arXiv subjects

Xiaohui Yu

Publications and source records attributed to Xiaohui Yu.

54 records · Page 3Linked to original sources

arXiv2021

Monoclinic EuSn$_2$As$_2$: A Novel High-Pressure Network Structure

The layered crystal of EuSn$_2$As$_2$ has a Bi$_2$Te$_3$-type structure in rhombohedral ($R\bar{3}m$) symmetry and has been confirmed to be an intrinsic magnetic topological insulator at ambient conditions. Combining {\it ab initio} calculations and \emph{in-situ} x-ray diffraction measurements, we identify a new monoclinic EuSn$_2$As$_2$ structure in $C2/m$ symmetry above $\sim$14 GPa. It has a three-dimensional network made up of honeycomb-like Sn sheets and zigzag As chains, transformed from the layered EuSn$_2$As$_2$ via a two-stage reconstruction mechanism with the connecting of Sn-Sn and As-As atoms successively between the buckled SnAs layers. Its dynamic structural stability has been verified by phonon mode analysis. Electrical resistance measurements reveal an insulator-metal-superconductor transition at low temperature around 5 and 15 GPa, respectively, according to the structural conversion, and the superconductivity with a \textit{T}${\rm {_C}}$ value of $\sim 4$ K is observed up to 30.8 GPa. These results establish a high-pressure EuSn$_2$As$_2$ phase with intriguing structural and electronic properties and expand our understandings about the layered magnetic topological insulators.

cond-mat.mtrl-sci↗

arXiv2020

Dimensional Crossover Tuned by Pressure in Layered Magnetic NiPS3

Layered magnetic transition-metal thiophosphate NiPS3 has unique two-dimensional (2D) magnetic properties and electronic behavior. The electronic band structure and corresponding magnetic state are expected to sensitive to the interlayer interaction, which can be tuned by external pressure. Here, we report an insulator-metal transition accompanied with magnetism collapse during the 2D-3D crossover in structure induced by hydrostatic pressure. A two-stage phase transition from monoclinic (C2=m) to trigonal (P-31m) lattice is identified by ab initio simulation and confirmed by high-pressure XRD and Raman data, corresponding to a layer by layer slip mechanism along the a-axis. Temperature dependence resistance measurements and room temperature infrared spectroscopy show that the insulator-metal transition occurs near 20 GPa as well as magnetism collapse, which is further confirmed by low temperature Raman measurement and theoretical calculation. These results establish a strong correlation among the structural change, electric transport, and magnetic phase transition and expand our understandings about the layered magnetic materials.

cond-mat.str-el↗

arXiv2020

Pressure induced metallization and possible unconventional superconductivity in spin liquid $NaYbSe_{2}$

Beyond the conventional electron pairing mediated by phonons, high-temperature superconductivity in cuprates is believed to stem from quantum spin liquid (QSL). The unconventional superconductivity by doping a spin liquid/Mott insulator, is a long-sought goal but a principal challenge in condensed matter physics because of the lack of an ideal QSL platform. Here we report the pressure induced metallization and possible unconventional superconductivity in $NaYbSe_{2}$, which belongs to a large and ideal family of triangular lattice spin liquid we revealed recently and is evidenced to possess a QSL ground state. The charge gap of NaYbSe2 is gradually reduced by applying pressures, and at ~20 GPa the crystal jumps into a superconducting (SC) phase with Tc ~ 5.8 K even before the insulating gap is completely closed. The metallization is confirmed by further high-pressure experiments but the sign of superconductivity is not well repeated. No symmetry breaking accompanies the SC transition, as indicated by X-ray diffraction and low-temperature Raman experiments under high pressures. This intrinsically connects QSL and SC phases, and suggests an unconventional superconductivity developed from QSL. We further observed the magnetic-field-tuned superconductor-insulator transition which is analogous to that found in the underdoped cuprate superconductor $La_{2-x}Sr_{x}CuO_{4}$. The study is expected to inspire interest in exploring new types of superconductors and sheds light into the intriguing physics from a spin liquid/Mott insulator to a superconductor.

cond-mat.supr-con↗

arXiv2020

Top-k queries over digital traces

Recent advances in social and mobile technology have enabled an abundance of digital traces (in the form of mobile check-ins, association of mobile devices to specific WiFi hotspots, etc.) revealing the physical presence history of diverse sets of entities (e.g., humans, devices, and vehicles). One challenging yet important task is to identify k entities that are most closely associated with a given query entity based on their digital traces. We propose a suite of indexing techniques and algorithms to enable fast query processing for this problem at scale. We first define a generic family of functions measuring the association between entities, and then propose algorithms to transform digital traces into a lower-dimensional space for more efficient computation. We subsequently design a hierarchical indexing structure to organize entities in a way that closely associated entities tend to appear together. We then develop algorithms to process top-k queries utilizing the index. We theoretically analyze the pruning effectiveness of the proposed methods based on a mobility model which we propose and validate in real life situations. Finally, we conduct extensive experiments on both synthetic and real datasets at scale, evaluating the performance of our techniques both analytically and experimentally, confirming the effectiveness and superiority of our approach over other applicable approaches across a variety of parameter settings and datasets.

cs.DB↗

arXiv2020

NLPMM: a Next Location Predictor with Markov Modeling

In this paper, we solve the problem of predicting the next locations of the moving objects with a historical dataset of trajectories. We present a Next Location Predictor with Markov Modeling (NLPMM) which has the following advantages: (1) it considers both individual and collective movement patterns in making prediction, (2) it is effective even when the trajectory data is sparse, (3) it considers the time factor and builds models that are suited to different time periods. We have conducted extensive experiments in a real dataset, and the results demonstrate the superiority of NLPMM over existing methods.

cs.AI↗

arXiv2020

MPE: A Mobility Pattern Embedding Model for Predicting Next Locations

The wide spread use of positioning and photographing devices gives rise to a deluge of traffic trajectory data (e.g., vehicle passage records and taxi trajectory data), with each record having at least three attributes: object ID, location ID, and time-stamp. In this paper, we propose a novel mobility pattern embedding model called MPE to shed the light on people's mobility patterns in traffic trajectory data from multiple aspects, including sequential, personal, and temporal factors. MPE has two salient features: (1) it is capable of casting various types of information (object, location and time) to an integrated low-dimensional latent space; (2) it considers the effect of ``phantom transitions'' arising from road networks in traffic trajectory data. This embedding model opens the door to a wide range of applications such as next location prediction and visualization. Experimental results on two real-world datasets show that MPE is effective and outperforms the state-of-the-art methods significantly in a variety of tasks.

cs.LG↗

arXiv2020

Eating Healthier: Exploring Nutrition Information for Healthier Recipe Recommendation

With the booming of personalized recipe sharing networks (e.g., Yummly), a deluge of recipes from different cuisines could be obtained easily. In this paper, we aim to solve a problem which many home-cooks encounter when searching for recipes online. Namely, finding recipes which best fit a handy set of ingredients while at the same time follow healthy eating guidelines. This task is especially difficult since the lions share of online recipes have been shown to be unhealthy. In this paper we propose a novel framework named NutRec, which models the interactions between ingredients and their proportions within recipes for the purpose of offering healthy recommendation. Specifically, NutRec consists of three main components: 1) using an embedding-based ingredient predictor to predict the relevant ingredients with user-defined initial ingredients, 2) predicting the amounts of the relevant ingredients with a multi-layer perceptron-based network, 3) creating a healthy pseudo-recipe with a list of ingredients and their amounts according to the nutritional information and recommending the top similar recipes with the pseudo-recipe. We conduct the experiments on two recipe datasets, including Allrecipes with 36,429 recipes and Yummly with 89,413 recipes, respectively. The empirical results support the framework's intuition and showcase its ability to retrieve healthier recipes.

cs.IR↗

arXiv2020

TTDM: A Travel Time Difference Model for Next Location Prediction

Next location prediction is of great importance for many location-based applications and provides essential intelligence to business and governments. In existing studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Unfortunately, due to the time and space complexity, these methods (e.g., Markov models) only use the just passed locations to predict next locations, without considering all the passed locations in the trajectory. In this paper, we seek to enhance the prediction performance by considering the travel time from all the passed locations in the query trajectory to a candidate next location. In particular, we propose a novel method, called Travel Time Difference Model (TTDM), which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Further, we integrate the TTDM with a Markov model via a linear interpolation to yield a joint model, which computes the probability of reaching each possible next location and returns the top-rankings as results. We have conducted extensive experiments on two real datasets: the vehicle passage record (VPR) data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over existing solutions. For example, compared with the Markov model, the top-1 accuracy improves by 40% on the VPR data and by 15.6% on the Taxi data.

cs.AI↗

arXiv2020

PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction

Traffic problems have seriously affected people's life quality and urban development, and forecasting the short-term traffic congestion is of great importance to both individuals and governments. However, understanding and modeling the traffic conditions can be extremely difficult, and our observations from real traffic data reveal that (1) similar traffic congestion patterns exist in the neighboring time slots and on consecutive workdays; (2) the levels of traffic congestion have clear multiscale properties. To capture these characteristics, we propose a novel method named PCNN based on deep Convolutional Neural Network, modeling Periodic traffic data for short-term traffic congestion prediction. PCNN has two pivotal procedures: time series folding and multi-grained learning. It first temporally folds the time series and constructs a two-dimensional matrix as the network input, such that both the real-time traffic conditions and past traffic patterns are well considered; then with a series of convolutions over the input matrix, it is able to model the local temporal dependency and multiscale traffic patterns. In particular, the global trend of congestion can be addressed at the macroscale; whereas more details and variations of the congestion can be captured at the microscale. Experimental results on a real-world urban traffic dataset confirm that folding time series data into a two-dimensional matrix is effective and PCNN outperforms the baselines significantly for the task of short-term congestion prediction.

eess.SP↗

arXiv2020

TraLFM: Latent Factor Modeling of Traffic Trajectory Data

The widespread use of positioning devices (e.g., GPS) has given rise to a vast body of human movement data, often in the form of trajectories. Understanding human mobility patterns could benefit many location-based applications. In this paper, we propose a novel generative model called TraLFM via latent factor modeling to mine human mobility patterns underlying traffic trajectories. TraLFM is based on three key observations: (1) human mobility patterns are reflected by the sequences of locations in the trajectories; (2) human mobility patterns vary with people; and (3) human mobility patterns tend to be cyclical and change over time. Thus, TraLFM models the joint action of sequential, personal and temporal factors in a unified way, and brings a new perspective to many applications such as latent factor analysis and next location prediction. We perform thorough empirical studies on two real datasets, and the experimental results confirm that TraLFM outperforms the state-of-the-art methods significantly in these applications.

cs.SI↗

arXiv2020

Evaluating Temporal Queries Over Video Feeds

Recent advances in Computer Vision and Deep Learning made possible the efficient extraction of a schema from frames of streaming video. As such, a stream of objects and their associated classes along with unique object identifiers derived via object tracking can be generated, providing unique objects as they are captured across frames. In this paper we initiate a study of temporal queries involving objects and their co-occurrences in video feeds. For example, queries that identify video segments during which the same two red cars and the same two humans appear jointly for five minutes are of interest to many applications ranging from law enforcement to security and safety. We take the first step and define such queries in a way that they incorporate certain physical aspects of video capture such as object occlusion. We present an architecture consisting of three layers, namely object detection/tracking, intermediate data generation and query evaluation. We propose two techniques,MFS and SSG, to organize all detected objects in the intermediate data generation layer, which effectively, given the queries, minimizes the number of objects and frames that have to be considered during query evaluation. We also introduce an algorithm called State Traversal (ST) that processes incoming frames against the SSG and efficiently prunes objects and frames unrelated to query evaluation, while maintaining all states required for succinct query evaluation. We present the results of a thorough experimental evaluation utilizing both real and synthetic data establishing the trade-offs between MFS and SSG. We stress various parameters of interest in our evaluation and demonstrate that the proposed query evaluation methodology coupled with the proposed algorithms is capable to evaluate temporal queries over video feeds efficiently, achieving orders of magnitude performance benefits.

cs.DB↗

arXiv2019

Pressure-driven switching of magnetism in layered CrCl3

Layered transition-metal compounds with controllable magnetic behaviors provide many fascinating opportunities for the fabrication of high-performance magneto-electric and spintronic devices. The tuning of their electronic and magnetic properties is usually limited to the change of layer thickness, electrostatic doping, and the control of electric and magnetic fields. However, pressure has been rarely exploited as a control parameter for tailoring their magneto-electric properties. Here, we report a unique pressure-driven isostructural phase transition in layered CrCl3 accompanied by a simultaneous switching of magnetism from a ferromagnetic to an antiferromagnetic ordering. Our experiments, in combination with ab initio calculations, demonstrate that such a magnetic transformation hinders the band-gap collapse under pressure, leading to an anomalous semiconductor-to-semiconductor transition. Our findings not only reveal high potential of CrCl3 in electronic and spintronic applications under ambient and extreme conditions but also establish the basis for exploring unusual phase transitions in layered transition-metal compounds.

cond-mat.mtrl-sci↗

arXiv2017

Liouville Type Theorem for Some Nonlocal Elliptic Equations

In this paper, we prove some Liouville theorem for the following elliptic equations involving nonlocal nonlinearity and nonlocal boundary value condition $$ \left\{ \begin{array}{ll} \displaystyle -Δu(y)=\intpr \frac{ F(u(x',0))}{|(x',0)-y|^{N-α}}dx'g(u(y)), &y\in\R, \\ \\ \displaystyle \frac{\partial u}{\partial ν}(x',0)=\intr \frac{G(u(y))}{|(x',0)-y|^{N-α}}\,dy f(u(x',0)), &(x',0)\in\partial \mathbb R_+^N, \end{array} \right. $$ where $\mathbb R_+^N=\{x\in \mathbb R^N:x_N>0\}$, $f,g,F,G$ are some nonlinear functions. Under some assumptions on the nonlinear functions $f,g,F,G$, we will show that this equation doesn't possess nontrivial positive solution. We extend the Liouville theorems from local problems to nonlocal problem. We use the moving plane method to prove our result.

math.AP↗

arXiv2015

Equations involving fractional Laplacian operator: Compactness and application

In this paper, we consider the following problem involving fractional Laplacian operator: \begin{equation}\label{eq:0.1} (-Δ)^α u= |u|^{2^*_α-2-\varepsilon}u + λu\,\, {\rm in}\,\, Ω,\quad u=0 \,\, {\rm on}\, \, \partialΩ, \end{equation} where $Ω$ is a smooth bounded domain in $\mathbb{R}^N$, $\varepsilon\in [0, 2^*_α-2)$, $0<α<1,\, 2^*_α= \frac {2N}{N-2α}$. We show that for any sequence of solutions $u_n$ of \eqref{eq:0.1} corresponding to $\varepsilon_n\in [0, 2^*_α-2)$, satisfying $\|u_n\|_{H}\le C$ in the Sobolev space $H$ defined in \eqref{eq:1.1a}, $u_n$ converges strongly in $H$ provided that $N>6α$ and $λ>0$. An application of this compactness result is that problem \eqref{eq:0.1} possesses infinitely many solutions under the same assumptions.

math.AP↗

arXiv2015

Fractional Hardy-Sobolev elliptic problems

In this paper, we study the following singular nonlinear elliptic problem \begin{equation}\label{eq:1} \left\{ \begin{array}{ll} \displaystyle (-Δ)^{\frac α2} u=λ|u|^{r-2}u+μ\frac{|u|^{q-2}u}{|x|^{s}}\quad &{\rm in }\quad Ω, \\ \\ u=0 &{\rm on }\quad \partialΩ, \end{array} \right. \end{equation} where $Ω$ is a smooth bounded domain in $\mathbb R^N$ with $0\in Ω$, $λ,μ>0,0<s\leqα$, $(-Δ)^{\frac α2}$ is the fractional Laplacian operator with $0<α<2$. We establish existence results of problem \eqref{eq:1} for subcritical, Sobolev critical and Hardy-Sobolev critical cases.

math.AP↗

arXiv2014

Liouville Type Theorems for Two Mixed Boundary Value Problems with General Nonlinearities

In this paper, we study the nonexistence of positive solutions for the following two mixed boundary value problems. The first problem is the mixed nonlinear-Neumann boundary value problem $$ \left\{ \begin{array}{ll} \displaystyle -Δu=f(u) &{\rm in}\quad \R, \\ \displaystyle \\ \frac{\partial u}{\partial ν}=g(u) &{\rm on}\quad Γ_1,\\ \displaystyle \\ \frac{\partial u}{\partial ν}=0 &{\rm on}\quad Γ_0 \end{array} \right. $$ and the second is the nonlinear-Dirichlet boundary value problem $$ \left\{ \begin{array}{ll} \displaystyle -Δu=f(u) &{\rm in}\quad \R, \\ \displaystyle \\ \frac{\partial u}{\partial ν}=g(u) &{\rm on}\quad Γ_1,\\ \displaystyle \\ u=0 &{\rm on}\quad Γ_0, \end{array} \right. $$ where $\R=\{x\in \mathbb R^N:x_N>0\}$, $Γ_1=\{x\in \mathbb R^N:x_N=0,x_1<0\}$ and $Γ_0=\{x\in \mathbb R^N:x_N=0,x_1>0\}$. We will prove that these problems possess no positive solution under some assumptions on the nonlinear terms. The main technique we use is the moving plane method in an integral form.

math.AP↗

arXiv2014

Existence results for superlinear elliptic equations with nonlinear boundary value conditions

In this paper, we study the existence of solutions for the following superlinear elliptic equation with nonlinear boundary value condition $$ \left\{ \begin{array}{ll} -Δu+u=|u|^{r-2}u &\text{in} \; Ω,\\ \\ \frac{\partial u}{\partial ν}=|u|^{q-2}u &\text{on}\;\partialΩ, \end{array} \right. $$ where $Ω\subset \mathbb R^N, N\geq 3$ is a bounded domain with smooth boundary. We will prove the existence results for the above equation under four different cases: (i) Both $q$ and $r$ are subcritical; (ii) $r$ is critical and $q$ is subcritical; (iii) $r$ is subcritical and $q$ is critical; (iv) Both $q$ and $r$ are critical.

math.AP↗

arXiv2011

Pressure-induced isostructural phase transition and correlation of FeAs coordination with the superconducting properties of 111-type Na1-xFeAs

The effect of pressure on the crystalline structure and superconducting transition temperature (Tc) of the 111-type Na1-xFeAs system using in situ high pressure synchrotron x-ray powder diffraction and diamond anvil cell techniques is studied. A pressure-induced tetragonal to tetragonal isostructural phase transition was found. The systematic evolution of the FeAs4 tetrahedron as a function of pressure based on Rietveld refinements on the powder x-ray diffraction patterns was obtained. The non-monotonic Tc(P) behavior of Na1-xFeAs is found to correlate with the anomalies of the distance between the anion (As) and the iron layer as well as the bond angle between As-Fe-As for the two tetragonal phases. This behavior provides the key structural information in understanding the origin of the pressure dependence of Tc for 111-type iron pnictide superconductors. A pressure-induced structural phase transition is also observed at 20 GPa.

cond-mat.supr-con↗