The use of computer decision-making support systems to justify address rehabilitation of the Semipalatinsk test site area

The paper describes the development of a range of optimal protective measures for remediation of the territory of the Semipalatinsk Test Site. The computer system for decision-making support, ReSCA, was employed for the estimations. Costs and radiological effectiveness of countermeasures were evaluated

Continuous CO2 and CH4 Observations in the Coastal Arctic Atmosphere of the Western Taimyr Peninsula, Siberia : The First Results from a New Measurement Station in Dikson

Atmospheric observations of sources and sinks of carbon dioxide (CO2) and methane (CH4) in the pan-Arctic domain are highly sporadic, limiting our understanding of carbon turnover in this climatically sensitive environment and the fate of enormous carbon reservoirs buried in permafrost. Particular gaps apply to the Arctic latitudes of Siberia, covered by the vast tundra ecosystems underlain by permafrost, where only few atmospheric sites are available. The paper presents the first results of continuous observations of atmospheric CO2 and CH4 dry mole fractions at a newly operated station "DIAMIS" (73.506828 degrees N, 80.519869 degrees E) deployed on the edge of the Dikson settlement on the western coast of the Taimyr Peninsula. Atmospheric mole fractions of CO2, CH4, and H2O are measured by a CRDS analyzer Picarro G2301-f, which is regularly calibrated against WMO-traceable gases. Meteorological records permit screening of trace gas series. Here, we give the scientific rationale of the site, describe the instrumental setup, analyze the local environments, examine the seasonal footprint, and show CO2 and CH4 fluctuations for the daytime mixed atmospheric layer that is representative over a vast Arctic domain (-500-1000 km), capturing both terrestrial and oceanic signals.Peer reviewe

Ex2^2MCMC: Sampling through Exploration Exploitation

We develop an Explore-Exploit Markov chain Monte Carlo algorithm ($\operatorname{Ex^2MCMC}$) that combines multiple global proposals and local moves. The proposed method is massively parallelizable and extremely computationally efficient. We prove $V$-uniform geometric ergodicity of $\operatorname{Ex^2MCMC}$ under realistic conditions and compute explicit bounds on the mixing rate showing the improvement brought by the multiple global moves. We show that $\operatorname{Ex^2MCMC}$ allows fine-tuning of exploitation (local moves) and exploration (global moves) via a novel approach to proposing dependent global moves. Finally, we develop an adaptive scheme, $\operatorname{FlEx^2MCMC}$, that learns the distribution of global moves using normalizing flows. We illustrate the efficiency of $\operatorname{Ex^2MCMC}$ and its adaptive versions on many classical sampling benchmarks. We also show that these algorithms improve the quality of sampling GANs as energy-based models

Chiral light in twisted Fabry-P\'erot cavities

Fundamental studies of the interaction of chiral light with chiral matter are important for the development of techniques that allow handedness-selective optical detection of chiral organic molecules. One approach to achieve this goal is the creation of a Fabry-P\'erot cavity that supports eigenmodes with a desired electromagnetic handedness, which interacts differently with left and right molecular enantiomers. In this paper, we theoretically study chiral Fabry-P\'erot cavities with mirrors comprising one-dimensional photonic crystal slabs made of van der Waals As$_2$S$_3$, a material with one of the highest known in-plane anisotropy. By utilizing the anisotropy degree of freedom provided by As$_2$S$_3$, we design Fabry-P\'erot cavities with constitutional and configurational geometrical chiralities. We demonstrate that in cavities with constitutional chirality, electromagnetic modes of left or right handedness exist due to the chirality of both mirrors, often referred to as handedness preserving mirrors in the literature. At the same time, cavities with configurational chirality support modes of both handednesses due to chiral morphology of the entire structure, set by the twist angle between the optical axes of the upper and lower non-chiral anisotropic mirrors. The developed chiral Fabry-P\'erot cavities can be tuned to the technologically available distance between the mirrors by properly twisting them, making such systems a prospective platform for the coupling of chiral light with chiral matter.Comment: 33 pages, 9 figure

Gradual Optimization Learning for Conformational Energy Minimization

Molecular conformation optimization is crucial to computer-aided drug discovery and materials design. Traditional energy minimization techniques rely on iterative optimization methods that use molecular forces calculated by a physical simulator (oracle) as anti-gradients. However, this is a computationally expensive approach that requires many interactions with a physical simulator. One way to accelerate this procedure is to replace the physical simulator with a neural network. Despite recent progress in neural networks for molecular conformation energy prediction, such models are prone to distribution shift, leading to inaccurate energy minimization. We find that the quality of energy minimization with neural networks can be improved by providing optimization trajectories as additional training data. Still, it takes around $5 \times 10^5$ additional conformations to match the physical simulator's optimization quality. In this work, we present the Gradual Optimization Learning Framework (GOLF) for energy minimization with neural networks that significantly reduces the required additional data. The framework consists of an efficient data-collecting scheme and an external optimizer. The external optimizer utilizes gradients from the energy prediction model to generate optimization trajectories, and the data-collecting scheme selects additional training data to be processed by the physical simulator. Our results demonstrate that the neural network trained with GOLF performs on par with the oracle on a benchmark of diverse drug-like molecules using $50$x less additional data.Comment: 17 pages, 5 figure

Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language Instructions

The adoption of pre-trained language models to generate action plans for embodied agents is a promising research strategy. However, execution of instructions in real or simulated environments requires verification of the feasibility of actions as well as their relevance to the completion of a goal. We propose a new method that combines a language model and reinforcement learning for the task of building objects in a Minecraft-like environment according to the natural language instructions. Our method first generates a set of consistently achievable sub-goals from the instructions and then completes associated sub-tasks with a pre-trained RL policy. The proposed method formed the RL baseline at the IGLU 2022 competition.Comment: 6 pages, 3 figure

The Calculation of the Spatial Distribution of Temperature Fields for Remote Monitoring of the Surface From an Unmanned Aerial Vehicle

В статье рассматривается расчет пространственного распределения температурных полей на поверхности и вглубь грунта. Представлена общая постановка задачи расчета пространственного распределения температурных полей в переменно-насыщенных пористых средах. Приведены результаты реализации предложенного способа расчета в ходе натурного экспериментаThe article deals with the calculation of the spatial distribution of temperature fields on the surface and deep into the soil. Presents a general formulation of the problem of calculating the spatial distribution of temperature fields in variably-saturated porous media. The results of the proposed method of calculation in the field experimen