Nicolaus Copernicus University in Toruń

About: Nicolaus Copernicus University in Toruń is a based out in . It is known for research contribution in the topics: Population & Membrane. The organization has 5921 authors who have published 15352 publications receiving 225625 citations.

Completely Positive Dynamical Semigroups of N Level Systems

Abstract: We establish the general form of the generator of a completely positive dynamical semigroup of an N‐level quantum system, and we apply the result to derive explicit inequalities among the physical parameters characterizing the Markovian evolution of a 2‐level system.

Using social and behavioural science to support COVID-19 pandemic response.

Abstract: The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behaviour with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic and highlight important gaps researchers should move quickly to fill in the coming weeks and months.

Trigonometric parallaxes of high mass star forming regions: the structure and kinematics of the milky way

Abstract: Over 100 trigonometric parallaxes and proper motions for masers associated with young, high- mass stars have been measured with the Bar and Spiral Structure Legacy Survey, a Very Long Baseline Array key science project, the European VLBI Network, and the Japanese VLBI Exploration of Radio Astrometry project. These measurements provide strong evidence for the existence of spiral arms in the MilkyWay, accurately locating many arm segments and yielding spiral pitch angles ranging from about 7 degrees to 20 degrees. The widths of spiral arms increase with distance from the Galactic center. Fitting axially symmetric models of the MilkyWay with the three- dimensional position and velocity information and conservative priors for the solar and average source peculiar motions, we estimate the distance to the Galactic center, R-0, to be 8.34 +/- 0.16 kpc, a circular rotation speed at the Sun, Theta(0), to be 240 +/- 8 km s(-1), and a rotation curve that is nearly flat ( i. e., a slope of -0.2 +/- 0.4 km s(-1) kpc(-1)) between Galactocentric radii of approximate to 5 and 16 kpc. Assuming a " universal" spiral galaxy form for the rotation curve, we estimate the thin disk scale length to be 2.44 +/- 0.16 kpc. With this large data set, the parameters R-0 and Theta(0) are no longer highly correlated and are relatively insensitive to different forms of the rotation curve. If one adopts a theoretically motivated prior that high- mass star forming regions are in nearly circular Galactic orbits, we estimate a global solar motion component in the direction of Galactic rotation, V-circle dot = 14.6 +/- 5.0 km s(-1). While Theta(0) and V-circle dot are significantly correlated, the sum of these parameters is well constrained, Theta(0) + V circle dot = 255.2 +/- 5.1 km s(-1), as is the angular speed of the Sun in its orbit about the Galactic center, ( Theta(0) + V-circle dot)/R-0 = 30.57 +/- 0.43 km s(-1) kpc(-1). These parameters improve the accuracy of estimates of the accelerations of the Sun and the Hulse-Taylor binary pulsar in their Galactic orbits, significantly reducing the uncertainty in tests of gravitational radiation predicted by general relativity.

A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update

Abstract: Objective: Most current Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately 10 years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs. Approach: We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons. Main results: We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods. Significance: This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these Review of Classification Algorithms for EEG-based BCI 2 methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI.

Trigonometric parallaxes of massive star-forming regions. vi. galactic structure, fundamental parameters, and noncircular motions

Abstract: We are using the Very Long Baseline Array and the Japanese VLBI Exploration of Radio Astronomy project to measure trigonometric parallaxes and proper motions of masers found in high-mass star-forming regions across the Milky Way. Early results from 18 sources locate several spiral arms. The Perseus spiral arm has a pitch angle of 16 degrees +/- 3 degrees, which favors four rather than two spiral arms for the Galaxy. Combining positions, distances, proper motions, and radial velocities yields complete three-dimensional kinematic information. We find that star-forming regions on average are orbiting the Galaxy approximate to 15 km s(-1) slower than expected for circular orbits. By fitting the measurements to a model of the Galaxy, we estimate the distance to the Galactic center R(0) = 8.4 +/- 0.6 kpc and a circular rotation speed Theta(0) = 254 +/- 16 km s(-1). The ratio Theta(0)/R(0) can be determined to higher accuracy than either parameter individually, and we find it to be 30.3 +/- 0.9 km s(-1) kpc(-1), in good agreement with the angular rotation rate determined from the proper motion of Sgr A*. The data favor a rotation curve for the Galaxy that is nearly flat or slightly rising with Galactocentric distance. Kinematic distances are generally too large, sometimes by factors greater than 2; they can be brought into better agreement with the trigonometric parallaxes by increasing Theta(0)/R(0) from the IAU recommended value of 25.9 km s(-1) kpc(-1) to a value near 30 km s(-1) kpc(-1). We offer a "revised" prescription for calculating kinematic distances and their uncertainties, as well as a new approach for defining Galactic coordinates. Finally, our estimates of Theta(0) and Theta(0)/R(0), when coupled with direct estimates of R(0), provide evidence that the rotation curve of the Milky Way is similar to that of the Andromeda galaxy, suggesting that the dark matter halos of these two dominant Local Group galaxy are comparably massive.