arXiv subjects

Zheng Chen

Publications and source records attributed to Zheng Chen.

At least 271 records · Page 15Linked to original sources

arXiv2019

Dynamic Resource Allocation in Co-Located and Cell-Free Massive MIMO

In this paper, we study joint power control and scheduling in uplink massive multiple-input multiple-output (MIMO) systems with randomly arriving data traffic. We consider both co-located and Cell-Free (CF) Massive MIMO, where the difference lies in whether the antennas are co-located at the base station or spread over a wide network area. The data is generated at each user according to an individual stochastic process. Using Lyapunov optimization techniques, we develop a dynamic scheduling algorithm (DSA), which decides at each time slot the amount of data to admit to the transmission queues and the transmission rates over the wireless channel. The proposed algorithm optimizes the long-term user throughput under various fairness policies while keeping the transmission queues stable. Simulation results show that the state-of-the-art power control schemes developed for Massive MIMO with infinite backlogs can fail to stabilize the system even when the data arrival rates are within the network capacity region. Our proposed DSA shows advantage in providing finite delay with performance optimization whenever the network can be stabilized.

cs.IT↗

arXiv2019

Autoignition and detonation development from a hot spot inside a closed chamber: effects of end wall reflection

The advancement of highly boosted internal combustion engines (ICEs) with high thermal efficiency is mainly constrained by knock and super-knock respectively due to the end gas autoignition and detonation development. At the end of the compression stroke, the pressure wave propagation and reflection in a small confined space may greatly influence the end gas autoignition, leading to different autoignition characteristics from those in a large or open space. The present study investigates the transient autoignition process in an iso-octane/air mixture inside a closed chamber under engine-relevant conditions. The emphasis is given to the assessing effects of the pressure wave-wall reflection and the mechanism of extremely strong pressure oscillations typical for super-knock. It is found that the hot spot induced autoignition in a closed chamber can be greatly affected by shock/pressure wave reflection from the end wall. Different autoignition modes respectively from the hot spot and end wall reflection are identified. A non-dimensional parameter quantifying the interplay between different length and time scales is introduced, which helps to identify different autoignition regimes including detonation development near the end wall. It is shown that detonation development from the hot spot may cause super-knock with devastating pressure oscillation. However, the detonation development from the end wall can hardly produce pressure oscillation strong enough for the super-knock. The obtained results provide a fundamental insight into the knocking mechanism in engines under highly boosted conditions.

physics.flu-dyn↗

arXiv2019

Creating Navigable Space from Sparse Noisy Map Points

We present a framework for creating navigable space from sparse and noisy map points generated by sparse visual SLAM methods. Our method incrementally seeds and creates local convex regions free of obstacle points along a robot's trajectory. Then a dense version of point cloud is reconstructed through a map point regulation process where the original noisy map points are first projected onto a series of local convex hull surfaces, after which those points falling inside the convex hulls are culled. The regulated and refined map points allow human users to quickly recognize and abstract the environmental information. We have validated our proposed framework using both a public dataset and a real environmental structure, and our results reveal that the reconstructed navigable free space has small volume loss (error) comparing with the ground truth, and the method is highly efficient, allowing real-time computation and online planning.

cs.RO↗

arXiv2019

Detecting Statistically Significant Communities

Community detection is a key data analysis problem across different fields. During the past decades, numerous algorithms have been proposed to address this issue. However, most work on community detection does not address the issue of statistical significance. Although some research efforts have been made towards mining statistically significant communities, deriving an analytical solution of p-value for one community under the configuration model is still a challenging mission that remains unsolved. The configuration model is a widely used random graph model in community detection, in which the degree of each node is preserved in the generated random networks. To partially fulfill this void, we present a tight upper bound on the p-value of a single community under the configuration model, which can be used for quantifying the statistical significance of each community analytically. Meanwhile, we present a local search method to detect statistically significant communities in an iterative manner. Experimental results demonstrate that our method is comparable with the competing methods on detecting statistically significant communities.

cs.SI↗

arXiv2019

A fast implicit solver for semiconductor models in one space dimension

Several different approaches are proposed for solving fully implicit discretizations of a simplified Boltzmann-Poisson system with a linear relaxation-type collision kernel. This system models the evolution of free electrons in semiconductor devices under a low-density assumption. At each implicit time step, the discretized system is formulated as a fixed-point problem, which can then be solved with a variety of methods. A key algorithmic component in all the approaches considered here is a recently developed sweeping algorithm for Vlasov-Poisson systems. A synthetic acceleration scheme has been implemented to accelerate the convergence of iterative solvers by using the solution to a drift-diffusion equation as a preconditioner. The performance of four iterative solvers and their accelerated variants has been compared on problems modeling semiconductor devices with various electron mean-free-path.

physics.comp-ph↗

arXiv2019

On the Benefits of Network-level Cooperation in IoT Networks with Aggregators

In this work, we consider a random access Internet of Things IoT wireless network assisted by two aggregators collecting information from two disjoint groups of sensors. The nodes and the aggregators are transmitting in a random access manner under slotted time, the aggregators perform network-level cooperation for the data collection. The aggregators are equipped with queues to store data packets that are transmitted by the network nodes and relaying them to the destination node. We characterize the throughput performance of the IoT network and we obtain the stability conditions for the queues at the aggregators. We apply the theory of boundary value problems to analyze the delay performance. Our results show that the presence of the aggregators provides significant gains in the IoT network performance, in addition, we provide useful insights regarding the scalability of the IoT network.

cs.IT↗

arXiv2019

Correlation-Based Community Detection

Mining community structures from the complex network is an important problem across a variety of fields. Many existing community detection methods detect communities through optimizing a community evaluation function. However, most of these functions even have high values on random graphs and may fail to detect small communities in the large-scale network (the so-called resolution limit problem). In this paper, we introduce two novel node-centric community evaluation functions by connecting correlation analysis with community detection. We will further show that the correlation analysis can provide a novel theoretical framework which unifies some existing evaluation functions in the context of a correlation-based optimization problem. In this framework, we can mitigate the resolution limit problem and eliminate the influence of random fluctuations by selecting the right correlation function. Furthermore, we introduce three key properties used in mining association rule into the context of community detection to help us choose the appropriate correlation function. Based on our introduced correlation functions, we propose a community detection algorithm called CBCD. Our proposed algorithm outperforms existing state-of-the-art algorithms on both synthetic benchmark networks and real-world networks.

cs.SI↗

arXiv2019

Dynamic Scheduling and Power Control in Uplink Massive MIMO with Random Data Arrivals

In this paper, we study the joint power control and scheduling in uplink massive multiple-input multiple-output (MIMO) systems with random data arrivals. The data is generated at each user according to an individual stochastic process. Using Lyapunov optimization techniques, we develop a dynamic scheduling algorithm (DSA), which decides at each time slot the amount of data to admit to the transmission queues and the transmission rates over the wireless channel. The proposed algorithm achieves nearly optimal performance on the long-term user throughput under various fairness policies. Simulation results show that the DSA can improve the time-average delay performance compared to the state-of-the-art power control schemes developed for Massive MIMO with infinite backlogs.

cs.NI↗

arXiv2019

The multiproximal linearization method for convex composite problems

Composite minimization involves a collection of smooth functions which are aggregated in a nonsmooth manner. In the convex setting, we design an algorithm by linearizing each smooth component in accordance with its main curvature. The resulting method, called the Multiprox method, consists in solving successively simple problems (e.g. constrained quadratic problems) which can also feature some proximal operators. To study the complexity and the convergence of this method we are led to study quantitative qualification conditions to understand the impact of multipliers on the complexity bounds. We obtain explicit complexity results of the form $O(\frac{1}{k})$ involving new types of constant terms. A distinctive feature of our approach is to be able to cope with oracles involving moving constraints. Our method is flexible enough to include the moving balls method, the proximal Gauss-Newton's method, or the forward-backward splitting, for which we recover known complexity results or establish new ones. We show through several numerical experiments how the use of multiple proximal terms can be decisive for problems with complex geometries.

math.OC↗

arXiv2019

Age of Information in a Multiple Access Channel with Heterogeneous Traffic and an Energy Harvesting Node

Age of Information (AoI) is a newly appeared concept and metric to characterize the freshness of data. In this work, we study the delay and AoI in a multiple access channel (MAC) with two source nodes transmitting different types of data to a common destination. The first node is grid-connected and its data packets arrive in a bursty manner, and at each time slot it transmits one packet with some probability. Another energy harvesting (EH) sensor node generates a new status update with a certain probability whenever it is charged. We derive the delay of the grid-connected node and the AoI of the EH sensor as functions of different parameters in the system. The results show that the mutual interference has a non-trivial impact on the delay and age performance of the two nodes.

cs.IT↗

arXiv2019

A Theoretical Analysis on Enthalpy of Vaporization: Temperature-Dependence and Singularity at the Critical State

Accurate evaluation of enthalpy of vaporization (or latent heat of vaporization) and its variation with temperature is of great interest in practical applications, especially for combustion of liquid fuels. Currently, a theoretically rigorous formulation for latent heat is still not in place; and the existing fitting formulas for various fluids are, in nature, system-dependent. In this paper, the enthalpy of vaporization and its change with temperature are derived rigorously from first principles at the reference and critical states. A composite formula for enthalpy of vaporization is constituted from physical plausibility. The composite formula contains no fitting parameters; and it can accurately predict the enthalpy of vaporization from the reference temperature to the critical temperature.

cond-mat.stat-mech↗

arXiv2019

Correlated Anomaly Detection from Large Streaming Data

Correlated anomaly detection (CAD) from streaming data is a type of group anomaly detection and an essential task in useful real-time data mining applications like botnet detection, financial event detection, industrial process monitor, etc. The primary approach for this type of detection in previous researches is based on principal score (PS) of divided batches or sliding windows by computing top eigenvalues of the correlation matrix, e.g. the Lanczos algorithm. However, this paper brings up the phenomenon of principal score degeneration for large data set, and then mathematically and practically prove current PS-based methods are likely to fail for CAD on large-scale streaming data even if the number of correlated anomalies grows with the data size at a reasonable rate; in reality, anomalies tend to be the minority of the data, and this issue can be more serious. We propose a framework with two novel randomized algorithms rPS and gPS for better detection of correlated anomalies from large streaming data of various correlation strength. The experiment shows high and balanced recall and estimated accuracy of our framework for anomaly detection from a large server log data set and a U.S. stock daily price data set in comparison to direct principal score evaluation and some other recent group anomaly detection algorithms. Moreover, our techniques significantly improve the computation efficiency and scalability for principal score calculation.

cs.LG↗

arXiv2019

Large-Scale Joint Topic, Sentiment & User Preference Analysis for Online Reviews

This paper presents a non-trivial reconstruction of a previous joint topic-sentiment-preference review model TSPRA with stick-breaking representation under the framework of variational inference (VI) and stochastic variational inference (SVI). TSPRA is a Gibbs Sampling based model that solves topics, word sentiments and user preferences altogether and has been shown to achieve good performance, but for large data set it can only learn from a relatively small sample. We develop the variational models vTSPRA and svTSPRA to improve the time use, and our new approach is capable of processing millions of reviews. We rebuild the generative process, improve the rating regression, solve and present the coordinate-ascent updates of variational parameters, and show the time complexity of each iteration is theoretically linear to the corpus size, and the experiments on Amazon data sets show it converges faster than TSPRA and attains better results given the same amount of time. In addition, we tune svTSPRA into an online algorithm ovTSPRA that can monitor oscillations of sentiment and preference overtime. Some interesting fluctuations are captured and possible explanations are provided. The results give strong visual evidence that user preference is better treated as an independent factor from sentiment.

cs.IR↗

arXiv2018

Thermal tuning of the carrier density in Dirac semimetal Cd3As2 nanoplates

The tunable carrier density plays a key role in the investigation of novel transport properties in three-dimensional topological semimetals. Here we demonstrate that the carrier density as well as the mobility of Dirac semimetal Cd3As2 nanoplates can be effectively tuned by the in-situ thermal treatment at 350 K for one hour, both showing a non-monotonic evolution with the thermal cycling treatments. The upward shift of Fermi level relative to the Dirac nodes blurs the surface Fermi-arc states, accompanying with an anomalous phase shift of oscillations of bulk states due to the change of topology of electrons. Meanwhile, the peaks of oscillations of bulk longitudinal magnetoresistivity shift at high fields due to their coupling to the oscillations of the surface Fermi-arc states. Our work provides a thermal control knob for manipulations of the quantum states through the carrier density in Dirac semimetal Cd3As2 at high temperature.

cond-mat.mes-hall↗

arXiv2018

Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews

This paper proposes a new HDP based online review rating regression model named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (i.e. product aspects), word sentiment and user preference as regression factors, and is able to perform topic clustering, review rating prediction, sentiment analysis and what we invent as "critical aspect" analysis altogether in one framework. TSPRA extends sentiment approaches by integrating the key concept "user preference" in collaborative filtering (CF) models into consideration, while it is distinct from current CF models by decoupling "user preference" and "sentiment" as independent factors. Our experiments conducted on 22 Amazon datasets show overwhelming better performance in rating predication against a state-of-art model FLAME (2015) in terms of error, Pearson's Correlation and number of inverted pairs. For sentiment analysis, we compare the derived word sentiments against a public sentiment resource SenticNet3 and our sentiment estimations clearly make more sense in the context of online reviews. Last, as a result of the de-correlation of "user preference" from "sentiment", TSPRA is able to evaluate a new concept "critical aspects", defined as the product aspects seriously concerned by users but negatively commented in reviews. Improvement to such "critical aspects" could be most effective to enhance user experience.

cs.LG↗

arXiv2018

Fast Botnet Detection From Streaming Logs Using Online Lanczos Method

Botnet, a group of coordinated bots, is becoming the main platform of malicious Internet activities like DDOS, click fraud, web scraping, spam/rumor distribution, etc. This paper focuses on design and experiment of a new approach for botnet detection from streaming web server logs, motivated by its wide applicability, real-time protection capability, ease of use and better security of sensitive data. Our algorithm is inspired by a Principal Component Analysis (PCA) to capture correlation in data, and we are first to recognize and adapt Lanczos method to improve the time complexity of PCA-based botnet detection from cubic to sub-cubic, which enables us to more accurately and sensitively detect botnets with sliding time windows rather than fixed time windows. We contribute a generalized online correlation matrix update formula, and a new termination condition for Lanczos iteration for our purpose based on error bound and non-decreasing eigenvalues of symmetric matrices. On our dataset of an ecommerce website logs, experiments show the time cost of Lanczos method with different time windows are consistently only 20% to 25% of PCA.

cs.LG↗

arXiv2018

Statistical inference methods for cumulative incidence function curves at a fixed point in time

Competing risks data arise frequently in clinical trials. When the proportional subdistribution hazard assumption is violated or two cumulative incidence function (CIF) curves cross, rather than comparing the overall treatment effects, researchers may be interested in focusing on a comparison of clinical utility at some fixed time points. This paper extend a series of tests that are constructed based on a pseudo-value regression technique or different transformation functions for CIFs and their variances based on Gaynor's or Aalen's work, and the differences among CIFs at a given time point are compared.

stat.AP↗

arXiv2018

Sample size determination in superiority or non-inferiority clinical trials with time-to-event data under exponential, Weibull and Gompertz distributions

To examine the effect of exponential, Weibull and Gompertz distributions on sample size determination for superiority trials (STs) or non-inferiority trials (NTs) with time-to-event data, we present two sample size formulas for STs or NTs based on Weibull and Gompertz distributions, respectively. The formulas are compared with the current exponential formula to examine their performance. The simulation results show that the sample size formula based on the Weibull distribution is the most robust among the three formulas in STs or NTs. We suggest that recognizing the appropriate distribution in advance is beneficial for proper project planning and that assuming a Weibull distributed survival time is most advantageous in STs or NTs.

stat.AP↗