arXiv subjects

Min Chen

Publications and source records attributed to Min Chen.

At least 253 records · Page 14Linked to original sources

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↗

arXiv2017

Gate-controlled magnonic-assisted switching of magnetization in ferroelectric/ferromagnetic junctions

Interfacing a ferromagnet with a polarized ferroelectric gate generates a non-uniform, interfacial spin density coupled to the ferroelectric polarization allowing so for an electric field control of effective transversal field to magnetization. Here we study the dynamic magnetization switching behavior of such a multilayer system based on the Landau-Lifshitz-Baryakhtar equation, demonstrating that interfacial magnetoelectric coupling is utilizable as a highly localized and efficient tool for manipulating magnetism.

cond-mat.str-el↗

arXiv2017

Energy and coverage efficiency trade-off in 5G small cell networks

When small cells are densely deployed in the fifth generation (5G) cellular networks, the base stations (BSs) switch-off strategy is an effective approach for saving energy consumption considering changes of traffic load. In general, the loss of coverage efficiency is an inevitable cost for cellular networks adopting BSs switch-off strategies. Based on the BSs switch-off strategy, an optimized energy density efficiency of hard core point process (HCPP) small cell networks is proposed to trade off the energy and coverage efficiency. Simulation results imply that the minimum active BS distance used for the BSs switch-off strategy is recommended as 150 meters to achieve a tradeoff between energy and coverage efficiency in 5G small cell networks.

cs.NI↗

arXiv2017

Detecting Small Signs from Large Images

In the past decade, Convolutional Neural Networks (CNNs) have been demonstrated successful for object detections. However, the size of network input is limited by the amount of memory available on GPUs. Moreover, performance degrades when detecting small objects. To alleviate the memory usage and improve the performance of detecting small traffic signs, we proposed an approach for detecting small traffic signs from large images under real world conditions. In particular, large images are broken into small patches as input to a Small-Object-Sensitive-CNN (SOS-CNN) modified from a Single Shot Multibox Detector (SSD) framework with a VGG-16 network as the base network to produce patch-level object detection results. Scale invariance is achieved by applying the SOS-CNN on an image pyramid. Then, image-level object detection is obtained by projecting all the patch-level detection results to the image at the original scale. Experimental results on a real-world conditioned traffic sign dataset have demonstrated the effectiveness of the proposed method in terms of detection accuracy and recall, especially for those with small sizes.

cs.CV↗

arXiv2017

Brain Intelligence: Go Beyond Artificial Intelligence

Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Japan's economy and solves various social problems. In recent years, AI has attracted attention as a key for growth in developed countries such as Europe and the United States and developing countries such as China and India. The attention has been focused mainly on developing new artificial intelligence information communication technology (ICT) and robot technology (RT). Although recently developed AI technology certainly excels in extracting certain patterns, there are many limitations. Most ICT models are overly dependent on big data, lack a self-idea function, and are complicated. In this paper, rather than merely developing next-generation artificial intelligence technology, we aim to develop a new concept of general-purpose intelligence cognition technology called Beyond AI. Specifically, we plan to develop an intelligent learning model called Brain Intelligence (BI) that generates new ideas about events without having experienced them by using artificial life with an imagine function. We will also conduct demonstrations of the developed BI intelligence learning model on automatic driving, precision medical care, and industrial robots.

cs.CV↗

arXiv2017

Persistent Spread Measurement for Big Network Data Based on Register Intersection

Persistent spread measurement is to count the number of distinct elements that persist in each network flow for predefined time periods. It has many practical applications, including detecting long-term stealthy network activities in the background of normal-user activities, such as stealthy DDoS attack, stealthy network scan, or faked network trend, which cannot be detected by traditional flow cardinality measurement. With big network data, one challenge is to measure the persistent spreads of a massive number of flows without incurring too much memory overhead as such measurement may be performed at the line speed by network processors with fast but small on-chip memory. We propose a highly compact Virtual Intersection HyperLogLog (VI-HLL) architecture for this purpose. It achieves far better memory efficiency than the best prior work of V-Bitmap, and in the meantime drastically extends the measurement range. Theoretical analysis and extensive experiments demonstrate that VI-HLL provides good measurement accuracy even in very tight memory space of less than 1 bit per flow.

cs.NI↗

arXiv2017

Underwater Optical Image Processing: A Comprehensive Review

Underwater cameras are widely used to observe the sea floor. They are usually included in autonomous underwater vehicles, unmanned underwater vehicles, and in situ ocean sensor networks. Despite being an important sensor for monitoring underwater scenes, there exist many issues with recent underwater camera sensors. Because of lights transportation characteristics in water and the biological activity at the sea floor, the acquired underwater images often suffer from scatters and large amounts of noise. Over the last five years, many methods have been proposed to overcome traditional underwater imaging problems. This paper aims to review the state-of-the-art techniques in underwater image processing by highlighting the contributions and challenges presented in over 40 papers. We present an overview of various underwater image processing approaches, such as underwater image descattering, underwater image color restoration, and underwater image quality assessments. Finally, we summarize the future trends and challenges in designing and processing underwater imaging sensors.

cs.CV↗

arXiv2016

High quality electron beam acceleration by ionization injection in laser wakefields with mid-infrared dual-color lasers

For the laser wakefield acceleration, suppression of beam energy spread while keeping sufficient charge is one of the key challenges. In order to achieve this, we propose bichromatic laser ionization injection with combined laser wavelengths of $2.4\rm μm$ and $0.8\rm μm$ for wakefield excitation and for triggering electron injection via field ionization, respectively. A laser pulse at $2.4\rm μm$ wavelength enables one to drive an intense acceleration structure with relatively low laser power. To further reduce the requirement of laser power, we also propose to use carbon dioxide as the working gas medium, where carbon acts as the injection element. Our full three dimensional particle-in-cell simulations show that electron beams at the GeV energy level with both low energy spreads (around one percent) and high charges (several tens of picocoulomb) can be obtained by this scheme with laser parameters achievable in the near future.

physics.plasm-ph↗

arXiv2016

POMDP-lite for Robust Robot Planning under Uncertainty

The partially observable Markov decision process (POMDP) provides a principled general model for planning under uncertainty. However, solving a general POMDP is computationally intractable in the worst case. This paper introduces POMDP-lite, a subclass of POMDPs in which the hidden state variables are constant or only change deterministically. We show that a POMDP-lite is equivalent to a set of fully observable Markov decision processes indexed by a hidden parameter and is useful for modeling a variety of interesting robotic tasks. We develop a simple model-based Bayesian reinforcement learning algorithm to solve POMDP-lite models. The algorithm performs well on large-scale POMDP-lite models with up to $10^{20}$ states and outperforms the state-of-the-art general-purpose POMDP algorithms. We further show that the algorithm is near-Bayesian-optimal under suitable conditions.

cs.AI↗

arXiv2016

Valley-protected backscattering suppression in silicon photonic graphene

In this paper, we study valley degree of freedom in all dielectric silicon photonic graphene. Photonic band gap opening physics under inversion symmetry breaking is revisited by the viewpoint of nonzero valley Chern number. Bulk valley modes with opposite orbital angular momentum are unveiled by inspecting time-varying electric fields. Topological transition is well illustrated through photonic Dirac Hamiltonian. Valley dependent edge states and the associated valley-protected backscattering suppression around Z-shape bend waveguide have been demonstrated.

physics.optics↗

arXiv2016

A compact tunable polarized X-ray source based on laser-plasma helical undulators

Laser wakefield accelerators have great potential as the basis for next generation compact radiation sources because their accelerating gradients are three orders of magnitude larger than traditional accelerators. However, X-ray radiation from such devices still lacks of tunability, especially the intensity and polarization distribution. Here we propose a tunable polarized radiation source from a helical plasma undulator based on plasma channel guided wakefield accelerator. When a laser pulse is initially incident with a skew angle relative to the channel axis, the laser and accelerated electrons experience collective spiral motions, which leads to elliptically polarized synchrotron-like radiation with flexible tunability on radiation intensity, spectra and polarization. We demonstrate that a radiation source with millimeter size and peak brilliance of $2\times10^{19} photons/s/mm^{2}/mrad^{2}/0.1%$ bandwidth can be made with moderate laser and electron beam parameters. This brilliance is comparable with the third generation of synchrotron radiation facilities running at similar photon energies, suggesting that laser plasma based radiation sources are promising for advanced applications.

physics.plasm-ph↗

arXiv2016

Plasma optical modulators for intense lasers

Optical modulators can be made nowadays with high modulation speed, broad bandwidth, while being compact, owing to the recent advance in material science and microfabrication technology. However, these optical modulators usually work for low intensity light beams. Here, we present an ultrafast, plasma-based optical modulator, which can directly modulate high power lasers with intensity up to 10^16 W/cm^2 level to produce an extremely broad spectrum with a fractional bandwidth over 100%, extending to the mid-infrared regime in the low-frequency side. This concept relies on two co-propagating laser beams in a sub-mm-scale underdense plasma, where a drive laser pulse first excites an electron plasma wave in its wake while a following carrier laser beam is modulated by the plasma wave. The laser and plasma parameters suitable for the modulator to work are presented. Such optical modulators may enable new applications in the high field physics.

physics.plasm-ph↗

arXiv2015

Service-Oriented Architectures and Web Services: Course Tutorial and Lab Notes

This document presents a number of quick-step instructions to get started on writing mini-service-oriented web services-based applications using OpenESB 2.31, Tomcat 6, GlassFish 2.x/3.0.1 with BPEL support, and Java 1.6+ primarily in Scientific Linux 6.6 with user quota restrictions. While the tutorial notes are oriented towards the students taking the SOEN487 on service-oriented architectures (SOA) at Computer Science and Software Engineering (CSE) Department, Faculty of Engineering and Computer Science (ENCS), other may find some of it useful as well outside of CSE or Concordia. The notes are compiled mostly based on the students' needs and feedback.

cs.DC↗

arXiv2015

RF-Based Charger Placement for Duty Cycle Guarantee in Battery-Free Sensor Networks

Battery-free sensor networks have emerged as a promising solution to conquer the lifetime limitation of battery-powered systems. In this paper, we study a sensor network built from battery-free sensor nodes which harvest energy from radio frequency (RF) signals transmitted by RF-based chargers, e.g., radio frequency identification (RFID) readers. Due to the insufficiency of harvested energy, the sensor nodes have to work in duty cycles to harvest enough energy before turning active and performing tasks. One fundamental issue in this kind of network design is how to deploy the chargers to ensure that the battery-free nodes can maintain a designated duty cycle for continuous operation. Based on a new wireless recharge model, we formulate the charger placement problem for node's duty cycle guarantee as a constrained optimization problem. We develop both greedy and efficient heuristics for solving the problem and validate our solutions through extensive simulations. The simulation results show that the proposed particle swarm optimization (PSO)-based divide-and-conquer approach can effectively reduce the number of chargers compared with the greedy approach.

cs.NI↗

arXiv2015

Perturbed Hankel determinant, correlation functions and Painlevé equations

We continue with the study of the Hankel determinant, $$ D_{n}(t,α,β):=\det\left(\int_{0}^{1}x^{j+k}w(x;t,α,β)dx\right)_{j,k=0}^{n-1}, $$ generated by a Pollaczek-Jacobi type weight, $$ w(x;t,α,β):=x^α(1-x)^β{\rm e}^{-t/x}, \quad x\in [0,1], \quad α>0, \quad β>0, \quad t\geq 0. $$ This reduces to the "pure" Jacobi weight at $t=0.$ We may take $α\in \mathbb{R}$, in the situation while $t$ is strictly greater than $0.$ It was shown in Chen and Dai (2010), that the logarithmic derivative of this Hankel determinant satisfies a Jimbo-Miwa-Okamoto $σ$-form of Painlevé \uppercase\expandafter{\romannumeral5} (${\rm P_{\uppercase\expandafter{\romannumeral5}}}$). In fact the logarithmic of the Hankel determinant has an integral representation in terms of a particular ${\rm P_{\uppercase\expandafter{\romannumeral5}}}.$ \\ In this paper, we show that, under a double scaling, where $n$ the dimension of the Hankel matrix tends to $\infty$, and $t$ tends to $0^{+},$ such that $s:=2n^2t$ is finite, the double scaled Hankel determinant (effectively an operator determinant) has an integral representation in terms of a particular ${\rm P_{\uppercase\expandafter{\romannumeral3}'}}.$ Expansions of the scaled Hankel determinant for small and large $s$ are found. A further double scaling with $α=-2n+λ,$ where $n\rightarrow \infty$ and $t,$ tends to $0^{+},$ such that $s:=nt$ is finite. In this situation the scaled Hankel determinant has an integral representation in terms of a particular ${\rm P_{\uppercase\expandafter{\romannumeral5}}},$ %which can be degenerate to a particular ${\rm P_{\uppercase\expandafter{\romannumeral3}}}$ and its small and large $s$ asymptotic expansions are also found.

math.CA↗