arXiv subjects

Xing Li

Publications and source records attributed to Xing Li.

At least 91 records · Page 5Linked to original sources

arXiv2021

LaP2: isostructural to MgB2 with charming superconductivity

The exploration of superconductivity dominated by structural units is of great interest in condense matter physics. MgB2, consisting of graphene-like B, becomes a typical representative of traditional superconductors. Phosphorus demonstrates diverse non-planar motifs through sp3 hybridization in allotropes and phosphides. Here, we report that a pressure-stabilized LaP2, isostructural to MgB2, shows superconductivity with a predicted Tc of 22.2 K, which is the highest among already known transition metal phosphides. Besides electron-phonon coupling of graphene-like P, alike the role of B layer in MgB2, La 5d/4f electrons are also responsible for the superconducting transition. Its dynamically stabilized pressure reaches as low as 7 GPa, a desirable feature of pressure-induced superconductors. The distinct P atomic arrangement is attributed to its sp2 hybridization and out-of-plane symmetric distribution of lone pair electrons. Although P is isoelectronic to N and As, we hereby find the different stable stoichiometries, structures, and electronic properties of La phosphides compared with La nitrides/arsenides at high pressure.

cond-mat.supr-con↗

arXiv2021

Design and evaluation of a FPGA-ADC prototype for the PET detector based on LYSO Crystals and SiPM arrays

The aim of this study is to design and evaluate a simple free running Analog-Digital Converter (ADC) based on the Field Programmable Gate Array (FPGA) device to accomplish the energy and position readout of the silicon photomultiplier (SiPM) array for application as PET scanners. This simple FPGA-ADC based on a carry chain Time-Digital Converter (TDC) implemented on a Kintex-7 FPGA consists of only one off-chip resistor so it has greater advantages in improving system integration and reducing cost than commercial chips. In this paper, a FPGA-ADC based front-end electronics prototype is presented, and both the design principle and implementation considerations are discussed. Experiments were performed using an 8 x 8 (crystal size: 4 x 4 x 15 mm3 ) and a 12 x 12 (crystal size: 2.65 x2.65 x 15 mm3 ) segmented LYSO crystals coupled with an 8 x 8 SiPM (Jseries, from ON Semiconductor) array which is under 22Na point source excitation. Initial test results indicate that the energy resolution of the two detectors after correction is around 13.2% and 13.5 % at 511 keV, and the profiles of the flood histograms show a clear visualization of the discrete scintillator element. All measurements were carried out at room temperature (~25 degree), without additional cooling

physics.ins-det↗

arXiv2021

Securing Serverless Computing: Challenges, Solutions, and Opportunities

Serverless computing is a new cloud service model that reduces both cloud providers' and consumers' costs through extremely agile development, operation, and charging mechanisms and has been widely applied since its emergence. Nevertheless, some characteristics of serverless computing, such as fragmented application boundaries, have raised new security challenges. Considerable literature work has been committed to addressing these challenges. Commercial and open-source serverless platforms implement many security measures to enhance serverless environments. This paper presents the first survey of serverless security that considers both literature work and industrial security measures. We summarize the primary security challenges, analyze corresponding solutions from the literature and industry, and identify potential research opportunities. Then, we conduct a gap analysis of the academic and industrial solutions as well as commercial and open-source serverless platforms' security capabilities, and finally, we present a complete picture of current serverless security research.

cs.CR↗

arXiv2021

Tomen: Application of Bitcoin Transaction Based on Tor

Bitcoin has emerged in 2008, and after decades of development, it has become the largest trading currency by far. The core of the blockchain is to ensure the anonymity of user transactions. As more and more analysis algorithms for blockchain transactions appear, the anonymity of the blockchain is increasingly threatened. We propose Tomen, an encryption application for the communication process in the bitcoin transaction process, combined with the encryption principle method of Tor. The goal is to achieve the application of the anonymization of bitcoin transaction communication.

cs.CR↗

arXiv2021

BEDS: Bagging ensemble deep segmentation for nucleus segmentation with testing stage stain augmentation

Reducing outcome variance is an essential task in deep learning based medical image analysis. Bootstrap aggregating, also known as bagging, is a canonical ensemble algorithm for aggregating weak learners to become a strong learner. Random forest is one of the most powerful machine learning algorithms before deep learning era, whose superior performance is driven by fitting bagged decision trees (weak learners). Inspired by the random forest technique, we propose a simple bagging ensemble deep segmentation (BEDs) method to train multiple U-Nets with partial training data to segment dense nuclei on pathological images. The contributions of this study are three-fold: (1) developing a self-ensemble learning framework for nucleus segmentation; (2) aggregating testing stage augmentation with self-ensemble learning; and (3) elucidating the idea that self-ensemble and testing stage stain augmentation are complementary strategies for a superior segmentation performance. Implementation Detail: https://github.com/xingli1102/BEDs.

cs.CV↗

arXiv2021

Human Action Recognition Based on Multi-scale Feature Maps from Depth Video Sequences

Human action recognition is an active research area in computer vision. Although great process has been made, previous methods mostly recognize actions based on depth data at only one scale, and thus they often neglect multi-scale features that provide additional information action recognition in practical application scenarios. In this paper, we present a novel framework focusing on multi-scale motion information to recognize human actions from depth video sequences. We propose a multi-scale feature map called Laplacian pyramid depth motion images(LP-DMI). We employ depth motion images (DMI) as the templates to generate the multi-scale static representation of actions. Then, we caculate LP-DMI to enhance multi-scale dynamic information of motions and reduces redundant static information in human bodies. We further extract the multi-granularity descriptor called LP-DMI-HOG to provide more discriminative features. Finally, we utilize extreme learning machine (ELM) for action classification. The proposed method yeilds the recognition accuracy of 93.41%, 85.12%, 91.94% on public MSRAction3D dataset, UTD-MHAD and DHA dataset. Through extensive experiments, we prove that our method outperforms state-of-the-art benchmarks.

cs.CV↗

arXiv2021

Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network

Graphs are often used to organize data because of their simple topological structure, and therefore play a key role in machine learning. And it turns out that the low-dimensional embedded representation obtained by graph representation learning are extremely useful in various typical tasks, such as node classification, content recommendation and link prediction. However, the existing methods mostly start from the microstructure (i.e., the edges) in the graph, ignoring the mesoscopic structure (high-order local structure). Here, we propose wGCN -- a novel framework that utilizes random walk to obtain the node-specific mesoscopic structures of the graph, and utilizes these mesoscopic structures to reconstruct the graph And organize the characteristic information of the nodes. Our method can effectively generate node embeddings for previously unseen data, which has been proven in a series of experiments conducted on citation networks and social networks (our method has advantages over baseline methods). We believe that combining high-order local structural information can more efficiently explore the potential of the network, which will greatly improve the learning efficiency of graph neural network and promote the establishment of new learning models.

cs.LG↗

arXiv2020

Symmetry-protected topological phase transitions and robust chiral order on a tunable zigzag lattice

Symmetry fractionalization, generating a large amount of symmetry-protected topological phases, provides scenarios for continuous phase transitions different from spontaneous symmetry breaking. However, it is hard to detect these symmetry-protected topological phase transitions experimentally. Motivated by the recent development of highly tunable ultracold polar molecules, we show that the setup in a zigzag optical lattice of this system provides a perfect platform to realize symmetry-protected topological phase transitions. By using infinite time-evolving block decimation, we obtain the phase diagram in a large parameter regions and find another scheme to realize the long-sought vector chiral phase, which is robust from quantum fluctuations. We discuss the existence of the chiral phase by an effective field analysis.

cond-mat.quant-gas↗

arXiv2020

A high resolution multi-phase clock Time-Digital Convertor implemented on Kintex-7 FPGA

Time-digital Converter (TDC) aims to measure the arrival time of the leading edge of the pulse signal. Our recent work presented a high resolution multi-phase TDC based on the Kintex-7 Field Programmable Gate Array (FPGA) device. A simple I/O tile based circular input buffer is employed to oscillate the input signal periodically, and then a multi-phase TDC based on ISERDES core with a 625 ps bin size is used to accomplish the multiple measurements for getting higher resolution performance. In this paper, the design concept, architecture, as well as kernel implementation considerations are all discussed. To evaluate the TDC's performance, we built a verification system based on Kintex-7 FPGA. Initial test results indicate that the TDC's effective bin size is successfully reduced from 625 ps to 78.125 ps, and the measured dual-channel time resolution is better than 35 ps RMS.

physics.ins-det↗

arXiv2020

Dynamic aspiration based on Win-Stay-Lose-Learn rule in Spatial Prisoner's Dilemma Gam

Prisoner's dilemma game is the most commonly used model of spatial evolutionary game which is considered as a paradigm to portray competition among selfish individuals. In recent years, Win-Stay-Lose-Learn, a strategy updating rule base on aspiration, has been proved to be an effective model to promote cooperation in spatial prisoner's dilemma game, which leads aspiration to receive lots of attention. But in many research the assumption that individual's aspiration is fixed is inconsistent with recent results from psychology. In this paper, according to Expected Value Theory and Achievement Motivation Theory, we propose a dynamic aspiration model based on Win-Stay-Lose-Learn rule in which individual's aspiration is inspired by its payoff. It is found that dynamic aspiration has a significant impact on the evolution process, and different initial aspirations lead to different results, which are called Stable Coexistence under Low Aspiration, Dependent Coexistence under Moderate aspiration and Defection Explosion under High Aspiration respectively. Furthermore, a deep analysis is performed on the local structures which cause cooperator's existence or defector's expansion, and the evolution process for different parameters including strategy and aspiration. As a result, the intrinsic structures leading to defectors' expansion and cooperators' survival are achieved for different evolution process, which provides a penetrating understanding of the evolution. Compared to fixed aspiration model, dynamic aspiration introduces a more satisfactory explanation on population evolution laws and can promote deeper comprehension for the principle of prisoner's dilemma.

physics.soc-ph↗

arXiv2020

Addressless: A New Internet Server Model to Prevent Network Scanning

Eliminating unnecessary exposure is a principle of server security. The huge IPv6 address space enhances security by making scanning infeasible, however, with recent advances of IPv6 scanning technologies, network scanning is again threatening server security. In this paper, we propose a new model named addressless server, which separates the server into an entrance module and a main service module, and assigns an IPv6 prefix instead of an IPv6 address to the main service module. The entrance module generates a legitimate IPv6 address under this prefix by encrypting the client address, so that the client can access the main server on a destination address that is different in each connection. In this way, the model provides isolation to the main server, prevents network scanning, and minimizes exposure. Moreover it provides a novel framework that supports flexible load balancing, high-availability, and other desirable features. The model is simple and does not require any modification to the client or the network. We implement a prototype and experiments show that our model can prevent the main server from being scanned at a slight performance cost.

cs.NI↗

arXiv2020

Driving Style Encoder: Situational Reward Adaptation for General-Purpose Planning in Automated Driving

General-purpose planning algorithms for automated driving combine mission, behavior, and local motion planning. Such planning algorithms map features of the environment and driving kinematics into complex reward functions. To achieve this, planning experts often rely on linear reward functions. The specification and tuning of these reward functions is a tedious process and requires significant experience. Moreover, a manually designed linear reward function does not generalize across different driving situations. In this work, we propose a deep learning approach based on inverse reinforcement learning that generates situation-dependent reward functions. Our neural network provides a mapping between features and actions of sampled driving policies of a model-predictive control-based planner and predicts reward functions for upcoming planning cycles. In our evaluation, we compare the driving style of reward functions predicted by our deep network against clustered and linear reward functions. Our proposed deep learning approach outperforms clustered linear reward functions and is at par with linear reward functions with a-priori knowledge about the situation.

cs.RO↗

arXiv2020

Planning on the fast lane: Learning to interact using attention mechanisms in path integral inverse reinforcement learning

General-purpose trajectory planning algorithms for automated driving utilize complex reward functions to perform a combined optimization of strategic, behavioral, and kinematic features. The specification and tuning of a single reward function is a tedious task and does not generalize over a large set of traffic situations. Deep learning approaches based on path integral inverse reinforcement learning have been successfully applied to predict local situation-dependent reward functions using features of a set of sampled driving policies. Sample-based trajectory planning algorithms are able to approximate a spatio-temporal subspace of feasible driving policies that can be used to encode the context of a situation. However, the interaction with dynamic objects requires an extended planning horizon, which depends on sequential context modeling. In this work, we are concerned with the sequential reward prediction over an extended time horizon. We present a neural network architecture that uses a policy attention mechanism to generate a low-dimensional context vector by concentrating on trajectories with a human-like driving style. Apart from this, we propose a temporal attention mechanism to identify context switches and allow for stable adaptation of rewards. We evaluate our results on complex simulated driving situations, including other moving vehicles. Our evaluation shows that our policy attention mechanism learns to focus on collision-free policies in the configuration space. Furthermore, the temporal attention mechanism learns persistent interaction with other vehicles over an extended planning horizon.

cs.RO↗

arXiv2020

Time-Varying and Nonlinearly Scaled Consensus of Multiagent Systems: A Generic Attracting Law Approach

This paper presents the design and analysis of the finite/fixed-time scaled consensus for multiagent systems. A study on a generic attracting law, the certain classes of nonlinear systems that admit attractors with finite/fixed-time convergence, is at first given for the consensus purpose. The estimates for the lower and upper bounds on the settling time functions are provided through the two-phase analysis. The given estimates are initial state dependent, but the durations are finite, without regarding the values that the initial states take. According to the generic attracting law, distributed protocols are proposed for multiagent systems with undirected and detail-balanced directed graphs, respectively, where the scaled strategies, including time-varying and nonlinear scales, are adopted. It is shown that the finite/fixed-time consensus for the multiagent system undertaken can still be achieved, even though both time-varying and nonlinear scales are taken among agents. Numerical simulation of two illustrative examples are given to verify effectiveness of the proposed finite-duration consensus protocols.

eess.SY↗

arXiv2020

Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs

The graph structure is a commonly used data storage mode, and it turns out that the low-dimensional embedded representation of nodes in the graph is extremely useful in various typical tasks, such as node classification, link prediction , etc. However, most of the existing approaches start from the binary relationship (i.e., edges) in the graph and have not leveraged the higher order local structure (i.e., motifs) of the graph. Here, we propose mGCMN -- a novel framework which utilizes node feature information and the higher order local structure of the graph to effectively generate node embeddings for previously unseen data. Through research we have found that different types of networks have different key motifs. And the advantages of our method over the baseline methods have been demonstrated in a large number of experiments on citation network and social network datasets. At the same time, a positive correlation between increase of the classification accuracy and the clustering coefficient is revealed. It is believed that using high order structural information can truly manifest the potential of the network, which will greatly improve the learning efficiency of the graph neural network and promote a brand-new learning mode establishment.

cs.SI↗

arXiv2020

Water-induced formation of an alkali-ion dimer in cryptomelane nanorods

Tunneled metal oxides such as $α-$Mn$_8$O$_{16}$ (hollandite) have proven to be compelling candidates for charge-storage materials in high-density batteries. In particular, the tunnels can support one-dimensional chains of K$^+$ ions (which act as structure-stabilizing dopants) and H$_2$O molecules, as these chains are favored by strong H-bonds and electrostatic interactions. In this work, we examine the role of water molecules in enhancing the stability of K$^+$-doped $α-$Mn$_8$O$_{16}$ (cryptomelane). The combined experimental and theoretical analyses show that for high enough concentrations of water and tunnel-ions, H$_2$O displaces K$^+$ ions from their natural binding sites. This displacement becomes energetically favorable due to the formation of K$_2^+$ dimers, thereby modifying the stoichiometric charge of the system. These findings have potentially significant technological implications for the consideration of cryptomelane as a Li$^+$/Na$^+$ battery electrode. Our work establishes the functional role of water in altering the energetics and structural properties of cryptomelane, an observation that has frequently been overlooked in previous studies.

cond-mat.mtrl-sci↗

arXiv2019

STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework

Simultaneous translation, which translates sentences before they are finished, is useful in many scenarios but is notoriously difficult due to word-order differences. While the conventional seq-to-seq framework is only suitable for full-sentence translation, we propose a novel prefix-to-prefix framework for simultaneous translation that implicitly learns to anticipate in a single translation model. Within this framework, we present a very simple yet surprisingly effective wait-k policy trained to generate the target sentence concurrently with the source sentence, but always k words behind. Experiments show our strategy achieves low latency and reasonable quality (compared to full-sentence translation) on 4 directions: zh<->en and de<->en.

cs.CL↗

arXiv2019

On the classification and false alarm of invalid prefixes in RPKI based BGP route origin validation

BGP is the default inter-domain routing protocol in today's Internet, but has serious security vulnerabilities\cite{murphy2005bgp}. One of them is (sub)prefix hijacking. IETF standardizes RPKI to validate the AS origin but RPKI has a lot of problems\cite{heilman2014consent}\cite{cooper2013risk}\cite{gilad2017we}\cite{gilad2017maxlength}, among which is potential false alarm. Although some previous work\cite{gilad2017we}\cite{heilman2014consent} points it out explicitly or implicitly, further measurement and analysis remain to be done. Our work measures and analyzes the invalid prefixes systematically. We first classify the invalid prefixes into six different types and then analyze their stability. We show that a large proportion of the invalid prefixes very likely result from traffic engineering, IP address transfer and failing to aggregate rather than real hijackings.

cs.NI↗