Search arXiv⌕ Search

arXiv subjects

Yu Wang

Publications and source records attributed to Yu Wang.

At least 1,351 records · Page 75Linked to original sources
arXiv2016

Characterizing Si:P quantum dot qubits with spin resonance techniques

Quantum dots patterned by atomically precise placement of phosphorus donors in single crystal silicon have long spin lifetimes, advantages in addressability, large exchange tunability, and are readily available few-electron systems. To be utilized as quantum bits, it is important to non-invasively characterise these donor quantum dots post fabrication and extract the number of bound electron and nuclear spins as well as their locations. Here, we propose a metrology technique based on electron spin resonance (ESR) measurements with the on-chip circuitry already needed for qubit manipulation to obtain atomic scale information about donor quantum dots and their spin configurations. Using atomistic tight-binding technique and Hartree self-consistent field approximation, we show that the ESR transition frequencies are directly related to the number of donors, electrons, and their locations through the electron-nuclear hyperfine interaction.

cond-mat.mes-hall↗
arXiv2016

Will Sanders Supporters Jump Ship for Trump? Fine-grained Analysis of Twitter Followers

In this paper, we study the likelihood of Bernie Sanders supporters voting for Donald Trump instead of Hillary Clinton. Building from a unique time-series dataset of the three candidates' Twitter followers, which we make public here, we first study the proportion of Sanders followers who simultaneously follow Trump (but not Clinton) and how this evolves over time. Then we train a convolutional neural network to classify the gender of Sanders followers, and study whether men are more likely to jump ship for Trump than women. Our study shows that between March and May an increasing proportion of Sanders followers are following Trump (but not Clinton). The proportion of Sanders followers who follow Clinton but not Trump has actually decreased. Equally important, our study suggests that the jumping ship behavior will be affected by gender and that men are more likely to switch to Trump than women.

cs.SI↗
arXiv2016

Pricing the Woman Card: Gender Politics between Hillary Clinton and Donald Trump

In this paper, we propose a data-driven method to measure the impact of the 'woman card' exchange between Hillary Clinton and Donald Trump. Building from a unique dataset of the two candidates' Twitter followers, we first examine the transition dynamics of the two candidates' Twitter followers one week before the exchange and one week after. Then we train a convolutional neural network to classify the gender of the followers and unfollowers, and study how women in particular are reacting to the 'woman card' exchange. Our study suggests that the 'woman card' comment has made women more likely to follow Hillary Clinton, less likely to unfollow her and that it has apparently not affected the gender composition of Trump followers.

cs.SI↗
arXiv2016

Stability and steady state analysis of distributed cooperative droop controlled DC microgrids

Distributed cooperative droop control consisting of the primary decentralized droop control and the {secondary} distributed correction control is studied in this paper, which aims to achieve an exact current sharing between generators, worked in the voltage control mode, of DC microgrids. For the DC microgrids with the distributed cooperative droop control, the dynamic stability has not been well investigated although its steady performance has been widely reported. This paper focuses on the stability problem and shows it is equivalent to the semistability problem of a class of second-order matrix systems. Some further sufficient conditions as well followed. The steady state is analyzed deeply for some special cases. A DC microgrid of three nodes is simulated on the Matlab/Simulink platform to illustrate the efficacy of analytic results.

math.OC↗
arXiv2016

High-Performance Complementary III-V Tunnel FETs with Strain Engineering

Strain engineering has recently been explored to improve tunnel field-effect transistors (TFETs). Here, we report design and performance of strained ultra-thin-body (UTB) III-V TFETs by quantum transport simulations. It is found that for an InAs UTB confined in [001] orientation, uniaxial compressive strain in [100] or [110] orientation shrinks the band gap meanwhile reduces (increases) transport (transverse) effective masses. Thus it improves the ON state current of both n-type and p-type UTB InAs TFETs without lowering the source density of states. Applying the strain locally in the source region makes further improvements by suppressing the OFF state leakage. For p-type TFETs, the locally strained area can be extended into the channel to form a quantum well, giving rise to even larger ON state current that is comparable to the n-type ones. Therefore strain engineering is a promising option for improving complementary circuits based on UTB III-V TFETs.

cond-mat.mes-hall↗
arXiv2016

Voting with Feet: Who are Leaving Hillary Clinton and Donald Trump?

From a crowded field with 17 candidates, Hillary Clinton and Donald Trump have emerged as the two front-runners in the 2016 U.S. presidential campaign. The two candidates each boast more than 5 million followers on Twitter, and at the same time both have witnessed hundreds of thousands of people leave their camps. In this paper we attempt to characterize individuals who have left Hillary Clinton and Donald Trump between September 2015 and March 2016. Our study focuses on three dimensions of social demographics: social capital, gender, and age. Within each camp, we compare the characteristics of the current followers with former followers, i.e., individuals who have left since September 2015. We use the number of followers to measure social capital, and profile images to infer gender and age. For classifying gender, we train a convolutional neural network (CNN). For age, we use the Face++ API. Our study shows that for both candidates followers with more social capital are more likely to leave (or switch camps). For both candidates females make up a larger presence among unfollowers than among current followers. Somewhat surprisingly, the effect is particularly pronounced for Clinton. Lastly, middle-aged individuals are more likely to leave Trump, and the young are more likely to leave Hillary Clinton.

cs.SI↗
arXiv2016

To Follow or Not to Follow: Analyzing the Growth Patterns of the Trumpists on Twitter

In this paper, we analyze the growth patterns of Donald Trump's followers (Trumpists, henceforth) on Twitter. We first construct a random walk model with a time trend to study the growth trend and the effects of public debates. We then analyze the relationship between Trump's activity on Twitter and the growth of his followers. Thirdly, we analyze the effects of such controversial events as calling for Muslim ban and his 'schlonged' remark.

cs.SI↗
arXiv2016

Homogeneous Geodesics in Generalized Wallach Spaces

We classify generalized Wallach spaces which are g.o. spaces. We also investigate homogeneous geodesics in generalized Wallach spaces for any given invariant Riemannian metric and we give some examples.

math.DG↗
arXiv2016

Deciphering the 2016 U.S. Presidential Campaign in the Twitter Sphere: A Comparison of the Trumpists and Clintonists

In this paper, we study follower demographics of Donald Trump and Hillary Clinton, the two leading candidates in the 2016 U.S. presidential race. We build a unique dataset US2016, which includes the number of followers for each candidate from September 17, 2015 to December 22, 2015. US2016 also includes the geographical location of these followers, the number of their own followers and, very importantly, the profile image of each follower. We use individuals' number of followers and profile images to analyze four dimensions of follower demographics: social status, gender, race and age. Our study shows that in terms of social influence, the Trumpists are more polarized than the Clintonists: they tend to have either a lot of influence or little influence. We also find that compared with the Clintonists, the Trumpists are more likely to be either very young or very old. Our study finds no gender affinity effect for Clinton in the Twitter sphere, but we do find that the Clintonists are more racially diverse.

cs.SI↗
arXiv2016

Catching Fire via "Likes": Inferring Topic Preferences of Trump Followers on Twitter

In this paper, we propose a framework to infer the topic preferences of Donald Trump's followers on Twitter. We first use latent Dirichlet allocation (LDA) to derive the weighted mixture of topics for each Trump tweet. Then we use negative binomial regression to model the "likes," with the weights of each topic serving as explanatory variables. Our study shows that attacking Democrats such as President Obama and former Secretary of State Hillary Clinton earns Trump the most "likes." Our framework of inference is generalizable to the study of other politicians.

cs.SI↗
arXiv2016

The environmental dependence of the stellar mass fundamental plane of early-type galaxies

Aims. We investigate the environmental dependence of the stellar mass fundamental plane (FP$_*$) using the early-type galaxy sample from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7). Methods. The FP$_*$ is calculated by replacing the luminosity in the fundamental plane (FP) with stellar mass. Based on the SDSS group catalog, we characterize the galaxy environment according to the mass of the host dark matter halo and the position in the halo. In halos with the same mass bin, the color distributions of central and satellite galaxies are different. Therefore, we calculate FP$_*$ coefficients of galaxies in different environments and compare them with those of the FP to study the contribution of the stellar population. Results. We find that coefficient $a$ of the FP$_*$ is systematically larger than that of the FP, but coefficient $b$ of the FP$_*$ is similar to the FP. Moreover, the environmental dependence of the FP$_*$ is similar to that of the FP. For central galaxies, FP$_*$ coefficients are significantly dependent on the halo mass. For satellite galaxies, the correlation between FP$_*$ coefficients and the halo mass is weak. Conclusions. We conclude that the tilt of the FP is not primarily driven by the stellar population.

astro-ph.GA↗
arXiv2015

Mott insulating states and quantum phase transitions of correlated SU(2N) Dirac fermions

The interplay between charge and spin degrees of freedom in strongly correlated fermionic systems, in particular of Dirac fermions, is a long-standing problem in condensed matter physics. We investigate the competing orders in the half-filled SU(2N) Hubbard model on a honeycomb lattice, which can be accurately realized in optical lattices with large-spin ultra-cold alkaline-earth fermions. Employing large-scale projector determinant quantum Monte Carlo simulations, we have explored quantum phase transitions from the gapless Dirac semi-metals to the gapped Mott-insulating phases in the SU(4) and SU(6) cases. Both of these Mott-insulating states are found to be columnar valence bond solid (cVBS) and to be absent of the antiferromagnetic Neel ordering and the loop current ordering. Inside the cVBS phases, the dimer ordering is enhanced by increasing fermion components and behaves non-monotonically as the interaction strength increases. Although the transitions generally should be of first order due to a cubic invariance possessed by the cVBS order, the coupling to gapless Dirac fermions can soften the transitions to second order through a non-analytic term in the free energy. Our simulations provide important guidance for the experimental exploration of novel states of matter with ultra-cold alkaline earth fermions.

cond-mat.quant-gas↗
arXiv2015

Linear Convergence of Adaptively Iterative Thresholding Algorithms for Compressed Sensing

This paper studies the convergence of the adaptively iterative thresholding (AIT) algorithm for compressed sensing. We first introduce a generalized restricted isometry property (gRIP). Then we prove that the AIT algorithm converges to the original sparse solution at a linear rate under a certain gRIP condition in the noise free case. While in the noisy case, its convergence rate is also linear until attaining a certain error bound. Moreover, as by-products, we also provide some sufficient conditions for the convergence of the AIT algorithm based on the two well-known properties, i.e., the coherence property and the restricted isometry property (RIP), respectively. It should be pointed out that such two properties are special cases of gRIP. The solid improvements on the theoretical results are demonstrated and compared with the known results. Finally, we provide a series of simulations to verify the correctness of the theoretical assertions as well as the effectiveness of the AIT algorithm.

math.OC↗
arXiv2015

Improving the Speed of Response of Learning Algorithms Using Multiple Models

This is the first of a series of papers that the authors propose to write on the subject of improving the speed of response of learning systems using multiple models. During the past two decades, the first author has worked on numerous methods for improving the stability, robustness, and performance of adaptive systems using multiple models and the other authors have collaborated with him on some of them. Independently, they have also worked on several learning methods, and have considerable experience with their advantages and limitations. In particular, they are well aware that it is common knowledge that machine learning is in general very slow. Numerous attempts have been made by researchers to improve the speed of convergence of algorithms in different contexts. In view of the success of multiple model based methods in improving the speed of convergence in adaptive systems, the authors believe that the same approach will also prove fruitful in the domain of learning. In this paper, a first attempt is made to use multiple models for improving the speed of response of the simplest learning schemes that have been studied. i.e. Learning Automata.

cs.LG↗
arXiv2015

NXgraph: An Efficient Graph Processing System on a Single Machine

Recent studies show that graph processing systems on a single machine can achieve competitive performance compared with cluster-based graph processing systems. In this paper, we present NXgraph, an efficient graph processing system on a single machine. With the abstraction of vertex intervals and edge sub-shards, we propose the Destination-Sorted Sub-Shard (DSSS) structure to store a graph. By dividing vertices and edges into intervals and sub-shards, NXgraph ensures graph data access locality and enables fine-grained scheduling. By sorting edges within each sub-shard according to their destination vertices, NXgraph reduces write conflicts among different threads and achieves a high degree of parallelism. Then, three updating strategies, i.e., Single-Phase Update (SPU), Double-Phase Update (DPU), and Mixed-Phase Update (MPU), are proposed in this paper. NXgraph can adaptively choose the fastest strategy for different graph problems according to the graph size and the available memory resources to fully utilize the memory space and reduce the amount of data transfer. All these three strategies exploit streamlined disk access pattern. Extensive experiments on three real-world graphs and five synthetic graphs show that NXgraph can outperform GraphChi, TurboGraph, VENUS, and GridGraph in various situations. Moreover, NXgraph, running on a single commodity PC, can finish an iteration of PageRank on the Twitter graph with 1.5 billion edges in 2.05 seconds; while PowerGraph, a distributed graph processing system, needs 3.6s to finish the same task.

cs.DB↗
arXiv2015

The Rationale for Second Level Adaptation

Recently, a new approach to the adaptive control of linear time-invariant plants with unknown parameters (referred to as second level adaptation), was introduced by Han and Narendra in [1]. Based on $N (\geq m+1)$ fixed or adaptive models of the plant, where $m$ is the dimension of the unknown parameter vector, an unknown parameter vector $α\in R^{N}$ is estimated in the new approach, and in turn, is used to control the overall system. Simulation studies were presented in [1] to demonstrate that the new method is significantly better than those that are currently in use. In this paper, we undertake a more detailed examination of the theoretical and practical advantages claimed for the new method. In particular, the need for many models, the proof of stability, and the improvement in performance and robustness are explored in depth both theoretically and experimentally.

eess.SY↗
arXiv2015

Analysis, Design and Testing of a Novel Quasi-Zero-Stiffness based Sensor System for Measurement of Absolute Vibration Motion

This study presents the analysis and design of a novel quasi-zero-stiffness (QZS) based vibration sensor system for measuring absolute displacement of vibrating platforms/objects. The sensor system is constructed by using positive and negative-stiffness springs, which makes it possible to achieve an equivalent QZS and consequently to create a broadband vibration-free point for absolute displacement measurement in vibrating platforms. Theoretic analysis is conducted for the analysis and design of the influence of structure parameters on system measurement performance. A prototype is designed which can avoid the drawback of instability in existing QZS systems with negative stiffness, and the corresponding data-processing software is developed to fulfill time domain and frequency domain measurements simultaneously. Both simulation and experiment results verify the effectiveness of this novel sensor system.

physics.ins-det↗