Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural network approach to control wall-following robot navigation

In any robotics application, the deployed robot has to navigate from source to a destination for performing task(s). Efficient control of this navigation is a major research challenge in the field. In this paper, an attempt has been made to develop a neural network (NN) based controller for navigation of wall following robot. The primary focus is to control the robot to take decision of changing direction based on a set of sensor readings, where the sensors are fit around of the waist of the robot (SCITOS G5 robot in this work). The NN is trained by these sensor readings dataset (a collection of multiple such instances) and predicts the future control strategy. The NN is trained with gradient descent algorithm. An extensive parametric study has been conducted to set the optimal number of nodes in the hidden layer and the learning rate. The experimental result shows that the proposed algorithm can control the robot with 92.67% accuracy and can take decision within 1 second.
Multi-task Feature Selection based Anomaly Detection

Network anomaly detection is still a vibrant research area. As the fast growth of network bandwidth and the tremendous traffic on the network, there arises an extremely challengeable question: How to efficiently and accurately detect the anomaly on multiple traffic? In multi-task learning, the traffic consisting of flows at different time periods is considered as a task. Multiple tasks at different time periods performed simultaneously to detect anomalies. In this paper, we apply the multi-task feature selection in network anomaly detection area which provides a powerful method to gather information from multiple traffic and detect anomalies on it simultaneously. In particular, the multi-task feature selection includes the well-known l1-norm based feature selection as a special case given only one task. Moreover, we show that the multi-task feature selection is more accurate by utilizing more information simultaneously than the l1-norm based method. At the evaluation stage, we preprocess the raw data trace from trans-Pacific backbone link between Japan and the United States, label with anomaly communities, and generate a 248-feature dataset. We show empirically that the multi-task feature selection outperforms independent l1-norm based feature selection on real traffic dataset.
A Comparative Study of N-glycolylneuraminic Acid (Neu5Gc) and Cytotoxic T Cell (CT) Carbohydrate Expression in Normal and Dystrophin-Deficient Dog and Human Skeletal Muscle

The expression of N-glycolylneuraminic acid (Neu5Gc) and the cytotoxic T cell (CT) carbohydrate can impact the severity of muscular dystrophy arising from the loss of dystrophin in mdx mice. Here, we describe the expression of these two glycans in skeletal muscles of dogs and humans with or without dystrophin-deficiency. Neu5Gc expression was highly reduced (>95%) in muscle from normal golden retriever crosses (GR, n = 3) and from golden retriever with muscular dystrophy (GRMD, n = 5) dogs at multiple ages (3, 6 and 13 months) when compared to mouse muscle, however, overall sialic acid expression in GR and GRMD muscles remained high at all ages. Neu5Gc was expressed on only a minority of GRMD satellite cells, CD8+ T lymphocytes and macrophages. Human muscle from normal (no evident disease, n = 3), Becker (BMD, n = 3) and Duchenne (DMD, n = 3) muscular dystrophy individuals had absent to very low Neu5Gc staining, but some punctate intracellular muscle staining was present in BMD and DMD muscles. The CT carbohydrate was localized to the neuromuscular junction in GR muscle, while GRMD muscles had increased expression on a subset of myofibers and macrophages. In humans, the CT carbohydrate was ectopically expressed on the sarcolemmal membrane of some BMD muscles, but not normal human or DMD muscles. These data are consistent with the notion that altered Neu5Gc and CT carbohydrate expression may modify disease severity resulting from dystrophin deficiency in dogs and humans.
Cognitive Radio Resource Management for QoS Guarantees in Autonomous Femtocell Networks

Deploying femtocell networks embedded in the Macrocell coverage greatly benefits communication quality in variety manners. However, the lack of schemes to effectively mitigate detractive interference, fully utilize radio resources and provide quality-of-service (QoS) guarantee (in terms of delay) creates challenges to practically facilitate the concept of femtocell. To tackle these challenges to achieve a successful dense femtocell deployment, this paper proposes a cognitve radio resource management (CRRM) scheme which is inspired by the spirit of cognitive radio technology. Instead of the need of a centralized manner, the femtocell with the proposed CRRM can autonomously sense the radio resource usage of the Macrocell so as to mitigate interference. By analytical deriving the effective capacity of the CRRM that specifies the QoS guarantee capability of the system, the optimum sensing period and radio resource allocation are proposed for the CRRM to achieve a fully radio resource utilization while statistically guaranteeing the QoS of the femtocell. Numerical results demonstrate that the proposed CRRM outperforms the randomized scheme (without CRRM) in terms of the radio resource utilization efficiency. Simulation results also support the effectiveness on the delay guarantee performance.
Path Planning for General Aircrafts Under Complex Scenarios Using an Improved NSGA-II Algorithm ⋆

General aviation rapidly becomes blossom with the growing economy. However, the complex flying environment and the flexibility for general aircrafts cause more difficulty to ensure the flying safety and efficiency. Hence it is vital to plan path for aircrafts to keep them away from threat and reduce fuel consumption. In this paper, we propose a path planning method based on an improved Nondominated Sorting Genetic Algorithm (NSGA-II) to generate ideal paths for aircraft(s) in a 2-D space to avoid conflict, prohibited flying zones and risk areas. In our method, the adaptive adjustment strategy for crossover and mutation is introduced to accelerate the computation speed and improve the solutions quality. Empirical studies under different scenarios with multiple aircrafts demonstrate that our algorithm can improve solution quality effectively and efficiently.
Communications in Mathematical Physics Strong Asymmetric Limit of the Quasi-Potential of the Boundary Driven Weakly Asymmetric Exclusion Process

We consider the weakly asymmetric exclusion process on a bounded interval with particles reservoirs at the endpoints. The hydrodynamic limit for the empirical density, obtained in the diffusive scaling, is given by the viscous Burgers equation with Dirichlet boundary conditions. In the case in which the bulk asymmetry is in the same direction as the drift due to the boundary reservoirs, we prove that the quasipotential can be expressed in terms of the solution to a one-dimensional boundary value problem which has been introduced by Enaud and Derrida [16]. We consider the strong asymmetric limit of the quasi-potential and recover the functional derived by Derrida, Lebowitz, and Speer [15] for the asymmetric exclusion process.
Expecting the end: Continuous expectancy ratings for tonal cadences

Cognitive accounts for the formation of expectations during music listening have largely centered around mental representations of scales using both melodic and harmonic stimuli. This study extends these findings to the most recurrent cadence patterns associated with tonal music using a real-time, continuous-rating paradigm. Musicians and nonmusicians heard cadential excerpts selected from Mozart’s keyboard sonatas (perfect authentic cadence [PAC], imperfect authentic cadence [IAC], half cadence [HC], deceptive cadence [DC], and evaded cadence [EV]), and continuously rated the strength of their expectations that the end of each excerpt is imminent. As predicted, expectations for closure increased over the course of each excerpt and then peaked at or near the target melodic tone and chord. Cadence categories for which tonic harmony was the expected goal (PAC, IAC, DC, EV) received the highest and earliest expectancy ratings, whereas cadence categories ending with dominant harmony (HC) received the lowest and latest ratings, suggesting that dominant harmony elicits weaker expectations in anticipation of its occurrence in cadential contexts. A regression analysis also revealed that longer excerpts featuring dense textures and a cadential six-four harmony received the highest ratings overall.
Internet-Based Evaluation and Prediction of Web Services Trustworthiness

As most Web services are delivered by third parties over unreliable Internet and are late bound at run-time, it is reasonable and useful to evaluate and predict the trustworthiness of Web services. In this paper, we propose a novel approach to evaluate and predict Web services trustworthiness using comprehensive trustworthy evidences collected from the Internet. First, we use an effective way to collect comprehensive trustworthy evidences from the Internet, which include both objective evidences (e.g. QoS) and subjective evidences (e.g. reputation). Second, Web services trustworthiness is evaluated with collected trustworthy evidences on a regular basis. Finally, the cumulative evaluation records are modeled as time series, and we propose a multi-step Web services trustworthiness prediction process, which can automatically and iteratively identify and optimize the model to fit the trustworthiness series data. Experiments conducted on a large-scale real-world dataset show that our method manages to collect comprehensive trustworthy evidences from the Internet and can effectively evaluate and predict the trustworthiness of Web services, which helps users to reuse Web services.
Generalized Nash Bargaining Solution for bandwidth allocation q

For over a decade, the Nash Bargaining Solution (NBS) concept from cooperative game theory has been used in networks to share resources fairly. Due to its many appealing properties, it has recently been used for assigning bandwidth in a general topology network between applications that have linear utility functions. In this paper, we use this concept for allocating the bandwidth between applications with general concave utilities. Our framework includes in fact several other fairness criteria, such as the max–min criteria. We study the impact of concavity on the allocation and present computational methods for obtaining fair allocations in a general topology, based on a dual Lagrangian approach and on SemiDefinite Programming. 2006 Elsevier B.V. All rights reserved.
Understanding Social Connection and a Possible Developmental Underpinning

UNDERSTANDING SOCIAL CONNECTION AND A POSSIBLE DEVELOPMENTAL UNDERPINNING Pamela Taylor Advisor: University of Guelph, 2018 Dr. Michael Grand Social connection is an important aspect of well-being and health, yet there is confusion about its definition and development. Study 1 examines common conceptualizations of social connection, namely quantity and closeness of relationships, and loneliness to determine how they interact to form a larger social connection construct. Data from 125 participants were analyzed using structural equation modelling (SEM), the results of which indicated that loneliness and relationship closeness best comprise the model. Study 2 replicated these findings with a larger, independent sample (N = 159), and examined a model of the possible development of social connection using Brown’s (2010a, 2012) notion that increased self-compassion, authenticity, and vulnerability lead to improved social connection. Parental (in)validation of negative emotions was added to provide developmental context. Results indicated that the concepts proposed by Brown relate to one’s level of social connection. Specifically, the data were consistent with the possibility that recalled parental emotional invalidation leads to decreased self-compassion, and increased ambivalence over emotional expression. Individuals with increased ambivalence over emotional expression, in turn, reported lower levels of authenticity, which may lead to less adaptive social connection, defined as closeness in relationships and loneliness. Overall, these findings have implications for the childhood development of emotional expression, and the farreaching effects of parenting.
Online learning of humanoid robot kinematics under switching tools contexts

In this paper a novel approach to kinematics learning and task space control, under switching contexts, is presented. Such non-stationary contexts may appear in many robotic tasks: in particular, the changing of the context due to the use of tools with different lengths and shapes is herein studied. We model the robot forward kinematics as a multi-valued function, in which different outputs for the same input query are related to actual different hidden contexts. To do that, we employ IMLE, a recent online learning algorithm that fits an infinite mixture of linear experts to the online stream of training data. This algorithm can directly provide multi-valued regression in a online fashion, while having, for classic single-valued regression, a performance comparable to state-of-the-art online learning algorithms. The context varying forward kinematics is learned online through exploration, not relying on any kind of prior knowledge. Using the proposed approach, the robot can dynamically learn how to use different tools, without forgetting the kinematic mappings concerning previously manipulated tools. No information is given about such tool changes to the learning algorithm, nor any assumption is made about the tool kinematics. To our knowledge this is the most general and efficient approach to learning and control under discrete varying contexts. Some experimental results obtained on a high-dimensional simulated humanoid robot provide a strong support to our approach.
Previous Lung Diseases and Lung Cancer Risk: A Systematic Review and Meta-Analysis

Background In order to review the epidemiologic evidence concerning previous lung diseases as risk factors for lung cancer, a meta-analysis and systematic review was conducted. Methods Relevant studies were identified through MEDLINE searches. Using random effects models, summary effects of specific previous conditions were evaluated separately and combined. Stratified analyses were conducted based on smoking status, gender, control sources and continent. Results A previous history of COPD, chronic bronchitis or emphysema conferred relative risks (RR) of 2.22 (95% confidence interval (CI): 1.66, 2.97) (from 16 studies), 1.52 (95% CI: 1.25, 1.84) (from 23 studies) and 2.04 (95% CI: 1.72, 2.41) (from 20 studies), respectively, and for all these diseases combined 1.80 (95% CI: 1.60, 2.11) (from 39 studies). The RR of lung cancer for subjects with a previous history of pneumonia was 1.43 (95% CI: 1.22–1.68) (from 22 studies) and for subjects with a previous history of tuberculosis was 1.76 (95% CI = 1.49, 2.08), (from 30 studies). Effects were attenuated when restricting analysis to never smokers only for COPD/emphysema/chronic bronchitis (RR = 1.22, 0.97–1.53), however remained significant for pneumonia 1.36 (95% CI: 1.10, 1.69) (from 8 studies) and tuberculosis 1.90 (95% CI: 1.45, 2.50) (from 11 studies). Conclusions Previous lung diseases are associated with an increased risk of lung cancer with the evidence among never smokers supporting a direct relationship between previous lung diseases and lung cancer.
Multi-temperature testing for core-based system-on-chip

Recent research has shown that different defects can manifest themselves as failures at different temperature spectra. Therefore, we need multi-temperature testing which applies tests at different temperature levels. In this paper, we discuss the need and problems for testing core-based systems-on-chip at different temperatures. To address the long test time problem for multi-temperature test, we propose a test scheduling technique that generates the shortest test schedules while keeping the cores under test within a temperature interval. Experimental results show the efficiency of the proposed technique.
Estimating Speaking Rate by Means of Rhythmicity Parameters

In this paper we present a speech rate estimator based on so-called rhythmicity features derived from a modified version of the short-time energy envelope. To evaluate the new method, it is compared to a traditional speech rate estimator on the basis of semi-automatic segmentation. Speech material from the Alcohol Language Corpus (ALC) covering intoxicated and sober speech of different speech styles provides a statistically sound foundation to test upon. The proposed measure clearly correlates with the semi-automatically determined speech rate and seems to be robust across speech styles and speaker states.
Investigation on fast MPLS restoration technique for a GbE wide area transport network: A disaster recovery case

In this work we report a simulation investigation, based on OPNET code, about the use of fast MPLS restoration procedures in a wide area transport network totally based on the WDM GbE transmission format. Our network models is the Italian transport network and we verified that, by means of the fast MPLS procedures consequent to link failures, we can reach restoration comparable with SDH cases. We also simulated cases of simultaneously failures of two main nodes, representing an example of disaster event, achieving service restoration in very fast times.
Technical Efficiency in Agricultural Production and Its Determinants : An Exploratory Study at the District Level

Given the importance of agriculture to the well being of a large percentage of India’s population, it becomes important to study how improvements can be made in the productivity of this sector. This study attempts to estimate the technical efficiency – a measure of how well inputs are being used towards producing output – of about 250 Indian districts in 1990-91. It employs the stochastic frontier function methodology. The results indicate that (i) the land elasticity is the highest followed by fertilizer; (ii) the mean efficiency of raising agricultural output is 79 per cent and therefore there is a scope for increasing output by 21 per cent without additional resources; (iii) states such as Madhya Pradesh, Uttar Pradesh, and Rajasthan have the largest number of districts with below average TE and they stand to gain the most from policy interventions towards improving technical efficiency. The results further indicate that health, education, and infrastructure are powerful drivers of efficiency at the district level and the relative importance of the determinants of efficiency across districts depends greatly on environmental factors, such as agro-climatic zones, technological factors, and crop mix. The results highlight the need for developing policy strategies at a more localized level.
“Reverse Ecology” and the Power of Population Genomics

Abstract Rapid and inexpensive sequencing technologies are making it possible to collect whole genome sequence data on multiple individuals from a population. This type of data can be used to quickly identify genes that control important ecological and evolutionary phenotypes by finding the targets of adaptive natural selection, and we therefore refer to such approaches as “reverse ecology. ” To quantify the power gained in detecting positive selection using population genomic data, we compare three statistical methods for identifying targets of selection: the McDonald–Kreitman test, the mkprf method, and a likelihood implementation for detecting dN/dS > 1. Because the first two methods use polymorphism data we expect them to have more power to detect selection. However, when applied to population genomic datasets from human, fly, and yeast, the tests using polymorphism data were actually weaker in two of the three datasets. We explore reasons why the simpler comparative method has identified more genes under selection, and suggest that the different methods may really be detecting different signals from the same sequence data. Finally, we find several statistical anomalies associated with the mkprf method, including an almost linear dependence between the number of positively selected genes identified and the prior distributions used. We conclude that interpreting the results produced by this method should be done with some caution.
Bootstrap Inference for Pre-averaged Realized Volatility based on Nonoverlapping Returns

The main contribution of this paper is to propose bootstrap methods for realized volatility-like estimators defined on pre-averaged returns. In particular, we focus on the pre-averaged realized volatility estimator proposed by Podolskij and Vetter (2009). This statistic can be written (up to a bias correction term) as the (scaled) sum of squared pre-averaged returns, where the pre-averaging is done over all possible non-overlapping blocks of consecutive observations. Pre-averaging reduces the influence of the noise and allows for realized volatility estimation on the pre-averaged returns. The non-overlapping nature of the pre-averaged returns implies that these are asymptotically independent, but possibly heteroskedastic. This motivates the application of the wild bootstrap in this context. We provide a proof of the first order asymptotic validity of this method for percentile and percentile-t intervals. Our Monte Carlo simulations show that the wild bootstrap can improve the finite sample properties of the existing first order asymptotic theory provided we choose the external random variable appropriately. We use empirical work to illustrate its use in practice.
Substance Use Among Adolescents: A Comparison Between Native Born and New American Students

iii ACKNOWLEDGEMENTS . iv LIST OF TABLES . viii LIST OF APPENDIX TABLES . ix CHAPTER I: INTRODUCTION . 1 Social Bond Theory and Social Capital . 1 Substance Use Among Immigrant Adolescents . 3 Purpose . 4 Research Questions . 5 CHAPTER II: A REVIEW OF THE LITERATURE. 6 Substance Use in Adolescence . 6 Tobacco . 6 Alcohol . 7 Marijuana . 8 Prescription Drugs . 8 Substance Use Among Immigrant Groups . 8 Social Bond Theory . 9 Social Capital and Substance Use . 12 Family and Substance Use . 13 Community and Substance Use . 14 School and Substance Use . 15 Peers and Substance Use . 16 Social Capital Among Immigrant Adolescents . 17 CHAPTER III: METHODS . 21
Online Participation in the UK: Testing a ‘Contextualised’ Model of Internet Effects 1

This article offers a new test of the mobilisation thesis of Internet effects on individual political participation using data from an NOP survey of 1,972 UK adults during May 2002. The analysis differs from that of previous studies in that it significantly widens the understanding of the dependent variable—online participation—as well as introducing new Internet-specific variables as explanatory factors for this new type of participation. Using this broader ‘contextualised’ model of online political activity we find support for the idea that the Internet is expanding the numbers of the politically active, specifically in terms of reaching groups that are typically inactive or less active in conventional or offline forms of politics. In drawing these conclusions our article joins with a growing body of literature calling for the re-evaluation of the so-called normalisation thesis which argues that ultimately the Internet will lead to a further narrowing of the pool of politically active citizens by reinforcing existing levels of engagement. At a broader level we consider the findings point to the need for scholars in the area to work towards a more sophisticated theoretical and empirical modelling of participation in the online environment.