arXiv subjects

Lei Chen

Publications and source records attributed to Lei Chen.

At least 631 records · Page 35Linked to original sources

arXiv2017

Predicting Audience's Laughter Using Convolutional Neural Network

For the purpose of automatically evaluating speakers' humor usage, we build a presentation corpus containing humorous utterances based on TED talks. Compared to previous data resources supporting humor recognition research, ours has several advantages, including (a) both positive and negative instances coming from a homogeneous data set, (b) containing a large number of speakers, and (c) being open. Focusing on using lexical cues for humor recognition, we systematically compare a newly emerging text classification method based on Convolutional Neural Networks (CNNs) with a well-established conventional method using linguistic knowledge. The advantages of the CNN method are both getting higher detection accuracies and being able to learn essential features automatically.

cs.CL↗

arXiv2017

Prediction-Based Task Assignment in Spatial Crowdsourcing (Technical Report)

Spatial crowdsourcing refers to a system that periodically assigns a number of location-based workers with spatial tasks nearby (e.g., taking photos or videos at some spatial locations). Previous works on the spatial crowdsourcing usually designed task assignment strategies that maximize some assignment scores, which are however only based on available workers/tasks in the system at the time point of assigning workers/tasks. These strategies may achieve local optimality, due to the neglect of future workers/tasks that may join the system. In contrast, in this paper, we aim to achieve "globally" optimal task assignments, by considering not only those present, but also future (via predictions), workers/tasks. Specifically, we formalize an important problem, namely prediction-based spatial crowdsourcing (PB-SC), which expects to obtain a "globally" optimal strategy for worker-and-task assignments, over both present and predicted task/worker locations, such that the total assignment quality score is maximized under the constraint of the traveling budget. In this paper, we design an effective grid-based prediction method to estimate spatial distributions of workers/tasks in the future, and then utilize the predicted ones in our procedure of task assignments. We prove that the PB-SC problem is NP-hard, and thus intractable. Therefore, we propose efficient approximate algorithms to tackle the PB-SC problem, including greedy and divide-and-conquer (D&C) approaches, which can efficiently assign workers to spatial tasks with high quality scores and low budget consumptions, by considering both current and future task/worker distributions. Through extensive experiments, we demonstrate the efficiency and effectiveness of our PB-SC processing approaches on real/synthetic data.

cs.DB↗

arXiv2017

CrowdFusion: A Crowdsourced Approach on Data Fusion Refinement

Data fusion has played an important role in data mining because high-quality data is required in a lot of applications. As on-line data may be out-of-date and errors in the data may propagate with copying and referring between sources, it is hard to achieve satisfying results with merely applying existing data fusion methods to fuse Web data. In this paper, we make use of the crowd to achieve high-quality data fusion result. We design a framework selecting a set of tasks to ask crowds in order to improve the confidence of data. Since data are correlated and crowds may provide incorrect answers, how to select a proper set of tasks to ask the crowd is a very challenging problem. In this paper, we design an approximation solution to address this challenge since we prove that the problem is at NP-hard. To further improve the efficiency, we design a pruning strategy and a preprocessing method, which effectively improve the performance of the proposed approximation solution. Furthermore, we find that under certain scenarios, we are not interested in all the facts, but only a specific set of facts. Thus, for these specific scenarios, we also develop another approximation solution which is much faster than the general approximation solution. We verify the solutions with extensive experiments on a real crowdsourcing platform.

cs.DB↗

arXiv2016

Performance and Compensation of I/Q Imbalance in Differential STBC-OFDM

Differential space time block coding (STBC) achieves full spatial diversity and avoids channel estimation overhead. Over highly frequency-selective channels, STBC is integrated with orthogonal frequency division multiplexing (OFDM) to achieve high performance. However, low-cost implementation of differential STBC-OFDM using direct-conversion transceivers is sensitive to In-phase/Quadrature-phase imbalance (IQI). In this paper, we quantify the performance impact of IQI at the receiver front-end on differential STBC-OFDM systems and propose a compensation algorithm to mitigate its effect. The proposed receiver IQI compensation works in an adaptive decision-directed manner without using known pilots or training sequences, which reduces the rate loss due to training overhead. Our numerical results show that our proposed compensation algorithm can effectively mitigate receive IQI in differential STBC-OFDM.

cs.IT↗

arXiv2016

Performance Analysis and Compensation of Joint TX/RX I/Q Imbalance in Differential STBC-OFDM

Differential space time block coding (STBC) achieves full spatial diversity and avoids channel estimation overhead. Over highly frequency-selective channels, STBC is integrated with orthogonal frequency division multiplexing (OFDM) to efficiently mitigate intersymbol interference effects. However, low-cost implementation of STBC-OFDM with direct-conversion transceivers is sensitive to In-phase/Quadrature-phase imbalance (IQI). In this paper, we quantify the performance impact of IQI at both the transmitter and receiver radio frequency front-ends on differential STBC-OFDM systems which has not been investigated before in the literature. In addition, we propose a widely-linear compensation algorithm at the receiver to mitigate the performance degradation caused by the IQI at the transmitter and receiver ends. Moreover, a parameter-based generalized algorithm is proposed to extract the IQI parameters and improve the performance under high-mobility. The adaptive compensation algorithms are blind and work in a decision-directed manner without using known pilots or training sequences. Numerical results show that our proposed compensation algorithms can effectively mitigate IQI in differential STBC-OFDM.

cs.IT↗

arXiv2016

A high performance Nb nano-SQUID with a three-dimensional structure

A superconducting quantum interference device (SQUID) miniaturized into nanoscale is promising in the inductive detection of a single electron spin. A nano-SQUID with a strong spin coupling coefficient, a low flux noise, and a wide working magnetic field range is highly desired in a single spin resonance measurement. Nano-SQUIDs with Dayem-bridge junctions excel in a high working field range and in the direct coupling from spins to the bridge. However, the common planar structure of nano-SQUIDs is known for problems such as a shallow flux modulation depth and a troublesome hysteresis in current-voltage curves. Here, we developed a fabrication process for creating three-dimensional (3-D) niobium (Nb) nano-SQUIDs with nano-bridge junctions that can be tuned independently. Characterization of the device shows up to 45.9 % modulation depth with a reversible current-voltage curve. Owning to the large modulation depth, the measured flux noise is as low as 0.34 μΦ$_0$/Hz$^{1/2}$. The working field range of the SQUID is greater than 0.5 T parallel to the SQUID plane. We believe that 3-D Nb nano-SQUIDs provide a promising step toward effective single-spin inductive detection.

cond-mat.mes-hall↗

arXiv2016

Bilayer Linearized Tensor Renormalization Group Approach for Thermal Tensor Networks

In this paper, we perform a comprehensive study of the renormalization group (RG) method on thermal tensor networks (TTN). By Trotter-Suzuki decomposition, one obtains the 1+1D TTN representing the partition function of 1D quantum lattice models, and then employs efficient RG contractions to obtain the thermodynamic properties with high precision. The linearized tensor renormalization group (LTRG) method, which can be used to contract TTN in an efficient and accurate way, is briefly reviewed. In addition, the single-layer LTRG can be generalized to a bilayer form, dubbed as LTRG++, in both finite- and infinite-size systems, with accuracies significantly improved. We provide the details of LTRG++ in finite-size system, comparing its accuracy with single-layer algorithm, and elaborate the infinite-size LTRG++ in the context of fermion chain model. We show that the LTRG++ algorithm in infinite-size system is in essence equivalent to transfer-matrix renormalization group method, while expressed in a tensor network language. LTRG++ is then applied to study an extended Hubbard model, where the phase separation phenomenon, groundstate phase diagram, as well as quantum criticality-enhanced magnetocaloric effects under external fields, are investigated.

cond-mat.str-el↗

arXiv2016

Dynamic Resource Allocation in Next Generation Cellular Networks with Full-Duplex Self-backhauls

With the dense deployment of small cell networks, low-cost backhaul schemes for small cell base stations (SBSs) have attracted great attentions. Self-backhaul using cellular communication technology is considered as a promising solution. Although some excellent works have been done on self-backhaul in small cell networks, most of them do not consider the recent advances of full-duplex (FD) and massive multiple-input and multiple-output (MIMO) technologies. In this paper, we propose a self-backhaul scheme for small cell networks by combining FD and massive MIMO technologies. In our proposed scheme, the macro base station (MBS) is equipped with massive MIMO antennas, and the SBSs have the FD communication ability. By treating the SBSs as \textit{special} macro users, we can achieve the simultaneous transmissions of the access link of users and the backhaul link of SBSs in the same frequency. Furthermore, considering the existence of inter-tier and intra-tier interference, we formulate the power allocation problem of the MBS and SBSs as an optimization problem. Because the formulated power allocation problem is a non-convex problem, we transform the original problem into a difference of convex program (DCP) by successive convex approximation method (SCAM) and variable transformation, and then solve it using a constrained concave convex procedure (CCCP) based iterative algorithm. Finally, extensive simulations are conducted with different system configurations to verify the effectiveness of the proposed scheme.

cs.NI↗

arXiv2016

Tuning Crowdsourced Human Computation

As the use of crowdsourcing increases, it is important to think about performance optimization. For this purpose, it is possible to think about each worker as a HPU(Human Processing Unit), and to draw inspiration from performance optimization on traditional computers or cloud nodes with CPUs. However, as we characterize HPUs in detail for this purpose, we find that there are important differences between CPUs and HPUs, leading to the need for completely new optimization algorithms. In this paper, we study the specific optimization problem of obtaining results fastest for a crowd sourced job with a fixed total budget. In crowdsourcing, jobs are usually broken down into sets of small tasks, which are assigned to workers one at a time. We consider three scenarios of increasing complexity: Identical Round Homogeneous tasks, Multiplex Round Homogeneous tasks, and Multiple Round Heterogeneous tasks. For each scenario, we analyze the stochastic behavior of the HPU clock-rate as a function of the remuneration offered. After that, we develop an optimum Budget Allocation strategy to minimize the latency for job completion. We validate our results through extensive simulations and experiments on Amazon Mechanical Turk.

cs.HC↗

arXiv2016

Zero-Shot Learning with Multi-Battery Factor Analysis

Zero-shot learning (ZSL) extends the conventional image classification technique to a more challenging situation where the test image categories are not seen in the training samples. Most studies on ZSL utilize side information such as attributes or word vectors to bridge the relations between the seen classes and the unseen classes. However, existing approaches on ZSL typically exploit a shared space for each type of side information independently, which cannot make full use of the complementary knowledge of different types of side information. To this end, this paper presents an MBFA-ZSL approach to embed different types of side information as well as the visual feature into one shared space. Specifically, we first develop an algorithm named Multi-Battery Factor Analysis (MBFA) to build a unified semantic space, and then employ multiple types of side information in it to achieve the ZSL. The close-form solution makes MBFA-ZSL simple to implement and efficient to run on large datasets. Extensive experiments on the popular AwA, CUB, and SUN datasets show its significant superiority over the state-of-the-art approaches.

cs.CV↗

arXiv2016

Processing SPARQL Queries Over Distributed RDF Graphs

We propose techniques for processing SPARQL queries over a large RDF graph in a distributed environment. We adopt a "partial evaluation and assembly" framework. Answering a SPARQL query Q is equivalent to finding subgraph matches of the query graph Q over RDF graph G. Based on properties of subgraph matching over a distributed graph, we introduce local partial match as partial answers in each fragment of RDF graph G. For assembly, we propose two methods: centralized and distributed assembly. We analyze our algorithms from both theoretically and experimentally. Extensive experiments over both real and benchmark RDF repositories of billions of triples confirm that our method is superior to the state-of-the-art methods in both the system's performance and scalability.

cs.DB↗

arXiv2016

Query Workload-based RDF Graph Fragmentation and Allocation

As the volume of the RDF data becomes increasingly large, it is essential for us to design a distributed database system to manage it. For distributed RDF data design, it is quite common to partition the RDF data into some parts, called fragments, which are then distributed. Thus, the distribution design consists of two steps: fragmentation and allocation. In this paper, we propose a method to explore the intrinsic similarities among the structures of queries in a workload for fragmentation and allocation, which aims to reduce the number of crossing matches and the communication cost during SPARQL query processing. Specifically, we mine and select some frequent access patterns to reflect the characteristics of the workload. Here, although we prove that selecting the optimal set of frequent access patterns is NP-hard, we propose a heuristic algorithm which guarantees both the data integrity and the approximation ratio. Based on the selected frequent access patterns, we propose two fragmentation strategies, vertical and horizontal fragmentation strategies, to divide RDF graphs while meeting different kinds of query processing objectives. Vertical fragmentation is for better throughput and horizontal fragmentation is for better performance. After fragmentation, we discuss how to allocate these fragments to various sites. Finally, we discuss how to process a query based on the results of fragmentation and allocation. Extensive experiments confirm the superior performance of our proposed solutions.

cs.DC↗

arXiv2016

Dynamic nuclear polarization in a magnetic resonance force microscope experiment

We report achieving enhanced nuclear magnetization in a magnetic resonance force microscope experiment at 0.6 tesla and 4.2 kelvin using the dynamic nuclear polarization (DNP) effect. In our experiments a microwire coplanar waveguide delivered radiowaves to excite nuclear spins and microwaves to excite electron spins in a 250 nm thick nitroxide-doped polystyrene sample. Both electron and proton spin resonance were observed as a change in the mechanical resonance frequency of a nearby cantilever having a micron-scale nickel tip. NMR signal, not observable from Curie-law magnetization at 0.6 tesla, became observable when microwave irradiation was applied to saturate the electron spins. The resulting NMR signal's size, buildup time, dependence on microwave power, and dependence on irradiation frequency was consistent with a transfer of magnetization from electron spins to nuclear spins. Due to the presence of an inhomogenous magnetic field introduced by the cantilever's magnetic tip, the electron spins in the sample were saturated in a microwave-resonant slice 10's of nm thick. The spatial distribution of the nuclear polarization enhancement factor $ε$ was mapped by varying the frequency of the applied radiowaves. The observed enhancement factor was zero for spins in the center of the resonant slice, was $ε= +10$ to $+20$ for spins proximal to the magnet, and was $ε= -10$ to $-20$ for spins distal to the magnet. We show that this bipolar nuclear magnetization profile is consistent with cross-effect DNP in a $\sim \! 10^{5} \: \mathrm{T} \: \mathrm{m}^{-1}$ magnetic field gradient. Potential challenges associated with generating and using DNP-enhanced nuclear magnetization in a nanometer-resolution magnetic resonance imaging experiment are elucidated and discussed.

cond-mat.mes-hall↗

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↗