arXiv subjects

Pu Wang

Publications and source records attributed to Pu Wang.

123 records · Page 7Linked to original sources

arXiv2019

Deep CSI Learning for Gait Biometric Sensing and Recognition

Gait is a person's natural walking style and a complex biological process that is unique to each person. Recently, the channel state information (CSI) of WiFi devices have been exploited to capture human gait biometrics for user identification. However, the performance of existing CSI-based gait identification systems is far from satisfactory. They can only achieve limited identification accuracy (maximum $93\%$) only for a very small group of people (i.e., between 2 to 10). To address such challenge, an end-to-end deep CSI learning system is developed, which exploits deep neural networks to automatically learn the salient gait features in CSI data that are discriminative enough to distinguish different people Firstly, the raw CSI data are sanitized through window-based denoising, mean centering and normalization. The sanitized data is then passed to a residual deep convolutional neural network (DCNN), which automatically extracts the hierarchical features of gait-signatures embedded in the CSI data. Finally, a softmax classifier utilizes the extracted features to make the final prediction about the identity of the user. In a typical indoor environment, a top-1 accuracy of $97.12 \pm 1.13\%$ is achieved for a dataset of 30 people.

cs.LG↗

arXiv2018

T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction

Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence. To capture the spatial and temporal dependence simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is in combination with the graph convolutional network (GCN) and gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures to capture spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data to capture temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://github.com/lehaifeng/T-GCN.

cs.LG↗

arXiv2018

Ultrafast Molecular Spectroscopy Using a Hollow-Core Photonic Crystal Fibre Light Source

We demonstrate, for the first time, the application of rare-gas filled hollow-core photonic crystal fibres (HC-PCFs) as tuneable ultraviolet light sources in femtosecond pump-probe spectroscopy. The time-resolved photoelectron imaging technique reveals non-adiabatic dynamical processes operating on three distinct timescales in the styrene molecule following excitation over the 242-258 nm region. These include ultrafast (<100 fs) internal conversion between the S2(pipi*) and S1(pipi*) electronic states and subsequent intramolecular vibrational energy redistribution within S1(pipi*). Compact, cost-effective and highly efficient bench-top HC-PCF sources have huge potential to open up many exciting new avenues for ultrafast spectroscopy in the ultraviolet and vacuum ultraviolet spectral regions. We anticipate that our initial validation of this approach will generate important impetus in this area

physics.chem-ph↗

arXiv2018

Advantages of the coherent state compared with squeeze state in unidimensional continuous variable quantum key distribution

In this work, a comparison study between unidimensional (UD) coherent-state and UD squeeze-state protocols is performed in the continuous variable quantum key distribution domain. First, the UD squeeze-state protocol is proposed and the equivalence between the prepare-and-measure and entanglement-based schemes of UD squeeze-state protocol is proved. Then, the security of the UD squeeze-state protocol under collective attack in realistic conditions is analyzed. Lastly, the performances of the two UD protocols are analyzed. Based on the uniform expressions established in our study, the squeeze-state and coherent-state protocols can be analyzed simultaneously. Our results show that the UD squeeze-state protocols are quite different from the two-dimensional protocols in that the UD squeeze-state protocols have a poorer performance compared with UD coherent-state protocols, which is opposite in the case of two-dimensional protocols.

quant-ph↗

arXiv2018

Finding and breaking the realistic rate-distance limit of continuous variable quantum key distribution

In this work, the rate-distance limit of continuous variable quantum key distribution is studied. We find that the excess noise generated on Bob's side and the method for calculating the excess noise restrict the rate-distance limit. Then, a realistic rate-distance limit is found. To break the realistic limit, a method for calculating the secret key rate using pure excess noise is proposed. The improvement in the rate-distance limit due to a higher reconciliation efficiency is analyzed. It is found that this improvement is dependent on the excess noise. From a finite-size analysis, the monotonicity of the Holevo bound versus the transmission efficiency is studied, and a tighter rate-distance limit is presented.

quant-ph↗

arXiv2017

Millimeter Wave Channel Estimation via Exploiting Joint Sparse and Low-Rank Structures

We consider the problem of channel estimation for millimeter wave (mmWave) systems, where, to minimize the hardware complexity and power consumption, an analog transmit beamforming and receive combining structure with only one radio frequency (RF) chain at the base station (BS) and mobile station (MS) is employed. Most existing works for mmWave channel estimation exploit sparse scattering characteristics of the channel. In addition to sparsity, mmWave channels may exhibit angular spreads over the angle of arrival (AoA), angle of departure (AoD), and elevation domains. In this paper, we show that angular spreads give rise to a useful low-rank structure that, along with the sparsity, can be simultaneously utilized to reduce the sample complexity, i.e. the number of samples needed to successfully recover the mmWave channel. Specifically, to effectively leverage the joint sparse and low-rank structure, we develop a two-stage compressed sensing method for mmWave channel estimation, where the sparse and low-rank properties are respectively utilized in two consecutive stages, namely, a matrix completion stage and a sparse recovery stage. Our theoretical analysis reveals that the proposed two-stage scheme can achieve a lower sample complexity than a direct compressed sensing method that exploits only the sparse structure of the mmWave channel. Simulation results are provided to corroborate our theoretical results and to show the superiority of the proposed two-stage method.

cs.IT↗

arXiv2016

Magneto-transport properties of CeRu$_2$Al$_{10}$: Similarities to URu$_2$Si$_2$

We report on magneto-transport properties of the Kondo semiconducting compound CeRu$_2$Al$_{10}$, focusing on its exotic phase below $T_0$ = 27 K. In this phase, an excess thermal conductivity $κ$ emerges and is gradually suppressed by magnetic field, strikingly resembling those observed in the hidden-order phase of URu$_2$Si$_2$. Our analysis indicates that low-energy magnetic excitation is the most likely origin, as was also proposed for URu$_2$Si$_2$ recently, despite the largely reduced magnetic moments. Likewise, other transport properties such as resistivity, thermopower and Nernst effect exhibit distinct features characterizing the very different charge dynamics above and below $T_0$, sharing similarities to URu$_2$Si$_2$, too. Given the exotic nature of the ordered phases in both compounds, whether a unified interpretation to all these observations exists appears to be extremely interesting.

cond-mat.str-el↗

arXiv2016

Low Bending Loss Nodeless Hollow-core Anti-Resonant Fiber

We report a high performance nodeless hollow-core anti-resonant fiber with a broadband guidance from 1050 nm to >1900 nm, a transmission attenuation of 200 dB/km, and a low bending loss of 0.25 dB/m for a 5 cm radius at wavelength of 1550 nm.

physics.optics↗

arXiv2015

On the role of spatial dynamics and topology on network flows

Particle flows in spatial networks are susceptible to congestion. In this paper, we analyze the phase transitions of these networks to a state of congested transport and the influence of both topology and spatial dynamics on its emergence. We systematically show that the value of the critical loading rate at which congestion emerges is affected by the addition of spatial dynamics, changing the nature of this transition from a continuous to a discontinuous one. Our numerical results are confirmed by introducing an analytical solvable framework. As a case of study, we explore the implications of our findings in the San Francisco road network where we can locate the roads that originate the congested phase. These roads are the spatially constrained, and not necessarily those with high betweenness as predicted by models without spatial dynamics.

physics.soc-ph↗

arXiv2014

Predicting commuter flows in spatial networks using a radiation model based on temporal ranges

Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and of human mobility. Here we show a first-principles based method for traffic prediction using a cost based generalization of the radiation model for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the lognormal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared to real traffic. Due to its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events.

physics.soc-ph↗

arXiv2013

Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals

We consider the problem of recovering block-sparse signals whose structures are unknown \emph{a priori}. Block-sparse signals with nonzero coefficients occurring in clusters arise naturally in many practical scenarios. However, the knowledge of the block structure is usually unavailable in practice. In this paper, we develop a new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster patterns. Specifically, a pattern-coupled hierarchical Gaussian prior model is introduced to characterize the statistical dependencies among coefficients, in which a set of hyperparameters are employed to control the sparsity of signal coefficients. Unlike the conventional sparse Bayesian learning framework in which each individual hyperparameter is associated independently with each coefficient, in this paper, the prior for each coefficient not only involves its own hyperparameter, but also the hyperparameters of its immediate neighbors. In doing this way, the sparsity patterns of neighboring coefficients are related to each other and the hierarchical model has the potential to encourage structured-sparse solutions. The hyperparameters, along with the sparse signal, are learned by maximizing their posterior probability via an expectation-maximization (EM) algorithm. Numerical results show that the proposed algorithm presents uniform superiority over other existing methods in a series of experiments.

cs.IT↗

arXiv2013

Convex Hull-Based Multi-objective Genetic Programming for Maximizing ROC Performance

ROC is usually used to analyze the performance of classifiers in data mining. ROC convex hull (ROCCH) is the least convex major-ant (LCM) of the empirical ROC curve, and covers potential optima for the given set of classifiers. Generally, ROC performance maximization could be considered to maximize the ROCCH, which also means to maximize the true positive rate (tpr) and minimize the false positive rate (fpr) for each classifier in the ROC space. However, tpr and fpr are conflicting with each other in the ROCCH optimization process. Though ROCCH maximization problem seems like a multi-objective optimization problem (MOP), the special characters make it different from traditional MOP. In this work, we will discuss the difference between them and propose convex hull-based multi-objective genetic programming (CH-MOGP) to solve ROCCH maximization problems. Convex hull-based sort is an indicator based selection scheme that aims to maximize the area under convex hull, which serves as a unary indicator for the performance of a set of points. A selection procedure is described that can be efficiently implemented and follows similar design principles than classical hyper-volume based optimization algorithms. It is hypothesized that by using a tailored indicator-based selection scheme CH-MOGP gets more efficient for ROC convex hull approximation than algorithms which compute all Pareto optimal points. To test our hypothesis we compare the new CH-MOGP to MOGP with classical selection schemes, including NSGA-II, MOEA/D) and SMS-EMOA. Meanwhile, CH-MOGP is also compared with traditional machine learning algorithms such as C4.5, Naive Bayes and Prie. Experimental results based on 22 well-known UCI data sets show that CH-MOGP outperforms significantly traditional EMOAs.

cs.NE↗

arXiv2012

Understanding Road Usage Patterns in Urban Areas

In this paper, we combine the most complete record of daily mobility, based on large-scale mobile phone data, with detailed Geographic Information System (GIS) data, uncovering previously hidden patterns in urban road usage. We find that the major usage of each road segment can be traced to its own - surprisingly few - driver sources. Based on this finding we propose a network of road usage by defining a bipartite network framework, demonstrating that in contrast to traditional approaches, which define road importance solely by topological measures, the role of a road segment depends on both: its betweeness and its degree in the road usage network. Moreover, our ability to pinpoint the few driver sources contributing to the major traffic flow allows us to create a strategy that achieves a significant reduction of the travel time across the entire road system, compared to a benchmark approach.

physics.soc-ph↗

arXiv2010

New generation of mobile phone viruses and corresponding countermeasures

The fast growing market for smart phones coupled with their almost continuous online presence makes these devices the new targets of virus writers. It has been recently found that the topological spread of MMS (Multimedia Message Services) viruses is highly restricted by the underlying fragmentation of the call graph. In this paper, we study MMS viruses under another type of spreading behavior: scanning. We find that hybrid MMS viruses including some level of scanning are more dangerous to the mobile community than their standard topological counterparts. However, the effectiveness of both scanning and topological behaviors in MMS viruses can generally be limited by two controlling methods: (i) decreasing susceptible handsets' market share (OS it runs) and (ii) improving monitoring capacity to limit the frequency in which MMS messages can be sent by the mobile viruses.

cs.NI↗

arXiv2010

Modeling the scaling properties of human mobility

While the fat tailed jump size and the waiting time distributions characterizing individual human trajectories strongly suggest the relevance of the continuous time random walk (CTRW) models of human mobility, no one seriously believes that human traces are truly random. Given the importance of human mobility, from epidemic modeling to traffic prediction and urban planning, we need quantitative models that can account for the statistical characteristics of individual human trajectories. Here we use empirical data on human mobility, captured by mobile phone traces, to show that the predictions of the CTRW models are in systematic conflict with the empirical results. We introduce two principles that govern human trajectories, allowing us to build a statistically self-consistent microscopic model for individual human mobility. The model not only accounts for the empirically observed scaling laws but also allows us to analytically predict most of the pertinent scaling exponents.

cond-mat.dis-nn↗