arXiv subjects

Min Chen

Publications and source records attributed to Min Chen.

At least 235 records · Page 13Linked to original sources

arXiv2018

Wearable Affective Robot

With the development of the artificial intelligence (AI), the AI applications have influenced and changed people's daily life greatly. Here, a wearable affective robot that integrates the affective robot, social robot, brain wearable, and wearable 2.0 is proposed for the first time. The proposed wearable affective robot is intended for a wide population, and we believe that it can improve the human health on the spirit level, meeting the fashion requirements at the same time. In this paper, the architecture and design of an innovative wearable affective robot, which is dubbed as Fitbot, are introduced in terms of hardware and algorithm's perspectives. In addition, the important functional component of the robot-brain wearable device is introduced from the aspect of the hardware design, EEG data acquisition and analysis, user behavior perception, and algorithm deployment, etc. Then, the EEG based cognition of user's behavior is realized. Through the continuous acquisition of the in-depth, in-breadth data, the Fitbot we present can gradually enrich user's life modeling and enable the wearable robot to recognize user's intention and further understand the behavioral motivation behind the user's emotion. The learning algorithm for the life modeling embedded in Fitbot can achieve better user's experience of affective social interaction. Finally, the application service scenarios and some challenging issues of a wearable affective robot are discussed.

cs.HC↗

arXiv2018

Background Subtraction using Compressed Low-resolution Images

Image processing and recognition are an important part of the modern society, with applications in fields such as advanced artificial intelligence, smart assistants, and security surveillance. The essential first step involved in almost all the visual tasks is background subtraction with a static camera. Ensuring that this critical step is performed in the most efficient manner would therefore improve all aspects related to objects recognition and tracking, behavior comprehension, etc.. Although background subtraction method has been applied for many years, its application suffers from real-time requirement. In this letter, we present a novel approach in implementing the background subtraction. The proposed method uses compressed, low-resolution grayscale image for the background subtraction. These low-resolution grayscale images were found to preserve the salient information very well. To verify the feasibility of our methodology, two prevalent methods, ViBe and GMM, are used in the experiment. The results of the proposed methodology confirm the effectiveness of our approach.

cs.CV↗

arXiv2018

Cognitive-LPWAN: Towards Intelligent Wireless Services in Hybrid Low Power Wide Area Networks

The relentless development of the Internet of Things (IoT) communication technologies and the gradual maturity of Artificial Intelligence (AI) have led to a powerful cognitive computing ability. Users can now access efficient and convenient smart services in smart-city, green-IoT and heterogeneous networks. AI has been applied in various areas, including the intelligent household, advanced health-care, automatic driving and emotional interactions. This paper focuses on current wireless-communication technologies, including cellular-communication technologies (4G, 5G), low-power wide-area (LPWA) technologies with an unlicensed spectrum (LoRa, SigFox), and other LPWA technologies supported by 3GPP working with an authorized spectrum (EC-GSM, LTE-M, NB-IoT). We put forward a cognitive low-power wide-area-network (Cognitive-LPWAN) architecture to safeguard stable and efficient communications in a heterogeneous IoT. To ensure that the user can employ the AI efficiently and conveniently, we realize a variety of LPWA technologies to safeguard the network layer. In addition, to balance the demand for heterogeneous IoT devices with the communication delay and energy consumption, we put forward the AI-enabled LPWA hybrid method, starting from the perspective of traffic control. The AI algorithm provides the smart control of wireless-communication technology, intelligent applications and services for the choice of different wireless-communication technologies. As an example, we consider the AIWAC emotion interaction system, build the Cognitive-LPWAN and test the proposed AI-enabled LPWA hybrid method. The experimental results show that our scheme can meet the demands of communication-delay applications. Cognitive-LPWAN selects appropriate communication technologies to achieve a better interaction experience.

cs.NI↗

arXiv2018

Label-less Learning for Traffic Control in an Edge Network

With the development of intelligent applications (e.g., self-driving, real-time emotion recognition, etc), there are higher requirements for the cloud intelligence. However, cloud intelligence depends on the multi-modal data collected by user equipments (UEs). Due to the limited capacity of network bandwidth, offloading all data generated from the UEs to the remote cloud is impractical. Thus, in this article, we consider the challenging issue of achieving a certain level of cloud intelligence while reducing network traffic. In order to solve this problem, we design a traffic control algorithm based on label-less learning on the edge cloud, which is dubbed as LLTC. By the use of the limited computing and storage resources at edge cloud, LLTC evaluates the value of data, which will be offloaded. Specifically, we first give a statement of the problem and the system architecture. Then, we design the LLTC algorithm in detail. Finally, we set up the system testbed. Experimental results show that the proposed LLTC can guarantee the required cloud intelligence while minimizing the amount of data transmission.

cs.NI↗

arXiv2018

A Dynamic Service-Migration Mechanism in Edge Cognitive Computing

Driven by the vision of edge computing and the success of rich cognitive services based on artificial intelligence, a new computing paradigm, edge cognitive computing (ECC), is a promising approach that applies cognitive computing at the edge of the network. ECC has the potential to provide the cognition of users and network environmental information, and further to provide elastic cognitive computing services to achieve a higher energy efficiency and a higher Quality of Experience (QoE) compared to edge computing. This paper firstly introduces our architecture of the ECC and then describes its design issues in detail. Moreover, we propose an ECC-based dynamic service migration mechanism to provide an insight into how cognitive computing is combined with edge computing. In order to evaluate the proposed mechanism, a practical platform for dynamic service migration is built up, where the services are migrated based on the behavioral cognition of a mobile user. The experimental results show that the proposed ECC architecture has ultra-low latency and a high user experience, while providing better service to the user, saving computing resources, and achieving a high energy efficiency.

cs.NI↗

arXiv2018

Collimated GeV attosecond electron-positron bunches from a plasma channel driven by 10 PW lasers

High-energy positrons and bright γ-ray sources are unique both for fundamental research and practical applications. However, GeV electron-positron pair jets and γ-ray flashes are still hardly produced in laboratories. Here we demonstrate that, by irradiating two 10 PW-scale laser pulses onto a near-critical density plasma channel, highly-directional GeV electron-positron pairs and bright γ-ray beams can be efficiently generated. Three-dimensional particle-in-cell simulations show that GeV positron jets show high density (8*10^21/cm^3), attosecond duration (400 as) and a divergence angle of 14°. Additionally, ultrabright (2*10^25 photons/s/mm2/mrad2/0.1%BW) collimated attosecond (370 as) γ-ray flashes with a laser energy conversion efficiency of 5.6% are emitted. Once realized in experiment, it may open up new possibilities for a wide variety of applications.

physics.plasm-ph↗

arXiv2018

Eigenfunctions for quasi-laplacian

To study the regularity of heat flow, Lin-Wang[1] introduced the quasi-harmonic sphere, which is a harmonic map from $M=(\mathbb{R}^m,e^{-\frac{|x|^2}{2(m-2)}}ds_0^2)$ to $N$ with finite energy. Here $ds_0^2$ is Euclidean metric in $\mathbb{R}^m$. Ding-Zhao [2] showed that if the target is a sphere, any equivariant quasi-harmonic spheres is discontinuous at infinity. The metric $g=e^{-\frac{|x|^2}{2(m-2)}}ds_0^2$ is quite singular at infinity and it is not complete. In this paper , we mainly study the eigenfunction of Quasi-Laplacian $Δ_g=e^{\frac{|x|^2}{2(m-2)}} ( Δ_{g_0} - \nabla_{g_0}h\cdot \nabla_{g_0}) =e^{\frac{|x|^2}{2(m-2)}} Δ_h$ for $h=\frac{|x|^2}{4}$. In particular, we show that non-constant eigenfunctions of $Δ_g$ must be discontinuous at infinity and non-constant eigenfunctions of drifted Laplacian $Δ_h=Δ_{g_0} - \nabla_{g_0} h\cdot \nabla_{g_0}$ is also discontinuous at infinity.

math.DG↗

arXiv2018

Magnetic field dependence of antiferromagnetic resonance in NiO

We report on measurements of magnetic field and temperature dependence of antiferromagnetic resonances in the prototypical antiferromagnet NiO. The frequencies of the magnetic resonances in the vicinity of 1 THz have been determined in the time-domain via time-resolved Faraday measurements after selective excitation by narrow-band superradiant terahertz (THz) pulses at temperatures down to 3K and in magnetic fields up to 10 T. The measurements reveal two antiferromagnetic resonance modes, which can be distinguished by their characteristic magnetic field dependencies. The nature of the two modes is discussed by comparison to an eight-sublattice antiferromagnetic model, which includes superexchange between the next-nearest-neighbor Ni spins, magnetic dipolar interactions, cubic magneto-crystalline anisotropy, and Zeeman interaction with the external magnetic field. Our study indicates that a two-sublattice model is insufficient for the description of spin dynamics in NiO, while the magnetic-dipolar interactions and magneto-crystalline anisotropy play important roles.

cond-mat.mtrl-sci↗

arXiv2018

Generation of GeV positron and γ-photon beams with controllable angular momentum by intense lasers

Although several laser-plasma-based methods have been proposed for generating energetic electrons, positrons and γ-photons, manipulation of their microstructures is still challenging, and their angular momentum control has not yet been achieved. Here, we present and numerically demonstrate an all-optical scheme to generate bright GeV γ-photon and positron beams with controllable angular momentum by use of two counter-propagating circularly-polarized lasers in a near-critical-density plasma. The plasma acts as a "switching medium", where the trapped electrons first obtain angular momentum from the drive laser pulse and then transfer it to the γ-photons via nonlinear Compton scattering. Further through the multiphoton Breit-Wheeler process, dense energetic positron beams are efficiently generated, whose angular momentum can be well controlled by laser-plasma interactions. This opens up a promising and feasible way to produce ultra-bright GeV γ-photons and positron beams with desirable angular momentum for a wide range of scientific research and applications.

physics.plasm-ph↗

arXiv2018

Relaxation rates of gene expression kinetics reveal the feedback signs of autoregulatory gene networks

The transient response to a stimulus and subsequent recovery to a steady state are the fundamental characteristics of a living organism. Here we study the relaxation kinetics of autoregulatory gene networks based on the chemical master equation model of single-cell stochastic gene expression with nonlinear feedback regulation. We report a novel relation between the rate of relaxation, characterized by the spectral gap of the Markov model, and the feedback sign of the underlying gene circuit. When a network has no feedback, the relaxation rate is exactly the decaying rate of the protein. We further show that positive feedback always slows down the relaxation kinetics while negative feedback always speeds it up. Numerical simulations demonstrate that this relation provides a possible method to infer the feedback topology of autoregulatory gene networks by using time-series data of gene expression.

q-bio.MN↗

arXiv2018

Cost-benefit Analysis of Visualization in Virtual Environments

Visualization and virtual environments (VEs) have been two interconnected parallel strands in visual computing for decades. Some VEs have been purposely developed for visualization applications, while many visualization applications are exemplary showcases in general-purpose VEs. Because of the development and operation costs of VEs, the majority of visualization applications in practice are yet to benefit from the capacity of VEs. In this paper, we examine this perplexity from an information-theoretic perspective. Our objectives are to conduct cost-benefit analysis on typical VE systems (including augmented and mixed reality, theatre-based systems, and large powerwalls), to explain why some visualization applications benefit more from VEs than others, and to sketch out pathways for the future development of visualization applications in VEs. We support our theoretical propositions and analysis using theories and discoveries in the literature of cognitive sciences and the practical evidence reported in the literatures of visualization and VEs.

cs.HC↗

arXiv2017

Hankel determinants for a perturbed Laguerre weight and Painleve V equation

In this paper, we study Hankel determinants generated from a perturbed Laguerre weight function, Under the double scaling scheme, we give the uniform asymptotic approximations of Hankel determinants in terms of a solution of a third-order nonlinear differential equation, which is equivalent to a particular Painleve V equation. In fact, this Painleve V equation is equivalent to the general Painleve III equation. The asymptotic approximations of the leading coefficients and the recurrence coefficients for the corresponding orthogonal polynomials also involve the painleve V equation. The asymptotic analysis is based on our earlier results by using the Deift-Zhou nonlinear steepest descent method.

nlin.SI↗

arXiv2017

Single-use MIMO system, Painlevé transcendents and double scaling

In this paper we study a particular Painlevé V (denoted ${\rm P_{V}}$) that arises from Multi-Input-Multi-Output (MIMO) wireless communication systems. Such a $P_V$ appears through its intimate relation with the Hankel determinant that describes the moment generating function (MGF) of the Shannon capacity. This originates through the multiplication of the Laguerre weight or the Gamma density $x^α {\rm e}^{-x},\;x> 0,$ for $α>-1$ by $(1+x/t)^λ$ with $t>0$ a scaling parameter. Here the $λ$ parameter "generates" the Shannon capacity, see Yang Chen and Matthew McKay, IEEE Trans. IT, 58 (2012) 4594--4634. It was found that the MGF has an integral representation as a functional of $y(t)$ and $y'(t)$, where $y(t)$ satisfies the "classical form" of $P_V$. In this paper, we consider the situation where $n,$ the number of transmit antennas, (or the size of the random matrix), tends to infinity, and the signal-to-noise ratio (SNR) $P$ tends to infinity, such that $s={4n^{2}}/{P}$ is finite. Under such double scaling the MGF, effectively an infinite determinant, has an integral representation in terms of a "lesser" $P_{III}$. We also consider the situations where $α=k+1/2,\;\;k\in \mathbb{N},$ and $α\in\{0,1,2,\dots\}$ $λ\in\{1,2,\dots\},$ linking the relevant quantity to a solution of the two dimensional sine-Gordon equation in radial coordinates and a certain discrete Painlevé-II. From the large $n$ asymptotic of the orthogonal polynomials, that appears naturally, we obtain the double scaled MGF for small and large $s$, together with the constant term in the large $s$ expansion. With the aid of these, we derive a number of cumulants and find that the capacity distribution function is non-Gaussian.

math-ph↗

arXiv2017

Critical edge behavior in the perturbed Laguerre ensemble and the Painleve V transcendent

In this paper, we consider the perturbed Laguerre unitary ensemble described by the weight function of $$w(x,t)=(x+t)^λx^αe^{-x}$$ with $ x\geq 0,\ t>0,\ α>0,\ α+λ+1 > 0.$ The Deift-Zhou nonlinear steepest descent approach is used to analyze the limit of the eigenvalue correlation kernel. It was found that under the double scaling $s=4nt,$ $n\to \infty,$ $t\to 0 $ such that $s$ is positive and finite, at the hard edge, the limiting kernel can be described by the $φ$-function related to a third-order nonlinear differential equation, which is equivalent to a particular Painlevé V (shorted as P$_{\rm V}$) transcendent via a simple transformation. Moreover, this P$_{\rm V}$ transcendent is equivalent to a general Painlevé P$_{\rm III}$ transcendent. For large $s,$ the P$_{\rm V}$ kernel reduces to the Bessel kernel $\mathbf{J}_{α+λ}.$ For small $s,$ the P$_{\rm V}$ kernel reduces to another Bessel kernel $\mathbf{J}_α.$ At the soft edge, the limiting kernel is the Airy kernel as the classical Laguerre weight.

math-ph↗

arXiv2017

LPG: a four-groups probabilistic approach to leveraging pleiotropy in genome-wide association studies

To date, genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants among a variety of traits/diseases, shedding a light on the genetic architecture of complex diseases. Polygenicity of complex diseases, which refers to the phenomenon that a vast number of risk variants collectively contribute to the heritability of complex diseases with modest individual effects, have been widely accepted. This imposes a major challenge towards fully characterizing the genetic bases of complex diseases. An immediate implication of polygenicity is that a much larger sample size is required to detect risk variants with weak/moderate effects. Meanwhile, accumulating evidence suggests that different complex diseases can share genetic risk variants, a phenomenon known as pleiotropy. In this study, we propose a statistical framework for Leveraging Pleiotropic effects in large-scale GWAS data (LPG). LPG utilizes a variational Bayesian expectation-maximization (VBEM) algorithm, making it computationally efficient and scalable for genome-wide scale analysis. To demon- strate the advantage of LPG over existing methods that do not leverage pleiotropy, we conducted extensive simulation studies and also applied LPG to analyze three au- toimmune disorders (Crohn's disease, rheumatoid arthritis and Type 1 diabetes). The results indicate that LPG can improve the power of prioritization of risk variants and accuracy of risk prediction by leveraging pleiotropy. The software is available at http- s://github.com/Shufeyangyi2015310117/LPG.

stat.ME↗

arXiv2017

Stochastic fluctuations can reveal the feedback signs of gene regulatory networks at the single-molecule level

Understanding the relationship between spontaneous stochastic fluctuations and the topology of the underlying gene regulatory network is of fundamental importance for the study of single-cell stochastic gene expression. Here by solving the analytical steady-state distribution of the protein copy number in a general kinetic model of stochastic gene expression with nonlinear feedback regulation, we reveal the relationship between stochastic fluctuations and feedback topology at the single-molecule level, which provides novel insights into how and to what extent a feedback loop can enhance or suppress molecular fluctuations. Based on such relationship, we also develop an effective method to extract the topological information of a gene regulatory network from single-cell gene expression data. The theory is demonstrated by numerical simulations and, more importantly, validated quantitatively by single-cell data analysis of a synthetic gene circuit integrated in human kidney cells.

q-bio.MN↗

arXiv2017

Extreme case of Faraday effect: magnetic splitting of ultrashort laser pulses in plasmas

The Faraday effect, caused by a magnetic-field-induced change in the optical properties, takes place in a vast variety of systems from a single atomic layer of graphenes to huge galaxies. Currently, it plays a pivot role in many applications such as the manipulation of light and the probing of magnetic fields and material's properties. Basically, this effect causes a polarization rotation of light during its propagation along the magnetic field in a medium. Here, we report an extreme case of the Faraday effect where a linearly polarized ultrashort laser pulse splits in time into two circularly polarized pulses of opposite handedness during its propagation in a highly magnetized plasma. This offers a new degree of freedom for manipulating ultrashort and ultrahigh power laser pulses. Together with technologies of ultra-strong magnetic fields, it may pave the way for novel optical devices, such as magnetized plasma polarizers. In addition, it may offer a powerful means to measure strong magnetic fields in laser-produced plasmas.

physics.plasm-ph↗

arXiv2017

Edge states in self-complementary checkerboard photonic crystals: Zak phase, surface impedance and experimental verification

Edge states of photonic crystals have attracted much attention for the potential applications such as high transmission waveguide bends, spin dependent splitters and one-way photonic circuits. Here, we theoretically discuss and experimentally observe the deterministic edge states in checkerboard photonic crystals. Due to the self-complementarity of checkerboard photonic crystals, a common band gap is structurally protected between two photonic crystals with different unit cells. Deterministic edge states are found inside the common band gap by exploiting the Zak phase analysis and surface impedance calculation. These edge states are also confirmed by a microwave experiment.

physics.optics↗