Searcharxiv⌕ Search

arXiv subjects

Yue Wang

Publications and source records attributed to Yue Wang.

At least 901 records · Page 50Linked to original sources
arXiv2017

Layer-by-layer epitaxial growth of scalable WSe2 on sapphire by molecular-beam epitaxy

Molecular-beam epitaxy (MBE) provides a simple but powerful way to synthesize large-area high-quality thin films and heterostructures of a wide variety of materials including accomplished group III-V and II-VI semiconductors as well as newly-developing oxides and chalcogenides, leading to major discoveries in condensed-matter physics. For two-dimensional (2D) materials, however, main fabrication routes have been mechanical exfoliation and chemical-vapor deposition by making good use of weak van der Waals bonding nature between neighboring layers, and MBE growth of 2D materials, in particular on insulating substrates for transport measurements, has been limited despite its fundamental importance for future advanced research. Here we report layer-by-layer epitaxial growth of scalable transition-metal dichalocogenide (TMDC) thin films on insulating substrates by MBE, and demonstrate ambipolar transistor operation. The proposed growth protocol is broadly applicable to other TMDC, providing a key milestone toward fabrication of van der Waals heterostructures with various 2D materials for novel properties and functionalities.

cond-mat.mtrl-sci↗
arXiv2017

End-to-end Learning for Short Text Expansion

Effectively making sense of short texts is a critical task for many real world applications such as search engines, social media services, and recommender systems. The task is particularly challenging as a short text contains very sparse information, often too sparse for a machine learning algorithm to pick up useful signals. A common practice for analyzing short text is to first expand it with external information, which is usually harvested from a large collection of longer texts. In literature, short text expansion has been done with all kinds of heuristics. We propose an end-to-end solution that automatically learns how to expand short text to optimize a given learning task. A novel deep memory network is proposed to automatically find relevant information from a collection of longer documents and reformulate the short text through a gating mechanism. Using short text classification as a demonstrating task, we show that the deep memory network significantly outperforms classical text expansion methods with comprehensive experiments on real world data sets.

cs.CL↗
arXiv2017

A Consumer-Centric Market for Database Computation in the Cloud

The availability of public computing resources in the cloud has revolutionized data analysis, but requesting cloud resources often involves complex decisions for consumers. Under the current pricing mechanisms, cloud service providers offer several service options and charge consumers based on the resources they use. Before they can decide which cloud resources to request, consumers have to estimate the completion time and cost of their computational tasks for different service options and possibly for different service providers. This estimation is challenging even for expert cloud users. We propose a new market-based framework for pricing computational tasks in the cloud. Our framework introduces an agent between consumers and cloud providers. The agent takes data and computational tasks from users, estimates time and cost for evaluating the tasks, and returns to consumers contracts that specify the price and completion time. Our framework can be applied directly to existing cloud markets without altering the way cloud providers offer and price services. In addition, it simplifies cloud use for consumers by allowing them to compare contracts, rather than choose resources directly. We present design, analytical, and algorithmic contributions focusing on pricing computation contracts, analyzing their properties, and optimizing them in complex workflows. We conduct an experimental evaluation of our market framework over a real-world cloud service and demonstrate empirically that our market ensures three key properties: competitiveness, fairness, and resilience. Finally, we present a fine-grained pricing mechanism for complex workflows and show that it can increase agent profits by more than an order of magnitude in some cases.

cs.DB↗
arXiv2017

Observation of Various and Spontaneous Magnetic Skyrmionic Bubbles at Room-Temperature in a Frustrated Kagome Magnet with Uniaxial Magnetic Anisotropy

Various and spontaneous magnetic skyrmionic bubbles are experimentally observed for the first time, at room temperature in a frustrated kagome magnet Fe3Sn2 with unixial magnetic anisotropy. The magnetization dynamics were investigated using in-situ Lorentz transmission electron microscopy, revealing that the transformation between different magnetic bubbles and domains are via the motion of Bloch lines driven by applied external magnetic field. The results demonstrate that Fe3Sn2 facilitates a unique magnetic control of topological spin textures at room temperature, making it a promising candidate for further skyrmion-based spintronic devices.

cond-mat.mtrl-sci↗
arXiv2017

Refined Asymptotics for Minimal Graphs in the Hyperbolic Space

We study the boundary behaviors of solutions $f$ to the Dirichlet problem for minimal graphs in the hyperbolic space with singular asymptotic boundaries and characterize the boundary behaviors of $f$ at the points strictly located in the tangent cones at the singular points on the boundary. For $n=2$, we also obtain a refined estimate of $f.$

math.AP↗
arXiv2017

Millimeter Wave LOS Coverage Enhancements with Coordinated High-Rise Access Points

Millimetre wave (mm-wave) communication is considered as one of the most important enablers for the fifth generation communication (5G) system to support data rate of Gbps and above. In some scenarios, it is crucial to maintain a line of sight (LOS) link for users enjoying 5G immersive experiences and thus requiring very high data rate. In this paper, we investigate the LOS probability in mm-wave systems. In particular, we study the impact of access point (AP) and blockage height on the LOS probability and propose a solution to effectively enhance the LOS coverage by using high-rise APs on top of low-rise APs normally installed on street furniture, e.g., lamp poles. Two deployment options are explored: 1) irregular deployment and 2) regular deployment, where LOS probability is derived for both cases. Simulation results show that the impact of AP height on LOS probability is significant and using coordinated high-rise APs jointly deployed with low-rise APs will substantially improve the LOS probability.

cs.IT↗
arXiv2017

Synthesizing Mapping Relationships Using Table Corpus

Mapping relationships, such as (country, country-code) or (company, stock-ticker), are versatile data assets for an array of applications in data cleaning and data integration like auto-correction and auto-join. However, today there are no good repositories of mapping tables that can enable these intelligent applications. Given a corpus of tables such as web tables or spreadsheet tables, we observe that values of these mappings often exist in pairs of columns in same tables. Motivated by their broad applicability, we study the problem of synthesizing mapping relationships using a large table corpus. Our synthesis process leverages compatibility of tables based on co-occurrence statistics, as well as constraints such as functional dependency. Experiment results using web tables and enterprise spreadsheets suggest that the proposed approach can produce high quality mappings.

cs.DB↗
arXiv2017

Revisiting Differentially Private Hypothesis Tests for Categorical Data

In this paper, we consider methods for performing hypothesis tests on data protected by a statistical disclosure control technology known as differential privacy. Previous approaches to differentially private hypothesis testing either perturbed the test statistic with random noise having large variance (and resulted in a significant loss of power) or added smaller amounts of noise directly to the data but failed to adjust the test in response to the added noise (resulting in biased, unreliable $p$-values). In this paper, we develop a variety of practical hypothesis tests that address these problems. Using a different asymptotic regime that is more suited to hypothesis testing with privacy, we show a modified equivalence between chi-squared tests and likelihood ratio tests. We then develop differentially private likelihood ratio and chi-squared tests for a variety of applications on tabular data (i.e., independence, sample proportions, and goodness-of-fit tests). Experimental evaluations on small and large datasets using a wide variety of privacy settings demonstrate the practicality and reliability of our methods.

cs.CR↗
arXiv2017

Thermodynamical Effects and High Resolution Methods for Compressible Fluid Flows

One of the fundamental differences of compressible fluid flows from incompressible fluid flows is the involvement of thermodynamics. This difference should be manifested in the design of numerical methods and seems often be neglected in addition that the entropy inequality, as a conceptual derivative, is taken into account to reflect irreversible processes and verified for some first order schemes. In this paper, we refine the GRP solver to illustrate how the thermodynamical variation is integrated into the design of high resolution methods for compressible fluid flows and demonstrate numerically the importance of thermodynamic effect in the resolution of strong waves. As a by-product, we show that the GRP solver works for generic equations of state, and is independent of technical arguments.

math.NA↗
arXiv2016

Sensitivity of the mixing current technique to detect nano-mechanical motion

Detection of nano-mechanical displacement by transport techniques has reached high level of sensitivity and versatility. In order to detect the amplitude of oscillation of nano-mechanical oscillator a widely used technique consists to couple this motion capacitively to a single-electron transistor and to detect the high-frequency modulation of the current through the non-linear mixing with an electric signal at a slighltly detuned frequency. The method known as current-mixing technique is employed in particular for the detection of suspended carbon nanotubes. In this paper we study theoretically the limiting conditions on the sensitivity of this method. The sensitivity is increased by increasing the response function to the signal, but also by reducing the noise. For these reasons we study systematically the response function, the effect of current-and displacement-fluctuations, and finally the case where the tunnelling rate of the electrons are of the same order or larger of the resonating frequency. We find thus upper bounds to the sensitivity of the detection technique.

cond-mat.mes-hall↗
arXiv2016

Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation

Many standard robotic platforms are equipped with at least a fixed 2D laser range finder and a monocular camera. Although those platforms do not have sensors for 3D depth sensing capability, knowledge of depth is an essential part in many robotics activities. Therefore, recently, there is an increasing interest in depth estimation using monocular images. As this task is inherently ambiguous, the data-driven estimated depth might be unreliable in robotics applications. In this paper, we have attempted to improve the precision of monocular depth estimation by introducing 2D planar observation from the remaining laser range finder without extra cost. Specifically, we construct a dense reference map from the sparse laser range data, redefining the depth estimation task as estimating the distance between the real and the reference depth. To solve the problem, we construct a novel residual of residual neural network, and tightly combine the classification and regression losses for continuous depth estimation. Experimental results suggest that our method achieves considerable promotion compared to the state-of-the-art methods on both NYUD2 and KITTI, validating the effectiveness of our method on leveraging the additional sensory information. We further demonstrate the potential usage of our method in obstacle avoidance where our methodology provides comprehensive depth information compared to the solution using monocular camera or 2D laser range finder alone.

cs.CV↗
arXiv2016

Generalization Error Bounds for Optimization Algorithms via Stability

Many machine learning tasks can be formulated as Regularized Empirical Risk Minimization (R-ERM), and solved by optimization algorithms such as gradient descent (GD), stochastic gradient descent (SGD), and stochastic variance reduction (SVRG). Conventional analysis on these optimization algorithms focuses on their convergence rates during the training process, however, people in the machine learning community may care more about the generalization performance of the learned model on unseen test data. In this paper, we investigate on this issue, by using stability as a tool. In particular, we decompose the generalization error for R-ERM, and derive its upper bound for both convex and non-convex cases. In convex cases, we prove that the generalization error can be bounded by the convergence rate of the optimization algorithm and the stability of the R-ERM process, both in expectation (in the order of $\mathcal{O}((1/n)+\mathbb{E}ρ(T))$, where $ρ(T)$ is the convergence error and $T$ is the number of iterations) and in high probability (in the order of $\mathcal{O}\left(\frac{\log{1/δ}}{\sqrt{n}}+ρ(T)\right)$ with probability $1-δ$). For non-convex cases, we can also obtain a similar expected generalization error bound. Our theorems indicate that 1) along with the training process, the generalization error will decrease for all the optimization algorithms under our investigation; 2) Comparatively speaking, SVRG has better generalization ability than GD and SGD. We have conducted experiments on both convex and non-convex problems, and the experimental results verify our theoretical findings.

stat.ML↗
arXiv2016

Nonvolatile Multi-level Memory and Boolean Logic Gates Based on a Single Memtranstor

Memtranstor that correlates charge and magnetic flux via nonlinear magnetoelectric effects has a great potential in developing next-generation nonvolatile devices. In addition to multi-level nonvolatile memory, we demonstrate here that nonvolatile logic gates such as NOR and NAND can be implemented in a single memtranstor made of the Ni/PMN-PT/Ni heterostructure. After applying two sequent voltage pulses (X1, X2) as the logic inputs on the memtranstor, the output magnetoelectric voltage can be positive high (logic "1"), positive low (logic "0"), or negative (logic "0"), depending on the levels of X1 and X2. The underlying physical mechanism is related to the complete or partial reversal of ferroelectric polarization controlled by inputting selective voltage pulses, which determines the magnitude and sign of the magnetoelectric voltage coefficient. The combined functions of both memory and logic could enable the memtranstor as a promising candidate for future computing systems beyond von Neumann architecture.

cs.ET↗
arXiv2016

Boundary Expansions for Constant Mean Curvature Surfaces in the Hyperbolic Space

We study expansions near the boundary of solutions to the Dirichlet problem for the constant mean curvature equation in the hyperbolic space. With a characterization of remainders of the expansion by multiple integrals, we establish optimal asymptotic expansions of solutions with boundary values of finite regularity and demonstrate a slight loss of regularity for coefficients.

math.AP↗
arXiv2016

Distributed Beam Scheduling for Multi-RAT Coexistence in mm-Wave 5G Networks

Millimetre-wave communication (licensed or unlicensed) is envisaged to be an important part of the fifth generation (5G) multi-RAT ecosystem. In this paper, we consider the spectrum bands shared by 5G cellular base stations and some existing networks, such as WiGig. Sharing the same band among such multiple radio access technologies (RATs) is very challenging due to the lack of centralized coordination and demands novel and efficient interference mitigation and coexistence mechanisms to reduce the mutual interference. To address this important challenge, we propose in this paper a novel multi-RAT coexistence mechanism where neighbouring 5G and WiGig base stations, each serving their own associated UEs, schedule their beam configurations in a distributed manner such that their own utility function, e.g. spectral efficiency, is maximized. We formulate the problem as a combinatorial optimization problem and show via simulations that our proposed distributed algorithms yield a comparable spectral efficiency for the entire networks as that using an exhaustive search, which requires global coordination among coexisting RATs and also has a much higher algorithmic complexity.

cs.IT↗
arXiv2016

A centrosymmetric hexagonal magnet with superstable biskyrmion magnetic nanodomains in a wide temperature range of 100K to 340K

Here, we report on the experimental discovery of biskyrmion magnetic nanodomains at RT and the observation of a biskyrmion-derived topological Hall effect (THE) in the centrosymmetric hexagonal MnNiGa magnet. Using a phase reconstruction technique based on a transport-of-intensity equation (TIE), we established the texture of the biskyrmion spin. Results from Lorentz transmission electron microscopy (TEM) and the topological Hall effect revealed that the biskyrmion phase is stable over a much wider temperature range (100 K to ~340K) and a larger magnetic field range in our material than in skyrmion-hosting bulk crystals reported previously[1-3,6]. The super-wide operating temperature and the broad range of material options indicate important progress toward the realization of skyrmion-based spintronic devices.

cond-mat.mtrl-sci↗
arXiv2016

Service Intelligence Oriented Distributed Data Stream Integration

Software as a service (SaaS) has recently enjoyed much attention as it makes the use of software more convenient and cost-effective. At the same time, the arising of users' expectation for high quality service such as real-time information or functionality provisioning brings about new challenges: to satisfy such (near) real-time requirements, real-time monitoring and effective processing of streaming data is necessary. However, due to the composition structure and multi-instance property of services, service data streams are often distributed, hard to synchronize and aggregate. We tackles these challenges by (1) proposing systematic associate strategies for relating distributed data; (2) introducing a new small window array mechanism for aggregating distributed data; (3) setting window parameters based on the cumulative distribution function (CDF) method; and (4) modeling streaming operators with queuing models for performance evaluation and prediction. Experiments show that our approach has good accuracy, completeness and acceptable performance measurement in processing distributed service data streams.

cs.SE↗