arXiv subjects

Yu Wang

Publications and source records attributed to Yu Wang.

At least 1,315 records · Page 73Linked to original sources

arXiv2018

Quantum communication protocols by quantum walks with two coins

We introduce some new perfect state transfer and teleportation schemes by quantum walks with two coins. Encoding the transferred information in coin 1 state and alternatively using two coin operators, we can perfectly recover the information on coin 1 state at target position only by at most two times of flipping operation. Based on quantum walks with two coins either on the line or on the $N$-circle, we can perfectly transfer any qubit state. In addition, using quantum walks with two coins either on complete graphs or regular graphs, we can first implement perfect qudit state transfer by quantum walks. Compared with existing schemes driven by one coin, more general graph structures can be used to perfectly transfer more general state. Moreover, the external control of coin operator during the transmitting process can be decreased greatly. Selecting coin 1 as the sender and coin 2 as the receiver, we also study how to realize generalized teleportation over long steps of walks by the above quantum walk models. Because quantum walks is an universal quantum computation model, our schemes may provide an universal platform for the design of quantum network and quantum computer.

quant-ph↗

arXiv2018

Gate-Induced Interfacial Superconductivity in 1T-SnSe2

Layered metal chalcogenide materials provide a versatile platform to investigate emergent phenomena and two-dimensional (2D) superconductivity at/near the atomically thin limit. In particular, gate-induced interfacial superconductivity realized by the use of an electric-double-layer transistor (EDLT) has greatly extended the capability to electrically induce superconductivity in oxides, nitrides and transition metal chalcogenides and enable one to explore new physics, such as the Ising pairing mechanism. Exploiting gate-induced superconductivity in various materials can provide us with additional platforms to understand emergent interfacial superconductivity. Here, we report the discovery of gate-induced 2D superconductivity in layered 1T-SnSe2, a typical member of the main-group metal dichalcogenide (MDC) family, using an EDLT gating geometry. A superconducting transition temperature Tc around 3.9 K was demonstrated at the EDL interface. The 2D nature of the superconductivity therein was further confirmed based on 1) a 2D Tinkham description of the angle-dependent upper critical field, 2) the existence of a quantum creep state as well as a large ratio of the coherence length to the thickness of superconductivity. Interestingly, the in-plane approaching zero temperature was found to be 2-3 times higher than the Pauli limit, which might be related to an electric field-modulated spin-orbit interaction. Such results provide a new perspective to expand the material matrix available for gate-induced 2D superconductivity and the fundamental understanding of interfacial superconductivity.

cond-mat.supr-con↗

arXiv2018

The Great Division

When information flow fails, when Democrats and Republicans do not talk to each other, when Israelis and Palestinians do not talk to each other, and when North Koreans and South Koreans do not talk to each other, mis-perceptions, biases and fake news arise. In this paper we present an in-depth study of political polarization and social division using Twitter data and Monte Carlo simulations. First, we study at the aggregate level people's inclination to retweet within their own ideological circle. Introducing the concept of cocoon ratio, we show that Donald Trump's followers are 2.56 more likely to retweet a fellow Trump follower than to retweet a Hillary Clinton follower. Second, going down to the individual level, we show that the tendency of retweeting exclusively within one's ideological circle is stronger for women than for men and that such tendency weakens as one's social capital grows. Third, we use a one-dimensional Ising model to simulate how a society with high cocoon ratios could end up becoming completely divided. We conclude with a discussion of our findings with respect to fake news.

cs.SI↗

arXiv2018

Phonetic and Graphemic Systems for Multi-Genre Broadcast Transcription

State-of-the-art English automatic speech recognition systems typically use phonetic rather than graphemic lexicons. Graphemic systems are known to perform less well for English as the mapping from the written form to the spoken form is complicated. However, in recent years the representational power of deep-learning based acoustic models has improved, raising interest in graphemic acoustic models for English, due to the simplicity of generating the lexicon. In this paper, phonetic and graphemic models are compared for an English Multi-Genre Broadcast transcription task. A range of acoustic models based on lattice-free MMI training are constructed using phonetic and graphemic lexicons. For this task, it is found that having a long-span temporal history reduces the difference in performance between the two forms of models. In addition, system combination is examined, using parameter smoothing and hypothesis combination. As the combination approaches become more complicated the difference between the phonetic and graphemic systems further decreases. Finally, for all configurations examined the combination of phonetic and graphemic systems yields consistent gains.

cs.SD↗

arXiv2018

Model-Based Speech Enhancement in the Modulation Domain

This paper presents an algorithm for modulation-domain speech enhancement using a Kalman filter. The proposed estimator jointly models the estimated dynamics of the spectral amplitudes of speech and noise to obtain an MMSE estimation of the speech amplitude spectrum with the assumption that the speech and noise are additive in the complex domain. In order to include the dynamics of noise amplitudes with those of speech amplitudes, we propose a statistical "Gaussring" model that comprises a mixture of Gaussians whose centers lie in a circle on the complex plane. The performance of the proposed algorithm is evaluated using the perceptual evaluation of speech quality measure, segmental SNR measure, and short-time objective intelligibility measure. For speech quality measures, the proposed algorithm is shown to give a consistent improvement over a wide range of SNRs when compared to competitive algorithms. Speech recognition experiments also show that the Gaussring-model-based algorithm performs well for two types of noise.

cs.SD↗

arXiv2018

Analog Circuit Applications based on Ambipolar Graphene/MoTe2 Vertical Transistors

The current integrated circuit (IC) technology based on conventional MOS-FET (metal-oxide-semiconductor field-effect transistor) is approaching the limit of miniaturization with increasing demand on energy. Several analog circuit applications based on graphene FETs have been demonstrated with less components comparing to the conventional technology. However, low on/off current ratio caused by the semimetal nature of graphene has severely hindered its practical applications. Here we report a graphene/MoTe2 van der Waals (vdW) vertical transistor with V-shaped ambipolar field effect transfer characteristics to overcome this challenge. Investigations on temperature dependence of transport properties reveal that gate tunable asymmetric barriers of the devices are account for the ambipolar behaviors. Furthermore, to demonstrate the analog circuit applications of such vdW vertical transistors, we successfully realized output polarity controllable (OPC) amplifier and frequency doubler. These results enable vdW heterojunction based electronic devices to open up new possibilities for wide perspective in telecommunication field.

cond-mat.mes-hall↗

arXiv2017

When Celebrities Endorse Politicians: Analyzing the Behavior of Celebrity Followers in the 2016 U.S. Presidential Election

Thanks to their name recognition and popularity, celebrities play an important role in American politics. Celebrity endorsements could add to the momentum of a politician's campaign and win the candidate extensive media coverage. There is one caveat though: the political preference of celebrity followers might differ from that of the celebrity. In this paper we explore that possibility. By carefully studying six prominent endorsements to the leading presidential candidates in the 2016 U.S. presidential election and statistically modeling Twitter "follow" behavior, we show (1) followers of all the celebrities with the exception of Lady Gaga are more likely to follow a large number of candidates and (2) the opinion of celebrity followers could systematically differ from that of the celebrity. Our methodology can be generalized to the study of such events as NBA players' refusing to visit the White House and pop singers' meeting with Dalai Lama.

cs.SI↗

arXiv2017

VoiceMask: Anonymize and Sanitize Voice Input on Mobile Devices

Voice input has been tremendously improving the user experience of mobile devices by freeing our hands from typing on the small screen. Speech recognition is the key technology that powers voice input, and it is usually outsourced to the cloud for the best performance. However, the cloud might compromise users' privacy by identifying their identities by voice, learning their sensitive input content via speech recognition, and then profiling the mobile users based on the content. In this paper, we design an intermediate between users and the cloud, named VoiceMask, to sanitize users' voice data before sending it to the cloud for speech recognition. We analyze the potential privacy risks and aim to protect users' identities and sensitive input content from being disclosed to the cloud. VoiceMask adopts a carefully designed voice conversion mechanism that is resistant to several attacks. Meanwhile, it utilizes an evolution-based keyword substitution technique to sanitize the voice input content. The two sanitization phases are all performed in the resource-limited mobile device while still maintaining the usability and accuracy of the cloud-supported speech recognition service. We implement the voice sanitizer on Android systems and present extensive experimental results that validate the effectiveness and efficiency of our app. It is demonstrated that we are able to reduce the chance of a user's voice being identified from 50 people by 84% while keeping the drop of speech recognition accuracy within 14.2%.

cs.CR↗

arXiv2017

Riemannian $M$-spaces with homogeneous geodesics

We investigate homogeneous geodesics in a class of homogeneous spaces called $M$-spaces, which are defined as follows. Let $G/K$ be a generalized flag manifold with $K=C(S)=S\times K_1$, where $S$ is a torus in a compact simple Lie group $G$ and $K_1$ is the semisimple part of $K$. Then the {\it associated $M$-space} is the homogeneous space $G/K_1$. These spaces were introduced and studied by H.C. Wang in 1954. We prove that for various classes of $M$-spaces the only g.o. metric is the standard metric. For other classes of $M$-spaces we give either necessary, or necessary and sufficient conditions, so that a $G$-invariant metric on $G/K_1$ is a g.o. metric. The analysis is based on properties of the isotropy representation $\mathfrak{m}=\mathfrak{m}_1\oplus \cdots\oplus \mathfrak{m}_s$ of the flag manifold $G/K$ (as Ad$(K)$-modules) and corresponding decomposition $\mathfrak{n}=\mathfrak{s}\oplus\mathfrak{m}_1\oplus \cdots\oplus \mathfrak{m}_s$ of the tangent space of the $M$-space $G/K_1$ (as Ad$(K_1)$-modules).

math.DG↗

arXiv2017

Pure state `really' informationally complete with rank-1 POVM

What is the minimal number of elements in a rank-1 positive-operator-valued measure (POVM) which can uniquely determine any pure state in $d$-dimensional Hilbert space $\mathcal{H}_d$? The known result is that the number is no less than $3d-2$. We show that this lower bound is not tight except for $d=2$ or 4. Then we give an upper bound of $4d-3$. For $d=2$, many rank-1 POVMs with four elements can determine any pure states in $\mathcal{H}_2$. For $d=3$, we show eight is the minimal number by construction. For $d=4$, the minimal number is in the set of $\{10,11,12,13\}$. We show that if this number is greater than 10, an unsettled open problem can be solved that three orthonormal bases can not distinguish all pure states in $\mathcal{H}_4$. For any dimension $d$, we construct $d+2k-2$ adaptive rank-1 positive operators for the reconstruction of any unknown pure state in $\mathcal{H}_d$, where $1\le k \le d$.

quant-ph↗

arXiv2017

How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers

Polarization in American politics has been extensively documented and analyzed for decades, and the phenomenon became all the more apparent during the 2016 presidential election, where Trump and Clinton depicted two radically different pictures of America. Inspired by this gaping polarization and the extensive utilization of Twitter during the 2016 presidential campaign, in this paper we take the first step in measuring polarization in social media and we attempt to predict individuals' Twitter following behavior through analyzing ones' everyday tweets, profile images and posted pictures. As such, we treat polarization as a classification problem and study to what extent Trump followers and Clinton followers on Twitter can be distinguished, which in turn serves as a metric of polarization in general. We apply LSTM to processing tweet features and we extract visual features using the VGG neural network. Integrating these two sets of features boosts the overall performance. We are able to achieve an accuracy of 69%, suggesting that the high degree of polarization recorded in the literature has started to manifest itself in social media as well.

cs.SI↗

arXiv2017

Social Network De-anonymization: More Adversarial Knowledge, More Users Re-Identified?

Following the trend of data trading and data publishing, many online social networks have enabled potentially sensitive data to be exchanged or shared on the web. As a result, users' privacy could be exposed to malicious third parties since they are extremely vulnerable to de-anonymization attacks, i.e., the attacker links the anonymous nodes in the social network to their real identities with the help of background knowledge. Previous work in social network de-anonymization mostly focuses on designing accurate and efficient de-anonymization methods. We study this topic from a different perspective and attempt to investigate the intrinsic relation between the attacker's knowledge and the expected de-anonymization gain. One common intuition is that the more auxiliary information the attacker has, the more accurate de-anonymization becomes. However, their relation is much more sophisticated than that. To simplify the problem, we attempt to quantify background knowledge and de-anonymization gain under several assumptions. Our theoretical analysis and simulations on synthetic and real network data show that more background knowledge may not necessarily lead to more de-anonymization gain in certain cases. Though our analysis is based on a few assumptions, the findings still leave intriguing implications for the attacker to make better use of the background knowledge when performing de-anonymization, and for the data owners to better measure the privacy risk when releasing their data to third parties.

cs.SI↗

arXiv2017

Cleavage Tendency of Anisotropic Two Dimensional Materials: ReX2 (X=S, Se) and WTe2

With unique distorted 1T structure and the associated in-plane anisotropic properties, mono- and few-layer ReX2 (X=S, Se) have recently attracted particular interest. Based on experiment and first-principles calculations, we investigate the fracture behavior of ReX2. We find that the cleaved edges of ReX2 flakes usually form an angle of ~120° or ~60°. In order to understand such phenomenon, we perform comprehensive investigations on the uniaxial tensile stress-strain relation of monolayer and multi-layer ReX2 sheets. Our numerical calculation shows that the particular cleaved edges of ReX2 flakes are caused by unique anisotropic ultimate tensile strengths and critical strains. We also calculate the stress-strain relation of WTe2, which explains why their cleaved edges are not corresponding to the principle axes. Our proposed mechanism about the fracture angle has also been supported by the calculated cleavage energies and surface energies for different edge surfaces.

cond-mat.mtrl-sci↗

arXiv2017

Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems

Designing an e-commerce recommender system that serves hundreds of millions of active users is a daunting challenge. From a human vision perspective, there're two key factors that affect users' behaviors: items' attractiveness and their matching degree with users' interests. This paper proposes Telepath, a vision-based bionic recommender system model, which understands users from such perspective. Telepath is a combination of a convolutional neural network (CNN), a recurrent neural network (RNN) and deep neural networks (DNNs). Its CNN subnetwork simulates the human vision system to extract key visual signals of items' attractiveness and generate corresponding activations. Its RNN and DNN subnetworks simulate cerebral cortex to understand users' interest based on the activations generated from browsed items. In practice, the Telepath model has been launched to JD's recommender system and advertising system. For one of the major item recommendation blocks on the JD app, click-through rate (CTR), gross merchandise value (GMV) and orders have increased 1.59%, 8.16% and 8.71% respectively. For several major ads publishers of JD demand-side platform, CTR, GMV and return on investment have increased 6.58%, 61.72% and 65.57% respectively by the first launch, and further increased 2.95%, 41.75% and 41.37% respectively by the second launch.

cs.IR↗

arXiv2017

LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions

We present LADDER, the first deep reinforcement learning agent that can successfully learn control policies for large-scale real-world problems directly from raw inputs composed of high-level semantic information. The agent is based on an asynchronous stochastic variant of DQN (Deep Q Network) named DASQN. The inputs of the agent are plain-text descriptions of states of a game of incomplete information, i.e. real-time large scale online auctions, and the rewards are auction profits of very large scale. We apply the agent to an essential portion of JD's online RTB (real-time bidding) advertising business and find that it easily beats the former state-of-the-art bidding policy that had been carefully engineered and calibrated by human experts: during JD.com's June 18th anniversary sale, the agent increased the company's ads revenue from the portion by more than 50%, while the advertisers' ROI (return on investment) also improved significantly.

cs.LG↗

arXiv2017

Future Word Contexts in Neural Network Language Models

Recently, bidirectional recurrent network language models (bi-RNNLMs) have been shown to outperform standard, unidirectional, recurrent neural network language models (uni-RNNLMs) on a range of speech recognition tasks. This indicates that future word context information beyond the word history can be useful. However, bi-RNNLMs pose a number of challenges as they make use of the complete previous and future word context information. This impacts both training efficiency and their use within a lattice rescoring framework. In this paper these issues are addressed by proposing a novel neural network structure, succeeding word RNNLMs (su-RNNLMs). Instead of using a recurrent unit to capture the complete future word contexts, a feedforward unit is used to model a finite number of succeeding, future, words. This model can be trained much more efficiently than bi-RNNLMs and can also be used for lattice rescoring. Experimental results on a meeting transcription task (AMI) show the proposed model consistently outperformed uni-RNNLMs and yield only a slight degradation compared to bi-RNNLMs in N-best rescoring. Additionally, performance improvements can be obtained using lattice rescoring and subsequent confusion network decoding.

cs.CL↗

arXiv2017

Intrinsic p-type W-based transition metal dichalcogenide by substitutional Ta-doping

Two-dimensional (2D) transition metal dichalcogenides (TMDs) have recently emerged as promising candidates for future electronics and optoelectronics. While most of TMDs are intrinsic n-type semiconductors due to electron donating which originates from chalcogen vacancies, obtaining intrinsic high-quality p-type semiconducting TMDs has been challenging. Here, we report an experimental approach to obtain intrinsic p-type Tungsten (W)-based TMDs by substitutional Ta-doping. The obtained few-layer Ta-doped WSe2 (Ta0.01W0.99Se2) field-effect transistor (FET) devices exhibit competitive p-type performances, including ~10^6 current on/off at room temperature. We also demonstrate high quality van der Waals (vdW) p-n heterojunctions based on Ta0.01W0.99Se2/MoS2 structure, which exhibit nearly ideal diode characteristics (with an ideality factor approaching 1 and a rectification ratio up to 10^5) and excellent photodetecting performance. Our study suggests that substitutional Ta-doping holds great promise to realize intrinsic p-type W-based TMDs for future electronic and photonic applications.

cond-mat.mtrl-sci↗

arXiv2017

Hierarchical Change Point Detection on Dynamic Networks

This paper studies change point detection on networks with community structures. It proposes a framework that can detect both local and global changes in networks efficiently. Importantly, it can clearly distinguish the two types of changes. The framework design is generic and as such several state-of-the-art change point detection algorithms can fit in this design. Experiments on both synthetic and real-world networks show that this framework can accurately detect changes while achieving up to 800X speedup.

cs.SI↗