A computational scheme to estimate the Leontief model matrix coefficients according to input-output table data for the southern regions of the Tyumen Oblast

Subject. This article deals with the control and management aspects of regional development on the basis of Leontief’s balance model. Objectives. The article aims to develop schemes for stable estimation of aggregate parameters of region balance models based on a shortened sample of input-output statistical data and rules for their subsequent regularization. Methods. For the study, we used multiple forms of regional economic balance model transformation based on the aggregation of data of the selected regional subsystems. Results. The primary estimates of aggregate input-output matrix for the southern regions of the Tyumen Oblast were obtained from the statistical input-output data for 2014–2018. To comply with the productivity conditions, additional information was introduced into the estimation algorithm reflecting the balance dependence for the reference input-output matrix for the Russian Federation and for the southern regions of the Tyumen Oblast in retrospective (2004–2013). Conclusions. The result of regularization of aggregate input-output matrix for the southern regions of the Tyumen Oblast obtained from the statistical input-output data on the basis of the least squares method indicates that the backward estimation technique cannot act as a basic tool for the primary construction of balance models of regional economies. However, backward estimation algorithms with subsequent regularization are effective in correcting the reference input-output matrix using actual data of the region’s socio-economic development.

A Sliding Window Data Compression Method for Spatial-Time DOA Estimation

This paper presents a sliding window data compression method for spatial-time direction-of-arrival (DOA) estimation using coprime array. The signal model is firstly formulated by jointly using the temporal and spatial information of the impinging sources. Then, a sliding window data compression processing is performed on the array output matrix to realize fast calculation of time average function, and the computational burden has been reduced accordingly. Based on the concept of sum and difference co-array (SDCA), the vectorized conjugate augmented MUSIC is adopted, with which more sources than twice of the physical sensors can be resolved. Additionally, the sparse array robustness to sensor failure has been evaluated by introducing the concept of essential sensors. The theoretical analysis and numerical simulations are provided to confirm the effectiveness performance of the proposed method.

Automatic Calibration Algorithm for English Text Translation Based on Semantic Features

At present, the existing methods of English article flip calibration neglect to extract English semantic features, which leads to errors in English flip results and has a great impact on the accuracy and time consumption of translation sentence calibration. Therefore, a semantic feature-based automatic text flipping calibration algorithm is proposed. According to the features of semantic information in machine translation, a semantic grammar tree is constructed to complete the machine turning of English articles. The CART decision tree attribute is obtained, and the random forest method is introduced to extract the input matrix and output matrix of the corpus feature as samples to determine the spatial attribute feature of the mistranslated sentences. Choose 10000 English sentences about human body parts as the experimental object and design the simulation experiment. The experimental results show that the minimum and maximum accuracy rates are 95.4% and 100.0%, respectively. The proposed algorithm is time-consuming, and the KSMR value is lower than that of the traditional method. It is proved that the error rate of English article flipping is significantly reduced.

The problem of measuring the quantity of output in the input–output model by W. Leontief in modeling the trends in economic reproduction of nations and regions

Subject. This article deals with the problem of constructing a Leontief's input–output matrix. Objectives. The article aims to determine the rules for constructing a Leontief's input–output matrix on the basis of data on production time and quantity of product output. Methods. For the study, I used the methods of logical and mathematical analyses. Results. The article formulates the rules for constructing a Leontief's input–output matrix, taking into account differences in the time of production, quantity of output, as well as the conditions for the reproduction of the resources expended. It summarizes these rules for the J. von Neumann model. Conclusions. The proposed approach to the analysis of the material mechanism of economic reproduction defines the relationship between the quantitative and cost characteristics of the production and consumption of products and resources. This relationship opens up new opportunities for the application of input–output models to create simple and accurate algorithms for identifying and predicting the macroeconomic trends.

Diversification of Nigeria Economy and National Development 2005-2012

Economic diversification has been the only solution to Nigeria’s economic challenges with the country in control of diversely untapped natural and human resources. This work has contextually x-rayed some much more considered theoretical paths of economic development through economic diversification and placed the blame for Nigeria’s economic backwardness on political will and lack of commitment to national course of political leaders. Since the diverse policy process of the government had yielded little or no sustainable results, even when the emphasis is to utilize the potentials in non-oil sectors to benefit ever-increasing population. Nigeria is relatively diversified but the positive impact of real diversification through surplus economic gains has not been achieved. A holistic approach to development was adopted in the theoretical framework used in this work that positively affects state, people and their relationship nationally and internationally. The thrust of the theory encourages free trade policy, efficient competition and democratic features to liberalize productivity through various guided legislation in line with Globalized Quality Standard. The research design was descriptive of the observed trend in the economy. It also analyses similar scholarly data collected for accuracy in exposing greatly a multi-sectoral approach in planning, dealing with interdependence using input-output matrix with reference to pre-independence and post-independence era of the national economy. This study looked at the positive intentions of some interventionist programmes and policies of the Government which were short-lived. Few years’ aggregate contribution and sectoral real GDP rate were stated. Recommendations were effectively based on keen interest in multi-sectoral diversification of an economy being the sub-structure that determines the effectiveness of super-structure.

Initial Assessment of the Impact of the SARS-CoV-19 on Tourism in Mexico

The main objectives of this document were to evaluate the impact of SARS-CoV-19 on the tourism industry and infer the share of tourism GDP in Mexico's national GDP. Information from the input-output matrix and the tourism satellite account was used. Results show that, when all tourism disappears, the Gross Domestic Product (GDP) decreases by 8.98%. By simulating a probable scenario of recovery of tourist activity for the year 2021 of 25%, the tourism GDP increases by 9% and for a scenario of 50%, GDP rises to 12%. It is suggested to project recovery plans in the local hotel and restaurant industries. The originality consisted in building a tourism input-output matrix based on data and information from the tourism satellite account. The main limitation is that we only worked with data from 2013, the most recent published by INEGI. It is recommended to replicate the study for tourism activity not only in GDP but also in employment and wages.

AUTOMAT[R]IX: learning simple matrix pipelines

Matrices are a very common way of representing and working with data in data science and artificial intelligence. Writing a small snippet of code to make a simple matrix transformation is frequently frustrating, especially for those people without an extensive programming expertise. We present AUTOMATIX, a system that is able to induce R program snippets from a single (and possibly partial) matrix transformation example provided by the user. Our learning algorithm is able to induce the correct matrix pipeline snippet by composing primitives from a library. Because of the intractable search space—exponential on the size of the library and the number of primitives to be combined in the snippet, we speed up the process with (1) a typed system that excludes all combinations of primitives with inconsistent mapping between input and output matrix dimensions, and (2) a probabilistic model to estimate the probability of each sequence of primitives from their frequency of use and a text hint provided by the user. We validate AUTOMATIX with a set of real programming queries involving matrices from Stack Overflow, showing that we can learn the transformations efficiently, from just one partial example.