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Hong Jiang

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Published work

32 published item(s)

preprint2026arXiv

The grip of grammar on meaning uncertainty: cross-linguistic evidence, neural correlates, and clinical relevance

Isolated word meanings are inherently uncertain. This uncertainty reduces when they are combined and anchored in context. We propose that grammar compresses meaning uncertainty cross-linguistically, which is reflected in brain and selectively disrupted in disorders. Compression was operationalized as the relative difference between non-contextual surprisal estimated from lexical frequency, and contextual surprisal from grammar-sensitive models. In narratives from 20 languages, contextual surprisal reduced frequency-based surprisal. This reduction closely tracked the surprisal cost of reversing word order, and scaled with richer, non-redundant lexis as organized by more complex but optimal dependency structure. During fMRI, surprisal and its reduction explained BOLD activity for comprehension and production in overlapping but distinct regions. Uncertainty reduction was significantly attenuated in aphasia, dementia, and schizophrenia, but remained intact where primary deficit is not language. These findings position uncertainty reduction via grammar as a foundational concept that illuminates principles, brain basis, and disruptions of language.

preprint2026arXiv

Time-delayed collective dynamics in waveguide QED and bosonic quantum networks

This work introduces a theoretical framework to model the collective dynamics of quantum emitters in highly non-Markovian environments, interacting through the exchange of photons with significant retardations. The formalism consists on a set of coupled delay differential equations for the emitter's polarizations $σ^\pm_i$, supplemented by input-output relations that describe the field mediating the interactions. These equations capture the dynamics of both linear (bosonic) and nonlinear (two-level) emitter arrays. It is exact in some limits$-$e.g., bosonic emitters or generic systems with up to one collective excitation$-$and can be integrated to provide accurate results for larger numbers of photons. These equations support a study of collective spontaneous emission of emitter arrays in open waveguide-QED environments. This study uncovers an effect we term cascaded super- and sub-radiance, characterized by light-cone-limited propagation and increasingly correlated photon emission across distant emitters. The collective nature of this dynamics for two-level systems is evident both in the enhancement of collective emission rates, as well as in a superradiant burst with a faster than linear growth. While these effects should be observable in existing circuit QED devices or slight generalizations thereof, the formalism put forward in this work can be extended to model other systems such as network of quantum emitters or the generation of correlated photon states.

preprint2023arXiv

Diagnostics for plasmon satellites and Hubbard bands in transition metal oxides

Coulomb correlations between the electrons imprint characteristic signatures to the spectral properties of materials. Among others, they are at the origin of a rich phenomenology of satellite features, either stemming from atomic-like multiplets or from interactions with particle-hole excitations or plasmons. While in many cases the latter lie at considerably higher energies than the former, suggesting clear distinction criteria, this picture has recently become blurred by indications that satellites of different types can coexist in the same energy range. It is now generally accepted that the identification of the nature of spectral features is a highly non-trivial task. In this article we propose a general procedure for tracing the origin of satellites of different types within modern ab initio calculations. As an illustration, we analyze the ternary transition metal oxides SrVO$_3$ and SrMoO$_3$, which are drosophila compounds for the coexistence of Hubbard and plasmonic satellites, reconciling previous seemingly contradictory findings in an unexpected manner.

preprint2022arXiv

DFT+$U$ within the framework of linear combination of numerical atomic orbitals

We present a formulation and implementation of the DFT+\textit{U} method within the framework of linear combination of numerical atomic orbitals (NAO). Our implementation not only enables single-point total energy and electronic-structure calculations but also provides access to atomic forces and stresses, hence allowing for full structure relaxations of periodic systems. Furthermore, our implementation allows one to deal with non-collinear spin texture, with the spin-orbit coupling (SOC) effect treated self-consistently. The key aspect behind our implementation is a suitable definition of the correlated subspace when multiple atomic orbitals with the same angular momentum are used, and this is addressed via the "Mulliken charge projector" constructed in terms of the first (most localized) atomic orbital within the $d/f$ angular momentum channel. The important Hubbard $U$ and Hund $J$ parameters can be estimated from a screened Coulomb potential of the Yukawa type, with the screening parameter either chosen semi-empirically or determined from the Thomas-Fermi screening model. Benchmark calculations are performed for four late transition metal monoxide bulk systems, i.e., MnO, FeO, CoO, and NiO, and for the 5$d$-electron compounds IrO$_2$. For the former type of systems, we check the performance of our DFT+$U$ implementation for calculating band gaps, magnetic moments, electronic band structures, as well as forces and stresses; for the latter, the efficacy of our DFT+$U$+SOC implementation is assessed. Systematic comparisons with available experimental results, and especially with the results from other implementation schemes are carried out, which demonstrate the validity of our NAO-based DFT+$U$ formalism and implementation.

preprint2021arXiv

All-electron periodic $G_0W_0$ implementation with numerical atomic orbital basis functions: algorithm and benchmarks

We present an all-electron, periodic {\GnWn} implementation within the numerical atomic orbital (NAO) basis framework. A localized variant of the resolution-of-the-identity (RI) approximation is employed to significantly reduce the computational cost of evaluating and storing the two-electron Coulomb repulsion integrals. We demonstrate that the error arising from localized RI approximation can be reduced to an insignificant level by enhancing the set of auxiliary basis functions, used to expand the products of two single-particle NAOs. An efficient algorithm is introduced to deal with the Coulomb singularity in the Brillouin zone sampling that is suitable for the NAO framework. We perform systematic convergence tests and identify a set of computational parameters, which can serve as the default choice for most practical purposes. Benchmark calculations are carried out for a set of prototypical semiconductors and insulators, and compared to independent reference values obtained from an independent $G_0W_0$ implementation based on linearized augmented plane waves (LAPW) plus high-energy localized orbitals (HLOs) basis set, as well as experimental results. With a moderate (FHI-aims \textit{tier} 2) NAO basis set, our $G_0W_0$ calculations produce band gaps that typically lie in between the standard LAPW and the LAPW+HLO results. Complementing \textit{tier} 2 with highly localized Slater-type orbitals (STOs), we find that the obtained band gaps show an overall convergence towards the LAPW+HLO results. The algorithms and techniques developed in this work pave the way for efficient implementations of correlated methods within the NAO framework.

preprint2021arXiv

C-for-Metal: High Performance SIMD Programming on Intel GPUs

The SIMT execution model is commonly used for general GPU development. CUDA and OpenCL developers write scalar code that is implicitly parallelized by compiler and hardware. On Intel GPUs, however, this abstraction has profound performance implications as the underlying ISA is SIMD and important hardware capabilities cannot be fully utilized. To close this performance gap we introduce C-For-Metal (CM), an explicit SIMD programming framework designed to deliver close-to-the-metal performance on Intel GPUs. The CM programming language and its vector/matrix types provide an intuitive interface to exploit the underlying hardware features, allowing fine-grained register management, SIMD size control and cross-lane data sharing. Experimental results show that CM applications from different domains outperform the best-known SIMT-based OpenCL implementations, achieving up to 2.7x speedup on the latest Intel GPU.

preprint2021arXiv

Phase transition and enhanced hardness of LaB4 under pressure

The crystalline structures of lanthanum tetraboride, LaB4, under pressure are investigated using a genetic algorithm method coupled with first-principles calculations. We find two low-enthalpy phases for LaB4 as the thermodynamic ground state up to 200 GPa: a phase previously observed in experiments (P4/mbm) and a novel orthorhombic phase (Pnma-I). The P4/mbm phase transforms to the Pnma-I phase at around 106 GPa, accompanied by breakage of the B-octahedron. The P4/mbm phase of LaB4 exhibits a high hardness (30.5 GPa) at ambient conditions, which is comparable to that of the hard material B4C. The hardness of the two low-enthalpy phases increases with pressure. The electronic band structures shows that all of the competitive phases (two stable and two metastable phases) are metallic. Phonon calculations shows the P4/mbm and Pnma-I phases are thermodynamically stable in their respective pressure ranges. This elucidation of the phase transition behavior and hardness of LaB4 provides new knowledge on the interesting high-pressure behaviors of the rare-earth metal borides.

preprint2020arXiv

The influence of high-energy local orbitals and electron-phonon interactions on the band gaps and optical spectra of hexagonal boron nitride

We report $ab$ $initio$ band diagram and optical absorption spectra of hexagonal boron nitride ($h$-BN), focusing on unravelling how the completeness of basis set for $GW$ calculations and how electron-phonon interactions (EPIs) impact on them. The completeness of basis set, an issue which was seldom discussed in previous optical spectra calculations of $h$-BN, is found crucial in providing converged quasiparticle band gaps. In the comparison among three different codes, we demonstrate that by including high-energy local orbitals in the all-electron linearized augmented plane waves based $GW$ calculations, the quasiparticle direct and fundamental indirect band gaps are widened by $\sim$0.2 eV, giving values of 6.81 eV and 6.25 eV respectively at the $GW_0$ level. EPIs, on the other hand, reduce them to 6.62 eV and 6.03 eV respectively at 0 K, and 6.60 eV and 5.98 eV respectively at 300 K. With clamped crystal structure, the first peak of the absorption spectrum is at 6.07 eV, originating from the direct exciton contributed by electron transitions around $K$ in the Brillouin zone. After including the EPIs-renormalized quasiparticles in the Bethe-Salpeter equation, the exciton-phonon coupling shifts the first peak to 5.83 eV at 300 K, lower than the experimental value of $\sim$6.00 eV. This accuracy is acceptable to an $ab$ $initio$ description of excited states with no fitting parameter.

preprint2016arXiv

Hubbard interactions in iron-based pnictides and chalcogenides: Slater parametrization, screening channels and frequency dependence

We calculate the strength of the frequency-dependent on-site electronic interactions in the iron pnictides LaFeAsO, BaFe2As2, BaRu2As2, and LiFeAs and the chalcogenide FeSe from first principles within the constrained random phase approximation. We discuss the accuracy of an atomic-like parametrization of the two-index density-density interaction matrices based on the calculation of an optimal set of three independent Slater integrals, assuming that the angular part of the Fe-d localized orbitals can be described within spherical harmonics as for isolated Fe atoms. We show that its quality depends on the ligand-metal bonding character rather than on the dimensionality of the lattice: it is excellent for ionic-like Fe-Se (FeSe) chalcogenides and a more severe approximation for more covalent Fe-As (LaFeAsO, BaFe2As2) pnictides. We furthermore analyze the relative importance of different screening channels, with similar conclusions for the different pnictides but a somewhat different picture for the benchmark oxide SrVO3: the ligand channel does not appear to be dominant in the pnictides, while oxygen screening is the most important process in the oxide. Finally, we analyze the frequency dependence of the interaction. In contrast to simple oxides, in iron pnictides its functional form cannot be simply modeled by a single plasmon, and the actual density of modes enters the construction of an effective Hamiltonian determining the low-energy properties.

preprint2016arXiv

Multi-resolution Compressive Sensing Reconstruction

We consider the problem of reconstructing an image from compressive measurements using a multi-resolution grid. In this context, the reconstructed image is divided into multiple regions, each one with a different resolution. This problem arises in situations where the image to reconstruct contains a certain region of interest (RoI) that is more important than the rest. Through a theoretical analysis and simulation experiments we show that the multi-resolution reconstruction provides a higher quality of the RoI compared to the traditional single-resolution approach.

preprint2015arXiv

Compressive Sensing via Low-Rank Gaussian Mixture Models

We develop a new compressive sensing (CS) inversion algorithm by utilizing the Gaussian mixture model (GMM). While the compressive sensing is performed globally on the entire image as implemented in our lensless camera, a low-rank GMM is imposed on the local image patches. This low-rank GMM is derived via eigenvalue thresholding of the GMM trained on the projection of the measurement data, thus learned {\em in situ}. The GMM and the projection of the measurement data are updated iteratively during the reconstruction. Our GMM algorithm degrades to the piecewise linear estimator (PLE) if each patch is represented by a single Gaussian model. Inspired by this, a low-rank PLE algorithm is also developed for CS inversion, constituting an additional contribution of this paper. Extensive results on both simulation data and real data captured by the lensless camera demonstrate the efficacy of the proposed algorithm. Furthermore, we compare the CS reconstruction results using our algorithm with the JPEG compression. Simulation results demonstrate that when limited bandwidth is available (a small number of measurements), our algorithm can achieve comparable results as JPEG.

preprint2015arXiv

Constrained and Preconditioned Stochastic Gradient Method

We consider stochastic approximations which arise from such applications as data communications and image processing. We demonstrate why constraints are needed in a stochastic approximation and how a constrained approximation can be incorporated into a preconditioning technique to derive the pre-conditioned stochastic gradient method (PSGM). We perform convergence analysis to show that the PSGM converges to the theoretical best approximation under some simple assumptions on the preconditioner and on the independence of samples drawn from a stochastic process. Simulation results are presented to demonstrate the effectiveness of the constrained and precondi-tioned stochastic gradient method.

preprint2015arXiv

Convergence of the Generalized Alternating Projection Algorithm for Compressive Sensing

The convergence of the generalized alternating projection (GAP) algorithm is studied in this paper to solve the compressive sensing problem $\yv = \Amat \xv + \epsilonv$. By assuming that $\Amat\Amat\ts$ is invertible, we prove that GAP converges linearly within a certain range of step-size when the sensing matrix $\Amat$ satisfies restricted isometry property (RIP) condition of $δ_{2K}$, where $K$ is the sparsity of $\xv$. The theoretical analysis is extended to the adaptively iterative thresholding (AIT) algorithms, for which the convergence rate is also derived based on $δ_{2K}$ of the sensing matrix. We further prove that, under the same conditions, the convergence rate of GAP is faster than that of AIT. Extensive simulation results confirm the theoretical assertions.

preprint2015arXiv

Lensless Compressive Imaging

We develop a lensless compressive imaging architecture, which consists of an aperture assembly and a single sensor, without using any lens. An anytime algorithm is proposed to reconstruct images from the compressive measurements; the algorithm produces a sequence of solutions that monotonically converge to the true signal (thus, anytime). The algorithm is developed based on the sparsity of local overlapping patches (in the transformation domain) and state-of-the-art results have been obtained. Experiments on real data demonstrate that encouraging results are obtained by measuring about 10% (of the image pixels) compressive measurements. The reconstruction results of the proposed algorithm are compared with the JPEG compression (based on file sizes) and the reconstructed image quality is close to the JPEG compression, in particular at a high compression rate.

preprint2014arXiv

Dynamical correlations and screened exchange on the experimental bench: spectral properties of the cobalt pnictide BaCo2As2

Understanding the Fermi surface and low-energy excitations of iron or cobalt pnictides is crucial for assessing electronic instabilities such as magnetic or superconducting states. Here, we propose and implement a new approach to compute the low-energy properties of correlated electron materials, taking into account both screened exchange beyond the local density approximation and local dynamical correlations. The scheme allows us to resolve the puzzle of BaCo2As2, for which standard electronic structure techniques predict a ferromagnetic instability not observed in nature.

preprint2014arXiv

Noise Analysis for Lensless Compressive Imaging

We analyze the signal to noise ratio (SNR) in a recently proposed lensless compressive imaging architecture. The architecture consists of a sensor of a single detector element and an aperture assembly of an array of aperture elements, each of which has a programmable transmittance. This lensless compressive imaging architecture can be used in conjunction with compressive sensing to capture images in a compressed form of compressive measurements. In this paper, we perform noise analysis of this lensless compressive imaging architecture and compare it with pinhole aperture imaging and lens aperture imaging. We will show that the SNR in the lensless compressive imaging is independent of the image resolution, while that in either pinhole aperture imaging or lens aperture imaging decreases as the image resolution increases. Consequently, the SNR in the lensless compressive imaging can be much higher if the image resolution is large enough.

preprint2014arXiv

Signal to Noise Ratio in Lensless Compressive Imaging

We analyze the signal to noise ratio (SNR) in a lensless compressive imaging (LCI) architecture. The architecture consists of a sensor of a single detecting element and an aperture assembly of an array of programmable elements. LCI can be used in conjunction with compressive sensing to capture images in a compressed form of compressive measurements. In this paper, we perform SNR analysis of the LCI and compare it with imaging with a pinhole or a lens. We will show that the SNR in the LCI is independent of the image resolution, while the SNR in either pinhole aperture imaging or lens aperture imaging decreases as the image resolution increases. Consequently, the SNR in the LCI is much higher if the image resolution is large enough.

preprint2013arXiv

A Hierarchical Modulation for Upgrading Digital Broadcast Systems

A hierarchical modulation scheme is proposed to upgrade an existing digital broadcast system, such as satellite TV, or satellite radio, by adding more data in its transmission. The hierarchical modulation consists of a basic constellation, which is the same as in the original system, and a secondary constellation, which carries the additional data for the upgraded system. The upgraded system with the hierarchical modulation is backward compatible in the sense that receivers that have been deployed in the original system can continue receiving data in the basic constellation. New receivers can be designed to receive data carried in the secondary constellation, as well as those in the basic constellation. Analysis will be performed to show the tradeoff between bit rate of the data in secondary constellation and the penalty to the performance of receiving the basic constellation.

preprint2013arXiv

A new compressive video sensing framework for mobile broadcast

A new video coding method based on compressive sampling is proposed. In this method, a video is coded using compressive measurements on video cubes. Video reconstruction is performed by minimization of total variation (TV) of the pixelwise DCT coefficients along the temporal direction. A new reconstruction algorithm is developed from TVAL3, an efficient TV minimization algorithm based on the alternating minimization and augmented Lagrangian methods. Video coding with this method is inherently scalable, and has applications in mobile broadcast.

preprint2013arXiv

A Stochastic Conjugate Gradient Method for Approximation of Functions

A stochastic conjugate gradient method for approximation of a function is proposed. The proposed method avoids computing and storing the covariance matrix in the normal equations for the least squares solution. In addition, the method performs the conjugate gradient steps by using an inner product that is based stochastic sampling. Theoretical analysis shows that the method is convergent in probability. The method has applications in such fields as predistortion for the linearization of power amplifiers.

preprint2013arXiv

Adaptive low rank and sparse decomposition of video using compressive sensing

We address the problem of reconstructing and analyzing surveillance videos using compressive sensing. We develop a new method that performs video reconstruction by low rank and sparse decomposition adaptively. Background subtraction becomes part of the reconstruction. In our method, a background model is used in which the background is learned adaptively as the compressive measurements are processed. The adaptive method has low latency, and is more robust than previous methods. We will present experimental results to demonstrate the advantages of the proposed method.

preprint2013arXiv

Digital predistortion for power amplifiers using separable functions

This paper is concerned with digital predistortion for linearization of RF high power amplifiers (HPAs). It has two objectives. First, we establish a theoretical framework for a generic predistorter system, and show that if a postdistorter exists, then it is also a predistorter, and therefore, the predistorter and postdistorter are equivalent. This justifies the indirect learning methods for a large class of HPAs. Secondly, we establish a systematic and general structure for a predistorter that is capable of compensating nonlinearity for a large variety of HPAs. This systematic structure is derived using approximation by separable functions, and avoids selection of predistorters based on the assumption of HPA models traditionally done in the literature.

preprint2013arXiv

Lensless Compressive Sensing Imaging

In this paper, we propose a lensless compressive sensing imaging architecture. The architecture consists of two components, an aperture assembly and a sensor. No lens is used. The aperture assembly consists of a two dimensional array of aperture elements. The transmittance of each aperture element is independently controllable. The sensor is a single detection element, such as a single photo-conductive cell. Each aperture element together with the sensor defines a cone of a bundle of rays, and the cones of the aperture assembly define the pixels of an image. Each pixel value of an image is the integration of the bundle of rays in a cone. The sensor is used for taking compressive measurements. Each measurement is the integration of rays in the cones modulated by the transmittance of the aperture elements. A compressive sensing matrix is implemented by adjusting the transmittance of the individual aperture elements according to the values of the sensing matrix. The proposed architecture is simple and reliable because no lens is used. Furthermore, the sharpness of an image from our device is only limited by the resolution of the aperture assembly, but not affected by blurring due to defocus. The architecture can be used for capturing images of visible lights, and other spectra such as infrared, or millimeter waves. Such devices may be used in surveillance applications for detecting anomalies or extracting features such as speed of moving objects. Multiple sensors may be used with a single aperture assembly to capture multi-view images simultaneously. A prototype was built by using a LCD panel and a photoelectric sensor for capturing images of visible spectrum.

preprint2013arXiv

Lensless Imaging by Compressive Sensing

In this paper, we propose a lensless compressive imaging architecture. The architecture consists of two components, an aperture assembly and a sensor. No lens is used. The aperture assembly consists of a two dimensional array of aperture elements. The transmittance of each aperture element is independently controllable. The sensor is a single detection element. A compressive sensing matrix is implemented by adjusting the transmittance of the individual aperture elements according to the values of the sensing matrix. The proposed architecture is simple and reliable because no lens is used. The architecture can be used for capturing images of visible and other spectra such as infrared, or millimeter waves, in surveillance applications for detecting anomalies or extracting features such as speed of moving objects. Multiple sensors may be used with a single aperture assembly to capture multi-view images simultaneously. A prototype was built by using a LCD panel and a photoelectric sensor for capturing images of visible spectrum.

preprint2013arXiv

Multi-view in Lensless Compressive Imaging

Multi-view images are acquired by a lensless compressive imaging architecture, which consists of an aperture assembly and multiple sensors. The aperture assembly consists of a two dimensional array of aperture elements whose transmittance can be individually controlled to implement a compressive sensing matrix. For each transmittance pattern of the aperture assembly, each of the sensors takes a measurement. The measurement vectors from the multiple sensors represent multi-view images of the same scene. We present theoretical framework for multi-view reconstruction and experimental results for enhancing quality of image using multi-view.

preprint2013arXiv

Providing Local Content in a Hybrid Single Frequency Network using Hierarchical Modulation

A hierarchical modulation method is proposed for providing local content in a hybrid satellite and terrestrial single frequency network such DVB-SH. The hierarchical modulation is used to transmit both global and local content in terrestrial transmitters. The global content is transmitted with high priority layer of the hierarchical modulation, and the local content is modulated with the low priority layer of the hierarchical modulation. The satellite transmits global content only. The performance of the hierarchical system for both global and local content is analyzed.

preprint2013arXiv

Sound localization using compressive sensing

In a sensor network with remote sensor devices, it is important to have a method that can accurately localize a sound event with a small amount of data transmitted from the sensors. In this paper, we propose a novel method for localization of a sound source using compressive sensing. Instead of sampling a large amount of data at the Nyquist sampling rate in time domain, the acoustic sensors take compressive measurements integrated in time. The compressive measurements can be used to accurately compute the location of a sound source.

preprint2013arXiv

Surveillance Video Processing Using Compressive Sensing

A compressive sensing method combined with decomposition of a matrix formed with image frames of a surveillance video into low rank and sparse matrices is proposed to segment the background and extract moving objects in a surveillance video. The video is acquired by compressive measurements, and the measurements are used to reconstruct the video by a low rank and sparse decomposition of matrix. The low rank component represents the background, and the sparse component is used to identify moving objects in the surveillance video. The decomposition is performed by an augmented Lagrangian alternating direction method. Experiments are carried out to demonstrate that moving objects can be reliably extracted with a small amount of measurements.

preprint2013arXiv

What about U on Surfaces? - Extended Hubbard Models for Adatom Systems from First Principles

Electronic correlations together with dimensional constraints lead to some of the most fascinating properties known in condensed matter physics. As possible candidates where these conditions are realized, semiconductor (111) surfaces and adatom systems on surfaces have been under investigation for quite some time. However, state-of-the-art theoretical studies on these materials that include many body effects beyond the band picture are rare. First principles estimates of inter-electronic Coulomb interactions for the correlated states are missing entirely, and usually these interactions are treated as adjustable parameters. In the present work, we report on calculations of the interaction parameters for the group IV surface-adatom systems in the α-phase series of Si(111):\{C, Si, Sn, Pb\}. For all systems investigated, interelectronic Coulomb interactions are indeed large compared to the kinetic energy of the states in question. Moreover, our study reveals that intersite interactions cannot be disregarded. We explicitly construct an extended Hubbard model for the series of group IV surface-adatom systems on silicon, which can be used for further many-body calculations.

preprint2012arXiv

Hubbard U and Hund's Exchange J in Transition Metal Oxides: Screening vs. Localization Trends from Constrained Random Phase Approximation

In this work, we address the question of calculating the local effective Coulomb interaction matrix in materials with strong electronic Coulomb interactions from first principles. To this purpose, we implement the constrained random phase approximation (cRPA) into a density functional code within the linearized augmented plane wave (LAPW) framework. We apply our approach to the 3d and 4d early transition metal oxides SrMO3 (M=V, Cr, Mn) and (M=Nb, Mo, Tc) in their paramagnetic phases. For these systems, we explicitly assess the differences between two physically motivated low-energy Hamiltonians: The first is the three-orbital model comprising the t2g states only, that is often used for early transition metal oxides. The second choice is a model where both, metal d- and oxygen p-states are retained in the construction of Wannier functions, but the Hubbard interactions are applied to the d-states only ("d-dp Hamiltonian"). Interestingly, since -- for a given compound -- both U and J depend on the choice of the model, so do their trends within a family of these compounds. In the 3d perovskite series SrMO3 the effective Coulomb interactions in the t2g Hamiltonian decrease along the series, due to the more efficient screening. The inverse -- generally expected -- trend, increasing interactions with increasing atomic number, is however recovered within the more localized "d-dp Hamiltonian". Similar conclusions are established in the layered 4d perovskites series Sr2MO4 (M=Mo, Tc, Ru, Rh). Compared to their isoelectronic and isostructural 3d analogues, the 4d 113 perovskite oxides SrMO3 (M=Nb, Mo, Tc) exhibit weaker screening effects. Interestingly, this leads to an effectively larger U on 4d shells than on 3d when a t2g model is constructed.

preprint2010arXiv

Efficient Knowledge Base Management in DCSP

DCSP (Distributed Constraint Satisfaction Problem) has been a very important research area in AI (Artificial Intelligence). There are many application problems in distributed AI that can be formalized as DSCPs. With the increasing complexity and problem size of the application problems in AI, the required storage place in searching and the average searching time are increasing too. Thus, to use a limited storage place efficiently in solving DCSP becomes a very important problem, and it can help to reduce searching time as well. This paper provides an efficient knowledge base management approach based on general usage of hyper-resolution-rule in consistence algorithm. The approach minimizes the increasing of the knowledge base by eliminate sufficient constraint and false nogood. These eliminations do not change the completeness of the original knowledge base increased. The proofs are given as well. The example shows that this approach decrease both the new nogoods generated and the knowledge base greatly. Thus it decreases the required storage place and simplify the searching process.

preprint2009arXiv

Localization in an Inhomogeneous Quantum Wire

We study interaction-induced localization of electrons in an inhomogeneous quasi-one-dimensional system--a wire with two regions, one at low density and the other high. Quantum Monte Carlo techniques are used to treat the strong Coulomb interactions in the low density region, where localization of electrons occurs. The nature of the transition from high to low density depends on the density gradient--if it is steep, a barrier develops between the two regions, causing Coulomb blockade effects. Ferromagnetic spin polarization does not appear for any parameters studied. The picture emerging here is in good agreement with measurements of tunneling between two wires.