Using Approximate Nearest Neighbor Methods to Rapidly Search Large Seismic Signal Archives.
Abstract not provided.
Slinkard, Megan Elizabeth↗
SEARCH · Engineering Papers
Results for “nearest neighbors”
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NEO-DNND: Communication-Optimized Distributed Nearest Neighbor Graph Construction
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Computer science↗
Enhancing quantum utility: Simulating large-scale quantum spin chains on superconducting quantum computers
We present the quantum simulation of the frustrated quantum spin- 1 2 antiferromagnetic Heisenberg spin chain with competing nearest-neighbor ( J 1 ) and next-nearest-neighbor ( J 2 ) exchange interactions in the real superconducting quantum computer with qubits ranging up to 100. In particular, we implement the Hamiltonian with the next-nearest neighbor exchange interaction in conjunction with the nearest-neighbor interaction on IBM's superconducting quantum computer and carry out the time evolution of the spin chain by employing the first-order Trotterization. Furthermore, our implementation of the second-order Trotterization for the isotropic Heisenberg spin chain, involving only nearest-neighbor exchange interaction, enables precise measurement of the expectation values of staggered magnetization observable across a range of up to 100 qubits. Notably, in both cases, our approach results in a constant circuit depth in each Trotter step, independent of the number of qubits. Our demonstration of the accurate measurement of expectation values for the large-scale quantum system using superconducting quantum computers designates the quantum utility of these devices for investigating various properties of many-body quantum systems. This will be a stepping stone to achieving the quantum advantage over classical ones in simulating quantum systems before the fault tolerance quantum era. Published by the American Physical Society 2024
97 MATHEMATICS AND COMPUTING↗
Diffusion behavior of lanthanide fission products in bcc Fe cladding: A first-principles study
Fuel-cladding chemical interaction poses significant challenges in nuclear reactors, where fission products generated from nuclear fuel interact with Fe-based cladding materials, potentially compromising their structural integrity. This study investigates the diffusion behavior of lanthanide fission products, Lanthanum (La), Cerium (Ce), Praseodymium (Pr), and Neodymium (Nd), within body-centered cubic (bcc) Fe cladding using the density functional theory, nudged elastic band method, and self-consistent mean field theory. Our results reveal significant vacancy binding energies, particularly with the 1st and 2nd nearest neighbors, which diminish beyond the 5th nearest neighbor, with La exhibiting the strongest binding affinity, followed by Nd, Ce, and Pr. The nudged elastic band calculations indicate significant high barriers for the dissociation of 1st nearest neighbor vacancy-solute pairs for all fission products. The tracer diffusion coefficients of these fission products were derived in an Arrhenius form, with a magnetic correction that accounts for the high-temperature paramagnetic state. The significant trapping effect of vacancies caused by a very dilute concentration of fission products reduces vacancy mobility, potentially leading to modifications in point defect supersaturation, void nucleation, and swelling under irradiation. These represent critical challenges for irradiated cladding materials. The tracer diffusion coefficients indicate that Nd diffuses the fastest, followed by La, Ce, and Pr. Furthermore, this study provides essential insights for understanding fission product transport in cladding materials and informs future design strategies to mitigate fuel-cladding chemical interaction, ultimately enhancing nuclear reactor safety and performance.
Diffusion↗
Superspin renormalization and slow relaxation in random spin systems
We develop an excited-state real-space renormalization group (RSRG-X) formalism to describe the dynamics of conserved densities in randomly interacting spin-12 systems. Our formalism is suitable for systems with U(1) and Z2 symmetries, and we apply it to chains of randomly positioned spins with dipolar XX+YY interactions, as arise in Rydberg quantum simulators and other platforms. The formalism generates a sequence of effective Hamiltonians that provide approximate descriptions for dynamics on successively smaller energy scales. These effective Hamiltonians involve “superspins”: two-level collective degrees of freedom constructed from (anti)aligned microscopic spins. Conserved densities can then be understood as relaxing via coherent collective spin flips. For the well-studied simpler case of randomly interacting nearest-neighbor XX+YY chains, the superspins reduce to single spins. Our formalism also leads to a numerical method capable of simulating the dynamics up to an otherwise inaccessible combination of large system size and late time. Focusing on disorder-averaged infinite-temperature autocorrelation functions, in particular the spin survival probability Sp¯(t), we demonstrate quantitative agreement between our algorithm and exact diagonalization (ED) at low but nonzero frequencies. Such agreement holds for chains with nearest-neighbor, next-nearest-neighbor, and long-range dipolar interactions. Our results indicate decay of Sp¯(t) slower than any power law and feature no significant deviation from the ∼1/ln2(t) asymptote expected from the infinite-randomness fixed-point of the nearest-neighbor model. We also apply the RSRG-X formalism to two-dimensional long-range systems of moderate size and find slow late-time decay of Sp¯(t).
Zhao, Yi J↗
A Fast Implementation of the ISOCLUS Algorithm
Unsupervised clustering is a fundamental building block in numerous image processing applications. One of the most popular and widely used clustering schemes for remote sensing applications is the ISOCLUS algorithm, which is based on the ISODATA method. The algorithm is given a set of n data points in d-dimensional space, an integer k indicating the initial number of clusters, and a number of additional parameters. The general goal is to compute the coordinates of a set of cluster centers in d-space, such that those centers minimize the mean squared distance from each data point to its nearest center. This clustering algorithm is similar to another well-known clustering method, called k-means. One significant feature of ISOCLUS over k-means is that the actual number of clusters reported might be fewer or more than the number supplied as part of the input. The algorithm uses different heuristics to determine whether to merge lor split clusters. As ISOCLUS can run very slowly, particularly on large data sets, there has been a growing .interest in the remote sensing community in computing it efficiently. We have developed a faster implementation of the ISOCLUS algorithm. Our improvement is based on a recent acceleration to the k-means algorithm of Kanungo, et al. They showed that, by using a kd-tree data structure for storing the data, it is possible to reduce the running time of k-means. We have adapted this method for the ISOCLUS algorithm, and we show that it is possible to achieve essentially the same results as ISOCLUS on large data sets, but with significantly lower running times. This adaptation involves computing a number of cluster statistics that are needed for ISOCLUS but not for k-means. Both the k-means and ISOCLUS algorithms are based on iterative schemes, in which nearest neighbors are calculated until some convergence criterion is satisfied. Each iteration requires that the nearest center for each data point be computed. Naively, this requires O(kn) time, where k denotes the current number of centers. Traditional techniques for accelerating nearest neighbor searching involve storing the k centers in a data structure. However, because of the iterative nature of the algorithm, this data structure would need to be rebuilt with each new iteration. Our approach is to store the data points in a kd-tree data structure. The assignment of points to nearest neighbors is carried out by a filtering process, which successively eliminates centers that can not possibly be the nearest neighbor for a given region of space. This algorithm is significantly faster, because large groups of data points can be assigned to their nearest center in a single operation. Preliminary results on a number of real Landsat datasets show that our revised ISOCLUS-like scheme runs about twice as fast.
Memarsadeghi, Nargess↗
Coarsening dynamics of Ising-nematic order in a frustrated Heisenberg antiferromagnet
We study the phase ordering dynamics of the classical antiferromagnetic 𝐽 1 −𝐽 2 (nearest-neighbor and next-nearest-neighbor couplings) Heisenberg model on the square lattice in the strong frustration regime (𝐽 2 /𝐽 1 > 1/2). While thermal fluctuations preclude any long-range magnetic order at finite temperatures, the system exhibits a long-range spin-driven nematic phase at low temperatures. The transition into the nematic phase is further shown to belong to the two-dimensional Ising universality class based on the critical exponents near the phase transition. Our large-scale stochastic Landau-Lifshitz-Gilbert simulations find a two-stage phase ordering when the system is quenched from a high-temperature paramagnetic state into the nematic phase. In the early stage, collinear alignments of spins lead to a locally saturated Ising-nematic order. Once domains of well-defined Ising order are developed, the late-stage relaxation is dominated by curvature-driven domain coarsening, as described by the Allen-Cahn equation. The characteristic size of Ising-nematic domains scales as the square root of time, similar to the kinetic Ising model described by the time-dependent Ginzburg-Landau theory. Our results confirm that the late-stage ordering kinetics of the spin-driven nematic, which is a vestigial order of the frustrated Heisenberg model, belongs to the dynamical universality class of a nonconserved Ising order. Interestingly, the system shows no violation of the superuniversality hypothesis under weak bond disorder. The dynamic scaling invariance is preserved in the presence of weak bond disorder. Here, we also discuss possible applications of our results to materials for which vestigial Ising-nematic order is realized.
Antiferromagnets↗
Robust d-Wave Superconductivity in the Square-Lattice t–J Model
Unravelling competing orders emergent in doped Mott insulators and their interplay with unconventional superconductivity is one of the major challenges in condensed matter physics. Here, to explore the possible superconducting state in a doped Mott insulator, we study the square-lattice t-J model with both the nearest-neighbor and next-nearest-neighbor electron hoppings and spin interactions. By using the state-of-the-art density matrix renormalization group calculation with imposing charge U(1) and spin SU(2) symmetries on the six-leg cylinders, we establish a quantum phase diagram including three phases: a stripe charge density wave phase, a superconducting phase without static charge order, and a superconducting phase coexistent with a weak charge stripe order. Crucially, we demonstrate that the superconducting phase has a power-law pairing correlation that decays much slower than the charge density and spin correlations, which is a quasi-1D descendant of the uniform d-wave superconductor in two dimensions. These findings reveal that enhanced charge and spin fluctuations with optimal doping is able to produce robust d-wave superconductivity in doped Mott insulators, providing a foundation for connecting theories of superconductivity to models of strongly correlated systems.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Explicit numerical formulas of improved stability and accuracy for the solution of parabolic equations
A class of explicit numerical formulas which involve next nearest neighbor as well as nearest neighbor points are explored in this paper. These formulas are formal approximations to the linear parabolic partial-differential equation of first order in time and second order in distance. It was found that some of these formulas can employ time steps as much as four times that for the conventional explicit technique without becoming unstable. Others showed improved accuracy for a given time step and spatial grid spacing. One formula achieved a steady-state solution of specified accuracy for an example problem in less than 4 percent of the total computational time required by the conventional explicit technique.
Olstad, W. B.↗
Finding maximum on an array processor with a global bus
The problem of finding the maximum of a set of values stored one/processor on an n x n array of processors is analyzed. The array has a time-shared global bus in addition to conventional processor-processor links. A two-phase algorithm for finding the maximum is presented that uses conventional links during the first phase and the global bus during the second. This algorithm is faster than algorithms that use either only the global bus or only the conventional links. Two types of interconnection patterns (the eighth nearest neighbor and the fourth nearest neighbor) are analyzed. In both cases it is shown that the time required to find the maximum using the two-phase algorithm is 0(n to the 2/3-power) assuming the propagation speed of the global bus to be a constant independent of the size of the array. In the case where the propagation speed is logarithmic in the number of processors, the time to find the maximum is 0(/n-squared log n/1/3), for both types of arrays. Extensions to q-dimensional arrays show that the two-phase algorithm is superior for any fixed value of q.
Bokhari, S. H.↗
Classification of multispectral image data by the Binary Diamond neural network and by nonparametric, pixel-by-pixel methods
The classification of multispectral image data obtained from satellites has become an important tool for generating ground cover maps. This study deals with the application of nonparametric pixel-by-pixel classification methods in the classification of pixels, based on their multispectral data. A new neural network, the Binary Diamond, is introduced, and its performance is compared with a nearest neighbor algorithm and a back-propagation network. The Binary Diamond is a multilayer, feed-forward neural network, which learns from examples in unsupervised, 'one-shot' mode. It recruits its neurons according to the actual training set, as it learns. The comparisons of the algorithms were done by using a realistic data base, consisting of approximately 90,000 Landsat 4 Thematic Mapper pixels. The Binary Diamond and the nearest neighbor performances were close, with some advantages to the Binary Diamond. The performance of the back-propagation network lagged behind. An efficient nearest neighbor algorithm, the binned nearest neighbor, is described. Ways for improving the performances, such as merging categories, and analyzing nonboundary pixels, are addressed and evaluated.
Salu, Yehuda↗
Inelastic Neutron Scattering Study of Magnetic Exchange Pathways in MnS
We report an investigation of the magnetic structure and magnetic exchange pathways in MnS via neutron scattering methods, aided by density functional theory (DFT) modeling. The material has been confirmed to undergo antiferromagnetic (AFM) ordering at 152 K, with the magnetic structure representing AFM stacking of ferromagnetic (FM) (111) planes of Mn magnetic moments. Correspondingly, the magnetic structure is described by a propagation vector k = (1/2, 1/2, 1/2), with the volume of the magnetic unit cell being 8 times larger than the volume of the nuclear unit cell. Analysis of inelastic neutron scattering (INS) data collected on a powder sample of MnS revealed that the next-nearest-neighbor magnetic exchange constant (J 2 ) exceeds the nearest-neighbor exchange constant (J 1 ) by more than 3 times, while in the case of MnO, which exhibits the same nuclear and magnetic structures as MnS, the J 2 /J 1 ratio was reported to be below 1.5. Although for MnO the signs of both J 1 and J 2 indicated AFM exchange interactions, machine-learning INS data analysis in combination with DFT calculations suggests that the INS data collected on MnS are best described with J 1 < 0 and J 2 > 0, corresponding to FM and AFM exchange couplings, respectively. To achieve a satisfactory fit to the experimentally observed data, the Hamiltonian used to model the INS spectra also included the next-next-nearest-neighbor magnetic exchange constant (J 3 ). The best-fit model has been obtained with the values of the exchange constants J 1 = -0.27 meV, J 2 = 1.05 meV, and J 3 = -0.19 meV.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Direct Visualization of Magnetic Correlations in Frustrated Spinel ZnFe 2 O 4
Magnetic materials with the spinel structure (A 2+ B 3+ 2 O 4 ) form the core of numerous magnetic devices, and ZnFe 2 O 4 constitutes a peculiar example where the nature of the magnetism is still unresolved. Susceptibility measurements revealed a cusp around T c = 13 K resembling an antiferromagnetic transition, despite the positive Curie–Weiss temperature determined to be Θ CW = 102.8(1) K. Bifurcation of field-cooled and zero-field-cooled data below T c in conjunction with a frequency dependence of the peak position and a non-zero imaginary component below T c shows it is in fact associated with a spin-glass transition. Highly structured magnetic diffuse neutron scattering from single crystals develops between 50 K and 25 K revealing the presence of magnetic disorder which is correlated in nature. Here, the 3D-mΔPDF method is used to visualize the local magnetic ordering preferences, and ferromagnetic nearest-neighbor and antiferromagnetic third nearest-neighbor correlations are shown to be dominant. Their temperature dependence is extraordinary with some flipping in sign and a strongly varying correlation length. The correlations can be explained by orbital interaction mechanisms for the magnetic pathways and a preferred spin cluster. This study demonstrates the power of the 3D-mΔPDF method in visualizing complex quantum phenomena thereby providing a way to obtain an atomic-scale understanding of magnetic frustration.
36 MATERIALS SCIENCE↗
Helimagnetism in the candidate ferroelectric CrI 2
CrI 2 is a van der Waals (vdW) layered material that exhibits helimagnetism that propagates along ribbon chains. This is determined from neutron time-of-flight diffraction measurements. Be low T N = 17 K in the orthorhombic structure, a screw-like helimagnetic order develops with an incommensurate wavevector of q ≈ (0.2492, 0, 0) at 8 K. Using density functional theory (DFT)+U calculations, the J 1 -J 2 model is leveraged to describe the helimagnetism, where J 1 (> 0) and J 2 (< 0) correspond, respectively, to a ferromagnetic nearest neighbor (NN) and antiferromagnetic next nearest neighbor (NNN) intrachain interaction. In conclusion, the DFT+U calculations suggest that orthorhombic CrI 2 satisfies conditions that favor formation of helimagnetic order.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Higher-order Van Hove singularities in kagome topological bands
Motivated by the growing interest in band structures featuring higher-order Van Hove singularities (HOVHS), we investigate a spinless fermion kagome system characterized by nearest-neighbor (NN) and next-nearest-neighbor (NNN) hopping amplitudes. While NN hopping preserves time-reversal symmetry, NNN hopping, akin to chiral hopping on the Haldane lattice, breaks time-reversal symmetry and leads to the formation of topological bands with Chern numbers ranging from 𝐶 = ±1 to ±4. We perform analytical and numerical analysis of the energy bands near the high-symmetry points Γ, ±𝐊, and 𝐌 𝑖 (𝑖 = 1, 2, and 3), which uncover a rich and complex landscape of HOVHS, controlled by the magnitude and phase of the NNN hopping. We observe power-law divergences in the density of states (DOS), 𝜌(𝜀)∼|𝜀| −𝜈 , with exponents 𝜈 = 1/2, 1/3, 1/4, which can significantly affect the anomalous Hall response at low temperatures when the Fermi level crosses the HOVHS. Additionally, the NNN hopping induces the formation of higher Chern number bands 𝐶 = ±2, ±4 in the middle of the spectrum obeying a sublattice interference whereupon electronic states are maximally localized in each of the sublattices when the momentum approaches the three high-symmetry points 𝐌 𝑖 (𝑖 = 1, 2, and 3) on the Brillouin zone boundary. Finally, this classification of HOVHS in kagome systems provides a platform to explore unconventional electronic orders induced by electronic correlations.
Chern insulators↗
Topological and magnetic properties of a noncollinear spin state on a honeycomb lattice in a magnetic field
Here, this paper studies the topological and magnetic properties of a noncollinear spin state on a honeycomb lattice that evolves from coplanar to ferromagnetic with a magnetic field applied along the z axis. The coplanar state is stabilized by nearest-neighbor ferromagnetic interactions, single-ion anisotropy along z, and DzyaloshinskiiMoriya interactions between next-nearest-neighbor sites. Below the critical field H$_c$ that aligns the spins, the magnetic unit cell contains six sites and the spin dynamics contains six magnon modes. Although the classical energy is degenerate with respect to the twist angle φ between nearest-neighbor spins, the dependence of the free energy on φ at low temperatures is dominated by the magnon zero-point energy, which contains extremum at φ = πl/3 for integer l. The only unique ground states GS(φ) have l = 0 or 1. For H < H$'_c$, the zero-point energy has minima at even l and the ground state is GS(0); for H$'_c$ < H < H$'_c$, the zero-point energy has minima at odd l and the ground state is GS(π/3). In GS(0), the magnon density of states exhibits five distinct topological phases with increasing field associated with the opening and closing of energy gaps between two or three magnonic bands. While the Berry curvature vanishes for the coplanar φ = 0 phase in zero field, the Berry curvature and Chern numbers exhibit signatures of the five topological phases below H$'_c$. Whereas the Berry curvature and Chern number are sensitive to changes in the magnon density of states within GS(π/3), the inelastic spectrum S(k,ω) is sensitive to changes in the intensity of the magnon modes in the different magnetic phases GS(0) and GS(π/3) rather than the five topological phases within GS(π/3).
Fishman, Randy S. [Oak Ridge National Laboratory (↗
Do the major axes of rich clusters of galaxies point toward their neighbors?
The major axis orientation of rich clusters of galaxies, determined from an analysis of X-ray images, is used to investigate whether these clusters point toward their nearest neighbors. No statistical significance is found for a pointing effect between clusters and their nearest neighbors in either X-ray, optical, or combined X-ray and optical samples. Using updated redshifts and permitting nonstatistical sample Abell clusters as nearest neighbors does not affect this conclusion. The lack of statistical significance for a pointing effect favors hierarchical models in which galaxies form first, followed by clusters and then superclusters. For clusters with well-defined X-ray orientations, it is found that cluster position angles determined from X-ray and optical data are in general agreement.
Ulmer, M. P.↗
Cluster characterization in atom probe tomography: Machine learning using multiple summary functions
In this work, we develop a machine learning-based method to characterize intracluster concentration (ρ c ), background concentration (ρ b ), clustering radius (r̄), and radius dispersity (δ r ) in simulated atom probe tomography data using multiple spatial statistics summary functions to train a Bayesian regularized neural network. Here, we build upon previous work that utilized Ripley’s K-function by incorporating additional features from nearest-neighbor spatial statistics summary functions to better characterize concentration-based metrics. The addition of nearest-neighbor based features allows for highly accurate estimates of ρ c and ρ b , both with 90% of the predictions within 4.0% of the real value; the root-mean-square errors are reduced by 81.5% and 92.8% from predictions using only K-function based features, respectively. Additionally, including these nearest-neighbor based features improves the ability to differentiate between r̄ and δ r .
36 MATERIALS SCIENCE↗