arXiv subjects

Lei Chen

Publications and source records attributed to Lei Chen.

At least 649 records · Page 36Linked to original sources

arXiv2015

Reliable Diversity-Based Spatial Crowdsourcing by Moving Workers

With the rapid development of mobile devices and the crowdsourcig platforms, the spatial crowdsourcing has attracted much attention from the database community, specifically, spatial crowdsourcing refers to sending a location-based request to workers according to their positions. In this paper, we consider an important spatial crowdsourcing problem, namely reliable diversity-based spatial crowdsourcing (RDB-SC), in which spatial tasks (such as taking videos/photos of a landmark or firework shows, and checking whether or not parking spaces are available) are time-constrained, and workers are moving towards some directions. Our RDB-SC problem is to assign workers to spatial tasks such that the completion reliability and the spatial/temporal diversities of spatial tasks are maximized. We prove that the RDB-SC problem is NP-hard and intractable. Thus, we propose three effective approximation approaches, including greedy, sampling, and divide-and-conquer algorithms. In order to improve the efficiency, we also design an effective cost-model-based index, which can dynamically maintain moving workers and spatial tasks with low cost, and efficiently facilitate the retrieval of RDB-SC answers. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches over both real and synthetic data sets.

cs.DB↗

arXiv2015

Distributed Virtual Resource Allocation in Small Cell Networks with Full Duplex Self-backhauls and Virtualization

Wireless network virtualization has attracted great attentions from both academia and industry. Another emerging technology for next generation wireless networks is in-band full duplex (FD) communications. Due to its promising performance, FD communication has been considered as an effective way to achieve self-backhauls for small cells. In this paper, we introduce wireless virtualization into small cell networks, and propose a virtualized small cell network architecture with FD self-backhauls. We formulate the virtual resource allocation problem in virtualized small cell networks with FD self-backhauls as an optimization problem. Since the formulated problem is a mixed combinatorial and non-convex optimization problem, its computational complexity is high. Moreover, the centralized scheme may suffer from signaling overhead, outdated dynamics information, and scalability issues. To solve it efficiently, we divide the original problem into two subproblems. For the first subproblem, we transfer it to a convex optimization problem, and then solve it by an efficient alternating direction method of multipliers (ADMM)-based distributed algorithm. The second subproblem is a convex problem, which can be solved by each infrastructure provider. Extensive simulations are conducted with different system configurations to show the effectiveness of the proposed scheme.

cs.NI↗

arXiv2015

Task Assignment on Multi-Skill Oriented Spatial Crowdsourcing

With the rapid development of mobile devices and crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community. Specifically, the spatial crowdsourcing refers to sending location-based requests to workers, based on their current positions. In this paper, we consider a spatial crowdsourcing scenario, in which each worker has a set of qualified skills, whereas each spatial task (e.g., repairing a house, decorating a room, and performing entertainment shows for a ceremony) is time-constrained, under the budget constraint, and required a set of skills. Under this scenario, we will study an important problem, namely multi-skill spatial crowdsourcing (MS-SC), which finds an optimal worker-and-task assignment strategy, such that skills between workers and tasks match with each other, and workers' benefits are maximized under the budget constraint. We prove that the MS-SC problem is NP-hard and intractable. Therefore, we propose three effective heuristic approaches, including greedy, g-divide-and-conquer and cost-model-based adaptive algorithms to get worker-and-task assignments. Through extensive experiments, we demonstrate the efficiency and effectiveness of our MS-SC processing approaches on both real and synthetic data sets.

cs.DB↗

arXiv2015

Filtered-OFDM - Enabler for Flexible Waveform in The 5th Generation Cellular Networks

The underlying waveform has always been a shaping factor for each generation of the cellular networks, such as orthogonal frequency division multiplexing (OFDM) for the 4th generation cellular networks (4G). To meet the diversified and pronounced expectations upon the upcoming 5G cellular networks, here we present an enabler for flexible waveform configuration, named as filtered-OFDM (f-OFDM). With the conventional OFDM, a unified numerology is applied across the bandwidth provided, balancing among the channel characteristics and the service requirements, and the spectrum efficiency is limited by the compromise we made. In contrast, with f-OFDM, the assigned bandwidth is split up into several subbands, and different types of services are accommodated in different subbands with the most suitable waveform and numerology, leading to an improved spectrum utilization. After outlining the general framework of f-OFDM, several important design aspects are also discussed, including filter design and guard tone arrangement. In addition, an extensive comparison among the existing 5G waveform candidates is also included to illustrate the advantages of f-OFDM. Our simulations indicate that, in a specific scenario with four distinct types of services, f-OFDM provides up to 46% of throughput gains over the conventional OFDM scheme.

cs.IT↗

arXiv2015

Deep Structured Models For Group Activity Recognition

This paper presents a deep neural-network-based hierarchical graphical model for individual and group activity recognition in surveillance scenes. Deep networks are used to recognize the actions of individual people in a scene. Next, a neural-network-based hierarchical graphical model refines the predicted labels for each class by considering dependencies between the classes. This refinement step mimics a message-passing step similar to inference in a probabilistic graphical model. We show that this approach can be effective in group activity recognition, with the deep graphical model improving recognition rates over baseline methods.

cs.CV↗

arXiv2015

Probing the Flat Band of Optically-Trapped Spin-Orbital Coupled Bose Gases Using Bragg Spectroscopy

Motivated by the recent efforts in creating the flat band in ultracold atomic systems, we investigate how to probe the flat band in an optically-trapped spin-orbital coupled Bose-Einstein condensate using Bragg spectroscopy. We have found that the excitation spectrum and the dynamic structure factor of the condensate alter dramatically, when the band structure exhibits various level of flatness. In particular, when the band exhibits perfect flatness around the band minima corresponding to a near infinite effective mass, a quadratic dispersion emerges in the low-energy excitation spectrum; in sharp contrast, for the opposite case when an ordinary band is present, the familiar linear dispersion arises. Such linear-to-quadratic crossover in the energy spectrum presents a striking manifestation of the transition of an ordinary band into a flat band, thereby allows the direct probe of the flat band by using Bragg spectroscopy.

cond-mat.quant-gas↗

arXiv2015

Non-equilibrium dynamics of an ultracold Bose gas under a multi-pulsed quantum quench in interaction

We investigate the nonequilibrium dynamical properties of a weakly-interacting Bose gas at zero temperature under the multi-pulsed quantum quench in interaction by calculating one-body, two-body correlation functions and Tan's contact of the model system. The multi-pulsed quench is represented as follows: first suddenly quenching the interatomic interaction from $g_{i}$ to $g_{f}$ at time $t=0$, holding time $t$, and then suddenly quenching interaction from $g_{f}$ back to $g_{i}$, holding the time $t$ sequence $n$ times. In particular, two typical kinds of quenching parameters are chosen, corresponding to $(g_{i}/g_{f}>1)$ and $(g_{i}/g_{f}<1)$ respectively. We find that the more the quenching times of $n$ are, the more the excitations are excited, which suggests that the multi-pulsed QQ is more powerful way of studying the non-equilibrium dynamics of many-body quantum system than the `one-off' quantum quench. Finally, we discuss the ultra-short-range properties of the two-body correlation function after the $n$th quenching, which can be used to probe the `Tan'scontact' in experiments. All our calculations can be tested in current cold atom experiments.

cond-mat.quant-gas↗

arXiv2014

Unusual behavior of sound velocity of a Bose gas in an optical superlattice at quasi-one-dimension

A Bose gas trapped in a one-dimensional optical superlattice has emerged as a novel superfluid characterized by tunable lattice topologies and tailored band structures. In this work, we focus on the propagation of sound in such a novel system and have found new features on sound velocity, which arises from the interplay between the two lattices with different periodicity and is not present in the case of a condensate in a monochromatic optical lattice. Particularly, this is the first time that the sound velocity is found to first increase and then decrease as the superlattice strength increases even at one dimension. Such unusual behavior can be analytically understood in terms of the competition between the decreasing compressibility and the increasing effective mass due to the increasing superlattice strength. This result suggests a new route to engineer the sound velocity by manipulating the superlattice's parameters. All the calculations based on the mean-field theory are justified by checking the exponent $γ$ of the off-diagonal one-body density matrix that is much smaller than 1. Finally, the conditions for possible experimental realization of our scenario are also discussed.

cond-mat.quant-gas↗

arXiv2014

Probing Sound Speed of an Optically-Trapped Bose Gas with Periodically Modulated Interactions by Bragg Spectroscopy

A Bose-Einstein condensate (BEC) with periodically modulated interactions (PMI) has emerged as a novel kind of periodic superfluid, which has been recently experimentally created using optical Feshbach resonance. In this paper, we are motivated to investigate the superfluidity of a BEC with PMI trapped in an optical lattice (OL). In particular, we explore the effects of PMI on the sound speed and the dynamical structure factor of the model system. Our numerical results, combined with the analytical results in both the weak-potential limit and the tight-binding limit, have shown that the PMI can strongly modify the sound speed of a BEC. Moreover, we have shown that the effects of PMI on sound speed can be experimentally probed via the dynamic structure factor, where the excitation strength toward the first Bogoliubov band exhibits marked difference from the non- PMI one. Our predictions of the effects of PMI on the sound speed can be tested using the Bragg spectroscopy.

cond-mat.quant-gas↗

arXiv2014

Quench Dynamics of Three-Dimensional Disordered Bose Gases: Condensation, Superfluidity and Fingerprint of Dynamical Bose Glass

In an equilibrium three-dimensional (3D) disordered condensate, it's well established that disorder can generate an amount of normal fluid equaling to $\frac{4}{3}$ of the condensate depletion. The concept that the superfluid is more volatile to the existence of disorder than the condensate is crucial to the understanding of Bose glass phase. In this Letter, we show that, by bringing a weakly disordered 3D condensate to nonequilibrium regime via a quantum quench in the interaction, disorder can destroy superfluid significantly more, leading to a steady state in which the normal fluid density far exceeds $\frac{4}{3}$ of the condensate depletion. This suggests a possibility of engineering Bose Glass in the dynamic regime. As both the condensate density and superfluid density are measurable quantities, our results allow an experimental demonstration of the dramatized interplay between the disorder and interaction in the nonequilibrium scenario.

cond-mat.quant-gas↗

arXiv2014

Majorana zero modes on a 1D chain for quantum computation

Numerical calculations for Majorana zero modes on a one-dimensional chain are performed using the technique of block diagonalization for general parameter settings. It is found that Majorana zero modes occur near the ends of the chain and decay exponentially away from the ends. The phase diagrams show that Majorana zero modes of a long-enough chain indeed have a parameter domain of $2t>|μ|$ as predicted from the bulk property of the chain, but a short chain has a much smaller parameter domain than the prediction. Through a numerical simulation Majorana zero modes are found to be robust under the disturbance of noise. Finally the reversion of the parity of the ground states is studied by applying a bias voltage on the quantum dot at an end of the chain. It is found that for a weak coupling between a chain and a quantum dot the parity of the ground states can be reversed through adiabatically tuning the bias voltage.

quant-ph↗

arXiv2014

Is getting the right answer just about choosing the right words? The role of syntactically-informed features in short answer scoring

Developments in the educational landscape have spurred greater interest in the problem of automatically scoring short answer questions. A recent shared task on this topic revealed a fundamental divide in the modeling approaches that have been applied to this problem, with the best-performing systems split between those that employ a knowledge engineering approach and those that almost solely leverage lexical information (as opposed to higher-level syntactic information) in assigning a score to a given response. This paper aims to introduce the NLP community to the largest corpus currently available for short-answer scoring, provide an overview of methods used in the shared task using this data, and explore the extent to which more syntactically-informed features can contribute to the short answer scoring task in a way that avoids the question-specific manual effort of the knowledge engineering approach.

cs.CL↗

arXiv2014

Probabilistic Nearest Neighbor Queries on Uncertain Moving Object Trajectories

Nearest neighbor (NN) queries in trajectory databases have received significant attention in the past, due to their application in spatio-temporal data analysis. Recent work has considered the realistic case where the trajectories are uncertain; however, only simple uncertainty models have been proposed, which do not allow for accurate probabilistic search. In this paper, we fill this gap by addressing probabilistic nearest neighbor queries in databases with uncertain trajectories modeled by stochastic processes, specifically the Markov chain model. We study three nearest neighbor query semantics that take as input a query state or trajectory $q$ and a time interval. For some queries, we show that no polynomial time solution can be found. For problems that can be solved in PTIME, we present exact query evaluation algorithms, while for the general case, we propose a sophisticated sampling approach, which uses Bayesian inference to guarantee that sampled trajectories conform to the observation data stored in the database. This sampling approach can be used in Monte-Carlo based approximation solutions. We include an extensive experimental study to support our theoretical results.

cs.DB↗

arXiv2013

Motionless Phase Stepping in X-Ray Phase Contrast Imaging with a Compact Source

X-ray phase contrast imaging offers a way to visualize the internal structures of an object without the need to deposit any radiation, and thereby alleviate the main concern in x-ray diagnostic imaging procedures today. Grating-based differential phase contrast imaging techniques are compatible with compact x-ray sources, which is a key requirement for the majority of clinical x-ray modalities. However, these methods are substantially limited by the need for mechanical phase stepping. We describe an electromagnetic phase stepping method that eliminates mechanical motion, and thus removing the constraints in speed, accuracy and flexibility. The method is broadly applicable to both projection and tomography imaging modes. The transition from mechanical to electromagnetic scanning should greatly facilitate the translation of x-ray phase contrast techniques into mainstream applications.

physics.med-ph↗

arXiv2013

Active Consensus over Sensor Networks via Randomized Communication

Distributed consensus has been widely studied for sensor network applications. Whereas the asymptotic convergence rate has been extensively explored in prior work, other important and practical issues, including energy efficiency and link reliability, have received relatively little attention. In this paper, we present a distributed consensus approach that can achieve a good balance between convergence rate and energy efficiency. The approach selects a subset of links that significantly contribute to the formation of consensus at each iteration, thus adapting the network's topology dynamically to the changes of the sensor states. A global optimization problem is formulated for optimal link selection, which is subsequently factorized into sub-problems that can be solved locally, and practically via approximation. An algorithm is derived to solve the approximation efficiently, using quadratic programming (QP) relaxation and random sampling. Simulations on networks of different types demonstrate that the proposed method reduces the communication energy costs without significantly impacting the convergence rate and that the approach is robust to link failures.

cs.NI↗

arXiv2013

Efficient Data Gathering in Wireless Sensor Networks Based on Matrix Completion and Compressive Sensing

Gathering data in an energy efficient manner in wireless sensor networks is an important design challenge. In wireless sensor networks, the readings of sensors always exhibit intra-temporal and inter-spatial correlations. Therefore, in this letter, we use low rank matrix completion theory to explore the inter-spatial correlation and use compressive sensing theory to take advantage of intra-temporal correlation. Our method, dubbed MCCS, can significantly reduce the amount of data that each sensor must send through network and to the sink, thus prolong the lifetime of the whole networks. Experiments using real datasets demonstrate the feasibility and efficacy of our MCCS method.

cs.NI↗

arXiv2012

Observation of Correlation between Route to Formation, Coherence, Noise, and Communication Performance of Kerr Combs

Microresonator optical frequency combs based on cascaded four-wave mixing are potentially attractive as a multi-wavelength source for on-chip optical communications. In this paper we compare time domain coherence, radio-frequency (RF) intensity noise, and individual line optical communications performance for combs generated from two different silicon nitride microresonators. The comb generated by one microresonator forms directly with lines spaced by a single free spectral range (FSR) and exhibits high coherence, low noise, and excellent 10 Gbit/s optical communications results. The comb generated by the second microresonator forms initially with multiple FSR line spacing, with additional lines later filling to reach single FSR spacing. This comb exhibits degraded coherence, increased intensity noise, and severely degraded communications performance. This study is to our knowledge the first to simultaneously investigate and observe a correlation between the route to comb formation, the coherence, noise, and optical communications performance of a Kerr comb.

physics.optics↗

arXiv2012

Efficient Multi-way Theta-Join Processing Using MapReduce

Multi-way Theta-join queries are powerful in describing complex relations and therefore widely employed in real practices. However, existing solutions from traditional distributed and parallel databases for multi-way Theta-join queries cannot be easily extended to fit a shared-nothing distributed computing paradigm, which is proven to be able to support OLAP applications over immense data volumes. In this work, we study the problem of efficient processing of multi-way Theta-join queries using MapReduce from a cost-effective perspective. Although there have been some works using the (key,value) pair-based programming model to support join operations, efficient processing of multi-way Theta-join queries has never been fully explored. The substantial challenge lies in, given a number of processing units (that can run Map or Reduce tasks), mapping a multi-way Theta-join query to a number of MapReduce jobs and having them executed in a well scheduled sequence, such that the total processing time span is minimized. Our solution mainly includes two parts: 1) cost metrics for both single MapReduce job and a number of MapReduce jobs executed in a certain order; 2) the efficient execution of a chain-typed Theta-join with only one MapReduce job. Comparing with the query evaluation strategy proposed in [23] and the widely adopted Pig Latin and Hive SQL solutions, our method achieves significant improvement of the join processing efficiency.

cs.DB↗