An experimental and analytical study of visual detection in a spacecraft environment, 1 July 1968 - 1 July 1969

Predicting star magnitude which can be seen with naked eye or sextant through spacecraft windo

Neuronal assembly dynamics in supervised and unsupervised learning scenarios

The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions

Human recognition based on gait poses

This paper introduces a new approach for gait analysis based on the Gait Energy Image (GEI). The main idea is to segment the gait cycle into some biomechanical poses, and to compute a particular GEI for eachpose. Pose-based GEIs can better represent body parts and dynamics descriptors with respect to the usually blurred depiction provided by a general GEI. Gait classification is carried out by fusing separatedpose-based decisions. Experiments on human identification prove the benefits of this new approach when compared to the original GEI method.Partially supported by projects CSD2007-00018 and CICYT TIN2009-14205-C04-04 from the Spanish Ministry of Innovation and Science, P1-1B2009-04 from Fundació Bancaixa and PREDOC/2008/04 grant from Universitat Jaume I. Portions of the research in this paper use the CASIA Gait Database collected by Institute of Automation, Chinese Academy of Science

Spatially Resolved Mapping of Local Polarization Dynamics in an Ergodic Phase of Ferroelectric Relaxor

Spatial variability of polarization relaxation kinetics in relaxor ferroelectric 0.9Pb(Mg1/3Nb2/3)O3-0.1PbTiO3 is studied using time-resolved Piezoresponse Force Microscopy. Local relaxation attributed to the reorientation of polar nanoregions is shown to follow stretched exponential dependence, exp(-(t/tau)^beta), with beta~~0.4, much larger than the macroscopic value determined from dielectric spectra (beta~~0.09). The spatial inhomogeneity of relaxation time distributions with the presence of 100-200 nm "fast" and "slow" regions is observed. The results are analyzed to map the Vogel-Fulcher temperatures on the nanoscale.Comment: 23 pages, 4 figures, supplementary materials attached; to be submitted to Phys. Rev. Let

Sparse Exploratory Factor Analysis

Sparse principal component analysis is a very active research area in the last decade. It produces component loadings with many zero entries which facilitates their interpretation and helps avoid redundant variables. The classic factor analysis is another popular dimension reduction technique which shares similar interpretation problems and could greatly benefit from sparse solutions. Unfortunately, there are very few works considering sparse versions of the classic factor analysis. Our goal is to contribute further in this direction. We revisit the most popular procedures for exploratory factor analysis, maximum likelihood and least squares. Sparse factor loadings are obtained for them by, first, adopting a special reparameterization and, second, by introducing additional [Formula: see text]-norm penalties into the standard factor analysis problems. As a result, we propose sparse versions of the major factor analysis procedures. We illustrate the developed algorithms on well-known psychometric problems. Our sparse solutions are critically compared to ones obtained by other existing methods

Space-Time Clustering and Correlations of Major Earthquakes

Earthquake occurrence in nature is thought to result from correlated elastic stresses, leading to clustering in space and time. We show that occurrence of major earthquakes in California correlates with time intervals when fluctuations in small earthquakes are suppressed relative to the long term average. We estimate a probability of less than 1% that this coincidence is due to random clustering.Comment: 5 pages, 3 figures. Submitted to PR

Study of Digital Competence of the Students and Teachers in Ukraine

Professional fulfillment of the personality at the conditions of the digital economy requires the high level of digital competency. One of the ways to develop these competencies is education. However, to provide the implementation of digital education at a high level, the digital competency of the teachers and students is a must. This paper presents explanations on the level determination of the digital competencies for teachers and students in Ukraine according to the DigComp recommendations. We tried to identify the main factors that reflect the degree of readiness teachers and students for digital education based on their self-evaluation. We also attempted to estimate the level of digital competencies based on the analysis of Case-Studies execution results. The complex analysis let us assess the connection between respondents’ self-evaluation and their real competencies. Here we provide a methodology and a model of level competencies determination by means of a survey, expert case rating and the results of the statistical analysis. On the basis of the obtained results, this paper suggests further research prospects and recommendations on the digital competency development in educational institutions in Ukraine

Relaxed 2-D Principal Component Analysis by LpL_p Norm for Face Recognition

A relaxed two dimensional principal component analysis (R2DPCA) approach is proposed for face recognition. Different to the 2DPCA, 2DPCA-$L_1$ and G2DPCA, the R2DPCA utilizes the label information (if known) of training samples to calculate a relaxation vector and presents a weight to each subset of training data. A new relaxed scatter matrix is defined and the computed projection axes are able to increase the accuracy of face recognition. The optimal $L_p$-norms are selected in a reasonable range. Numerical experiments on practical face databased indicate that the R2DPCA has high generalization ability and can achieve a higher recognition rate than state-of-the-art methods.Comment: 19 pages, 11 figure

Timing and Dose of Upper Limb Motor Intervention After Stroke: A Systematic Review

This systematic review aimed to investigate timing, dose, and efficacy of upper limb intervention during the first 6 months poststroke. Three online databases were searched up to July 2020. Titles/abstracts/full-text were reviewed independently by 2 authors. Randomized and nonrandomized studies that enrolled people within the first 6 months poststroke, aimed to improve upper limb recovery, and completed preintervention and postintervention assessments were included. Risk of bias was assessed using Cochrane reporting tools. Studies were examined by timing (recovery epoch), dose, and intervention type. Two hundred and sixty-one studies were included, representing 228 (n=9704 participants) unique data sets. The number of studies completed increased from one (n=37 participants) between 1980 and 1984 to 91 (n=4417 participants) between 2015 and 2019. Timing of intervention start has not changed (median 38 days, interquartile range [IQR], 22–66) and study sample size remains small (median n=30, IQR 20–48). Most studies were rated high risk of bias (62%). Study participants were enrolled at different recovery epochs: 1 hyperacute (<24 hours), 13 acute (1–7 days), 176 early subacute (8–90 days), 34 late subacute (91–180 days), and 4 were unable to be classified to an epoch. For both the intervention and control groups, the median dose was 45 (IQR, 600–1430) min/session, 1 (IQR, 1–1) session/d, 5 (IQR, 5–5) d/wk for 4 (IQR, 3–5) weeks. The most common interventions tested were electromechanical (n=55 studies), electrical stimulation (n=38 studies), and constraint-induced movement (n=28 studies) therapies. Despite a large and growing body of research, intervention dose and sample size of included studies were often too small to detect clinically important effects. Furthermore, interventions remain focused on subacute stroke recovery with little change in recent decades. A united research agenda that establishes a clear biological understanding of timing, dose, and intervention type is needed to progress stroke recovery research. Prospective Register of Systematic Reviews ID: CRD42018019367/CRD42018111629