Miracle-dz/UniFAR-FTUs huggingface dataset:
Network low density parity check codes designed for multiple-access relay channel

A relay scheme combining network coding with channel coding is proposed for multiple-access relay channel. Via a newly designed network low density parity check code, this scheme permits the relay node to jointly encode the data information from two source nodes. To predict the system performance, a newly generalized Gaussian approximation algorithm is derived, based on which an optimization algorithm is developed to search the degree distribution of the network LDPC code. Simulations show that, in a network with two sources, one relay and one destination, the system with the designed network low density parity check code hold a performance gap less than 0.6dB from the threshold and a performance gain of about 0.3dB can be achieved to the same rate XOR network coding scheme at BER=10−5.
Relationships between tennis experience, executive function, and self-regulation of dietary behaviour in children

Physical inactivity and unbalanced dietary behaviour in childhood have become worldwide public health concerns. This study aimed to evaluate the relationship between sports activity (tennis), executive functions, and dietary related self-regulation in children. Fifty-two participants (6–12 years old, 31 males and 21 females) were enrolled in regular tennis lessons prior to the study (mean ± SD = 3 ± 2 years). Executive functions and basic processing speed were evaluated using three tasks (the Stroop test, the 2-back task, and the Local-global task). Dietary behaviour (extent of dislike for food and ratio of eating disliked food) was evaluated using questionnaires. An established questionnaire available for use in clinical investigations, the Food Frequency Questionnaire based on food groups, was used to obtain a detailed assessment of food intake. Regression analyses demonstrated that tennis experience was positively associated with basic processing speed and executive functions. Furthermore, higher executive functions were related to a higher ratio of eating disliked food, while no such association was observed for basic processing speed. A higher ratio for eating disliked food was related to better dietary intake (i.e. larger vegetable intake). Structural equation modelling revealed that the model had an adequate fit to the data (χ2 = 3.05, p = .55, GFI = .98, AGFI = .92, RMSEA = .00). The model indicated the following cascade associations: tennis experience → executive functions → rate of eating disliked food → vegetable intake. This study implied that longer sports experience is associated with better dietary related self-regulation via higher executive functions.
A FOSS4G-based procedure to compare OpenStreetMap and authoritative road network datasets

OpenStreetMap (OSM), the most popular VGI crowdsourcing project, is an excellent example of an open-license spatial database. But what is the quality of OSM road network datasets compared to authoritative counterparts? Several comparisons of this kind are detailed in literature but these cannot be easily adapted to other scenarios. Developing a generic automated procedure is very challenging. This paper proposes a FOSS4G-based procedure for automated quality comparison of OSM and any authoritative road network datasets. We detail work-in-progress which has great potential. Our procedure is currently implemented into a GRASS command with future plans to extend this to a QGIS plugin and a FOSS4G-based WPS.
Development of cognitive flexibility in late adolescence: investigating behavioral performance and neural activation in a task-switching paradigm

Successful cognitive control relies on both the ability to instantiate higher-order cognitive functions and the ability to flexibly switch between them in service of changing task demands, i.e. cognitive flexibility. While a wealth of important work on the development of cognitive control in adolescence has focused on the development of executive functions, there has been a relative lack of work on the development of cognitive flexibility. Here we address this limitation by investigating the development of cognitive flexibility using a task-switching paradigm in a large sample of adolescents and young adults (ages 14-32, n = 82). For a subset of subjects that had usable fMRI data (n=56), we assessed task-switching performance and analyzed fMRI data collected in-scanner while they performed the task-switching paradigm. We observed that successful task-switching was associated with widespread activation of frontoparietal and visual processing brain areas. A component of this larger task-switching system, the left inferior parietal cortex, showed age-related reductions in neural activation specifically during task-switching into trials that taxed inhibitory control. These neural findings occurred in parallel with age-related improvements in successful task-switching performance in the same context. This pattern of results suggests that task-switching into the most cognitively demanding contexts follows a protracted development that extends through adolescence and young adulthood. Further, the age-related reduction in parietal cortex activation suggests that adolescents have greater reliance on the frontoparietal system, which has been implicated in transient aspects of cognitive control, to achieve adult-like performance. Taken together, our results suggest that a key aspect of cognitive maturation in adulthood is the ability to flexibly switch between cognitive tasks with limited cost to performance and a decreasing reliance on frontoparietal regions across adolescence.
CHARACTERIZING PANTOGRAPHIC SHEETS BY MEANS OF COMPUTATIONAL EXPERIMENTS

Metamaterials is the new class of materials which are able to show a desired behavior at a macroscopic level. In the last years, properties of pantographic fabrics have been studied in the metamaterial framework. These systems are made of two families of orthogonal fibers, jointed in the intersection points by pivots. Such systems show interesting mechanical properties. The fibers and the pivots react differently to strain. In particular, bending and extension deformations involve only fibers (which are straight in the reference configuration), whereas torsion involves pivots. Such metamaterials is able to undergo large macroscopic deformation with small deformation of its microstructure. Since it is able to store a large amount of energy also beyond the elastic regime and it undergoes plastic deformation before rupture, it shows high toughness. Furthermore, an advantageous strength to weight ratio is observed. To predict the macroscopic behavior of these materials an efficient computational discrete model is needed. In this work, we will present a new numerical study of 2D pantographic fabric, in which its macroscopic behavior is modeled in terms of a second gradient continuum theory obtained by means of a heuristic homogenization procedure. In particular, we will consider a bi-axial extension test. We will discuss the distribution of the internal stored energy and we will compare the results with the uni-axial case.
Developing methods to measure sentence severity and assess disparity in sentencing in the criminal courts of England and Wales

This doctoral research focuses on sentencing, and more specifically, disparity in sentencing in the courts of England and Wales. Previous research in this area has introduced bias into the samples by focusing solely on custodial sentences and therefore failing to consider the more common non-custodial sentences. This thesis overcomes this methodological problem. There are two parts to this research: the first part develops a sentence severity scale based on the principle of proportionality, and includes the full range of sentences meted out by the courts in England and Wales. The second part of this research then uses the new scale to assess disparity based on the extra-legal factors of the case. Data from the Offender Assessment System (OASys) was merged with data from the Police National Computer (PNC), which provided information on the offence and resultant sentence, as well as information relating to the socio-demographic characteristics of the offender. Goodman Row Column Association analysis was used to model the association between the offences and sentence categories. This model was then extended to control for three legal factors offence plea, previous history of offending, and the number of offences the offender was sentenced for which must all be taken into consideration when sentencing offenders. This analysis provided a series of scores for each of the sentence categories allowing them to be arranged by magnitude, ranging from the least to most severe sentence. To anchor the scale and make the scores less arbitrary, linear
Twenty-Five Moves Suffice for Rubik's Cube

How many moves does it take to solve Rubik's Cube? Positions are known that require 20 moves, and it has already been shown that there are no positions that require 27 or more moves; this is a surprisingly large gap. This paper describes a program that is able to find solutions of length 20 or less at a rate of more than 16 million positions a second. We use this program, along with some new ideas and incremental improvements in other techniques, to show that there is no position that requires 26 moves.
Leveraging a corpus of natural language descriptions for program similarity

Program similarity is a central challenge in many programming-related applications, such as code search, clone detection, automatic translation, and programming education. We present a novel approach for establishing the similarity of code fragments by: (i) obtaining textual descriptions of code fragments captured in millions of posts on question-answering sites, blogs and other sources, and (ii) using natural language processing techniques to establish similarity between textual descriptions, and thus between their corresponding code fragments. To improve precision, we use a simple static analysis that extracts type signatures, and combine the results of textual similarity with similarity of the signatures. Because our notion of code similarity is based on similarity of textual descriptions, our approach can determine semantic relatedness and similarity of code across different libraries and even across different programming languages, a task considered extremely difficult using traditional approaches. To evaluate our approach, we use data obtained from the popular question-answering site, Stackoverflow. To obtain a ground-truth to compare against, we developed a crowdsourcing system, Like2Drops, that allows users to label the similarity of code fragments. We used the system to collect similarity classifications for a massive corpus of 6,500 program pairs. Our results show that our technique is effective in determining similarity, and achieves more than 85 percent precision, recall and accuracy.
Multiple Cue Data Fusion with Particle Filters for Road Course Detection in Vision Systems

In the following we present a robust, real time applicable fusion system for detection of the road course up to 100m. For that, using a particle filter we developed a robust and flexible system which fuses the information from a digital map system and different cues from an image sensor. In addition we present a new image based feature for road surface detection. The system works in realtime
Applying AOSE Concepts to Model Crosscutting Variability in Variant-Rich Processes

Software process models need to be variant-rich, in the sense that they should be systematically customizable to specific project goals and project environments. It is currently very difficult to model Variant-Rich Process (VRP) because variability mechanisms are largely missing in modern process modeling languages. Variability mechanisms from other domains, such as programming languages, might be suitable for the representation of variability and could be adapted to the modeling of software processes. Mechanisms from Software Product Line Engineering (SPLE) and concepts from Aspect-Oriented Software Engineering (AOSE) show particular promise when modeling variability. This paper presents an approach that integrates variability concepts from SPLE and AOSE in the design of a VRP approach for the systematic support of tailoring in software processes. This approach has also been implemented in SPEM, resulting in the vSPEM notation. It has been used in a pilot application, which indicates that our approach based on AOSE can make process tailoring easier and more productive.
Fast-Response Liquid Crystal Microlens

Electrically tunable liquid crystal microlenses have attracted strong research attention due to their advantages of tunable focusing, voltage actuation, low power consumption, simple fabrication, compact structure, and good stability. They are expected to be essential optical devices with widespread applications. However, the slow response time of nematic liquid crystal (LC) microlenses has been a significant technical barrier to practical applications and commercialization. LC/polymer composites, consisting of LC and monomer, are an important extension of pure LC systems, which offer more flexibility and much richer functionality than LC alone. Due to the anchoring effect of a polymer network, microlenses, based on LC/polymer composites, have relatively fast response time in comparison with pure nematic LC microlenses. In addition, polymer-stabilized blue phase liquid crystal (PS-BPLC) based on Kerr effect is emerging as a promising candidate for new photonics application. The major attractions of PS-BPLC are submillisecond response time and no need for surface alignment layer. In this paper, we review two types of fast-response microlenses based on LC/polymer composites: polymer dispersed/stabilized nematic LC and polymer-stabilized blue phase LC. Their basic operating principles are introduced and recent progress is reviewed by examples from recent literature. Finally, the major challenges and future perspectives are discussed.
Comparison of two different methods for coherent averaging in online ECG analysis

The coherent averaging is an often used method in ECG systems for noise removing. Most algorithms are developed for the offline data analysis either with automatic or with manual pattern determination for cycles averaging. These algorithms can't be used for a wearable long time ECG analysis system, which processes the biosignal continuously in real time, because the continuous analysis needs a lot of computational power, which can be provided only by PCs. Therefore, an online averaging algorithm was developed for the ECG signal filtering for wearable long time analysis systems. This article presents an effective algorithm for online ECG complex averaging. Also a comparison of two methods for cycle allocation is offered
Simulated Gastrointestinal pH Condition Improves Antioxidant Properties of Wheat and Rice Flours

The present study was conducted to evaluate the antioxidant properties of wheat and rice flours under simulated gastrointestinal pH condition. After subjecting the wheat and rice flour slurries to simulated gastrointestinal pH condition, both slurries were centrifuged to obtain the crude phenolic extracts for further analyses. Extraction yield, total contents of phenolic and flavonoids were determined as such (untreated) and under simulated gastrointestinal pH condition (treated). 1,1-diphenyl-2-picrylhydrazyl radical (DPPH•) scavenging activity, 2,2′-azino-bis(3-ethylbenzthiazoline-6-sulphonic acid) radical cation (ABTS•+) scavenging activity, ferric reducing antioxidant power (FRAP), beta-carotene bleaching (BCB) and iron chelating activity assays were employed for the determination of antioxidant activity of the tested samples. In almost all of the assays performed, significant improvements in antioxidant properties (p < 0.05) were observed in both flours after treatment, suggesting that wheat and rice flours contain considerably heavy amounts of bound phenolics, and that their antioxidant properties might be improved under gastrointestinal digestive conditions.
Bullies, victims and bully-victims in South African schools: Examining the risk factors

School bullying is a complex social phenomenon that negatively impacts the psychosocial well-being of students, as well as the overall culture and climate of schools. Designing appropriate interventions to combat bullying in South African schools requires nuanced information about this phenomenon. This paper examines the extent and nature of bullying in schools located in different and unequal socio-economic contexts. It then examines the risk factors associated with being a victim of bullying. Self-reported data from a nationally representative sample of 12,514 Grade Nine South African students, who participated in the 2015 Trends in International Mathematics and Science Study, was used. Data were subjected to analysis using independent samples t-tests and hierarchical generalised linear modelling. The results revealed different patterns of bullying victimisation and perpetration by the socio-economic status (SES) of the school, with students attending schools with a low SES reporting higher levels of bullying. Factors resulting in higher odds of being a victim were students’ gender and psychosocial characteristics. Perpetration as a risk factor for victimisation (bully-victims) was found across bullying types. The results suggest that students play different participant roles as bully and victim, and that the two behaviours reinforce one another. Keywords: bullying; bullying risk factors; bully-victim; school safety; South Africa; TIMSS
An efficient distributed intrusion detection scheme

In general, intrusion detection systems are accurate and comfortable instruments for securing computer and network resources. But one of the great unsolved problems of deploying such systems in real-life environments is the low efficiency, especially in the context of applying the host-based attack identification paradigm. In this paper, a new approach for overcoming the efficiency problem is presented: HEIDI (high-efficient intrusion detection infrastructure). By this approach, flexible and efficiency-oriented modules and mechanisms are combined to obtain a distributed intrusion detection scheme.
The effect of oral contraceptives on orthodontic tooth movement in rat

Introduction: The purpose of this study was to investigate the effect of ethinyl estradiol/norgestrel � used in some oral contraceptives- on orthodontic tooth movement in Wistar rats. Material and Methods: Forty eight female three-month old Wistar rats with an average weight of 250�25gr were divided into two experimental and control groups. One week prior to appliance insertion and during the appliance therapy period, 100 mcg/kg/day of ethinyl estradiol and 1mg/kg/days of norgestrel were administered to the experimental group by gavage; meanwhile the control group received an equivalent volume of Sodium Chloride 0.9 % (Saline). Maxillary central incisors were tipped distally by insertion of springs exerting 30g force. Two, seven and fourteen days after spring insertion animals were sacrificed. The mesioincisal distance between maxillary incisors were measured. Subsequently, histological sections were prepared for histomorphometric studies. Results: 14 days after force application the orthodontic tooth movement was significantly lower in the experimental group (p<0.05). The number of osteoclasts were significantly lower in the experimental group 2, 7 and 14 days after spring insertion (p<0.05). Conclusion: Ethinyl estradiol/norgestrel (oral contraceptives) can significantly decrease the amount of tooth movement in the linear phase. Key words:Oral contraceptives, orthodontic tooth movement, ethinyl estradiol, norgestrel.
A novel local patch framework for fixing supervised learning models

In the past decades, machine learning models, especially supervised learning algorithms, have been widely used in various real world applications. However, no matter how strong a learning model is, it will suffer from the prediction errors when it is applied to real world problems. Due to the black box nature of supervised learning models, it is a challenging problem to fix the supervised learning models by further learning from the failure cases it generates. In this paper, we propose a novel Local Patch Framework (LPF) to locally fix supervised learning models by learning from its predicted failure cases. Since the learning models are generally globally optimized during training process, our proposed LPF assumes that most of the learning errors are led by local errors in the model. Thus we aim to break the black boxes of learning models by identifying and fixing the local errors of various models automatically. The proposed LPF has two key steps, which are local error region subspace learning and local patch model learning. Through this way, we aim to fix the errors of learning models locally and automatically with certain generalization ability on unseen testing data. Experiments on both classification and ranking problems show that the proposed LPF is effective and outperforms the original algorithms and the incremental learning model.
Automatic heart rate estimation from painful faces

Non-contact measurement of the heart rate is more comfortable than classical methods and can facilitate new applications. However, current approaches are very susceptible to motion. Aiming at overcoming this limitation, we propose a new, more robust approach to estimate the heart rate from a videotaped face. It features non-planar motion compensation, fusion of multiple ROI signals, and a RANSAC-like time-domain heart rate estimation algorithm. In experiments with a comprehensive pain recognition dataset we show that our approach outperforms previous methods in the presence of spontaneous head movement and facial expression.
ASAP: Scalable Collision Arbitration for Large RFID Systems

The growing importance of operations such as identification, location sensing, and object tracking has led to increasing interests in contactless Radio Frequency Identification (RFID) systems. Enjoying the low cost of RFID tags, modern RFID systems tend to be deployed for large-scale mobile objects. Both the theoretical and experimental results suggest that when tags are in large numbers, most existing collision arbitration protocols do not satisfy the scalability and time-efficiency requirements of many applications. To address this problem, we propose Adaptively Splitting-based Arbitration Protocol (ASAP), a scheme that provides efficient RFID identification for both small and large deployment of RFID tags, in terms of time and energy cost. Theoretical analysis and simulation evaluation show that the performance of ASAP is better than most existing collision-arbitration solutions and the time efficiency is close to the theoretically optimal values.
Research on Resource Allocation Algorithm Based on Energy Optimizationand Immune Algorithm in Cloud Computing Environment

This paper will explore the key factors affecting the resource allocation performance; reveal the essential relati- onship among CPU dynamic power consumption, resources computing capacity, resources supply voltage strategy, the amount of resources, supply voltage, and the frequency of CPU; and solve the huge energy consumption problem and the poor performance of load balancing in the process of resource allocation. The issues include: design and analysis of the key technology of resource allocation in cloud computing environment; combined with time span, load balancing and energy optimization, design of multi-objective optimization model for resource allocation and heuristic resource alloca- tion algorithm. This research has important significance for further study on resource allocation and energy optimization in cloud computing environment.