arXiv subjects

Hao Zhang

Publications and source records attributed to Hao Zhang.

At least 1,171 records · Page 65Linked to original sources

arXiv2019

A Seft-adaptive Multicellular GEP Algorithm Based On Fuzzy Control For Function Optimization

To improve the global optimization ability of traditional GEP algorithm, a Multicellular gene expression programming algorithm based on fuzzy control (Multicellular GEP Algorithm Based On Fuzzy Control, MGEP-FC) is proposed. The MGEP-FC algorithm describes the size of cross rate, mutation rate and real number mutation rate by constructing fuzzy membership function. According to the concentration and dispersion of individual fitness values in population, the crossover rate, mutation rate and real number set mutation rate of genetic operation are dynamically adjusted. In order to make the diversity of the population continue in the iterative process, a new genetic operation scheme is designed, which combines the new individuals with the parent population to build a temporary population, and the diversity of the temporary and subpopulation are optimized. The results of 12 Benchmark optimization experiments show that the MGEP-FC algorithm has been greatly improved in stability, global convergence and optimization speed.

cs.NE↗

arXiv2019

A Hybrid Precipitation Prediction Method based on Multicellular Gene Expression Programming

Prompt and accurate precipitation forecast is very important for development management of regional water resource, flood disaster prevention and people's daily activity and production plan; however, non-linear and nonstationary characteristics of precipitation data and noise seriously affect forecast accuracy. This paper combines multicellular gene expression programming with more powerful function mining ability and wavelet analysis with more powerful denoising and extracting data fine feature capability for precipitation forecast modeling, proposing to estimate meteorological precipitation with WTGEPRP algorithm. Comparative result for simulation experiment with actual precipitation data in Zhengzhou, Nanning and Melbourne in Australia indicated that: fitting and forecasting performance of WTGEPRP algorithm is better than the algorithm Multicellular Gene Expression Programming-based Hybrid Model for Precipitation Prediction Coupled with EMD, Supporting Vector Regression, BP Neural Network, Multicellular Gene Expression Programming and Gene Expression Programming, and has good application prospect.

cs.NE↗

arXiv2019

Biomechanics of Collective Cell Migration in Cancer Progression -- Experimental and Computational Methods

Cell migration is essential for regulating many biological processes in physiological or pathological conditions, including embryonic development and cancer invasion. In vitro and in silico studies suggest that collective cell migration is associated with some biomechanical particularities, such as restructuring of extracellular matrix, stress and force distribution profiles, and reorganization of cytoskeleton. Therefore, the phenomenon could be understood by an in-depth study of cells' behavior determinants, including but not limited to mechanical cues from the environment and from fellow travelers. This review article aims to cover the recent development of experimental and computational methods for studying the biomechanics of collective cell migration during cancer progression and invasion. We also summarized the tested hypotheses regarding the mechanism underlying collective cell migration enabled by these methods. Together, the paper enables a broad overview on the methods and tools currently available to unravel the biophysical mechanisms pertinent to cell collective migration, as well as providing perspectives on future development towards eventually deciphering the key mechanisms behind the most lethal feature of cancer.

q-bio.CB↗

arXiv2019

Predicting Dirac semimetals based on Sodium Ternary Compounds

Predicting a new Dirac semimetal (DSM), as well as other topological materials, is quite challenging, since the relationship between crystal structure, composing atoms and the band topology is complex and elusive. Here, we demonstrate an approach to design DSMs via exploring the chemical degree of freedom. Based on the understanding of the well-known DSM Na$_3$Bi, three compounds in one family, namely Na$_2$MgSn, Na$_2$MgPb and Na$_2$CdSn, have been exactly located. Further hybrid-functional calculations with improved estimation of band inversion show that two of them, Na$_2$MgPb and Na$_2$CdSn, have band topology of DSMs. The nontrivial surface states with Fermi arcs on the (010) and (100) side surfaces are shown to connect the projection of bulk Dirac nodes. Most importantly, the candidate compounds are dynamically stable and have been experimentally synthesized. The ideas in this work would stimulate more designs on locating topological materials based on the understanding of existing ones.

cond-mat.mtrl-sci↗

arXiv2019

High thermoelectric efficiency in monolayer PbI$_2$ from 300 K to 900 K

By using a first-principles approach, monolayer PbI$_2$ is found to have great potential in thermoelectric applications. The linear Boltzmann transport equation is applied to obtain the perturbation to the electron distribution by different scattering mechanisms. The mobility is mainly limited by the deformation-potential interaction with long-wavelength acoustic vibrations at low carrier concentrations. At high concentrations, ionized impurity scattering becomes stronger. The electrical conductivity and Seebeck coefficient are calculated accurately over various ranges of temperature and carrier concentration. The lattice thermal conductivity of PbI$_2$, 0.065 W/mK at 300 K, is the lowest among other 2D thermoelectric materials. Such ultralow thermal conductivity is attributed to large atomic mass, weak interatomic bonding, strong anharmonicity, and localized vibrations in which the vast majority of heat is trapped. These electrical and phonon transport properties enable high thermoelectric figure of merit over 1 for both p-type and n-type doping from 300 K to 900 K. A maximum $zT$ of 4.9 is achieved at 900 K with an electron concentration of 1.9$\times$10$^{12}$ cm$^{-2}$. Our work shows exceptionally good thermoelectric energy conversion efficiency in monolayer PbI$_2$, which can be integrated to the existing photovoltaic devices.

cond-mat.mtrl-sci↗

arXiv2019

Quantum superhet based on microwave-dressed Rydberg atoms

The highly sensitive, phase- and frequency-resolved detection of microwave electric fields is of central importance for diverse fields ranging from astronomy, remote sensing, communication and microwave quantum technology. However, present quantum sensing of microwave electric fields primarily relies on atom-based electrometers only enabling amplitude measurement. Moreover, the best sensitivity of atom-based electrometers is limited by photon shot noise to few $μ$Vcm$^{-1}$Hz$^{-1/2}$: While going beyond is in principle possible by using squeezed light or Schrödinger-cat state, the former is very challenging for atomic experiments while the latter is feasible in all but very small atomic systems. Here we report a novel microwave electric field quantum sensor termed as quantum superhet, which, for the first time, enables experimental measurement of phase and frequency, and makes a sensitivity few tens of nVcm$^{-1}$Hz$^{-1/2}$ readily accessible for current experiments. This sensor is based on microwave-dressed Rydberg atoms and tailored optical spectrum, with very favorable scalings on sensitivity gains. We can experimentally achieve a sensitivity of $55$ nVcm$^{-1}$Hz$^{-1/2}$, with the minimum detectable field being three orders of magnitude smaller than existing quantum electrometers. We also measure phase and frequency, being able to reach a frequency accuracy of few tens of $μ$Hz for microwave field of just few tens of nVcm$^{-1}$. Our technique can be also applied to sense electric fields at terahertz or radio frequency. This work is a first step towards realizing the long sought-after electromagnetic-wave quantum sensors with quantum projection noise limited sensitivity, promising broad applications such as in radio telescope, terahertz communication and quantum control.

physics.atom-ph↗

arXiv2019

Controllable SH wave radiation patterns generated by finite-size face-shear piezoelectric transducers for structural health monitoring

The fundamental shear horizontal (SH0) wave in plate-like structures is of practical importance in structural health monitoring due to its non-dispersive characteristics. However, compared with Lamb wave, SH0 wave is less used in practice since it is difficult to be excited by using conventional piezoelectric transducers. Recently, face-shear piezoelectric mode has been realized in PZT ceramics and several face-shear piezoelectric transducers have been developed to excite pure SH0 wave in plates. However, when controllable radiation pattern of SH0 wave is required, it is found that no mathematical basis is available for designers to optimize the transducer's parameters. In this work, based on the reciprocal theorem of elastodynamics and Huygens' principle, a theoretical model is proposed to describe the SH0 wave field generated by arbitrary-shaped face-shear transducers bonded on an infinite isotropic plate. It is proved that the wave field generated by a face-shear transducer is equivalent to that generated by uniform in-plane tractions along its perimeter. Then the proposed model is applied to two specific cases of a single square face-shear (d24) PZT wafer and a bidirectional SH0 wave piezoelectric transducer(BSH-PT).Explicit analytical expressions for both cases are successfully obtained. The accuracy of the obtained analytical model is verified by both experiments and FEM simulations. Furthermore, some general guidelines based on the analytical model are presented for optimization of the transducer's dimensions. Finally, based on the guidelines, a new bidirectional SH0 wave piezoelectric transducer is proposed with very small beam divergence.

physics.app-ph↗

arXiv2019

Superionic UO2: A Model Anharmonic Crystalline Material

Crystalline materials at elevated temperatures and pressures can exhibit properties more reminiscent of simple liquids than ideal crystalline materials. Superionic crystalline materials having a liquid-like conductivity σ are particularly interesting for battery, fuel cell, and other energy applications, and we study UO2 as a prototypical superionic material since this material is widely studied given its commercial importance as reactor fuel. Using molecular dynamics, we first investigate basic thermodynamic and structural properties. We then quantify structural relaxation, dynamic heterogeneity, and average ions mobility. We find that the non-Arrhenius diffusion and structural relaxation time of this prototypical superionic material can be quantitatively described in terms of a generalized activated transport model ('string model') in which the activation energy varies in direct proportion to the average string length. Our transport data can also be described equally well by an Adam-Gibbs model in which the excess entropy density of the crystalline material is estimated from specific heat and thermal expansion data, consistent with the average scale of string-like collective motion scaling inversely with the excess entropy of the crystal. Strong differences in the temperature dependence of the interfacial mobility from non-ionic materials are observed, and we suggest that this difference is due to the relatively high cohesive interaction of ionic materials. In summary, the study of superionic UO2 provides insight into the role of cooperative motion in enhancing ion mobility in ionic materials and offers design principles for the development of new superionic materials for use in diverse energy applications.

cond-mat.mtrl-sci↗

arXiv2019

Production and hadronic decays of Higgs bosons in heavy ion collisions

We examine Higgs boson production and decay in heavy-ion collisions at the LHC and future colliders. Owing to the long lifetime of the Higgs boson, its hadronic decays may experience little or no screening from the hot and dense quark-gluon plasma whereas jets from hard scattering processes and from decays of the electro-weak gauge bosons and the top-quark suffer significant energy loss. This distinction can lead to enhanced signal to background ratios in hadronic decay channels and thus, for example, provide alternative ways to probe the Yukawa coupling of the Higgs boson to the bottom quark and its lifetime.

hep-ph↗

arXiv2019

Spin Transport in a Mott Insulator of Ultracold Fermions

Strongly correlated materials are expected to feature unconventional transport properties, such that charge, spin, and heat conduction are potentially independent probes of the dynamics. In contrast to charge transport, the measurement of spin transport in such materials is highly challenging. We observed spin conduction and diffusion in a system of ultracold fermionic atoms that realizes the half-filled Fermi-Hubbard model. For strong interactions, spin diffusion is driven by super-exchange and doublon-hole-assisted tunneling, and strongly violates the quantum limit of charge diffusion. The technique developed in this work can be extended to finite doping, which can shed light on the complex interplay between spin and charge in the Hubbard model.

cond-mat.quant-gas↗

arXiv2019

Chemical network reduction in protoplanetary disks

Protoplanetary disks (PPDs) are characterized by different kinds of gas dynamics and chemistry, which are coupled via ionization, heating and cooling processes, as well as advective and turbulent transport. However, directly coupling gas dynamics with time-dependent chemistry is prohibitively computationally expensive when using comprehensive chemical reaction networks. In this paper, we evaluate the utility of a species-based network reduction method in different disk environments to produce small chemical networks that reproduce the abundances of major species found in large gas-phase chemistry networks. We find that the method works very well in disk midplane and surfaces regions, where approximately 20-30 gas phase species, connected by $\sim$50-60 gas phase reactions, are sufficient to reproduce the targeted ionization fraction and chemical abundances. Most species of the reduced networks, including major carriers of oxygen, carbon and nitrogen, also have similar abundances in the reduced and complete network models. Our results may serve as an initial effort for future hydrodynamic/magnetohydrodynamic simulations of PPDs incorporating time-dependent chemistry in appropriate regions. Accurately modeling the abundances of major species at intermediate disk heights, however, will require much more extended network incorporating gas-grain chemistry and are left for future studies.

astro-ph.EP↗

arXiv2019

High-accuracy mass, spin, and recoil predictions of generic black-hole merger remnants

We present accurate fits for the remnant properties of generically precessing binary black holes, trained on large banks of numerical-relativity simulations. We use Gaussian process regression to interpolate the remnant mass, spin, and recoil velocity in the 7-dimensional parameter space of precessing black-hole binaries with mass ratios $q\leq2$, and spin magnitudes $χ_1,χ_2\leq0.8$. For precessing systems, our errors in estimating the remnant mass, spin magnitude, and kick magnitude are lower than those of existing fitting formulae by at least an order of magnitude (improvement is also reported in the extrapolated region at high mass ratios and spins). In addition, we also model the remnant spin and kick directions. Being trained directly on precessing simulations, our fits are free from ambiguities regarding the initial frequency at which precessing quantities are defined. We also construct a model for remnant properties of aligned-spin systems with mass ratios $q\leq8$, and spin magnitudes $χ_1,χ_2\leq0.8$. As a byproduct, we also provide error estimates for all fitted quantities, which can be consistently incorporated into current and future gravitational-wave parameter-estimation analyses. Our model(s) are made publicly available through a fast and easy-to-use Python module called surfinBH.

gr-qc↗

arXiv2019

Mining Deep And-Or Object Structures via Cost-Sensitive Question-Answer-Based Active Annotations

This paper presents a cost-sensitive active Question-Answering (QA) framework for learning a nine-layer And-Or graph (AOG) from web images. The AOG explicitly represents object categories, poses/viewpoints, parts, and detailed structures within the parts in a compositional hierarchy. The QA framework is designed to minimize an overall risk, which trades off the loss and query costs. The loss is defined for nodes in all layers of the AOG, including the generative loss (measuring the likelihood of the images) and the discriminative loss (measuring the fitness to human answers). The cost comprises both the human labor of answering questions and the computational cost of model learning. The cost-sensitive QA framework iteratively selects different storylines of questions to update different nodes in the AOG. Experiments showed that our method required much less human supervision (e.g., labeling parts on 3--10 training objects for each category) and achieved better performance than baseline methods.

cs.CV↗

arXiv2019

An arbitrary two-particle high-dimensional Bell state measurement by auxiliary entanglement

Bell state measurement (BSM) plays a vital role in quantum information. There are many researches on BSM of qubit Bell state, however, there is no definite solution of how to realize high-dimensional Bell state measurement (HDBSM). In this paper, We present a scheme for realizing arbitrary high-dimensional two-particle Bell state measurement by auxiliary entanglement. In our scheme, the maximal entangled states with different degrees of freedom (DOF) are used as auxiliary states, and then the HDBSM can be achieved by implementing Bell measurement with different degrees of freedom. We present the detailed HDBSM scheme for three and four-dimensional Bell state, and the general formula for arbitrary high-dimensional Bell state. Furthermore, We have designed the experimental scheme for three-dimensional BSM, and given the method of arbitrary dimension. This method can promote the high-dimensional quantum information task.

quant-ph↗

arXiv2019

Deep Poisson gamma dynamical systems

We develop deep Poisson-gamma dynamical systems (DPGDS) to model sequentially observed multivariate count data, improving previously proposed models by not only mining deep hierarchical latent structure from the data, but also capturing both first-order and long-range temporal dependencies. Using sophisticated but simple-to-implement data augmentation techniques, we derived closed-form Gibbs sampling update equations by first backward and upward propagating auxiliary latent counts, and then forward and downward sampling latent variables. Moreover, we develop stochastic gradient MCMC inference that is scalable to very long multivariate count time series. Experiments on both synthetic and a variety of real-world data demonstrate that the proposed model not only has excellent predictive performance, but also provides highly interpretable multilayer latent structure to represent hierarchical and temporal information propagation.

stat.ML↗

arXiv2018

Nearly-tight bounds on linear regions of piecewise linear neural networks

The developments of deep neural networks (DNN) in recent years have ushered a brand new era of artificial intelligence. DNNs are proved to be excellent in solving very complex problems, e.g., visual recognition and text understanding, to the extent of competing with or even surpassing people. Despite inspiring and encouraging success of DNNs, thorough theoretical analyses still lack to unravel the mystery of their magics. The design of DNN structure is dominated by empirical results in terms of network depth, number of neurons and activations. A few of remarkable works published recently in an attempt to interpret DNNs have established the first glimpses of their internal mechanisms. Nevertheless, research on exploring how DNNs operate is still at the initial stage with plenty of room for refinement. In this paper, we extend precedent research on neural networks with piecewise linear activations (PLNN) concerning linear regions bounds. We present (i) the exact maximal number of linear regions for single layer PLNNs; (ii) a upper bound for multi-layer PLNNs; and (iii) a tighter upper bound for the maximal number of liner regions on rectifier networks. The derived bounds also indirectly explain why deep models are more powerful than shallow counterparts, and how non-linearity of activation functions impacts on expressiveness of networks.

cs.LG↗

arXiv2018

One-dimensional van der Waals heterostructures

Property by design is one appealing idea in material synthesis but hard to achieve in practice. A recent successful example is the demonstration of van der Waals (vdW) heterostructures,1-3 in which atomic layers are stacked on each other and different ingredients can be combined beyond symmetry and lattice matching. This concept, usually described as a nanoscale Lego blocks, allows to build sophisticated structures layer by layer. However, this concept has been so far limited in two dimensional (2D) materials. Here we show a class of new material where different layers are coaxially (instead of planarly) stacked. As the structure is in one dimensional (1D) form, we name it "1D vdW heterostructures". We demonstrate a 5 nm diameter nanotube consisting of three different materials: an inner conductive carbon nanotube (CNT), a middle insulating hexagonal boron nitride nanotube (BNNT) and an outside semiconducting MoS2 nanotube. As the technique is highly applicable to other materials in the current 2D libraries,4-6 we anticipate our strategy to be a starting point for discovering a class of new semiconducting nanotube materials. A plethora of function-designable 1D heterostructures will appear after the combination of CNTs, BNNTs and semiconducting nanotubes.

cond-mat.mtrl-sci↗

arXiv2018

Estimating hourly population distribution change at high spatiotemporal resolution in urban areas using geo-tagged tweets, land use data, and dasymetric maps

This paper introduces a spatiotemporal analysis framework for estimating hourly changing population distribution in urban areas using geo-tagged tweets (the messages containing users' physical locations), land use data, and dasymetric maps. We collected geo-tagged social media (tweets) within the County of San Diego during one year (2015) by using Twitter's Streaming Application Programming Interfaces (APIs). A semi-manual Twitter content verification procedure for data cleaning was applied first to separate tweets created by humans and non-human users (bots). The next step is to calculate the number of unique Twitter users every hour with the two different geographical units: (1) census blocks, and (2) 1km by 1km resolution grids of LandScan. The final step is to estimate actual dynamic population by transforming the numbers of unique Twitter users in each census block or grid into estimated population densities with spatial and temporal variation factors. A temporal factor was based on hourly frequency changes of unique Twitter users within San Diego County, CA. A spatial factor was estimated by using the dasymetric method with land use maps and 2010 census data. Several comparison maps were created to visualize the spatiotemporal pattern changes of dynamic population distribution.

physics.soc-ph↗