Miracle-dz/UniFAR-FTUs huggingface dataset:
Filtering Normalizing Flows

Dynamical systems are the governing force behind many real world phenomena and temporally correlated data. Recently, a number of neural network architectures have been proposed to address inference for nonlinear dynamical systems. We introduce two different methods based on normalizing flows for posterior inference in latent non-linear dynamical systems. We also present gradient-based amortized posterior inference approaches using the auto-encoding variational Bayes framework that can be applied to a wide range of generative models with nonlinear dynamics. We call our method Filtering Normalizing Flows (FNF). FNF performs favorably against state-of-the-art inference methods in terms of accuracy of predictions and quality of uncovered codes and dynamics on synthetic data.
Mathematical modelling and optimal control of typhoid fever

Typhoid fever is a disease caused by a salmonella bacterium (Salmonella typhi) and transmitted by ingestion of water and/or food contaminated with faeces (stool). In this paper, we derive and analyse a model for the control of typhoid fever which takes into account an imperfect vaccine combined with some other control measures already studied in the literature. We begin by analysing the model without control. We compute the basic reproduction numberR0 and prove the local and global stability of the disease–free equilibrium whenever R0 is less than one through Lyapunov’s theory. When R0 is greater than one, we prove the local asymptotic stability of the unique endemic equilibrium through the Centre Manifold Theory and we find that the model exhibits a forward bifurcation. Then, we extend the model by reformulating it as an optimal control problem, with the use of three time dependent controls, to assess the impact of vaccination combined with protection/environment sanitation and treatment on the spread of the disease in human population. By using optimal control theory, we establish conditions under which the spread of the disease can be stopped, and we examine the impact of combined control tools on the transmission dynamic of the disease. Pontryagin’s maximum principle is used to characterize the optimal control. Numerical simulations and efficiency analysis show that, if we want to reduce significantly the spread of typhoid fever, treatment must be taken into account in all control strategies.
Molecular diversity and association mapping in a collection of synthetic hexaploid wheat

Ogbonnaya FC , Emebiri LC, Jilal A, Trethowan R, Abdalla O, Brettell R, Moody D, van Ginkel M Department of Primary Industries, Primary Industries Research Victoria (PIRVic), Private Bag 260, Horsham, Victoria 3401, Australia International Centre for Agricultural Research in the Dry Areas, PO Box 5466, Aleppo, Syria Plant Breeding Institute, University of Sydney, PMB 11, Camden, NSW 2570, Australia
Switching costs, Relationship banking and Micro, Small and Medium Enterprises formal bank credit in Zambia

This paper uses data collected from a sample of Micro, Small and Medium Enterprises (MSMEs) located in urban areas of five major districts in Zambia to investigate the role of switching costs and relationship banking among MSMEs when borrowing formal bank credit. We find that MSMEs choice of the main-bank does not persist signaling the absence of switching costs and the vague role of relationship banking. Moreover, switching a main-bank has no effect on lending interest rates. Our results imply that MSMEs are not ’locked-in’ and long-term relationships between banks and MSMEs barely exist. Banks also don’t use discounts on lending interest rates to attract enterprises from competitor banks. These results point to the existence of asymmetric information as the main explanation for the occurrence of high interest rates and low volume of credit banks allocate to MSMEs. Therefore, we recommend strengthening relationship banking as a single opportunity to promote repeated interaction between banks and MSMEs and greater absorption of MSMEs ’soft’ information by commercial banks. JEL Classification: D43, G21, L13, O16
In-class social networks and academic performance: how good connections can improve grades

Understanding how different variables affect student performance is an important requirement for improving educational practices. Since humans are highly social beings, social factors should play a significant role in the academic context. This paper analyzes the impact on academic performance of social indicators such as students friendship circle and in-class clustering. The analysis is based on data from six different classes of the topic Databases taken by students of computing-related majors. We assessed students’ friendship circle in terms of density (sociability) and also quality (grades) of their friends. The paper shows results with strong, statistically relevant relationships between the social factors and student performance. Among other results, the analysis indicates that (i) students with higher social capital tend to perform better, and (ii) students with friends with higher grades have better chances of recovering from a low exam grade.
Feature Extraction Performance Verification based on Ultra-wideband Imaging and Artificial Neural Network

Breast cancer is a second leading case among the women. Therefore, an efficiency breast cancer system is very important for accurate early detection. The selection of number of features is very important to avoid the system complexity and large processing time. This paper proposed a modified feature selection method for early breast cancer detection. Ultra-wideband (UWB) signals are transmitted and received using a pair of antenna. 1632 features are extracted from the received UWB signal and four statistical features (mean, median, maximum and minimum numbers) are selected from the extracted 1632 features. These features are fed into feed forward backpropagation neural network for breast cancer detection. The proposed features selection method is able to use to detect the breast cancer in terms of existence, location and size with average accuracy of 86.28%. The proposed feature selection method is able to increase the system performance.
Application of a direct current circuit to pick up and to store bioelectricity produced by microbial fuel cells

Every year the demand for energy worldwide is increasing. There are some alternatives to reduce these problems, such as clean energy or renewable energy. A particular alternative is the microbial fuel cells. These cells are biochemical reactors that convert chemical energy into electricity. The present research evaluated the dairy serum to produce bioelectricity from micro fuel cells (MFC) that were constructed with low-cost materials and with isolated bacteria in anaerobic sediments, located in Ecuadorian national territory, producing maximum voltages of 0.830 V in the circuit and a maximum power density of 30mW / m2. This low voltage was worked with 50 mL MFCs and with an output voltage of 300 mV. Under these conditions, a FLYBACK lift circuit isolated by the transformer was designed. This new circuit could increase the voltage from 30 mV to enough voltage to light a 2.5 V LED. Therefore, the energy produced by the MFC can be directly used to light a LED and to charge capacitors. This study shows that these MFCs, together with the designed circuit, could be used potentially to generate clean energy.
Tails for Minimally-Actuated Milli-Robots

Minimally actuated robots such as VelociRoACH [6], which uses only 2 motors, can be limited in maneuverability. Generating differential thrust for turns is difficult, particularly with high forward velocity. However, adding an extra element to generate moments improves maneuverability, stabilizes body motion, or provides recovery from inversion. For the small moment-of-inertia of millirobots, tails can be particularly effective at changing orientation compared to adding extra degrees of freedom for legs, as surfaces may be low friction. Also due to favorable scaling, aerodynamic control effects can be used in running and even jumping robots. Tail principles have been explored on a variety of milli-robots at UC Berkeley shown in Fig. 1. While aerodynamic effects are of course dominant in devices such as a 2.5 gram glider [7], at running speeds of 3 m/s, aerodynamic forces are significant and useable for passive stabilization as in the 30 gram VelociRoACH [6], or steering as in SailRoACH [3] (Fig. 1bc). The 100 gram jumping robot Salto [5] uses active thrusters (Fig. 1d), while actively pushing against the ground with a tail can be used for either inversion or turning (Fig. 1g) [2]. Table I summarizes turning performance for several of these robots.
DPG: DEEP DIFFERENTIABLE DETERMINISTIC POL-

Over the last decade, two competing control strategies have emerged for solving complex control tasks with high efficacy. Model-based control algorithms, such as model-predictive control (MPC) and trajectory optimization, peer into the gradients of underlying system dynamics in order to solve control tasks with high sample efficiency. However, like all gradient-based numerical optimization methods, model-based control methods are sensitive to intializations and are prone to becoming trapped in local minima. Deep reinforcement learning (DRL), on the other hand, can somewhat alleviate these issues by exploring the solution space through sampling — at the expense of computational cost. In this paper, we present a hybrid method that combines the best aspects of gradient-based methods and DRL. We base our algorithm on the deep deterministic policy gradients (DDPG) algorithm and propose a simple modification that uses true gradients from a differentiable physical simulator to increase the convergence rate of both the actor and the critic. We demonstrate our algorithm on seven 2D robot control tasks, with the most complex one being a differentiable half cheetah with hard contact constraints. Empirical results show that our method boosts the performance of DDPG without sacrificing its robustness to local minima.
Uncertainty quantification with compound density networks

Despite the huge success of deep neural networks (NNs), finding good mechanisms for quantifying their prediction uncertainty is still an open problem. Bayesian neural networks are one of the most popular approaches to uncertainty quantification. On the other hand, it was recently shown that ensembles of NNs, which belong to the class of mixture models, can also be used to quantify prediction uncertainty. In this paper, we enhance the flexibility of mixture models by replacing the fixed mixing weights by an adaptive, input-dependent distribution represented by NNs, and by considering uncountable many mixture components. The resulting class of models can be seen as the continuous counterpart to mixture density networks and is therefore referred to as compound density networks (CDNs). We employ likelihood maximization to train CDNs, and empirically show that they yield better uncertainty estimates on out-of-distribution data and are more robust to adversarial examples than previous approaches. 1 Compound density networks We generalize mixture density networks (MDNs) [1] from finite mixture distributions to mixtures of an uncountable set of components. The resulting model is given by p(y|x;ψ) = ∫ p(y; f(x;θ))p(θ; g(x;ψ)) dθ = Ep(θ;g(x;ψ))[ p(y; f(x;θ))] , (1) and, in correspondence to MDNs, is referred to as compound density network (CDN). Here, f(x;θ) corresponds to a neural network (NN) with parameters θ that outputs the parameters of the compound distribution (e.g. the mean and variance of a Gaussian). The parameters θ are random parameters themselves with a distribution that is parametrized by another function g(x;ψ) modelled by NNs. As MDNs, CDNs can be trained by maximizing the log-likelihood of ψ given the dataset D = (xn,yn)n=1. For regularization, one can subtract a Kullback-Leibler divergence (DKL) term encouraging the mixing distribution to stay close to some distribution p(θ), which leads to the objective
Dynamics of an Ellipse-Shaped Meniscus on a Substrate-Supported Drop under an Electric Field

The behavior of a conducting droplet and a dielectric droplet placed under an electric potential is analyzed. Expressions for drop height based on electrode separation and the applied voltage are found, and problem parameters associated with breakup and droplet ejection are classified. Similar to previous theoretical work, the droplet interface is restricted to an ellipse shape. However, contrary to previous work, the added complexity of the boundary condition at the electrode is taken into account. To gain insight into this problem, a two-dimensional droplet is addressed. This allows for conformal maps to be used to solve for the potential surrounding the drop, which gives the total upward electrical force on the drop that is then balanced by surface tension and gravitational forces. For the conducting case, the maximum droplet height is attained when the distance between the electrode and the drop becomes sufficiently large, in which case, the droplet can stably grow to about 2.31 times its initial height before instabilities occur. In the dielectric case, hysteresis can occur for certain values of electrode separation and relative permittivity.
Robust RST Controller Design by Convex Optimization G

A convex optimization-based robust RST controller design approach is proposed in this paper. It is shown that the system specifications given as the Nyquist robust stability criteria, absorption of effects for different types of external disturbances and the pole placement problem for LTI systems can be transformed into convex constraints. The controller design problem is then reduced to solving the standard quadraticallyconstrained convex program. The analysis also illustrates the potential limitations of the method through inherent tradeoff between robustness stability and performance specifications. The design procedure is illustrated on a practical industrial problem, showing that the proposed method can provide very robust solutions with good performance.
CHALLENGES THAT NURSES FACE IN PALLIATIVE CARE FOR THE ELDERLY

The aim of this thesis is to gather and summarize the challenges that nurses undergo while offering care to the elderly in palliative care. The purpose of this thesis was to bring out the challenges that nurses face in offering palliative care to the elderly. This can help in finding solutions to these challenges and to help nurses have coping mechanisms to make palliative care more effective. The articles that were used in the thesis were obtained from CINAHL, PubMed and Elsevier Science Direct. Selection of the appraised articles was carried out using the inclusion and exclusion criteria. In addition, the articles which were selected had full text access, answered the research questions, were peer-reviewed, were related to the research topic, were scientific based, published between 2010 and 2018 and their language of publication was English. A thematic analysis of the six selected articles was carried out for the purposes of identifying patterns of meaning and significance of the data in relation to the research questions. Data from the selected articles was grouped into five themes that included; stress from family and friends, nurse-patient relationship and emotional attachment, time management, inadequacy of resources and communication. Upon analyzing the appraised articles in line with the identified themes, it was discovered that indeed nurses play an integral role in offering effective palliative care. However, there are several challenges that they face in offering effective palliative care. These challenges include stress from family and friends, poor time management, poor patient-nurse relationship and emotional attachment, inadequate resources and poor communication. The thesis suggested ways to eliminate the challenges that prevent nurses from offering effective palliative care. Lahden ammattikorkeakoulu Hoitotyön koulutusohjelma JULIET, ODHIAMBO; MERCY, RUTTO Hoitotyön opinnäytetyö, Sairaanhoitajien kohtaamat haasteet ikääntyvien palliatiivisessa hoitotyössä 36 sivua, 4 liitesivua Kevät 2018
DESIGN OF UNMANNED SHIP HEADING CONTROLLER BASED ON FCMAC-PID

To solve the problems of inaccuracy of path tracking and untimely response of trajectory tracking for small unmanned ship, a FCMAC-PID course controller with fast response, good robustness and strong anti-jamming ability is designed. This method combines the advantages of Fuzzy-PID control and CMAC neural network, and overcomes the shortcomings of the Fuzzy-PID control which has steady-state error and weak anti-interference ability. On the basis of analyzing the kinematics equation of ship manufacturing, the model of ship hull is established according to the second-order wild model. Simulink simulation is carried out under the condition of no disturbance and comprehensive disturbance. The control effects of Fuzzy-PID and FCMAC-PID controllers are compared. The results show that FCMAC-PID path tracking controller has stronger self-adaptability, fast response speed, strong anti-interference ability and better tracking performance.
Experimental Study on Jet Acoustics of Subscale Rocket Nozzles with a Different Divergent Contour Design

To design launch pads and test facilities properly it is necessary to predict the noise emission of rocket engines as well as specific exhaust jet created tones. As existing prediction models are limited to jet engines with low jet Mach numbers, tests were performed to study the application in rocket engines with significant higher jet Mach numbers. Two subscale rocket nozzles were tested, differing in the contour type and the related exhaust jet shock pattern. The tests reveal several prominent tones of different types that appear only during fully-attached flow state.
GRAM-GAUSS-NEWTON METHOD: LEARNING OVER-

First-order methods such as stochastic gradient descent (SGD) are currently the standard algorithm for training deep neural networks. Second-order methods, despite their better convergence rate, are rarely used in practice due to the prohibitive computational cost in calculating the second-order information. In this paper, we propose a novel Gram-Gauss-Newton (GGN) algorithm to train deep neural networks for regression problems with square loss. Our method draws inspiration from the connection between neural network optimization and kernel regression of neural tangent kernel (NTK). Different from typical second-order methods that have heavy computational cost in each iteration, GGN only has minor overhead compared to first-order methods such as SGD. We also give theoretical results to show that for sufficiently wide neural networks, the convergence rate of GGN is quadratic. Furthermore, we provide convergence guarantee for mini-batch GGN algorithm, which is, to our knowledge, the first convergence result for the mini-batch version of a second-order method on overparameterized neural networks. Preliminary experiments on regression tasks demonstrate that for training standard networks, our GGN algorithm converges much faster and achieves better performance than SGD.
Difference-Seeking Generative Adversarial Network-Unseen Sample Generation

Unseen data, which are not samples from the distribution of training data and are difficult to collect, have exhibited importance in numerous applications, ({\em e.g.,} novelty detection, semi-supervised learning, and adversarial training). In this paper, we introduce a general framework called \textbf{d}ifference-\textbf{s}eeking \textbf{g}enerative \textbf{a}dversarial \textbf{n}etwork (DSGAN), to generate various types of unseen data. Its novelty is the consideration of the probability density of the unseen data distribution as the difference between two distributions $p_{\bar{d}}$ and $p_{d}$ whose samples are relatively easy to collect. The DSGAN can learn the target distribution, $p_{t}$, (or the unseen data distribution) from only the samples from the two distributions, $p_{d}$ and $p_{\bar{d}}$. In our scenario, $p_d$ is the distribution of the seen data, and $p_{\bar{d}}$ can be obtained from $p_{d}$ via simple operations, so that we only need the samples of $p_{d}$ during the training. Two key applications, semi-supervised learning and novelty detection, are taken as case studies to illustrate that the DSGAN enables the production of various unseen data. We also provide theoretical analyses about the convergence of the DSGAN.
Implementing Apache Spark Jobs Execution and Apache Spark Cluster Creation for Openstack Sahara 1

In this paper the problem of creating virtual clusters in clouds for big data analysis with Apache Hadoop and Apache Spark is discussed. Both clouds and MapReduce models are popular nowadays for a bunch of reasons: cheapness and efficient big data analysis respectively. For these thoughts, having an open source solution for building clusters is important. The article gives an overview on existing methods for Apache Spark cluster creation in clouds. We consider two open source cloud engines OpenStack and Eucalyptus and the most popular proprietary cloud service Amazon Web Services and examine cloud related features presented by these systems. Afterwards, we regard possible ways of creating virtual clusters for big data processing in OpenStack and describe their pros and cons. In the second part we describe in details one of these solutions that uses service Sahara. Sahara represents a cluster management system for OpenStack and it is used for setting up virtual clusters and executing MapReduce jobs. Sahara did not support contemporary versions of Apache Spark. The article introduces the results of our work that led to a Sahara modification, describes its idea and implementation details. By virtue of our modification, Sahara is able to create and use virtual clusters with contemporary versions of Apache Spark in OpenStack clouds.
Stories of Sameness and Difference: The Views and Experiences of Children and Adolescents with a trans* Parent

Abstract Scholarly attention has recently been given to the ways in which gender and parenting identities intersect, yet little is known about how children and adolescents with a trans* parent experience family life and relationships. This article reports data from a qualitative study in the UK based on interviews with 29 children and adolescents aged 5–18, all of whom have at least one parent who is trans*. Drawing upon theories of symbolic interactionism, the study investigated children and adolescents’ meaning-making and experiences of family life and relationships in the context of their parent’s gender identity. Data were analyzed using a theoretically informed thematic approach, which resulted in the identification of four main themes. Overall, findings suggest that parental gender identity does not adversely impact upon how children and adolescents perceive their relationship with their parent, but may be otherwise relevant to their experiences both within and outside of the home. Findings are discussed in relation to theories of family display (Finch, 2007) and discourse dependence (Galvin, 2006), suggesting that combining and extending these perspectives might be useful for understanding the perspectives of children and adolescents in different contexts of family diversity.
Announcement wars as an equilibrium selection device

This paper attempts to give a rationale to public announcements, so often observed in the real world, and to formalize the idea that they can be used as a form of equilibrium selection device when multiple equilibria arise. It also shows how announcements solve the problems of coordination failures as predicted by the empirical literature based on experimental studies.