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Qi Wang

Publications and source records attributed to Qi Wang.

At least 559 records · Page 31Linked to original sources
arXiv2021

Stabilization of a nonlinear bullet coexisting with a Bose-Einstein condensate in a rapidly cooled magnonic system driven by a spin-orbit torque

We have recently shown that injection of magnons into a magnetic dielectric via the spin-orbit torque (SOT) effect in the adjacent layer of a heavy metal subjected to the action of short (0.1 $μ$s) current pulses allows for control of a magnon Bose-Einstein Condensate (BEC). Here, the BEC was formed in the process of rapid cooling (RC), when the electric current heating the sample is abruptly terminated. In the present study, we show that the application of a longer (1.0 $μ$s) electric current pulse triggers the formation of a nonlinear localized magnonic bullet below the linear magnon spectrum. After pulse termination, the magnon BEC, as before, is formed at the bottom of the linear spectrum, but the nonlinear bullet continues to exist, stabilized for additional 30 ns by the same process of RC-induced magnon condensation. Our results suggest that a stimulated condensation of excess magnons to all highly populated magnonic states occurs.

cond-mat.quant-gas↗
arXiv2021

Stimulated-Raman-Adiabatic-Passage mechanism in a magnonic environment

We discuss the realization of a magnonic version of the STImulated-Raman-Adiabatic-Passage (m-STIRAP) mechanism using micromagnetic simulations. We consider the propagation of magnons in curved magnonic directional couplers. Our results demonstrate that quantum-classical analogy phenomena are accessible in magnonics. Specifically, the inherent advantages of the STIRAP mechanism, associated with dark states, can now be utilized in magnonics. Applications of this effect for future magnonic device functionalities and designs are discussed.

cond-mat.mes-hall↗
arXiv2021

From wall observations to turbulence: The difficulty of flow reconstruction

Estimation of the initial state of turbulent channel flow from spatially and temporally resolved wall data is performed using adjoint-variational data assimilation. The accuracy of the predicted flow deteriorates with distance from the wall, most precipitously across the buffer layer beyond which only large-scale structures are reconstructed. To quantify the difficulty of the state estimation, the Hessian of the associated cost function is evaluated at the true solution. The forward-adjoint duality is exploited to efficiently compute the Hessian matrix from the ensemble-averaged cross-correlation of the adjoint fields due to impulses at the sensing locations and times. Characteristics of the Hessian are examined when observations correspond to the streamwise or spanwise wall shear stress or wall pressure. Most of the eigenmodes decay beyond the buffer layer, thus demonstrating weak sensitivity of wall observations to the turbulence in the bulk. However, when the measurement time $t_m^+ \gtrsim 20$, some streamwise-elongated Hessian eigenfunctions remain finite in the outer flow, which corresponds to the sensitivity of wall observations to outer large-scale motions. Furthermore, we report statistics of the adjoint-field kinetic-energy budget at long times, which are distinctly different from those of the forward model. One notable difference is the high concentration of energy within the buffer layer and its narrower support. A large and exponentially amplifying adjoint field in that region leads to large gradients of the cost function, smaller step size in the gradient-based optimization, and difficulty to achieve convergence especially elsewhere in the domain where the gradients are comparatively small.

physics.flu-dyn↗
arXiv2021

The subfield codes and subfield subcodes of a family of MDS codes

Maximum distance separable (MDS) codes are very important in both theory and practice. There is a classical construction of a family of $[2^m+1, 2u-1, 2^m-2u+3]$ MDS codes for $1 \leq u \leq 2^{m-1}$, which are cyclic, reversible and BCH codes over $\mathrm{GF}(2^m)$. The objective of this paper is to study the quaternary subfield subcodes and quaternary subfield codes of a subfamily of the MDS codes for even $m$. A family of quaternary cyclic codes is obtained. These quaternary codes are distance-optimal in some cases and very good in general. Furthermore, infinite families of $3$-designs from these quaternary codes are presented.

cs.IT↗
arXiv2021

Pressure-induced Superconductivity at 32 K in MoB2

Since the discovery of superconductivity in MgB2 (Tc ~ 39 K), the search for superconductivity in related materials with similar structures or ingredients has never stopped. Although about 100 binary borides have been explored, only few of them show superconductivity with relatively low Tc. In this work, we report the discovery of superconductivity up to 32 K in MoB2 under pressure which is the highest Tc in transition-metal borides. Although the Tc can be well explained by theoretical calculations in the framework of electron-phonon coupling, the d-electrons and phonon modes of transition metal Mo atoms play utterly important roles in the emergence of superconductivity in MoB2, distinctly different from the case of well-known MgB2. Our study sheds light on the exploration of high-Tc superconductors in transition metal borides.

cond-mat.supr-con↗
arXiv2021

MT: Multi-Perspective Feature Learning Network for Scene Text Detection

Text detection, the key technology for understanding scene text, has become an attractive research topic. For detecting various scene texts, researchers propose plenty of detectors with different advantages: detection-based models enjoy fast detection speed, and segmentation-based algorithms are not limited by text shapes. However, for most intelligent systems, the detector needs to detect arbitrary-shaped texts with high speed and accuracy simultaneously. Thus, in this study, we design an efficient pipeline named as MT, which can detect adhesive arbitrary-shaped texts with only a single binary mask in the inference stage. This paper presents the contributions on three aspects: (1) a light-weight detection framework is designed to speed up the inference process while keeping high detection accuracy; (2) a multi-perspective feature module is proposed to learn more discriminative representations to segment the mask accurately; (3) a multi-factor constraints IoU minimization loss is introduced for training the proposed model. The effectiveness of MT is evaluated on four real-world scene text datasets, and it surpasses all the state-of-the-art competitors to a large extent.

cs.CV↗
arXiv2021

Spatial-Spectral Clustering with Anchor Graph for Hyperspectral Image

Hyperspectral image (HSI) clustering, which aims at dividing hyperspectral pixels into clusters, has drawn significant attention in practical applications. Recently, many graph-based clustering methods, which construct an adjacent graph to model the data relationship, have shown dominant performance. However, the high dimensionality of HSI data makes it hard to construct the pairwise adjacent graph. Besides, abundant spatial structures are often overlooked during the clustering procedure. In order to better handle the high dimensionality problem and preserve the spatial structures, this paper proposes a novel unsupervised approach called spatial-spectral clustering with anchor graph (SSCAG) for HSI data clustering. The SSCAG has the following contributions: 1) the anchor graph-based strategy is used to construct a tractable large graph for HSI data, which effectively exploits all data points and reduces the computational complexity; 2) a new similarity metric is presented to embed the spatial-spectral information into the combined adjacent graph, which can mine the intrinsic property structure of HSI data; 3) an effective neighbors assignment strategy is adopted in the optimization, which performs the singular value decomposition (SVD) on the adjacent graph to get solutions efficiently. Extensive experiments on three public HSI datasets show that the proposed SSCAG is competitive against the state-of-the-art approaches.

cs.CV↗
arXiv2021

Asymptotic and Quenching Behavior for a family of Parabolic System with General Singular Nonlinearities

This study is concerned with a family of parabolic system with general singular nonlinearities, which is a generalization of MEMS system. To some extent, the classification of global existence and quenching according to parameters and initial data is given. Moreover, the convergence rate is also obtained. We point out that compared to single MEMS equation, new ideas and techniques are introduced in obtaining the convergence rate for system in our study. In fact, due to the lack of variational characterization for the first eigenvalue of the linearized elliptic system, the methods in obtaining convergence rate for single equation cannot work completely here.

math.AP↗
arXiv2021

Network percolation reveals adaptive bridges of the mobility network response to COVID-19

Human mobility is crucial to understand the transmission pattern of COVID-19 on spatially embedded geographic networks. This pattern seems unpredictable, and the propagation appears unstoppable, resulting in over 350,000 death tolls in the U.S. by the end of 2020. Here, we create the spatiotemporal inter-county mobility network using 10 TB (Terabytes) trajectory data of 30 million smart devices in the U.S. in the first six months of 2020. We investigate its bound percolation by removing the weakly connected edges. The mobility network becomes vulnerable and prone to reach its criticality and thus experience surprisingly abrupt phase transitions. Despite the complex behaviors of the mobility network, we devised a novel approach to identify a small, manageable set of recurrent critical bridges, connecting the giant component and the second-largest component. These adaptive links, located across the United States, played a key role as valves connecting components in divisions and regions during the pandemic. Beyond, our numerical results unveil that network characteristics determine the critical thresholds and the bridge locations. The findings provide new insights into managing and controlling the connectivity of mobility networks during unprecedented disruptions. The work can also potentially offer practical future infectious diseases both globally and locally.

physics.soc-ph↗
arXiv2021

Auto-weighted Multi-view Feature Selection with Graph Optimization

In this paper, we focus on the unsupervised multi-view feature selection which tries to handle high dimensional data in the field of multi-view learning. Although some graph-based methods have achieved satisfactory performance, they ignore the underlying data structure across different views. Besides, their pre-defined laplacian graphs are sensitive to the noises in the original data space, and fail to get the optimal neighbor assignment. To address the above problems, we propose a novel unsupervised multi-view feature selection model based on graph learning, and the contributions are threefold: (1) during the feature selection procedure, the consensus similarity graph shared by different views is learned. Therefore, the proposed model can reveal the data relationship from the feature subset. (2) a reasonable rank constraint is added to optimize the similarity matrix to obtain more accurate information; (3) an auto-weighted framework is presented to assign view weights adaptively, and an effective alternative iterative algorithm is proposed to optimize the problem. Experiments on various datasets demonstrate the superiority of the proposed method compared with the state-of-the-art methods.

cs.LG↗
arXiv2021

Thermodynamically Consistent Algorithms for Models of Diblock Copolymer Solutions Interacting with Electric and Magnetic Fields

We derive thermodynamically consistent models for diblock copolymer solutions coupled with the electric and magnetic field, respectively. These models satisfy the second law of thermodynamics and therefore are therefore thermodynamically consistent. We then design a set of 2nd order, linear, semi-discrete schemes for the models using the energy quadratization method and the supplementary variable method, respectively, which preserve energy dissipation rates of the models. The spatial discretization is carried out subsequently using 2nd order finite difference methods, leading to fully discrete algorithms that preserve discrete energy-dissipation-rates of the models so that the resulting fully discrete models are thermodynamically consistent. Convergence rates are numerically confirmed through mesh refinement tests and several numerical examples are given to demonstrate the role of the mobility in pattern formation, defect removing effect of both electric and magnetic fields as well as the hysteresis effect for applied external fields in copolymer solutions.

math.NA↗
arXiv2021

Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning

Graph-based semi-supervised learning methods, which deal well with the situation of limited labeled data, have shown dominant performance in practical applications. However, the high dimensionality of hyperspectral images (HSI) makes it hard to construct the pairwise adjacent graph. Besides, the fine spatial features that help improve the discriminability of the model are often overlooked. To handle the problems, this paper proposes a novel spatial-spectral HSI classification method via multiple random anchor graphs ensemble learning (RAGE). Firstly, the local binary pattern is adopted to extract the more descriptive features on each selected band, which preserves local structures and subtle changes of a region. Secondly, the adaptive neighbors assignment is introduced in the construction of anchor graph, to reduce the computational complexity. Finally, an ensemble model is built by utilizing multiple anchor graphs, such that the diversity of HSI is learned. Extensive experiments show that RAGE is competitive against the state-of-the-art approaches.

cs.CV↗
arXiv2021

An Offline Delegatable Cryptocurrency System

Blockchain-based cryptocurrencies, facilitating the convenience of payment by providing a decentralized online solution, have not been widely adopted so far due to slow confirmation of transactions. Offline delegation offers an efficient way to exchange coins. However, in such an approach, the coins that have been delegated confront the risk of being spent twice since the delegator's behaviour cannot be restricted easily on account of the absence of effective supervision. Even if a third party can be regarded as a judge between the delegator and delegatee to secure transactions, she still faces the threat of being compromised or providing misleading assure. Moreover, the approach equipped with a third party contradicts the real intention of decentralized cryptocurrency systems. In this paper, we propose \textit{DelegaCoin}, an offline delegatable cryptocurrency system to mitigate such an issue. We exploit trusted execution environments (TEEs) as decentralized "virtual agents" to prevent malicious delegation. In DelegaCoin, an owner can delegate his coins through offline-transactions without interacting with the blockchain network. A formal model and analysis, prototype implementation, and further evaluation demonstrate that our scheme is provably secure and practically feasible.

cs.CR↗
arXiv2021

Examining mobility data justice during 2017 Hurricane Harvey

Natural disasters can significantly disrupt human mobility in urban areas. Studies have attempted to understand and quantify such disruptions using crowdsourced mobility data sets. However, limited research has studied the justice issues of mobility data in the context of natural disasters. The lack of research leaves us without an empirical foundation to quantify and control the possible biases in the data. This study, using 2017 Hurricane Harvey as a case study, explores three aspects of mobility data that could potentially cause injustice: representativeness, quality, and precision. We find representativeness being a major factor contributing to mobility data injustice. There is a persistent disparity of representativeness across neighborhoods of different socioeconomic characteristics before, during, and after the hurricane's landfall. Additionally, we observed significant drops of data precision during the hurricane, adding uncertainty to locate people and understand their movements during extreme weather events. The findings highlight the necessity in understanding and controlling the possible bias of mobility data as well as developing practical tools through data justice lenses in collecting and analyzing data during disasters.

cs.SI↗
arXiv2021

Multi-channel Deep Supervision for Crowd Counting

Crowd counting is a task worth exploring in modern society because of its wide applications such as public safety and video monitoring. Many CNN-based approaches have been proposed to improve the accuracy of estimation, but there are some inherent issues affect the performance, such as overfitting and details lost caused by pooling layers. To tackle these problems, in this paper, we propose an effective network called MDSNet, which introduces a novel supervision framework called Multi-channel Deep Supervision (MDS). The MDS conducts channel-wise supervision on the decoder of the estimation model to help generate the density maps. To obtain the accurate supervision information of different channels, the MDSNet employs an auxiliary network called SupervisionNet (SN) to generate abundant supervision maps based on existing groundtruth. Besides the traditional density map supervision, we also use the SN to convert the dot annotations into continuous supervision information and conduct dot supervision in the MDSNet. Extensive experiments on several mainstream benchmarks show that the proposed MDSNet achieves competitive results and the MDS significantly improves the performance without changing the network structure.

cs.CV↗
arXiv2021

An Identity-based Batch Verification Scheme for VANETs Based on Ring Signature with Efficient Revocation

Vehicular ad-hoc networks (VANETs) are one of the most important components in Intelligent Transportation System (ITS), which aims to provide secure and efficient communication between vehicles. Safety-critical vehicular communication requires security, privacy, and auditability. To satisfy these requirements simultaneously, several conditional privacy-preserving authentication schemes are proposed by employing ring signatures. However, these methods have paid little attention to the issues like how to choose the valid ring members or how to set up a ring. In this paper, we introduce an efficient conditional privacy-preserving scheme which provides an appropriate approach establishing the list of ring members with efficient revocation. Moreover, our proposed scheme also provides batch verification to significantly reduce the computational cost. According to the analysis of security, our scheme is sufficiently resistant against several common attacks in VANETs. The performance results show that the proposed scheme is efficient and practical with both low computation and communication cost.

cs.CR↗
arXiv2021

Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models

Reinforcement learning is a promising paradigm for solving sequential decision-making problems, but low data efficiency and weak generalization across tasks are bottlenecks in real-world applications. Model-based meta reinforcement learning addresses these issues by learning dynamics and leveraging knowledge from prior experience. In this paper, we take a closer look at this framework, and propose a new Thompson-sampling based approach that consists of a new model to identify task dynamics together with an amortized policy optimization step. We show that our model, called a graph structured surrogate model (GSSM), outperforms state-of-the-art methods in predicting environment dynamics. Additionally, our approach is able to obtain high returns, while allowing fast execution during deployment by avoiding test time policy gradient optimization.

cs.LG↗