arXiv subjects

Rui Zhang

Publications and source records attributed to Rui Zhang.

At least 1,135 records · Page 63Linked to original sources

arXiv2019

Consensus-based Distributed Discrete Optimal Transport for Decentralized Resource Matching

Optimal transport has been used extensively in resource matching to promote the efficiency of resources usages by matching sources to targets. However, it requires a significant amount of computations and storage spaces for large-scale problems. In this paper, we take a consensus-based approach to decentralize discrete optimal transport problems and develop fully distributed algorithms with alternating direction method of multipliers. We show that our algorithms guarantee certain levels of efficiency and privacy besides the distributed nature. We further derive primal and dual algorithms by exploring the primal and dual problems of discrete optimal transport with linear utility functions and prove the equivalence between them. We verify the convergence, online adaptability, and the equivalence between the primal algorithm and the dual algorithm with numerical experiments. Our algorithms reflect the bargaining between sources and targets on the amounts and prices of transferred resources and reveal an averaging principle which can be used to regulate resource markets and improve resource efficiency.

math.OC↗

arXiv2019

Multiple agile Earth observation satellites, oversubscribed targets scheduling using complex networks theory

The Earth observation satellites (EOSs) scheduling is of great importance to achieve efficient observation missions. The agile EOSs (AEOS) with stronger attitude maneuvering capacity can greatly improve observation efficiency while increasing scheduling complexity. The multiple AEOSs, oversubscribed targets scheduling problem with multiple observations are addressed, and the potential observation missions are modeled as nodes in the complex networks. To solve the problem, an improved feedback structured heuristic is designed by defining the node and target importance factors. On the basis of a real world Chinese AEOS constellation, simulation experiments are conducted to validate the heuristic efficiency in comparison with a constructive algorithm and a structured genetic algorithm.

astro-ph.IM↗

arXiv2019

Temperature dependence of normalized sensitivity of Love wave sensor with unidirectional carbon fiber epoxy composite/Mn-doped 0.24PIN-0.46PMN-0.30PT ternary single crystal configuration

We have derived a general formula for sensitivity optimization of gravimetric sensors and use it to design a high precision and high sensitivity gravimetric sensor using unidirectional carbon fiber epoxy composite (CFEC) guiding layer on single crystal Mn-doped yPb(In1/2Nb1/2)O3-(1-x-y)Pb(Mg1/3Nb2/3)O3-xPbTiO3 (Mn: PIN-PMN-PT) piezoelectric substrate. The normalized maximum sensitivity exhibits a decreasing tendency with temperature up to 55 degrees Celsius. For the CFEC-on-Mn: PIN-PMN-PT sensor configuration with wavelength 24 {mu}m at 25 degrees Celsius, the maximum sensitivity can reach as high as 760.88 cm2/g, which is nearly twice that of traditional SiO2/ST quartz configuration gravimetric sensor.

physics.app-ph↗

arXiv2019

Few-shot machine learning in the three-dimensional Ising model

We investigate theoretically the phase transition in three dimensional cubic Ising model utilizing state-of-the-art machine learning algorithms. Supervised machine learning models show high accuracies (~99\%) in phase classification and very small relative errors ($< 10^{-4}$) of the energies in different spin configurations. Unsupervised machine learning models are introduced to study the spin configuration reconstructions and reductions, and the phases of reconstructed spin configurations can be accurately classified by a linear logistic algorithm. Based on the comparison between various machine learning models, we develop a few-shot strategy to predict phase transitions in larger lattices from trained sample in smaller lattices. The few-shot machine learning strategy for three dimensional(3D) Ising model enable us to study 3D ising model efficiently and provides a new integrated and highly accurate approach to other spin models.

cond-mat.dis-nn↗

arXiv2019

Trajectory Design for Cellular-Connected UAV Under Outage Duration Constraint

In this paper, we study the trajectory design for a cellular-connected unmanned aerial vehicle (UAV) with given initial and final locations, while communicating with the ground base stations (GBSs) along its flight. We consider delay-limited communications between the UAV and its associated GBSs, where a given signal-to-noise ratio (SNR) target needs to be satisfied at the receiver. However, in practice, due to various factors such as quality-of-service (QoS) requirement, GBSs' availability and UAV mobility constraints, the SNR target may not be met at certain time periods during the flight, each termed as an outage duration. In this paper, we aim to optimize the UAV trajectory to minimize its mission completion time, subject to a constraint on the maximum tolerable outage duration in its flight. To tackle this non-convex problem, we first transform it into a more tractable form and thereby reveal some useful properties of the optimal trajectory solution. Based on these properties, we then further simplify the problem and propose efficient algorithms to check the feasibility of the problem as well as to obtain its optimal and high-quality suboptimal solutions, by leveraging graph theory and convex optimization techniques. Numerical results show that our proposed trajectory designs outperform the conventional method based on dynamic programming, in terms of both performance and complexity.

cs.IT↗

arXiv2019

Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond

Unmanned aerial vehicles (UAVs) have found numerous applications and are expected to bring fertile business opportunities in the next decade. Among various enabling technologies for UAVs, wireless communication is essential and has drawn significantly growing attention in recent years. Compared to the conventional terrestrial communications, UAVs' communications face new challenges due to their high altitude above the ground and great flexibility of movement in the three-dimensional (3D) space. Several critical issues arise, including the line-of-sight (LoS) dominant UAV-ground channels and resultant strong aerial-terrestrial network interference, the distinct communication quality of service (QoS) requirements for UAV control messages versus payload data, the stringent constraints imposed by the size, weight and power (SWAP) limitations of UAVs, as well as the exploitation of the new design degree of freedom (DoF) brought by the highly controllable 3D UAV mobility. In this paper, we give a tutorial overview of the recent advances in UAV communications to address the above issues, with an emphasis on how to integrate UAVs into the forthcoming fifth-generation (5G) and future cellular networks. In particular, we partition our discussions into two promising research and application frameworks of UAV communications, namely UAV-assisted wireless communications and cellular-connected UAVs,where UAVs serve as aerial communication platforms and users, respectively. Furthermore, we point out promising directions for future research and investigation.

eess.SP↗

arXiv2019

Harmonic Unpaired Image-to-image Translation

The recent direction of unpaired image-to-image translation is on one hand very exciting as it alleviates the big burden in obtaining label-intensive pixel-to-pixel supervision, but it is on the other hand not fully satisfactory due to the presence of artifacts and degenerated transformations. In this paper, we take a manifold view of the problem by introducing a smoothness term over the sample graph to attain harmonic functions to enforce consistent mappings during the translation. We develop HarmonicGAN to learn bi-directional translations between the source and the target domains. With the help of similarity-consistency, the inherent self-consistency property of samples can be maintained. Distance metrics defined on two types of features including histogram and CNN are exploited. Under an identical problem setting as CycleGAN, without additional manual inputs and only at a small training-time cost, HarmonicGAN demonstrates a significant qualitative and quantitative improvement over the state of the art, as well as improved interpretability. We show experimental results in a number of applications including medical imaging, object transfiguration, and semantic labeling. We outperform the competing methods in all tasks, and for a medical imaging task in particular our method turns CycleGAN from a failure to a success, halving the mean-squared error, and generating images that radiologists prefer over competing methods in 95% of cases.

cs.CV↗

arXiv2019

Throughput Optimization for Wireless Powered Interference Channels

This paper studies a general multi-user wireless powered interference channel (IFC) under the harvest-then-transmit protocol, where the communication in channel coherence time consists of two phases, namely wireless energy transfer (WET) and wireless information transfer (WIT). In the first phase, all energy transmitters (ETs) transmit energy signals to information transmitters (ITs) via collaborative waveform design, while in the second phase, each IT transmits an information signal to its intended ET using the harvested energy in the previous phase. The aim is to jointly design the WET-WIT time allocation, the (deterministic) transmit signal at the first phase, and the transmit power of ITs in the second phase to optimize the network throughput. The design problems are non-convex and hence difficult to solve globally. To deal with them, we propose efficient iterative algorithms based on alternating projections; then, the majorization-minimization technique is used to tackle the nonconvex sub-problems in each iteration. We also extend the devised design methodology by considering imperfect channel state information (CSI) and non-linearity in energy harvesting circuit. The proposed algorithms are locally convergent and can provide high-quality suboptimal solutions to the design problems. Simulation results show the effectiveness of the proposed algorithms under various setups.

eess.SP↗

arXiv2019

Jamming-assisted Eavesdropping over Parallel Fading Channels

This paper advances the proactive eavesdropping research by considering a practical half-duplex mode for the legitimate monitor and dealing with the challenging case that the suspicious link opportunistically communicates over parallel fading channels. To increase eavesdropping success probability, we propose cognitive jamming for the monitor to change the suspicious link's long-term belief on the parallel channels' distributions, and thereby induce it to transmit more likely over a smaller subset of unjammed channels with a lower transmission rate. As the half-duplex monitor cannot eavesdrop the channel that it is simultaneously jamming to, our jamming design should also control the probability of such "own goal" that occurs when the suspicious link chooses one of the jammed (uneavesdroppable) channels to transmit. We formulate the optimal jamming design problem as a mixed integer nonlinear programming and show that it is non-convex. Nevertheless, we prove that the monitor should optimally use the maximum jamming power if it decides to jam, for maximally reducing suspicious link's communication rate and driving the suspicious link out of the jammed channels. Then we manage to simplify the MINLP to integer programming and reveal a fundamental trade-off in deciding the number of jammed channels: jamming more channels helps reduce the suspicious link's communication rate for overhearing more clearly, but increases own goal probability and thus decreases eavesdropping success probability. Finally, we extend our study to the two-way suspicious communication scenario, and show there is another interesting trade-off in deciding the common jammed channels for balancing bidirectional eavesdropping performances. Numerical results show that our optimized jamming-assisted eavesdropping scheme greatly increase eavesdropping success probability as compared with the conventional passive eavesdropping.

cs.IT↗

arXiv2019

Slow Liquid Scintillator Candidates for MeV-scale Neutrino Experiments

Slow liquid scintillator Cherenkov detectors have been proposed as part of several future neutrino experiments because they can provide both directionality and energy measurements. This feature is expected to enhance the sensitivities for MeV-scale neutrino physics, including solar physics, the search for supernova relic neutrino, and the study of geo-sciences. In this study, the characteristics of a slow liquid scintillator were investigated, along with the light yields and decay time constants for various combinations of linear alkylbenzene (LAB), 2,5-diphenyloxazole (PPO), and 1,4-bis (2-methylstyryl)-benzene (bis-MSB). The results of our study indicated that LAB with 0.07 g/L of PPO and 13 mg/L of bis-MSB was the best candidate for an effective separation between Cherenkov and scintillation lights with a reasonably high light yield.

physics.ins-det↗

arXiv2019

Uplink Cooperative NOMA for Cellular-Connected UAV

Aerial-ground interference mitigation is a challenging issue in the cellular-connected unmanned aerial vehicle (UAV) communications. Due to the strong line-of-sight (LoS) air-to-ground (A2G) channels, the UAV may impose/suffer more severe uplink/downlink interference to/from the cellular base stations (BSs) than the ground users. To tackle this challenge, we propose to apply the non-orthogonal multiple access (NOMA) technique to the uplink communication from a UAV to cellular BSs, under spectrum sharing with the existing ground users. However, for our considered system, traditional NOMA with local interference cancellation (IC), termed non-cooperative NOMA, may provide very limited gain compared to the OMA. This is because there are many co-channel BSs due to the LoS A2G channels and thus the UAV's rate performance is severely limited by the BS with the worst channel condition with the UAV. To improve the UAV's achievable rate, a new cooperative NOMA scheme is proposed by exploiting the backhaul links among BSs. Specifically, some BSs with better channel conditions are selected to decode the UAV's signals first, and then forward the decoded signals to their backhaul-connected BSs for IC. To investigate the optimal design of cooperative NOMA, we maximize the weighted sum-rate of the UAV and ground users by jointly optimizing the UAV's rate and power allocations over multiple resource blocks. However, this problem is hard to be solved optimally. To obtain useful insights, we first consider two special cases with egoistic and altruistic transmission strategies of the UAV, respectively, and solve their corresponding problems optimally. Next, we consider the general case and propose an efficient suboptimal solution via the alternating optimization. Numerical results show that the proposed cooperative NOMA yields significant throughput gains than the OMA and the non-cooperative NOMA benchmark.

cs.IT↗

arXiv2019

Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task

We present Spider, a large-scale, complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 college students. It consists of 10,181 questions and 5,693 unique complex SQL queries on 200 databases with multiple tables, covering 138 different domains. We define a new complex and cross-domain semantic parsing and text-to-SQL task where different complex SQL queries and databases appear in train and test sets. In this way, the task requires the model to generalize well to both new SQL queries and new database schemas. Spider is distinct from most of the previous semantic parsing tasks because they all use a single database and the exact same programs in the train set and the test set. We experiment with various state-of-the-art models and the best model achieves only 12.4% exact matching accuracy on a database split setting. This shows that Spider presents a strong challenge for future research. Our dataset and task are publicly available at https://yale-lily.github.io/spider

cs.CL↗

arXiv2019

Doping Dependence of the Second Magnetization Peak, Critical Current Density and Pinning Mechanism in BaFe$_{2-x}$Ni$_x$As$_2$ Pnictide Superconductors

A series of high quality BaFe$_{2-x}$Ni$_x$As$_2$ pnictide superconductors were studied using magnetic relaxation and isothermal magnetic measurements in order to study the second magnetization peak (SMP) and critical current behaviour in Ni-doped 122 family. The temperature dependence of the magnetic relaxation rate suggests a pinning crossover, whereas, it's magnetic field dependence hints a vortex-lattice structural phase-transition. The activation energy ($U$) estimated using the magnetic relaxation data was analyzed in detail for slightly-underdoped, slightly-overdoped and an overdoped samples, using Maley's method and collective creep theory. Our results confirm that the SMP in these samples is due to the collective (elastic) to plastic creep crossover as has been observed for the other members of 122-family. In addition, we also investigated the doping dependence of the critical current density ($J_c$) and the vortex-pinning behaviour in these compounds. The observed $J_c$ is higher than the threshold limit (10$^5$ A/cm$^2$) considered for the technological potential and even greater than 1 MA/cm$^2$ for slightly underdoped Ni-content, x = 0.092 sample. The pinning characteristics were analyzed in terms of the models developed by Dew-Hughes and Griessen $et$ $al$, which suggest the dominant role of $δl$-type pinning.

cond-mat.supr-con↗

arXiv2019

Generative Adversarial Classifier for Handwriting Characters Super-Resolution

Generative Adversarial Networks (GAN) receive great attentions recently due to its excellent performance in image generation, transformation, and super-resolution. However, GAN has rarely been studied and trained for classification, leading that the generated images may not be appropriate for classification. In this paper, we propose a novel Generative Adversarial Classifier (GAC) particularly for low-resolution Handwriting Character Recognition. Specifically, involving additionally a classifier in the training process of normal GANs, GAC is calibrated for learning suitable structures and restored characters images that benefits the classification. Experimental results show that our proposed method can achieve remarkable performance in handwriting characters 8x super-resolution, approximately 10% and 20% higher than the present state-of-the-art methods respectively on benchmark data CASIA-HWDB1.1 and MNIST.

cs.CV↗

arXiv2019

Limiting Top-Higgs Interaction and Higgs-Boson Width from Multi-Top Productions

We demonstrate that the multi-top productions efficiently probe the CP-property of top-Higgs interaction and the Higgs-boson width at the LHC. The four top-quark production alone can exclude a purely CP-odd top-quark Yukawa coupling at the 13~TeV LHC with an integrated luminosity of $430~{\rm fb}^{-1}$, regardless the size of the Yukawa coupling. Combining the single Higgs-boson production, the $t\bar{t}H$ associated production and the four top-quark production, we show that the CP-phase of the top-quark Yukawa coupling and the Higgs-boson width can be stringently bounded at the LHC with integrated luminosities of $300~{\rm fb}^{-1}$ and $3000~{\rm fb}^{-1}$.

hep-ph↗

arXiv2019

Distributed Change Detection via Average Consensus over Networks

Distributed change-point detection has been a fundamental problem when performing real-time monitoring using sensor-networks. We propose a distributed detection algorithm, where each sensor only exchanges CUSUM statistic with their neighbors based on the average consensus scheme, and an alarm is raised when local consensus statistic exceeds a pre-specified global threshold. We provide theoretical performance bounds showing that the performance of the fully distributed scheme can match the centralized algorithms under some mild conditions. Numerical experiments demonstrate the good performance of the algorithm especially in detecting asynchronous changes.

eess.SP↗

arXiv2019

Phase-dependent quantum correlation in cavity-atom system

We propose a scheme to manipulate quantum correlation of output lights from two sides of a cavity by phase control. A probe laser is set to split into two beams in an interferometer with a relative phase in two arms which drive the cavity mode in opposite directions along cavity axis, individually. This phase, here named as driving-field phase, is important to build up quantum correlation in HBT (Hanbury Brown-Twiss) setup. Three control lasers propagate vertically to the cavity axis and drive the corresponding atomic transitions with a closed-loop phase. This type of closed-loop phase has been utilized to realize quantum correlation and even quantum entanglement of the atomic system in previous work [Phys. Rev. A 81 033836 (2010)]. The scheme here is useful to manipulate steady and maximum quantum correlation.

quant-ph↗

arXiv2018

Securing UAV Communications via Joint Trajectory and Power Control

Unmanned aerial vehicle (UAV) communication is anticipated to be widely applied in the forthcoming fifth-generation (5G) wireless networks, due to its many advantages such as low cost, high mobility, and on-demand deployment. However, the broadcast and line-of-sight (LoS) nature of air-to-ground wireless channels gives rise to a new challenge on how to realize secure UAV communications with the destined nodes on the ground. This paper aims to tackle this challenge by applying the physical layer security technique. We consider both the downlink and uplink UAV communications with a ground node, namely UAV-to-ground (U2G) and ground-to-UAV (G2U) communications, respectively, subject to a potential eavesdropper on the ground. In contrast to the existing literature on wireless physical layer security only with ground nodes at fixed or quasi-static locations, we exploit the high mobility of the UAV to proactively establish favorable and degraded channels for the legitimate and eavesdropping links, respectively, via its trajectory design. We formulate new problems to maximize the average secrecy rates of the U2G and G2U transmissions, respectively, by jointly optimizing the UAV's trajectory and the transmit power of the legitimate transmitter over a given flight period of the UAV. Although the formulated problems are non-convex, we propose iterative algorithms to solve them efficiently by applying the block coordinate descent and successive convex optimization methods. Specifically, the transmit power and UAV trajectory are each optimized with the other being fixed in an alternating manner, until the algorithms converge. Simulation results show that the proposed algorithms can improve the secrecy rates for both U2G and G2U communications, as compared to other benchmark schemes without power control and/or trajectory optimization.

cs.IT↗