Estimation of Density from a Sample of Joint Point and Nearest-Neighbor Distances

Electron diffraction and direct transmission have been used extensively to study the local atomic arrangement in amorphous solids and in particular Ge. Nearest neighbor distances had been calculated from E.D. profiles and the results have been interpreted in terms of the microcrystalline or the random network models. Direct transmission electron microscopy appears the most direct and accurate method to resolve this issue since the spacial resolution of the better instruments are of the order of 3Å. In particular the tilted beam interference method is used regularly to show fringes corresponding to 1.5 to 3Å lattice planes in crystals as resolution tests.

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Nearest-Neighbor Distances Change Over Short Time Intervals in Foraging Flocks of Shorebirds

ORNITHOLOGICAL SCIENCE ◽

10.2326/osj.19.107 ◽

2020 ◽

Vol 19 (1) ◽

pp. 107

Author(s):

Ivana Novcic

Keyword(s):

Nearest Neighbor ◽

Time Intervals ◽

Short Time ◽

Nearest Neighbor Distances

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STUDY OF THERMODYNAMIC PROPERTIES OF ZINC-BLENDE-TYPE SEMICONDUCTORS: TEMPERATURE AND PRESSURE DEPENDENCES

Modern Physics Letters B ◽

10.1142/s0217984911026760 ◽

2011 ◽

Vol 25 (12n13) ◽

pp. 1041-1051 ◽

Cited By ~ 7

Author(s):

HO KHAC HIEU ◽

VU VAN HUNG

Keyword(s):

Nearest Neighbor ◽

Thermodynamic Quantities ◽

Atomic Displacements ◽

Zinc Blende ◽

The Mean ◽

Temperature And Pressure ◽

Analytical Expressions ◽

Theoretical Results ◽

Nearest Neighbor Distances ◽

Statistical Moment Method

Using the statistical moment method (SMM), the temperature and pressure dependences of thermodynamic quantities of zinc-blende-type semiconductors have been investigated. The analytical expressions of the nearest-neighbor distances, the change of volumes and the mean-square atomic displacements (MSDs) have been derived. Numerical calculations have been performed for a series of zinc-blende-type semiconductors: GaAs , GaP , GaSb , InAs , InP and InSb . The agreement between our calculations and both earlier other theoretical results and experimental data is a support for our new theory in investigating the temperature and pressure dependences of thermodynamic quantities of semiconductors.

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Determining the Ratio of the Precipitated versus Substituted Arsenic by XAFS and SIMS in Heavy Dose Arsenic Implants in Silicon

MRS Proceedings ◽

10.1557/proc-669-j5.8 ◽

2001 ◽

Vol 669 ◽

Cited By ~ 6

Author(s):

M. A. Sahiner ◽

S. W. Novak ◽

J. C. Woicik ◽

J. Liu ◽

V. Krishnamoorty

Keyword(s):

Solid Solubility ◽

Amorphous Materials ◽

Nearest Neighbor ◽

Secondary Ion Mass Spectrometry ◽

Local Environment ◽

Coordination Numbers ◽

Annealing Conditions ◽

X Ray Absorption ◽

Secondary Ion ◽

Nearest Neighbor Distances

ABSTRACTDoping silicon with arsenic by ion implantation above the solid solubility level leads to As clusters and/or precipitates in the form of monoclinic SiAs causing electrical deactivation of the dopant. Information on the local structure around the As atom, and the As concentration depth profiles is important for the implantation and annealing process in order to reduce the precipitated As and maximize the electrically activated As. In this study, we determined the local As structure and the precipitated versus substituted As for As implants in CZ (001) Si wafers, with implant energies between 20 keV and 100 keV, and implant doses ranging from 1 × 1015/cm2 to 1 × 1018/cm2. The samples were subjected to different thermal annealing conditions. We used secondary ion mass spectrometry (SIMS) and UT- MARLOWE simulations to determine the region where the As-concentration is above the solid solubility level. By x-ray absorption fine structure spectroscopy (XAFS), we probed the structure of the local environment around As. XAFS being capable of probing the short-range order in crystalline and amorphous materials provides information on the number, distance and chemical identity of the neighbors of the main absorbing atom. Using Fourier analysis, the coordination numbers (N) and the nearest-neighbor distances (R) to As atoms in the first shell were extracted from the XAFS data. When As precipitates as monoclinic SiAs, the nearest-neighbor distances and coordination numbers are ∼2.37 Å and ∼3, as opposed to ∼2.40 Å and ∼4 when As is substitutional. Based on this information, the critical implant dose where the precipitation/clustering of As starts, and the ratio of the substitutional versus cluster/precipitate form As in the samples were determined.

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Assessment of Two-Dimensional Separative Systems Using Nearest-Neighbor Distances Approach. Part 1: Orthogonality Aspects

Analytical Chemistry ◽

10.1021/ac4012705 ◽

2013 ◽

Vol 85 (20) ◽

pp. 9449-9458 ◽

Cited By ~ 19

Author(s):

Witold Nowik ◽

Sylvie Héron ◽

Myriam Bonose ◽

Mateusz Nowik ◽

Alain Tchapla

Keyword(s):

Nearest Neighbor ◽

Two Dimensional ◽

Nearest Neighbor Distances

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Evaluating the Stability of Embedding-based Word Similarities

Transactions of the Association for Computational Linguistics ◽

10.1162/tacl_a_00008 ◽

2018 ◽

Vol 6 ◽

pp. 107-119 ◽

Cited By ~ 17

Author(s):

Maria Antoniak ◽

David Mimno

Keyword(s):

Nearest Neighbor ◽

Word Embeddings ◽

Training Set ◽

Word Associations ◽

Training Corpus ◽

Study Word ◽

Highly Sensitive ◽

The Stability ◽

Nearest Neighbor Distances

Word embeddings are increasingly being used as a tool to study word associations in specific corpora. However, it is unclear whether such embeddings reflect enduring properties of language or if they are sensitive to inconsequential variations in the source documents. We find that nearest-neighbor distances are highly sensitive to small changes in the training corpus for a variety of algorithms. For all methods, including specific documents in the training set can result in substantial variations. We show that these effects are more prominent for smaller training corpora. We recommend that users never rely on single embedding models for distance calculations, but rather average over multiple bootstrap samples, especially for small corpora.

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Conformity in the collective: differences in hunger affect individual and group behavior in a shoaling fish

Behavioral Ecology ◽

10.1093/beheco/arz036 ◽

2019 ◽

Vol 30 (4) ◽

pp. 968-974 ◽

Cited By ~ 3

Author(s):

Alexander D M Wilson ◽

Alicia L J Burns ◽

Emanuele Crosato ◽

Joseph Lizier ◽

Mikhail Prokopenko ◽

...

Keyword(s):

Collective Behavior ◽

Nearest Neighbor ◽

Collective Motion ◽

Internal State ◽

Group Behavior ◽

Fundamental Interest ◽

Individual Level ◽

Group Members ◽

The Individual ◽

Nearest Neighbor Distances

Abstract Animal groups are often composed of individuals that vary according to behavioral, morphological, and internal state parameters. Understanding the importance of such individual-level heterogeneity to the establishment and maintenance of coherent group responses is of fundamental interest in collective behavior. We examined the influence of hunger on the individual and collective behavior of groups of shoaling fish, x-ray tetras (Pristella maxillaris). Fish were assigned to one of two nutritional states, satiated or hungry, and then allocated to 5 treatments that represented different ratios of satiated to hungry individuals (8 hungry, 8 satiated, 4:4 hungry:satiated, 2:6 hungry:satiated, 6:2 hungry:satiated). Our data show that groups with a greater proportion of hungry fish swam faster and exhibited greater nearest neighbor distances. Within groups, however, there was no difference in the swimming speeds of hungry versus well-fed fish, suggesting that group members conform and adapt their swimming speed according to the overall composition of the group. We also found significant differences in mean group transfer entropy, suggesting stronger patterns of information flow in groups comprising all, or a majority of, hungry individuals. In contrast, we did not observe differences in polarization, a measure of group alignment, within groups across treatments. Taken together these results demonstrate that the nutritional state of animals within social groups impacts both individual and group behavior, and that members of heterogenous groups can adapt their behavior to facilitate coherent collective motion.

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Likelihood and nearest-neighbor distance properties of multidimensional Poisson cluster processes

Journal of Applied Probability ◽

10.2307/3213062 ◽

1981 ◽

Vol 18 (4) ◽

pp. 879-888 ◽

Cited By ~ 13

Author(s):

Michel Baudin

Keyword(s):

Nearest Neighbor ◽

Likelihood Function ◽

Practical Application ◽

Expected Number ◽

Generating Functional ◽

Functional Representation ◽

Distance Properties ◽

Poisson Cluster ◽

Nearest Neighbor Distances ◽

Asymptotic Developments

The probability generating functional representation of a multidimensional Poisson cluster process is utilized to derive a formula for its likelihood function, but the prohibitive complexity of this formula precludes its practical application to statistical inference. In the case of isotropic processes, it is however feasible to compute functions such as the probability Q(r) of finding no point in a disc of radius r and the probability Q(r | 0) of nearest-neighbor distances greater than r, as well as the expected number C(r | 0) of points at a distance less than r from a given point. Explicit formulas and asymptotic developments are derived for these functions in the n-dimensional case. These can effectively be used as tools for statistical analysis.

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