Характерні особливості розвитку вітчизняних хімічних наукових шкіл

Проведено аналіз діяльності відомих учених-хіміків, які стали лідерами формування наукових шкіл у хімічних науках. Установлено актуальність їх діяльності та визначено характерні особливості розвитку вітчизняних наукових шкіл, котрі мали широкі та важливі напрямки досліджень.Activity analysis of the famous chemist-scientists, which have become leaders in the forming of scientific schools in chemistry sciences is transacted. Urgency of their activity is inserted and characters domestic scientific schools development, which had wide and important directions of the investigations are rated

A characterization of strongly chordal graphs

AbstractIn this paper, we present a simple charactrization of strongly chordal graphs. A chordal graph is strongly chordal if and only if every cycle on six or more vertices has an induced triangle with exactly two edges of the triangle as the chords of the cycle

Wigner-Poisson statistics of topological transitions in a Josephson junction

The phase-dependent bound states (Andreev levels) of a Josephson junction can cross at the Fermi level, if the superconducting ground state switches between even and odd fermion parity. The level crossing is topologically protected, in the absence of time-reversal and spin-rotation symmetry, irrespective of whether the superconductor itself is topologically trivial or not. We develop a statistical theory of these topological transitions in an N-mode quantum-dot Josephson junction, by associating the Andreev level crossings with the real eigenvalues of a random non-Hermitian matrix. The number of topological transitions in a 2pi phase interval scales as sqrt(N) and their spacing distribution is a hybrid of the Wigner and Poisson distributions of random-matrix theory.Comment: 12 pages, 15 figures; v2 to appear in PRL, with appendix in the supplementary materia

Zero-voltage conductance peak from weak antilocalization in a Majorana nanowire

We show that weak antilocalization by disorder competes with resonant Andreev reflection from a Majorana zero-mode to produce a zero-voltage conductance peak of order e^2/h in a superconducting nanowire. The phase conjugation needed for quantum interference to survive a disorder average is provided by particle-hole symmetry - in the absence of time-reversal symmetry and without requiring a topologically nontrivial phase. We identify methods to distinguish the Majorana resonance from the weak antilocalization effect.Comment: 13 pages, 8 figures. Addendum, February 2014: Appendix B shows results for weak antilocalization in the circular ensemble. (This appendix is not in the published version.

Spring School on Language, Music, and Cognition: Organizing Events in Time

The interdisciplinary spring school “Language, music, and cognition: Organizing events in time” was held from February 26 to March 2, 2018 at the Institute of Musicology of the University of Cologne. Language, speech, and music as events in time were explored from different perspectives including evolutionary biology, social cognition, developmental psychology, cognitive neuroscience of speech, language, and communication, as well as computational and biological approaches to language and music. There were 10 lectures, 4 workshops, and 1 student poster session. Overall, the spring school investigated language and music as neurocognitive systems and focused on a mechanistic approach exploring the neural substrates underlying musical, linguistic, social, and emotional processes and behaviors. In particular, researchers approached questions concerning cognitive processes, computational procedures, and neural mechanisms underlying the temporal organization of language and music, mainly from two perspectives: one was concerned with syntax or structural representations of language and music as neurocognitive systems (i.e., an intrapersonal perspective), while the other emphasized social interaction and emotions in their communicative function (i.e., an interpersonal perspective). The spring school not only acted as a platform for knowledge transfer and exchange but also generated a number of important research questions as challenges for future investigations

Complexity of Discrete Energy Minimization Problems

Discrete energy minimization is widely-used in computer vision and machine learning for problems such as MAP inference in graphical models. The problem, in general, is notoriously intractable, and finding the global optimal solution is known to be NP-hard. However, is it possible to approximate this problem with a reasonable ratio bound on the solution quality in polynomial time? We show in this paper that the answer is no. Specifically, we show that general energy minimization, even in the 2-label pairwise case, and planar energy minimization with three or more labels are exp-APX-complete. This finding rules out the existence of any approximation algorithm with a sub-exponential approximation ratio in the input size for these two problems, including constant factor approximations. Moreover, we collect and review the computational complexity of several subclass problems and arrange them on a complexity scale consisting of three major complexity classes -- PO, APX, and exp-APX, corresponding to problems that are solvable, approximable, and inapproximable in polynomial time. Problems in the first two complexity classes can serve as alternative tractable formulations to the inapproximable ones. This paper can help vision researchers to select an appropriate model for an application or guide them in designing new algorithms.Comment: ECCV'16 accepte

Analysis of High Dimensional Data from Intensive Care Medicine

As high dimensional data occur as a rule rather than an exception in critical care today, it is of utmost importance to improve acquisition, storage, modelling, and analysis of medical data, which appears feasable only with the help of bedside computers. The use of clinical information systems offers new perspectives of data recording and also causes a new challenge for statistical methodology. A graphical approach for analysing patterns in statistical time series from online monitoring systems in intensive care is proposed here as an example of a simple univariate method, which contains the possibility of a multivariate extension and which can be combined with procedures for dimension reduction

A New Method to Estimate the Noise in Financial Correlation Matrices

Financial correlation matrices measure the unsystematic correlations between stocks. Such information is important for risk management. The correlation matrices are known to be ``noise dressed''. We develop a new and alternative method to estimate this noise. To this end, we simulate certain time series and random matrices which can model financial correlations. With our approach, different correlation structures buried under this noise can be detected. Moreover, we introduce a measure for the relation between noise and correlations. Our method is based on a power mapping which efficiently suppresses the noise. Neither further data processing nor additional input is needed.Comment: 25 pages, 8 figure

On Measuring Non-Recursive Trade-Offs

We investigate the phenomenon of non-recursive trade-offs between descriptional systems in an abstract fashion. We aim at categorizing non-recursive trade-offs by bounds on their growth rate, and show how to deduce such bounds in general. We also identify criteria which, in the spirit of abstract language theory, allow us to deduce non-recursive tradeoffs from effective closure properties of language families on the one hand, and differences in the decidability status of basic decision problems on the other. We develop a qualitative classification of non-recursive trade-offs in order to obtain a better understanding of this very fundamental behaviour of descriptional systems