arXiv subjects

Gang Wang

Publications and source records attributed to Gang Wang.

At least 487 records · Page 27Linked to original sources

arXiv2015

Video Tracking Using Learned Hierarchical Features

In this paper, we propose an approach to learn hierarchical features for visual object tracking. First, we offline learn features robust to diverse motion patterns from auxiliary video sequences. The hierarchical features are learned via a two-layer convolutional neural network. Embedding the temporal slowness constraint in the stacked architecture makes the learned features robust to complicated motion transformations, which is important for visual object tracking. Then, given a target video sequence, we propose a domain adaptation module to online adapt the pre-learned features according to the specific target object. The adaptation is conducted in both layers of the deep feature learning module so as to include appearance information of the specific target object. As a result, the learned hierarchical features can be robust to both complicated motion transformations and appearance changes of target objects. We integrate our feature learning algorithm into three tracking methods. Experimental results demonstrate that significant improvement can be achieved using our learned hierarchical features, especially on video sequences with complicated motion transformations.

cs.CV↗

arXiv2015

DAG-Recurrent Neural Networks For Scene Labeling

In image labeling, local representations for image units are usually generated from their surrounding image patches, thus long-range contextual information is not effectively encoded. In this paper, we introduce recurrent neural networks (RNNs) to address this issue. Specifically, directed acyclic graph RNNs (DAG-RNNs) are proposed to process DAG-structured images, which enables the network to model long-range semantic dependencies among image units. Our DAG-RNNs are capable of tremendously enhancing the discriminative power of local representations, which significantly benefits the local classification. Meanwhile, we propose a novel class weighting function that attends to rare classes, which phenomenally boosts the recognition accuracy for non-frequent classes. Integrating with convolution and deconvolution layers, our DAG-RNNs achieve new state-of-the-art results on the challenging SiftFlow, CamVid and Barcelona benchmarks.

cs.CV↗

arXiv2015

Towards Predicting the Likeability of Fashion Images

In this paper, we propose a method for ranking fashion images to find the ones which might be liked by more people. We collect two new datasets from image sharing websites (Pinterest and Polyvore). We represent fashion images based on attributes: semantic attributes and data-driven attributes. To learn semantic attributes from limited training data, we use an algorithm on multi-task convolutional neural networks to share visual knowledge among different semantic attribute categories. To discover data-driven attributes unsupervisedly, we propose an algorithm to simultaneously discover visual clusters and learn fashion-specific feature representations. Given attributes as representations, we propose to learn a ranking SPN (sum product networks) to rank pairs of fashion images. The proposed ranking SPN can capture the high-order correlations of the attributes. We show the effectiveness of our method on our two newly collected datasets.

cs.CV↗

arXiv2015

High-gradient High-charge CW Superconducting RF gun with CsK2Sb photocathode

High-gradient CW photo-injectors operating at high accelerating gradients promise to revolutionize many sciences and applications. They can establish the basis for super-bright monochromatic X-ray free-electron lasers, super-bright hadron beams, nuclear- waste transmutation or a new generation of microchip production. In this letter we report on our operation of a superconducting RF electron gun with a record-high accelerating gradient at the CsK2Sb photocathode (i.e. ~ 20 MV/m) generating a record-high bunch charge (i.e., 3 nC). We briefly describe the system and then detail our experimental results. This achievement opens new era in generating high-power electron beams with a very high brightness.

physics.acc-ph↗

arXiv2015

Defect-induced magnetism in SiC: Interplay between ferromagnetism and paramagnetism

Defect-induced ferromagnetism has triggered a lot of investigations and controversies. The major issue is that the induced ferromagnetic signal is so weak that it can sufficiently be accounted for by trace contamination. To resolve this issue, we studied the variation of the magnetic properties of SiC after neutron irradiation with fluence covering four orders of magnitude. A large paramagnetic component has been induced and scales up with defect concentration, which can be well accounted for by uncoupled divacancies. However, the ferromagnetic contribution is still weak and only appears in the low fluence range of neutrons or after annealing treatments. First-principles calculations hint towards a mutually exclusive role of the concentration of defects: Defects favor spin polarization at the expense of magnetic interaction. Combining both experimental and first-principles calculation results, the defect-induced ferromagnetism can be understood as a local effect which cannot be scaled up with the volume. Therefore, our investigation answers the long-standing question why the defect-induced ferromagnetic signal is weak.

cond-mat.mtrl-sci↗

arXiv2015

Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders

Hyperspectral image (HSI) classification is a hot topic in the remote sensing community. This paper proposes a new framework of spectral-spatial feature extraction for HSI classification, in which for the first time the concept of deep learning is introduced. Specifically, the model of autoencoder is exploited in our framework to extract various kinds of features. First we verify the eligibility of autoencoder by following classical spectral information based classification and use autoencoders with different depth to classify hyperspectral image. Further in the proposed framework, we combine PCA on spectral dimension and autoencoder on the other two spatial dimensions to extract spectral-spatial information for classification. The experimental results show that this framework achieves the highest classification accuracy among all methods, and outperforms classical classifiers such as SVM and PCA-based SVM.

cs.CV↗

arXiv2015

An Investigation of Collisions between Fiber Positioning Units in LAMOST

The arrangement of the fiber positioning units in LAMOST focal plane may lead to the collisions during the fiber allocation. To avoid these collisions, the soft protection system has to abandon some targets located in the overlapped field of the adjacent fiber units. In this paper, we firstly analyzed the probability of the collisions between fibers and inferred their possible reasons. It is useful to solve the problem of the fiber-positioning units collisions so as to improve LAMOST efficiency. Based on it, a collision handling system is designed by using the master-slave control structure between the micro control unit (MCU) and the microcomputer. The simulated experiments validate that the system can provide real-time inspection and swap the information between the fiber unit controllers and the main controller.

astro-ph.IM↗

arXiv2015

Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification

In order to encode the class correlation and class specific information in image representation, we propose a new local feature learning approach named Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to hierarchically learn feature transformation filter banks to transform raw pixel image patches to features. The learned filter banks are expected to: (1) encode common visual patterns of a flexible number of categories; (2) encode discriminative information; and (3) hierarchically extract patterns at different visual levels. Particularly, in each single layer of DDSFL, shareable filters are jointly learned for classes which share the similar patterns. Discriminative power of the filters is achieved by enforcing the features from the same category to be close, while features from different categories to be far away from each other. Furthermore, we also propose two exemplar selection methods to iteratively select training data for more efficient and effective learning. Based on the experimental results, DDSFL can achieve very promising performance, and it also shows great complementary effect to the state-of-the-art Caffe features.

cs.CV↗

arXiv2015

Multimodal Multipart Learning for Action Recognition in Depth Videos

The articulated and complex nature of human actions makes the task of action recognition difficult. One approach to handle this complexity is dividing it to the kinetics of body parts and analyzing the actions based on these partial descriptors. We propose a joint sparse regression based learning method which utilizes the structured sparsity to model each action as a combination of multimodal features from a sparse set of body parts. To represent dynamics and appearance of parts, we employ a heterogeneous set of depth and skeleton based features. The proper structure of multimodal multipart features are formulated into the learning framework via the proposed hierarchical mixed norm, to regularize the structured features of each part and to apply sparsity between them, in favor of a group feature selection. Our experimental results expose the effectiveness of the proposed learning method in which it outperforms other methods in all three tested datasets while saturating one of them by achieving perfect accuracy.

cs.CV↗

arXiv2015

Spin-orbit engineering in transition metal dichalcogenide alloy monolayers

Transition metal dichalcogenide (TMDC) monolayers are newly discovered semiconductors for a wide range of applications in electronics and optoelectronics. Most studies have focused on binary monolayers that share common properties: direct optical bandgap, spin-orbit (SO) splittings of hundreds of meV, light-matter interaction dominated by robust excitons and coupled spin-valley states of electrons. Studies on alloy-based monolayers are more recent, yet they may not only extend the possibilities for TMDC applications through specific engineering but also help understanding the differences between each binary material. Here, we synthesized highly crystalline Mo$_{(1-x)}$W$_{x}$Se$_2$ to show engineering of the direct optical bandgap and the SO coupling in ternary alloy monolayers. We investigate the impact of the tuning of the SO spin splitting on the optical and polarization properties. We show a non-linear increase of the optically generated valley polarization as a function of tungsten concentration, where 40% tungsten incorporation is sufficient to achieve valley polarization as high as in binary WSe2. We also probe the impact of the tuning of the conduction band SO spin splitting on the bright versus dark state population i.e. PL emission intensity. We show that the MoSe2 PL intensity decreases as a function of temperature by an order of magnitude, whereas for WSe2 we measure surprisingly an order of magnitude increase over the same temperature range (T=4-300K). The ternary material shows a trend between these two extreme behaviors. These results show the strong potential of SO engineering in ternary TMDC alloys for optoelectronics and applications based on electron spin- and valley-control.

cond-mat.mtrl-sci↗

arXiv2015

Model Independent Description of Amplification and Saturation Using Green's Function

High-gain Free Electron Laser (FEL) is one of the many electron-beam instabilities that have a number of common features linking the shot noise, the amplification and the saturation. In this paper, we present a new, model-independent description of the interplay between these effects. We derive a simple formula for a maximum attainable gain before instability saturates. Application of this model-independent formula to FELs is compared with FEL theory and simulations. We apply the limitations resulting from these findings to FEL amplifiers used for seeded FELs and for Coherent electron Cooling.

physics.acc-ph↗

arXiv2015

ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images

Feature-based object matching is a fundamental problem for many applications in computer vision, such as object recognition, 3D reconstruction, tracking, and motion segmentation. In this work, we consider simultaneously matching object instances in a set of images, where both inlier and outlier features are extracted. The task is to identify the inlier features and establish their consistent correspondences across the image set. This is a challenging combinatorial problem, and the problem complexity grows exponentially with the image number. To this end, we propose a novel framework, termed ROML, to address this problem. ROML optimizes simultaneously a partial permutation matrix (PPM) for each image, and feature correspondences are established by the obtained PPMs. Two of our key contributions are summarized as follows. (1) We formulate the problem as rank and sparsity minimization for PPM optimization, and treat simultaneous optimization of multiple PPMs as a regularized consensus problem in the context of distributed optimization. (2) We use the ADMM method to solve the thus formulated ROML problem, in which a subproblem associated with a single PPM optimization appears to be a difficult integer quadratic program (IQP). We prove that under wildly applicable conditions, this IQP is equivalent to a linear sum assignment problem (LSAP), which can be efficiently solved to an exact solution. Extensive experiments on rigid/non-rigid object matching, matching instances of a common object category, and common object localization show the efficacy of our proposed method.

cs.CV↗

arXiv2015

Coils and the Electromagnet Used in the Joule Balance at the NIM

In the joule balance developed at National Institute of Metrology (NIM), the dynamic phase of a watt balance is replaced by the mutual inductance measurement in an attempt to provide an alternative method for the kg redefinition. But for this method a rather large current in the exciting coil, is needed to offer the necessary magnetic field in the force weighing phase, and the coil heating becomes an important uncertainty source. To reduce coil heating, a new coil system, in which a ferromagnetic material is used to increase the magnetic field was designed recently. But adopting the ferromagnetic material brings the difficulty from the nonlinear characteristic of material. This problem can be removed by measuring the magnetic flux linkage difference of the suspended coil at two vertical positions directly to replace the mutual inductance parameter. Some systematic effects of this magnet are discussed.

physics.ins-det↗

arXiv2015

Dressing Actions on Proper Definite Affine Spheres

We will first clarify the loop group formulations for both hyperbolic and elliptic definite affine spheres in R^3. Then we classify the rational elements with 3 poles or 6 poles in a real twisted loop group, and compute dressing actions of them on such surfaces. Some new examples with pictures will be produced at last.

math.DG↗

arXiv2014

Stochastic Reactive Power Management in Microgrids with Renewables

Distribution microgrids are being challenged by reverse power flows and voltage fluctuations due to renewable generation, demand response, and electric vehicles. Advances in photovoltaic (PV) inverters offer new opportunities for reactive power management provided PV owners have the right investment incentives. In this context, reactive power compensation is considered here as an ancillary service. Accounting for the increasing time-variability of distributed generation and demand, a stochastic reactive power compensation scheme is developed. Given uncertain active power injections, an online reactive control scheme is devised. This scheme is distribution-free and relies solely on power injection data. Reactive injections are updated using the Lagrange multipliers of a second-order cone program. Numerical tests on an industrial 47-bus microgrid and the residential IEEE 123-bus feeder corroborate the reactive power management efficiency of the novel stochastic scheme over its deterministic alternative, as well as its capability to track variations in solar generation and household demand.

math.OC↗

arXiv2014

Tunable Cobalt Vacancies and Related Properties in LaCoxAs2

The origin of transition metal vacancies and their effects on the properties of ThCr2Si2-type compounds have been less studied and poorly understood. Here we carefully investigate the structure, physical properties, and electronic structure for a series of lanthanum cobalt arsenides with nominal composition of LaCoxAs2 (1.6 < = x < = 2.1). It is revealed that the occupancy of Co can be tuned between 1.98(1) and 1.61(1). The structural analyses based on X-ray and neutron diffractions show the existence of Co vacancies results from charge balance due to the formation of bond between As-As. These Co vacancies affect the magnetic and electrical properties greatly, adjusting the Curie temperature from 205 to 47 K and increasing the resistivity by more than 100%. First principles calculations indicate that the Co vacancies weaken the spin polarization and reduce the density of states at the Fermi level, resulting in decreased Curie temperature and increased resistivity, respectively. Our results address the importance of transition metal vacancies in ThCr2Si2-type materials and offer a reliable route to tune the magnetism of ThCr2Si2-type structure.

cond-mat.str-el↗

arXiv2014

Learning Invariant Color Features for Person Re-Identification

Matching people across multiple camera views known as person re-identification, is a challenging problem due to the change in visual appearance caused by varying lighting conditions. The perceived color of the subject appears to be different with respect to illumination. Previous works use color as it is or address these challenges by designing color spaces focusing on a specific cue. In this paper, we propose a data driven approach for learning color patterns from pixels sampled from images across two camera views. The intuition behind this work is that, even though pixel values of same color would be different across views, they should be encoded with the same values. We model color feature generation as a learning problem by jointly learning a linear transformation and a dictionary to encode pixel values. We also analyze different photometric invariant color spaces. Using color as the only cue, we compare our approach with all the photometric invariant color spaces and show superior performance over all of them. Combining with other learned low-level and high-level features, we obtain promising results in ViPER, Person Re-ID 2011 and CAVIAR4REID datasets.

cs.CV↗

arXiv2014

Orbit optimization and time delay interferometry for inclined ASTROD-GW formation with half-year precession-period

ASTROD-GW (ASTROD [Astrodynamical Space Test of Relativity using Optical Devices] optimized for Gravitational Wave detection) is a gravitational-wave mission with the aim of detecting gravitational waves from massive black holes, extreme mass ratio inspirals (EMRIs) and galactic compact binaries, together with testing relativistic gravity and probing dark energy and cosmology. Mission orbits of the 3 spacecrafts forming a nearly equilateral triangular array are chosen to be near the Sun-Earth Lagrange points L3, L4 and L5. The 3 spacecrafts range interferometrically with one another with arm length about 260 million kilometers. For 260 times longer arm length, the detection sensitivity of ASTROD-GW is 260 fold better than that of eLISA/NGO in the lower frequency region by assuming the same acceleration noise. Therefore, ASTROD-GW will be a better cosmological probe. In previous papers, we have worked out the time delay interferometry (TDI) for the ecliptic formation. To resolve the reflection ambiguity about the ecliptic plane in source position determination, we have changed the basic formation into slightly inclined formation with half-year precession-period. In this paper, we optimize a set of 10-year inclined ASTROD-GW mission orbits numerically using ephemeris framework starting at June 21, 2035, including cases of inclination angle is 0° (no inclination), 0.5°, 1.0°, 1.5°, 2.0°, 2.5° and 3.0°. We simulate the time delays of the first and second generation TDI configurations for the different inclinations, and compare/analyse the numerical results to attain the requisite sensitivity of ASTROD-GW by suppressing laser frequency noise below the secondary noises. To explicate our calculation process for different inclination cases, we take the 1.0° as example to show the orbit optimization and TDI simulation.

gr-qc↗