Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Selected Macroeconomic Variables on Banking Sector Interest Rate Spread in Nigeria ( 1981-2016 )

This study examined the macroeconomic variables that affect banking sector interest rate spread in Nigeria using time series data form 1981-2016. The specific objectives of the study are: to investigate the effect of exchange rate on interest rate spreads in Nigeria, to ascertain the effect of inflation on interest rate spreads in Nigeria, to explore the effect of financial depth on interest rate spreads in Nigeria, and to investigate the effect of monetary policy rate on interest rate spreads in Nigeria. An ex post facto research designs were employed in analyzing the objectives. Base on the regression result, the study found that; exchange rate has a positive and significant relationship with interest rate spread in the long and short run. Inflation in the long run does not have positive and significant effect on interest rate spread in Nigeria, while the reverse is the case in the short run where inflation has significant effect on interest spread in Nigeria. Financial depth has a negative significant effect on interest rate spread in Nigeria both in long and short run. Monetary policy rate as one of the strong monetary instrument designed to influence interest rate spread in positive direction had positive and significant effect on interest rate spread in Nigeria both in the long and short run given the empirical result obtained in this study.
Statistical modeling support for calibration of a multiphysics model of subcooled boiling flows

Nuclear reactor system analyses rely on multiple complex models which describe the physics of reactor neutronics, thermal hydraulics, structural mechanics, coolant physico-chemistry, etc. Such coupled multiphysics models require extensive calibration and validation before they can be used in practical system safety study and/or design/technology optimization. This paper presents an application of statistical modeling and Bayesian inference in calibrating an example multiphysics model of subcooled boiling flows which is widely used in reactor thermal hydraulic analysis. The presence of complex coupling of physics in such a model together with the large number of model inputs, parameters and multidimensional outputs poses significant challenge to the model calibration method. However, the method proposed in this work is shown to be able to overcome these difficulties while allowing data (observation) uncertainty and model inadequacy to be taken into consideration.
Handling the Transition in the Locomotion of an Articulated Quadruped Robot by Adaptive CPG

—A control strategy based on the theory of Central Pattern Generators (CPG) implemented through recurrent neural network (RNN) for the development of trotting and walking locomotion of an articulated quadruped robot is presented in this paper. The control strategy was enriched with fuzzy logic to ensure the stability and balance of the robot in transitions among locomotion modes. The control strategy was simulated and then implemented in a prototype. The results are very promising. A quadruped robot with three joints per leg, name R4A3, was built for this purpose. The R4A3 learnt to walk by itself; for its locomotion, it is not necessary to develop the kinematic equations. A novel Nonlinear First Order Differential Equation System was developed to simulate the CPG as oscillators. The control strategy is distributed and autonomous, operating through three levels of control. Each level determines the degree of complexity of the action concerning the consequence of how it affects the entire robot. Level 1 aims to those repetitive continuous movements occurring on each joint. These movements are action primitives. Level 2 governs repetitive movements occurring on each leg. It involves the basic actions that identify the type of locomotion. Level 3 considers the recognition of the motion of the R4A3 determining its stability and balance, planning transitions between types of locomotion and avoiding overturning. With the appropriated parameterization the same Differential Equation System is used in each CPG on each level reducing the mathematical complexity to reproduce the quadruped movement resulting in a low computational cost suitable for hardware implementation.
Characterization of rural extension workers in Mexico a contribution from the correspondence analysis

Rural innovation remains a crucial element to solve problems of low productivity in agriculture and rural poverty, rural extension constitutes an instrument of agricultural policy that promotes innovation through its extension workers. In order to characterize the profile of these actors, a typology was made based on their professional attributes and the problems they face. The survey of extension workers of the monitoring and evaluation system of the rural extension policy 20162017 was used, it collected information in ten states of the republic and its sample size was 609 questionnaires. The methodology used multivariate statistical procedures, at first, the correspondence analysis estimated principal dimensions, later clusters were constructed to define the typology. Three strata were detected, which grouped the predominant problem and area of the professional studies, this allows a better understanding of the characteristics of the extension workers and establish strategies for solving problems in the extension policy focused on the knowledge of the capacities and conditions of one of its main actors.
SEASONAL VARIATION IN THE SHOOT- REGENERATION POTENTIAL OF THE NODAL EXPLANTS OF ROSA SETIGERA

The present paper reports seasonal variation in the regeneration potential of nodal explant of Rosa setigera over MS medium. We evaluated the shoot regeneration potential of the explants harvested from mature plants viz. shoot-tip, nodal segment (1.5cm), internodal segment (1.5cm) and leaf disc. The explants were harvested in rainy, winter and summer seasons and inoculated over MS medium fortified with various combinations of NAA, 2,4-D, BAP and kinetin. Among the explants, only nodal segments harvested in rainy and winter seasons induced microshoots. The response of explant varied with the concentration and combination of plant growth regulators and the season of harvest. The best response, in terms of multiple shoot induction, by nodal explants was exhibited over the medium fortified with 2.2M BAP in rainy season and 0.5M NAA + 8.8M BAP in winter season.
Effect of Silver Nanoparticles on Fluorescence Intensity of Fluoreseina Dye Mixed in One Solution

Article Info. Metal enhanced fluorescence (MEF) is an unequaled phenomenon of metal nanoparticle surface plasmons, when light interacts with the metal nanostructures (silver nanoparticles) which result electromagnetic fields to promote the sensitivity of fluorescence. This work endeavor to study the influence of silver nanoparticles on fluorescence intensity of Fluoreseina dye by employment mixture solution with different mixing ratio. Silver nanoparticles had been manufactured by the chemical reduction method so that Ag NP layer coating had been done by hot rotation liquid method. The optical properties of the prepared samples (mixture solution of Fluoreseina dye solutions and colloidal solution with 5 minutes prepared of Ag NPs) tested by using UV-VIS absorption and Fluorescence spectrophotometer. by using AFM, SEM testes, the structure of silver nanoparticles had been estimated. the result of this work showed that adding Ag NPs colloidal to Fluoreseina dye solution help get a significant increase in the fluorescence intensity of this dye. this study results show that its significant to recent related studies in MEF.
Double Machine Learning Density Estimation for Local Treatment Effects with Instruments

It is common to quantify causal effects with mean values, which, however, may 1 fail to capture significant distribution differences of the outcome under different 2 treatments. We study the problem of estimating the density of the causal effect 3 of a binary treatment on a continuous outcome given a binary instrumental vari4 able in the presence of covariates. Specifically, we consider the local treatment 5 effect, which measures the effect of treatment among those who comply with the 6 assignment under the assumption of monotonicity (only the ones who were offered 7 the treatment take it). We develop two families of methods for this task, kernel8 smoothing and model-based approximations – the former smoothes the density by 9 convoluting with a smooth kernel function; the latter projects the density onto a 10 finite-dimensional density class. For both approaches, we derive double/debiased 11 machine learning (DML) based estimators. We study the asymptotic convergence 12 rates of the estimators and show that they are robust to the biases in nuisance 13 function estimation. We illustrate the proposed methods on synthetic data and a 14 real dataset called 401(k). 15
How COVID-19 Affects Democracy Discourses in China: Evidence from Weibo and Zhihu∗

How did COVID-19 affect the discourses of democracy on the Chinese Internet? With surging COVID-19 cases and deaths in Western democracies contrasting to the relatively quick defence in China, whether the democratic decision-making process is an optimal model in handling a large-scale crisis has been heatedly debated globally, including the Chinese internet. To understand the changes in online discussions on democracy in China, we analyze two most popular social media platforms in China: Weibo (Chinese version of Twitter) and Zhihu (Chinese version of Quora). We use the keyword-matching and machine-learning approach to select 336,351 Weibo posts related to democracy and COVID-19. For Zhihu, we crawl all 204 questions and 23,630 answers directly on democracy since 2011. We then classify each post from Weibo to one of three types of sentiments – positive, negative, and satirical – and one from Zhihu into positive and negative sentiment. We illustrate the temporal trends of sentiments and analyze the relationship among various democratic values and different countries. Our analyses draw several important findings regarding democracy discourses in China: 1) Since the outbreak of COVID19, the online discussion of democracy has drastically increased and then decreased; 2) since the period when COVID-19 was not effectively controlled in many democracies, the Chinese view of democracy has become more negative and cynical; 3) The negative and cynical sentiments are strong in the context of “Western” democracies; 4) When the discussion is about democratic values in China, the sentiments are more likely positive. ∗Preliminary draft. Please do not cite or circulate. All errors are our own. †Department of Economics, Hong Kong Baptist University; tingchen@hkbu.edu. hk. ‡Division of Social Science, Hong Kong University of Science and Technology; jyhong@ust.hk. §Division of Social Science, Hong Kong University of Science and Technology; hanying.wei@connect.ust.hk.
Super Resolution with Generative Adversarial Networks

Recently, GANs can be widely used in image generation, feature extraction, image recovery and Image Super-Resolution. Single Image Super-Resolution (SISR) has a board range of applications, for speciﬁc ﬁelds including security video surveillance and medical imaging. In our project, we ﬁrst present how can the Generative Adversarial networks can be applied on Image Super-Resolution. Then we modify the problem settings and generate results based on different problem settings. Finally, we evaluate and compare the different results based on several common measures to get a deeper insight of the Generative Adversarial Networks Image Super Resolution Algorithm. Our quality measures include metrics such as Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and human options to judge the results generated. The goal of our project is to implement a Generative Adversarial Networks model for the image super-resolution and analyses its performance of image reconstruction with different parameter settings.
Growth onset of perylene-based nanocrystals

1 Laboratory of Thermal Physics of Multiphase Systems, Arifov Institute of Ion-Plasma and Laser Technologies, Academy of Sciences of Uzbekistan, Durmon yuli str. , 33, 100125, Tashkent, Uzbekistan 2 Research Group PLASMANT, NANOLabCenter of Excellence, Department of Chemistry, University of Antwerp, Universiteits plein 1, 2610, Antwerp, Belgium 3 University of Geological Sciences, Olimlar str. 64, 100041, Tashkent, Uzbekistan 4 National University of Uzbekistan named after Mirzo Ulugbek, University str. 4, 100174, Tashkent, Uzbekistan
The Effect of Gold Nano Particles with Different Sizes on Streptococcus Species

Statement of the Problem: Streptococcus mutans, Streptococcus sanguinis, and Streptococcus salivarius are most common etiologic bacteria for dental caries. Different sizes of gold nanoparticles may have different antibacterial effects on these species. Purpose: This study aimed to compare the antibacterial effect of chlorhexidine and three sizes of gold nano particles (25, 60, 90nm) against clinical and standard strains of Streptococcus mutans, Streptococcus sanguinis, and Streptococcus salivarius. Materials and Method: In this cross-sectional study, the specimens were collected from 75 children aged 3-5 years old. Antibacterial effect of chlorhexidine and three sizes of gold nano particles (25, 60, 90nm) were investigated by evaluating the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) against three bacterial strains. Results: The MIC and MBC of gold nanoparticles with different sizes against Streptococcus mutans, Streptococcus sanguinis, and Streptococcus salivarius were statistically different. The MIC and MBC of smaller gold nano particles (25nm) were significantly lower (p<0.001) than larger ones. Patient-derived bacteria had significantly higher values of MIC and MBC in comparison to standard species (p<0.001). Conclusion: The results of this study confirmed the significant size-dependency of gold nano particles for antibacterial activity. As the size of gold nano particles decrease, the antibacterial properties enhance.
An Introduction to Quantum Computing for Statisticians

Quantum computing has the potential to revolutionise and change the way we live and understand the world. This review aims to provide an accessible introduction to quantum computing with a focus on applications in statistics and data analysis. We start with an introduction to the basic concepts necessary to understand quantum computing and the differences between quantum and classical computing. We describe the core quantum subroutines that serve as the building blocks of quantum algorithms. We then review a range of quantum algorithms expected to deliver a computational advantage in statistics and machine learning. We highlight the challenges and opportunities in applying quantum computing to problems in statistics and discuss potential future research directions.
μDep: Mutation-Based Dependency Generation for Precise Taint Analysis on Android Native Code

The existence of native code in Android apps plays an important role in triggering inconspicuous propagation of secrets and circumventing malware detection. However, the state-of-the-art information-flow analysis tools for Android apps all have limited capabilities of analyzing native code. Due to the complexity of binary-level static analysis, most static analyzers choose to build conservative models for a selected portion of native code. Though the recent inter-language analysis improves the capability of tracking information flow in native code, it is still far from attaining similar effectiveness of the state-of-the-art information-flow analyzers that focus on non-native Java methods. To overcome the above constraints, we propose a new analysis framework, <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math><alternatives><mml:math><mml:mi>μ</mml:mi></mml:math><inline-graphic xlink:href="sun-ieq2-3155693.gif"/></alternatives></inline-formula>Dep, to detect sensitive information flows of the Android apps containing native code. In this framework, we combine a control-flow based static binary analysis with a mutation-based dynamic analysis to model the tainting behaviors of native code in the apps. Based on the result of the analyses, <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math><alternatives><mml:math><mml:mi>μ</mml:mi></mml:math><inline-graphic xlink:href="sun-ieq3-3155693.gif"/></alternatives></inline-formula>Dep conducts a stub generation for the related native functions to facilitate the state-of-the-art analyzer DroidSafe with fine-grained tainting behavior summaries of native code. The experimental results show that our framework is competitive on the accuracy, and effective in analyzing the information flows in real-world apps and malware compared with the state-of-the-art inter-language static analysis.
CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation

Sequential recommendation methods are increasingly important in cutting-edge recommender systems. Through leveraging historical records, the systems can capture user interests and perform recommendations accordingly. State-of-the-art sequential recommendation models proposed very recently combine contrastive learning techniques for obtaining high-quality user representations. Though effective and performing well, the models based on contrastive learning require careful selection of data augmentation methods and pretext tasks, efficient negative sampling strategies, and massive hyper-parameters validation. In this paper, we propose an ultra-simple alternative for obtaining better user representations and improving sequential recommendation performance. Specifically, we present a simple yet effective Consistency T braining method for sequential Recommendation (CT4Rec) in which only two extra training objectives are utilized without any structural modifications and data augmentation. Experiments on three benchmark datasets and one large newly crawled industrial corpus demonstrate that our proposed method outperforms SOTA models by a large margin and with much less training time than these based on contrastive learning. Online evaluation on real-world content recommendation system also achieves 2.717% improvement on the click-through rate and 3.679% increase on the average click number per capita. Further exploration reveals that such a simple method has great potential for CTR prediction. Our code is available at https://github.com/ct4rec/CT4Rec.git.
Beyond Ads: Sequential Decision-Making Algorithms in Public Policy

We explore the promises and challenges of employing sequential decision-making algorithms – such as bandits, reinforcement learning, and active learning – in the public sector. While such algorithms have been heavily studied in settings that are suitable for the private sector (e.g., online advertising), the public sector could greatly beneﬁt from these approaches, but poses unique methodological challenges for machine learning. We highlight several applications of sequential decision-making algorithms in regulation and governance, and discuss areas for further research which would enable them to be more widely applicable, fair, and effective. In particular, ensuring that these systems learn rational, causal decision-making policies can be difﬁcult and requires great care. We also note the potential risks of such deployments and urge caution when conducting work in this area. We hope our work inspires more investigation of public-sector sequential decision making applications, which provide unique challenges for machine learning researchers and can be socially beneﬁcial.
Learning distributed channel access policies for networked estimation: data-driven optimization in the mean-field regime

The problem of communicating sensor measurements over shared networks is prevalent in many modern large-scale distributed systems such as cyber-physical systems, wireless sensor networks, and the internet of things. Due to bandwidth constraints, the system designer must jointly design decentralized medium access transmission and estimation policies that accommodate a very large number of devices in extremely contested environments such that the collection of all observations is reproduced at the destination with the best possible fidelity. We formulate a remote estimation problem in the mean-field regime where a very large number of sensors communicate their observations to an access point, or base station, under a strict constraint on the maximum fraction of transmitting devices. We show that in the mean-field regime, this problem exhibits a structure that enables tractable optimization algorithms. More importantly, we obtain a data-driven learning scheme that admits a finite sample-complexity guarantee on the performance of the resulting estimation system under minimal assumptions on the data's probability density function.
Learning Contraction Policies from Offline Data

This paper proposes a data-driven method for learning convergent control policies from offline data using Contraction theory. Contraction theory enables constructing a policy that makes the closed-loop system trajectories inherently convergent towards a unique trajectory. At the technical level, identifying the contraction metric, which is the distance metric with respect to which a robot's trajectories exhibit contraction is often non-trivial. We propose to jointly learn the control policy and its corresponding contraction metric while enforcing contraction. To achieve this, we learn an implicit dynamics model of the robotic system from an offline data set consisting of the robot's state and input trajectories. Using this learned dynamics model, we propose a data augmentation algorithm for learning contraction policies. We randomly generate samples in the state-space and propagate them forward in time through the learned dynamics model to generate auxiliary sample trajectories. We then learn both the control policy and the contraction metric such that the distance between the trajectories from the offline data set and our generated auxiliary sample trajectories decreases over time. We evaluate the performance of our proposed framework on simulated robotic goal-reaching tasks and demonstrate that enforcing contraction results in faster convergence and greater robustness of the learned policy.