arXiv subjects

Gang Wang

Publications and source records attributed to Gang Wang.

At least 379 records · Page 21Linked to original sources

arXiv2019

MAC Protocols for Wireless Mesh Networks with Multi-beam Antennas: A Survey

Multi-beam antenna technologies have provided lots of promising solutions to many current challenges faced in wireless mesh networks. The antenna can establish several beamformings simultaneously and initiate concurrent transmissions or receptions using multiple beams, thereby increasing the overall throughput of the network transmission. Multi-beam antenna has the ability to increase the spatial reuse, extend the transmission range, improve the transmission reliability, as well as save the power consumption. Traditional Medium Access Control (MAC) protocols for wireless network largely relied on the IEEE 802.11 Distributed Coordination Function(DCF) mechanism, however, IEEE 802.11 DCF cannot take the advantages of these unique capabilities provided by multi-beam antennas. This paper surveys the MAC protocols for wireless mesh networks with multi-beam antennas. The paper first discusses some basic information in designing multi-beam antenna system and MAC protocols, and then presents the main challenges for the MAC protocols in wireless mesh networks compared with the traditional MAC protocols. A qualitative comparison of the existing MAC protocols is provided to highlight their novel features, which provides a reference for designing the new MAC protocols. To provide some insights on future research, several open issues of MAC protocols are discussed for wireless mesh networks using multi-beam antennas.

cs.NI↗

arXiv2019

ChainSplitter: Towards Blockchain-based Industrial IoT Architecture for Supporting Hierarchical Storage

The fast developing Industrial Internet of Things (IIoT) technologies provide a promising opportunity to build large-scale systems to connect numerous heterogeneous devices into the Internet. Most existing IIoT infrastructures are based on a centralized architecture, which is easier for management but cannot effectively support immutable and verifiable services among multiple parties. Blockchain technology provides many desired features for large-scale IIoT infrastructures, such as decentralization, trustworthiness, trackability, and immutability. This paper presents a blockchain-based IIoT architecture to support immutable and verifiable services. However, when applying blockchain technology to the IIoT infrastructure, the required storage space posts a grant challenge to resource-constrained IIoT infrastructures. To address the storage issue, this paper proposes a hierarchical blockchain storage structure, \textit{ChainSplitter}. Specially, the proposed architecture features a hierarchical storage structure where the majority of the blockchain is stored in the clouds, while the most recent blocks are stored in the overlay network of the individual IIoT networks. The proposed architecture seamlessly binds local IIoT networks, the blockchain overlay network, and the cloud infrastructure together through two connectors, the \textit{blockchain connector} and the \textit{cloud connector}, to construct the hierarchical blockchain storage. The blockchain connector in the overlay network builds blocks in blockchain from data generated in IIoT networks, and the cloud connector resolves the blockchain synchronization issues between the overlay network and the clouds. We also provide a case study to show the efficiency of the proposed hierarchical blockchain storage in a practical Industrial IoT case.

cs.CR↗

arXiv2019

Astrodynamical middle-frequency interferometric gravitational wave observatory AMIGO: Mission concept and orbit design

AMIGO is a first-generation Astrodynamical Middle-frequency Interferometric GW Observatory. The scientific goals of AMIGO are: to bridge the spectra gap between first-generation high-frequency and low-frequency GW sensitivities; to detect intermediate mass BH coalescence; to detect inspiral phase and predict time of binary black hole coalescence together with neutron star coalescence for ground interferometers; to detect compact binary inspirals for studying stellar evolution and galactic population. The mission concept is to use time delay interferometry for a nearly triangular formation of 3 drag-free spacecraft with nominal arm length 10,000 km, emitting laser power 2-10 W and telescope diameter 300-500 mm. The design GW sensitivity in the middle frequency band is 3 x 10^(-21) Hz^(-1/2). Both heliocentric and geocentric orbits are under study. All options have LISA-like formations, that is the triangular formation is 60 deg inclined to the orbit plane. For AMIGO, the first-generation time delay interferometry is good enough for the laser frequency noise suppression. We also investigate for each options of orbits under study, whether constant equal-arm implementation is feasible. For the solar-orbit option, the acceleration to maintain the formation can be designed to be less than 15 nm/s2 with the thruster requirement in the 15 μN range. AMIGO would be a good place to implement the constant equal-arm option. Fuel requirement, thruster noise requirement and test mass acceleration actuation requirement are briefly considered. From the orbit study, the solar orbit option is the first mission orbit choice. We study the deployment for this orbit option. A last-stage launch from 300 km LEO (Low Earth Orbit) to an appropriate 2-degree-behind-the-Earth AMIGO formation in 95 days requires only a δv of 75 m/s.

gr-qc↗

arXiv2019

Background evaluations for the chiral magnetic effect with normalized correlators using a multiphase transport model

The chiral magnetic effect (CME) induces an electric charge separation in a chiral medium along the magnetic field that is mostly produced by spectator protons in heavy-ion collisions. The experimental searches for the CME, based on the charge-dependent angular correlations ($γ$), however, have remained inconclusive, because the non-CME background contributions are not well understood. Experimentally, the $γ$ correlators have been measured with respect to the second-order ($Ψ_{2}$) and the third-order ($Ψ_{3}$) symmetry planes, defined as $γ_{112}$ and $γ_{123}$, respectively. The expectation was that with a proper normalization, $γ_{123}$ would provide a data-driven estimate for the background contributions in $γ_{112}$. In this work, we calculate different harmonics of the $γ$ correlators using a charge-conserving version of a multiphase transport (AMPT) model to examine the validity of the said assumption. We find that the pure-background AMPT simulations do not yield an equality in the normalized $γ_{112}$ and $γ_{123}$, quantified by $κ_{112}$ and $κ_{123}$, respectively. Furthermore, we test another correlator, $γ_{132}$, within AMPT, and discuss the relation between different $γ$ correlators.

hep-ph↗

arXiv2019

Semantic Correlation Promoted Shape-Variant Context for Segmentation

Context is essential for semantic segmentation. Due to the diverse shapes of objects and their complex layout in various scene images, the spatial scales and shapes of contexts for different objects have very large variation. It is thus ineffective or inefficient to aggregate various context information from a predefined fixed region. In this work, we propose to generate a scale- and shape-variant semantic mask for each pixel to confine its contextual region. To this end, we first propose a novel paired convolution to infer the semantic correlation of the pair and based on that to generate a shape mask. Using the inferred spatial scope of the contextual region, we propose a shape-variant convolution, of which the receptive field is controlled by the shape mask that varies with the appearance of input. In this way, the proposed network aggregates the context information of a pixel from its semantic-correlated region instead of a predefined fixed region. Furthermore, this work also proposes a labeling denoising model to reduce wrong predictions caused by the noisy low-level features. Without bells and whistles, the proposed segmentation network achieves new state-of-the-arts consistently on the six public segmentation datasets.

cs.CV↗

arXiv2019

Boundary-Aware Feature Propagation for Scene Segmentation

In this work, we address the challenging issue of scene segmentation. To increase the feature similarity of the same object while keeping the feature discrimination of different objects, we explore to propagate information throughout the image under the control of objects' boundaries. To this end, we first propose to learn the boundary as an additional semantic class to enable the network to be aware of the boundary layout. Then, we propose unidirectional acyclic graphs (UAGs) to model the function of undirected cyclic graphs (UCGs), which structurize the image via building graphic pixel-by-pixel connections, in an efficient and effective way. Furthermore, we propose a boundary-aware feature propagation (BFP) module to harvest and propagate the local features within their regions isolated by the learned boundaries in the UAG-structured image. The proposed BFP is capable of splitting the feature propagation into a set of semantic groups via building strong connections among the same segment region but weak connections between different segment regions. Without bells and whistles, our approach achieves new state-of-the-art segmentation performance on three challenging semantic segmentation datasets, i.e., PASCAL-Context, CamVid, and Cityscapes.

cs.CV↗

arXiv2019

Non-Abelian gauge potential driven localization transition in quasiperiodic optical lattices

Gauge potential is an emergent concept in systems of ultracold atomic gases. Derived from quantum waves immersed in an \emph{Abelian} gauge, the quasiperiodic Aubry-Andre-Harper (AAH) model is a simple yet powerful Hamiltonian to study the Anderson localization of ultracold atoms. In this work, we investigate the localization properties of ultracold atoms trapped in quasiperiodic optical lattices subject to a non-Abelian gauge, which can be depicted by a family of non-Abelian AAH models. We identify that the non-Abelian AAH models can bear the self-duality under the Fourier transformation. We thus analyze the localization transition of this self-dual non-Abelian quasiperiodic optical lattices, revealing that the non-Abelian gauge involved drives a transition from a pure delocalization phase, then to coexistence phases, and then finally to a pure localization phase. This is in stark contrast to the Abelian AAH model that does not support the coexistence phases. Our results thus comprise a new insight on the fundamental aspects of Anderson localization in quasiperiodic systems, from the perspective of non-Abelian gauge.

cond-mat.quant-gas↗

arXiv2019

Unpaired Image Captioning via Scene Graph Alignments

Most of current image captioning models heavily rely on paired image-caption datasets. However, getting large scale image-caption paired data is labor-intensive and time-consuming. In this paper, we present a scene graph-based approach for unpaired image captioning. Our framework comprises an image scene graph generator, a sentence scene graph generator, a scene graph encoder, and a sentence decoder. Specifically, we first train the scene graph encoder and the sentence decoder on the text modality. To align the scene graphs between images and sentences, we propose an unsupervised feature alignment method that maps the scene graph features from the image to the sentence modality. Experimental results show that our proposed model can generate quite promising results without using any image-caption training pairs, outperforming existing methods by a wide margin.

cs.CV↗

arXiv2019

Orbit design and thruster requirement for various constant-arm space mission concepts for gravitational-wave observation

In previous papers, we have addressed the issues of orbit design and thruster requirement for the constant arm versions of AMIGO (Astrodynamical Middle-frequency Interferometric Gravitational-wave Observatory) mission concept and for the constant arm GW (gravitational wave) mission concept of AIGSO (Atom Interferometric Gravitational-wave Space Observatory). In this paper, we apply similar methods to the orbit design and thruster requirement for the constant arm GW missions B-DECIGO and DECIGO, and estimate the yearly propellant requirements at the specific impulse Isp = 300 sec and Isp = 1000 sec. For the geocentric orbit options of B-DECIGO which we have explored, the fuel mass requirement is a concern. For the heliocentric orbit options of B-DECIGO and DECIGO, the fuel requirement to keep the arm equal and constant should be easily satisfied. Furthermore, we explore the thruster and propellant requirements for constant arm versions of LISA and TAIJI missions and find the fuel mass requirement is not a show stopper either. The proof mass actuation noise is a concern. To have enough dynamical range, an alternate proof mass is required. Detailed laboratory study is warranted

gr-qc↗

arXiv2019

Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks

Caching is envisioned to play a critical role in next-generation content delivery infrastructure, cellular networks, and Internet architectures. By smartly storing the most popular contents at the storage-enabled network entities during off-peak demand instances, caching can benefit both network infrastructure as well as end users, during on-peak periods. In this context, distributing the limited storage capacity across network entities calls for decentralized caching schemes. Many practical caching systems involve a parent caching node connected to multiple leaf nodes to serve user file requests. To model the two-way interactive influence between caching decisions at the parent and leaf nodes, a reinforcement learning framework is put forth. To handle the large continuous state space, a scalable deep reinforcement learning approach is pursued. The novel approach relies on a deep Q-network to learn the Q-function, and thus the optimal caching policy, in an online fashion. Reinforcing the parent node with ability to learn-and-adapt to unknown policies of leaf nodes as well as spatio-temporal dynamic evolution of file requests, results in remarkable caching performance, as corroborated through numerical tests.

cs.IT↗

arXiv2019

NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding

Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realistic number of distinct class categories, diversity in camera views, varied environmental conditions, and variety of human subjects. In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. Furthermore, we investigate a novel one-shot 3D activity recognition problem on our dataset, and a simple yet effective Action-Part Semantic Relevance-aware (APSR) framework is proposed for this task, which yields promising results for recognition of the novel action classes. We believe the introduction of this large-scale dataset will enable the community to apply, adapt, and develop various data-hungry learning techniques for depth-based and RGB+D-based human activity understanding. [The dataset is available at: http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp]

cs.CV↗

arXiv2019

ZAIGA: Zhaoshan Long-baseline Atom Interferometer Gravitation Antenna

The Zhaoshan long-baseline Atom Interferometer Gravitation Antenna (ZAIGA) is a new type of underground laser-linked interferometer facility, and is currently under construction. It is in the 200-meter-on-average underground of a mountain named Zhaoshan which is about 80 km southeast to Wuhan. ZAIGA will be equipped with long-baseline atom interferometers, high-precision atom clocks, and large-scale gyros. ZAIGA facility will take an equilateral triangle configuration with two 1-km-apart atom interferometers in each arm, a 300-meter vertical tunnel with atom fountain and atom clocks mounted, and a tracking-and-ranging 1-km-arm-length prototype with lattice optical clocks linked by locked lasers. The ZAIGA facility will be used for experimental research on gravitation and related problems including gravitational wave detection, high-precision test of the equivalence principle of micro-particles, clock based gravitational red-shift measurement, rotation measurement and gravito-magnetic effect.

physics.atom-ph↗

arXiv2019

Feature Boosting Network For 3D Pose Estimation

In this paper, a feature boosting network is proposed for estimating 3D hand pose and 3D body pose from a single RGB image. In this method, the features learned by the convolutional layers are boosted with a new long short-term dependence-aware (LSTD) module, which enables the intermediate convolutional feature maps to perceive the graphical long short-term dependency among different hand (or body) parts using the designed Graphical ConvLSTM. Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation. To improve the reliability of the features for representing each body part and enhance the LSTD module, we further introduce a context consistency gate (CCG) in this paper, with which the convolutional feature maps are modulated according to their consistency with the context representations. We evaluate the proposed method on challenging benchmark datasets for 3D hand pose estimation and 3D full body pose estimation. Experimental results show the effectiveness of our method that achieves state-of-the-art performance on both of the tasks.

cs.CV↗

arXiv2019

On the Evolution of Ion Bunch Profile in the Presence of Longitudinal Coherent Electron Cooling

In the presence of longitudinal coherent electron cooling, the evolution of the line-density profile of a circulating ion bunch can be described by the 1-D Fokker-Planck equation. We show that, in the absence of diffusion, the 1-D equation can be solved analytically for certain dependence of cooling force on the synchrotron amplitude. For more general cases with arbitrary diffusion, we solved the 1-D Fokker-Planck equation numerically and the numerical solutions have been compared with results from macro-particle tracking.

physics.acc-ph↗

arXiv2019

Orbit Design for Space Atom-Interferometer AIGSO

Atom Interferometric Gravitational-wave (GW) Space Observatory (AIGSO) is a mission concept mainly aimed at the middle-frequency (0.1 Hz - 10 Hz) GW detection. AIGSO proposes to have three spacecraft in linear formation with extension of 10 km. The three spacecraft need to maintain 5 km + 5 km constant arm-length formation. In this study, we address the issue of orbit design and thruster requirement. The acceleration to maintain the formation can be designed to be less than 30 pm/s$^2$ and the thruster requirement is in the 30 nN range. Application to other arm-length-maintaining missions is also discussed.

gr-qc↗

arXiv2019

Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization

Neural networks with REctified Linear Unit (ReLU) activation functions (a.k.a. ReLU networks) have achieved great empirical success in various domains. Nonetheless, existing results for learning ReLU networks either pose assumptions on the underlying data distribution being e.g. Gaussian, or require the network size and/or training size to be sufficiently large. In this context, the problem of learning a two-layer ReLU network is approached in a binary classification setting, where the data are linearly separable and a hinge loss criterion is adopted. Leveraging the power of random noise perturbation, this paper presents a novel stochastic gradient descent (SGD) algorithm, which can \emph{provably} train any single-hidden-layer ReLU network to attain global optimality, despite the presence of infinitely many bad local minima, maxima, and saddle points in general. This result is the first of its kind, requiring no assumptions on the data distribution, training/network size, or initialization. Convergence of the resultant iterative algorithm to a global minimum is analyzed by establishing both an upper bound and a lower bound on the number of non-zero updates to be performed. Moreover, generalization guarantees are developed for ReLU networks trained with the novel SGD leveraging classic compression bounds. These guarantees highlight a key difference (at least in the worst case) between reliably learning a ReLU network as well as a leaky ReLU network in terms of sample complexity. Numerical tests using both synthetic data and real images validate the effectiveness of the algorithm and the practical merits of the theory.

stat.ML↗

arXiv2019

Skeleton-Based Online Action Prediction Using Scale Selection Network

Action prediction is to recognize the class label of an ongoing activity when only a part of it is observed. In this paper, we focus on online action prediction in streaming 3D skeleton sequences. A dilated convolutional network is introduced to model the motion dynamics in temporal dimension via a sliding window over the temporal axis. Since there are significant temporal scale variations in the observed part of the ongoing action at different time steps, a novel window scale selection method is proposed to make our network focus on the performed part of the ongoing action and try to suppress the possible incoming interference from the previous actions at each step. An activation sharing scheme is also proposed to handle the overlapping computations among the adjacent time steps, which enables our framework to run more efficiently. Moreover, to enhance the performance of our framework for action prediction with the skeletal input data, a hierarchy of dilated tree convolutions are also designed to learn the multi-level structured semantic representations over the skeleton joints at each frame. Our proposed approach is evaluated on four challenging datasets. The extensive experiments demonstrate the effectiveness of our method for skeleton-based online action prediction.

cs.CV↗

arXiv2019

A Novel Neural Network Structure Constructed according to Logical Relations

To solve more complex things, computer systems becomes more and more complex. It becomes harder to be handled manually for various conditions and unknown new conditions in advance. This situation urgently requires the development of computer technology of automatic judgement and decision according to various conditions. Current ANN (Artificial Neural Network) models are good at perceptual intelligence while they are not good at cognitive intelligence such as logical representation, making them not deal with the above situation well. Therefore, researchers have tried to design novel models so as to represent and store logical relations into the neural network structures, the type of which is called KBNN (Knowledge-Based Neural Network). In this type models, the neurons and links are designed specific for logical relation representation, and the neural network structures are constructed according to logical relations, allowing us to construct automatically the rule libraries of expert systems. In this paper, the further improvement is made based on KBNN by redesigning the neurons and links. This improvement can make neurons solely for representing things while making links solely for representing logical relations between things, and thus no extra logical neurons are needed. Moreover, the related construction and adjustment methods of the neural network structure are also designed based on the redesigned neurons and links, making the neural network structure dynamically constructed and adjusted according to the logical relations. The probabilistic mechanism for the weight adjustment can make the neural network model further represent logical relations in the uncertainty.

cs.NE↗