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Correlation between atomic structure evolution and strength in a bulk metallic glass at cryogenic temperature
2014
Scientific Reports
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An accurate physical mechanism quantitatively describing the relationship between the deformation units and the strength of BMGs at cryogenic temperature is still unclear. ...
To further quantitatively describe the change in the first maximum of S(q), the position and the full width at half maximum (FWHM) of the first maximum at different temperatures are calculated by Gaussian ...
(b) Deconvolution of the first nearest neighbor shell into two Gaussians at 98 K. (c) Coordination numbers versus temperature. ...
doi:10.1038/srep03897
pmid:24469299
pmcid:PMC3904144
fatcat:zedbme755neuza5ehl2wdrthxa
Threshold response to stochasticity in morphogenesis
2019
PLoS ONE
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Establishing the R8 cell is crucial in setting up the geometric, and functional, relationships of cells within an ommatidium and among neighboring ommatidia. ...
During development of biological organisms, multiple complex structures are formed. ...
Computation for the work described in this paper was supported by the University of Southern California's Center for High-Performance Computing (hpc.usc.edu). ...
doi:10.1371/journal.pone.0210088
pmid:30699125
pmcid:PMC6353092
fatcat:dkfkxonqtrcs5m5ltfpbbx26ya
Pattern recognition approach to classifying CYP 2C19 isoform
2012
Open Medicine
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AbstractIn this paper a pattern recognition approach to classifying quantitative structure-property relationships (QSPR) of the CYP2C19 isoform is presented. ...
Presented solution deals with those problems, additionally incorporating a throughout feature selection for improving the stability of received results. ...
Quantitative structure-property relationship Quantitative structure-property relationship (QSPR) is the process by which chemical structure is quantitatively correlated with a well defined process, such ...
doi:10.2478/s11536-011-0120-3
fatcat:axbhk2pconhg5fp56r4ax3t5ge
Quantitative analyses of the 3D nuclear landscape recorded with super-resolved fluorescence microscopy
2017
Methods
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In this paper we describe and discuss tools for (semi-) automated, quantitative 3D analyses of the spatial nuclear organization. ...
KEYWORDS super-resolution fluorescence microscopy; nucleome; chromatin compaction maps; quantitative image analysis; 3D nuclear topography of DNA, RNA and proteins ABREVIATIONS 3D-SIM, 3D structured illumination ...
The mapping of specific DNA sequences and nuclear proteins on the different chromatin compaction classes allows new insights into the relationships between structural and functional processes of the 4D ...
doi:10.1016/j.ymeth.2017.03.013
pmid:28323041
fatcat:nspqczt3vfc7dhhpwfkav6wiqm
Towards a systematic characterization of protein complex function: a natural language processing and machine-learning framework
[article]
2021
bioRxiv
pre-print
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PCfun leverages the embedding for rapid annotation of protein complex function by integrating two approaches: (1) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for ...
It is a general assumption of molecular biology that the ensemble of expressed molecules, their activities and interactions determine biological processes, cellular states and phenotypes. ...
tree (k-dimensional tree) (Freidman et al., 1977) , a space-174 partitioning structure for storing the sub-embeddings' vectors of GO terms to enable rapid application 175 of a nearest neighbor algorithm ...
doi:10.1101/2021.02.24.432789
fatcat:qgoj3ntf4nhplhexwkix63kvgm
Local indicators of geocoding accuracy (LIGA): theory and application
2009
International Journal of Health Geographics
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Herein lies a paradox for spatial analysis: For a given level of positional error increasing sample density to more accurately follow the underlying population distribution increases perturbability and ...
We therefore must understand the relationships between positional accuracy and the perturbability of the spatial weights in order to have confidence in a study's results. ...
process might be, and is able to articulate that process quantitatively. ...
doi:10.1186/1476-072x-8-60
pmid:19863795
pmcid:PMC2774310
fatcat:qtgsiyxywvezva7lcnjbmqdk5e
HUMAN ACTIVITY DETECTION AND ACTION RECOGNITION IN VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS
2020
Journal of Information and Communication Technology
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Tracking of human activity in the video is implemented using the Gaussian Mixture Model. Convolutional Neural Network based classification approach is used for database training and testing purposes. ...
Several approaches have been presented for human activity recognition using machine learning techniques. ...
This research received no specific grant from any funding agency in the public, commercial, or not-for profit sectors. ...
doi:10.32890/jict2020.19.2.1
fatcat:cafcrtepljesvnidrwfjuo2foa
Transport of Information along Unidimensional Layered Networks of Dissociated Hippocampal Neurons and Implications for Rate Coding
2006
Journal of Neuroscience
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The balance of excitatory and inhibitory synapses is crucial for this transmission. ...
We demonstrate that propagation along the line is precisely described by information theory as a chain of Gaussian communication channels. ...
We checked for asymmetry in 18 nearest neighbor ROIs in n ϭ 3 cultures. ...
doi:10.1523/jneurosci.4692-05.2006
pmid:16641232
pmcid:PMC6674052
fatcat:oorevdvlhneahkmnge5nqyoqsy
On the relationship between cyclic and hierarchical three-species predator-prey systems and the two-species Lotka-Volterra model
2012
European Physical Journal B : Condensed Matter Physics
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2011 pre-print arXiv:1111.1674v1 fatcat:vaxpmwhmizdi5omq3gfrobfvniOther Versions
If spreading occurs only through nearest-neighbor hopping, small population clusters emerge; yet the requirement of an intermediate species cluster obviously disrupts spatio-temporal correlations between ...
In the presence of pair exchange processes, the system remains essentially well-mixed, and we generally find the Monte Carlo simulation results for the spatially extended model (2) to be consistent with ...
This work is in part supported by Virginia Tech's Institute for Critical Technology and Applied Science (ICTAS) through a Doctoral Scholarship, and the US National Science Foundation through grant No. ...
doi:10.1140/epjb/e2012-20918-4
fatcat:f6szph4qd5fi7ak3abs277u3ui
Covert Network Analysis for Key Player Detection and Event Prediction Using a Hybrid Classifier
2014
The Scientific World Journal
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The proposed system calculates certain centrality measures for each node in the network and then applies novel hybrid classifier for detection of key players. ...
Our system also applies anomaly detection to predict any terrorist activity in order to help law enforcement agencies to destabilize the involved network. ...
A new hybrid classifier as an ensemble of -nearest neighbors (kNN), Gaussian mixture model (GMM), and support vector machine (SVM) is proposed here for accurate detection of key players. ...
doi:10.1155/2014/615431
pmid:25136674
pmcid:PMC4127216
fatcat:5kxdhtr735go7j7tvhrdniavh4
A new model for the diffusion behavior of hydrogen in metallic glasses
1999
Acta Materialia
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The eect of the alloying elements on the activation energy of hydrogen diusion in amorphous iron is discussed in terms of their electronic structure and mean volume. # ...
This deviation was traditionally explained by the existence of various kinds of jumps or in terms of continuous distributions of activation energies due to dierent kinds of disorder. ...
This ratio is equal to 2 in a f.c.c. structure, having 12 ®rst-nearest neighbors and six second-nearest neighbors. ...
doi:10.1016/s1359-6454(99)00157-3
fatcat:j5oz7s36xne37b7xfwd2gdxzhu
Repurposing de novo designed entities reveals phosphodiesterase 3B and cathepsin L modulators
2015
Chemical Communications
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for explicit use with de novo designed molecules. 6 We have recently reported a machine-learning approach (Gaussian process regression, GP) for quantitative structure-activity relationship (SAR) modeling ...
Still, low structural fingerprint similarity (T c o 0.2) between these de novo designed entities and their respective nearest neighbors in ChEMBL suggests, in this case, that straightforward similarity ...
doi:10.1039/c5cc01376c
pmid:25828577
fatcat:h3cpapld6nhdxduzssdx7fxviq
Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning
[article]
2020
arXiv
pre-print
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Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs. ...
A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. ...
This ML work highlights the predictive power of local static structure to quantitatively connect with β processes in MGs. ...
arXiv:2006.13552v1
fatcat:kfzmm4ei2bbplky7ghaldqsgkm
Electronic Nature of Step-Edge Barriers against Adatom Descent on Transition-Metal Surfaces
2008
Physical Review Letters
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We also find an approximate linear relation between the adatom step-edge hopping barriers and the adatom-surface bonding strength with slope roughly proportional to the number of the adatom's nearest neighbors ...
for adatom descent at a step and the relative degree of electronic shell filling of the adatom and the substrate. ...
is roughly proportional to the number of the nearest neighbors of the adatom at the initial state. ...
doi:10.1103/physrevlett.101.216101
pmid:19113426
fatcat:zffttb5j3fdjvk2o7bcjel374y
Range and energetics of charge hopping in organic semiconductors
2017
Physical review B
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In particular, the degree to which hops beyond the nearest neighbor must be accounted for at RT is still largely unknown. ...
The recent upswing in attention for the thermoelectric properties of organic semiconductors (OSCs) adds urgency to the need for a quantitative description of the range and energetics of hopping transport ...
The possibility to search for an optimal, i.e., giving the highest conductivity, combination of R * and E * does not exist in NNH, as R * is constant and equal to the nearest-neighbor distance. ...
doi:10.1103/physrevb.96.241202
fatcat:gg4vh453mbc3tn6mhbvod3pufi
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