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Register a new userX-model: further development and possible modifications
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Sergei Kulakov
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Despite its critical importance, the famous X-model elaborated by Ziel and Steinert (2016) has neither bin been widely studied nor further developed. And yet, the possibilities to improve the model are as numerous as the fields it can be applied to. The present paper takes advantage of a technique proposed by Coulon et al. (2014) to enhance the X-model. Instead of using the wholesale supply and demand curves as inputs for the model, we rely on the transforme
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In this paper we develop the new semi-analitical model of a tidally perturbed or tidally disrupted star proposed recently by two of us. This model is effectively a one dimensional Lagrangian model and it can be evolved numerically much faster that the conventional 3D models. A self-consistent derivation of the dynamical equations of the model is performed and several important theorems about the dynamics of the model are proved without any particular assumption about the equation of state of the stellar gas. The dynamical equations are solved numerically for the case of $n=1.5$ polytropic star evolving in the relativistic field of a $10^7M_{odot}$ Kerr black hole. Some results of these calculations are compared with the results of calculations based on finite-difference 3D simulations. The comparison shows a very good agreement between both approaches to the problem. Then we show that the strength of the tidal encounter depends significantly on the relative orientation of the orbital angular momentum of the star and the spin of the black hole.
We develop a machine learning based tool for accurate prediction of socio-economic indicators from daytime satellite imagery. The diverse set of indicators are often not intuitively related to observable features in satellite images, and are not even always well correlated with each other. Our predictive tool is more accurate than using night light as a proxy, and can be used to predict missing data, smooth out noise in surveys, monitor development progress of a region, and flag potential anomalies. Finally, we use predicted variables to do robustness analysis of a regression study of high rate of stunting in India.
Proposed in 1991, Least Mean Square Error Reconstruction for self-organizing network, shortly Lmser, was a further development of the traditional auto-encoder (AE) by folding the architecture with respect to the central coding layer and thus leading to the features of symmetric weights and neurons, as well as jointly supervised and unsupervised learning. However, its advantages were only demonstrated in a one-hidden-layer implementation due to the lack of computing resources and big data at that time. In this paper, we revisit Lmser from the perspective of deep learning, develop Lmser network based on multiple convolutional layers, which is more suitable for image-related tasks, and confirm several Lmser functions with preliminary demonstrations on image recognition, reconstruction, association recall, and so on. Experiments demonstrate that Lmser indeed works as indicated in the original paper, and it has promising performance in various applications.
The efficiency of the recently proposed iCIPT2 [iterative configuration interaction (iCI) with selection and second-order perturbation theory (PT2); J. Chem. Theory Comput. 16, 2296 (2020)] for strongly correlated electrons is further enhanced (by up to 20x) by using (1) a new ranking criterion for configuration selection, (2) a new particle-hole algorithm for Hamiltonian construction over randomly selected configuration state functions (CSF), and (3) a new data structure for the quick sorting of the joint variational and first-order interaction spaces. Meanwhile, the memory requirement is also reduced greatly. As a result, this improved implementation of iCIPT2 can handle one order of magnitude more CSFs than the previous version, as revealed by taking the chromium dimer and an iron-sulfur cluster, [2Fe-2S], as examples.
Within the national innovation system literature, empirical analyses are severely lacking for developing economies. Particularly, the low- and middle-income countries (LMICs) eligible for the World Banks International Development Association (IDA) support, are rarely part of any empirical discourse on growth, development, and innovation. One major issue hindering panel analyses in LMICs, and thus them being subject to any empirical discussion, is the lack of complete data availability. This work offers a new complete panel dataset with no missing values for LMICs eligible for IDAs support. I use a standard, widely respected multiple imputation technique (specifically, Predictive Mean Matching) developed by Rubin (1987). This technique respects the structure of multivariate continuous panel data at the country level. I employ this technique to create a large dataset consisting of many variables drawn from publicly available established sources. These variables, in turn, capture six crucial country-level capacities: technological capacity, financial capacity, human capital capacity, infrastructural capacity, public policy capacity, and social capacity. Such capacities are part and parcel of the National Absorptive Capacity Systems (NACS). The dataset (MSK dataset) thus produced contains data on 47 variables for 82 LMICs between 2005 and 2019. The dataset has passed a quality and reliability check and can thus be used for comparative analyses of national absorptive capacities and development, transition, and convergence analyses among LMICs.
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