arXiv subjects

Ke Xu

Publications and source records attributed to Ke Xu.

At least 415 records · Page 23Linked to original sources

arXiv2017

Extroverts Tweet Differently from Introverts in Weibo

Being dominant factors driving the human actions, personalities can be excellent indicators in predicting the offline and online behavior of different individuals. However, because of the great expense and inevitable subjectivity in questionnaires and surveys, it is challenging for conventional studies to explore the connection between personality and behavior and gain insights in the context of large amount individuals. Considering the more and more important role of the online social media in daily communications, we argue that the footprint of massive individuals, like tweets in Weibo, can be the inspiring proxy to infer the personality and further understand its functions in shaping the online human behavior. In this study, a map from self-reports of personalities to online profiles of 293 active users in Weibo is established to train a competent machine learning model, which then successfully identifies over 7,000 users as extroverts or introverts. Systematical comparisons from perspectives of tempo-spatial patterns, online activities, emotion expressions and attitudes to virtual honor surprisingly disclose that the extrovert indeed behaves differently from the introvert in Weibo. Our findings provide solid evidence to justify the methodology of employing machine learning to objectively study personalities of massive individuals and shed lights on applications of probing personalities and corresponding behaviors solely through online profiles.

cs.CY↗

arXiv2017

Atomically sharp 1D SbSeI, SbSI and SbSBr with high stability and novel properties for microelectronic, optoelectronic, and thermoelectric applications

In scaling of transistor dimensions with low source-to-drain currents, 1D semiconductors with certain electronic properties are highly desired. We discover three new 1D materials, SbSeI, SbSI and SbSBr with high stability and novel electronic properties based on first principles calculations. Both dynamical and thermal stability of these 1D materials are examined. The bulk-to-1D transition results in dramatic changes in band gap, effective mass and static dielectric constant due to quantum confinement, making 1D SbSeI a highly promising channel material for transistors with gate length shorter than 1 nm. Under small uniaxial strain, these materials are transformed from indirect into direct band gap semiconductors, paving the way for optoelectronic devices and mechanical sensors. Moreover, the thermoelectric performance of these materials is significantly improved over their bulk counterparts. Finally, we demonstrate the experimental feasibility of synthesizing such atomically sharp V-VI-VII compounds. These highly desirable properties render SbSeI, SbSI and SbSBr promising 1D materials for applications in future microelectronics, optoelectronics, mechanical sensors, and thermoelectrics.

cond-mat.mtrl-sci↗

arXiv2017

Mode-Division Multiplexing for Silicon Photonic Network-on-chip

Optical interconnect is a potential solution to attain the large bandwidth on-chip communications needed in high performance computers in a low power and low cost manner. Mode-division multiplexing (MDM) is an emerging technology that scales the capacity of a single wavelength carrier by the number of modes in a multimode waveguide, and is attractive as a cost-effective means for high bandwidth density on-chip communications. Advanced modulation formats with high spectral efficiency in MDM networks can further improve the data rates of the optical link. Here, we demonstrate an intra-chip MDM communications link employing advanced modulation formats with two waveguide modes. We demonstrate a compact single wavelength carrier link that is expected to support 2x100 Gb/s mode multiplexed capacity. The network comprised integrated microring modulators at the transmitter, mode multiplexers, multimode waveguide interconnect, mode demultiplexers and integrated germanium on silicon photodetectors. Each of the mode channels achieves 100 Gb/s line rate with 84 Gb/s net payload data rate at 7% overhead for hard-decision forward error correction (HD-FEC) in the OFDM/16-QAM signal transmission.

cs.ET↗

arXiv2017

A GPU-Based Solution to Fast Calculation of Betweenness Centrality on Large Weighted Networks

Recent decades have witnessed the tremendous development of network science, which indeed brings a new and insightful language to model real systems of different domains. Betweenness, a widely employed centrality in network science, is a decent proxy in investigating network loads and rankings. However, the extremely high computational cost greatly prevents its applying on large networks. Though several parallel algorithms have been presented to reduce its calculation cost on unweighted networks, a fast solution for weighted networks, which are in fact more ubiquitous than unweighted ones in reality, is still missing. In this study, we develop an efficient parallel GPU-based approach to boost the calculation of betweenness centrality on very large and weighted networks. Comprehensive and systematic evaluations on both synthetic and real-world networks demonstrate that our solution can arrive the performance of 30x to 150x speedup over the CPU implementation by integrating the work-efficient and warp-centric strategies. Our algorithm is completely open-sourced and free to the community and it is public available through https://dx.doi.org/10.6084/m9.figshare.4542405. Considering the pervasive deployment and declining price of GPU on personal computers and servers, our solution will indeed offer unprecedented opportunities for exploring the betweenness related problems in network science.

cs.SI↗

arXiv2016

Higher contagion and weaker ties mean anger spreads faster than joy in social media

Increasing evidence suggests that, similar to face-to-face communications, human emotions also spread in online social media. However, the mechanisms underlying this emotional contagion, for example, whether different feelings spread in unlikely ways or how the spread of emotions relates to the social network, is rarely investigated. Indeed, because of high costs and spatio-temporal limitations, explorations of this topic are challenging using conventional questionnaires or controlled experiments. Because they are collection points for natural affective responses of massive individuals, online social media sites offer an ideal proxy for tackling this issue from the perspective of computational social science. In this paper, based on the analysis of millions of tweets in Weibo, surprisingly, we find that anger is more contagious than joy, indicating that it can spark more angry follow-up tweets. Moreover, regarding dissemination in social networks, anger travels easily along weaker ties than joy, meaning that it can infiltrate different communities and break free of local traps because strangers share such content more often. Through a simple diffusion model, we reveal that greater contagion and weaker ties function cooperatively to speed up anger's spread. The diffusion of real-world events with different dominant emotions provides further testimony to the findings. To the best of our knowledge, this is the first time that quantitative long-term evidence has been presented that reveals a difference in the mechanism by which joy and anger are disseminated. Our findings shed light on both personal anger management in human communications and on controlling collective outrage in cyberspace.

cs.SI↗

arXiv2016

Sleeping Beauties in Meme Diffusion

A sleeping beauty in diffusion indicates that the information, can be ideas or innovations, will experience a hibernation before a sudden spike of popularity and it is widely found in citation history of scientific publications. However, in this study, we demonstrate that the sleeping beauty is an interesting and unexceptional phenomenon in information diffusion and even more inspiring, there exist two consecutive sleeping beauties in the entire lifetime of propagation, suggesting that the information, including scientific topics, search queries or Wikipedia entries, which we call memes, will go unnoticed for a period and suddenly attracts some attention, and then it falls asleep again and later wakes up with another unexpected popularity peak. Further explorations on this phenomenon show that intervals between two wake ups follow an exponential distribution and the second wake up generally reaches its peak at a higher velocity. In addition, higher volume of the first wake up will lead to even much higher popularity of the second wake up with great odds. Taking these findings into consideration, an upgraded Bass model is presented to well describe the diffusion dynamics of memes on different media. Our results can help understand the common mechanism behind propagation of different memes and are instructive to locate the tipping point in marketing or find innovative publications in science.

cs.SI↗

arXiv2016

PipeCNN: An OpenCL-Based FPGA Accelerator for Large-Scale Convolution Neuron Networks

Convolutional neural networks (CNNs) have been widely employed in many applications such as image classification, video analysis and speech recognition. Being compute-intensive, CNN computations are mainly accelerated by GPUs with high power dissipations. Recently, studies were carried out exploiting FPGA as CNN accelerator because of its reconfigurability and energy efficiency advantage over GPU, especially when OpenCL-based high-level synthesis tools are now available providing fast verification and implementation flows. Previous OpenCL-based design only focused on creating a generic framework to identify performance-related hardware parameters, without utilizing FPGA's special capability of pipelining kernel functions to minimize memory bandwidth requirement. In this work, we propose an FPGA accelerator with a new architecture of deeply pipelined OpenCL kernels. Data reuse and task mapping techniques are also presented to improve design efficiency. The proposed schemes are verified by implementing two representative large-scale CNNs, AlexNet and VGG on Altera Stratix-V A7 FPGA. We have achieved a similar peak performance of 33.9 GOPS with a 34% resource reduction on DSP blocks compared to previous work. Our design is openly accessible and thus can be reused to explore new architectures for neural network accelerators.

cs.AR↗

arXiv2016

Learning Influence Functions from Incomplete Observations

We study the problem of learning influence functions under incomplete observations of node activations. Incomplete observations are a major concern as most (online and real-world) social networks are not fully observable. We establish both proper and improper PAC learnability of influence functions under randomly missing observations. Proper PAC learnability under the Discrete-Time Linear Threshold (DLT) and Discrete-Time Independent Cascade (DIC) models is established by reducing incomplete observations to complete observations in a modified graph. Our improper PAC learnability result applies for the DLT and DIC models as well as the Continuous-Time Independent Cascade (CIC) model. It is based on a parametrization in terms of reachability features, and also gives rise to an efficient and practical heuristic. Experiments on synthetic and real-world datasets demonstrate the ability of our method to compensate even for a fairly large fraction of missing observations.

cs.SI↗

arXiv2016

All passive photonic power divider with arbitrary split ratio

Integrated optical power splitter is one of the fundamental building blocks in photonic integrated circuits (PIC). Conventional multimode interferometer based power splitter is widely used as it has reasonable footprint and is easy to fabricate. However, it is challenging to realize arbitrary split ratio especially for multi-outputs. In this work, an ultra-compact power splitter with a QR code-like nanostructure is designed by a nonlinear fast search method (FSM). The highly functional structure is composed of a number of freely designed square pixels with the size of 120nm x 120nm which could be either dielectric or air. The lightwaves are scattered by a number of etched squares with optimized locations and the scattered waves superimpose at the outputs with the desired power ratio. We demonstrate 1x2 splitters with 1:1, 1:2, 1:3 split ratios and a 1x3 splitter with the ratio of 1:2:1. The footprint for all the devices is only 3.6umx3.6 um. Well-controlled split ratios are measured for all the cases. The measured transmission efficiencies of all the splitters are close to 80% over 30 nm wavelength range.

physics.optics↗

arXiv2016

Blind Quantum Computing with Decoy States

In this paper, we study the Universal Blind Quantum Computing (UBQC) protocol, which allows a client to perform quantum computation on a remote quantum server and the Remote Blind qubit state Preparation (RBSP) protocol which allows the client to prepare single qubits on the server's location with week coherent pulses. This RBSP protocol is inefficient for small $ T $, because the required number of pulses for generating one single qubit scales as $ \mbox{O}(1/T^4) $, where $ T $ is the transmittance of the quantum channe between the client and the server. Our contribution in this paper is a modified RBSP protocol with decoy states. This new protocol allows us to generate $ S $ single qubits simultaneously in a single instance. With our new rotocol, we are able to reduce the number of weak coherent pulses required for generating one single qubit from $ \mbox{O}(1/T^4) $ to nearly $ \mbox{O}(1/T) $, significantly increasing the efficiency. For a 100 km standard telecom fiber of loss 0.2 dB/km, our method gives a six orders of magnitude improvement in the speed, which is very significant.

quant-ph↗

arXiv2016

Can Online Emotions Predict the Stock Market in China?

Whether the online social media, like Twitter or its variant Weibo, can be a convincing proxy to predict the stock market has been debated for years, especially for China. However, as the traditional theory in behavioral finance states, the individual emotions can influence decision-making of investors, so it is reasonable to further explore this controversial topic from the perspective of online emotions, which is richly carried by massive tweets in social media. Surprisingly, through thorough study on over 10 million stock-relevant tweets from Weibo, both correlation analysis and causality test show that five attributes of the stock market in China can be competently predicted by various online emotions, like disgust, joy, sadness and fear. Specifically, the presented model significantly outperforms the baseline solutions on predicting five attributes of the stock market under the $K$-means discretization. We also employ this model in the scenario of realistic online application and its performance is further testified.

cs.SI↗

arXiv2016

Designing New Improper Ferroelectrics with a General Strategy

The presence of a switchable spontaneous electric polarization makes ferroelectrics ideal candidates for the use in many applications such as memory and sensors devices. Since known ferroelectrics are rather limited, finding new ferroelectric (FE) materials has become a flourishing field. One promising route is to design the so-called hybrid improper ferroelectricity. However, the previous approach based on the Landau theory is not easily adopted to the systems that are unrelated to the Pbnm perovskite structure. To this end, we develop a general design rule that is applicable to any systems. By combining this rule with density functional theory calculations, we identify previously unrecognized classes of FE materials. It shows that the R-3c perovskite structure can become FE by substituting half of the B-site cations. ZnSrO2 with a non-perovskite layered structure can also be FE through the anion substitution. Moreover, our approach can be used to design new multiferroics as illustrated in the case of fluorine substituted LaMnO3.

cond-mat.mtrl-sci↗

arXiv2015

Unusual Ferroelectricity Induced by the Jahn-Teller Effect: A Case Study on Lacunar Spinel Compounds

The Jahn-Teller effect refers to the symmetry-lowering geometrical distortion in a crystal (or non-linear molecule) due to the presence of a degenerate electronic state. Usually, the Jahn-Teller distortion is not polar. Recently, GaV4S8 with the lacunar spinel structure was found to undergoes a Jahn-Teller distortion from cubic to ferroelectric rhombohedral structure at TJT = 38K. In this work, we carry out a general group theory analysis to show how and when the Jahn-Teller effect gives rise to ferroelectricity. On the basis of this theory, we find that the ferroelectric Jahn-Teller distort in GaV4S8 is due to the non-centrosymmetric nature of the parent phase and strong electron-phonon interaction related to two low-energy T2 phonon modes. Interestingly, GaV4S8 is not only ferroelectric, but also ferromagnetic with the magnetic easy axis along the ferroelectric direction. This suggests that GaV4S8 is a multiferroic in which an external electric field may control its magnetization direction. Our study not only explains the Jahn-Teller physics in GaV4S8, but also paves a new way for searching and designing new ferroelectrics and multiferroics.

cond-mat.mtrl-sci↗

arXiv2015

A Statistical Parsing Framework for Sentiment Classification

We present a statistical parsing framework for sentence-level sentiment classification in this article. Unlike previous works that employ syntactic parsing results for sentiment analysis, we develop a statistical parser to directly analyze the sentiment structure of a sentence. We show that complicated phenomena in sentiment analysis (e.g., negation, intensification, and contrast) can be handled the same as simple and straightforward sentiment expressions in a unified and probabilistic way. We formulate the sentiment grammar upon Context-Free Grammars (CFGs), and provide a formal description of the sentiment parsing framework. We develop the parsing model to obtain possible sentiment parse trees for a sentence, from which the polarity model is proposed to derive the sentiment strength and polarity, and the ranking model is dedicated to selecting the best sentiment tree. We train the parser directly from examples of sentences annotated only with sentiment polarity labels but without any syntactic annotations or polarity annotations of constituents within sentences. Therefore we can obtain training data easily. In particular, we train a sentiment parser, s.parser, from a large amount of review sentences with users' ratings as rough sentiment polarity labels. Extensive experiments on existing benchmark datasets show significant improvements over baseline sentiment classification approaches.

cs.CL↗

arXiv2015

A comparative study of ab initio nonradiative recombination rate calculations under different formalisms

Nonradiative carrier recombination is of both great applied and fundamental importance.But the correct ab initio approaches to calculate it remains to be inconclusive. Here we used 5 different formalisms to calculate the nonradiative carrier recombinations of two complex defect structures GaP:Zn_Ga-O_P and GaN:Zn_Ga-V_N, and compared the results with experiments.In order to apply different multiphonon assisted electron transition formalisms, we have calculated the electron-phonon coupling constants by ab initio density functional theory for all phonon modes. Compared with different methods, the capture coefficients calculated by the static coupling theory are 4.30*10^-8 and 1.46*10^-7 cm^3/s for GaP:Zn_Ga-O_P and GaN:Zn_Ga-V_N, which are in good agreement with the experiment results, 4*10^-8 and 3.0*10^-7 cm^3/s respectively. We also provided arguments for why the static coupling theory should be used to calculate the nonradiative decays of semiconductors.

cond-mat.mtrl-sci↗

arXiv2015

Characterizing Information Spreading in Online Social Networks

Online social networks (OSNs) are changing the way in which the information spreads throughout the Internet. A deep understanding of the information spreading in OSNs leads to both social and commercial benefits. In this paper, we characterize the dynamic of information spreading (e.g., how fast and widely the information spreads against time) in OSNs by developing a general and accurate model based on the Interactive Markov Chains (IMCs) and mean-field theory. This model explicitly reveals the impacts of the network topology on information spreading in OSNs. Further, we extend our model to feature the time-varying user behaviors and the ever-changing information popularity. The complicated dynamic patterns of information spreading are captured by our model using six key parameters. Extensive tests based on Renren's dataset validate the accuracy of our model, which demonstrate that it can characterize the dynamic patterns of video sharing in Renren precisely and predict future spreading tendency successfully.

cs.SI↗

arXiv2014

Word Network Topic Model: A Simple but General Solution for Short and Imbalanced Texts

The short text has been the prevalent format for information of Internet in recent decades, especially with the development of online social media, whose millions of users generate a vast number of short messages everyday. Although sophisticated signals delivered by the short text make it a promising source for topic modeling, its extreme sparsity and imbalance brings unprecedented challenges to conventional topic models like LDA and its variants. Aiming at presenting a simple but general solution for topic modeling in short texts, we present a word co-occurrence network based model named WNTM to tackle the sparsity and imbalance simultaneously. Different from previous approaches, WNTM models the distribution over topics for each word instead of learning topics for each document, which successfully enhance the semantic density of data space without importing too much time or space complexity. Meanwhile, the rich contextual information preserved in the word-word space also guarantees its sensitivity in identifying rare topics with convincing quality. Furthermore, employing the same Gibbs sampling with LDA makes WNTM easily to be extended to various application scenarios. Extensive validations on both short and normal texts testify the outperformance of WNTM as compared to baseline methods. And finally we also demonstrate its potential in precisely discovering newly emerging topics or unexpected events in Weibo at pretty early stages.

cs.CL↗

arXiv2014

Competition Between Homophily and Information Entropy Maximization in Social Networks

In social networks, it is conventionally thought that two individuals with more overlapped friends tend to establish a new friendship, which could be stated as homophily breeding new connections. While the recent hypothesis of maximum information entropy is presented as the possible origin of effective navigation in small-world networks. We find there exists a competition between information entropy maximization and homophily in local structure through both theoretical and experimental analysis. This competition means that a newly built relationship between two individuals with more common friends would lead to less information entropy gain for them. We conjecture that in the evolution of the social network, both of the two assumptions coexist. The rule of maximum information entropy produces weak ties in the network, while the law of homophily makes the network highly clustered locally and the individuals would obtain strong and trust ties. Our findings shed light on the social network modeling from a new perspective.

cs.SI↗