arXiv subjects

Wei Zhu

Publications and source records attributed to Wei Zhu.

At least 307 records · Page 17Linked to original sources

arXiv2018

Measurement of Source Star Colors with the K2C9-CFHT Multi-color Microlensing Survey

K2 Campaign 9 (K2C9) was the first space-based microlensing parallax survey capable of measuring microlensing parallaxes of free-floating planet candidate microlensing events. Simultaneous to K2C9 observations we conducted the K2C9 Canada-France-Hawaii Telescope Multi-Color Microlensing Survey (K2C9-CFHT MCMS) in order to measure the colors of microlensing source stars to improve the accuracy of K2C9's parallax measurements. We describe the difference imaging photometry analysis of the K2C9-CFHT MCMS observations, and present the project's first data release. This includes instrumental difference flux lightcurves of 217 microlensing events identified by other microlensing surveys, reference image photometry calibrated to PanSTARRS data release 1 photometry, and tools to convert between instrumental and calibrated flux scales. We derive accurate analytic transformations between the PanSTARRS bandpasses and the Kepler bandpass, as well as angular diameter-color relations in the PanSTARRS bandpasses. To demonstrate the use of our data set, we analyze ground-based and K2 data of a short timescale microlensing event, OGLE-2016-BLG-0795. We find the event has a timescale $t_{\rm E}=4.5 \pm 0.1$~days and microlens parallax $π_{\rm E}=0.12 \pm 0.03$ or $0.97 \pm 0.04$, subject to the standard satellite parallax degeneracy. We argue that the smaller value of the parallax is more likely, which implies that the lens is likely a stellar-mass object in the Galactic bulge as opposed to a super-Jupiter mass object in the Galactic disk.

astro-ph.EP↗

arXiv2018

Scalable low dimensional manifold model in the reconstruction of noisy and incomplete hyperspectral images

We present a scalable low dimensional manifold model for the reconstruction of noisy and incomplete hyperspectral images. The model is based on the observation that the spatial-spectral blocks of a hyperspectral image typically lie close to a collection of low dimensional manifolds. To emphasize this, the dimension of the manifold is directly used as a regularizer in a variational functional, which is solved efficiently by alternating direction of minimization and weighted nonlocal Laplacian. Unlike general 3D images, the same similarity matrix can be shared across all spectral bands for a hyperspectral image, therefore the resulting algorithm is much more scalable than that for general 3D data. Numerical experiments on the reconstruction of hyperspectral images from sparse and noisy sampling demonstrate the superiority of our proposed algorithm in terms of both speed and accuracy.

cs.CV↗

arXiv2018

Iteratively Linearized Reweighted Alternating Direction Method of Multipliers for a Class of Nonconvex Problems

In this paper, we consider solving a class of nonconvex and nonsmooth problems frequently appearing in signal processing and machine learning research. The traditional alternating direction method of multipliers encounters troubles in both mathematics and computations in solving the nonconvex and nonsmooth subproblem. In view of this, we propose a reweighted alternating direction method of multipliers. In this algorithm, all subproblems are convex and easy to solve. We also provide several guarantees for the convergence and prove that the algorithm globally converges to a critical point of an auxiliary function with the help of the Kurdyka-Łojasiewicz property. Several numerical results are presented to demonstrate the efficiency of the proposed algorithm.

math.NA↗

arXiv2018

Pairing from strong repulsion in triangular lattice Hubbard model

We propose a paring mechanism between holes in the dilute limit of doped frustrated Mott insulators. Hole pairing arises from a hole-hole-magnon three-body bound state. This pairing mechanism has its roots on single-hole kinetic energy frustration, which favors antiferromagnetic (AFM) correlations around the hole. We demonstrate that the AFM polaron (hole-magnon bound state) produced by a single-hole propagating on a field-induced polarized background is strong enough to bind a a second hole. The effective interaction between these three-body bound states is repulsive, implying that this pairing mechanism is relevant for superconductivity.

cond-mat.supr-con↗

arXiv2018

Entanglement signatures of emergent Dirac fermions: kagome spin liquid & quantum criticality

Quantum spin liquids (QSL) are exotic phases of matter that host fractionalized excitations. It is difficult for local probes to characterize QSL, whereas quantum entanglement can serve as a powerful diagnostic tool due to its non-locality. The kagome antiferromagnetic Heisenberg model is one of the most studied and experimentally relevant models for QSL, but its solution remains under debate. Here, we perform a numerical Aharonov-Bohm experiment on this model and uncover universal features of the entanglement entropy. By means of the density-matrix renormalization group, we reveal the entanglement signatures of emergent Dirac spinons, which are the fractionalized excitations of the QSL. This scheme provides qualitative insights into the nature of kagome QSL, and can be used to study other quantum states of matter. As a concrete example, we also benchmark our methods on an interacting quantum critical point between a Dirac semimetal and a charge ordered phase.

cond-mat.str-el↗

arXiv2017

Artificial-noise-aided Secure Multicast Precoding for Directional Modulation Systems

In multi-cast scenario, all desired users are divided into $K$ groups. Each group receives its own individual confidential message stream. Eavesdropper group aims to intercept $K$ confidential-message streams. To achieve a secure transmission, two secure schemes are proposed: maximum group receive power plus null-space (NS) projection (Max-GRP plus NSP) and leakage. The former obtains its precoding vector per group by maximizing its own group receive power subject to the orthogonal constraint, and its AN projection matrix consist of all bases of NS of all desired steering vectors from all groups. The latter attains its desired precoding vector per group by driving the current confidential message power to its group steering space and reducing its power leakage to eavesdropper group and other $K-1$ desired ones by maximizing signal to leakage and noise ratio (Max-SLNR). And its AN projection matrix is designed by forcing AN power into the eavesdropper steering space by viewing AN as a useful signal for eavesdropper group and maximizing AN to leakage-and-noise ratio (Max-ANLNR). Simulation results show that the proposed two methods are better than conventional method in terms of both bit-error-rate (BER) and secrecy sum-rate per group. Also, the leakage scheme performs better than Max-GRP-NSP , especially in the presence of direction measurement errors. However, the latter requires no channel statistical parameters and thus is simpler compared to the former.

cs.IT↗

arXiv2017

Inertial Proximal Incremental Aggregated Gradient Method

In this paper, we introduce an inertial version of the Proximal Incremental Aggregated Gradient method (PIAG) for minimizing the sum of smooth convex component functions and a possibly nonsmooth convex regularization function. Theoretically, we show that the inertial Proximal Incremental Aggregated Gradiend (iPIAG) method enjoys a global linear convergence under a quadratic growth condition, which is strictly weaker than strong convexity, provided that the stepsize is not larger than a constant. Moreover, we present two numerical expreiments which demonstrate that iPIAG outperforms the original PIAG.

math.OC↗

arXiv2017

Multi-appearance Segmentation and Extended 0-1 Program for Dense Small Object Tracking

Aiming to address the fast multi-object tracking for dense small object in the cluster background, we review track orientated multi-hypothesis tracking(TOMHT) with consideration of batch optimization. Employing autocorrelation based motion score test and staged hypotheses merging approach, we build our homologous hypothesis generation and management method. A new one-to-many constraint is proposed and applied to tackle the track exclusions during complex occlusions. Besides, to achieve better results, we develop a multi-appearance segmentation for detection, which exploits tree-like topological information and realizes one threshold for one object. Experimental results verify the strength of our methods, indicating speed and performance advantages of our tracker.

cs.CV↗

arXiv2017

LDMNet: Low Dimensional Manifold Regularized Neural Networks

Deep neural networks have proved very successful on archetypal tasks for which large training sets are available, but when the training data are scarce, their performance suffers from overfitting. Many existing methods of reducing overfitting are data-independent, and their efficacy is often limited when the training set is very small. Data-dependent regularizations are mostly motivated by the observation that data of interest lie close to a manifold, which is typically hard to parametrize explicitly and often requires human input of tangent vectors. These methods typically only focus on the geometry of the input data, and do not necessarily encourage the networks to produce geometrically meaningful features. To resolve this, we propose a new framework, the Low-Dimensional-Manifold-regularized neural Network (LDMNet), which incorporates a feature regularization method that focuses on the geometry of both the input data and the output features. In LDMNet, we regularize the network by encouraging the combination of the input data and the output features to sample a collection of low dimensional manifolds, which are searched efficiently without explicit parametrization. To achieve this, we directly use the manifold dimension as a regularization term in a variational functional. The resulting Euler-Lagrange equation is a Laplace-Beltrami equation over a point cloud, which is solved by the point integral method without increasing the computational complexity. We demonstrate two benefits of LDMNet in the experiments. First, we show that LDMNet significantly outperforms widely-used network regularizers such as weight decay and DropOut. Second, we show that LDMNet can be designed to extract common features of an object imaged via different modalities, which proves to be very useful in real-world applications such as cross-spectral face recognition.

cs.CV↗

arXiv2017

Chiral liquid phase of simple quantum magnets

We study a $T=0$ quantum phase transition between a quantum paramagnetic state and a magnetically ordered state for a spin $S=1$ XXZ Heisenberg antiferromagnet on a two-dimensional triangular lattice. The transition is induced by an easy plane single-ion anisotropy $D$. At the mean-field level, the system undergoes a direct transition at a critical $D = D_c$ between a paramagnetic state at $D > D_c$ and an ordered state with broken U(1) symmetry at $D < D_c$. We show that beyond mean field the phase diagram is very different and includes an intermediate, partially ordered chiral liquid phase. Specifically, we find that inside the paramagnetic phase the Ising ($J_z$) component of the Heisenberg exchange binds magnons into a two-particle bound state with zero total momentum and spin. This bound state condenses at $D > D_c$, before single-particle excitations become unstable, and gives rise to a chiral liquid phase, which spontaneously breaks spatial inversion symmetry, but leaves the spin-rotational U(1) and time-reversal symmetries intact. This chiral liquid phase is characterized by a finite vector chirality without long range dipolar magnetic order. In our analytical treatment, the chiral phase appears for arbitrarily small $J_z$ because the magnon-magnon attraction becomes singular near the single-magnon condensation transition. This phase exists in a finite range of $D$ and transforms into the magnetically ordered state at some $D<D_c$. We corroborate our analytic treatment with numerical density matrix renormalization group calculations.

cond-mat.str-el↗

arXiv2017

Toward a Galactic Distribution of Planets. I. Methodology & Planet Sensitivities of the 2015 High-Cadence Spitzer Microlens Sample

We analyze an ensemble of microlensing events from the 2015 Spitzer microlensing campaign, all of which were densely monitored by ground-based high-cadence survey teams. The simultaneous observations from Spitzer and the ground yield measurements of the microlensing parallax vector $π_{\rm E}$, from which compact constraints on the microlens properties are derived, including $\lesssim$25\% uncertainties on the lens mass and distance. With the current sample, we demonstrate that the majority of microlenses are indeed in the mass range of M dwarfs. The planet sensitivities of all 41 events in the sample are calculated, from which we provide constraints on the planet distribution function. In particular, assuming a planet distribution function that is uniform in $\log{q}$, where $q$ is the planet-to-star mass ratio, we find a $95\%$ upper limit on the fraction of stars that host typical microlensing planets of 49\%, which is consistent with previous studies. Based on this planet-free sample, we develop the methodology to statistically study the Galactic distribution of planets using microlensing parallax measurements. Under the assumption that the planet distributions are the same in the bulge as in the disk, we predict that $\sim$1/3 of all planet detections from the microlensing campaigns with Spitzer should be in the bulge. This prediction will be tested with a much larger sample, and deviations from it can be used to constrain the abundance of planets in the bulge relative to the disk.

astro-ph.EP↗

arXiv2017

Depth dependent decoherence caused by surface and external spins for NV centers in diamond

By efficient nanoscale plasma etching, the nitrogen-vacancy (NV) centers in diamond were brought to the sample surface step by step successfully. At each depth, we used the relative ratios of spin coherence times before and after applying external spins on the surface to present the decoherence, and investigated the relationships between depth and ratios. The values of relative ratios declined and then rised with the decreasing depth, which was attributed to the decoherence influenced by external spins, surface spins, discrete surface spin effects and electric field noise. Moreover, our work revealed a characteristic depth at which the NV center would experience relatively the strongest decoherence caused by external spins in consideration of inevitable surface spins. And the characteristic depth was found depending on the adjacent environments of NV centers and the density of surface spins.

cond-mat.mes-hall↗

arXiv2017

An Isolated Microlens Observed from K2, Spitzer and Earth

We present the result of microlensing event MOA-2016-BLG-290, which received observations from the two-wheel Kepler (K2), Spitzer, as well as ground-based observatories. A joint analysis of data from K2 and the ground leads to two degenerate solutions of the lens mass and distance. This degeneracy is effectively broken once the (partial) Spitzer light curve is included. Altogether, the lens is found to be an extremely low-mass star located in the Galactic bulge. MOA-2016-BLG-290 is the first microlensing event for which we have signals from three well-separated ($\sim1$ AU) locations. It demonstrates the power of two-satellite microlensing experiment in reducing the ambiguity of lens properties, as pointed out independently by S. Refsdal and A. Gould several decades ago.

astro-ph.EP↗

arXiv2017

Extracting Microlensing Signals from K2 Campaign 9

The reduction of the \emph{K2}'s Campaign 9 (K2C9) microlensing data is challenging mostly because of the very crowded field and the unstable pointing of the spacecraft. In this work, we present the first method that can extract microlensing signals from this K2C9 data product. The raw light curves and the astrometric solutions are first derived, using the techniques from Soares-Furtado et al. and Huang et al. for \emph{K2} dense field photometry. We then minimize and remove the systematic effect by performing simultaneous modeling with the microlensing signal. We also derive precise $(K_p-I)$ vs. $(V-I)$ color-color relations that can predict the microlensing source flux in the \emph{Kepler} bandpass. By implementing the color-color relation in the light curve modeling, we show that the microlensing parameters can be better constrained. In the end, we use two example microlensing events, OGLE-2016-BLG-0980 and OGLE-2016-BLG-0940, to test our method.

astro-ph.IM↗

arXiv2017

Robust Secure Transmission of Using Main-Lobe-Integration Based Leakage Beaforming in Directional Modulation MU-MIMO Systems

In the paper, we make an investigation of robust beamforming for secure directional modulation in the multi-user multiple-input and multiple output (MU-MIMO) systems in the presence of direction angle measurement errors. When statistical knowledge of direction angle measurement errors is unavailable, a novel robust beamforming scheme of combining main-lobe-integration (MLI) and leakage is proposed to simultaneously transmit multiple different independent parallel confidential message streams to the associated multiple distinct desired users. The proposed scheme includes two steps: designing the beamforming vectors of the useful confidential messages and constructing artificial noise (AN) projection matrix. Here, in its first step, the beamforming vectors for the useful confidential messages of desired user k are given by minimizing the useful signal power leakage from main-lobe of desired user k to the sum of main-lobes of the remaining desired directions plus main-lobes of all eavesdropper directions. In its second step, the AN projection matrix is constructed by simultaneously maximizing the AN power leakage to all eavesdropper directions such that all eavesdroppers are disrupted seriously, where AN is viewed by the transmitter as a useful signal for eavedroppers. Due to independent beamforming vectors for different desired users, a more secure transmission is achieved. Compared with conventional non-robust methods, the proposed method can provide a significant improvement in bit error rate along the desired directions and secrecy-sum-rate towards multiple desired users without needing statistical property or distribution of angle measurement errors.

cs.IT↗

arXiv2017

Ground-based parallax confirmed by Spitzer: binary microlensing event MOA-2015-BLG-020

We present the analysis of the binary gravitational microlensing event MOA-2015-BLG-020. The event has a fairly long timescale (about 63 days) and thus the light curve deviates significantly from the lensing model that is based on the rectilinear lens-source relative motion. This enables us to measure the microlensing parallax through the annual parallax effect. The microlensing parallax parameters constrained by the ground-based data are confirmed by the Spitzer observations through the satellite parallax method. By additionally measuring the angular Einstein radius from the analysis of the resolved caustic crossing, the physical parameters of the lens are determined. It is found that the binary lens is composed of two dwarf stars with masses $M_1 = 0.606 \pm 0.028M_\odot$ and $M_2 = 0.125 \pm 0.006M_\odot$ in the Galactic disk. Assuming the source star is at the same distance as the bulge red clump stars, we find the lens is at a distance $D_L = 2.44 \pm 0.10 kpc$. In the end, we provide a summary and short discussion of all published microlensing events in which the annual parallax effect is confirmed by other independent observations.

astro-ph.SR↗

arXiv2017

Scientific Data Interpolation with Low Dimensional Manifold Model

We propose to apply a low dimensional manifold model to scientific data interpolation from regular and irregular samplings with a significant amount of missing information. The low dimensionality of the patch manifold for general scientific data sets has been used as a regularizer in a variational formulation. The problem is solved via alternating minimization with respect to the manifold and the data set, and the Laplace-Beltrami operator in the Euler-Lagrange equation is discretized using the weighted graph Laplacian. Various scientific data sets from different fields of study are used to illustrate the performance of the proposed algorithm on data compression and interpolation from both regular and irregular samplings.

math.NA↗

arXiv2017

The Strong Cell-based Hydrogen Peroxide Generation Triggered by Cold Atmospheric Plasma

Hydrogen peroxide (H2O2) is an important signaling molecule in cancer cells. However, the significant secretion of H2O2 by cancer cells have been rarely observed. Cold atmospheric plasma (CAP) is a near room temperature ionized gas composed of neutral particles, charged particles, reactive species, and electrons. Here, we first demonstrated that breast cancer cells and pancreatic adenocarcinoma cells generated micromolar level H2O2 during just 1 min of direct CAP treatment on these cells. The cell-based H2O2 generation is affected by the medium volume, the cell confluence, as well as the discharge voltage. The application of cold atmospheric plasma (CAP) in the cancer treatment has been intensively investigated over the past decade. Several cellular responses to the CAP treatment have been observed including the consumption of the CAP-originated reactive species, the rise of intracellular reactive oxygen species, the damage on DNA and mitochondria, as well as the activation of apoptotic events. This is a new previously unknown cellular response to CAP, which provides a new prospective to understand the interaction between CAP and cells.

q-bio.CB↗