Miracle-dz/UniFAR-FTUs huggingface dataset:
INFLATION THRESHOLDS AND RELATIVE PRICE VARIABILITY IN THE ECOWAS REGION

While the relationship between inflation and relative price variability (RPV) has been widely investigated in developed and emerging market economies because of its direct relevance to monetary policy implementation, research has neglected this linkage in most developing countries especially in the ECOWAS region. This paper examines whether RPV responds differently to different inflation regimes across the ECOWAS region, determining the inflation threshold that would minimize the distortionary impact of expected inflation on RPV in the region. Using the Panel Smooth Transition Regression (PSTR) model over the period 2008:M1 to 2019:M12, the paper finds that ECOWAS countries would achieve lower RPV in the moderate/intermediate inflation regime where the undesirable effect of expected inflation on RPV fades out. This regime ranges from an inflation rate of 7.1 percent to 8.3 percent per annum. The paper further establishes different inflation thresholds for the WAEMU and WAMZ countries, which reflects the differences in inflation tolerance levels between the Zones. It shows that inflation targets below the thresholds of 5.5 percent and 8.3 percent per annum, are desirable in the WAEMU and WAMZ, respectively. Along these lines, this paper concludes that ECOWAS countries should adopt a two-tier inflation compliance regime, until a common monetary policy framework is adopted across the region. The determination of appropriate inflation targets in line with these thresholds in both Zones would pave the way for the adoption of an inflation targeting (IT) regime to anchor inflation expectations and enhance credibility to monetary policy across the wider ECOWAS region. JEL Classification : C23, E31, E52, O55
Comparison of various machine learning approaches uses in heart ailments prediction

Heart disease has been the leading cause of a huge number of deaths in recent years. As a result, an accurate and feasible system is required to diagnose this disease early to provide better treatment. Advances in machine learning have the potential to enhance healthcare access. Given the importance of a crucial organ like the heart, medical professionals and physicians have made it a priority to forecast heart failure-related events in clinical practice, nevertheless, forecasting heart failure-related events in clinical practice has generally failed to achieve high accuracy. The objective here is to demonstrate how machine learning may be used to solve the problem. By analyzing hundreds of healthcare data and other semantics, machine learning algorithms can analyze related cases with diseases and health conditions. Here a demonstration of how to load the data, generate predictions through different models from patient data is shown. The metrics are then compared for a better understanding of their function and what impact can be inferred from them.
Next frame prediction using ConvLSTM

Intelligent decision-making systems require the potential for forecasting, foreseeing, and reasoning about future events. The issue of video frame prediction has aroused a lot of attention due to its usefulness in many computer vision applications such as autonomous vehicles and robots. Recent deep learning advances have significantly improved video prediction performance. Nevertheless, as top-performing systems attempt to foresee even more future frames, their predictions become increasingly foggy. We developed a method for predicting a future frame based on a series of prior frames that services the Convolutional Long-Short Term Memory (ConvLSTM) model. The input video is segmented into frames, fed to the ConvLSTM model to extract the features and forecast a future frame which can be beneficial in a variety of applications. We have used two metrics to measure the quality of the predicted frame: structural similarity index (SSIM) and perceptual distance, which help in understanding the difference between the actual frame and the predicted frame. The UCF101 data set is used for testing and training in the project. It is a data collection of realistic action videos taken from YouTube with 101 action categories for action detection. The ConvLSTM model is trained and tested for 24 categories from this dataset and a future frame is predicted which yields satisfactory results. We obtained SSIM as 0.95 and perceptual similarity as 24.28 for our system. The suggested work’s results are also compared to those of state-of-the-art approaches, which are shown to be superior.
Design and Simulation of Waveguide Bragg Grating based Temperature Sensor in COMSOL

With the advancements in the domain of photonics and optical sensors, Fibre Bragg Grating (FBG) sensors, owing to their increased advantages, have been researched widely and have proved to be useful in sensing applications. Moreover, the advent of Photonic Integrated Circuits (PICs) demands the incorporation of optical sensing in waveguides, which can be integrated on silicon photonic chips. In this paper, the design of a sub-micron range Waveguide Bragg Grating (WBG) based temperature sensor with high peak reflectivity and thermal sensitivity is proposed. The flexibility of COMSOL Multiphysics software is explored to simulate the sensor and the results are verified with the analytical values calculated using MATLAB. The simulation is carried out for the proposed design having 16000 gratings and a corresponding peak reflectivity of 0.953 is obtained. A thermal sensitivity of 80 pm/K is achieved, which is approximately eight times better than that of FBG based sensor.
Machine Learning Approaches in Stock Price Prediction: A Systematic Review

Prediction of stock prices is one of the most researched topics and gathers interest from academia and the industry alike. With the emergence of Artificial Intelligence, various algorithms have been employed in order to predict the equity market movement. The combined application of statistics and machine learning algorithms have been designed either for predicting the opening price of the stock the very next day or understanding the long term market in the future. This paper explores the different techniques that are used in the prediction of share prices from traditional machine learning and deep learning methods to neural networks and graph-based approaches. It draws a detailed analysis of the techniques employed in predicting the stock prices as well as explores the challenges entailed along with the future scope of work in the domain.
Grow-IoT (smart analytics app for comprehensive plant health analysis and remote farm monitoring using smart sensors)

Agriculture is the primary source of livelihood for a large section of the society in India, and the ever-increasing demand for high quality and high quantity yield calls for highly efficient and effective farming methods. Grow-IoT is a smart analytics app for comprehensive plant health analysis and remote farm monitoring platform to ensure that the farmer is aware of all the critical factors affecting the farm status. The cameras installed on the field facilitate capturing images of the plants to determine plant health based on phenotypic characteristics. Visual feedback is provided by the computer vision algorithm using image segmentation to classify plant health into three distinct categories. The sensors installed on the field relay crucial information to the Cloud for real-time optimized farm status management. All the data relayed can then be viewed using the user-friendly Grow-IoT app to remotely monitor integral aspects of the farm and take the required actions in case of critical conditions. Thus, the mobile platform combined with computer vision for plant health analysis and smart sensor modules gives the farmer a technical perspective. The simplistic design of the application makes sure that the user has the least cognitive load while using it. Overall, the smart module is a significant technical step to facilitate efficient produce across all seasons in a year.
Fake News Detection from Online media using Machine learning Classifiers

With the advancement in technology, the consumption of news has shifted from Print media to social media. The convenience and accessibility are major factors that have contributed to this shift in consumption of the news. However, this change has bought upon a new challenge in the form of “Fake news” being spread with not much supervision available on the net. In this paper, this challenge has been addressed through a Machine learning concept. The algorithms such as K-Nearest Neighbor, Support Vector Machine, Decision Tree, Naïve Bayes and Logistic regression Classifiers to identify the fake news from real ones in a given dataset and also have increased the efficiency of these algorithms by pre-processing the data to handle the imbalanced data more appropriately. Additionally, comparison of the working of these classifiers is presented along with the results. The model proposed has achieved an accuracy of 89.98% for KNN, 90.46% for Logistic Regression, 86.89% for Naïve Bayes, 73.33% for Decision Tree and 89.33% for SVM in our experiment.
Design and Implementation of Carbon Nano-tube based Full Adder at 32nm Technology for High Speed and Power Efficient Arithmetic Applications

Due to physical, material, technological, power-thermal and economical difficulties, scaling of CMOS transistors will stop very soon. Due to efficiency of power and speed compared to CMOS transistors, Carbon Nano-tube transistors are best suitable element to design logic circuits. So, CNTFETS have been utilized in designing of proposed full adder (FA) and 4-bit ripple carry adder (RCA) in this paper. Proposed FA and RCA have been compared to rival designs on bases of power, speed and power-delay-product (PDP). FA and RCA circuits have been analysed with the variation of temperature from 0°C to 100°C while the variation of supply voltages is from 0.7V to 1.3V. For all temperatures and all supply voltages, proposed FA and proposed RCA have the least power consumption, shortest delay and lowest PDP. SPICE has been utilized for simulating FAs and RCAs in 32 nm process node. Even though the fabrication is complicated than CMOS counterparts but simulation results confirm usefulness of proposed FA and RCA for high speed and power efficient arithmetic applications.
Deep Self-Dissimilarities as Powerful Visual Fingerprints

Features extracted from deep layers of classiﬁcation networks are widely used as image descriptors. Here, we exploit an unexplored property of these features: their internal dissimilarity. While small image patches are known to have similar statistics across image scales, it turns out that the internal distribution of deep features varies distinctively between scales. We show how this deep self dissimilarity (DSD) property can be used as a powerful visual ﬁngerprint. Particularly, we illustrate that full-reference and no-reference image quality measures derived from DSD are highly correlated with human preference. In addition, incorporating DSD as a loss function in training of image restoration networks, leads to results that are at least as photo-realistic as those obtained by GAN based methods, while not requiring adversarial training.
Performance Improvement in E-Gun Deposited SiOx- Based RRAM Device by Switching Material Thickness Reduction

A performance improvement by reduction in switching material thickness in a e-gun deposited SiOx based resistive switching memory device was investigated. Reduction in thickness cause thinner filamentary path formation during ON-state by controlling the vacancy defects. Thinner filament cause lowering of operation current from 500 μA to 100 μA and also improves the reset current (from >400 μA to <100 μA). Switching material thickness reduction also cause the forming free ability in the device. All these electrical parametric improvements enhance the device reliability performances. The device show >200 dc endurance, >3-hour data retention and >1000 P/E endurance with 100 ns pulses.
Cardiac Disease Prediction using Supervised Machine Learning Techniques.

Diagnosis of cardiac disease requires being more accurate, precise, and reliable. The number of death cases due to cardiac attacks is increasing exponentially day by day. Thus, practical approaches for earlier diagnosis of cardiac or heart disease are done to achieve prompt management of the disease. Various supervised machine learning techniques like K-Nearest Neighbour, Decision Tree, Logistic Regression, Naïve Bayes, and Support Vector Machine (SVM) model are used for predicting cardiac disease using a dataset that was collected from the repository of the University of California, Irvine (UCI). The results depict that Logistic Regression was better than all other supervised classifiers in terms of the performance metrics. The model is also less risky since the number of false negatives is low as compared to other models as per the confusion matrix of all the models. In addition, ensemble techniques can be approached for the accuracy improvement of the classifier. Jupyter notebook is the best tool, for the implementation of Python Programming having many types of libraries, header files, for accurate and precise work.
ON TOLERANCE TO PARASITIC INFECTIOUS DISEASES By

Immune responses to infectious parasitic diseases appear to be simply directed towards killing or clearing the parasite. However, the immunological strategies constitute a complex incorporated network of immune mechanisms to eradicate the parasite itself in addition to preservation of the integrity of host tissues. Resistance and tolerance are believed to be two distinct complementary host defense immune strategies. Mechanisms triggering infectious tolerance to parasitic infections are still poorly recognized but seem to be centered on controlling parasiteinduced tissue damage in the infected host. It is becoming obvious that realizing infectious tolerance as a prominent constituent of defense strategy deliver significant conceptions in coexistence of host-parasite relationship. This data may also pave the way to new therapeutic approaches in many parasitosis.
Using Flow Maps to Explore Migrations Over Time

This paper presents JFlowMap, a graphical tool offering various visualization techniques for producing and analyzing flow maps, one of the most often used visualizations of migration flows. It explains how these techniques can be used in combination to create task-oriented views, i.e. views that help to solve specific user tasks. The article further demonstrates on the UNHCR Refugee Dataset how the tool can be applied to the analysis of temporal changes in migration flows, and finally, sketches the possible directions of future work.
SCHUBERT CALCULUS

In this paper we motivate and develop the basic machinery of Schubert calculus as a method for counting intersections of subspaces, an important problem historically in enumerative geometry. After introducing basic objects of study such as Schubert cells and Schubert varieties in the Grassmannian and showing how intersections of these varieties can express the motivating enumerative questions we discuss how properties of these objects can be interpereted algebraically using cohomology. We then discuss symmetric function theory in order to establish an isomorphism from the cohomology ring of the Grassmannian to a quotient of the ring of symmetric functions. Finally, we discuss the Littlewood-Richardson rule for multiplying arbitrary Schur polynomials, which via the aformentioned isomorphism allows us to compute in the cohomology ring thus solving our original enumerative geometry problems.
On the Statistical Complexity of Sample Amplification

Given n i.i. d. samples drawn from an unknown distribution P , when is it possible to produce a larger set of n+m samples which cannot be distinguished from n+m i.i. d. samples drawn from P? Axelrod et al. [AGSV20] formalized this question as the sample amplification problem, and gave optimal amplification procedures for discrete distributions and Gaussian location models. However, these procedures and associated lower bounds are tailored to the specific distribution classes, and a general statistical understanding of sample amplification is still largely missing. In this work, we place the sample amplification problem on a firm statistical foundation by deriving generally applicable amplification procedures, lower bound techniques and connections to existing statistical notions. Our techniques apply to a large class of distributions including the exponential family, and establish a rigorous connection between sample amplification and distribution learning.
The Origin of Exponential Star-forming Disks

The disk components of galaxies generally show an exponential profile extending over several scale lengths, both in mass and star formation rate, but the physical origin is not well understood. We explore a physical model in which the galactic gas disk is viewed as a “modified accretion disk” in which coplanar gas inflow, driven by viscous stresses in the disk, provides the fuel for star formation, which progressively removes gas as it flows inward. We show that magnetic stresses from magneto-rotational instability are the most plausible source of the required viscosity, and construct a simple physical model to explore this. A key feature is to link the magnetic field strength to the local star formation surface density, Btot∝ΣSFRα . This provides a feedback loop between star formation and the flow of gas. We find that the model naturally produces stable steady-state exponential disks, as long as α ∼ 0.15, the value indicated from spatially resolved observations of nearby galaxies. The disk scale length h R is set by the rate at which the disk is fed, by the normalization of the B tot–ΣSFR relation and by the circular velocity of the halo. The angular momentum distribution of the gas and stars within the disk is a consequence of the transfer of angular momentum that is inherent to the operation of an accretion disk, rather than the initial angular momentum of the inflowing material. We suggest that magnetic stresses likely play a major role in establishing the stable exponential form of galactic disks.
Fighting Money Laundering With Statistics and Machine Learning

Money laundering is a profound global problem. Nonetheless, there is little scientific literature on statistical and machine learning methods for anti-money laundering. In this paper, we focus on anti-money laundering in banks and provide an introduction and review of the literature. We propose a unifying terminology with two central elements: (i) client risk profiling and (ii) suspicious behavior flagging. We find that client risk profiling is characterized by diagnostics, i.e., efforts to find and explain risk factors. On the other hand, suspicious behavior flagging is characterized by non-disclosed features and hand-crafted risk indices. Finally, we discuss directions for future research. One major challenge is the need for more public data sets. This may potentially be addressed by synthetic data generation. Other possible research directions include semi-supervised and deep learning, interpretability, and fairness of the results.
A PTAS for parallel two-stage flowshops under makespan constraint

As a hybrid of the Parallel Two-stage Flowshop problem and the Multiple Knapsack problem, we investigate the scheduling of parallel two-stage ﬂowshops under makespan constraint, which was motivated by applications in cloud computing and introduced by Chen et al. [3] recently. A set of two-stage jobs are selected and scheduled on parallel two-stage ﬂowshops to achieve the maximum total proﬁt while maintaining the given makespan constraint. We give a positive answer to an open question about its approximability proposed by Chen et al. [3]. More speciﬁcally, based on guessing strategies and rounding techniques for linear programs, we present a polynomial time approximation scheme (PTAS) for the case when the number of ﬂowshops is a ﬁxed constant. As the case with two ﬂowshops is already strongly NP-hard, our PTAS achieves the best possible approximation ratio.
Lempel: Developing the pattern recognition skill in computational thinking through an online educational game

Computational thinking is a key set of skills for the 21st century's digital literacy. Taking advantage of computers to solve complex problems automatically will be helpful in most future jobs. Among the skills that comprise Computational Thinking, pattern recognition plays an important role in managing and compressing information. To foster the development of this skill among primary and secondary school students, we have developed Lempel. In this game, we pro-pose a set of challenges of increasing complexity in which players have to provide a compressed version of the information presented. Lempel's fine-grained interaction data logging system allows us to use Learning Analytics techniques to better understand how the learning of this skill takes place.
Emotional intelligence and coping with stress in foster parents

Although much research on emotional intelligence (EI) and coping with stress has been performed in recent years, little is known about these dimensions of individual differences between parents. The main purpose of this study was to examine emotional intelligence and coping styles in foster parents. The study included 124 individual participants aged between 30 and 64 years old (M = 45.18, SD = 8.72), including foster parents (n = 63, 50.81%) and biological parents (n = 61, 49.19%). The cross-sectional survey study was conducted using the Schutte Self-Report Emotional Intelligence Test (SSEIT) and Coping Inventory for Stressful Situations (CISS). In comparison to biological parents, foster parents demonstrated significantly higher levels of emotional intelligence (EI), more frequently used task-oriented coping styles, and less often pursued emotional and avoidant coping strategies to deal with stress. EI was positively correlated with task-oriented coping and negatively with emotional coping. Hierarchical regression analysis indicated that EI was a strong predictor of task-oriented coping. Training focused on the enhancement of both EI and coping with stress should be considered as an effective way to improve parents’ competence. 166 Aleksandra M. Rogowska, Barbara Zmaczyńska-Witek, Ilona Łatka, Zofia Kardasz
Rational Decay of A Multilayered Structure-Fluid PDE System

In this work, we consider a certain multilayered (thick layer) wave–(thin layer) wave–heat (fluid) interactive PDE system. Such coupled PDE systems have been used in the literature to describe the blood transport process in mammalian vascular systems. In particular, the deformations of the boundary interface (thin layer) are described via the two dimensional elastic equation. The present work constitutes an investigation of the extent of the stabilizing effects of the underlying fluid dissipation – across the boundary interface – upon both the thick and thin structural components. (All three PDE components evolve on their respective geometries.) In this regard, our main result is the derivation of uniform decay rates for classical solutions of this multilayered PDE model. To obtain these estimates, necessary a priori inequalities for certain static multilayered PDE models are generated here to ultimately allow an application of a wellknown resolvent criterion for rational decay. Key terms: Fluid-Structure Interaction, Multilayered System, Semigroup, Rational Decay