arXiv subjects

Cheng Wang

Publications and source records attributed to Cheng Wang.

At least 505 records · Page 28Linked to original sources

arXiv2015

Asynchronous Byzantine Agreement with Optimal Resilience and Linear Complexity

Given a system with $n > 3t + 1$ processes, where $t$ is the tolerated number of faulty ones, we present a fast asynchronous Byzantine agreement protocol that can reach agreement in $O(t)$ expected running time. This improves the $O(n^2)$ expected running time of Abraham, Dolev, and Halpern [PODC 2008]. Furthermore, if $n = (3 + \varepsilon) t$ for any $\varepsilon > 0$, our protocol can reach agreement in $O (1 / \varepsilon)$ expected running time. This improves the result of Feldman and Micali [STOC 1988] (with constant expected running time when $n > 4 t$).

cs.DC↗

arXiv2015

Beyond One Third Byzantine Failures

The Byzantine agreement problem requires a set of $n$ processes to agree on a value sent by a transmitter, despite a subset of $b$ processes behaving in an arbitrary, i.e. Byzantine, manner and sending corrupted messages to all processes in the system. It is well known that the problem has a solution in a (an eventually) synchronous message passing distributed system iff the number of processes in the Byzantine subset is less than one third of the total number of processes, i.e. iff $n > 3b+1$. The rest of the processes are expected to be correct: they should never deviate from the algorithm assigned to them and send corrupted messages. But what if they still do? We show in this paper that it is possible to solve Byzantine agreement even if, beyond the $ b$ ($< n/3 $) Byzantine processes, some of the other processes also send corrupted messages, as long as they do not send them to all. More specifically, we generalize the classical Byzantine model and consider that Byzantine failures might be partial. In each communication step, some of the processes might send corrupted messages to a subset of the processes. This subset of processes - to which corrupted messages might be sent - could change over time. We compute the exact number of processes that can commit such faults, besides those that commit classical Byzantine failures, while still solving Byzantine agreement. We present a corresponding Byzantine agreement algorithm and prove its optimality by giving resilience and complexity bounds.

cs.DC↗

arXiv2015

Robust Unit Commitment Considering Strategic Wind Generation Curtailment

Wind generation is traditionally treated as a non-dispatchable resource and is fully absorbed unless there are security issues. To tackle the operational reliability issues caused by the volatile and non-dispatchable wind generation, many dispatch frameworks have been proposed, including robust unit commitment (RUC) considering wind variation. One of the drawbacks that commonly exist in those dispatch frameworks is increased demand on flexibility resources and associated costs. To improve wind dispatchability and reduce flexibility resource costs, in this paper, we propose a novel RUC model considering strategic wind generation curtailment (WGC). Strategic WGC can reduce wind uncertainty and variability and increase the visibility of wind generation capacity. As a result, the ramping requirement for wind generation will be reduced and ramp-up capability of wind generation can be increased, leading to reduced day-ahead operational cost with guaranteed operational reliability requirement of power systems. The economic benefits also include profits gained by wind farm by providing ramping-up capacities other auxiliary services. We also propose a solution algorithm based on the column and constraint generation (C&CG). Simulations on the IEEE 39-bus system and two larger test systems demonstrate the effectiveness of the proposed RUC model and efficiency of the proposed computational methodology.

math.OC↗

arXiv2015

Risk-Based Admissibility Assessment of Wind Generation Integrated into a Bulk Power System

The increasing integration of large-scale volatile and uncertain wind generation has brought great challenges to power system operations. In this paper, a risk-based admissibility assessment approach is proposed to quantitatively evaluate how much wind generation can be accommodated by the bulk power system under a given unit commitment (UC) strategy. Firstly, the operational risk brought by the variation and uncertainty of wind generation is developed as an admissibility measure of wind generation. Then its linear approximation is derived for practical implementation. Furthermore, a risk-minimization model is established to mathematically characterize the admissible region of wind generation. This model can be solved effectively by a modified column and constraint generation (C&CG) algorithm. Simulations on the IEEE 9-bus system and the real Guangdong power grid demonstrate the effectiveness and efficiency of the proposed methodology.

math.OC↗

arXiv2015

Direct observation of laser guided corona discharges

Laser based lightning control holds a promising way to solve the problem of the long standing disaster of lightning strikes. But it is a challenging project due to insufficient understanding of the interaction between laser plasma channel and high voltage electric filed. In this work, a direct observation of laser guided corona discharge is reported. The high voltage corona discharge can be guided along laser plasma filament, and enhanced through the interaction with laser filaments. The fluorescence lifetime of laser filament guided corona discharge was measured to be several microseconds, which is 3 orders of magnitude longer than the fluorescence lifetime of laser filaments. This could be advantageous towards laser assisted leader development in the atmosphere.

physics.optics↗

arXiv2015

STC: Coarse-Grained Vehicular Data Based Travel Speed Sensing by Leveraging Spatial-Temporal Correlation

As an important information for traffic condition evaluation, trip planning, transportation management, etc., average travel speed for a road means the average speed of vehicles travelling through this road in a given time duration. Traditional ways for collecting travel-speed oriented traffic data always depend on dedicated sensors and supporting infrastructures, and are therefore financial costly. Differently, vehicular crowdsensing as an infrastructure-free way, can be used to collect data including real-time locations and velocities of vehicles for road travel speed estimation, which is a quite low-cost way. However, vehicular crowdsensing data is always coarse-grained. This coarseness can lead to the incompleteness of travel speeds. Aiming to handle this problem as well as estimate travel speed accurately, in this paper, we propose an approach named STC that exploits the spatial-temporal correlation among travel speeds for roads by introducing the time-lagged cross correlation function. The time lagging factor describes the time consumption of traffic feature diffusion along roads. To properly calculate cross correlation, we novelly make the determination of the time lagging factor self-adaptive by recording the locations of vehicles at different roads. Then, utilizing the local stationarity of cross correlation, we further reduce the problem of single-road travel speed vacancy completion to a minimization problem. Finally, we fill all the vacancies of travel speed for roads in a recursive way using the geometric structure of road net. Elaborate experiments based on real taxi trace data show that STC can settle the incompleteness problem of vehicle crowdsensing data based travel speed estimation and ensure the accuracy of estimated travel speed better, in comparison with representative existing methods such as KNN, Kriging and ARIMA.

cs.OH↗

arXiv2015

General Capacity for Deterministic Dissemination in Wireless Ad Hoc Networks

In this paper, we study capacity scaling laws of the deterministic dissemination (DD) in random wireless networks under the generalized physical model (GphyM). This is truly not a new topic. Our motivation to readdress this issue is two-fold: Firstly, we aim to propose a more general result to unify the network capacity for general homogeneous random models by investigating the impacts of different parameters of the system on the network capacity. Secondly, we target to close the open gaps between the upper and the lower bounds on the network capacity in the literature. The generality of this work lies in three aspects: (1) We study the homogeneous random network of a general node density $λ\in [1,n]$, rather than either random dense network (RDN, $λ=n$) or random extended network (REN, $λ=1$) as in the literature. (2) We address the general deterministic dissemination sessions, \ie, the general multicast sessions, which unify the capacities for unicast and broadcast sessions by setting the number of destinations for each session as a general value $n_d\in[1,n]$. (3) We allow the number of sessions to change in the range $n_s\in(1,n]$, instead of assuming that $n_s=Θ(n)$ as in the literature. We derive the general upper bounds on the capacity for the arbitrary case of $(λ, n_d, n_s)$ by introducing the Poisson Boolean model of continuum percolation, and prove that they are tight according to the existing general lower bounds constructed in the literature.

cs.IT↗

arXiv2015

Rule Optimization for Real-Time Query Service in Software-Defined Internet of Vehicles

Internet of Vehicles (IoV) has recently gained considerable attentions from both industry and research communities since the development of communication technology and smart city. However, a proprietary and closed way of operating hardwares in network equipments slows down the progress of new services deployment and extension in IoV. Moreover, the tightly coupled control and data planes in traditional networks significantly increase the complexity and cost of network management. By proposing a novel architecture, called Software-Defined Internet of Vehicles (SDIV), we adopt the software-defined network (SDN) architecture to address these problems by leveraging its separation of the control plane from the data plane and a uniform way to configure heterogeneous switches. However, the characteristics of IoV introduce the very challenges in rule installation due to the limited size of Flow Tables at OpenFlow-enabled switches which are the main component of SDN. It is necessary to build compact Flow Tables for the scalability of IoV. Accordingly, we develop a rule optimization approach for real-time query service in SDIV. Specifically, we separate wired data plane from wireless data plane and use multicast address in wireless data plane. Furthermore, we introduce a destination-driven model in wired data plane for reducing the number of rules at switches. Experiments show that our rule optimization strategy reduces the number of rules while keeping the performance of data transmission.

cs.NI↗

arXiv2015

Three-dimensional phenomena in microbubble acoustic streaming

Ultrasound-driven oscillating micro-bubbles have been used as active actuators in microfluidic devices to perform manifold tasks such as mixing, sorting and manipulation of microparticles. A common configuration consists on side-bubbles, created by trapping air pockets in blind channels perpendicular to the main channel direction. This configuration consists of acoustically excited bubbles with a semi-cylindrical shape that generate significant streaming flow. Due to the geometry of the channels, such flows have been generally considered as quasi two-dimensional. Similar assumptions are often made in many other microfluidic systems based on \emph{flat} micro-channels. However, in this paper we show that microparticle trajectories actually present a much richer behavior, with particularly strong out-of-plane dynamics in regions close to the microbubble interface. Using Astigmatism Particle Tracking Velocimetry, we reveal that the apparent planar streamlines are actually projections of a \emph{streamsurface} with a pseudo-toroidal shape. We therefore show that acoustic streaming cannot generally be assumed as a two-dimensional phenomenon in confined systems. The results have crucial consequences for most of the applications involving acoustic streaming as particle trapping, sorting and mixing.

physics.flu-dyn↗

arXiv2015

Electrically Tunable Band Gap in Antiferromagnetic Mott Insulator Sr2IrO4

The electronic band gap in conventional semiconductor materials, such as silicon, is fixed by the material's crystal structure and chemical composition. The gap defines the material's transport and optical properties and is of great importance for performance of semiconductor devices like diodes, transistors and lasers. The ability to tune its value would allow enhanced functionality and flexibility of future electronic and optical devices. Recently, an electrically tunable band gap was realized in a 2D material - electronically gated bilayer graphene [1-3]. Here we demonstrate the realization of an electrically tunable band gap in a 3D antiferromagnetic Mott insulator Sr2IrO4. Using nano-scale contacts between a sharpened Cu tip and a single crystal of Sr2IrO4, we apply a variable external electric field up to a few MV/m and demonstrate a continuous reduction in the band gap of Sr2IrO4 by as much as 16%. We further demonstrate the feasibility of reversible resistive switching and electrically tunable anisotropic magnetoresistance,which provide evidence of correlations between electronic transport, magnetic order, and orbital states in this 5d oxide. Our findings suggest a promising path towards band gap engineering in 5d transition-metal oxides that could potentially lead to appealing technical solutions for next-generation electronic devices.

cond-mat.mes-hall↗

arXiv2014

Stability and Convergence of a Second Order Mixed Finite Element Method for the Cahn-Hilliard Equation

In this paper we devise and analyze an unconditionally stable, second-order-in-time numerical scheme for the Cahn-Hilliard equation in two and three space dimensions. We prove that our two-step scheme is unconditionally energy stable and unconditionally uniquely solvable. Furthermore, we show that the discrete phase variable is bounded in $L^\infty (0,T;L^\infty)$ and the discrete chemical potential is bounded in $L^\infty (0,T;L^2)$, for any time and space step sizes, in two and three dimensions, and for any finite final time $T$. We subsequently prove that these variables converge with optimal rates in the appropriate energy norms in both two and three dimensions. We include in this work a detailed analysis of the initialization of the two-step scheme.

math.NA↗

arXiv2014

Non-parametric shrinkage mean estimation for quadratic loss functions with unknown covariance matrices

In this paper, a shrinkage estimator for the population mean is proposed under known quadratic loss functions with unknown covariance matrices. The new estimator is non-parametric in the sense that it does not assume a specific parametric distribution for the data and it does not require the prior information on the population covariance matrix. Analytical results on the improvement of the proposed shrinkage estimator are provided and some corresponding asymptotic properties are also derived. Finally, we demonstrate the practical improvement of the proposed method over existing methods through extensive simulation studies and real data analysis. Keywords: High-dimensional data; Shrinkage estimator; Large $p$ small $n$; $U$-statistic.

stat.ME↗

arXiv2014

Integrated high quality factor lithium niobate microdisk resonators

Lithium Niobate (LN) is an important nonlinear optical material. Here we demonstrate LN microdisk resonators that feature optical quality factor ~ 100,000, realized using robust and scalable fabrication techniques, that operate over a wide wavelength range spanning visible and near infrared. Using our resonators, and leveraging LN's large second order optical nonlinearity, we demonstrate on-chip second harmonic generation with a conversion efficiency of 0.109 W-1.

physics.optics↗

arXiv2014

Anisotropic magnetoresistance in antiferromagnetic Sr2IrO4

We report point-contact measurements of anisotropic magnetoresistance (AMR) in a single crystal of antiferromagnetic (AFM) Mott insulator Sr2IrO4. The point-contact technique is used here as a local probe of magnetotransport properties on the nanoscale. The measurements at liquid nitrogen temperature revealed negative magnetoresistances (MRs) (up to 28%) for modest magnetic fields (250 mT) applied within the IrO2 a-b plane and electric currents flowing perpendicular to the plane. The angular dependence of MR shows a crossover from four-fold to two-fold symmetry in response to an increasing magnetic field with angular variations in resistance from 1-14%. We tentatively attribute the four-fold symmetry to the crystalline component of AMR and the field-induced transition to the effects of applied field on the canting of AFM-coupled moments in Sr2IrO4. The observed AMR is very large compared to the crystalline AMRs in 3d transition metal alloys/oxides (0.1-0.5%) and can be associated with the large spin-orbit interactions in this 5d oxide while the transition provides evidence of correlations between electronic transport, magnetic order and orbital states. The finding of this work opens an entirely new avenue to not only gain a new insight into physics associated with spin-orbit coupling but also better harness the power of spintronics in a more technically favorable fashion.

cond-mat.mes-hall↗

arXiv2014

A Fourier Pseudospectral Method for the "Good" Boussinesq Equation with Second Order Temporal Accuracy

In this paper, we discuss the nonlinear stability and convergence of a fully discrete Fourier pseudospectral method coupled with a specially designed second order time-stepping for the numerical solution of the "good" Boussinesq equation. Our analysis improves the existing results presented in earlier literature in two ways. First, an $l_\infty(0, T^*; H2)$ convergence for the solution and $l_\infty(0, T^*; l_2)$ convergence for the time-derivative of the solution are obtained in this paper, instead of the $l_\infty(0, T^*; l_2)$ convergence for the solution and the $l_\infty(0, T^*; H^{-2})$ convergence for the time-derivative, given in [17]. In addition, the stability and convergence of this method is shown to be unconditional for the time step in terms of the spatial grid size, compared with a severe restriction time step restriction $Δt \leq Ch^2$ reported in [17].

math.NA↗

arXiv2013

Data Center Cost Optimization Via Workload Modulation Under Real-World Electricity Pricing

We formulate optimization problems to study how data centers might modulate their power demands for cost-effective operation taking into account three key complex features exhibited by real-world electricity pricing schemes: (i) time-varying prices (e.g., time-of-day pricing, spot pricing, or higher energy prices during coincident peaks) and (ii) separate charge for peak power consumption. Our focus is on demand modulation at the granularity of an entire data center or a large part of it. For computational tractability reasons, we work with a fluid model for power demands which we imagine can be modulated using two abstract knobs of demand dropping and demand delaying (each with its associated penalties or costs). Given many data center workloads and electric prices can be effectively predicted using statistical modeling techniques, we devise a stochastic dynamic program (SDP) that can leverage such predictive models. Since the SDP can be computationally infeasible in many real platforms, we devise approximations for it. We also devise fully online algorithms that might be useful for scenarios with poor power demand or utility price predictability. For one of our online algorithms, we prove a competitive ratio of 2-1/n. Finally, using empirical evaluation with both real-world and synthetic power demands and real-world prices, we demonstrate the efficacy of our techniques. As two salient empirically-gained insights: (i) demand delaying is more effective than demand dropping regarding to peak shaving (e.g., 10.74% cost saving with only delaying vs. 1.45% with only dropping for Google workload) and (ii) workloads tend to have different cost saving potential under various electricity tariffs (e.g., 16.97% cost saving under peak-based tariff vs. 1.55% under time-varying pricing tariff for Facebook workload).

eess.SY↗

arXiv2013

Improving Data Forwarding in Mobile Social Networks with Infrastructure Support: A Space-Crossing Community Approach

In this paper, we study two tightly coupled issues: space-crossing community detection and its influence on data forwarding in Mobile Social Networks (MSNs) by taking the hybrid underlying networks with infrastructure support into consideration. The hybrid underlying network is composed of large numbers of mobile users and a small portion of Access Points (APs). Because APs can facilitate the communication among long-distance nodes, the concept of physical proximity community can be extended to be one across the geographical space. In this work, we first investigate a space-crossing community detection method for MSNs. Based on the detection results, we design a novel data forwarding algorithm SAAS (Social Attraction and AP Spreading), and show how to exploit the space-crossing communities to improve the data forwarding efficiency. We evaluate our SAAS algorithm on real-life data from MIT Reality Mining and UIM. Results show that space-crossing community plays a positive role in data forwarding in MSNs in terms of deliver ratio and delay. Based on this new type of community, SAAS achieves a better performance than existing social community-based data forwarding algorithms in practice, including Bubble Rap and Nguyen's Routing algorithms.

cs.SI↗

arXiv2013

Optimal feature selection for sparse linear discriminant analysis and its applications in gene expression data

This work studies the theoretical rules of feature selection in linear discriminant analysis (LDA), and a new feature selection method is proposed for sparse linear discriminant analysis. An $l_1$ minimization method is used to select the important features from which the LDA will be constructed. The asymptotic results of this proposed two-stage LDA (TLDA) are studied, demonstrating that TLDA is an optimal classification rule whose convergence rate is the best compared to existing methods. The experiments on simulated and real datasets are consistent with the theoretical results and show that TLDA performs favorably in comparison with current methods. Overall, TLDA uses a lower minimum number of features or genes than other approaches to achieve a better result with a reduced misclassification rate.

stat.ME↗