arXiv subjects

Han Zhang

Publications and source records attributed to Han Zhang.

At least 451 records · Page 25Linked to original sources

arXiv2019

Building change detection based on multi-scale filtering and grid partition

Building change detection is of great significance in high resolution remote sensing applications. Multi-index learning, one of the state-of-the-art building change detection methods, still has drawbacks like incapability to find change types directly and heavy computation consumption of MBI. In this paper, a two-stage building change detection method is proposed to address these problems. In the first stage, a multi-scale filtering building index (MFBI) is calculated to detect building areas in each temporal with fast speed and moderate accuracy. In the second stage, images and the corresponding building maps are partitioned into grids. In each grid, the ratio of building areas in time T2 and time T1 is calculated. Each grid is classified into one of the three change patterns, i.e., significantly increase, significantly decrease and approximately unchanged. Exhaustive experiments indicate that the proposed method can detect building change types directly and outperform the current multi-index learning method.

eess.IV↗

arXiv2019

Multiple instance dense connected convolution neural network for aerial image scene classification

With the development of deep learning, many state-of-the-art natural image scene classification methods have demonstrated impressive performance. While the current convolution neural network tends to extract global features and global semantic information in a scene, the geo-spatial objects can be located at anywhere in an aerial image scene and their spatial arrangement tends to be more complicated. One possible solution is to preserve more local semantic information and enhance feature propagation. In this paper, an end to end multiple instance dense connected convolution neural network (MIDCCNN) is proposed for aerial image scene classification. First, a 23 layer dense connected convolution neural network (DCCNN) is built and served as a backbone to extract convolution features. It is capable of preserving middle and low level convolution features. Then, an attention based multiple instance pooling is proposed to highlight the local semantics in an aerial image scene. Finally, we minimize the loss between the bag-level predictions and the ground truth labels so that the whole framework can be trained directly. Experiments on three aerial image datasets demonstrate that our proposed methods can outperform current baselines by a large margin.

cs.CV↗

arXiv2019

DynWalks: Global Topology and Recent Changes Awareness Dynamic Network Embedding

Learning topological representation of a network in dynamic environments has recently attracted considerable attention due to the time-evolving nature of many real-world networks i.e. nodes/links might be added/removed as time goes on. Dynamic network embedding aims to learn low dimensional embeddings for unseen and seen nodes by using any currently available snapshots of a dynamic network. For seen nodes, the existing methods either treat them equally important or focus on the $k$ most affected nodes at each time step. However, the former solution is time-consuming, and the later solution that relies on incoming changes may lose the global topology---an important feature for downstream tasks. To address these challenges, we propose a dynamic network embedding method called DynWalks, which includes two key components: 1) An online network embedding framework that can dynamically and efficiently learn embeddings based on the selected nodes; 2) A novel online node selecting scheme that offers the flexible choices to balance global topology and recent changes, as well as to fulfill the real-time constraint if needed. The empirical studies on six real-world dynamic networks under three different slicing ways show that DynWalks significantly outperforms the state-of-the-art methods in graph reconstruction tasks, and obtains comparable results in link prediction tasks. Furthermore, the wall-clock time and complexity analysis demonstrate its excellent time and space efficiency. The source code of DynWalks is available at https://github.com/houchengbin/DynWalks

cs.SI↗

arXiv2019

Brain Network Construction and Classification Toolbox (BrainNetClass)

Brain functional network has become an increasingly used approach in understanding brain functions and diseases. Many network construction methods have been developed, whereas the majority of the studies still used static pairwise Pearson's correlation-based functional connectivity. The goal of this work is to introduce a toolbox namely "Brain Network Construction and Classification" (BrainNetClass) to the field to promote more advanced brain network construction methods. It comprises various brain network construction methods, including some state-of-the-art methods that were recently developed to capture more complex interactions among brain regions along with connectome feature extraction, reduction, parameter optimization towards network-based individualized classification. BrainNetClass is a MATLAB-based, open-source, cross-platform toolbox with graphical user-friendly interfaces for cognitive and clinical neuroscientists to perform rigorous computer-aided diagnosis with interpretable result presentations even though they do not possess neuroimage computing and machine learning knowledge. We demonstrate the implementations of this toolbox on real resting-state functional MRI datasets. BrainNetClass (v1.0) can be downloaded from https://github.com/zzstefan/BrainNetClass.

q-bio.NC↗

arXiv2019

Self-Attention Generative Adversarial Networks

In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape.

stat.ML↗

arXiv2019

Multi-Antenna Channel Interpolation via Tucker Decomposed Extreme Learning Machine

Channel interpolation is an essential technique for providing high-accuracy estimation of the channel state information (CSI) for wireless systems design where the frequency-space structural correlations of multi-antenna channel are typically hidden in matrix or tensor forms. In this letter, a modified extreme learning machine (ELM) that can process tensorial data, or ELM model with tensorial inputs (TELM), is proposed to handle the channel interpolation task. The TELM inherits many good properties from ELMs. Based on the TELM, the Tucker decomposed extreme learning machine (TDELM) is proposed for further improving the performance. Furthermore, we establish a theoretical argument to measure the interpolation capability of the proposed learning machines. Experimental results verify that our proposed learning machines can achieve comparable mean squared error (MSE) performance against the traditional ELMs but with 15% shorter running time, and outperform the other methods for a 20% margin measured in MSE for channel interpolation.

eess.SP↗

arXiv2019

ERNIE: Enhanced Representation through Knowledge Integration

We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration). Inspired by the masking strategy of BERT, ERNIE is designed to learn language representation enhanced by knowledge masking strategies, which includes entity-level masking and phrase-level masking. Entity-level strategy masks entities which are usually composed of multiple words.Phrase-level strategy masks the whole phrase which is composed of several words standing together as a conceptual unit.Experimental results show that ERNIE outperforms other baseline methods, achieving new state-of-the-art results on five Chinese natural language processing tasks including natural language inference, semantic similarity, named entity recognition, sentiment analysis and question answering. We also demonstrate that ERNIE has more powerful knowledge inference capacity on a cloze test.

cs.CL↗

arXiv2019

Chiral phase transition from the Dyson-Schwinger equations in a finite spherical volume

Within the framework of Dyson-Schwinger equations and by means of Multiple Reflection Expansion, we study the finite volume effects on the chiral phase transition in a sphere, especially discuss its influence on the location of the possible critical end point (CEP). According to our calculations, when we take the sphere instead of cube as a research, the influence of finite volume effects on phase transition is not as significant as previously calculated. For instance, as the radius of spherical volume decreases from infinite to $2 \mathrm{fm}$, at zero chemical potential and finite temperature, the critical temperature $T_{c}$ has only a slight drop. And at finite chemical potential and finite temperature, the location of CEP shifts toward smaller temperature and higher chemical potential, but the amplitude of variation does not exceed $20\%$. So we find that not only the size of the volume, but also the shape of the volume will have a considerable impact on the phase transition.

hep-ph↗

arXiv2019

Signal Demodulation with Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset and Algorithms

In this paper, we investigate the design and implementation of machine learning (ML) based demodulation methods in the physical layer of visible light communication (VLC) systems. We build a flexible hardware prototype of an end-to-end VLC system, from which the received signals are collected as the real data. The dataset is available online, which contains eight types of modulated signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Specifically, the CNN based demodulator converts the modulated signals to images and recognizes the signals by the image classification. The proposed DBN based demodulator contains three restricted Boltzmann machines (RBMs) to extract the modulation features. The AdaBoost method includes a strong classifier that is constructed by the weak classifiers with the k-nearest neighbor (KNN) algorithm. These three demodulators are trained and tested by our online open dataset. Experimental results show that the demodulation accuracy of the three data-driven demodulators drops as the transmission distance increases. A higher modulation order negatively influences the accuracy for a given transmission distance. Among the three ML methods, the AdaBoost modulator achieves the best performance.

eess.SP↗

arXiv2019

Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics

In this paper, we investigate deep learning (DL)-enabled signal demodulation methods and establish the first open dataset of real modulated signals for wireless communication systems. Specifically, we propose a flexible communication prototype platform for measuring real modulation dataset. Then, based on the measured dataset, two DL-based demodulators, called deep belief network (DBN)-support vector machine (SVM) demodulator and adaptive boosting (AdaBoost) based demodulator, are proposed. The proposed DBN-SVM based demodulator exploits the advantages of both DBN and SVM, i.e., the advantage of DBN as a feature extractor and SVM as a feature classifier. In DBN-SVM based demodulator, the received signals are normalized before being fed to the DBN network. Furthermore, an AdaBoost based demodulator is developed, which employs the $k$-Nearest Neighbor (KNN) as a weak classifier to form a strong combined classifier. Finally, experimental results indicate that the proposed DBN-SVM based demodulator and AdaBoost based demodulator are superior to the single classification method using DBN, SVM, and maximum likelihood (MLD) based demodulator.

eess.SP↗

arXiv2019

Experimental test of contextuality in quantum and classical systems

Contextuality is considered as an intrinsic signature of non-classicality, and a crucial resource for achieving unique advantages of quantum information processing. However, recently there have been debates on whether classical fields may also demonstrate contextuality. Here we experimentally configure a contextuality test for optical fields, adopting various definitions of measurement events, and analyse how the definitions affect the emergence of non-classical correlations. The heralded single photon state, a typical non-classical light field, manifests contextuality in our setup, while contextuality for classical coherent fields strongly depends on the specific definition of measurement events which is equivalent to filtering the non-classical component of the input state. Our results highlight the importance of definition of measurement events to demonstrate contextuality, and link the contextual correlations to non-classicality defined by quasi-probabilities in phase space.

quant-ph↗

arXiv2019

Transmission of Video Image in Underwater Acoustic Communication

Acoustic communication is currently considered as the best way to transmit information over long distances under water, since acoustic waves have lower attenuation in water than the other information transmission media. However, current information transmission rates of the underwater acoustic communication is usually limited by low-frequency bandwidth due to the large attenuation which characterized in high-frequency sound waves in water. Spiral acoustic beams with helical wavefront dislocations carrying orbital angular momentum have been applied to acoustic levitation and capture. Recently, harnessing the orthogonality and the infinite dimension of Hilbert space in underwater acoustic communication has been considered promising. Here, we construct a real-time underwater acoustic communication system to transmit video image. The system multiplexes eight orbital angular momentum topology charges ranging from +1 to +8 and encoded with on-off keying modulation format, achieving a spectral efficiency of 8 (bit/s)/Hz. This demonstration suggests that multiplexed information-carrying orbital angular momentum acoustic beams presents tremendous potential for increasing the capacity of underwater communication systems.

eess.SP↗

arXiv2019

Ferroelectric driven exciton and trion modulation in monolayer MoSe2 and WSe2

In this work, we show how domain engineered lithium niobate can be used to selectively dope monolayer MoSe2 and WSe2 and demonstrate that these ferroelectric domains can significantly enhance or inhibit photoluminescence (PL) with the most dramatic modulation occurring at the heterojunction interface between two domains. A micro-PL and Raman system is used to obtain spatially resolved images of the differently doped transition metal dichalcogenides (TMDs). The domain inverted lithium niobate causes changes in the TMDs due to electrostatic doping as a result of the remnant polarization from the substrate. Moreover, the differently doped TMDs (n-type MoSe2 and p-type WSe2) exhibit opposite PL modulation. Distinct oppositely charged domains were obtained with a 9-fold PL enhancement for the same single MoSe2 sheet when adhered to the positive (P+) and negative (P-) domains. This sharp PL modulation on the ferroelectric domain results from different free electron or hole concentrations in the materials conduction band or valence band. Moreover, excitons dissociate rapidly at the interface between the P+ and P- domains due to the built-in electric field. We are able to adjust the charge on the P+ and P- domains using temperature via the pyroelectric effect and observe rapid PL quenching over a narrow temperature range illustrating the observed PL modulation is electronic in nature. This observation creates an opportunity to harness the direct bandgap TMD 2D materials as an active optical component for the lithium niobate platform using domain engineering of the lithium niobate substrate to create optically active heterostructures that could be used for photodetectors or even electrically driven optical sources on-chip.

cond-mat.mtrl-sci↗

arXiv2019

On-Chip Chiral Single-Photon Interface: Isolation and Unidirectional Emission

Chiral quantum systems have received intensive attention in fundamental physics and applications in quantum information processing including optical isolation and photon unidirectional emission. Here, we design an on-chip emitter-resonator system with strong chiral light-matter interaction for a chiral single-photon interface. The system includes a microring resonator with a strong evanescent field and a near-unity optical chirality along both of the whole outside and inside walls, allowing a strong and chiral coupling of the Whispering-Gallery mode to a quantum emitter. By initializing a quantum dot in a specific spin ground state or shifting the transition energy with a polarization-selective optical Stark effect, we show a broadband optical isolation at the single-photon level over several GHz. Furthermore, a quantum emitter chirally coupling to the microring resonator can emit single photons unidirectionally. Our protocol paves a way to realize multifunctional chiral single-photon interface in on-chip quantum information processing and quantum networks.

quant-ph↗

arXiv2018

High-speed and high-responsivity hybrid silicon/black-phosphorus waveguide photodetectors at 2 μm

Silicon photonics is being extended from the near-infrared (near-IR) window of 1.3-1.5 μm for optical fiber communications to the mid-infrared (mid-IR) wavelength-band of 2 μm or longer for satisfying the increasing demands in many applications. Mid-IR waveguide photodetectors on silicon have attracted intensive attention as one of the indispensable elements for various photonic systems. Previously high-performance waveguide photodetectors on silicon were realized for the near-IR window of 1.3-1.5 μm by introducing another semiconductor material (e.g., Ge, and III-V compounds) in the active region. Unfortunately, these traditional semiconductor materials do not work well for the wavelength of ~2 μm or longer because the light absorption becomes very weak. As an alternative, two-dimensional materials provide a new and promising option for enabling active photonic devices on silicon. Here black-phosphorus (BP) thin films with optimized medium thicknesses (~40 nm) are introduced as the active material for light absorption and silicon/BP hybrid ridge waveguide photodetectors are demonstrated with a high responsivity at a low bias voltage. And up to 4.0Gbps data transmission is achieved at 2μm.

physics.app-ph↗

arXiv2018

Projected Primal-Dual Gradient Flow of Augmented Lagrangian with Application to Distributed Maximization of the Algebraic Connectivity of a Network

In this paper, a projected primal-dual gradient flow of augmented Lagrangian is presented to solve convex optimization problems that are not necessarily strictly convex. The optimization variables are restricted by a convex set with computable projection operation on its tangent cone as well as equality constraints. As a supplement of the analysis in \cite{niederlander2016distributed}, we show that the projected dynamical system converges to one of the saddle points and hence finding an optimal solution. Moreover, the problem of distributedly maximizing the algebraic connectivity of an undirected network by optimizing the port gains of each nodes (base stations) is considered. The original semi-definite programming (SDP) problem is relaxed into a nonlinear programming (NP) problem that will be solved by the aforementioned projected dynamical system. Numerical examples show the convergence of the aforementioned algorithm to one of the optimal solutions. The effect of the relaxation is illustrated empirically with numerical examples. A methodology is presented so that the number of iterations needed to reach the equilibrium is suppressed. Complexity per iteration of the algorithm is illustrated with numerical examples.

math.OC↗

arXiv2018

Inverse Quadratic Optimal Control for Discrete-Time Linear Systems

In this paper, we consider the inverse optimal control problem for the discrete-time linear quadratic regulator, over finite-time horizons. Given observations of the optimal trajectories, and optimal control inputs, to a linear time-invariant system, the goal is to infer the parameters that define the quadratic cost function. The well-posedness of the inverse optimal control problem is first justified. In the noiseless case, when these observations are exact, we analyze the identifiability of the problem and provide sufficient conditions for uniqueness of the solution. In the noisy case, when the observations are corrupted by additive zero-mean noise, we formulate the problem as an optimization problem and prove the statistical consistency of the problem later. The performance of the proposed method is illustrated through numerical examples.

math.OC↗

arXiv2018

A Unified Mammogram Analysis Method via Hybrid Deep Supervision

Automatic mammogram classification and mass segmentation play a critical role in a computer-aided mammogram screening system. In this work, we present a unified mammogram analysis framework for both whole-mammogram classification and segmentation. Our model is designed based on a deep U-Net with residual connections, and equipped with the novel hybrid deep supervision (HDS) scheme for end-to-end multi-task learning. As an extension of deep supervision (DS), HDS not only can force the model to learn more discriminative features like DS, but also seamlessly integrates segmentation and classification tasks into one model, thus the model can benefit from both pixel-wise and image-wise supervisions. We extensively validate the proposed method on the widely-used INbreast dataset. Ablation study corroborates that pixel-wise and image-wise supervisions are mutually beneficial, evidencing the efficacy of HDS. The results of 5-fold cross validation indicate that our unified model matches state-of-the-art performance on both mammogram segmentation and classification tasks, which achieves an average segmentation Dice similarity coefficient (DSC) of 0.85 and a classification accuracy of 0.89. The code is available at https://github.com/angrypudding/hybrid-ds.

cs.CV↗