arXiv subjects

Dan Yang

Publications and source records attributed to Dan Yang.

At least 55 records · Page 3Linked to original sources

arXiv2023

On triharmonic hypersurfaces in space forms

In this paper we study triharmonic hypersurfaces immersed in a space form $N^{n+1}(c)$. We prove that any proper CMC triharmonic hypersurface in the sphere $\mathbb S^{n+1}$ has constant scalar curvature; any CMC triharmonic hypersurface in the hyperbolic space $\mathbb H^{n+1}$ is minimal. Moreover, we show that any CMC triharmonic hypersurface in the Euclidean space $\mathbb R^{n+1}$ is minimal provided that the multiplicity of the principal curvature zero is at most one. In particular, we are able to prove that every CMC triharmonic hypersurface in the Euclidean space $\mathbb R^{6}$ is minimal.These results extend some recent works due to Montaldo-Oniciuc-Ratto and Chen-Guan, and give affirmative answer to the generalized Chen's conjecture.

math.DG↗

arXiv2023

Secure Communication for Spatially Correlated RIS-Aided Multiuser Massive MIMO Systems: Analysis and Optimization

This letter investigates the secure communication in a reconfigurable intelligent surface (RIS)-aided multiuser massive multiple-input multiple-output (MIMO) system exploiting artificial noise (AN). We first derive a closed-form expression of the ergodic secrecy rate under spatially correlated MIMO channels. By using this derived result, we further optimize the power fraction of AN in closed form and the RIS phase shifts by developing a gradient-based algorithm, which requires only statistical channel state information (CSI). Our analysis shows that spatial correlation at the RIS provides an additional dimension for optimizing the RIS phase shifts. Numerical simulations validate the analytical results which show the insightful interplay among the system parameters and the degradation of secrecy performance due to high spatial correlation at the RIS.

cs.IT↗

arXiv2023

Secure Communication for Spatially Correlated Massive MIMO with Low-Resolution DACs

In this paper, the performance of a secure massive multiple-input multiple-output (MIMO) system adopting low-resolution digital-to-analog converters (DACs) is analyzed over spatially correlated wireless channels. A tight lower bound for the achievable secrecy rate is derived with artificial noise (AN) transmitted in the null space of the user channels. Using the analytical results, the impact of spatial correlation on the secrecy rate is explicitly evaluated in the presence of low-resolution DACs. The analytical observations reveal that using low-resolution DACs can be beneficial to the secrecy performance compared with ideal DACs, when the channels are strongly correlated and optimal power allocation is not employed.

cs.IT↗

arXiv2022

Imaging individual active regions on the Sun's far side with improved helioseismic holography

Helioseismic holography is a useful method to detect active regions on the Sun's far side and improve space weather forecasts. We aim to improve helioseismic holography by using a clear formulation of the problem, an accurate forward solver in the frequency domain, and a better understanding of the noise properties. Building on the work of Lindsey et al., we define the forward- and backward-propagated wave fields (ingression and egression) in terms of a Green's function. This Green's function is computed using an accurate forward solver in the frequency domain. We analyse overlapping segments of 31 hr of SDO/HMI dopplergrams, with a cadence of 24 hr. Phase shifts between the ingression and the egression are measured and averaged to detect active regions on the far side. The phase maps are compared with direct EUV intensity maps from STEREO/EUVI. We confirm that medium-size active regions can be detected on the far side with high confidence. Their evolution (and possible emergence) can be monitored on a daily time scale. Seismic maps averaged over 3 days provide an active region detection rate as high as 75% and a false discovery rate only as low as 7%, for active regions with areas above one thousandth of an hemisphere. For a large part, these improvements can be attributed to the use of a complete Green's function (all skips) and to the use of all observations on the front side (full pupil). Improved helioseismic holography enables the study of the evolution of medium-size active regions on the Sun's far side.

astro-ph.SR↗

arXiv2021

Biconservative hypersurfaces with constant scalar curvature in space forms

Biconservative hypersurfaces are hypersurfaces which have conservative stress-energy tensor with respect to the bienergy, containing all minimal and constant mean curvature hypersurfaces. The purpose of this paper is to study biconservative hypersurfaces $M^n$ with constant scalar curvature in a space form $N^{n+1}(c)$. We prove that every biconservative hypersurface with constant scalar curvature in $N^4(c)$ has constant mean curvature. Moreover, we prove that any biconservative hypersurface with constant scalar curvature in $N^5(c)$ is ether an open part of a certain rotational hypersurface or a constant mean curvature hypersurface. These solve an open problem proposed recently by D. Fetcu and C. Oniciuc for $n\leq4$.

math.DG↗

arXiv2021

Testing and Support Recovery of Correlation Structures for Matrix-Valued Observations with an Application to Stock Market Data

Estimation of the covariance matrix of asset returns is crucial to portfolio construction. As suggested by economic theories, the correlation structure among assets differs between emerging markets and developed countries. It is therefore imperative to make rigorous statistical inference on correlation matrix equality between the two groups of countries. However, if the traditional vector-valued approach is undertaken, such inference is either infeasible due to limited number of countries comparing to the relatively abundant assets, or invalid due to the violations of temporal independence assumption. This highlights the necessity of treating the observations as matrix-valued rather than vector-valued. With matrix-valued observations, our problem of interest can be formulated as statistical inference on covariance structures under sub-Gaussian distributions, i.e., testing non-correlation and correlation equality, as well as the corresponding support estimations. We develop procedures that are asymptotically optimal under some regularity conditions. Simulation results demonstrate the computational and statistical advantages of our procedures over certain existing state-of-the-art methods for both normal and non-normal distributions. Application of our procedures to stock market data reveals interesting patterns and validates several economic propositions via rigorous statistical testing.

stat.ME↗

arXiv2021

How to Estimate the Far-Side Open Flux using STEREO Coronal Holes

Global magnetic field models use as input synoptic data, which usually show "aging effects" as the longitudinal 360° information is not obtained simultaneously. Especially during times of increased solar activity, the evolution of the magnetic field may yield large uncertainties. A significant source of uncertainty is the Sun's magnetic field on the side of the Sun that is not visible to the observer. Various methods have been used to complete the picture: synoptic charts, flux-transport models, and far side helioseismology. In this study, we present a new method to estimate the far-side open flux within coronal holes using STEREO EUV observations. First, we correlate the structure of the photospheric magnetic field as observed with the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory (HMI/SDO) with features in the transition region. From the 304A intensity distribution, which we found to be specific to coronal holes, we derive an empirical estimate for the open flux. Then we use a large sample of 313 SDO coronal hole observations to verify this relation. Finally, we perform a cross-instrument calibration from SDO to STEREO data to enable the estimation of the open flux at solar longitudes not visible from Earth. We find that the properties of strong, unipolar magnetic elements in the photosphere, which determine the coronal hole's open flux, can be approximated by open fields in the transition region. We find that structures below a threshold of 78% (STEREO) or 94% (SDO) of the solar disk median intensity as seen in 304A filtergrams are reasonably well correlated with the mean magnetic flux density of coronal holes (cc = 0.59). Using the area covered by these structures (A_of) and the area of the coronal hole (A_ch), we model the open magnetic flux of a coronal hole as |Phi_ch| = 0.25 A_ch exp(0.032 A_of) with an estimated uncertainty of 40 to 60%.

astro-ph.SR↗

arXiv2021

Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning

Data augmentation is a powerful technique for improving the performance of the few-shot classification task. It generates more samples as supplements, and then this task can be transformed into a common supervised learning issue for solution. However, most mainstream data augmentation based approaches only consider the single modality information, which leads to the low diversity and quality of generated features. In this paper, we present a novel multi-modal data augmentation approach named Dizygotic Conditional Variational AutoEncoder (DCVAE) for addressing the aforementioned issue. DCVAE conducts feature synthesis via pairing two Conditional Variational AutoEncoders (CVAEs) with the same seed but different modality conditions in a dizygotic symbiosis manner. Subsequently, the generated features of two CVAEs are adaptively combined to yield the final feature, which can be converted back into its paired conditions while ensuring these conditions are consistent with the original conditions not only in representation but also in function. DCVAE essentially provides a new idea of data augmentation in various multi-modal scenarios by exploiting the complement of different modality prior information. Extensive experimental results demonstrate our work achieves state-of-the-art performances on miniImageNet, CIFAR-FS and CUB datasets, and is able to work well in the partial modality absence case.

cs.CV↗

arXiv2021

Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network

Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely imbalanced. Considering the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API categories. We collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs.

cs.SE↗

arXiv2021

Representation Evaluation Block-based Teacher-Student Network for the Industrial Quality-relevant Performance Modeling and Monitoring

Quality-relevant fault detection plays an important role in industrial processes, while the current quality-related fault detection methods based on neural networks main concentrate on process-relevant variables and ignore quality-relevant variables, which restrict the application of process monitoring. Therefore, in this paper, a fault detection scheme based on the improved teacher-student network is proposed for quality-relevant fault detection. In the traditional teacher-student network, as the features differences between the teacher network and the student network will cause performance degradation on the student network, representation evaluation block (REB) is proposed to quantify the features differences between the teacher and the student networks, and uncertainty modeling is used to add this difference in modeling process, which are beneficial to reduce the features differences and improve the performance of the student network. Accordingly, REB and uncertainty modeling is applied in the teacher-student network named as uncertainty modeling teacher-student uncertainty autoencoder (TSUAE). Then, the proposed TSUAE is applied to process monitoring, which can effectively detect faults in the process-relevant subspace and quality-relevant subspace simultaneously. The proposed TSUAE-based fault detection method is verified in two simulation experiments illustrating that it has satisfactory fault detection performance compared to other fault detection methods.

cs.LG↗

arXiv2020

Phase diagrams of interacting spreading dynamics in complex networks

Epidemic spreading processes in the real world can interact with each other in a cooperative, competitive, or asymmetric way, requiring a description based on coevolution dynamics. Rich phenomena such as discontinuous outbreak transitions and hystereses can arise, but a full picture of these behaviors in the parameter space is lacking. We develop a theory for interacting spreading dynamics on complex networks through spectral dimension reduction. In particular, we derive from the microscopic quenched mean-field equations a two-dimensional system in terms of the macroscopic variables, which enables a full phase diagram to be determined analytically. The diagram predicts critical phenomena that were known previously but only numerically, such as the interplay between discontinuous transition and hysteresis as well as the emergence and role of tricritical points.

physics.soc-ph↗

arXiv2020

Active Gate Drive with Gate-Drain Discharge Compensation for Voltage Balancing in Series-Connected SiC MOSFETs

Imbalanced voltage sharing during the turn-off transient is a challenge for series-connected silicon carbide (SiC) MOSFET application. This article first discusses the influence of the gate-drain discharge deviation on the voltage imbalance ratio, and its primary causes are also presented and verified by LTspice simulation. Accordingly, a novel active gate drive, which aims to compensate the discharge difference between devices connected in series, is proposed and analyzed. By only using the original output of the driving IC, the proposed gate drive is realized by implementing an auxiliary circuit on the existing commercial gate drive. Therefore, unlike other active gate drives for balancing control, no extra isolations for power/signal are needed, and the number of the devices in series is unlimited. The auxiliary circuit includes three sub-circuits as a high-bandwidth current sink for regulating switching performance, a relative low-frequency but reliable sampling and control circuit for closed-loop control, and a trigger combining the former and the latter. The operational principle and the design guideline for each part are presented in detail. Experimental results validate the performance of the proposed gate drive and its voltage balancing control algorithm.

eess.SY↗

arXiv2020

Factor Models for High-Dimensional Tensor Time Series

Large tensor (multi-dimensional array) data are now routinely collected in a wide range of applications, due to modern data collection capabilities. Often such observations are taken over time, forming tensor time series. In this paper we present a factor model approach for analyzing high-dimensional dynamic tensor time series and multi-category dynamic transport networks. Two estimation procedures along with their theoretical properties and simulation results are presented. Two applications are used to illustrate the model and its interpretations.

stat.ME↗

arXiv2020

Effective edge-based approach for promoting the spreading of SIR model

Promoting some typical spreading dynamics, for instance, the spreading of information, commercial message, vaccination guidance, innovation, and political movement, can bring benefits to all aspects of the socio-economic systems. In this study, we propose a strategy for promoting the spreading of the susceptible-infected-recovered model, which is widely applied to describe these common spreading dynamics in real life. Specifically, we first quantify the potential influence that the addition of each latent edge (that is, edges that do not exist before) could cause to the spreading dynamics. Then, we strategically add the latent edges to the original networks according to the potential influence of each latent edge. Numerical simulations verify the effectiveness of our strategy and demonstrate that our strategy outperforms several static strategies, namely, adding the latent edges between nodes with the largest degree or eigenvector centrality. This study provides an effective way of promoting the spreading of the susceptible-infected-recovered model by modifying the network structure slightly and helps in understanding what a better network structure for the spreading dynamics is. Besides, the theoretical framework established in this study provides inspirations for the further investigations of edge-based promoting strategies for other spreading models.

physics.soc-ph↗

arXiv2020

The optimal edge for containing the spreading of SIS model

Numerous real-world systems, for instance, the communication platforms and transportation systems, can be abstracted into complex networks. Containing spreading dynamics (e.g., epidemic transmission and misinformation propagation) in networked systems is a hot topic in multiple fronts. Most of the previous strategies are based on the immunization of nodes. However, sometimes, these node--based strategies can be impractical. For instance, in the train transportation networks, it is dramatic to isolating train stations for flu prevention. On the contrary, temporarily suspending some connections between stations is more acceptable. Thus, we pay attention to the edge-based containing strategy. In this study, we develop a theoretical framework to find the optimal edge for containing the spreading of the susceptible-infected-susceptible model on complex networks. In specific, by performing a perturbation method to the discrete-Markovian-chain equations of the SIS model, we derive a formula that approximately provides the decremental outbreak size after the deactivation of a certain edge in the network. Then, we determine the optimal edge by simply choosing the one with the largest decremental outbreak size. Note that our proposed theoretical framework incorporates the information of both network structure and spreading dynamics. Finally, we test the performance of our method by extensive numerical simulations. Results demonstrate that our strategy always outperforms other strategies based only on structural properties (degree or edge betweenness centrality). The theoretical framework in this study can be extended to other spreading models and offers inspirations for further investigations on edge-based immunization strategies.

physics.soc-ph↗

arXiv2019

Containing rumors spreading on correlated multiplex networks

Rumors flooding on rapidly-growing online social networks has geared much attention from many fronts. Individuals can transmit rumors via numerous channels since they can be active on multiple platforms. However, no systematic theoretical research of rumors containing dynamics on multiplex networks has been conducted yet. In this study, we propose a family of containing strategies based on the degree product $\mathcal{K}$ of each user on the multiplex networks. Then, we develop a heterogeneous edge-based compartmental theory to comprehend the containing dynamics. The simulation results demonstrate that strategies with preference to block users with large $\mathcal{K}$ can significantly reduce the rumor outbreak size and enlarge the threshold. Besides, better performance can be expected on heterogeneous multiplex networks with the increasing of preference intensity and degree heterogeneity. Moreover, take the inter-layer degree correlations $r_s$ into consideration, the strategy performs best on multiplex networks with $r_s=-1$, $r_s=1$ the second, and $r_s=0$ the last. On the contrary, if we prefer to block users with small $\mathcal{K}$ rather than large $\mathcal{K}$, the containing performance will be worse than that of blocking users randomly on most multiplex networks except for uncorrelated multiplex networks with uniform degree distribution. We found that the blocking preferences have no influence on the containing results on uncorrelated multiplex networks with uniform degree distribution. Our theoretical analysis can well predict the rumors containing results and performance differences in all the cases studied. The systematic theoretical research of rumors containing dynamics on multiplex networks in this study will offer inspirations for further investigations on this issue.

physics.soc-ph↗

arXiv2019

Misinformation spreading on correlated multiplex networks

The numerous expanding online social networks offer fast channels for misinformation spreading, which could have a serious impact on socioeconomic systems. Researchers across multiple areas have paid attention to this issue with a view of addressing it. However, no systematical theoretical study has been performed to date on observing misinformation spreading on correlated multiplex networks. In this study, we propose a multiplex network-based misinformation spreading model, considering the fact that each individual can obtain misinformation from multiple platforms. Subsequently, we develop a heterogeneous edge-base compartmental theory to comprehend the spreading dynamics of our proposed model. In addition, we establish an analytical method based on stability analysis to obtain the misinformation outbreak threshold. On the basis of these theories, we finally analyze the influence of different dynamical and structural parameters on the misinformation spreading dynamics. Results show that the misinformation outbreak size $R(\infty)$ grows continuously with the effective transmission probability $β$ once $β$ exceeds a certain value, that is, the outbreak threshold $β_c$. A large average degrees, strong degree heterogeneity, or positive inter-layer correlation will reduce $β_c$, accelerating the outbreak of misinformation. Besides, increasing the degree heterogeneity or a more positive inter-layer correlation will both enlarge (reduce) $R(\infty)$ for small (large) values of $β$. Our systematic theoretical analysis results agree well with the numerical simulation results. Our proposed model and accurate theoretical analysis will serve as a useful framework to understand and predict the spreading dynamics of misinformation on multiplex networks, and thereby pave the way to address this serious issue.

physics.soc-ph↗

arXiv2019

Autoregressive Models for Matrix-Valued Time Series

In finance, economics and many other fields, observations in a matrix form are often generated over time. For example, a set of key economic indicators are regularly reported in different countries every quarter. The observations at each quarter neatly form a matrix and are observed over many consecutive quarters. Dynamic transport networks with observations generated on the edges can be formed as a matrix observed over time. Although it is natural to turn the matrix observations into a long vector, and then use the standard vector time series models for analysis, it is often the case that the columns and rows of the matrix represent different types of structures that are closely interplayed. In this paper we follow the autoregressive structure for modeling time series and propose a novel matrix autoregressive model in a bilinear form that maintains and utilizes the matrix structure to achieve a greater dimensional reduction, as well as more interpretable results. Probabilistic properties of the models are investigated. Estimation procedures with their theoretical properties are presented and demonstrated with simulated and real examples.

stat.ME↗