arXiv subjects

Yan Zhang

Publications and source records attributed to Yan Zhang.

At least 667 records · Page 37Linked to original sources

arXiv2017

Role of the orbital degree of freedom in iron-based superconductors

Almost a decade has passed since the serendipitous discovery of the iron-based high temperature superconductors (FeSCs) in 2008. The question of how much similarity the FeSCs have with the copper oxide high temperature superconductors emerged since the initial discovery of long-range antiferromagnetism in the FeSCs in proximity to superconductivity. Despite the great resemblance in their phase diagrams, there exist important disparities between FeSCs and cuprates that need to be considered in order to paint a full picture of these two families of high temperature superconductors. One of the key differences lies in the multi-orbital multi-band nature of FeSCs, in contrast to the effective single-band model for cuprates. Due to the complexity of multi-orbital band structures, the orbital degree of freedom is often neglected in formulating the theoretical models for FeSCs. On the experimental side, systematic studies of the orbital related phenomena in FeSCs have been largely lacking. In this review, we summarize angle-resolved photoemission spectroscopy (ARPES) measurements across various FeSC families in literature, focusing on the systematic trend of orbital dependent electron correlations and the role of different Fe 3d orbitals in driving the nematic transition, the spin-density-wave transition, and implications for superconductivity.

cond-mat.supr-con↗

arXiv2017

SNR-based adaptive acquisition method for fast Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a computational imaging technique with both high resolution and large field-of-view. However, the effective numerical aperture (NA) achievable with a typical LED panel is ambiguous and usually relies on the repeated tests of different illumination NAs. The imaging quality of each raw image usually depends on the visual assessments, which is subjective and inaccurate especially for those dark field images. Moreover, the acquisition process is really time-consuming.In this paper, we propose a SNR-based adaptive acquisition method for quantitative evaluation and adaptive collection of each raw image according to the signal-to-noise ration (SNR) value, to improve the FPM's acquisition efficiency and automatically obtain the maximum achievable NA, reducing the time of collection, storage and subsequent calculation. The widely used EPRY-FPM algorithm is applied without adding any algorithm complexity and computational burden. The performance has been demonstrated in both USAF targets and biological samples with different imaging sensors respectively, which have either Poisson or Gaussian noises model. Further combined with the sparse LEDs strategy, the number of collection images can be shorten to around 25 frames while the former needs 361 images, the reduction ratio can reach over 90%. This method will make FPM more practical and automatic, and can also be used in different configurations of FPM.

eess.IV↗

arXiv2017

When Traffic Flow Prediction Meets Wireless Big Data Analytics

Traffic flow prediction is an important research issue for solving the traffic congestion problem in an Intelligent Transportation System (ITS). Traffic congestion is one of the most serious problems in a city, which can be predicted in advance by analyzing traffic flow patterns. Such prediction is possible by analyzing the real-time transportation data from correlative roads and vehicles. This article first gives a brief introduction to the transportation data, and surveys the state-of-the-art prediction methods. Then, we verify whether or not the prediction performance is able to be improved by fitting actual data to optimize the parameters of the prediction model which is used to predict the traffic flow. Such verification is conducted by comparing the optimized time series prediction model with the normal time series prediction model. This means that in the era of big data, accurate use of the data becomes the focus of studying the traffic flow prediction to solve the congestion problem. Finally, experimental results of a case study are provided to verify the existence of such performance improvement, while the research challenges of this data-analytics-based prediction are presented and discussed.

cs.AI↗

arXiv2017

Deep Learning for Secure Mobile Edge Computing

Mobile edge computing (MEC) is a promising approach for enabling cloud-computing capabilities at the edge of cellular networks. Nonetheless, security is becoming an increasingly important issue in MEC-based applications. In this paper, we propose a deep-learning-based model to detect security threats. The model uses unsupervised learning to automate the detection process, and uses location information as an important feature to improve the performance of detection. Our proposed model can be used to detect malicious applications at the edge of a cellular network, which is a serious security threat. Extensive experiments are carried out with 10 different datasets, the results of which illustrate that our deep-learning-based model achieves an average gain of 6% accuracy compared with state-of-the-art machine learning algorithms.

cs.CR↗

arXiv2017

Evidence of Electron-Hole Imbalance in WTe2 from High-Resolution Angle-Resolved Photoemission Spectroscopy

WTe2 has attracted a great deal of attention because it exhibits extremely large and nonsaturating magnetoresistance. The underlying origin of such a giant magnetoresistance is still under debate. Utilizing laser-based angle-resolved photoemission spectroscopy with high energy and momentum resolutions, we reveal the complete electronic structure of WTe2. This makes it possible to determine accurately the electron and hole concentrations and their temperature dependence. We find that, with increasing the temperature, the overall electron concentration increases while the total hole concentration decreases. It indicates that the electron-hole compensation, if it exists, can only occur in a narrow temperature range, and in most of the temperature range there is an electron-hole imbalance. Our results are not consistent with the perfect electron-hole compensation picture that is commonly considered to be the cause of the unusual magnetoresistance in WTe2. We identified a flat band near the Brillouin zone center that is close to the Fermi level and exhibits a pronounced temperature dependence. Such a flat band can play an important role in dictating the transport properties of WTe2. Our results provide new insight on understanding the origin of the unusual magnetoresistance in WTe2.

cond-mat.mtrl-sci↗

arXiv2017

Information Potential Auto-Encoders

In this paper, we suggest a framework to make use of mutual information as a regularization criterion to train Auto-Encoders (AEs). In the proposed framework, AEs are regularized by minimization of the mutual information between input and encoding variables of AEs during the training phase. In order to estimate the entropy of the encoding variables and the mutual information, we propose a non-parametric method. We also give an information theoretic view of Variational AEs (VAEs), which suggests that VAEs can be considered as parametric methods that estimate entropy. Experimental results show that the proposed non-parametric models have more degree of freedom in terms of representation learning of features drawn from complex distributions such as Mixture of Gaussians, compared to methods which estimate entropy using parametric approaches, such as Variational AEs.

cs.LG↗

arXiv2017

Predicting disease-related genes by path-based similarity and community structure in protein-protein interaction network

Network-based computational approaches to predict unknown genes associated with certain diseases are of considerable significance for uncovering the molecular basis of human diseases. In this paper, we proposed a kind of new disease-gene-prediction methods by combining the path-based similarity with the community structure in the human protein-protein interaction network. Firstly, we introduced a set of path-based similarity indices, a novel community-based similarity index, and a new similarity combining the path-based similarity index. Then we assessed the statistical significance of the measures in distinguishing the disease genes from non-disease genes, to confirm their availability in predicting disease genes. Finally, we applied these measures to the disease-gene prediction of single disease-gene family, and analyzed the performance of these measures in disease-gene prediction, especially the effect of the community structure on the prediction performance in detail. The results indicated that genes associated with the same or similar diseases commonly reside in the same community of the protein-protein interaction network, and the community structure is greatly helpful for the disease-gene prediction.

q-bio.MN↗

arXiv2017

Highly efficient quantitative phase microscopy using programmable annular LED illumination

In this work, we present a highly efficient quantitative phase imaging (QPI) approach using programmable annular LED illumination based on traditional bright-field microscope. As a new type of coded illumination, the LED array provides a flexible and compatible way to realize QPI. The proposed method modulates the transfer function of system by changing the LED illumination pattern, which achieves twice resolution of objective NA and gives noise-robust response of transfer function. The phase of a sample could be recovered from the intensity images with the inversion of transfer function. Moreover, the weak object transfer function (WOTF) of axisymmetric oblique source is derived, and the noise-free and noisy simulation results sufficiently validate the applicability of discrete annular source. The quantitative phase measurements of micro polystyrene bead and visible blazed transmission grating demonstrate the accuracy of proposed method. Finally, the experimental investigations of unstained human cancer cells using different types objective are presented, and these results show the possibility of widespread adoption of QPI in the morphology study of cellular processes and biomedical community.

physics.optics↗

arXiv2017

A Probabilistic Embedding Clustering Method for Urban Structure Detection

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by learning via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

cs.SI↗

arXiv2017

Reduced Electron Exposure for Energy-Dispersive Spectroscopy using Dynamic Sampling

Analytical electron microscopy and spectroscopy of biological specimens, polymers, and other beam sensitive materials has been a challenging area due to irradiation damage. There is a pressing need to develop novel imaging and spectroscopic imaging methods that will minimize such sample damage as well as reduce the data acquisition time. The latter is useful for high-throughput analysis of materials structure and chemistry. In this work, we present a novel machine learning based method for dynamic sparse sampling of EDS data using a scanning electron microscope. Our method, based on the supervised learning approach for dynamic sampling algorithm and neural networks based classification of EDS data, allows a dramatic reduction in the total sampling of up to 90%, while maintaining the fidelity of the reconstructed elemental maps and spectroscopic data. We believe this approach will enable imaging and elemental mapping of materials that would otherwise be inaccessible to these analysis techniques.

cs.LG↗

arXiv2017

Electronic structure of heavy fermion system CePt2In7 from angle-resolved photoemission spectroscopy

We have carried out high-resolution angle-resolved photoemission measurements on the Cebased heavy fermion compound CePt2In7 that exhibits stronger two-dimensional character than the prototypical heavy fermion system CeCoIn5. Multiple Fermi surface sheets and a complex band structure are clearly resolved. We have also performed detailed band structure calculations on CePt2In7. The good agreement found between our measurements and the calculations suggests that the band renormalization effect is rather weak in CePt2In7. A comparison of the common features of the electronic structure of CePt2In7 and CeCoIn5 indicates that CeCoIn5 shows a much stronger band renormalization effect than CePt2In7. These results provide new information for understanding the heavy fermion behaviors and unconventional superconductivity in Ce-based heavy fermion systems.

cond-mat.str-el↗

arXiv2017

Two Categories of Indoor Interactive Dynamics of a Large-scale Human Population in a WiFi covered university campus

To explore large-scale population indoor interactions, we analyze 18,715 users' WiFi access logs recorded in a Chinese university campus during 3 months, and define two categories of human interactions, the event interaction (EI) and the temporal interaction (TI). The EI helps construct a transmission graph, and the TI helps build an interval graph. The dynamics of EIs show that their active durations are truncated power-law distributed, which is independent on the number of involved individuals. The transmission duration presents a truncated power-law behavior at the daily timescale with weekly periodicity. Besides, those `leaf' individuals in the aggregated contact network may participate in the `super-connecting cliques' in the aggregated transmission graph. Analyzing the dynamics of the interval graph, we find that the probability distribution of TIs' inter-event duration also displays a truncated power-law pattern at the daily timescale with weekly periodicity, while the pairwise individuals with burst interactions are prone to randomly select their interactive locations, and those individuals with periodic interactions have preferred interactive locations.

cs.SI↗

arXiv2017

Data preprocessing methods for robust Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a recently proposed computational imaging technique with both high resolution and wide field-of-view. In current FP experimental setup, the dark-field images with high-angle illuminations are easily submerged by stray light and background noise due to the low signal-to-noise ratio, thus significantly degrading the reconstruction quality and also imposing a major restriction on the synthetic numerical aperture (NA) of the FP approach. To this end, an overall and systematic data preprocessing scheme for noise removal from FP's raw dataset is provided, which involves sampling analysis as well as underexposed/overexposed treatments, then followed by the elimination of unknown stray light and suppression of inevitable background noise, especially Gaussian noise and CCD dark current in our experiments. The reported non-parametric scheme facilitates great enhancements of the FP's performance, which has been demonstrated experimentally that the benefits of noise removal by these methods far outweigh its defects of concomitant signal loss. In addition, it could be flexibly cooperated with the existing state-of-the-art algorithms, producing a stronger robustness of the FP approach in various applications.

cs.CV↗

arXiv2017

Truncating Wide Networks using Binary Tree Architectures

Recent study shows that a wide deep network can obtain accuracy comparable to a deeper but narrower network. Compared to narrower and deeper networks, wide networks employ relatively less number of layers and have various important benefits, such that they have less running time on parallel computing devices, and they are less affected by gradient vanishing problems. However, the parameter size of a wide network can be very large due to use of large width of each layer in the network. In order to keep the benefits of wide networks meanwhile improve the parameter size and accuracy trade-off of wide networks, we propose a binary tree architecture to truncate architecture of wide networks by reducing the width of the networks. More precisely, in the proposed architecture, the width is continuously reduced from lower layers to higher layers in order to increase the expressive capacity of network with a less increase on parameter size. Also, to ease the gradient vanishing problem, features obtained at different layers are concatenated to form the output of our architecture. By employing the proposed architecture on a baseline wide network, we can construct and train a new network with same depth but considerably less number of parameters. In our experimental analyses, we observe that the proposed architecture enables us to obtain better parameter size and accuracy trade-off compared to baseline networks using various benchmark image classification datasets. The results show that our model can decrease the classification error of baseline from 20.43% to 19.22% on Cifar-100 using only 28% of parameters that baseline has. Code is available at https://github.com/ZhangVision/bitnet.

cs.CV↗

arXiv2017

Raman scattering study of tetragonal magnetic phase in Sr$_{1-x}$Na$_x$Fe$_2$As$_2$: structural symmetry and electronic gap

We use inelastic light scattering to study Sr$_{1-x}$Na$_x$Fe$_2$As$_2$ ($x\approx0.34$), which exhibits a robust tetragonal magnetic phase that restores the four-fold rotation symmetry inside the orthorhombic magnetic phase. With cooling, we observe splitting and recombination of an $E_g$ phonon peak upon entering the orthorhombic and tetragonal magnetic phases, respectively, consistent with the reentrant phase behavior. Our electronic Raman data reveal a pronounced feature that is clearly associated with the tetragonal magnetic phase, suggesting the opening of an electronic gap. No phonon back-folding behavior can be detected above the noise level, which implies that any lattice translation symmetry breaking in the tetragonal magnetic phase must be very weak.

cond-mat.supr-con↗

arXiv2017

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures which bring network functions and contents to the network edge are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks including definition, architecture and advantages. Next, a comprehensive survey of issues on computing, caching and communication techniques at the network edge is presented respectively. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks such as cloud technology, SDN/NFV and smart devices are discussed. Finally, open research challenges and future directions are presented as well.

cs.NI↗

arXiv2017

System calibration method for Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a recently proposed quantitative phase imaging technique with high resolution and wide field-of-view (FOV). In current FPM imaging platforms, systematic error sources come from the aberrations, LED intensity fluctuation, parameter imperfections and noise, which will severely corrupt the reconstruction results with artifacts. Although these problems have been researched and some special methods have been proposed respectively, there is no method to solve all of them. However, the systematic error is a mixture of various sources in the real situation. It is difficult to distinguish a kind of error source from another due to the similar artifacts. To this end, we report a system calibration procedure, termed SC-FPM, based on the simulated annealing (SA) algorithm, LED intensity correction and adaptive step-size strategy, which involves the evaluation of an error matric at each iteration step, followed by the re-estimation of accurate parameters. The great performance has been achieved both in simulation and experiments. The reported system calibration scheme improves the robustness of FPM and relaxes the experiment conditions, which makes the FPM more pragmatic.

physics.ins-det↗

arXiv2017

Community detection based on significance optimization in complex networks

Community structure is an important structural property that extensively exists in various complex networks. In the past decade, much attention has been paid to the design of community-detection methods, but analyzing the behaviors of the methods is also of interest in the theoretical research and real applications. Here, we focus on an important measure for community structure, significance [Sci. Rep. 3 (2013) 2930]. Specifically, we study the effect of various network parameters on this measure in detail, analyze the critical behaviors of it in partition transition, and analytically give the formula of the critical points and the phase diagrams. The results shows that the critical number of communities in partition transition increases dramatically with the difference between inter- and intra-community link densities, and thus significance optimization displays higher resolution in community detection than many other methods, but it is also easily to lead to the excessive splitting of communities. By Louvain algorithm for significance optimization, we confirmed the theoretical results on artificial and real-world networks, and give a series of comparisons with some classical methods.

physics.soc-ph↗