Whispering Vortices

Experiments indicating the excitation of whispering gallery type electromagnetic modes by a vortex moving in an annular Josephson junction are reported. At relativistic velocities the Josephson vortex interacts with the modes of the superconducting stripline resonator giving rise to novel resonances on the current-voltage characteristic of the junction. The experimental data are in good agreement with analysis and numerical calculations based on the two-dimensional sine--Gordon model.Comment: 5 pages, 5 figures, text shortened to fit 4 pages, correction of typo

Insights from the NeurIPS 2021 NetHack Challenge

In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge. Participants were tasked with developing a program or agent that can win (i.e., ‘ascend’ in) the popular dungeon-crawler game of NetHack by interacting with the NetHack Learning Environment (NLE), a scalable, procedurally generated, and challenging Gym environment for reinforcement learning (RL). The challenge showcased community-driven progress in AI with many diverse approaches significantly beating the previously best results on NetHack. Furthermore, it served as a direct comparison between neural (e.g., deep RL) and symbolic AI, as well as hybrid systems, demonstrating that on NetHack symbolic bots currently outperform deep RL by a large margin. Lastly, no agent got close to winning the game, illustrating NetHack’s suitability as a long-term benchmark for AI research

ФОТОЭЛЕКТРИЧЕСКИЕ ПРЕОБРАЗОВАТЕЛИ В СИСТЕМЕ СО СПЕКТРАЛЬНЫМ РАСЩЕПЛЕНИЕМСОЛНЕЧНОЙ ЭНЕРГИИ

This paper presents results on the simulation of photo converters in a spectral splitting system where solar radiation is separated into three spectral ranges (∆λ1<500 nm, ∆λ2 = 500−725 nm and ∆λ3>725 nm) by means of dichroic filters and then converted to electrical energy by photoconverters based on InGaN/GaN, GaAs/AlGaAs single−junction heterostructures and monocrystalline silicon c−Si. Special attention is paid to the absorption spectrum spreading due to more efficient conversion of the ultraviolet part of the spectrum. The total efficiency of the system varies from 21% to 37% depending on the design of heterostructures.Представлены результаты моделирования фотоэлектрических преобразователей в системе со спектральным расщеплением солнечной энергии, в которой солнечное излучение разделяется с помощью дихроичных фильтров на три спектральных диапазона (∆λ1 < 500 нм, ∆λ2 = 500÷725 нм, ∆λ3 > 725 нм) и затем преобразуется в электроэнергию фотоэлектрическими преобразователями на основе однопереходных гетероструктур InGaN/GaN, GaAs/AlGaAs и монокристаллического кремния c−Si. Особое внимание уделено исследованию расширения спектрального диапазона поглощения системы за счет более эффективного преобразования ультрафиолетовой части спектра. Суммарный КПД системы на всем спектре варьируется от 21 до 37 % в зависимости от дизайна гетероструктур однопереходных фотоэлектрических пре-образователей и вариантов оптических систем

НОВЫЕ НАПРАВЛЕНИЯ РАЗВИТИЯ ТЕХНОЛОГИИ ПРОИЗВОДСТВА УЛЬТРАФИОЛЕТОВЫХ СВЕТОДИОДОВ

The paper presents results of the development of ultraviolet light−emitting diodes based on GaN/AlGaN heterostructures grown on AlN substrates by chloride−hydride vapor phase epitaxy. The peak wavelengths are in the range of 360—365 nm with a spectral width of 10—13 nm; the output optical power of LED dies is 50 mW at 350 mA.Представлены результаты по созданию ультрафиолетовых светодиодов на основе гетеро-структур GaN/AlGaN, выращенных на подложках AlN методом хлоридно-гидридной эпитаксии. Пиковые длины волн находятся в диапазоне 360—365 нм, ширина спектральной кривой составляет 10—13 нм, выходная оптическая мощность чипов светодиодов — 50 мВт при токе 350 мА

Deep coordination graphs

This paper introduces the deep coordination graph (DCG) for collaborative multi-agent reinforcement learning. DCG strikes a flexible trade-off between representational capacity and generalization by factoring the joint value function of all agents according to a coordination graph into payoffs between pairs of agents. The value can be maximized by local message passing along the graph, which allows training of the value function end-to-end with Q-learning. Payoff functions are approximated with deep neural networks that employ parameter sharing and low-rank approximations to significantly improve sample efficiency. We show that DCG can solve predator-prey tasks that highlight the relative overgeneralization pathology, as well as challenging StarCraft II micromanagement tasks