arXiv subjects

Wenjun Zhang

Publications and source records attributed to Wenjun Zhang.

At least 235 records · Page 13Linked to original sources

arXiv2021

The characteristics of cycle-nodes-ratio and its application to network classification

Cycles, which can be found in many different kinds of networks, make the problems more intractable, especially when dealing with dynamical processes on networks. On the contrary, tree networks in which no cycle exists, are simplifications and usually allow for analyticity. There lacks a quantity, however, to tell the ratio of cycles which determines the extent of network being close to tree networks. Therefore we introduce the term Cycle Nodes Ratio (CNR) to describe the ratio of number of nodes belonging to cycles to the number of total nodes, and provide an algorithm to calculate CNR. CNR is studied in both network models and real networks. The CNR remains unchanged in different sized Erdös Rényi (ER) networks with the same average degree, and increases with the average degree, which yields a critical turning point. The approximate analytical solutions of CNR in ER networks are given, which fits the simulations well. Furthermore, the difference between CNR and two-core ratio (TCR) is analyzed. The critical phenomenon is explored by analysing the giant component of networks. We compare the CNR in network models and real networks, and find the latter is generally smaller. Combining the coarse-graining method can distinguish the CNR structure of networks with high average degree. The CNR is also applied to four different kinds of transportation networks and fungal networks, which give rise to different zones of effect. It is interesting to see that CNR is very useful in network recognition of machine learning.

physics.soc-ph↗

arXiv2021

Evidence of Potts-Nematic Superfluidity in a Hexagonal $sp^2$ Optical Lattice

As in between liquid and crystal phases lies a nematic liquid crystal, which breaks rotation with preservation of translation symmetry, there is a nematic superfluid phase bridging a superfluid and a supersolid. The nematic order also emerges in interacting electrons and has been found to largely intertwine with multi-orbital correlation in high-temperature superconductivity, where Ising nematicity arises from a four-fold rotation symmetry $C_4$ broken down to $C_2$. Here we report an observation of a three-state ($\mathbb{Z}_3$) quantum nematic order, dubbed "Potts-nematicity", in a system of cold atoms loaded in an excited band of a hexagonal optical lattice described by an $sp^2$-orbital hybridized model. This Potts-nematic quantum state spontaneously breaks a three-fold rotation symmetry of the lattice, qualitatively distinct from the Ising nematicity. Our field theory analysis shows that the Potts-nematic order is stabilized by intricate renormalization effects enabled by strong inter-orbital mixing present in the hexagonal lattice. This discovery paves a way to investigate quantum vestigial orders in multi-orbital atomic superfluids.

cond-mat.quant-gas↗

arXiv2020

Uplink Transmission Design for Crowded Correlated Cell-Free Massive MIMO-OFDM Systems

In cell-free massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, user equipments (UEs) are served by many distributed access points (APs), where channels are correlated due to finite angle-delay spread in realistic outdoor wireless propagation environments. Meanwhile, the number of UEs is growing rapidly for future fully networked society. In this paper, we focus on the uplink transmission design in crowded correlated cell-free massive MIMO-OFDM systems with limited number of orthogonal pilots. For the pilot transmission phase, we identify active UEs based on non-orthogonal pilot phase shift hopping patterns and non-orthogonal adjustable phase shift pilots (APSP). We derive a closed-form expression of mean square error of channel estimation (MSE-CE) and obtain an optimal condition for minimizing MSE-CE. According to this condition, the APSP set allocation scheme is proposed. Furthermore, for the data transmission, the max-min power control algorithm is devised to maximize the minimum spectral efficiency (SE) lower bound among active UEs. Simulation results indicate significant performance gains in terms of MSE-CE for the proposed APSP set allocation scheme. The proposed power control scheme can further improve the minimum SE among active UEs. Hence, they are crucial for crowded correlated cell-free massive MIMO-OFDM systems.

cs.IT↗

arXiv2020

Massive Unsourced Random Access for Massive MIMO Correlated Channels

This paper investigates the massive random access for a huge amount of user devices served by a base station (BS) equipped with a massive number of antennas. We consider a grant-free unsourced random access (U-RA) scheme where all users possess the same codebook and the BS aims at declaring a list of transmitted codewords and recovering the messages sent by active users. Most of the existing works concentrate on applying U-RA in the oversimplified independent and identically distributed (i.i.d.) channels. In this paper, we consider a fairly general joint-correlated MIMO channel model with line-of-sight components for the realistic outdoor wireless propagation environments. We conduct the activity detection for the emitted codewords by performing an improved coordinate descent approach with Bayesian learning automaton to solve a covariance-based maximum likelihood estimation problem. The proposed algorithm exhibits a faster convergence rate than traditional descent approaches. We further employ a coupled coding scheme to resolve the issue that the dimensions of the common codebook expand exponentially with user payload size in the practical massive machine-type communications scenario. Our simulations reveal that to achieve an error probability of 0.05 for reliable communications in correlated channels, one must pay a 0.9 to 1.3 dB penalty comparing to the minimum signal to noise ratio needed in i.i.d. channels on condition that a sufficient number of receiving antennas is equipped at the BS.

cs.IT↗

arXiv2020

Energy-efficiency of Massive Random Access with Individual Codebook

The massive machine-type communication has been one of the most representative services for future wireless networks. It aims to support massive connectivity of user equipments (UEs) which sporadically transmit packets with small size. In this work, we assume the number of UEs grows linearly and unboundedly with blocklength and each UE has an individual codebook. Among all UEs, an unknown subset of UEs are active and transmit a fixed number of data bits to a base station over a shared-spectrum radio link. Under these settings, we derive the achievability and converse bounds on the minimum energy-per-bit for reliable random access over quasi-static fading channels with and without channel state information (CSI) at the receiver. These bounds provide energy-efficiency guidance for new schemes suited for massive random access. Simulation results indicate that the orthogonalization scheme TDMA is energy-inefficient for large values of UE density $μ$. Besides, the multi-user interference can be perfectly cancelled when $μ$ is below a critical threshold. In the case of no-CSI, the energy-per-bit for random access is only a bit more than that with the knowledge UE activity.

cs.IT↗

arXiv2020

Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation

Transferring knowledges learned from multiple source domains to target domain is a more practical and challenging task than conventional single-source domain adaptation. Furthermore, the increase of modalities brings more difficulty in aligning feature distributions among multiple domains. To mitigate these problems, we propose a Learning to Combine for Multi-Source Domain Adaptation (LtC-MSDA) framework via exploring interactions among domains. In the nutshell, a knowledge graph is constructed on the prototypes of various domains to realize the information propagation among semantically adjacent representations. On such basis, a graph model is learned to predict query samples under the guidance of correlated prototypes. In addition, we design a Relation Alignment Loss (RAL) to facilitate the consistency of categories' relational interdependency and the compactness of features, which boosts features' intra-class invariance and inter-class separability. Comprehensive results on public benchmark datasets demonstrate that our approach outperforms existing methods with a remarkable margin. Our code is available at \url{https://github.com/ChrisAllenMing/LtC-MSDA}

cs.CV↗

arXiv2020

Toward Better Understanding of Saliency Prediction in Augmented 360 Degree Videos

Augmented reality (AR) overlays digital content onto the reality. In AR system, correct and precise estimations of user's visual fixations and head movements can enhance the quality of experience by allocating more computation resources on the areas of interest. However, there is inadequate research about understanding the visual exploration of users when using an AR system or modeling AR visual attention. To bridge the gap between the saliency prediction on real-world scene and on scene augmented by virtual information, we construct the ARVR saliency dataset with 12 diverse videos viewed by 20 people. The virtual reality (VR) technique is employed to simulate the real-world. Annotations of object recognition and tracking as augmented contents are blended into the omnidirectional videos. The saliency annotations of head and eye movements for both original and augmented videos are collected and together constitute the ARVR dataset. We also design a model which is capable of solving the saliency prediction problem in AR. Local block images are extracted to simulate the viewport and offset the projection distortion. Conspicuous visual cues in local viewports are extracted to constitute the spatial features. The optical flow information is estimated as the important temporal feature. We also consider the interplay between virtual information and reality. The composition of the augmentation information is distinguished, and the joint effects of adversarial augmentation and complementary augmentation are estimated. We generate a graph by taking each block image as one node. Both the visual saliency mechanism and the characteristics of viewing behaviors are considered in the computation of edge weights on the graph which are interpreted as Markov chains. The fraction of the visual attention that is diverted to each block image is estimated through equilibrium distribution on of this chain.

eess.IV↗

arXiv2020

Polar Coding and Sparse Spreading for Massive Unsourced Random Access

In this paper, we propose a new polar coding scheme for the unsourced, uncoordinated Gaussian random access channel. Our scheme is based on sparse spreading, treat interference as noise and successive interference cancellation (SIC). On the transmitters side, each user randomly picks a code-length and a transmit power from multiple choices according to some probability distribution to encode its message, and an interleaver to spread its encoded codeword bits across the entire transmission block. The encoding configuration of each user is transmitted by compressive sensing, similar to some previous works. On the receiver side, after recovering the encoding configurations of all users, it applies single-user polar decoding and SIC to recover the message list. Numerical results show that our scheme outperforms all previous schemes for active user number $K_a\geq 250$, and provides competitive performance for $K_a\leq 225$. Moreover, our scheme has much lower complexity compared to other schemes as we only use single-user polar coding.

cs.IT↗

arXiv2020

Pricing variance swaps with stochastic volatility and stochastic interest rate under full correlation structure

This paper considers the case of pricing discretely-sampled variance swaps under the class of equity-interest rate hybridization. Our modeling framework consists of the equity which follows the dynamics of the Heston stochastic volatility model, and the stochastic interest rate is driven by the Cox-Ingersoll-Ross (CIR) process with full correlation structure imposed among the state variables. This full correlation structure possess the limitation to have fully analytical pricing formula for hybrid models of variance swaps, due to the non-affinity property embedded in the model itself. We address this issue by obtaining an efficient semi-closed form pricing formula of variance swaps for an approximation of the hybrid model via the derivation of characteristic functions. Subsequently, we implement numerical experiments to evaluate the accuracy of our pricing formula. Our findings confirmed that the impact of the correlation between the underlying and the interest rate is significant for pricing discretely-sampled variance swaps.

q-fin.PR↗

arXiv2020

Cross-domain Detection via Graph-induced Prototype Alignment

Applying the knowledge of an object detector trained on a specific domain directly onto a new domain is risky, as the gap between two domains can severely degrade model's performance. Furthermore, since different instances commonly embody distinct modal information in object detection scenario, the feature alignment of source and target domain is hard to be realized. To mitigate these problems, we propose a Graph-induced Prototype Alignment (GPA) framework to seek for category-level domain alignment via elaborate prototype representations. In the nutshell, more precise instance-level features are obtained through graph-based information propagation among region proposals, and, on such basis, the prototype representation of each class is derived for category-level domain alignment. In addition, in order to alleviate the negative effect of class-imbalance on domain adaptation, we design a Class-reweighted Contrastive Loss to harmonize the adaptation training process. Combining with Faster R-CNN, the proposed framework conducts feature alignment in a two-stage manner. Comprehensive results on various cross-domain detection tasks demonstrate that our approach outperforms existing methods with a remarkable margin. Our code is available at https://github.com/ChrisAllenMing/GPA-detection.

cs.CV↗

arXiv2020

Chinese cities' air quality pattern and correlation

Air quality impacts people's health and daily life, affects the sensitive ecosystems, and even restrains a country's development. By collecting and processing the time series data of Air Quality Index (AQI) of 363 cities of China from Jan. 2015 to Mar. 2019, we dedicated to characterize the universal patterns, the clustering and correlation of air quality of different cities by using the methods of complex network and time series analysis. The main results are as follows: 1) The Air Quality Network of China (AQNC) is constructed by using the Planar Maximally Filtered Graph (PMFG) method. The geographical distances on the correlation of air quality of different cities have been studied, it is found that 100 km is a critical distance for strong correlation. 2) Seven communities of AQNC have been detected, and their patterns have been analyzed by taking into account the Hurst exponent and climate environment, it is shown that the seven communities are reasonable, and they are significantly influenced by the climate factors, such as monsoon, precipitation, geographical regions, etc. 3) The motifs of air quality time series of seven communities have been investigated by the visibility graph, for some communities, the evolutionary patterns of the motifs are a bit stable, and they have the long-term memory effects. While for others, there are no stable patterns.

physics.soc-ph↗

arXiv2020

Implementation of a double-path multimode interferometer using a spinor Bose-Einstein condensate

We realize a double-path multimode matter wave interferometer with spinor Bose-Einstein condensate and observe clear spatial interference fringes as well as a periodic change of the visibility in the time domain, which we refer to as the time domain interference and which is different from the traditional double-path interferometer. By changing the relative phase of the two paths, we find that the spatial fringes first lose coherence and recover. As the number of modes increases, the time domain interference signal with the narrower peaks is observed, which is beneficial to the improvement of the resolution of the phase measurement. We also investigated the influence of initial phase configuration and phase evolution rate between different modes in the two paths. With enhanced resolution, the sensitivity of interferometric measurements of physical observables can also be improved by properly assigning measurable quantities to the relative phase between two paths.

physics.atom-ph↗

arXiv2019

Adversarial Domain Adaptation with Domain Mixup

Recent works on domain adaptation reveal the effectiveness of adversarial learning on filling the discrepancy between source and target domains. However, two common limitations exist in current adversarial-learning-based methods. First, samples from two domains alone are not sufficient to ensure domain-invariance at most part of latent space. Second, the domain discriminator involved in these methods can only judge real or fake with the guidance of hard label, while it is more reasonable to use soft scores to evaluate the generated images or features, i.e., to fully utilize the inter-domain information. In this paper, we present adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples' difference relative to source and target domains. Domain mixup is jointly conducted on pixel and feature level to improve the robustness of models. Extensive experiments prove that the proposed approach can achieve superior performance on tasks with various degrees of domain shift and data complexity.

cs.CV↗

arXiv2019

Double-Slit Interferometer Measurements at SPEAR3

The resolution of a conventional telescope used to image visible-light synchrotron radiation is often limited by diffraction effects. To improve resolution, the double-slit interferometer method was developed at KEK and has since become popular around the world. Based on the Van Cittert-Zernike theorem relating transverse source profile to transverse spatial coherence, the particle beam size can be inferred by recording fringe contrast as a function of interferometer slit separation. In this paper, we describe the SPEAR3 double-slit interferometer, develop a theoretical framework for the interferometer and provide experimental results. Of note the double-slit system is 'rotated' about the beam axis to map the dependence of photon beam coherence on angle.

physics.acc-ph↗

arXiv2019

Statistical Properties of Links of Network: A Survey on the Shipping Lines of Worldwide Marine Transport Network

Node properties and node importance identification of networks have been vastly studied in the last decades. While in this work, we analyse the links' properties of networks by taking the Worldwide Marine Transport Network (WMTN) as an example, i.e., statistical properties of the shipping lines of WMTN have been investigated in various aspects: Firstly, we study the feature of loops in the shipping lines by defining the line saturability. It is found that the line saturability decays exponentially with the increase of line length. Secondly, to detect the geographical community structure of shipping lines, the Label Propagation Algorithm with compression of Flow (LPAF) and Multi-Dimensional Scaling (MDS) method are employed, which show rather consistent communities. Lastly, to analyse the redundancy property of shipping lines of different marine companies, the multilayer networks were constructed by aggregating the shipping lines of different marine companies. It is observed that the topological quantities, such as average degree, average clustering coefficient, etc., increase smoothly when marine companies are randomly merged (randomly choose two marine companies, then merge the shipping lines of them together), while the relative entropy decreases when the merging sequence is determined by the Jensen-Shannon distance (choose two marine companies when the J-S distance between them is the lowest). This indicates the low redundancy of shipping lines among different marine companies.

physics.soc-ph↗

arXiv2019

Asymmetric population of momentum distribution by quasi-periodically driving a triangular optical lattice

Ultracold atoms in periodical driven optical lattices enable us to investigate novel band structures and explore the topology of the bands. In this work, we investigate the impact of the ramping process of the driving signal and propose a simple but effective method to realize desired asymmetric population in momentum distribution by controlling the initial phase of the driving signal. A quasi-momentum oscillation along the shaking direction in the frame of reference co-moving with the lattice is formed, causing the formation of the mix of ground energy band and first excited band in laboratory frame, within the regime that the driving frequency is far less than the coupling frequency between ground band and higher energy bands. This method avoids the construction of intricate lattices or complex control sequence. With a triangular lattice, we experimentally investigate the influence of the initial phase, frequency, amplitude of the driving signal on the population difference, and observe good agreement with our theoretical model. This provides guidance on how to load a driving signal in driven optical lattice experiment and also potentially supplies a useful tool to form a qubit that can be used in quantum computation.

cond-mat.quant-gas↗

arXiv2019

An Algorithm for Transmitting VR Video Based on Adaptive Modulation

Virtual reality (VR) is making waves around the world recently. However, traditional video streaming is not suitable for VR video because of the huge size and view switch requirements of VR videos. Since the view of each user is limited, it is unnecessary to send the whole 360-degree scene at high quality which can be a heavy burden for the transmission system. Assuming filed-of-view (FoV) of each user can be predicted with high probability, we can divide the video screen into partitions and send those partitions which will appear in FoV at high quality. Hence, we propose an novel strategy for VR video streaming. First, we define a quality-of-experience metric to measure the viewing experience of users and define a channel model to reflect the fluctuation of the wireless channel. Next, we formulate the optimization problem and find its feasible solution by convex optimization. In order to improve bandwidth efficiency, we also add adaptive modulation to this part. Finally, we compare our algorithm with other VR streaming algorithm in the simulation. It turns out that our algorithm outperforms other algorithms.

cs.NI↗

arXiv2019

Towards Locally Consistent Object Counting with Constrained Multi-stage Convolutional Neural Networks

High-density object counting in surveillance scenes is challenging mainly due to the drastic variation of object scales. The prevalence of deep learning has largely boosted the object counting accuracy on several benchmark datasets. However, does the global counts really count? Armed with this question we dive into the predicted density map whose summation over the whole regions reports the global counts for more in-depth analysis. We observe that the object density map generated by most existing methods usually lacks of local consistency, i.e., counting errors in local regions exist unexpectedly even though the global count seems to well match with the ground-truth. Towards this problem, in this paper we propose a constrained multi-stage Convolutional Neural Networks (CNNs) to jointly pursue locally consistent density map from two aspects. Different from most existing methods that mainly rely on the multi-column architectures of plain CNNs, we exploit a stacking formulation of plain CNNs. Benefited from the internal multi-stage learning process, the feature map could be repeatedly refined, allowing the density map to approach the ground-truth density distribution. For further refinement of the density map, we also propose a grid loss function. With finer local-region-based supervisions, the underlying model is constrained to generate locally consistent density values to minimize the training errors considering both the global and local counts accuracy. Experiments on two widely-tested object counting benchmarks with overall significant results compared with state-of-the-art methods demonstrate the effectiveness of our approach.

cs.CV↗