Entropy Penalized Semidefinite Programming

Low-rank methods for semidefinite programming (SDP) have gained a lot of interest recently, especially in machine learning applications. Their analysis often involves determinant-based or Schatten-norm penalties, which are hard to implement in practice due to high computational efforts. In this paper, we propose Entropy Penalized Semi-definite programming (EP-SDP) which provides a unified framework for a wide class of penalty functions used in practice to promote a low-rank solution. We show that EP-SDP problems admit efficient numerical algorithm having (almost) linear time complexity of the gradient iteration which makes it useful for many machine learning and optimization problems. We illustrate the practical efficiency of our approach on several combinatorial optimization and machine learning problems.Comment: 28th International Joint Conference on Artificial Intelligence, 201

Prediction and prevention of pandemics via graphical model inference and convex programming

Hard-to-predict bursts of COVID-19 pandemic revealed significance of statistical modeling which would resolve spatio-temporal correlations over geographical areas, for example spread of the infection over a city with census tract granularity. In this manuscript, we provide algorithmic answers to the following two inter-related public health challenges of immense social impact which have not been adequately addressed (1) Inference Challenge assuming that there are N census blocks (nodes) in the city, and given an initial infection at any set of nodes, e.g. any N of possible single node infections, any N(N - 1)/2 of possible two node infections, etc, what is the probability for a subset of census blocks to become infected by the time the spread of the infection burst is stabilized? (2) Prevention Challenge What is the minimal control action one can take to minimize the infected part of the stabilized state footprint? To answer the challenges, we build a Graphical Model of pandemic of the attractive !sing (pair-wise, binary) type, where each node represents a census tract and each edge factor represents the strength of the pairwise interaction between a pair of nodes, e.g. representing the inter-node travel, road closure and related, and each local bias/field represents the community level of immunization, acceptance of the social distance and mask wearing practice, etc. Resolving the Inference Challenge requires finding the Maximum-A-Posteriory (MAP), i.e. most probable, state of the !sing Model constrained to the set of initially infected nodes. (An infected node is in the +1state and a node which remained safe is in the - 1 state.) We show that almost all attractive !sing Models on dense graphs result in either of the two possibilities (modes) for the MAP state: either all nodes which were not infected initially became infected, or all the initially uninfected nodes remain uninfected (susceptible). This bi-modal solution of the Inference Challenge allows us to re-state the Prevention Challenge as the following tractable convex programming: for the bare !sing Model with pair-wise and bias factors representing the system without prevention measures, such that the MAP state is fully infected for at least one of the initial infection patterns, find the closest, for example in l(1), l(2) or any other convexity-preserving norm, therefore prevention-optimal, set of factors resulting in all the MAP states of the !sing model, with the optimal prevention measures applied, to become safe. We have illustrated efficiency of the scheme on a quasi-realistic model of Seattle. Our experiments have also revealed useful features, such as sparsity of the prevention solution in the case of the l(1) norm, and also somehow unexpected features, such as localization of the sparse prevention solution at pair-wise links which are NOT these which are most utilized/traveled.Funding Agencies|NSF [2027072]; Analytical center at Skoltech [000000D730321P5Q0002, 70-2021-00145 02.11.2021]; Swedish Research Council; Swedish Transport Administration</p

Optimization of a light collection in the Shashlyk-type electromagnetic calorimeter with projective geometry for the NICA/MPD experiment.

The MPD spectrometer at the NICA collider complex is currently under construction in Dubna. The main goal of the experiment is to obtain fundamental knowledge about the properties of hot and dense baryonic matter formed in heavy-ion collisions in the energy range of (4-11) A*GeV. Crucial detector of the MPD experiment is a large-sized barrel electromagnetic calorimeter (ECal), which (together with the tracking system) will provide unique opportunities for the measurement and identification of a wide variety of charged and neutral particles carrying information about early stages of the interactions. Important tasks related to the construction of the Shashlyk-type MPD ECal are the development, production and study of the calorimeter modules with projective geometry. To improve performance of ECal, the light collection in the modules should be optimized. We present the methods and technologies developed to increase the light yield with different types and configurations of reflectors on the end of wavelengths shifting fibers. Expected characteristics of the calorimeter in detection of photons and electrons are presented and discusse

Optimization of a light collection in the Shashlyk-type electromagnetic calorimeter with projective geometry for the NICA/MPD experiment.

The MPD spectrometer at the NICA collider complex is currently under construction in Dubna. The main goal of the experiment is to obtain fundamental knowledge about the properties of hot and dense baryonic matter formed in heavy-ion collisions in the energy range of (4-11) A*GeV. Crucial detector of the MPD experiment is a large-sized barrel electromagnetic calorimeter (ECal), which (together with the tracking system) will provide unique opportunities for the measurement and identification of a wide variety of charged and neutral particles carrying information about early stages of the interactions. Important tasks related to the construction of the Shashlyk-type MPD ECal are the development, production and study of the calorimeter modules with projective geometry. To improve performance of ECal, the light collection in the modules should be optimized. We present the methods and technologies developed to increase the light yield with different types and configurations of reflectors on the end of wavelengths shifting fibers. Expected characteristics of the calorimeter in detection of photons and electrons are presented and discusse

Data on the temporal changes in soil properties and microbiome composition after a jet-fuel contamination during the pot and field experiments

The soil response to a jet-fuel contamination is uncertain. In this article, original data on the influence of a jet-fuel spillage on the topsoil properties are presented. The data set is obtained during a one-year long pot and field experiments with Dystric Arenosols, Fibric Histosols and Albic Luvisols. Kerosene loads were 1, 5, 10, 25 and 100 g/kg. The data set includes information about temporal changes in kerosene concentration; physicochemical properties, such as рН, moisture, cation exchange capacity, content of soil organic matter, available P and K, exchangeable NH4+, and water-soluble NO3–; and biological properties, such as biological consumption of oxygen, and cellulolytic activity. Also, we provide sequencing data on variable regions of 16S ribosomal RNA of microbial communities from the respective soil samples

The Influence of Kerosene on Microbiomes of Diverse Soils

One of the most important challenges for soil science is to determine the limits for the sustainable functioning of contaminated ecosystems. The response of soil microbiomes to kerosene pollution is still poorly understood. Here, we model the impact of kerosene leakage on the composition of the topsoil microbiome in pot and field experiments with different loads of added kerosene (loads up to 100 g/kg; retention time up to 360 days). At four time points we measured kerosene concentration and sequenced variable regions of 16S ribosomal RNA in the microbial communities. Mainly alkaline Dystric Arenosols with low content of available phosphorus and soil organic matter had an increased fraction of Actinobacteriota, Firmicutes, Nitrospirota, Planctomycetota, and, to a lesser extent, Acidobacteriota and Verrucomicobacteriota. In contrast, in highly acidic Fibric Histosols, rich in soil organic matter and available phosphorus, the fraction of Acidobacteriota was higher, while the fraction of Actinobacteriota was lower. Albic Luvisols occupied an intermediate position in terms of both physicochemical properties and microbiome composition. The microbiomes of different soils show similar response to equal kerosene loads. In highly contaminated soils, the proportion of anaerobic bacteria-metabolizing hydrocarbons increased, whereas the proportion of aerobic bacteria decreased. During the field experiment, the soil microbiome recovered much faster than in the pot experiments, possibly due to migration of microorganisms from the polluted area. The microbial community of Fibric Histosols recovered in 6 months after kerosene had been loaded, while microbiomes of Dystric Arenosols and Albic Luvisols did not restore even after a year