arXiv subjects

Tao Zhou

Publications and source records attributed to Tao Zhou.

At least 289 records · Page 16Linked to original sources

arXiv2021

Quantifying the efficacy of childcare services on women employment

Women are set back in the labor market after becoming mother. Intuitively, childcare services are able to promote women employment as they may reconciliate the motherhood penalty. However, most known studies concentrated on the effects of childcare services on fertility rate, instead of quantitative analyses about the effects on women employment. Using worldwide panel data and Chinese data at province level, this paper unfolds the quantitative relationship between childcare services and women employment, that is, the attendance rate of childcare services is positively correlated with the relative employment rate of women to men. Further analysis suggests that such a positive impact may largely resulted from breaking the vulnerable employment dilemma.

physics.soc-ph↗

arXiv2021

Collaborative Filtering Approach to Link Prediction

Link prediction is a fundamental challenge in network science. Among various methods, local similarity indices are widely used for their high cost-performance. However, the performance is less robust: for some networks local indices are highly competitive to state-of-the-art algorithms while for some other networks they are very poor. Inspired by techniques developed for recommender systems, we propose an enhancement framework for local indices based on collaborative filtering (CF). Considering the delicate but important difference between personalized recommendation and link prediction, we further propose an improved framework named as self-included collaborative filtering (SCF). The SCF framework significantly improved the accuracy and robustness of well-known local indices. The combination of SCF framework and a simple local index can produce an index with competitive performance and much lower complexity compared with elaborately-designed state-of-the-art algorithms.

cs.SI↗

arXiv2021

Multi-level Cross-modal Interaction Network for RGB-D Salient Object Detection

Depth cues with affluent spatial information have been proven beneficial in boosting salient object detection (SOD), while the depth quality directly affects the subsequent SOD performance. However, it is inevitable to obtain some low-quality depth cues due to limitations of its acquisition devices, which can inhibit the SOD performance. Besides, existing methods tend to combine RGB images and depth cues in a direct fusion or a simple fusion module, which makes they can not effectively exploit the complex correlations between the two sources. Moreover, few methods design an appropriate module to fully fuse multi-level features, resulting in cross-level feature interaction insufficient. To address these issues, we propose a novel Multi-level Cross-modal Interaction Network (MCINet) for RGB-D based SOD. Our MCI-Net includes two key components: 1) a cross-modal feature learning network, which is used to learn the high-level features for the RGB images and depth cues, effectively enabling the correlations between the two sources to be exploited; and 2) a multi-level interactive integration network, which integrates multi-level cross-modal features to boost the SOD performance. Extensive experiments on six benchmark datasets demonstrate the superiority of our MCI-Net over 14 state-of-the-art methods, and validate the effectiveness of different components in our MCI-Net. More important, our MCI-Net significantly improves the SOD performance as well as has a higher FPS.

cs.CV↗

arXiv2021

A new discrete energy technique for multi-step backward difference formulas

The backward differentiation formula (BDF) is a useful family of implicit methods for the numerical integration of stiff differential equations. It is well noticed that the stability and convergence of the $A$-stable BDF1 and BDF2 schemes for parabolic equations can be directly established by using the standard discrete energy analysis. However, such classical analysis technique seems not directly applicable to the BDF-$\mathbf{k}$ schemes for $3\leq \mathbf{k}\leq 5$. To overcome the difficulty, a powerful analysis tool based on the Nevanlinna-Odeh multiplier technique [Numer. Funct. Anal. Optim., 3:377-423, 1981] was developed by Lubich et al. [IMA J. Numer. Anal., 33:1365-1385, 2013]. In this work, by using the so-called discrete orthogonal convolution kernels technique, we will recover the classical energy analysis so that the stability and convergence of the BDF-$\mathbf{k}$ schemes for $3\leq \mathbf{k}\leq 5$ can be established. One of the theoretical advantages of our analysis technique is that less spacial regularity requirement is needed on the initial data.

math.NA↗

arXiv2021

Impurity effect as a probe for the pairing symmetry of graphene-based superconductors

The single impurity effect on the graphene-based superconductor is studied theoretically. Four different pairing symmetries are discussed. Sharp resonance peaks are found near the impurity site for the $d+id$-wave pairing symmetry and the $p+ip$-wave pairing symmetry when the chemical potential is large. As the chemical potential decreases, the in-gap states are robust for the $d+id$ pairing symmetry while they disappear for the $p+ip$ pairing symmetry. Such in-gap peaks are absent for the fully gapped extended $s$-wave pairing symmetry and the nodal $f$-wave pairing symmetry. The existence of the in-gap resonance peaks can be explained well based on the sign-reversal of the superconducting gap along different Fermi pockets and by analyzing the denominator of the $T$-matrix. All of the features can be accessed by the experiments, which provide a useful probe for the pairing symmetry of graphene-based superconductors.

cond-mat.supr-con↗

arXiv2021

Averaged fidelity-based steering criteria

In the present work, the averaged fidelity is introduced as the steering parameter. According to the definitions of steering from Alice to Bob, a general scheme for designing linear steering criteria is developed for a high-dimensional system. For a given set of measurements on Bob's side, two quantities, the so-called nonsteering thresholds, can be defined. If the measured averaged fidelity exceeds these thresholds, the state shared by Alice and Bob is steerable from Alice to Bob, and the measurements performed by Alice are also verified to be incompatible. Within the general scheme, we also construct a linear steering inequality when the set of measurements performed by Bob has a continuous setting. Some applications are also provided.

quant-ph↗

arXiv2020

An energy stable and maximum bound preserving scheme with variable time steps for time fractional Allen-Cahn equation

In this work, we propose a Crank-Nicolson-type scheme with variable steps for the time fractional Allen-Cahn equation. The proposed scheme is shown to be unconditionally stable (in a variational energy sense), and is maximum bound preserving. Interestingly, the discrete energy stability result obtained in this paper can recover the classical energy dissipation law when the fractional order $α\rightarrow 1.$ That is, our scheme can asymptotically preserve the energy dissipation law in the $α\rightarrow 1$ limit. This seems to be the first work on variable time-stepping scheme that can preserve both the energy stability and the maximum bound principle. Our Crank-Nicolson scheme is build upon a reformulated problem associated with the Riemann-Liouville derivative. As a by product, we build up a reversible transformation between the L1-type formula of the Riemann-Liouville derivative and a new L1-type formula of the Caputo derivative, with the help of a class of discrete orthogonal convolution kernels. This is the first time such a \textit{discrete} transformation is established between two discrete fractional derivatives. We finally present several numerical examples with an adaptive time-stepping strategy to show the effectiveness of the proposed scheme.

math.NA↗

arXiv2020

Improving EEG Decoding via Clustering-based Multi-task Feature Learning

Accurate electroencephalogram (EEG) pattern decoding for specific mental tasks is one of the key steps for the development of brain-computer interface (BCI), which is quite challenging due to the considerably low signal-to-noise ratio of EEG collected at the brain scalp. Machine learning provides a promising technique to optimize EEG patterns toward better decoding accuracy. However, existing algorithms do not effectively explore the underlying data structure capturing the true EEG sample distribution, and hence can only yield a suboptimal decoding accuracy. To uncover the intrinsic distribution structure of EEG data, we propose a clustering-based multi-task feature learning algorithm for improved EEG pattern decoding. Specifically, we perform affinity propagation-based clustering to explore the subclasses (i.e., clusters) in each of the original classes, and then assign each subclass a unique label based on a one-versus-all encoding strategy. With the encoded label matrix, we devise a novel multi-task learning algorithm by exploiting the subclass relationship to jointly optimize the EEG pattern features from the uncovered subclasses. We then train a linear support vector machine with the optimized features for EEG pattern decoding. Extensive experimental studies are conducted on three EEG datasets to validate the effectiveness of our algorithm in comparison with other state-of-the-art approaches. The improved experimental results demonstrate the outstanding superiority of our algorithm, suggesting its prominent performance for EEG pattern decoding in BCI applications.

eess.SP↗

arXiv2020

High resolution functional imaging through Lorentz transmission electron microscopy and differentiable programming

Lorentz transmission electron microscopy is a unique characterization technique that enables the simultaneous imaging of both the microstructure and functional properties of materials at high spatial resolution. The quantitative information such as magnetization and electric potentials is carried by the phase of the electron wave, and is lost during imaging. In order to understand the local interactions and develop structure-property relationships, it is necessary to retrieve the complete wavefunction of the electron wave, which requires solving for the phase shift of the electrons (phase retrieval). Here we have developed a method based on differentiable programming to solve the inverse problem of phase retrieval, using a series of defocused microscope images. We show that our method is robust and can outperform widely used \textit{transport of intensity equation} in terms of spatial resolution and accuracy of the retrieved phase under same electron dose conditions. Furthermore, our method shares the same basic structure as advanced machine learning algorithms, and is easily adaptable to various other forms of phase retrieval in electron microscopy.

cond-mat.mtrl-sci↗

arXiv2020

Positive definiteness of real quadratic forms resulting from the variable-step approximation of convolution operators

The positive definiteness of real quadratic forms with convolution structures plays an important role in stability analysis for time-stepping schemes for nonlocal operators.In this work, we present a novel analysis tool to handle discrete convolution kernels resulting from variable-step approximations for convolution operators. More precisely, for a class of discrete convolution kernels relevant to variable-step time discretizations,we show that the associated quadratic form is positive definite under some easy-to-check algebraic conditions. Our proof is based on an elementary constructing strategy using the properties of discrete orthogonal convolution kernels and complementary convolution kernels. To the best of our knowledge, this is the first general result on simple algebraic conditions for the positive definiteness of variable-step discrete convolution kernels. Using the unified theory, the stability for some simple non-uniform time-stepping schemes can be obtained in a straightforward way.

math.NA↗

arXiv2020

Topological phases of spinless $p$-orbital fermions in zigzag optical lattices

Motivated by the experiment [St-Jean {\it et al}., Nature Photon. {\bf 11}, 651 (2017)] on topological phases with collective photon modes in a zigzag chain of polariton micropillars, we study spinless $p$-orbital fermions with local interorbital hoppings and repulsive interactions between $p_x$ and $p_y$ bands in zigzag optical lattices. We show that spinless $p$-band fermions in zigzag optical lattices can mimic the interacting Su-Schrieffer-Heeger model and the effective transverse field Ising model in the presence of local hoppings. We analytically and numerically discuss the ground-state phases and quantum phase transitions of the model. This work provides a simple scheme to simulate topological phases and the quench dynamics of many-body systems in optical lattices.

cond-mat.quant-gas↗

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

Dynamic Fusion based Federated Learning for COVID-19 Detection

Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, sharing diagnostic images across medical institutions is usually not allowed due to the concern of patients' privacy. This causes the issue of insufficient datasets for training the image classification model. Federated learning is an emerging privacy-preserving machine learning paradigm that produces an unbiased global model based on the received updates of local models trained by clients without exchanging clients' local data. Nevertheless, the default setting of federated learning introduces huge communication cost of transferring model updates and can hardly ensure model performance when data heterogeneity of clients heavily exists. To improve communication efficiency and model performance, in this paper, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections. First, we design an architecture for dynamic fusion-based federated learning systems to analyse medical diagnostic images. Further, we present a dynamic fusion method to dynamically decide the participating clients according to their local model performance and schedule the model fusion-based on participating clients' training time. In addition, we summarise a category of medical diagnostic image datasets for COVID-19 detection, which can be used by the machine learning community for image analysis. The evaluation results show that the proposed approach is feasible and performs better than the default setting of federated learning in terms of model performance, communication efficiency and fault tolerance.

cs.DC↗

arXiv2020

Sparse approximation of data-driven Polynomial Chaos expansions: an induced sampling approach

One of the open problems in the field of forward uncertainty quantification (UQ) is the ability to form accurate assessments of uncertainty having only incomplete information about the distribution of random inputs. Another challenge is to efficiently make use of limited training data for UQ predictions of complex engineering problems, particularly with high dimensional random parameters. We address these challenges by combining data-driven polynomial chaos expansions with a recently developed preconditioned sparse approximation approach for UQ problems. The first task in this two-step process is to employ the procedure developed in (Ahlfeld et al. 2016) to construct an "arbitrary" polynomial chaos expansion basis using a finite number of statistical moments of the random inputs. The second step is a novel procedure to effect sparse approximation via $\ell^1$ minimization in order to quantify the forward uncertainty. To enhance the performance of the preconditioned $\ell^1$ minimization problem, we sample from the so-called induced distribution, instead of using Monte Carlo (MC) sampling from the original, unknown probability measure. We demonstrate on test problems that induced sampling is a competitive and often better choice compared with sampling from asymptotically optimal measures (such as the equilibrium measure) when we have incomplete information about the distribution. We demonstrate the capacity of the proposed induced sampling algorithm via sparse representation with limited data on test functions, and on a Kirchoff plating bending problem with random Young's modulus.

math.NA↗

arXiv2020

Analysis of the second order BDF scheme with variable steps for the molecular beam epitaxial model without slope selection

In this work, we are concerned with the stability and convergence analysis of the second order BDF (BDF2) scheme with variable steps for the molecular beam epitaxial model without slope selection. We first show that the variable-step BDF2 scheme is convex and uniquely solvable under a weak time-step constraint. Then we show that it preserves an energy dissipation law if the adjacent time-step ratios $r_k:=τ_k/τ_{k-1}<3.561.$ Moreover, with a novel discrete orthogonal convolution kernels argument and some new discrete convolutional inequalities, the $L^2$ norm stability and rigorous error estimates are established, under the same step-ratios constraint that ensuring the energy stability., i.e., $0<r_k<3.561.$ This is known to be the best result in literature. We finally adopt an adaptive time-stepping strategy to accelerate the computations of the steady state solution and confirm our theoretical findings by numerical examples.

math.NA↗

arXiv2020

Rotational symmetry breaking and partial Majorana corner states in a high-Tc superconductor based heterostructure

Applying a microscopic model, we study theoretically the quasiparticle excitation of a twodimensional topological insulator (TI) being in proximity to a high-Tc superconductor. In the momentum space, the proximity induced pairing term in the TI layer includes both the singlet channel and triplet channel, leading to the C4 rotational symmetry breaking of the energy bands and the quasiparticle spectra. For a cylinder geometry, the zero energy edge states may appear but they are localized at the upper boundary. For the finite-size system with open boundaries, the zero energy states at the upper boundary disappear and the Majorana bound states emerge at the two lower corners. All of the results can be understood well through exploring the pairing order parameter and the anomalous Green's function.

cond-mat.supr-con↗

arXiv2020

M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients

Early and accurate prediction of overall survival (OS) time can help to obtain better treatment planning for brain tumor patients. Although many OS time prediction methods have been developed and obtain promising results, there are still several issues. First, conventional prediction methods rely on radiomic features at the local lesion area of a magnetic resonance (MR) volume, which may not represent the full image or model complex tumor patterns. Second, different types of scanners (i.e., multi-modal data) are sensitive to different brain regions, which makes it challenging to effectively exploit the complementary information across multiple modalities and also preserve the modality-specific properties. Third, existing methods focus on prediction models, ignoring complex data-to-label relationships. To address the above issues, we propose an end-to-end OS time prediction model; namely, Multi-modal Multi-channel Network (M2Net). Specifically, we first project the 3D MR volume onto 2D images in different directions, which reduces computational costs, while preserving important information and enabling pre-trained models to be transferred from other tasks. Then, we use a modality-specific network to extract implicit and high-level features from different MR scans. A multi-modal shared network is built to fuse these features using a bilinear pooling model, exploiting their correlations to provide complementary information. Finally, we integrate the outputs from each modality-specific network and the multi-modal shared network to generate the final prediction result. Experimental results demonstrate the superiority of our M2Net model over other methods.

eess.IV↗

arXiv2020

PraNet: Parallel Reverse Attention Network for Polyp Segmentation

Colonoscopy is an effective technique for detecting colorectal polyps, which are highly related to colorectal cancer. In clinical practice, segmenting polyps from colonoscopy images is of great importance since it provides valuable information for diagnosis and surgery. However, accurate polyp segmentation is a challenging task, for two major reasons: (i) the same type of polyps has a diversity of size, color and texture; and (ii) the boundary between a polyp and its surrounding mucosa is not sharp. To address these challenges, we propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images. Specifically, we first aggregate the features in high-level layers using a parallel partial decoder (PPD). Based on the combined feature, we then generate a global map as the initial guidance area for the following components. In addition, we mine the boundary cues using a reverse attention (RA) module, which is able to establish the relationship between areas and boundary cues. Thanks to the recurrent cooperation mechanism between areas and boundaries, our PraNet is capable of calibrating any misaligned predictions, improving the segmentation accuracy. Quantitative and qualitative evaluations on five challenging datasets across six metrics show that our PraNet improves the segmentation accuracy significantly, and presents a number of advantages in terms of generalizability, and real-time segmentation efficiency.

eess.IV↗