380,837 research outputs found
The periodic table of data structures
http://sites.computer.org/debull/A18sept/p64.pdfPublished versio
Toward a periodic table of personality: Mapping personality scales between the five-factor model and the circumplex model
In this study, we examine the structures of 10 personality inventories (PIs) widely used for personnel assessment by mapping the scales of PIs to the lexical Big Five circumplex model resulting in a Periodic Table of Personality. Correlations between 273 scales from 10 internationally popular PIs with independent markers of the lexical Big Five are reported, based on data from samples in 2 countries (United Kingdom, N 286; United States, N 1,046), permitting us to map these scales onto the Abridged Big Five Dimensional Circumplex model (Hofstee, de Raad, & Goldberg, 1992). Emerging from our findings we propose a common facet framework derived from the scales of the PIs in our study. These results provide important insights into the literature on criterion-related validity of personality traits, and enable researchers and practitioners to understand how different PI scales converge and diverge and how compound PI scales may be constructed or replicated. Implications for research and practice are considered
Feature Optimization for Atomistic Machine Learning Yields A Data-Driven Construction of the Periodic Table of the Elements
Machine-learning of atomic-scale properties amounts to extracting
correlations between structure, composition and the quantity that one wants to
predict. Representing the input structure in a way that best reflects such
correlations makes it possible to improve the accuracy of the model for a given
amount of reference data. When using a description of the structures that is
transparent and well-principled, optimizing the representation might reveal
insights into the chemistry of the data set. Here we show how one can
generalize the SOAP kernel to introduce a distance-dependent weight that
accounts for the multi-scale nature of the interactions, and a description of
correlations between chemical species. We show that this improves substantially
the performance of ML models of molecular and materials stability, while making
it easier to work with complex, multi-component systems and to extend SOAP to
coarse-grained intermolecular potentials. The element correlations that give
the best performing model show striking similarities with the conventional
periodic table of the elements, providing an inspiring example of how machine
learning can rediscover, and generalize, intuitive concepts that constitute the
foundations of chemistry.Comment: 9 pages, 4 figure
Measuring temperature - dependent propagating disturbances in coronal fan loops using multiple SDO/AIA channels and surfing transform technique
A set of co-aligned high resolution images from the Atmospheric Imaging
Assembly (AIA) on board the Solar Dynamics Observatory (SDO) is used to
investigate propagating disturbances (PDs) in warm fan loops at the periphery
of a non-flaring active region NOAA AR 11082. To measure PD speeds at multiple
coronal temperatures, a new data analysis methodology is proposed enabling
quantitative description of subvisual coronal motions with low signal-to-noise
ratios of the order of 0.1 %. The technique operates with a set of
one-dimensional "surfing" signals extracted from position-time plots of several
AIA channels through a modified version of Radon transform. The signals are
used to evaluate a two-dimensional power spectral density distribution in the
frequency - velocity space which exhibits a resonance in the presence of
quasi-periodic PDs. By applying this analysis to the same fan loop structures
observed in several AIA channels, we found that the traveling velocity of PDs
increases with the temperature of the coronal plasma following the square root
dependence predicted for the slow mode magneto-acoustic wave which seems to be
the dominating wave mode in the studied loop structures. This result extends
recent observations by Kiddie et al. (Solar Phys., 2012) to a more general
class of fan loop systems not associated with sunspots and demonstrating
consistent slow mode activity in up to four AIA channels.Comment: 23 pages, 8 figures, 2 table
2DMatPedia: An open computational database of two-dimensional materials from top-down and bottom-up approaches
Two-dimensional (2D) materials have been a hot research topic in the last
decade, due to novel fundamental physics in the reduced dimension and appealing
applications. Systematic discovery of functional 2D materials has been the
focus of many studies. Here, we present a large dataset of 2D materials, with
more than 6,000 monolayer structures, obtained from both top-down and bottom-up
discovery procedures. First, we screened all bulk materials in the database of
Materials Project for layered structures by a topology-based algorithm, and
theoretically exfoliate them into monolayers. Then, we generated new 2D
materials by chemical substitution of elements in known 2D materials by others
from the same group in the periodic table. The structural, electronic and
energetic properties of these 2D materials are consistently calculated, to
provide a starting point for further material screening, data mining, data
analysis and artificial intelligence applications. We present the details of
computational methodology, data record and technical validation of our publicly
available data (http://www.2dmatpedia.org/)
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