Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimax Rate Optimal Adaptive Nearest Neighbor Classification and Regression

<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> Nearest Neighbor (kNN) method is a simple and popular statistical method for classification and regression. For both classification and regression problems, existing works have shown that, if the distribution of the feature vector has bounded support and the probability density function is bounded away from zero in its support, the convergence rate of the standard kNN method, in which <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is the same for all test samples, is minimax optimal. On the contrary, if the distribution has unbounded support, we show that there is a gap between the convergence rate achieved by the standard kNN method and the minimax bound. To close this gap, we propose an adaptive kNN method, in which different <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is selected for different samples. Our selection rule does not require precise knowledge of the underlying distribution of features. The proposed adaptive method significantly outperforms the standard one. We characterize the convergence rate of the proposed adaptive method, and show that it matches the minimax lower bound.
On the Exponential Stability of a Stratified Flow to the 2D Ideal MHD Equations with Damping

We study the stability of a type of stratified flows of the two dimensional inviscid incompressible MHD equations with velocity damping. The exponential stability for the perturbation near certain stratified flow is investigated in a strip-type area R*[0,1]. Although the magnetic filed potential is governed by a transport equation, by using the algebraic structure of the incompressible condition, it turns out that the linearized MHD equations around the given stratified flow retain a non-local damping mechanism. After carefully analyzing the non-linear structure and introducing some suitable weighted energy norms, we get the exponential stability by combining the exponential decay in time in the lower order energy with that in the high order energy.
A Unifying Framework of Bilinear LSTMs

This paper presents a novel unifying framework of bilinear LSTMs that can represent and utilize the nonlinear interaction of the input features present in sequence datasets for achieving superior performance over a linear LSTM and yet not incur more parameters to be learned. To realize this, our unifying framework allows the expressivity of the linear vs. bilinear terms to be balanced by correspondingly trading off between the hidden state vector size vs. approximation quality of the weight matrix in the bilinear term so as to optimize the performance of our bilinear LSTM, while not incurring more parameters to be learned. We empirically evaluate the performance of our bilinear LSTM in several language-based sequence learning tasks to demonstrate its general applicability.
Instance-based Model Adaptation for Direct Speech Translation

Despite recent technology advancements, the effectiveness of neural approaches to end-to-end speech-to-text translation is still limited by the paucity of publicly available training corpora. We tackle this limitation with a method to improve data exploitation and boost the system’s performance at inference time. Our approach allows us to customize "on the fly" an existing model to each incoming translation request. At its core, it exploits an instance selection procedure to retrieve, from a given pool of data, a small set of samples similar to the input query in terms of latent properties of its audio signal. The retrieved samples are then used for an instance-specific fine-tuning of the model. We evaluate our approach in three different scenarios. In all data conditions (different languages, in/out-of-domain adaptation), our instance-based adaptation yields coherent performance gains over static models.
Robust Domain Randomization for Reinforcement Learning

Producing agents that can generalize to a wide range of environments is a significant challenge in reinforcement learning. One method for overcoming this issue is domain randomization, whereby at the start of each training episode some parameters of the environment are randomized so that the agent is exposed to many possible variations. However, domain randomization is highly inefficient and may lead to policies with high variance across domains. In this work, we formalize the domain randomization problem, and show that minimizing the policy's Lipschitz constant with respect to the randomization parameters leads to low variance in the learned policies. We propose a method where the agent only needs to be trained on one variation of the environment, and its learned state representations are regularized during training to minimize this constant. We conduct experiments that demonstrate that our technique leads to more efficient and robust learning than standard domain randomization, while achieving equal generalization scores.
Linearly involved generalized Moreau enhanced models and their proximal splitting algorithm under overall convexity condition

The convex envelopes of the direct discrete measures, for the sparsity of vectors or for the low-rankness of matrices, have been utilized extensively as practical penalties in order to compute a globally optimal solution of the corresponding regularized least-squares models. Motivated mainly by the ideas in Zhang (2010 Ann. Stat. 38 894–942; Selesnick 2017 IEEE Trans. Signal Process. 65 4481–94; Yin et al 2019 IEEE Trans. Signal Process. 67 2595–607) to exploit nonconvex penalties in the regularized least-squares models without losing their overall convexities, this paper presents the linearly involved generalized Moreau enhanced (LiGME) model as a unified extension of such utilizations of nonconvex penalties. The proposed model can admit multiple nonconvex penalties without losing its overall convexity and thus is applicable to much broader scenarios in the sparsity-rank-aware signal processing. Under the general overall-convexity condition of the LiGME model, we also present a novel proximal splitting type algorithm of guaranteed convergence to a globally optimal solution. Numerical experiments in typical examples of the sparsity-rank-aware signal processing demonstrate the effectiveness of the LiGME models and the proposed proximal splitting algorithm.
Complexity Results on Register Pushdown Automata

Register pushdown automata (RPDA) is an extension of classical pushdown automata to handle data values in a restricted way. RPDA attracts attention as a model of a query language for structured documents with data values. The membership and emptiness problems for RPDA are known to be EXPTIME-complete. This paper shows the membership problem becomes PSPACE-complete and NP-complete for nondecreasing and growing RPDA, respectively, while the emptiness problem remains EXPTIME-complete for these subclasses.
Knowledge, attitudes, and practices of healthcare workers about healthy lifestyles : a study in an urban-based district hospital in KwaZulu-Natal.

13 CHAPTER ONE: INTRODUCTION 14 Overview of chapter 14 Introduction 15 Backgro und 16 Study Aims 19 Specific objectives of the study 19 Overview of research report 20 CHAPTER TWO: LITERATURE REVIEW 21 Overview of chapter 21 Search strategies 21 SECTION A: BURDEN OF CHRONIC DISEASES OF LIFESTYLE 23 SECTION B: MAJOR CONTRIBUTING FACTORS TO THE INCREASE IN PREVALENCE OF CHRONIC DISEASES OF LIFESTYLE 28 Cardiovascular disease ; 28 Diabetes 28 Hypertension 31 SECTION C: LIFESTYLE FACTORS INFLUENCING THE INCREASE IN CHRONIC DISEASES OF LIFESTYLE 33 Obesity, diet and nutrition 33 Physical activity 35 Use of tobacco products 36 SECTION D: EFFECTS OF KNOWLEDGE, ATTITUDES AND PRACTICES ON LIFESTYLE RISK FACTO RS 37 SECTION E: ECONOMIC TREND S INFLUENCING LIFESTYLE CHOICES . 43 Urbanization 43 Globalization 45 SECTION F : ROLE OF THE WORKP LACE IN PROMO TING A HEALTHY LIFESTYLE 47 SECTION G: MODELS FOR PROMOTING HEALTHY LIFESTYLES 51 Chapter summary 55 CHAPTER THREE: METHODOLOGy 56 Overview of chapter 56 SECTION A: STUDY SETTING 57 SECTION B: STUDY PERIOD 58 SECTION C: STUDY DESIGN 58 SECTION D: STUDY POPULATION 59 Inclusion criteria: 59 Exclusion criteria: 59 SECTION E: SAMPLING STRATEGY AND SIZE 60 SECTION F: DATA COLLECTION METHODS 60 Variables 60 Data collection instruments 61 Data collection process 61 Measures to ensure validity and reliability 61 SECTION G: DATA MANAGEMENT 62 SECTION H: DATA ANALYSIS 62 Overview of data analysis 62 Data classification 63 Significance testing 66 SECTION I: ETHICAL CONSIDERATIONS 66 Respect for persons 67 Principle of beneficence 67 Principle of justice 67 Chapter summary 68 CHAPTER FOUR: RESULTS 69 Overview of chapter 69 SECTION A: Descriptive profile of respondents 70 SECTION B: Knowledge of the staff with regards to healthy lifestyle issues 73 SECTION C: Attitudes of the staff 74 SECTION D: Practices of the staff 75 SECTION E: Weight perception and body mass index 76
The Most Massive Galaxies with Large Depleted Cores: Structural Parameter Relations and Black Hole Masses

Luminous spheroids (MV ≲ −21.50 ± 0.75 mag) contain partially depleted cores with sizes (Rb) typically 0.02–0.5 kpc. However, galaxies with Rb > 0.5 kpc are rare and poorly understood. Here, we perform detailed decompositions of the composite surface brightness profiles, extracted from archival Hubble Space Telescope and ground-based images, of 12 extremely luminous “large-core” galaxies that have Rb > 0.5 kpc and MV ≲ −23.50 ± 0.10 mag, fitting a core-Sérsic model to the galaxy spheroids. Using 28 “normal-core” (i.e., Rb < 0.5 kpc) galaxies and one “large-core” (i.e., Rb > 0.5 kpc) galaxy from the literature, we constructed a final sample of 41 core-Sérsic galaxies. We find that large-core spheroids (with stellar masses M* ≳ 1012M☉) are not simple high-mass extensions of the less luminous normal-core spheroids having M* ∼ 8 × 1010–1012M☉. While the two types follow the same strong relations between the spheroid luminosity LV and Rb ( ), and the spheroid half-light radius Re ( , for ellipticals plus Brightest Cluster Galaxies), we discover a break in the core-Sérsic σ–LV relation occurring at MV ∼ −23.50 ± 0.10 mag. Furthermore, we find a strong log-linear Rb–MBH relation for the 11 galaxies in the sample with directly determined supermassive black hole (SMBH) masses MBH—3/11 galaxies are large-core galaxies—such that . However, for the large-core galaxies the SMBH masses estimated from the MBH–σ and core-Sérsic MBH–L relations are undermassive, by up to a factor of 40, relative to expectations from their large Rb values, confirming earlier results. Our findings suggest that large-core galaxies harbor overmassive SMBHs (MBH ≳ 1010M☉), considerably (∼3.7–15.6σ and ∼0.6–1.7σ) larger than expectations from the spheroid σ and L, respectively. We suggest that the Rb–MBH relation can be used to estimate SMBH masses in the most massive galaxies.
Semantic Segmentation of Skin Lesions using a Small Data Set

Early detection of melanoma is difficult for the human eye but a crucial step towards reducing its death rate. Computerized detection of these melanoma and other skin lesions is necessary. The central research question in this paper is "How to segment skin lesion images using a neural network with low available data? ". This question is divided into three sub questions regarding best performing network structure, training data and training method. First theory associated with these questions is discussed. Literature states that U-net CNN structures have excellent performances on the segmentation task, more training data increases network performance and utilizing transfer learning enables networks to generalize to new data better. To validate these findings in the literature two experiments are conducted. The first experiment trains a network on data sets of different size. The second experiment proposes twelve network structures and trains them on the same data set. The experimental results support the findings in the literature. The FCN16 and FCN32 networks perform best in the accuracy, intersection over union and mean BF1 Score metric. Concluding from these results the skin lesion segmentation network is a fully convolutional structure with a skip architecture and an encoder depth of either one or two. Weights of this network should be initialized using transfer learning from the pre trained VGG16 network. Training data should be cropped to reduce complexity and augmented during training to reduce the likelihood of overfitting.
Primordial black hole formation in inflationary 
α
-attractor models

The formation of primordial black holes (PBHs) is studied using superconformal inflationary $\ensuremath{\alpha}$ attractors. An inflaton potential is constructed with a plateaulike region, which brings about the onset of an ultra slow-roll region where the required enhancement in the curvature power spectrum takes place. This is accomplished by carefully fine-tuning the parameters in the potential. For the parameter sets, the amount of observable inflation is such that the curvature perturbation ${\mathcal{P}}_{\mathcal{R}}$ peaks at $\ensuremath{\sim}{10}^{14}\text{ }\text{ }{\mathrm{Mpc}}^{\ensuremath{-}1}$, producing PBHs in the mass window $1{0}^{16}\text{ }\text{ }\mathrm{g}\ensuremath{\le}M\ensuremath{\le}{10}^{18}\text{ }\text{ }\mathrm{g}$. The reheating period after inflation is taken into account to determine whether or not PBH formation takes place in such a phase. Finally, the spectrum of second order gravitational waves that can arise due to the curvature perturbation enhancement is calculated.
Advanced strategy representations for the iterated prisoner ’ s dilemma

Although many studies have been conducted on evolving strategies for the iterated prisoners dilemma (IPD) using evolutionary algorithms, most of them use rather simple representations like look-up tables. Knowledge about the evolution of strategies with complex representations remains limited. Complex representations may lead to new strategies and may be closer to the human decision making process (“thinking"’). Our main question is: Do complex represantations have substantial advantages over simpler ones in context of the IPD? In this paper we introduce two novel advanced representations: an exhaustive approach based on linear genetic programming and an evolution strategy based approach which reflects a psychological model. We show that both approaches lead to the generation of usable tournament strategies. We also use a cooevolutionary environment to study the evolution of cooperation, leading to diverse results. Additionally, the application of decision tree techniques to generate a strategy training set from real world tournaments is discussed. Strategies evolved with such training sets may perform well in subsequent real world tournaments, as other participants tend to imitate successful strategies of previuos tournaments.
FEATURES OF COMMERCIALIZATION IN BIOTECHNOLOGY

The purpose of the study is to determine the specific features of the commercialization of scientific and engineering developments in the field of biotechnology. According to the goal, the main tasks are: to find out specific features of biotechnology production and determine their impact on the commercialization of scientific and technological developments in the field of biotechnology; to analyse existing forms of commercialization of innovative developments and to find out the possibility of their application in biotechnology; to outline the features and related problems of commercialization of scientific and engineering developments in the biotechnology industry. The article defines commercialization, consider on commercialization of scientific and technical innovative developments. It demonstrates urgency and necessity of innovative developments commercialization as an answer to the growing needs of consumers and market evolution. The study argues the interdisciplinary of biotechnology and its distribution in various spheres of life through colour classification. It determines the features of commercialization in biotechnology. The author describes the main characteristics of biotechnologies: complexity of the structure of biotechnological products, the complexity of production, the imperfection of quality control methods, and the lack of clinical history of drugs. She insists on their great influence on the commercialization of developments of the industry. The article shows that all the characteristics of biotechnologies can be divided into those that have a direct and indirect impact on their commercialization. It enumerates the forms of commercialization of scientific and technical developments: use in their own production, leasing, engineering, franchising, industrial cooperation, license agreements and consider on the possibility of their application in biotechnology. Not all forms of commercialization can be applied in the industry and franchising definitely can’t. The scientific novelty of this study is to substantiate the influence of the characteristic features of the industry on the processes of commercialization in biotechnology, the choice of forms of commercialization, the definition and systematization of problems, the solution of which will simplify the processes of commercialization of biotechnological developments.
Three-Dimensional Constrained Optimal Motion Planning for a Six-Degree-of-Freedom Quadrotor Helicopter for Urban Traffic Purposes

Original Research Paper Received 08 November 2014 Accepted 15 February 2015 Available Online 04 April 2015 The issue of traffic is an international challenge in the sophisticated countries in which over population is considered an important factor in creating this problem. Studies show that accident reports during the minimum time are the best way to control the traffic. For this purpose, this paper has been done in such way that after modeling the flying robot using Newton-Euler equations, three-dimensional constrained optimal trajectory has been generated through Direct Collocation Approach. In other words, the proposed problem in this paper is first formulated as an optimal control problem. Afterwards, the optimal control problem is discretized through Direct Collocation Technique, which is one of the numerical solving methods of the optimal control problems, and it is transformed into Nonlinear Programing Problem (NLP). Eventually, the aforementioned nonlinear programming problem is solved via SNOPT which works based on the gradient algorithm like SQP. It should be noted that since the main objective of motion planning in this paper is controlling the urban traffic, the urban constrains are utilized during the trajectory optimization. In other words, all of the high-rise buildings located during the course are modeled by the various cylinders. The efficacy of the aforementioned method is demonstrated by extensive simulations, and in particular it is confirmed that this method is capable of producing suitable solution for three-dimensional constrained optimal motion planning for six-degree-of-freedom quadrotor helicopter for urban traffic purposes.
Vitamina C, carotenoides, fenólicos totais e atividade antioxidante de goiaba, manga e mamão procedentes da Ceasa do Estado de Minas Gerais

This study aimed to analyze the content of antioxidant compounds (ascorbic acid - AA, dehydroascorbic acid - DHA, total vitamin C, lycopene, β-carotene, β-cryptoxanthin and phenolic compounds) and to evaluate the antioxidant activity in guava, mango and papaya. The analysis of carotenoids and vitamin C was performed by high performance liquid chromatography (HPLC). The content of phenolic compounds was determined using the Folin-Ciocalteu reagent and spectrophotometric reading. Antioxidant activity was evaluated by testing the 2.2-diphenyl-2-picryl-hydrazil (DPPH•) and reducing power. ANOVA was used for data analysis (α = 0.05). The levels of antioxidant constituents differed among the three fruits; guava was the fruit that had the highest levels of phenolic compounds, total vitamin C, lycopene and DHA, and the highest values for antioxidant activity. There was a strong correlation between tests that evaluated antioxidant activity and the content of phenolic compounds, demonstrating that these are the main antioxidant compounds to contribute to antioxidant activity of fruits examined in both tests. It is important to encourage the use of fruits evaluated in this study, both at home and in food service establishments, in order to increase the intake of natural antioxidants by the population.
Integration of Feature Selection with Data Level Approach for Software Defect Prediction

The dataset of software metrics in general are not balanced (unbalanced). An imbalance distribution of classes and attributes that are not relevant may decrease the performance of the model prediction software defect, because the majority of the class predictions tend to produce than minority class. This research uses a public dataset from NASA (National Aeronautics and Space Administration) MDP (Metrics Data Program) repository. This research aims to reduce the influence of class imbalance in the dataset, so that performance can be improved in the classification of defect prediction software. The model proposed in this research is applying the technique feature selection with particle swarm optimization (PSO), approaches the level of data using Random Under Sampling (RUS) and SMOTE (Synthetic Minority Over-sampling Technique) and (ensemble) Bagging with Naive Bayes Classifier. Research results show that the proposed model can improve the performance of naive bayes of the overall value of the AUC reached > 0.8. Statistical tests indicate that there is a significant difference between a naive bayes model with the model proposed by the p value (0.043) smaller than the alpha values (0.05) which means there is a significant difference between the two models.
Gut Microbiota Modulation on Intestinal Mucosal Adaptive Immunity

The mammalian intestine harbors a remarkable number of microbes and their components and metabolites, which are fundamental for the instigation and development of the host immune system. The intestinal innate and adaptive immunity coordinate and interact with the symbionts contributing to the intestinal homeostasis through establishment of a mutually beneficial relationship by tolerating to symbiotic microbiota and retaining the ability to exert proinflammatory response towards invasive pathogens. Imbalance between the intestinal immune system and commensal organisms disrupts the intestinal microbiological homeostasis, leading to microbiota dysbiosis, compromised integrity of the intestinal barrier, and proinflammatory immune responses towards symbionts. This, in turn, exacerbates the degree of the imbalance. Intestinal adaptive immunity plays a critical role in maintaining immune tolerance towards symbionts and the integrity of intestinal barrier, while the innate immune system regulates the adaptive immune responses to intestinal commensal bacteria. In this review, we will summarize recent findings on the effects and mechanisms of gut microbiota on intestinal adaptive immunity and the plasticity of several immune cells under diverse microenvironmental settings.