arXiv subjects

Chen Zhang

Publications and source records attributed to Chen Zhang.

At least 577 records · Page 32Linked to original sources

arXiv2020

Probing up-down quark matter via gravitational waves

Recently, it was shown that quark matter with only $u$ and $d$ quarks ($ud$QM) can be the ground state of matter for baryon numbers $A>A_\textrm{min}$ with $A_{\rm min}\gtrsim 300$. In this paper, we explore $ud$ quark stars ($ud$QSs) that are composed of $ud$QM, in the context of the two-families scenario in which $ud$QSs and hadronic stars (HSs) can coexist. Distinct signatures are discussed compared to the conventional study regarding strange quark stars (SQSs). We show that the requirements of $A_{\rm min}\gtrsim 300$ and the most massive compact star observed being a $ud$QS together may put stringent constraints on the allowed parameter space of $ud$QSs. Then, we study the related gravitational-wave probe of the tidal deformability in binary star mergers, including the $ud$QS-$ud$QS and $ud$QS-HS cases. The obtained values of the tidal deformability at 1.4 solar masses and the average tidal deformability are all in good compatibility with the experimental constraints of GW170817. This study points to a new possible interpretation of the GW170817 binary merger event, where $ud$QS may be at least one component of the binary system detected.

astro-ph.HE↗

arXiv2020

Analytical effective-one-body formalism for extreme-mass-ratio inspirals: eccentric orbits

Extreme-mass-ratio-inspiral (EMRI) is one of the most important sources for the future space-borne gravitational wave detectors. In such kind of systems, the compact objects usually orbit around the central supermassive black holes with complicated trajectories. Usually, the trajectory is approximated as geodesic of a test-particle in Kerr space-time, and the orbital evolution are simulated with the help of adiabatic approximation. However, this omits the influence of the compact object on the back ground. In the present paper, employing effective-one-body formalism, we analytically calculate out the trajectories of a compact object around a massive Kerr black hole in equatorial-eccentric orbit, and express the fundamental orbital frequencies in explicit forms. Our formalism include the first-order corrections of mass-ratio in the conservative orbital motion. Furthermore, we insert the mass-ratio related terms in the first post-Newtonian energy fluxes. By calculating the gravitational waves from the Teukolsky equations, we quantitatively reveal the influence of the mass of the compact object on the data analysis. We find that the shrinking of geodesic motion by taking the small objects as test particles may be not appropriate for the detection of EMRIs.

gr-qc↗

arXiv2020

Testing dispersion of gravitational waves from eccentric extreme-mass-ratio inspirals

In general relativity, there is no dispersion in gravitational waves, while some modified gravity theories predict dispersion phenomena in the propagation of gravitational waves. In this paper, we demonstrate that this dispersion will induce an observable deviation of waveforms if the orbits have large eccentricities. The mechanism is that the waveform modes with different frequencies will be emitted at the same time due to the existence of eccentricity. During the propagation, because of the dispersion, the arrival time of different modes will be different, then produce the deviation and dephasing of waveforms compared with general relativity. This kind of dispersion phenomena related with extreme-mass-ratio inspirals could be observed by space-borne detectors, and the constraint on the graviton mass could be improved . Moreover, we find that the dispersion effect may also be constrained by ground detectors better than the current result if a highly eccentric intermediate-mass-ratio inspirals be observed.

gr-qc↗

arXiv2020

Probabilistic Residual Learning for Aleatoric Uncertainty in Image Restoration

Aleatoric uncertainty is an intrinsic property of ill-posed inverse and imaging problems. Its quantification is vital for assessing the reliability of relevant point estimates. In this paper, we propose an efficient framework for quantifying aleatoric uncertainty for deep residual learning and showcase its significant potential on image restoration. In the framework, we divide the conditional probability modeling for the residual variable into a deterministic homo-dimensional level, a stochastic low-dimensional level and a merging level. The low-dimensionality is especially suitable for sparse correlation between image pixels, enables efficient sampling for high dimensional problems and acts as a regularizer for the distribution. Preliminary numerical experiments show that the proposed method can give not only state-of-the-art point estimates of image restoration but also useful associated uncertainty information.

eess.IV↗

arXiv2020

Deeper Insights into Weight Sharing in Neural Architecture Search

With the success of deep neural networks, Neural Architecture Search (NAS) as a way of automatic model design has attracted wide attention. As training every child model from scratch is very time-consuming, recent works leverage weight-sharing to speed up the model evaluation procedure. These approaches greatly reduce computation by maintaining a single copy of weights on the super-net and share the weights among every child model. However, weight-sharing has no theoretical guarantee and its impact has not been well studied before. In this paper, we conduct comprehensive experiments to reveal the impact of weight-sharing: (1) The best-performing models from different runs or even from consecutive epochs within the same run have significant variance; (2) Even with high variance, we can extract valuable information from training the super-net with shared weights; (3) The interference between child models is a main factor that induces high variance; (4) Properly reducing the degree of weight sharing could effectively reduce variance and improve performance.

cs.LG↗

arXiv2019

Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction

Low-rank tensor decomposition and completion have attracted significant interest from academia given the ubiquity of tensor data. However, the low-rank structure is a global property, which will not be fulfilled when the data presents complex and weak dependencies given specific graph structures. One particular application that motivates this study is the spatiotemporal data analysis. As shown in the preliminary study, weakly dependencies can worsen the low-rank tensor completion performance. In this paper, we propose a novel low-rank CANDECOMP / PARAFAC (CP) tensor decomposition and completion framework by introducing the $L_{1}$-norm penalty and Graph Laplacian penalty to model the weakly dependency on graph. We further propose an efficient optimization algorithm based on the Block Coordinate Descent for efficient estimation. A case study based on the metro passenger flow data in Hong Kong is conducted to demonstrate improved performance over the regular tensor completion methods.

cs.LG↗

arXiv2019

Composite Higgs Meets Planck Scale: Partial Compositeness from Partial Unification

Providing an Ultra-Violet completion valid up to the Planck scale is of paramount importance to validate the composite Higgs paradigm, at par with supersymmetry. We propose the first complete and feasible framework, based on partial unification of a confining hypercolor gauge group, where couplings of the standard model fermions are mediated by both gauge and scalar bosons. We demonstrate our approach by providing an explicit model based on a Techni-Pati-Salam unification, $SU(8)_{\rm PS}\times SU(2)_L\times SU(2)_R$, able to generate masses for all fermion generations, including neutrinos, via partial compositeness. We predict an $Sp(4)$ hypercolor group, and lattice studies will be crucial to validate the model.

hep-ph↗

arXiv2019

Angle-resolved photoemission spectroscopy study of crystal electric field in heavy fermion compound CePt2In7

The three-dimensional electronic structure and Ce 4f electrons of the heavy fermion superconductor CePt2In7 is investigated. Angle-resolved photoemission spectroscopy using variable photon energy establishes the existence of quasi-two and three dimensional Fermi surface topologies. Temperature-dependent 4d-4f on-resonance photoemission spectroscopies reveal that heavy quasiparticle bands begin to form at a temperature well above the characteristic (coherence) temperature T*. T* emergence may be closely related to crystal electric field splitting, particularly the low-lying heavy band formed by crystal electric field splitting.

cond-mat.str-el↗

arXiv2019

Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks

Due to their inherent capability in semantic alignment of aspects and their context words, attention mechanism and Convolutional Neural Networks (CNNs) are widely applied for aspect-based sentiment classification. However, these models lack a mechanism to account for relevant syntactical constraints and long-range word dependencies, and hence may mistakenly recognize syntactically irrelevant contextual words as clues for judging aspect sentiment. To tackle this problem, we propose to build a Graph Convolutional Network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies. Based on it, a novel aspect-specific sentiment classification framework is raised. Experiments on three benchmarking collections illustrate that our proposed model has comparable effectiveness to a range of state-of-the-art models, and further demonstrate that both syntactical information and long-range word dependencies are properly captured by the graph convolution structure.

cs.CL↗

arXiv2019

Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network

It has been widely accepted that Long Short-Term Memory (LSTM) network, coupled with attention mechanism and memory module, is useful for aspect-level sentiment classification. However, existing approaches largely rely on the modelling of semantic relatedness of an aspect with its context words, while to some extent ignore their syntactic dependencies within sentences. Consequently, this may lead to an undesirable result that the aspect attends on contextual words that are descriptive of other aspects. In this paper, we propose a proximity-weighted convolution network to offer an aspect-specific syntax-aware representation of contexts. In particular, two ways of determining proximity weight are explored, namely position proximity and dependency proximity. The representation is primarily abstracted by a bidirectional LSTM architecture and further enhanced by a proximity-weighted convolution. Experiments conducted on the SemEval 2014 benchmark demonstrate the effectiveness of our proposed approach compared with a range of state-of-the-art models.

cs.CL↗

arXiv2019

Constraining CP-violating electron-gluonic operators

We present an analysis of constraints on two types of CP-odd electron-gluonic operators $(\bar{e}iγ^5 e)G_{μν}^a G^{aμν}$ and $(\bar{e}e)G_{μν}^a \tilde{G}^{aμν}$ from current and future electric dipole moment (EDM) experiments. The recent result from the ACME experiment using ThO molecules is used to derive an impressive lower bound on the effective scale for $(\bar{e}iγ^5 e)G_{μν}^a G^{aμν}$ at 8 TeV, assuming a QCD one-loop factor and no helicity suppression from new physics. One interesting aspect is that $(\bar{e}iγ^5 e)G_{μν}^a G^{aμν}$ contributes to the observable EDM in ThO experiment mainly through CP-odd electron-nucleon interaction rather than direct electron EDM which arises from three-loop running and matching. For $(\bar{e}e)G_{μν}^a \tilde{G}^{aμν}$ the current bound is much weaker and suffers from large uncertainties. We also discuss the QCD running and matching for the CP-odd electron-gluonic operators and give an estimate of the relevant nucleon matrix elements and uncertainties that are needed in the calculation.

hep-ph↗

arXiv2019

A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning

Distributed Denial of Service (DDoS) attack has become one of the most destructive network attacks which can pose a mortal threat to Internet security. Existing detection methods can not effectively detect early attacks. In this paper, we propose a detection method of DDoS attacks based on generalized multiple kernel learning (GMKL) combining with the constructed parameter R. The super-fusion feature value (SFV) and comprehensive degree of feature (CDF) are defined to describe the characteristic of attack flow and normal flow. A method for calculating R based on SFV and CDF is proposed to select the combination of kernel function and regularization paradigm. A DDoS attack detection classifier is generated by using the trained GMKL model with R parameter. The experimental results show that kernel function and regularization parameter selection method based on R parameter reduce the randomness of parameter selection and the error of model detection, and the proposed method can effectively detect DDoS attacks in complex environments with higher detection rate and lower error rate.

cs.CR↗

arXiv2019

Adaptive DDoS attack detection method based on multiple-kernel learning

Distributed denial of service (DDoS) attacks have caused huge economic losses to society. They have become one of the main threats to Internet security. Most of the current detection methods based on a single feature and fixed model parameters cannot effectively detect early DDoS attacks in cloud and big data environment. In this paper, an adaptive DDoS attack detection method (ADADM) based on multiple kernel learning (MKL) is proposed. Based on the burstiness of DDoS attack flow, the distribution of addresses and the interactivity of communication, we define five features to describe the network flow characteristic. Based on the ensemble learning framework, the weight of each dimension is adaptively adjusted by increasing the inter-class mean with a gradient ascent and reducing the intra-class variance with a gradient descent, and the classifier is established to identify an early DDoS attack by training simple multiple kernel learning (SMKL) models with two characteristics including inter-class mean squared difference growth (M-SMKL) and intra-class variance descent (S-SMKL). The sliding window mechanism is used to coordinate the S-SMKL and M-SMKL to detect the early DDoS attack. The experimental results indicate that this method can detect DDoS attacks early and accurately.

cs.CR↗

arXiv2019

Expectation Propagation for Poisson Data

The Poisson distribution arises naturally when dealing with data involving counts, and it has found many applications in inverse problems and imaging. In this work, we develop an approximate Bayesian inference technique based on expectation propagation for approximating the posterior distribution formed from the Poisson likelihood function and a Laplace type prior distribution, e.g., the anisotropic total variation prior. The approach iteratively yields a Gaussian approximation, and at each iteration, it updates the Gaussian approximation to one factor of the posterior distribution by moment matching. We derive explicit update formulas in terms of one-dimensional integrals, and also discuss stable and efficient quadrature rules for evaluating these integrals. The method is showcased on two-dimensional PET images.

math.NA↗

arXiv2019

Lead Halide Perovskite based Dynamic Metasurfaces

Lead halide perovskites (MAPbX3) are known to have high refractive index and controllable bandgap, making them attractive for all-dielectric and tunable metasurfaces. Till now, perovskite metasurfaces have only been used in structural colors. More interesting meta-devices with 2π phase control are still absent. Here we experimentally demonstrate the MAPbX3 perovskite based metasurfaces with a complete control of phase shift in a reflection mode. By utilizing MAPbBr3 cut-wires as meta-atoms on a ground metal film, we find that the MAPbBr3 perovskite metasurface can produce full phase control from 0 to 2π and high reflection efficiency simultaneously. Consequently, high-efficiency polarization conversion, anomalous reflection and meta-hologram have been successfully produced. Most interestingly, the bandgap of MAPbX3 perovskite can be post-synthetically and reversibly tuned via anion exchange, providing a new approach to dynamically control of the all-dielectric meta-devices with novel function such as anomalous reflection and hologram et al.

physics.app-ph↗

arXiv2019

Phenomenology of A Little Higgs Pseudo-Axion

In models where the Higgs is realized as a pseudo-Nambu-Goldstone boson (pNGB) of some global symmetry breaking, there are often remaining pNGBs of some $U(1)$ groups (called `pseudo-axions'), which could lead to smoking gun signatures of such scenarios and provide important clues on the electroweak symmetry breaking mechanism. As a concrete example, we investigate the phenomenology of the pseudo-axion in the anomaly-free Simplest Little Higgs (SLH) model. After clarifying a subtle issue related to the effect of symmetric vector-scalar-scalar (VSS) vertices (e.g. $Z_μ(H\partial^μη+η\partial^μH)$), we show that for natural region in the parameter space, the SLH pseudo-axion is top-philic, decaying almost exclusively to a pair of top quarks. The direct and indirect (i.e. via heavy particle decay) production of such a pseudo-axion at the $14\,\mbox{TeV}$ (HL-)LHC turn out to suffer from either large backgrounds or small rates, making its detection quite challenging. A $pp$ collider with higher energy and luminosity, such as the $27\,\mbox{TeV}$ HE-LHC, or even the $100\,\mbox{TeV}$ FCC-hh or SppC, is therefore motivated to capture the trace of such a pNGB.

hep-ph↗

arXiv2018

Lattice-mismatched semiconductor heterostructures

Semiconductor heterostructure is a critical building block for modern semiconductor devices. However, forming semiconductor heterostructures of lattice-mismatch has been a great challenge for several decades. Epitaxial growth is infeasible to form abrupt heterostructures with large lattice-mismatch while mechanical-thermal bonding results in a high density of interface defects and therefore severely limits device applications. Here we show an ultra-thin oxide-interfaced approach for the successful formation of lattice-mismatched semiconductor heterostructures. Following the depiction of a theory on the role of interface oxide in forming the heterostructures, we describe experimental demonstrations of Ge/Si (diamond lattices), Si/GaAs (zinc blende lattice), GaAs/GaN (hexagon lattice), and Si/GaN heterostructures. Extraordinary electrical performances in terms of ideality factor, current on/off ratio, and reverse breakdown voltage are measured from p-n diodes fabricated from the four types of heterostructures, significantly outperforming diodes derived from other methods. Our demonstrations indicate the versatility of the ultra-thin-oxide-interface approach in forming lattice-mismatched heterostructures, open up a much larger possibility for material combinations for heterostructures, and pave the way toward broader applications in electronic and optoelectronic realms.

physics.app-ph↗

arXiv2018

Robust functional ANOVA model with t-process

Robust estimation approaches are of fundamental importance for statistical modelling. To reduce susceptibility to outliers, we propose a robust estimation procedure with t-process under functional ANOVA model. Besides common mean structure of the studied subjects, their personal characters are also informative, especially for prediction. We develop a prediction method to predict the individual effect. Statistical properties, such as robustness and information consistency, are studied. Numerical studies including simulation and real data examples show that the proposed method performs well.

stat.ME↗