PREPARATION A SERIES OF ATROPISOMERIC BIPY-DIOXIDES BY OXIDATIVE COUPLING AND THEIR APPLICATION IN ASYMMETRIC CATALYSIS

The authors thank the Russian Science Foundation for Grant No. 18-73-10156

Corrosion-electrochemical behavior of nickel in an alkali metal carbonate melt under a chlorine-containing atmosphere

The corrosion-electrochemical behavior of a nickel electrode is studied in the melt of lithium, sodium, and potassium (40: 30: 30 mol %) carbonates in the temperature range 500-600°C under an oxidizing atmosphere CO2 + 0.5O2 (2: 1), which is partly replaced by gaseous chlorine (30, 50, 70%) in some experiments. In other experiments, up to 5 wt % chloride of sodium peroxide is introduced in a salt melt. A change in the gas-phase composition is shown to affect the mechanism of nickel corrosion. © 2013 Pleiades Publishing, Ltd

Enantioselective propargylation of aldehydes catalyzed by new chiral Lewis bases

In this work we’ve designed a series of new chiral Lewis Bases and show their excellent catalytic ability in the reaction of asymmetric propargylation of aromatic and α-unsaturated aldehydes.The authors thank the Russian Science Foundation for Grant 18-73-10156

In which shell-type SNRs should we look for gamma-rays and neutrinos from p-p collisions?

We present a simple analytic model for the various contributions to the non-thermal emission from shell type SNRs, and show that this model's results reproduce well the results of previous detailed calculations. We show that the \geq 1 TeV gamma ray emission from the shell type SNRs RX J1713.7-3946 and RX J0852.0-4622 is dominated by inverse-Compton scattering of CMB photons (and possibly infra-red ambient photons) by accelerated electrons. Pion decay (due to proton-proton collisions) is shown to account for only a small fraction, \lesssim10^-2, of the observed flux, as assuming a larger fractional contribution would imply nonthermal radio and X-ray synchrotron emission and thermal X-ray Bremsstrahlung emission that far exceed the observed radio and X-ray fluxes. Models where pion decay dominates the \geq 1 TeV flux avoid the implied excessive synchrotron emission (but not the implied excessive thermal X-ray Bremsstrahlung emission) by assuming an extremely low efficiency of electron acceleration, K_ep \lesssim 10^-4 (K_ep is the ratio of the number of accelerated electrons and the number of accelerated protons at a given energy). We argue that observations of SNRs in nearby galaxies imply a lower limit of K_ep \gtrsim 10^-3, and thus rule out K_ep values \lesssim 10^-4 (assuming that SNRs share a common typical value of K_ep). It is suggested that SNRs with strong thermal X-ray emission, rather than strong non-thermal X-ray emission, are more suitable candidates for searches of gamma rays and neutrinos resulting from proton-proton collisions. In particular, it is shown that the neutrino flux from the SNRs above is probably too low to be detected by current and planned neutrino observatories (Abridged).Comment: 13 pages, 1 figure, accepted for publication in JCAP, minor revision

Stereoselective Synthesis of Atropisomeric Bipyridine N,N′-Dioxides by Oxidative Coupling

Bipyridine N,N′-dioxide is a structural fragment found in many bioactive compounds. Furthermore, chiral analogues secured their place as powerful Lewis base catalysts. The scope of the existing methods for the synthesis of atropisomeric bipyridine N,N′-dioxides is limited. Herein, we present a practical, highly chemo- and stereoselective method for oxidative dimerization of chiral pyridine N-oxides using O2 as a terminal oxidant. A series of 13 axially chiral bipyridine N,N′-dioxides were synthesized in up to 75% yield. © 2019 American Chemical Society

Knowledge is at the Edge! How to Search in Distributed Machine Learning Models

With the advent of the Internet of Things and Industry 4.0 an enormous amount of data is produced at the edge of the network. Due to a lack of computing power, this data is currently send to the cloud where centralized machine learning models are trained to derive higher level knowledge. With the recent development of specialized machine learning hardware for mobile devices, a new era of distributed learning is about to begin that raises a new research question: How can we search in distributed machine learning models? Machine learning at the edge of the network has many benefits, such as low-latency inference and increased privacy. Such distributed machine learning models can also learn personalized for a human user, a specific context, or application scenario. As training data stays on the devices, control over possibly sensitive data is preserved as it is not shared with a third party. This new form of distributed learning leads to the partitioning of knowledge between many devices which makes access difficult. In this paper we tackle the problem of finding specific knowledge by forwarding a search request (query) to a device that can answer it best. To that end, we use a entropy based quality metric that takes the context of a query and the learning quality of a device into account. We show that our forwarding strategy can achieve over 95% accuracy in a urban mobility scenario where we use data from 30 000 people commuting in the city of Trento, Italy.Comment: Published in CoopIS 201

Breather lattice and its stabilization for the modified Korteweg-de Vries equation

We obtain an exact solution for the breather lattice solution of the modified Korteweg-de Vries (MKdV) equation. Numerical simulation of the breather lattice demonstrates its instability due to the breather-breather interaction. However, such multi-breather structures can be stabilized through the concurrent application of ac driving and viscous damping terms.Comment: 6 pages, 3 figures, Phys. Rev. E (in press

Asymmetric Propargylation of Aldehydes Catalyzed by New Chiral Lewis Bases

The authors thank the Russian Science Foundation for Grant № 18-73-10156

Dehydration of Amides to Nitriles

The authors thank Russian Science Foundation for grant № 18-73-10156

Accurate and Fast Retrieval for Complex Non-metric Data via Neighborhood Graphs

We demonstrate that a graph-based search algorithm-relying on the construction of an approximate neighborhood graph-can directly work with challenging non-metric and/or non-symmetric distances without resorting to metric-space mapping and/or distance symmetrization, which, in turn, lead to substantial performance degradation. Although the straightforward metrization and symmetrization is usually ineffective, we find that constructing an index using a modified, e.g., symmetrized, distance can improve performance. This observation paves a way to a new line of research of designing index-specific graph-construction distance functions