better retaining the MRI image details, where a novel feedback mechanism is introduced to guide the reconstruction of high-resolution images. The deep sub-network learns from the residuals of the high-frequency image information, where multiple residual blocks are cascaded to magnify the MRI images at the last network layer. Finally, the features from the shallow and deep sub-networks are fused for the reconstruction of high-resolution MRI images. For effective fusion of features from the deep and shallow sub-networks, a step-wise connection (CPRN S) is designed as inspired by the human cognitive processes (from simple to complex). Experiments over three public MRI datasets show that our proposed CPRN achieves superior MRI super-resolution performance as compared with the state-of-the-art. Our source code will be publicly available at http://www.yongxu.org/lunwen.html.
In this work we use density functional theory (DFT) calculations to benchmark empirical potentials for the interaction between nickel and sp$^2$ bonded carbon nanoparticles. These potentials are then used in order to investigate how Ni decorated or coated carbon nanotubes (CNT) affect the mechanical properties of Al/CNT composites. In particular we look at the pull-out behaviour of pristine as well as Ni-decorated and Ni-coated CNT from an Al matrix. Our result shows that Ni coating may produce an extended interface (interphase) where a significant amount of energy is dissipated during CNT pull-out, leading to a high pull-out force. We also demonstrate that surface decorated CNT may act as efficient nano-crystallization agents and thus provide a novel strengthening mechanism not previously discussed in the literature. We discuss our results in view of promising approaches for engineering CNT-metal interfaces such as to achieve high strength metal-CNT composite.
This paper presents our approach for the engagement intensity regression task of EmotiW 2019. The task is to predict the engagement intensity value of a student when he or she is watching an online MOOCs video in various conditions. Based on our winner solution last year, we mainly explore head features and body features with a bootstrap strategy and two novel loss functions in this paper. We maintain the framework of multi-instance learning with long short-term memory (LSTM) network, and make three contributions. First, besides of the gaze and head pose features, we explore facial landmark features in our framework. Second, inspired by the fact that engagement intensity can be ranked in values, we design a rank loss as a regularization which enforces a distance margin between the features of distant category pairs and adjacent category pairs. Third, we use the classical bootstrap aggregation method to perform model ensemble which randomly samples a certain training data by several times and then averages the model predictions. We evaluate the performance of our method and discuss the influence of each part on the validation dataset. Our methods finally win 3rd place with MSE of 0.0626 on the testing set.
In this paper, we propose an accurate edge detector using richer convolutional features (RCF). Since objects in nature images have various scales and aspect ratios, the automatically learned rich hierarchical representations by CNNs are very critical and effective to detect edges and object boundaries. And the convolutional features gradually become coarser with receptive fields increasing. Based on these observations, our proposed network architecture makes full use of multiscale and multi-level information to perform the image-to-image edge prediction by combining all of the useful convolutional features into a holistic framework. It is the first attempt to adopt such rich convolutional features in computer vision tasks. Using VGG16 network, we achieve \sArt results on several available datasets. When evaluating on the well-known BSDS500 benchmark, we achieve ODS F-measure of \textbf{.811} while retaining a fast speed (\textbf{8} FPS). Besides, our fast version of RCF achieves ODS F-measure of \textbf{.806} with \textbf{30} FPS.
Bipartite networks are of great importance in many real-world applications. In bipartite networks, butterfly (i.e., a complete 2 x 2 biclique) is the smallest non-trivial cohesive structure and plays a key role. In this paper, we study the problem of efficient counting the number of butterflies in bipartite networks. The most advanced techniques are based on enumerating wedges which is the dominant cost of counting butterflies. Nevertheless, the existing algorithms cannot efficiently handle large-scale bipartite networks. This becomes a bottleneck in large-scale applications. In this paper, instead of the existing layer-priority-based techniques, we propose a vertex-priority-based paradigm BFC-VP to enumerate much fewer wedges; this leads to a significant improvement of the time complexity of the state-of-the-art algorithms. In addition, we present cache-aware strategies to further improve time efficiency while theoretically retaining the time complexity of BFC-VP. Moreover, we also show that our proposed techniques can work efficiently in external and parallel contexts. Our extensive empirical studies demonstrate that the proposed techniques can speed up the state-of-the-art techniques by up to two orders of magnitude for the real datasets.
Directly learning features from the point cloud has become an active research direction in 3D understanding. Existing learning-based methods usually construct local regions from the point cloud and extract the corresponding features. However, most of these processes do not adequately take the spatial distribution of the point cloud into account, limiting the ability to perceive fine-grained patterns. We design a novel Local Spatial Aware (LSA) layer, which can learn to generate Spatial Distribution Weights (SDWs) hierarchically based on the spatial relationship in local region for spatial independent operations, to establish the relationship between these operations and spatial distribution, thus capturing the local geometric structure sensitively.We further propose the LSANet, which is based on LSA layer, aggregating the spatial information with associated features in each layer of the network better in network design.The experiments show that our LSANet can achieve on par or better performance than the state-of-the-art methods when evaluating on the challenging benchmark datasets. For example, our LSANet can achieve 93.2% accuracy on ModelNet40 dataset using only 1024 points, significantly higher than other methods under the same conditions. The source code is available at https://github.com/LinZhuoChen/LSANet.
Quantum interference between optical absorption processes can excite carriers with a polarized distribution in the Brillouin zone depending on properties of the incident optical fields. The polarized distribution of carriers introduces a current that can be controlled by the phases and polarizations of the incident optical fields. Here we study the quantum interference of 2- and 3-photon absorption processes in AlGaAs. We present theoretical predictions for carrier and current injection rates considering different frequencies, phases, and polarizations of the incident fields. We also discuss the important features that result from only nonlinear optical processes being involved, which leads for instance to a sharper distribution of carriers in the Brillouin zone.
We report experiments demonstrating Quantum Interference Control (QuIC) based on two nonlinear optical absorption processes in semiconductors. We use two optical beams of frequencies $ω$ and $3ω/2$ incident on AlGaAs and measure the injection current due to the interference between 2- and 3-photon absorption processes. We analyze the dependence of the injection current on the intensities and phases of the incident fields.
The success of neural summarization models stems from the meticulous encodings of source articles. To overcome the impediments of limited and sometimes noisy training data, one promising direction is to make better use of the available training data by applying filters during summarization. In this paper, we propose a novel Bi-directional Selective Encoding with Template (BiSET) model, which leverages template discovered from training data to softly select key information from each source article to guide its summarization process. Extensive experiments on a standard summarization dataset were conducted and the results show that the template-equipped BiSET model manages to improve the summarization performance significantly with a new state of the art.
Although TMDC monolayers offer giant optical nonlinearity within few-angstrom thickness, it is still elusive to modulate and engineer the wavefront of nonlinear emissions. The grain size of high-quality monolayers also restricts possibilities of imparting inhomogeneous or gradient profiles of phases and amplitudes by classical light sources. Plasmonic nanosieves can support huge field enhancement and precise nonlinear phase control with hundred-nanometer pixel-level resolution, however they suffer from intrinsically weak nonlinear susceptibility. In nature, epoxy represents a strong glue whose magic adhesion comes from the bonding of two intrinsic loose and nonsticky resins. Inspired by the nature of epoxy, we report a multifunctional and powerful nonlinear interfaces via integrating transition-metal dichalcogenide (TMDC) monolayers with linear plasmonic nanosieves, which cannot be accessed by either constituents. We experimentally demonstrate orbital angular momentum (OAM) generation, beam steering, versatile polarization control and holograms and of SH emission, with the proposed nonlinear interfaces. In addition, an effective second-harmonic (SH) nonlinearity \c{hi}(2) of ~25 nm/V is obtained for such systems. This designer platform synergizes the complementary advantages of TMDC monolayer, plasmonic nanosieves, geometric phases, and field enhancement, paving a distinct avenue toward designer, multi-functional, and ultra-compact nonlinear optical devices.
It is shown that the Fizeau drag can be used to cause nonreciprocity. We propose the use of a nanostructured toroid cavity made of $χ^{(2)}$ nonlinear materials to achieve nonreciprocal photon blockade (PB) through the Fizeau drag. Under the weak driving condition, we discuss the origins of the PB based on the doubly resonant modes with good spatial overlap at the fundamental and second-harmonic frequencies. We also find that for the fundamental mode, the PB happens when we drive the system from one side but the photon-induced tunneling happens when we drive the system from the other side. However, there is no such phenomenon in the second-harmonic mode. Remarkably, the PB phenomenon occurs with a reasonably small optical nonlinearity thus bringing the system parameters closer to the reasonably achievable realm by the current technology.
Jet identification is one of the fields in high energy physics that machine learning has begun to make an impact. More often than not, convolutional neural networks are used to classify jet images with the benefit that essentially no physics input is required. Inspired by a recent work by Datta and Larkoski, we study the classification of quark/gluon-initiated jets based on fully-connected neural networks (FNNs), where expert-designed physical variables are taken as input. FNNs are applied in two ways: trained separately on various narrow jet transverse momentum $p_{TJ}$ bins; trained on a wide region of $p_{TJ} \in [200,~1000]$ GeV. We find their performances are almost the same. The performance is better when the $p_{TJ}$ is larger. Jet discrimination with FNN is studied on both particle and detector level data. The results based on particle level data are comparable with those from deep convolutional neural networks, while the significance improvement characteristic (SIC) from detector level data would at most decrease by $15\%$. We also test the performance of FNNs with full set or subsets of jet observables as input features. The FNN with one subset consisting of fourteen observables shows nearly no degradation of performance. This indicates that these fourteen expert-designed observables could have captured the most necessary information for separating quark and gluon jets.
We compiled a sample of 73 active galactic nuclei (AGNs) with reverberation mapping (RM) observations from RM campaigns including our ongoing campaign of monitoring super-Eddington accreting massive black holes (SEAMBHs). This sample covers a large range of black hole (BH) mass $(M_{\bullet}=10^{6-9}~M_\odot)$, dimensionless accretion rates $(\dot{\mathscr{M}}=10^{-2.7}-10^{2.7})$ and 5100~Å~luminosity $(L_{5100}=10^{42-46}~\rm erg~s^{-1})$, allowing us to systematically study the AGN variability and their relations with BH mass, accretion rates, and optical luminosity. We employed the damped random walk (DRW) model to delineate the optical variability of continuum at 5100~Å~and obtained damped variability timescale ($τ_{\rm d}$) and amplitude ($σ_{\rm d}$) using a Markov Chain Monte Carlo (MCMC) method. We also estimated the traditional variability amplitudes ($F_{\rm var}$), which provide a model-independent measure and therefore are used to test the DRW results. We found that AGN variability characteristics are generally correlated with $(M_{\bullet},\dot{\mathscr{M}},L_{5100})$. These correlations are smooth from sub-Eddington to super-Eddington accretion AGNs, probably implying that the AGN variability may be caused by the same physical mechanism.
To help the blind people walk to the destination efficiently and safely in indoor environment, a novel wearable navigation device is presented in this paper. The locating, way-finding, route following and obstacle avoiding modules are the essential components in a navigation system, while it remains a challenging task to consider obstacle avoiding during route following, as the indoor environment is complex, changeable and possibly with dynamic objects. To address this issue, we propose a novel scheme which utilizes a dynamic sub-goal selecting strategy to guide the users to the destination and help them bypass obstacles at the same time. This scheme serves as the key component of a complete navigation system deployed on a pair of wearable optical see-through glasses for the ease of use of blind people's daily walks. The proposed navigation device has been tested on a collection of individuals and proved to be effective on indoor navigation tasks. The sensors embedded are of low cost, small volume and easy integration, making it possible for the glasses to be widely used as a wearable consumer device.