Natural data structure extracted from neighborhood-similarity graphs

'Big' high-dimensional data are commonly analyzed in low-dimensions, after performing a dimensionality-reduction step that inherently distorts the data structure. For the same purpose, clustering methods are also often used. These methods also introduce a bias, either by starting from the assumption of a particular geometric form of the clusters, or by using iterative schemes to enhance cluster contours, with uncontrollable consequences. The goal of data analysis should, however, be to encode and detect structural data features at all scales and densities simultaneously, without assuming a parametric form of data point distances, or modifying them. We propose a novel approach that directly encodes data point neighborhood similarities as a sparse graph. Our non-iterative framework permits a transparent interpretation of data, without altering the original data dimension and metric. Several natural and synthetic data applications demonstrate the efficacy of our novel approach

Developing experimental estimates of regional skill demand

This paper shows how novel data, in the form of online job adverts, can be used to enrich official labour market statistics. We use millions of job adverts to provide granular estimates of the vacancy stock broken down by location, occupation and skill category. To derive these estimates, we build on previous work and deploy methodologies for a) converting the flow of job adverts into a stock and b) adjusting this stock to ensure it is representative of the underlying economy. Our results benefit from the use of duration data at the level of individual vacancies. We also introduce a new iteration of Nesta’s skills taxonomy. This is the first iteration to blend an expert-derived collection of skills with the skills extracted from job adverts. These methodological advances allow us to analyse which skill sets are sought by employers, how these vary across Travel To Work Areas in the UK and how skill demand evolves over time. For example, we find that there is considerable geographical variability in skill demand, with the stock varying more than five-fold across locations. At the same time, most of the demand is concentrated among three categories: “Business, law & finance”, “Science, manufacturing & engineering” and “Digital”. Together, these account for more than 60% of all skills demanded. The type of intelligence presented in this report could be used to support both local and national decision makers in responding to recent labour market disruptions

Tribological and Mechanical Properties of the Nanostructured Superlattice Coatings with Respect to Surface Texture

This research is funded by the Latvian Council of Science, project “Carbon-rich self-healing multifunctional nanostructured smart coatings (NSC) for high-tech applications using high-power confined plasma technology for their deposition”, project No. 2019/1-0385.Ceramic Nanostructured Superlattice Coatings (NSC) have broad applicability to improve the parts’ and assemblies’ tribological and mechanical properties for the needs of the automotive and aerospace industries. Improving the material properties using nanocoatings for such a widely used material as, for example, bearing steel 100Cr6 makes it possible to improve the service life of machine parts. In this paper, the correlation dependence between tribological and mechanical properties of the NSC and its surface texture are considered to determine how much surface texture will affect the tribological performance of the coated workpieces, as well as the measuring and evaluation procedure of the nanocoatings, are presented. Three different NSC described by a general empirical formula {TiMe1Me2-CN/TiAlSi-N}n and based on the modified carbonitride/nitride non-stoichiometric chemical composition were created, and their tribological and mechanical properties measured and analyzed in the context with surface texture. NSC deposited by the advanced PVD (Physical vapor deposition) technique demonstrated significantly higher wear resistance (up to 28 times), reasonably lower friction coefficient (CoF) (up to 4 times), and significantly higher hardness of the coated workpieces (up to 7 times) versus substrate material. A strong correlation between the steady-state dry sliding friction, CoF, and the amplitude and functional surface texture parameters of tribo-track were observed. The first results of the initiated research regarding the correlation analysis of the tribological and mechanical properties, on the one hand, and surface texture, on the other hand, of the NSC are reported here. © 2022 by the authors.Latvian Council of Science project No. 2019/1-0385; Institute of Solid-State Physics, University of Latvia has received funding from the European Union's Horizon 2020 Framework Programme H2020-WIDESPREAD-01-2016-2017-Teaming Phase 2 under grant agreement No. 739508, project CAMART2

Nanoindentation Response analysis of Thin Film Substrates-I: Strain Gradient-Divergence Approach

Nanoindentation is a widely-used method for sensitive exploration of the mechanical properties of micromechanical systems. We derive a simple empirical analysis technique to extract stress-strain field (SSF) gradient and divergence representations from nanoindentation data sets. Using this approach, local SSF gradients and structural heterogeneities can be discovered to obtain more detail about the sample’s microstructure, thus enhancing the analytic capacity of the nanoindentation technique. We demonstrate the application of the SSF gradient-divergence analysis approach to nanoindentation measurements of bulk silicon

Nanoindentation response analysis of thin film substrates-II: Strain hardening-softening oscillations in subsurface layer

We have extracted stress-strain field (SSF) gradient and divergence rep-resentations from nanoindentation data sets of bulk solids often used as thin film substrates: bearing and tooling steels, silicon, glasses, and fused silica. Oscillations of the stress-strain field gradient and divergence induced in the subsurface layer by the nanoindentation have been revealed. The oscillations are especially prominent in single indentation tests at shallow penetration depths, h<100 nm, whereas they are concealed in the averaged datasets of 10 and more single tests. The amplitude of the SSF divergence oscillations decays as a sublinear power-law when the indenter approaches deeper atomic layers, with an exponent -0.9 for the steel and -0.8 for the fused silica. The oscillations are interpreted as alternating strain hardening-softening plastic deformation cycles induced in the subsurface layer under the indenter load

Universality in the firing of minicolumnar-type neural networks

An open question in biological neural networks is whether changes in firing modalities are mainly an individual network property or whether networks follow a joint pathway. For the early developmental period, our study focusing on a simple network class of excitatory and inhibitory neurons suggests the following answer: Networks with considerable variation of topology and dynamical parameters follow a universal firing paradigm that evolves as the overall connectivity strength and firing level increase, as seen in the process of network maturation. A simple macroscopic model reproduces the main features of the paradigm as a result of the competition between the fundamental dynamical system notions of synchronization vs chaos and explains why in simulations the paradigm is robust regarding differences in network topology and largely independent from the neuron model used. The presented findings reflect the first dozen days of dissociated neuronal in vitro cultures (upon following the developmental period bears similarly universal features but is characterized by the processes of neuronal facilitation and depression that do not require to be considered for the first developmental period). A key element for explaining processes in nature by physics has been the art of choosing the optimal level of description for the effects to be described. In our current challenge to explain important aspects of our brain by means of physics, we still largely miss such a handle at many levels: To what detail, e.g., is it necessary to model neurons and their connectivity to understand what their neural network is doing? For simple small-size networks of minicolumnar type (by many considered as a potential module underlying the function of the cortex), we show that all networks from this large network class follow the same—universal—behavior, as their overall connectivity strength is enhanced. Moreover, the paradigm that they follow can be explained in terms of low-dimensional dynamical systems theory, which reveals the origin of the universal behavior. Our findings suggest that other network classes could be treated in a similar manner. The uncovered universality permits us to substantially limit the degree of details required to model cortical computation, which opens up a novel perspective toward more effective simulations of and investigations into close-to-biology neural networks and sheds a novel perspective on biological multiscale information processing. From the practical side, our findings imply that biological neural networks with strong parallels to the increase of a connectivity strength will develop closely along the uncovered paradigm. Examples are neuronal cultures at the early stage of their development or biochemical processes that globally enhance the connectivity strength among the elements of the neural network