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Zhicheng Wang

Publications and source records attributed to Zhicheng Wang.

108 records · Page 6Linked to original sources
arXiv2020

Active- and transfer-learning applied to microscale-macroscale coupling to simulate viscoelastic flows

Active- and transfer-learning are applied to polymer flows for the multiscale discovery of effective constitutive approximations required in viscoelastic flow simulation. The result is macroscopic rheology directly connected to a microstructural model. Micro and macroscale simulations are adaptively coupled by means of Gaussian process regression to run the expensive microscale computations only as necessary. This active-learning guided multiscale method can automatically detect the inaccuracy of the learned constitutive closure and initiate simulations at new sampling points informed by proper acquisition functions, leading to an autonomic microscale-macroscale coupled system. Also, we develop a new dissipative particle dynamics model with the range of interaction cutoff between particles allowed to vary with the local strain-rate invariant, which is able to capture both the shear-thinning viscosity and the normal stress difference functions consistent with rheological experiments for aqueous polyacrylamide solutions. Our numerical experiments demonstrate the effectiveness of using active- and transfer-learning schemes to on-the-fly couple a spectral element solver and a mesoscopic particle-based simulator, and verify that the microscale-macroscale coupled model with effective constitutive closure learned from microscopic dynamics can outperform empirical constitutive models compared to experimental observations. The effective closure learned in a channel simulation is then transferred directly to the flow past a circular cylinder, where the results show that only two additional microscopic simulations are required to achieve a satisfactory constitutive model to once again close the continuum equations. This new paradigm of active- and transfer-learning for multiscale modeling is readily applicable to other microscale-macroscale coupled simulations of complex fluids and other materials.

physics.comp-ph↗
arXiv2020

High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification

Occluded person re-identification (ReID) aims to match occluded person images to holistic ones across dis-joint cameras. In this paper, we propose a novel framework by learning high-order relation and topology information for discriminative features and robust alignment. At first, we use a CNN backbone and a key-points estimation model to extract semantic local features. Even so, occluded images still suffer from occlusion and outliers. Then, we view the local features of an image as nodes of a graph and propose an adaptive direction graph convolutional (ADGC)layer to pass relation information between nodes. The proposed ADGC layer can automatically suppress the message-passing of meaningless features by dynamically learning di-rection and degree of linkage. When aligning two groups of local features from two images, we view it as a graph matching problem and propose a cross-graph embedded-alignment (CGEA) layer to jointly learn and embed topology information to local features, and straightly predict similarity score. The proposed CGEA layer not only take full use of alignment learned by graph matching but also re-place sensitive one-to-one matching with a robust soft one. Finally, extensive experiments on occluded, partial, and holistic ReID tasks show the effectiveness of our proposed method. Specifically, our framework significantly outperforms state-of-the-art by6.5%mAP scores on Occluded-Duke dataset.

cs.CV↗
arXiv2020

Remarks on the theta correspondence over finite fields

S.-Y. Pan decomposes the uniform projection of the Weil representation of a finite symplectic-odd orthogonal dual pair, in terms of Deligne-Lusztig virtual characters, assuming that the order of the finite field is large enough. In this paper we use Pan's decomposition to study the theta correspondence for this kind of dual pairs, following the approach of Adams-Moy and Aubert-Michel-Rouquier. Our results give the theta correspondence between unipotent representations and certain quadratic unipotent representations.

math.RT↗
arXiv2019

Differentiating Features for Scene Segmentation Based on Dedicated Attention Mechanisms

Semantic segmentation is a challenge in scene parsing. It requires both context information and rich spatial information. In this paper, we differentiate features for scene segmentation based on dedicated attention mechanisms (DF-DAM), and two attention modules are proposed to optimize the high-level and low-level features in the encoder, respectively. Specifically, we use the high-level and low-level features of ResNet as the source of context information and spatial information, respectively, and optimize them with attention fusion module and 2D position attention module, respectively. For attention fusion module, we adopt dual channel weight to selectively adjust the channel map for the highest two stage features of ResNet, and fuse them to get context information. For 2D position attention module, we use the context information obtained by attention fusion module to assist the selection of the lowest-stage features of ResNet as supplementary spatial information. Finally, the two sets of information obtained by the two modules are simply fused to obtain the prediction. We evaluate our approach on Cityscapes and PASCAL VOC 2012 datasets. In particular, there aren't complicated and redundant processing modules in our architecture, which greatly reduces the complexity, and we achieving 82.3% Mean IoU on PASCAL VOC 2012 test dataset without pre-training on MS-COCO dataset.

cs.CV↗
arXiv2019

Descents of unipotent representations of finite unitary groups

Inspired by the Gan-Gross-Prasad conjecture and the descent problem for classical groups, in this paper we study the descents of unipotent representations of unitary groups over finite fields. We give the first descents of unipotent representations explicitly, which are unipotent as well. Our results include both the Bessel case and Fourier-Jacobi case, which are related via theta correspondence.

math.RT↗
arXiv2019

Enhancement of the upper critical field in the cubic Laves-phase superconductor HfV$_{2}$ by Nb doping

We report the effect of Nb doping on the upper critical field of the cubic Laves-phase superconductor HfV$_{2}$ studied in a series of HfV$_{2-x}$Nb$_{x}$ samples with 0 $\leq$ $x$ $\leq$ 0.3 under pulsed fields up to 30 T. The undoped HfV$_{2}$ undergoes a martensitic structural transition around 110 K, and becomes superconducting below $T_{\rm c}$ = 9.4 K. Upon Nb doping, while the structural transition is suppressed for $x$ $\geq$ 0.1, a maximum in $T_{\rm c}$ of 10.1 K and zero-temperature upper critical field $B_{\rm c2}$(0) of 22.4 T is found at $x$ = 0.2, which is ascribed to an increase of the density of states at the Fermi level. For all samples, the temperature dependence of $B_{\rm c2}$ can be well described by the Werthamer-Helfand-Hohenberg (WHH) theory that takes into account both the spin paramagnetic effect and spin orbit scattering. In addition, a comparison is made between the $B_{\rm c2}$ behavior of HfV$_{2-x}$Nb$_{x}$ and those of Nb-Ti and Nb$_{3}$Sn.

cond-mat.supr-con↗
arXiv2019

Neutron powder diffraction study on the non-superconducting phases of ThFeAsN$_{1-x}$O$_x$ ($x=0.15, 0.6$) iron pnictide

We use neutron powder diffraction to study on the non-superconducting phases of ThFeAsN$_{1-x}$O$_x$ with $x=0.15, 0.6$. In our previous results on the superconducting phase ThFeAsN with $T_c=$ 30 K, no magnetic transition is observed by cooling down to 6 K, and possible oxygen occupancy at the nitrogen site is shown in the refinement(H. C. Mao \emph{et al.}, EPL, 117, 57005 (2017)). Here, in the oxygen doped system ThFeAsN$_{1-x}$O$_x$, two superconducting region ($0\leqslant x \leqslant 0.1$ and $0.25\leqslant x \leqslant 0.55$) have been identified by transport experiments (B. Z. Li \emph{et al.}, J. Phys.: Condens. Matter 30, 255602 (2018)). However, within the resolution of our neutron powder diffraction experiment, neither the intermediate doping $x=0.15$ nor the heavily overdoped compound $x= 0.6$ shows any magnetic order from 300 K to 4 K. Therefore, while it shares the common phenomenon of two superconducting domes as most of 1111-type iron-based superconductors, the magnetically ordered parent compound may not exist in this nitride family.

cond-mat.supr-con↗
arXiv2019

Superconducting phase diagram and nontrivial band topology of structurally modulated Sn$_{1-x}$Sb$_{x}$

We report the discovery of superconductivity in binary alloy Sn$_{1-x}$Sb$_{x}$ with $x$ in the range of 0.43 to 0.6, which possesses a modulated rhombohedral structure due to the incommensurate ordering of Sn and Sb layers along the $c$-axis. The specific heat measurements indicate a weakly coupled, fully gapped superconducting state in this homogeneity range with a maximum bulk $T_{\rm c}$ of 1.58 K at $x$ = 0.46, though the electronic specific heat and Hall coefficients remain nearly $x$-independent. The nonmonotonic dependence of the bulk $T_{\rm c}$ is discussed in relation to the effects of Sb-layer intercalation between the [Sn$_{4}$Sb$_{3}$] seven-layer lamellae that are the essential building block for superconductivity. On the other hand, a zero-resistivity transition is found to take place well above the bulk superconducting transition, and the corresponding $T_{\rm c}$ increases monotonically with $x$ from 2.06 K to 3.29 K. This contrast, together with the uniform elements distribution revealed by energy dispersive x-ray mapping, implies that the resistive transition is due to the strain effect at the grain boundary rather than the compositional inhomogeneity. The first-principles calculations on the representative composition Sn$_{4}$Sb$_{3}$ ($x$ = 0.43) indicate that it is topologically nontrivial similar to Sb, but with different Z$_{2}$ invariants (0;111). Our results not only identify a new superconducting region in the Sn-Sb phase diagram, but also provide a viable platform to study the interplay between structural modulation, nontrivial band topology and superconductivity.

cond-mat.supr-con↗
arXiv2019

Rethinking on Multi-Stage Networks for Human Pose Estimation

Existing pose estimation approaches fall into two categories: single-stage and multi-stage methods. While multi-stage methods are seemingly more suited for the task, their performance in current practice is not as good as single-stage methods. This work studies this issue. We argue that the current multi-stage methods' unsatisfactory performance comes from the insufficiency in various design choices. We propose several improvements, including the single-stage module design, cross stage feature aggregation, and coarse-to-fine supervision. The resulting method establishes the new state-of-the-art on both MS COCO and MPII Human Pose dataset, justifying the effectiveness of a multi-stage architecture. The source code is publicly available for further research.

cs.CV↗
arXiv2019

Type-II superconductivity in W5Si3-type Nb5Sn2Al

We report the discovery of superconductivity in the ternary aluminide Nb$_{5}$Sn$_{2}$Al, which crystallizes in the W$_{5}$Si$_{3}$-type structure with one-dimensional Nb chains along the $c$-axis. It is found that the compound has a multiband nature and becomes a weakly coupled, type-II superconductor below 2.0 K. The bulk nature of superconductivity is confirmed by the specific heat jump, whose temperature dependence shows apparent deviation from a single isotropic gap behavior. The lower and upper critical fields are estimated to be 2.0 mT and 0.3 T, respectively. From these values, we derive the penetration depth, coherence length and Ginzburg-Landau parameter to be 516 nm, 32.8 nm and 15.6, respectively. By contrast, the isostructural compound Ti$_{5}$Sn$_{2}$Al dose not superconduct above 0.5 K. A comparison of these results with other W$_{5}$Si$_{3}$-type superconductors suggests that $T_{\rm c}$ of these compounds correlates with the average number of valence electrons per atom.

cond-mat.supr-con↗
arXiv2018

Supercondutivity in SnSb with natural superlattice structure

We report the results of electrical resistivity, magnetic and thermodynamic measurements on polycrystalline SnSb, whose structure consists of stacks of Sb bilayers and Sn4Sb3 septuple layers along the c-axis. The material is found to be a weakly coupled, fully gapped, type-II superconductor with a bulk Tc of 1.50 K, while showing a zero resistivity transition at a significantly higher temperature of 2.48 K. The Sommerfeld coefficient and upper critical field, obtained from specific heat measurements, are 2.29 mJ/mol K and 520 Oe, respectively. Compositional inhomogeneity and strain effect at the grain boundaries are proposed as possible origins for the difference in resistive and bulk superconducting transitions.In addition, a comparison with the rock-salt structure SnAs superconductor is presented. Our results provide the first clear evidence of bulk superconductivity in a natural superlattice derived from a topological semimetal.

cond-mat.supr-con↗
arXiv2018

Deep Learning of Vortex Induced Vibrations

Vortex induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and drag forces on the structure given some limited and scattered information on the velocity field. This is an inverse problem that is not straightforward to solve using standard computational fluid dynamics (CFD) methods, especially since no information is provided for the pressure. An even greater challenge is to infer the lift and drag forces given some dye or smoke visualizations of the flow field. Here we employ deep neural networks that are extended to encode the incompressible Navier-Stokes equations coupled with the structure's dynamic motion equation. In the first case, given scattered data in space-time on the velocity field and the structure's motion, we use four coupled deep neural networks to infer very accurately the structural parameters, the entire time-dependent pressure field (with no prior training data), and reconstruct the velocity vector field and the structure's dynamic motion. In the second case, given scattered data in space-time on a concentration field only, we use five coupled deep neural networks to infer very accurately the vector velocity field and all other quantities of interest as before. This new paradigm of inference in fluid mechanics for coupled multi-physics problems enables velocity and pressure quantification from flow snapshots in small subdomains and can be exploited for flow control applications and also for system identification.

physics.flu-dyn↗
arXiv2018

Study of the rare earth effects on the magnetic fluctuations in RbLn$_2$Fe$_4$As$_4$O$_2$ (Ln = Tb, Dy and Ho) by Mössbauer spectroscopy

In the current work, we report the investigation of RbLn$_2$Fe$_4$As$_4$O$_2$ (Ln = Tb, Dy and Ho) and RbFe$_2$As$_2$ by $^{57}$Fe Mössbauer spectroscopy. Singlet pattern has been observed for all the samples indicating the absence of static magnetic ordering down to 5.9\,K on the Fe sublattice. The observed intermediate value of the isomer shift confirms the effective self charge transfer effect for the studied superconductors. Debye temperatures of these samples have been determined by the temperature dependence of the isomer shift. Most importantly, we observe different spectral line width broadening behaviors for samples with different rare earth elements, and no line width broadenings for samples Ln=Tb and Ho. These results suggest that the observed magnetic fluctuations at the Fe site might be sensitive to the magnetic behaviors of the rare earth magnetic moments.

cond-mat.str-el↗
arXiv2018

Cascaded Pyramid Network for Multi-Person Pose Estimation

The topic of multi-person pose estimation has been largely improved recently, especially with the development of convolutional neural network. However, there still exist a lot of challenging cases, such as occluded keypoints, invisible keypoints and complex background, which cannot be well addressed. In this paper, we present a novel network structure called Cascaded Pyramid Network (CPN) which targets to relieve the problem from these "hard" keypoints. More specifically, our algorithm includes two stages: GlobalNet and RefineNet. GlobalNet is a feature pyramid network which can successfully localize the "simple" keypoints like eyes and hands but may fail to precisely recognize the occluded or invisible keypoints. Our RefineNet tries explicitly handling the "hard" keypoints by integrating all levels of feature representations from the GlobalNet together with an online hard keypoint mining loss. In general, to address the multi-person pose estimation problem, a top-down pipeline is adopted to first generate a set of human bounding boxes based on a detector, followed by our CPN for keypoint localization in each human bounding box. Based on the proposed algorithm, we achieve state-of-art results on the COCO keypoint benchmark, with average precision at 73.0 on the COCO test-dev dataset and 72.1 on the COCO test-challenge dataset, which is a 19% relative improvement compared with 60.5 from the COCO 2016 keypoint challenge.Code (https://github.com/chenyilun95/tf-cpn.git) and the detection results are publicly available for further research.

cs.CV↗
arXiv2018

Mössbauer spectroscopy study of magnetic fluctuations in superconducting RbGd$_2$Fe$_4$As$_4$O$_2$

$^{57}$Fe Mössbauer spectra were measured at different temperatures between 5.9 K and 300 K on the recently discovered self-doped superconducting RbGd$_2$Fe$_4$As$_4$O$_2$ with T$_c$ as high as 35 K. Singlet pattern was observed down to the lowest temperature measured in this work, indicating the absence of static magnetic order on the Fe site. The intermediate isomer shift in comparison with that of the samples RbFe$_2$As$_2$ and GdFeAsO confirms the self doping induced local electronic structure change. Surprisingly, we observe two magnetic fluctuation induced spectral broadenings below $\sim$15 K and $\sim$100 K which are believed to be originated from the transferred magnetic fluctuations of the Gd$^{3+}$ moments and that of the magnetic fluctuations of the Fe atoms, respectively.

cond-mat.supr-con↗
arXiv2017

Multigap superconductivity in ThAsFeN investigated using muSR measurements

We have investigated the superconducting ground state of the newly discovered superconductor ThFeAsN with a tetragonal layered crystal structure using resistivity, magnetization, heat capacity and transverse-field (TF) muon-spin rotation ($μ$SR) measurements. Our resistivity and magnetization measurements reveal an onset of bulk superconductivity with $T_{\bf c}\sim$ 30 K. The heat capacity results show a very small anomaly, $Δ$C$_{ele}$$\sim$0.214 (J/mol-K) at $T_{\bf c}\sim$ 30 K and exhibits exponential behavior below $T_{\bf c}$, which fits better to two superconducting gaps rather than a single gap. Further a nonlinear magnetic field dependence of the electronic specific heat coefficient $γ$(H) has been found in the low temperature limit, which indicates that the smaller energy gap is nodal. Our analysis of the TF-$μ$SR results shows that the temperature dependence of the superfluid density is better described by a two-gap model either isotropic $s$+$s$-wave or $s$+$d$-wave than a single gap isotropic $s$-wave model for the superconducting gap, consistent with other Fe-based superconductors. The combine $γ$(H) and TF-$μ$SR results confirm $s$+$d$-wave model for the gap structure of ThFeAsN. The observation of two gaps in ThFeAsN suggests multiband nature of the superconductivity possibly arising from the d-bands of Fe ions. Furthermore, from our TF-$μ$SR study we have estimated the magnetic penetration depth, in the polycrystalline sample, of $λ_{\mathrm{L}}$$(0)$ = 375 nm, superconducting carrier density $n_s = 4.6 \times 10^{27}~ $m$^{-3}$, and carrier's effective-mass $m^*$ = 2.205\textit{m}$_{e}$. We will compare the results of our present study with those reported for the Fe-pnictide family of superconductors.

cond-mat.supr-con↗
arXiv2017

Neutron powder diffraction study on the iron-based nitride superconductor ThFeAsN

We report neutron diffraction and transport results on the newly discovered superconducting nitride ThFeAsN with $T_c=$ 30 K. No magnetic transition, but a weak structural distortion around 160 K, is observed cooling from 300 K to 6 K. Analysis on the resistivity, Hall transport and crystal structure suggests this material behaves as an electron optimally doped pnictide superconductors due to extra electrons from nitrogen deficiency or oxygen occupancy at the nitrogen site, which together with the low arsenic height may enhance the electron itinerancy and reduce the electron correlations, thus suppress the static magnetic order.

cond-mat.supr-con↗