Mathematical Models for Calculation of the Parameters of the Bank and the Borrower in the Mortgage Lending

The article covers the consideration of the issues of analysis and development of mathematical models to improve the efficiency of the mortgage lending system. The relevance of the study is due to the fact that the current mechanisms of mortgage lending in Russia do not correspond to global trends: by interest rates level; in terms of the volume of loans issued and other conditions. However, it is one of the main tools that allows to improve the population’s living conditions and, as a result, to release the socio-economic tension caused by this factor, as well as to attract additional input in the investment and construction sector, which in modern conditions is one of the most important problems. As a result of the research carried out, the article offers a mathematical model for calculating the parameters of the bank and the borrower, in order to form the most acceptable conditions for the loan. In the mathematical model, such parameters of the borrower and the lender as the price of the apartment, the percentage of the down payment from its price, the mortgage loan rate, the total debt and the loan term, as well as the share of the borrower's income allocated to monthly payments are interconnected. This model will allow the bank to determine the most suitable loan conditions regarding the payment amount, term, and available credit limit, and the borrower to calculate the parameters of the loan in order to make an informed decision on attracting it. All this, in the end, will allow banks to reduce the level of risk on issued mortgage loans, and the borrower — confidence in the ability to pay off the mortgage loan.

Determination of PM1 Sources at a Prague Background Site during the 2012–2013 Period Using PMF Analysis of Combined Aerosol Mass Spectra

Two intensive measurement campaigns using a compact time-of-flight aerosol mass spectrometer were carried out at the suburban site in Prague (Czech Republic) in summer (2012) and winter (2013). The aim was to determine the aerosol sources of the NR-PM1 fraction by PMF analysis of organic (OA) and inorganic aerosol mass spectra. Firstly, an analysis of the OA mass spectra was performed. Hydrocarbon-like OA (HOA), biomass burning OA (BBOA), and two types of oxygenated OA (OOA1) and (OOA2) were identified in summer. In winter, HOA, BBOA, long-range oxygenated OA (LROOA), and local oxygenated OA (LOOA) were determined. The identified HOA and BBOA factors were then used as additional input for the subsequent ME-2 analysis of the combined organic and inorganic spectra. This analysis resulted in six factors in both seasons. All of the previously reported organic factors were reidentified and expanded with the inorganic part of the spectra in both seasons. Two predominantly inorganic factors ammonium sulphate (AMOS) and ammonium nitrate (AMON) were newly identified in both seasons. Despite very similar organic parts of the mass profiles, the daily cycles of HOA and LOOA differed significantly in winter. It appears that the addition of the inorganic part of the mass profile, in some cases, reduces the ability of the model to identify physically meaningful factors.

POMACS V5, N3, December 2021 Editorial

The ACM Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the ACM Special Interest Group SIGMETRICS. All papers in this issue of POMACS will be presented during the ACM SIGMETRICS/Performance 2022 conference. The issue contains papers selected by the editorial board via a rigorous review process that follows a hybrid conference and journal model, with reviews conducted by the 93 members of our POMACS editorial board. Each paper was either conditionally accepted (and shepherded), allowed a "one-shot" revision (to be resubmitted to one of the subsequent two deadlines), or rejected (with resubmission allowed after a year). For this issue, which represents the summer deadline, POMACS publishes 17 papers out of 71 submissions. All submitted papers received at least 3 reviews and we held an online TPC meeting. Based on the indicated primary track, roughly 37% of the submissions were in the Theory track, 30% were in the Measurement & Applied Modeling track, 20% were in the Systems track, and 14% were in the Learning track. Many people contributed to the success of this issue of POMACS. First, we would like to thank the authors, who submitted their best work to SIGMETRICS/POMACS. Second, we would like to thank the TPC members who provided constructive feedback in their reviews to authors and participated in the online discussions and TPC meetings. We also thank the several external reviewers who provided their expert opinion on specific submissions that required additional input. We are also grateful to the SIGMETRICS Board Chair, Giuliano Casale, and to past TPC Chairs, Anshul Gandhi, Negar Kiyavash, and Jia Wang, who provided a wealth of information and guidance (including a template for writing this editorial note!). Finally, we are grateful to the Organization Committee and to the SIGMETRICS Board for their ongoing efforts and initiatives for creating an exciting program for ACM SIGMETRICS/Performance 2022.

Indonesia's socio-political developments during Jokowi's leadership

This paper tries to discuss the latest socio-political developments in Indonesia during President Jokowi, a study of political journals and perspectives from the international community. The sources of literature that we use are various international publications and media highlights published in the last five years, both national and international journals. At the same time, the method is literature analysis involving data coding system, data evaluation, and interpreting conclusion drawing so that this finding is under the study question with high validity principle. Our searches are electronic. This study relies on secondary data. The series of reports for this study are in descriptive qualitative data format. The findings that we can convey are that the politics of the Jokowi era was the division of power between the executive and the legislature in the form of the Unitary State of the Republic of Indonesia with a presidential system with a parliamentary system. In other words, Indonesia does not adhere to a system of separation of powers but rather a system of power-sharing between the executive at the center and the regions. Thus, these findings serve as additional input for future research on the same theme.

Simple superphosphate by two-stage acid treatment of phosphate raw materials

The distinguishing feature of the proposed flow method before the classical (chamber) method is that the entire production cycle of natural phosphate processing is carried out in two stages. At the first stage, the phosphorite is treated with a stoichiometric flow rate of concentrated sulphuric acid (at least 93%), under conditions of complete decomposition of phosphorite to form phosphoric acid and crystals of anhydrite (calcium sulfate). The reaction temperature is 122 °C. In the second stage, the resulting concentrated solution of phosphoric acid in a mixture with sulphur is involved in a reaction with an additional input of phosphorite, which is the basis for the mechanism of chemical formation of monocalciumphosphate and granulation of superphosphate mass. The processes for neutralizing phosphoric acid on monocalciumphosphate and for granulating the product by coagulation are combined in one apparatus. The drying stage of the product is excluded from the scheme.

Reservoir Computing with Delayed Input for Fast and Easy Optimisation

Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.

Robust and unbiased estimation of the background distribution for automated quantitative imaging

Quantitative analysis of high-throughput microscopy images requires robust automated algorithms. Background estimation is usually the first step and has an impact on all subsequent analysis, in particular for foreground detection and calculation of ratiometric quantities. Most methods recover only a single background value, such as the median. Those that aim to retrieve a background distribution by dividing the intensity histogram yield a biased estimation in images in non-trivial cases. In this work, we present the first method to recover an unbiased estimation of the background distribution directly from an image and without any additional input. Through a robust statistical test, our method leverages the lack of local spatial correlation in background pixels to select a subset of pixels that accurately represent the background distribution. This method is both fast and simple to implement, as it only uses standard mathematical operations and an averaging filter. Additionally, the only parameter, the size of the averaging filter, does not require fine tuning. The obtained background distribution can be used to test for foreground membership of individual pixels, or to estimate confidence intervals in derived quantities. We expect that the concepts described in this work can help to develop a novel family of robust segmentation methods.

Reservoir Computing with Delayed Input for Fast and Easy Optimization

Reservoir computing is a machine learning method that uses the response of a dynamical system to a certain input in order to solve a task. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.

Learning a Transform Base for the Multi- to Hyperspectral Sensor Network with K-SVD

A promising low-cost solution for monitoring spectral information, e.g., on agricultural fields, is that of wireless sensor networks. In contrast to remote sensing, these can achieve more continuous monitoring due to their long-term deployment and are less impacted by the atmosphere, making them a promising solution for the calibration of satellite data. In this paper, we explore an alternative approach for processing data from such a network. Hyperspectral sensors were found to be too complex for such a network. While previous work considered fusing the data from different multispectral sensors in order to derive hyperspectral data, we shift the assessment of the hyperspectral modeling in a separate preprocessing step based on machine learning. We then use the learned data as additional input while using identical multispectral sensors, further reducing the complexity of the sensors. Despite requiring careful parametrization, the approach delivers hyperspectral data of similar and in some cases even better quality.

Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma

PET/CT images provide a rich data source for clinical prediction models in head and neck squamous cell carcinoma (HNSCC). Deep learning models often use images in an end-to-end fashion with clinical data or no additional input for predictions. However, in the context of HNSCC, the tumor region of interest may be an informative prior in the generation of improved prediction performance. In this study, we utilize a deep learning framework based on a DenseNet architecture to combine PET images, CT images, primary tumor segmentation masks, and clinical data as separate channels to predict progression-free survival (PFS) in days for HNSCC patients. Through internal validation (10-fold cross-validation) based on a large set of training data provided by the 2021 HECKTOR Challenge, we achieve a mean C-index of 0.855 +- 0.060 and 0.650 +- 0.074 when observed events are and are not included in the C-index calculation, respectively. Ensemble approaches applied to cross-validation folds yield C-index values up to 0.698 in the independent test set (external validation). Importantly, the value of the added segmentation mask is underscored in both internal and external validation by an improvement of the C-index when compared to models that do not utilize the segmentation mask. These promising results highlight the utility of including segmentation masks as additional input channels in deep learning pipelines for clinical outcome prediction in HNSCC.