Semi-local Approach Research Articles

Local N-electron valence state perturbation theory using pair-natural orbitals based on localized virtual molecular orbitals.

Second-order N-electron valence state perturbation theory (NEVPT2) is an exactly size-consistent and intruder-state-free multi-reference theory. To accelerate the NEVPT2 computation, Guo and Neese combined it with the local pair-natural orbital (PNO) method using the projected atomic orbitals (PAOs) as the underlying local basis [Guo et al., J. Chem. Phys. 144, 094111 (2016)]. In this paper, we report the further development of the PNO-NEVPT2 method using the orthonormal and non-redundant localized virtual molecular orbitals (LVMOs) instead of PAOs. The LVMOs were previously considered to perform relatively poor compared to PAOs because the resulting orbital domains were unacceptably large. Our prior work, however, showed that this drawback can be remedied by re-forming the domain construction scheme using differential overlap integrals [Saitow et al., J. Chem. Phys. 157, 084101 (2022)]. In this work, we develop further refinements to enhance the feasibility of using LVMOs. We first developed a two-level semi-local approach for screening out so-called weak-pairs to select or truncate the pairs for PNO constructions more flexibly. As a refinement specific to the Pipek-Mezey localization for LVMOs, we introduced an iterative scheme to truncate the Givens rotations using varying thresholds. We assessed the LVMO-based PNO-NEVPT2 method through benchmark calculations for linear phenyl alkanes, which demonstrate that it performs comparably well relative to the PAO-based approach. In addition, we evaluated the Co-C bond dissociation energies for the cobalamin derivatives composed of 200 or more atoms, which confirms that the LVMO-based method can recover more than 99.85% of the canonical NEVPT2 correlation energy.

A novel semi local measure of identifying influential nodes in complex networks

How to identify influencers is very significance in mastering the nature of node, controlling spreading process in complex networks. In this research field, each method has its own advantages and limitations. For example, local metrics are relatively simple, global metrics can give better results, but the computational complexity is also relatively high. A semi-local approach on basis of node dimension is proposed to identify influencers. The node dimension can detect regions with different dimensional structures by scaling the local dimension on the scale. The saturation effect is discovered in the process of identifying influencers by the node dimension. When the maximum dimension radius is close to the mean shortest path length of networks, the method has better performance. Through the saturation effect, our approach can be a tradeoff between local and global metrics. In addition, we show the correlation between different measures and node dimension with different maximum dimension radii. We employ Susceptible-Infected-Recovered (SIR) model to verify the effectiveness of our designed approach. Simulation results indicate the superiority of our algorithm.

Region-Based Prediction for Image Compression in the Cloud.

Thanks to the increasing number of images stored in the cloud, external image similarities can be leveraged to efficiently compress images by exploiting inter-images correlations. In this paper, we propose a novel image prediction scheme for cloud storage. Unlike current state-of-the-art methods, we use a semi-local approach to exploit inter-image correlation. The reference image is first segmented into multiple planar regions determined from matched local features and super-pixels. The geometric and photometric disparities between the matched regions of the reference image and the current image are then compensated. Finally, multiple references are generated from the estimated compensation models and organized in a pseudo-sequence to differentially encode the input image using classical video coding tools. Experimental results demonstrate that the proposed approach yields significant rate-distortion performance improvements compared with the current image inter-coding solutions such as high efficiency video coding.

Theoretical and experimental investigations of optical, structural and electronic properties of the lower-dimensional hybrid [NH3-(CH2)10-NH3]ZnCl4

In the current study, a combination between theoretical and experimental studies has been made for the hybrid perovskite [NH3-(CH2)10-NH3]ZnCl4. The density functional theory (DFT) was performed to investigate structural and electronic properties of the tilted compound. A local approximation (LDA) and semi-local approach (GGA) were employed. The results are obtained using, respectively, the local exchange correlation functional of Perdew-Wang 92 (PW92) and semi local functional of Perdew-Burke-Ernzerhof (PBE). The optimized cell parameters are in good agreement with the experimental results. Electronic properties have been studied through the calculation of band structures and density of state (DOS), while structural properties are investigated by geometry optimization of the cell. Fritz-Haber-Institute (FHI) pseudopotentials were employed to perform all calculations. The optical diffuse reflectance spectra was mesured and applied to deduce the refractive index (n), the extinction coefficient (k), the absorption coefficient ( $\alpha$ ), the real and imaginary dielectric permittivity parts ( $\varepsilon_{r},\varepsilon_{i})$ ) and the optical band gap energy Eg. The optical band gap energy value shows good consistent with that obtained from DFT calculations and reveals the insulating behavior of the material.

Symmetry detection based on multiscale pairwise texture boundary segment interactions

In this paper, we propose a new unsupervised and simple approach to local symmetry detection of ribbon-like structure in natural images. The proposed model consists in quantifying the presence of a partial medial axis segment, existing between each pair of (preliminary detected) line segments delineating the boundary of two textured regions, by a set of heuristics related both to the geometrical structure of each pair of line segments and its ability to locally delimit a homogeneous texture region in the image. This semi-local approach is finally embedded in a two-step algorithm with an amplification step, via a Hough-style voting approach achieved at different scales and coordinate spaces which aims at determining the dominant local symmetries present in the image and a final denoising step, via an averaging procedure, which aims at removing noise and spurious local symmetries. The experiments, reported in this paper and conducted on the recent extension of the Berkeley Segmentation Dataset for the local symmetry detection task, demonstrate that the proposed symmetry detector performs well compared to the best existing state-of-the-art algorithms recently proposed in the literature.

A comparison of global and semi-local approximation in T-stage stochastic optimization

The paper presents a comparison between two different flavors of nonlinear models to be used for the approximate solution of T-stage stochastic optimization (TSO) problems, a typical paradigm of Markovian decision processes. Specifically, the well-known class of neural networks is compared with a semi-local approach based on kernel functions, characterized by less demanding computational requirements. To this purpose, two alternative methods for the numerical solution of TSO are considered, one corresponding to the classic approximate dynamic programming (ADP) and the other based on a direct optimization of the optimal control functions, introduced here for the first time. Advantages and drawbacks in the TSO context of the two classes of approximators are analyzed, in terms of computational burden and approximation capabilities. Then, their performances are evaluated through simulations in two important high-dimensional TSO test cases, namely inventory forecasting and water reservoirs management.

Suppression of the Peierls instability in 1D system: Semi-local approach to Little’s conjecture

A self-consistent perturbative evaluation of the cohesive energy is proposed for correlated half-filled systems. The method uses a localized N-electron zeroth-order wavefunction without eliminating the translational invariance. The evaluation of charge excitations contributions to the wavefunction allows one to rationalize the mechanisms for instability. Following Little’s conjecture, we demonstrate that side chains consisting of alternating fixed point charges affords for a suppression of the Peierls instability. Not only does our description offer an original inspection into 1D correlated systems but it is also to be considered as a guide for future experimental works.

Curvature of Indoor Sensor Network: Clustering Coefficient

We investigate the geometric properties of the communication graph in realistic low-power wireless networks. In particular, we explore the concept of the curvature of a wireless network via the clustering coefficient. Clustering coefficient analysis is a computationally simplified, semilocal approach, which nevertheless captures such a large-scale feature as congestion in the underlying network. The clustering coefficient concept is applied to three cases of indoor sensor networks, under varying thresholds on the link packet reception rate (PRR). A transition from positive curvature (meshed network) to negative curvature (core concentric network) is observed by increasing the threshold. Even though this paper deals with network curvature per se, we nevertheless expand on the underlying congestion motivation, propose several new concepts (network inertia and centroid), and finally we argue that greedy routing on a virtual positively curved network achieves load balancing on the physical network.

Potassium intercalation in graphite: A van der Waals density-functional study

Potassium intercalation in graphite is investigated by first-principles theory. The bonding in the potassium-graphite compound is reasonably well accounted for by traditional semilocal density functional theory (DFT) calculations. However, to investigate the intercalate formation energy from pure potassium atoms and graphite requires use of a description of the graphite interlayer binding and thus a consistent account of the nonlocal dispersive interactions. This is included seamlessly with ordinary DFT by a van der Waals density functional (vdW-DF) approach [Phys. Rev. Lett. 92, 246401 (2004)]. The use of the vdW-DF is found to stabilize the graphite crystal, with crystal parameters in fair agreement with experiments. For graphite and potassium-intercalated graphite structural parameters such as binding separation, layer binding energy, formation energy, and bulk modulus are reported. Also the adsorption and sub-surface potassium absorption energies are reported. The vdW-DF description, compared with the traditional semilocal approach, is found to weakly soften the elastic response.

A semi-local quasi-harmonic model to compute the thermodynamic and mechanicalproperties of silicon nanostructures

This paper presents a semi-local quasi-harmonic model with local phonon density of states(LPDOS) to compute the thermodynamic and mechanical properties of siliconnanostructures at finite temperature. In contrast to an earlier approach (Tang and Aluru2006 Phys. Rev. B 74 235441), where a quasi-harmonic model with LPDOS computed by aGreen’s function technique (QHMG) was developed considering many layers of atoms, thesemi-local approach considers only two layers of atoms to compute the LPDOS. We showthat the semi-local approach combines the accuracy of the QHMG approach and thecomputational efficiency of the local quasi-harmonic model. We present results for severalsilicon nanostructures to address the accuracy and efficiency of the semi-local approach.

Mixed mode fracture of a higher order beam model in gradient-dependent plasticity

A higher-order shear-deformable beam model with gradient-dependent plasticity regularisation is presented in this work. The model takes into account a realistic non-linear variation of stresses and strains through the beam thickness. This enables mixed mode fracture analysis. Numerical results are presented for a multilayered beam. The discrete problem size is significantly reduced through this semi-local approach.

Semilocal approach to nonlocal equations

Abstract One can classify nonlocal equations according to the value of a parameter, defined as the ratio between the range of nonlocality and the characteristic length of the diagonal part of the integral kernel. When this ratio is small, one can derive a local (differential) equation, which approximately reproduces the solutions of the nonlocal equation. Then using the methods already existing for the analysis of differential equations one can, with a relatively small effort, describe the properties of the solutions of nonlocal equations. It is not possible to attach to every nonlocal Schrodinger equation an equivalent local one (i.e. a differential equation of second order). The reason is that the spectrum of nonlocal equations is much richer and cannot be reproduced with a second order differential equation. When this is the case, the behaviour of the solutions of nonlocal equations is similar to that corresponding to nonlinear equations. The method presented here provides one not only with a quite simple approach for studying the properties of nonlocal equations, but also with a fairly accurate method of approximate solution, when the above-mentioned parameter is reasonably small.