arXiv subjects

Yanwei Cui

Publications and source records attributed to Yanwei Cui.

32 records · Page 2Linked to original sources

arXiv2021

Metal-insulator-like transition, superconducting dome and topological electronic structure in Ga-doped Re$_{3}$Ge$_{7}$

Superconductivity frequently appears by doping compounds that show a collective phase transition. So far, however, this has not been observed in topological materials. Here we report the discovery of superconductivity induced by Ga doping in orthorhombic Re$_{3}$Ge$_{7}$, which undergoes a second-order metal-insulator-like transition at $\sim$58 K and is predicted to have a nontrivial band topology. It is found that the substitution of Ga for Ge leads to hole doping in Re$_{3}$Ge$_{7-x}$Ga$_{x}$. As a consequence, the phase transition is gradually suppressed and disappears above $x$ = 0.2. At this $x$ value, superconductivity emerges and $T_{\rm c}$ exhibits a dome-like doping dependence with a maximum value of 3.37 K at $x$ = 0.25. First-principles calculations suggest that the phase transition in Re$_{3}$Ge$_{7}$ is associated with an electronic instability driven by Fermi surface nesting and the nontrival band topology is preserved after Ga doping. Our results indicate that Ga-doped Re$_{3}$Ge$_{7}$ provides a rare opportunity to study the interplay between superconductivity and competing electronic states in a topologically nontrivial system.

cond-mat.supr-con↗

arXiv2021

Superconductivity in ThMo2Si2C with Mo2C Square Net

We report the superconductivity of a new quaternary compound ThMo$_2$Si$_2$C, synthesized with the arc-melting technique. The compound crystallizes in a tetragonal CeCr$_2$Si$_2$C-type structure with cell parameters of $a$ = 4.2296 Åand $c$ = 5.3571 Å. An interlayer Si-Si covalent bonding is suggested by the atomic distance. The electrical resistivity and magnetic susceptibility measurements indicate a Pauli-paramagnetic metal with dominant electron-electron scattering in the normal-state. Bulk superconductivity at 2.2 K is demonstrated with a dimensionless specific-heat jump of $ΔC/γ_{\rm n}T$ = 0.98. The superconducting parameters of the critical magnetic fields, coherence length, penetration depth, and superconducting energy gap are given.

cond-mat.supr-con↗

arXiv2020

Doping-induced superconductivity in the topological semimetal Mo5Si3

Chemical doping of topological materials may provide a possible route for realizing topological superconductivity. However, all such cases known so far are based on chalcogenides. Here we report the discovery of superconductivity induced by Re doping in the topological semimetal Mo$_{5}$Si$_{3}$ with a tetragonal structure. Partial substitution of Re for Mo in Mo$_{5-x}$Re$_{x}$Si$_{3}$ results in an anisotropic shrinkage of the unit cell up to the solubility limit of approximately $x$ = 2. Over a wide doping range (0.5 $\leq$ $x$ $\leq$ 2), these silicides are found to be weakly coupled superconductors with a fully isotropic gap. $T_{\rm c}$ increases monotonically with $x$ from 1.67 K to 5.78 K, the latter of which is the highest among superconductors of the same structural type. This trend in $T_{\rm c}$ correlates well with the variation of the number of valence electrons, and is mainly ascribed to the enhancement of electron-phonon coupling. In addition, band structure calculations reveal that superconducting Mo$_{5-x}$Re$_{x}$Si$_{3}$ exhibits nontrivial band topology characterized by $Z_{2}$ invariants (1;000) or (1;111) depending on the Re doping level. Our results suggest that transition metal silicides are a fertile ground for the exploration of candidate topological superconductors.

cond-mat.supr-con↗

arXiv2020

Superconductivity in hexagonal Nb-Mo-Ru-Rh-Pd high-entropy alloys

We report the superconducting properties of new hexagonal Nb$_{10+2x}$Mo$_{35-x}$Ru$_{35-x}$Rh$_{10}$Pd$_{10}$ high-entropy alloys (HEAs) (0 $\leq$ $x$ $\leq$ 5). With increasing $x$, the superconducting transition temperature $T_{\rm c}$ shows a maximum of 6.19 K at $x$ = 2.5, while the zero-temperature upper critical field $B_{\rm c2}$(0) increases monotonically, reaching 8.3 T at $x$ = 5. For all $x$ values, the specific heat jump deviates from the Bardeen-Cooper-Schreiffer behavior. In addition, we show that $T_{\rm c}$ of these HEAs is not determined mainly by the density of states at the Fermi level and would be enhanced by lowering the valence electron concentration.

cond-mat.supr-con↗

arXiv2020

Polymorphism and superconductivity in the V-Nb-Mo-Al-Ga high-entropy alloys

High-entropy alloys (HEAs) are at the focus of current research for their diverse properties, including superconductivity and structural polymorphism. However, the polymorphic transition has been observed only in nonsuperconducting HEAs and mostly under high pressure. Here we report the discovery of superconductivity and temperature-driven polymorphism in the (V$_{0.5}$Nb$_{0.5}$)$_{3-x}$Mo$_{x}$Al$_{0.5}$Ga$_{0.5}$ (0.2 $\leq$ $x$ $\leq$ 1.4) HEAs. It is found that the as-cast HEA is of a single body-centered cubic (bcc) structure for $x$ = 0.2 and a mixture of the bcc and A15 structures for higher $x$ values. Upon annealing, the bcc structure undergoes a polymorphic transformation to the A15 one and all HEAs exhibits bulk superconductivity. For $x$ = 0.2, whereas the bcc polymorph is not superconducting down to 1.8 K, the A15 polymorph has a superconducting transition temperature $T_{\rm c}$ of 10.2 K and an estimated zero-temperature upper critical field $B_{\rm c2}$(0) of 20.1 T, both of which are the highest among HEA superconductors. With increasing Mo content $x$, both $T_{\rm c}$ and $B_{\rm c2}$(0) of the A15-type HEAs decrease, yet the large ratio of $B_{\rm c2}$(0)/$T_{\rm c}$ signifies a disorder-induced enhancement of the upper critical field over a wide $x$ range. The decrease in $T_{\rm c}$ is attributed to the decrease in both the electronic specific-heat coefficient and electron-phonon coupling strength. Furthermore, the valence electron count dependence of $T_{\rm c}$, which is different from both the binary A15 and other structurally different HEA superconductors, suggests that $T_{\rm c}$ may be increased further by reducing the number of valence electrons. Our results not only uncover HEA superconductors of a new structural type, but also provide the first example of polymorphism dependent superconductivity in HEAs.

cond-mat.supr-con↗

arXiv2019

Understand customer reviews with less data and in short time: pretrained language representation and active learning

In this paper, we address customer review understanding problems by using supervised machine learning approaches, in order to achieve a fully automatic review aspects categorisation and sentiment analysis. In general, such supervised learning algorithms require domain-specific expert knowledge for generating high quality labeled training data, and the cost of labeling can be very high. To achieve an in-production customer review machine learning enabled analysis tool with only a limited amount of data and within a reasonable training data collection time, we propose to use pre-trained language representation to boost model performance and active learning framework for accelerating the iterative training process. The results show that with integration of both components, the fully automatic review analysis can be achieved at a much faster pace.

cs.CL↗

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

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

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

Modelling customer online behaviours with neural networks: applications to conversion prediction and advertising retargeting

In this paper, we apply neural networks into digital marketing world for the purpose of better targeting the potential customers. To do so, we model the customer online behaviours using dedicated neural network architectures. Starting from user searched keywords in a search engine to the landing page and different following pages, until the user left the site, we model the whole visited journey with a Recurrent Neural Network (RNN), together with Convolution Neural Networks (CNN) that can take into account of the semantic meaning of user searched keywords and different visited page names. With such model, we use Monte Carlo simulation to estimate the conversion rates of each potential customer in the future visiting. We believe our concept and the preliminary promising results in this paper enable the use of largely available customer online behaviours data for advanced digital marketing analysis.

cs.LG↗

arXiv2016

Combining multiple resolutions into hierarchical representations for kernel-based image classification

Geographic object-based image analysis (GEOBIA) framework has gained increasing interest recently. Following this popular paradigm, we propose a novel multiscale classification approach operating on a hierarchical image representation built from two images at different resolutions. They capture the same scene with different sensors and are naturally fused together through the hierarchical representation, where coarser levels are built from a Low Spatial Resolution (LSR) or Medium Spatial Resolution (MSR) image while finer levels are generated from a High Spatial Resolution (HSR) or Very High Spatial Resolution (VHSR) image. Such a representation allows one to benefit from the context information thanks to the coarser levels, and subregions spatial arrangement information thanks to the finer levels. Two dedicated structured kernels are then used to perform machine learning directly on the constructed hierarchical representation. This strategy overcomes the limits of conventional GEOBIA classification procedures that can handle only one or very few pre-selected scales. Experiments run on an urban classification task show that the proposed approach can highly improve the classification accuracy w.r.t. conventional approaches working on a single scale.

cs.CV↗

arXiv2016

Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach

Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy. One of the most popular way to integrate such information is to extract hierarchical features from a multiscale segmentation. In the classification context, the extracted features are commonly concatenated into a long vector (also called stacked vector), on which is applied a conventional vector-based machine learning technique (e.g. SVM with Gaussian kernel). In this paper, we rather propose to use a sequence structured kernel: the spectrum kernel. We show that the conventional stacked vector-based kernel is actually a special case of this kernel. Experiments conducted on various publicly available hyperspectral datasets illustrate the improvement of the proposed kernel w.r.t. conventional ones using the same hierarchical spatial features.

cs.CV↗

arXiv2016

A Subpath Kernel for Learning Hierarchical Image Representations

Tree kernels have demonstrated their ability to deal with hierarchical data, as the intrinsic tree structure often plays a discriminative role. While such kernels have been successfully applied to various domains such as nature language processing and bioinformatics, they mostly concentrate on ordered trees and whose nodes are described by symbolic data. Meanwhile, hierarchical representations have gained increasing interest to describe image content. This is particularly true in remote sensing, where such representations allow for revealing different objects of interest at various scales through a tree structure. However, the induced trees are unordered and the nodes are equipped with numerical features. In this paper, we propose a new structured kernel for hierarchical image representations which is built on the concept of subpath kernel. Experimental results on both artificial and remote sensing datasets show that the proposed kernel manages to deal with the hierarchical nature of the data, leading to better classification rates.

cs.CV↗