Spectroscopic Microwave Dielectric Model of Moist Soils

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Technical report: Cost-benefit analysis of cooking banana seed propagation methods

<p>Confusion matrix obtained as a result of Bayesian classification of EEG patterns, corresponding to various eye movements and blinking, after EOG artifact removal.</p

Increased motor cortex excitability during motor imagery in brain-computer interface trained subjects

Background: Motor imagery (MI) is the mental performance of movement without muscle activity. It is generally accepted that MI and motor performance have similar physiological mechanisms. Purpose: To investigate the activity and excitability of cortical motor areas during MI in subjects who were previously trained with an MI-based brain-computer interface (BCI). Subjects and Methods: Eleven healthy volunteers without neurological impairments (mean age, 36 years; range: 24–68 years) were either trained with an MI-based BCI (BCI-trained, n = 5) or received no BCI training (n = 6, controls). Subjects imagined grasping in a blocked paradigm task with alternating rest and task periods. For evaluating the activity and excitability of cortical motor areas we used functional MRI and navigated transcranial magnetic stimulation (nTMS). Results: fMRI revealed activation in Brodmann areas 3 and 6, the cerebellum, and the thalamus during MI in all subjects. The primary motor cortex was activated only in BCI-trained subjects. The associative zones of activation were larger in non-trained subjects. During MI, motor evoked potentials recorded from two of the three targeted muscles were significantly higher only in BCI-trained subjects. The motor threshold decreased (median = 17%) during MI, which was also observed only in BCI-trained subjects. Conclusion: Previous BCI training increased motor cortex excitability during MI. These data may help to improve BCI applications, including rehabilitation of patients with cerebral palsy.Web of Science7art. no. 0016

Electrical, Hemodynamic, and Motor Activity in BCI Post-stroke Rehabilitation: Clinical Case Study

The goal of the paper is to present an example of integrated analysis of electrical, hemodynamic, and motor activity accompanying the motor function recovery in a post-stroke patient having an extensive cortical lesion. The patient underwent a course of neurorehabilitation assisted with the hand exoskeleton controlled by brain-computer interface based on kinesthetic motor imagery. The BCI classifier was based on discriminating covariance matrices of EEG corresponding to motor imagery. The clinical data from three successive 2 weeks hospitalizations with 4 and 8 month intervals, respectively were under analysis. The rehabilitation outcome was measured by Fugl-Meyer scale and biomechanical analysis. Both measures indicate prominent improvement of the motor function of the paretic arm after each hospitalization. The analysis of brain activity resulted in three main findings. First, the sources of EEG activity in the intact brain areas, most specific to motor imagery, were similar to the patterns we observed earlier in both healthy subjects and post-stroke patients with mild subcortical lesions. Second, two sources of task-specific activity were localized in primary somatosensory areas near the lesion edge. The sources exhibit independent mu-rhythm activity with the peak frequency significantly lower than that of mu-rhythm in healthy subjects. The peculiarities of the detected source activity underlie changes in EEG covariance matrices during motor imagery, thus serving as the BCI biomarkers. Third, the fMRI data processing showed significant reduction in size of areas activated during the paretic hand movement imagery and increase for those activated during the intact hand movement imagery, shifting the activations to the same level. This might be regarded as the general index of the motor recovery. We conclude that the integrated analysis of EEG, fMRI, and motor activity allows to account for the reorganization of different levels of the motor system and to provide a comprehensive basis for adequate assessment of the BCI+ exoskeleton rehabilitation efficiency

Late Pleistocene and Holocene vegetation and climate on the northern Taymyr Peninsula, Arctic Russia

Pollen data from a Levinson-Lessing Lake sediment core (74°28'N, 98°38'E) and Cape Sabler, Taymyr Lake permafrost sequences (74°33'N, 100°32'E) reveal substantial environmental changes on the northern Taymyr Peninsula during the last c. 32000 14C years. The continuous records confirm that a scarce steppe-like vegetation with Poaceae, Artemisia and Cyperaceae dominated c. 32 000–10300 14C yr BP, while tundra-like vegetation with Oxyria, Ranunculaceae and Caryophyllaceae grew in wetter areas. The coldest interval occurred c. 18000 yr BP. Lateglacial pollen data show several warming events followed by a climate deterioration c. 10500 14C yr BP, which may correspond with the Younger Dryas. The Late Pleistocene/Holocene transition, c. 10300–10000 14C yr BP, is characterized by a change from the herb-dominated vegetation to shrubby tundra with Betula sect. Nanae and Salix. Alnus fruticosa arrived locally c. 9000–8500 14Cyr BP and disappeared c. 4000–3500 14Cyr BP. Communities of Betula sect. Nanae, broadly distributed at c. 10000–3500 14Cyr BP, almost disappeared when vegetation became similar to the modern herb tundra after 3500–3000 14Cyr BP. Quantitative climate reconstructions show Last Glacial Maximum summer temperature about 4°C below the present and Preboreal (c. 10 000 14C yr BP) temperature 2–4°C above the present. Maximum summer temperature occurred between 10 000 and 5500 14C yr BP; later summers were similar to present or slightly warmer

Solving Data Quality Problems with Desbordante: a Demo

Data profiling is an essential process in modern data-driven industries. One of its critical components is the discovery and validation of complex statistics, including functional dependencies, data constraints, association rules, and others. However, most existing data profiling systems that focus on complex statistics do not provide proper integration with the tools used by contemporary data scientists. This creates a significant barrier to the adoption of these tools in the industry. Moreover, existing systems were not created with industrial-grade workloads in mind. Finally, they do not aim to provide descriptive explanations, i.e. why a given pattern is not found. It is a significant issue as it is essential to understand the underlying reasons for a specific pattern's absence to make informed decisions based on the data. Because of that, these patterns are effectively rest in thin air: their application scope is rather limited, they are rarely used by the broader public. At the same time, as we are going to demonstrate in this presentation, complex statistics can be efficiently used to solve many classic data quality problems. Desbordante is an open-source data profiler that aims to close this gap. It is built with emphasis on industrial application: it is efficient, scalable, resilient to crashes, and provides explanations. Furthermore, it provides seamless Python integration by offloading various costly operations to the C++ core, not only mining. In this demonstration, we show several scenarios that allow end users to solve different data quality problems. Namely, we showcase typo detection, data deduplication, and data anomaly detection scenarios

Brain-Computer Interface Based on Generation of Visual Images

This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects) and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive Bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP) classifier

Opposite-side flavour tagging of B mesons at the LHCb experiment

The calibration and performance of the oppositeside flavour tagging algorithms used for the measurements of time-dependent asymmetries at the LHCb experiment are described. The algorithms have been developed using simulated events and optimized and calibrated with B + →J/ψK +, B0 →J/ψK ∗0 and B0 →D ∗− μ + νμ decay modes with 0.37 fb−1 of data collected in pp collisions at √ s = 7 TeV during the 2011 physics run. The oppositeside tagging power is determined in the B + → J/ψK + channel to be (2.10 ± 0.08 ± 0.24) %, where the first uncertainty is statistical and the second is systematic

Measurement of the Bs0→J/ψKS0B_s^0\to J/\psi K_S^0 branching fraction

The $B_s^0\to J/\psi K_S^0$ branching fraction is measured in a data sample corresponding to 0.41$fb^{-1}$ of integrated luminosity collected with the LHCb detector at the LHC. This channel is sensitive to the penguin contributions affecting the sin2$\beta$ measurement from $B^0\to J/\psi K_S^0$ The time-integrated branching fraction is measured to be $BF(B_s^0\to J/\psi K_S^0)=(1.83\pm0.28)\times10^{-5}$. This is the most precise measurement to date