Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of morphology on walking ability in the modern broiler: a gait analysis study

This study tested the hypothesis that growth rate and liveweight affect walking ability in broilers by comparing objective measurements of the spatial and temporal gait parameters of several groups of birds. Two strains of birds were used (relaxed and selected), raised on two feeding regimes (ad-libitum and restricted), and culled at the same final liveweight (commercial cull weight of 2.4 kg). The ad-libitum-fed selected birds walked more slowly, with lower cadences, and took shorter steps. The steps were wider, and the toes were pointed outwards, resulting in a wider walking base. They kept their feet in contact with the ground for longer periods, having longer percentage stance times, shorter percentage swing times and increased double-contact times compared to the relaxed birds. These changes serve to increase stability during walking and are a likely consequence of the morphological changes in the selected broiler - in particular, the rapid growth of breast muscle moving the centre of gravity forward, and the relatively short legs compared to their liveweight. This altered gait would be very inefficient and would rapidly tire the birds, and could help to explain the low level of activity seen in the modern broiler.
Graph-based Latent Embedding, Annotation and Representation Learning in Neural Networks for Semi-supervised and Unsupervised Settings

vii CHAPTER 1: INTRODUCTION 1 1.1 Dissertation Outline 5 1.1.1 GAR: An Efficient and Scalable Graph-based Activity Regularization for Semi-supervised Learning 5 1.1. 2 Auto-clustering Output Layer: Automatic Learning of Latent Annotations in Neural Networks 5 1.1. 3 Learning Latent Representations in Neural Networks for Unsupervised Clustering through Pseudo Supervision and Graph-based Activity Regularization 6 CHAPTER 2: GAR: AN EFFICIENT AND SCALABLE GRAPH-BASED ACTIVITY REGULARIZATION FOR SEMI-SUPERVISED LEARNING 8 2.1 Related Work 10 2.2 Proposed Framework 12 2.2.1 Bipartite Graph Approach 12 2.2.2 Adaptive Adjacency 14 2.2.3 The Optimality of B 15 2.2. 4 Activity Regularization 16 2.2. 5 Training 18 2.3 Experiments 19 2.3.1 Datasets Used in the Experiments 20 2.3.2 Models Used in the Experiments 20 2.3.3 Results 22 2.3.3.1 MNIST 23 2.3.3.2 SVHN and NORB 26 2.3.3. 3 Effects of Hyperparameters 28
Nonlinear system identification with kernels

This thesis will focus exclusively on the application of kernel-based nonparametric methods to nonlinear identification problems. As for other nonlinear methods, two key questions in kernel-based identification are the questions of how to define a nonlinear model (kernel selection) and how to tune the complexity of the model (regularisation). The following chapter will discuss how these questions are usually dealt with in the literature. The principal contribution of this thesis is the presentation and investigation of two optimisation criteria (one existing in the literature and one novel proposition) for structural approximation and complexity tuning in kernel-based nonlinear system identification. Both methods are based on the idea of incorporating feature-based complexity constraints into the optimisation criterion, by penalising derivatives of functions. Essentially, such methods offer the user flexibility in the definition of a kernel function and the choice of regularisation term, which opens new possibilities with respect to how nonlinear models can be estimated in practice. Both methods bear strong links with other methods from the literature, which will be examined in detail in Chapters 2 and 3 and will form the basis of the subsequent developments of the thesis. Whilst analogy will be made with parallel frameworks, the discussion will be rooted in the framework of Reproducing Kernel Hilbert Spaces (RKHS). Using RKHS methods will allow analysis of the methods presented from both a theoretical and a practical point-of-view. Furthermore, the methods developed will be applied to several identification ‘case studies’, comprising of both simulation and real-data examples, notably: • Structural detection in static nonlinear systems. • Controlling smoothness in LPV models. • Complexity tuning using structural penalties in NARX systems. • Internet traffic modelling using kernel methods
Electrical Current Injection into Exciton-Polariton Condensates and Polariton-Enhanced Exciton Transport

Exciton-polaritons are composite quasiparticles that are the result of coupling excitons with photons. They bring together many of the advantages of both underlying parts: The excitons lend their strong interactions, and the photons give an extremely light mass. Polaritons exist in solid-state systems, which gives them a high potential for device applications, especially as their most interesting quantum effects, Bose-Einstein condensation in particular, become accessible at higher temperatures. In this thesis, I focus primarily on the effects of injecting electrical current into a polariton condensate. I was able to take advantage of the well-established fabrication methods for GaAs-based systems, as well as the high-quality samples developed by my predecessors, which allow the formation of nearly equilibrium condensates over large areas. My colleagues and I then made devices that allow direct injection of electrical charge carriers through polariton condensates in an in-plane direction of the 2D system. I report here three major observations. The first is a superlinear increase in photocurrent, which we attribute to stimulation of an Auger-like scattering of excitons into both the condensate and free carrier states. The second is an increase in the condensate density with current injection, which we explain as stimulated pairing of injected free electrons with intrinsic free holes. The third effect is a shift in the in-plane momentum of polariton condensates with injected current, which is evidence of drag on the polaritons by the free electrons. In addition to the work with electrical current injection, I also report on exciton transport distances in the polariton system. Specifically, very highly excitonic particles are found far from the creation point, with transport distances comparable to those of the much more photonic polaritons. We explain this as an effect of the unusual shape of the polariton energy dispersion.
Convergence analysis and stability of perturbed three-step iterative algorithm for generalized mixed ordered quasi-variational inclusion involving XOR operator

ABSTRACT In this article, we introduce a new type of generalized mixed ordered quasi-variational inclusion problem in the setting of real ordered Hilbert spaces. Further, we propose a perturbed three-step iterative algorithm for solving generalized mixed ordered quasi-variational inclusion problem. By using the relaxed resolvent operator method with XOR technique, we prove the existence of solution of generalized mixed ordered quasi-variational inclusion problem and discuss the convergence analysis and stability of the proposed algorithm. The iterative algorithm and results presented in this paper generalize, improve and significantly refine many previously known results of this area. Moreover, we give a numerical example to illustrate and show the convergence of the proposed algorithm in support of our main result.
Book Review: More Than a Tool?, Failure to Connect: How Computers Affect Our Children's Minds–For Better and Worse

Jane Healy has organized Failure to Connect: How Computers Affect Our Children’s Minds-For Better and Worse into three sections: an overview of the current status of children and computers, a discussion of the interrelationships between computers and the growing child, and a look at the successful uses of information technologies in homes and schools and a glimpse at what the future may hold. The book could easily have been split into three or four separate books, as Healy herself admits. Faced with such a broad landscape and in spite of her long list of references, we struggled to find the basis of some of her many opinions. The reader is constantly trying to figure out what the author is advocating; is it a positive or a negative opinion of computer use with children? Her attempt to present both sides of the issue and remain neutral on the use of technology leaves the reader with the dizzy feeling of disorientation throughout the first half of the book. No doubt about it, though, using the word FAILURE in a book title is sure to get the attention of educators. But whose failure is Jane Healy referring to in this fast-paced, sometimes infuriating book? Are the computers failing? Or are the students failing? Should we be scrutinizing teachers or, perhaps, parents and taxpayers? Or, perhaps, our profit-driven businesses or the philistines in our midst who confuse “information” with “knowledge. ” Healy has captured for our attention a host of culprits and, using a veritable arsenal of very recent references (over 300 ranging from the Wall Street Journal to Robert Sternberg’s Successful Intelligences), relentlessly takes potshots at one and all. And easy targets they are. How can anyone doubt, after the horrors of Columbine High School, that “computers, used incorrectly, may do far more harm than good,” as the book jacket puts it? Violence-aggrandizing computer games and sick virtual realities can be very damaging for susceptible young people. But is J. EDUCATIONAL COMPUTING RESEARCH, Vol. 24(1) 93–97, 2001
Mixed convection casson fluid flow over an exponentially stretching sheet with Newtonian heating effect

This paper deals with mixed convection of Casson fluid which flows over a heated surface that has been stretched exponentially. The governing equations that govern the fluid flow are reduced to ordinary differential equations by imposing suitable similarity variables. Numerical computational was carried out to solve for the f “(0) and θ (0) for some arbitrary values of the mixed convection parameter λ, Biot number Bi and Newtonian fluid parameter β when Pr =7.
Nonlinear Optical Tweezers As an Optical Method for Controlling Particles with High Trap Efficiency

Optical tweezers have seen as an essential tool for the manipulation dielectric microparticles and nanoparticles due to its non-contact action and high resolution of optical force. Up to now, there has been a lot of optical tweezers applications in the fields of biophysics, chemistry, medical science and nanoscience. Recently, optical tweezers have been theoretically and experimentally developing for the nanomechanical characterization of various kinds of biological cells. The configuration of optical tweezers has been day after day improving to enhance the trapping efficiency, spatial and temporal resolution and easy to control trapped objects. In common trend of optical tweezers improvements, we will discuss in detail of the several configurations of nonlinear optical tweezers using nonlinear materials as the added lens. We will also address the advantages of nonlinear optical tweezers, such as enhance optical efficiency, reduce trapping region, simplify controlling all-optical method. Finally, we present discussions about the specific properties of nonlinear optical tweezers used for stretch DNA molecule as example and an ideal to improve nonlinear optical tweezers using thin layer of organic dye proposed for going time.
Split credibility: A two-dimensional semi-linear credibility model

In the thesis, we introduce a two-dimensional semi-linear credibility model, which is an extension of the classical credibility or split credibility models used by practicing actuaries. Our model predicts the future expected losses of a policyholder by considering its historical primary and excess losses. The optimal split point is derived based on the mean squared error criterion. We show when and why splitting a policyholder’s historical losses into primary and excess parts work analytically. In addition, we derived formulas for estimating our model parameters nonparametrically. Finally, we show the application of our model through three examples.
ISPRS Journal of Photogrammetry and Remote Sensing

Incremental Structure-from-Motion (SfM) techniques have exhibited superior practicability in many recent studies; however, efficiency and robustness remain key challenges for these techniques. In this work, we propose a new incremental SfM method that overcomes these problems in a united framework that contains two iteration loops. The inner loop is a track selection loop, where a well-conditioned subset of the feature tracks is iteratively selected to accelerate the time-consuming bundle adjustment. The outer loop is a camera registration loop, where the a priori camera rotations are estimated via rotation averaging on multiple orthogonal maximum spanning trees (OMSTs) of the view-graph and used as weak supervision for the registration. The calibrated camera poses that agree with the a priori camera rotations are preferentially registered, and after all the consistent cameras have been calibrated, the remaining cameras are incrementally registered. The results of extensive experiments demonstrate that our system can reconstruct both general and ambiguous image datasets, and our system outperforms many state-of-the-art SfM systems in terms of efficiency and robustness.
What is Visual Quantum Learning Effect to Student Learning Results

The problem faced by developing countries, including Indonesia, are still the lowest thinking of student ability, proved the low value of the PISA test. An alternative to improving the problem of student ability is the application of visual quantum learning. The research aims to determine how the influence of visual quantum learning on the result of learning. Which is this research experimental with pretest-posttest control group design? Data collection was done using the test method. The test used is a written that is a matter of multiple choice with 4 answers choices. The data obtained than in the analysis using a t-test. The result showed that students learning outcomes in the learning using visual quantum learning with an average score of 80.74 compared to students whose learning using classical learning, with an average of 78.28. this is reinforced by the results of the t-test analysis showing significant differences between students who were treated through visual quantum learning models with students who were not treated through classical learning. The results of this research can be concluded that visual quantum learning model learning model influences the 4th grade of science learning outcomes in elementary school. In order for science learning to obtain good results, then learning can be done by using visual quantum learning of models. Keywords—quantum learning; learning result
A multiscale procedure based on the stochastic kinetic mean field and the phase-field models for coarsening

We report a quantitative multiscale (MS) procedure based on the recently developed Stochastic Kinetic Mean Field approach (SKMF) [Erdelyi, M. Pasichnyy, V. Bezpalchuk, J. J. Toman, B. Gajdics, and A. M. Gusak, Comput. Phys. Commun. 204, 31–37 (2016)], combined with the Phase Field model (PFM) and CALPHAD database, to study the nucleation-growth-coarsening process in alloys. The SKMF approach reproduces the nucleation and early growth of precipitates in the matrix, and the PFM then simulates the coarsening of the microstructure. To ensure the consistency of the procedure, the length and time scales of SKMF and PFM are explicitly connected. Moreover, both the effective interaction energies used in the SKMF and the free energy used in the PFM are taken from CALPHAD database. Two different implementations of the procedure are proposed. First, the postnucleation microstructure as provided by SKMF is used as the initial condition for subsequent PFM simulations. Second, only the particle size distribution and particle density are transferred to PFM, thereby giving access to bigger systems. The proposed procedure is tested in the specific case of the Ag-Cu model alloy.We report a quantitative multiscale (MS) procedure based on the recently developed Stochastic Kinetic Mean Field approach (SKMF) [Erdelyi, M. Pasichnyy, V. Bezpalchuk, J. J. Toman, B. Gajdics, and A. M. Gusak, Comput. Phys. Commun. 204, 31–37 (2016)], combined with the Phase Field model (PFM) and CALPHAD database, to study the nucleation-growth-coarsening process in alloys. The SKMF approach reproduces the nucleation and early growth of precipitates in the matrix, and the PFM then simulates the coarsening of the microstructure. To ensure the consistency of the procedure, the length and time scales of SKMF and PFM are explicitly connected. Moreover, both the effective interaction energies used in the SKMF and the free energy used in the PFM are taken from CALPHAD database. Two different implementations of the procedure are proposed. First, the postnucleation microstructure as provided by SKMF is used as the initial condition for subsequent PFM simulations. Second, only the particle size distribution and pa.
Fast and Scalable Approaches to Accelerate the Fuzzy k-Nearest Neighbors Classifier for Big Data

One of the best-known and most effective methods in supervised classification is the k-nearest neighbors algorithm (kNN). Several approaches have been proposed to improve its accuracy, where fuzzy approaches prove to be among the most successful, highlighting the classical fuzzy k-nearest neighbors (FkNN). However, these traditional algorithms fail to tackle the large amounts of data that are available today. There are multiple alternatives to enable kNN classification in big datasets, spotlighting the approximate version of kNN called hybrid spill tree. Nevertheless, the existing proposals of FkNN for big data problems are not fully scalable, because a high computational load is required to obtain the same behavior as the original FkNN algorithm. This article proposes global approximate hybrid spill tree FkNN and local hybrid spill tree FkNN, two approximate approaches that speed up runtime without losing quality in the classification process. The experimentation compares various FkNN approaches for big data with datasets of up to 11 million instances. The results show an improvement in runtime and accuracy over literature algorithms.
Fault detection and classification with feature representation based on deep residual convolutional neural network

This paper proposes a novel fault detection and classification method via deep residual convolutional neural network (DRCNN). The DRCNN captures the deep process features represented by convolutional layers from local to global. Unlike traditional methods, this feature representation can extract the deep fault information and learn the latent fault patterns. Besides, a data preprocessing approach is also proposed to transform the shape of original data into the shape available for convolutional neural network. Finally, experiments based on the data set of Tennessee Eastman process (TEP), a chemical industrial process benchmark, show that the proposed method achieves superior fault detection and better classification performance compared with the state‐of‐the‐art methods.
Strengths and limitations of using mobile technology in community based rehabilitation in a resource-poor setting

Limited access to rehabilitation, especially in the world’s poorest areas, is global health, development, and human rights challenge. Mobile technology and the increase of smartphones in Sub-Saharan Africa has created new opportunities to improve and scale rehabilitation in situations where rehabilitation professionals are few. Research on mobile technology and Community Based Rehabilitation in resource-poor setting is limited both in number and quality. This research aims to increase knowledge on how mobile technology could improve and scale access to rehabilitation in resource-poor setting. The study has been done using qualitative content analysis. Altogether 28 caretakers from households with disabilities and rehabilitation personnel from Tanga, Tanzania were interviewed. Aim was to get views from present availability and quality of health, rehabilitation, and other relevant services including factors contributing towards self-management and motivation of households. Data was also collected relating to the role of mobile technology in households and in homebased rehabilitation. Participants tested the applicability of AIpowered mobile solution that could provide automated advice and support. Inductive content analysis was used during the analysis phase. The analysis found external and internal contributors that impact home-based rehabilitation outcomes. Data also shows how mobiles are presently used in rehabilitation and what participants expect from mobile technology in future. This research provides evidence that when households with disabilities have access to evidence-based information and learning, rehabilitation can be effective, even when rehabilitation professionals are few or not available. Community Based Rehabilitation provides an opportunity for tele-rehabilitation and especially for using AI-powered technologies to automate rehabilitation advise and support. More research is needed on cost effectiveness and how to ensure equal access.
Estimating graph properties with HyperLogLog-type algorithms

When one is dealing with algorithms in very large networks, the main memory often becomes too small. Therefore, other techniques are necessary to analyse these graphs. Boldi and Vigna showed that vertex ball cardinality can be estimated well using HyperBall, which is based on HyperLogLog probabilistic counters. In this paper HyperEdgeball, similar to HyperBall, is created to estimate edgeball cardinality. Both algorithms are used in HyperSurplus to estimate the number of surplus edges locally, which gives good first practical results. Theory is deduced to count triangles and 4-cycles in a similar fashion, this yielded not yet in a properly performing algorithm.