arXiv subjects

Lei Liu

Publications and source records attributed to Lei Liu.

At least 271 records · Page 15Linked to original sources

arXiv2022

OmniUV: A Multi-Purpose Simulation Toolkit for VLBI Observation

We present OmniUV, a multi-purpose simulation toolkit for space and ground VLBI observations. It supports various kinds of VLBI stations, including Earth (ground) fixed, Earth orbit, Lunar fixed, Lunar orbit, Moon-Earth and Earth-Sun Lagrange 1 and 2 points, etc. The main functionalities of this toolkit are: (1) Trajectory calculation; (2) Baseline uv calculation, by taking the vailability of each station into account; (3) Visibility simulation for the given uv distribution, source structure and system noise; (4) Image and beam reconstruction. Two scenarios, namely space VLBI network and wide field array, are presented as demonstrations of the toolkit applications in completely different scales. OmniUV is the acronym of "Omnipotent UV". We hope it could work as a general framework, in which various kinds of stations could be easily incorporated and the functionalities could be further extended. The toolkit has been made publicly available.

astro-ph.IM↗

arXiv2022

The number of languages with maximum state complexity

Câmpeanu and Ho (2004) determined the maximum finite state complexity of finite languages, building on work of Champarnaud and Pin (1989). They stated that it is very difficult to determine the number of maximum-complexity languages. Here we give a formula for this number. We also generalize their work from languages to functions on finite sets.

cs.FL↗

arXiv2022

A discrete-module-finite-element hydroelasticity method in analyzing dynamic response of floating flexible structures

A discrete-module-finite element (DMFE) based hydroelasticity method has been proposed and well developed. Firstly, a freely floating flexible structure is discretized into several macro-submodules in two horizontal directions to perform a multi-rigid-body hydrodynamic analysis. Each macro-submodule is then abstracted to a lumped mass at the center of gravity that bears the external forces including inertia force, hydrodynamic force and hydrostatic force. Apart from external forces, all lumped masses are also subjected to structural forces that reflect the structural deformation features of the original flexible structure. The key to calculating the structural forces is derivation of the equivalent overall structural stiffness matrix with respect to the displacements of all lumped masses, which is tackled following the finite element procedure. More specifically, each macro-submodule is discretized into a number of microelements to derive the corresponding structural stiffness matrix, which is manipulated to a new one including only the nodes at the position of the lumped masses and surrounding boundaries by using the substructure approach, and subsequently the target overall stiffness matrix is obtained by combining together all macro-submodules. Finally, based on equivalence between external and structural forces, the DMFE method establishes the hydroelastic equation on all lumped masses with their displacements as unknown variables. Solving the equation gives the displacement response of all lumped masses. Displacement and structural force responses are first calculated on the interfaces of every two adjacent macro-submodules, after which at any given position of the flexible structure, the recovery of displacement is based on the structural stiffness matrix of the corresponding macro-submodule and the recovery of structural force uses the spline interpolation scheme.

physics.flu-dyn↗

arXiv2022

Massive Unsourced Random Access over Rician Fading Channels: Design, Analysis, and Optimization

In this paper, we investigate an unsourced random access scheme for massive machine-type communications (mMTC) in the sixth-generation (6G) wireless networks with sporadic data traffic. Firstly, we establish a general framework for massive unsourced random access based on a two-layer signal coding, i.e., an outer code and an inner code. In particular, considering Rician fading in the scenario of mMTC, we design a novel codeword activity detection algorithm for the inner code of unsourced random access based on the distribution of received signals by exploiting the maximum likelihood (ML) method. Then, we analyze the performance of the proposed codeword activity detection algorithm exploiting Fisher Information Matrix, which facilitates the derivative of the approximated distribution of the estimation error of the codeword activity vector when the number of base station (BS) antennas is sufficiently large. Furthermore, for the outer code, we propose an optimization algorithm to allocate the lengths of message bits and parity check bits, so as to strike a balance between the error probability and the complexity required for outer decoding. Finally, extensive simulation results validate the effectiveness of the proposed detection algorithm and the optimized length allocation scheme compared with an existing detection algorithm and a fixed length allocation scheme.

cs.IT↗

arXiv2022

Large Andreev bound state zero bias peaks in a weakly dissipative environment

We study Andreev bound states in hybrid InAs-Al nanowire devices. The energy of these states can be tuned to zero by gate voltage or magnetic field, revealing large zero bias peaks (ZBPs) near 2e^2/h in tunneling conductance. Probing these large ZBPs using a weakly dissipative lead reveals non-Fermi liquid temperature (T) dependence due to environmental Coulomb blockade (ECB), an interaction effect from the lead acting on the nanowire junction. By increasing T, these large ZBPs either show a height increase or a transition from split peaks to a ZBP, both deviate significantly from non-dissipative devices where a Fermi-liquid T dependence is revealed. Our result demonstrates the competing effect between ECB and thermal broadening on Andreev bound states.

cond-mat.mes-hall↗

arXiv2022

Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning

Network virtualization (NV) is a technology with broad application prospects. Virtual network embedding (VNE) is the core orientation of VN, which aims to provide more flexible underlying physical resource allocation for user function requests. The classical VNE problem is usually solved by heuristic method, but this method often limits the flexibility of the algorithm and ignores the time limit. In addition, the partition autonomy of physical domain and the dynamic characteristics of virtual network request (VNR) also increase the difficulty of VNE. This paper proposed a new type of VNE algorithm, which applied reinforcement learning (RL) and graph neural network (GNN) theory to the algorithm, especially the combination of graph convolutional neural network (GCNN) and RL algorithm. Based on a self-defined fitness matrix and fitness value, we set up the objective function of the algorithm implementation, realized an efficient dynamic VNE algorithm, and effectively reduced the degree of resource fragmentation. Finally, we used comparison algorithms to evaluate the proposed method. Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics. By changing the resource attributes of physical network and virtual network, it can be proved that the algorithm has good flexibility.

cs.NI↗

arXiv2022

Incorporating Distributed DRL into Storage Resource Optimization of Space-Air-Ground Integrated Wireless Communication Network

Space-air-ground integrated network (SAGIN) is a new type of wireless network mode. The effective management of SAGIN resources is a prerequisite for high-reliability communication. However, the storage capacity of space-air network segment is extremely limited. The air servers also do not have sufficient storage resources to centrally accommodate the information uploaded by each edge server. So the problem of how to coordinate the storage resources of SAGIN has arisen. This paper proposes a SAGIN storage resource management algorithm based on distributed deep reinforcement learning (DRL). The resource management process is modeled as a Markov decision model. In each edge physical domain, we extract the network attributes represented by storage resources for the agent to build a training environment, so as to realize the distributed training. In addition, we propose a SAGIN resource management framework based on distributed DRL. Simulation results show that the agent has an ideal training effect. Compared with other algorithms, the resource allocation revenue and user request acceptance rate of the proposed algorithm are increased by about 18.15\% and 8.35\% respectively. Besides, the proposed algorithm has good flexibility in dealing with the changes of resource conditions.

cs.NI↗

arXiv2022

Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method

Traditional ground wireless communication networks cannot provide high-quality services for artificial intelligence (AI) applications such as intelligent transportation systems (ITS) due to deployment, coverage and capacity issues. The space-air-ground integrated network (SAGIN) has become a research focus in the industry. Compared with traditional wireless communication networks, SAGIN is more flexible and reliable, and it has wider coverage and higher quality of seamless connection. However, due to its inherent heterogeneity, time-varying and self-organizing characteristics, the deployment and use of SAGIN still faces huge challenges, among which the orchestration of heterogeneous resources is a key issue. Based on virtual network architecture and deep reinforcement learning (DRL), we model SAGIN's heterogeneous resource orchestration as a multi-domain virtual network embedding (VNE) problem, and propose a SAGIN cross-domain VNE algorithm. We model the different network segments of SAGIN, and set the network attributes according to the actual situation of SAGIN and user needs. In DRL, the agent is acted by a five-layer policy network. We build a feature matrix based on network attributes extracted from SAGIN and use it as the agent training environment. Through training, the probability of each underlying node being embedded can be derived. In test phase, we complete the embedding process of virtual nodes and links in turn based on this probability. Finally, we verify the effectiveness of the algorithm from both training and testing.

cs.NI↗

arXiv2021

Transfer Learning in High-dimensional Semi-parametric Graphical Models with Application to Brain Connectivity Analysis

Transfer learning has drawn growing attention with the target of improving statistical efficiency of one study (dataset) by digging information from similar and related auxiliary studies (datasets). In the article, we consider transfer learning problem in estimating undirected semi-parametric graphical model. We propose an algorithm called Trans-Copula-CLIME for estimating undirected graphical model while digging information from similar auxiliary studies, characterizing the similarity between the target graph and each auxiliary graph by the sparsity of a divergence matrix. The proposed method relaxes the restrictive assumption that data follows a Gaussian distribution, which deviates from reality for the fMRI dataset related to Attention Deficit Hyperactivity Disorder (ADHD) considered here. Nonparametric rank-based correlation coefficient estimators are utilized in the Trans-Copula-CLIME procedure to achieve robustness against normality. We establish the convergence rate of the Trans-Copula-CLIME estimator under some mild conditions, which demonstrates that when the similarity between the auxiliary studies and the target study is sufficiently high and the number of informative auxiliary samples is sufficiently large, then the Trans-Copula-CLIME estimator shows great advantage over the existing non-transfer-learning ones. Simulation studies also show that Trans-Copula-CLIME estimator has better performance especially when data are not from Gaussian distribution. At last, the proposed method is applied to infer functional brain connectivity pattern for ADHD patients in the target Beijing site by leveraging the fMRI datasets from New York site.

stat.ME↗

arXiv2021

The effects of halide anions on the electroreduction of CO2 to C2H4: a density functional theory study

The halide anions present in the electrolyte gradually improves the Faradaic efficiencies (FEs) of the multi-hydrocarbon (C2+) products for the electrochemical reduction of CO2 over copper (Cu) catalysts in the order of F- < Cl- < Br- < I-. However, the mechanism behind the increased yield of C2+ products with the addition of halide anions still remains indistinct. In this study, we analysed the mechanism by investigating the electronic structures and computing the relative free energies of intermediates formed from CO2 to C2H4 on the Cu (100) facet based on density functional theory (DFT) calculations. The results show that formyl *CHO species from the hydrogenation reaction of the adsorbed *CO acts as the key intermediate, and the C-C coupling reaction occurs preferentially between the *CHO and *CO with the formation of a *CHO-CO intermediate. Subsequently, the free-energy pathway of C2H4 formation has been proposed, and we found that the presence of halide anions significantly decreases the free energy of the *CHOCH intermediate, and enhances the desorption capacity of C2H4 in the order of F- < Cl- < Br- < I-. Lastly, the obtained results are rationalized by the Bader charge analysis.

physics.chem-ph↗

arXiv2021

Electrocatalytic reduction of nitrogen to ammonia in ionic liquids

Ammonia NH3 is an important raw material for nitrogen fertilizer production and is widely used in industry. It is also a carbon free renewable fuel and an excellent hydrogen storage material. Reaction conditions of traditional Haber Bosch process are very harsh although it supports most of todays NH3 production. Recently, electrocatalytic nitrogen reduction reaction NRR has become one of the effective alternative methods to the traditional Haber Bosch process due to the advantages of mild operation conditions and almost no environmental pollutions. Ionic liquids ILs act as one of green solvents possess a lot of unique features and have shown excellent performance in electrocatalytic NRR process. In this review, we summarize fundamentals of the electrocatalytic NRR, the role of ILs in several typical electrocatalytic processes, and the possible role of ILs in the electrocatalytic NRR process. Moreover, the selection principles and issues of the application of ILs, as well as the future development direction for the electrocatalytic NRR process are concluded.

physics.chem-ph↗

arXiv2021

Ferroelectric materials and their applications in green catalysis

The demand for renewable and environmentally friendly energy source has attracted extensive research on high performance catalysts. Ferroelectrics which are a class of materials with a switchable polarization are the unique and promising catalyst candidates due to the significant effects of polarization on surfaces physical and chemical properties. The band bending at the ferroelectric/semiconductor interface induced by the polarization flip promotes the charge separation and transfer, there by enhancing the photocatalytic performance. More importantly, the reactants can be selectively adsorbed on the surface of the ferroelectric materials depending on the polarization direction, which can effectively lift the basic limitations as imposed by Sabatier principle on catalytic activity. This review summarizes the latest developments of ferroelectric materials, and introduces the ferroelectric-related catalytic application. The possible research directions of 2D ferroelectric materials in chemical catalysis is discussed at the end. The review is expected to inspire the extensive research interests from physical, chemical and material science communities.

cond-mat.mtrl-sci↗

arXiv2021

Number of New Top 2% Researchers from China and USA Over Time

In this paper we compare the numbers of new top 2% researchers from China and USA annually since 1980. We find that the log ratio of the numbers decreases almost linearly over time. As early as 2009, the total number of new top 2% researchers across all subfields from China exceeds that of USA. In particular, such trend is more striking in many subfields, e.g., Engineering, Chemistry, and Enabling & Strategic Technologies.

stat.AP↗

arXiv2021

Cross-Modal Object Tracking: Modality-Aware Representations and A Unified Benchmark

In many visual systems, visual tracking often bases on RGB image sequences, in which some targets are invalid in low-light conditions, and tracking performance is thus affected significantly. Introducing other modalities such as depth and infrared data is an effective way to handle imaging limitations of individual sources, but multi-modal imaging platforms usually require elaborate designs and cannot be applied in many real-world applications at present. Near-infrared (NIR) imaging becomes an essential part of many surveillance cameras, whose imaging is switchable between RGB and NIR based on the light intensity. These two modalities are heterogeneous with very different visual properties and thus bring big challenges for visual tracking. However, existing works have not studied this challenging problem. In this work, we address the cross-modal object tracking problem and contribute a new video dataset, including 654 cross-modal image sequences with over 481K frames in total, and the average video length is more than 735 frames. To promote the research and development of cross-modal object tracking, we propose a new algorithm, which learns the modality-aware target representation to mitigate the appearance gap between RGB and NIR modalities in the tracking process. It is plug-and-play and could thus be flexibly embedded into different tracking frameworks. Extensive experiments on the dataset are conducted, and we demonstrate the effectiveness of the proposed algorithm in two representative tracking frameworks against 17 state-of-the-art tracking methods. We will release the dataset for free academic usage, dataset download link and code will be released soon.

cs.CV↗

arXiv2021

Open-Set Crowdsourcing using Multiple-Source Transfer Learning

We raise and define a new crowdsourcing scenario, open set crowdsourcing, where we only know the general theme of an unfamiliar crowdsourcing project, and we don't know its label space, that is, the set of possible labels. This is still a task annotating problem, but the unfamiliarity with the tasks and the label space hampers the modelling of the task and of workers, and also the truth inference. We propose an intuitive solution, OSCrowd. First, OSCrowd integrates crowd theme related datasets into a large source domain to facilitate partial transfer learning to approximate the label space inference of these tasks. Next, it assigns weights to each source domain based on category correlation. After this, it uses multiple-source open set transfer learning to model crowd tasks and assign possible annotations. The label space and annotations given by transfer learning will be used to guide and standardize crowd workers' annotations. We validate OSCrowd in an online scenario, and prove that OSCrowd solves the open set crowdsourcing problem, works better than related crowdsourcing solutions.

cs.HC↗

arXiv2021

Cross-modal Zero-shot Hashing by Label Attributes Embedding

Cross-modal hashing (CMH) is one of the most promising methods in cross-modal approximate nearest neighbor search. Most CMH solutions ideally assume the labels of training and testing set are identical. However, the assumption is often violated, causing a zero-shot CMH problem. Recent efforts to address this issue focus on transferring knowledge from the seen classes to the unseen ones using label attributes. However, the attributes are isolated from the features of multi-modal data. To reduce the information gap, we introduce an approach called LAEH (Label Attributes Embedding for zero-shot cross-modal Hashing). LAEH first gets the initial semantic attribute vectors of labels by word2vec model and then uses a transformation network to transform them into a common subspace. Next, it leverages the hash vectors and the feature similarity matrix to guide the feature extraction network of different modalities. At the same time, LAEH uses the attribute similarity as the supplement of label similarity to rectify the label embedding and common subspace. Experiments show that LAEH outperforms related representative zero-shot and cross-modal hashing methods.

cs.CV↗

arXiv2021

MetaMIML: Meta Multi-Instance Multi-Label Learning

Multi-Instance Multi-Label learning (MIML) models complex objects (bags), each of which is associated with a set of interrelated labels and composed with a set of instances. Current MIML solutions still focus on a single-type of objects and assumes an IID distribution of training data. But these objects are linked with objects of other types, %(i.e., pictures in Facebook link with various users), which also encode the semantics of target objects. In addition, they generally need abundant labeled data for training. To effectively mine interdependent MIML objects of different types, we propose a network embedding and meta learning based approach (MetaMIML). MetaMIML introduces the context learner with network embedding to capture semantic information of objects of different types, and the task learner to extract the meta knowledge for fast adapting to new tasks. In this way, MetaMIML can naturally deal with MIML objects at data level improving, but also exploit the power of meta-learning at the model enhancing. Experiments on benchmark datasets demonstrate that MetaMIML achieves a significantly better performance than state-of-the-art algorithms.

cs.LG↗

arXiv2021

Large-composition-range pure-phase homogeneous InAs$_{1-x}$Sb$_x$ nanowires

Narrow bandgap InAs$_{1-x}$Sb$_x$ nanowires show broad prospects for applications in wide spectrum infrared detectors, high-performance transistors and quantum computation. Realizing such applications require the fine control of composition and crystal structure of nanowires. However, to date, the fabrication of large-composition-range pure-phase homogeneous InAs$_{1-x}$Sb$_x$ nanowires remains a huge challenge. Here, we first report the growth of large-composition-range stemless InAs$_{1-x}$Sb$_x$ nanowires (x = 0-0.63) on Si (111) substrates by molecular-beam epitaxy. It is found that pure-phase InAs$_{1-x}$Sb$_x$ nanowires can be successfully obtained by controlling the antimony content x, nanowire diameter and nanowire growth direction. Detailed EDS data show that the antimony is uniformly distributed along the axial and radial directions of InAs$_{1-x}$Sb$_x$ nanowires and no spontaneous core-shell nanostructures form in the nanowires. Based on field-effect measurements, we confirm that InAs$_{1-x}$Sb$_x$ nanowires exhibit good conductivity and their mobilities can be up to 4200 cm^2/V.s at 7 K. Our work lays the foundation for the development of InAs$_{1-x}$Sb$_x$ nanowire optoelectronic, electronic and quantum devices.

physics.app-ph↗