Miracle-dz/UniFAR-FTUs huggingface dataset:
PCR and Sequence Analysis of a Polymorphic Direct Repeat ( DR ) Chromosomal Region

Molecular genetic approaches that differentiate isolates of a pathogenic microbial species have revolutionized contemporary epidemiologic investigations of putative disease outbreaks. The human gram-positive bacterium group A Streptococcus (GAS) has more than 80 M-protein serotypes, but isolates expressing the M1 serotype are disproportionately represented among invasive disease episodes in most case series (1). M1 organisms also commonly cause pharyngitis. For reasons that are unknown, M1 isolates and organisms expressing other M serologic types can undergo rapid temporal variation in disease frequency and severity (1). Serotype M1 isolates have been studied by several molecular typing approaches, including multilocus enzyme electrophoresis; pulsed-field gel electrophoresis; rRNA gene polymorphism typing (ribotyping); random amplified polymorphic DNA analysis; and sequencing of the genes encoding streptokinase, C5a peptidase, M protein, hyaluronidase, and pyrogenic exotoxin A, B, and C (1-5). The common theme of these analyses is that most M1 isolates cultured from patients with invasive disease episodes are closely allied in overall chromosomal relationship as a consequence of sharing a recent common ancestor (1,3,5). Lack of readily detectable chromosomal variation has limited insights on the molecular origin of new virulent strains, velocity of strain spread in human populations, and association of genetic subtypes with certain clinical syndromes, including necrotizing fasciitis and acute rheumatic fever. Recently, Akesson et al. (6) identified a GAS extracellular protein made by M1 strains that inhibits human complement. This streptococcal inhibitor of complement (Sic) protein is incorporated into the membrane-attack complex (C5b-C9) and inhibits target cell lysis by an undetermined mechanism. Analysis of molecular diversity among 16 M1 GAS isolates from patients with pharyngitis identified seven alleles of the sic gene (7). The high level of sic polymorphism was unanticipated, given that other methods of molecular analysis had failed to identify substantial variation among M1 isolates Rapid Molecular Genetic Subtyping of Serotype M1 Group A Streptococcus Strains
The Image Processing Handbook

Consistently rated as the best overall introduction to computer-based image processing, The Image Processing Handbook covers two-dimensional (2D) and three-dimensional (3D) imaging techniques, image printing and storage methods, image processing algorithms, image and feature measurement, quantitative image measurement analysis, and more. Incorporating image processing and analysis examples at all scales, from nano- to astro-, this Seventh Edition: Features a greater range of computationally intensive algorithms than previous versions Provides better organization, more quantitative results, and new material on recent developments Includes completely rewritten chapters on 3D imaging and a thoroughly revamped chapter on statistical analysis Contains more than 1700 references to theory, methods, and applications in a wide variety of disciplines Presents 500+ entirely new figures and images, with more than two-thirds appearing in color The Image Processing Handbook, Seventh Edition delivers an accessible and up-to-date treatment of image processing, offering broad coverage and comparison of algorithms, approaches, and outcomes.
Predictors of immigrants ’ second-language proficiency

This article examines the predictors of second-language proficiency for a group that until now has hardly been investigated: immigrants who rarely participate in the host society and who have a low level of secondlanguage proficiency (sample characteristics are for example: no paid job, low educational and literacy level, high mean age and number of years since migration). In contrast with earlier research, not only self-assessments were used as indicator for second-language proficiency, but also language test scores. Results from a sample of 624 immigrants partly replicate findings from earlier studies: self-assessed second-language proficiency is higher among immigrants who have followed a language course, do voluntary work, have a high educational level, high mother-tongue proficiency, a low migration age, and a large number of years since migration. No links, however, were found between having psychological problems, gender, and migration motive and self-assessed second-language proficiency. Furthermore, some new predictors of self-assessed second-language proficiency were identified, namely similarity in alphabet between mother tongue and second language, daily interactions with natives in the public domain, and speaking the second language at home. For a subsample (N1⁄4 98) second-language proficiency was also assessed via (objective) lexicon tests. When using scores on this test as dependent variables, only years since migration turned out to be a significant predictor. Though certainly tentative, this finding indicates that different predictors of second-language proficiency may apply depending on how it has been measured. *Corresponding author: Coby van Niejenhuis, University of Groningen, The Netherlands, E-mail: c.y. van.niejenhuis@rug.nl Margaretha P. C. van der Werf: E-mail: m.p.c.van. der.werf@rug.nl, Sabine Otten: E-mail: s.otten@rug.nl, University of Groningen, The Netherlands IJSL 2015; 236: 75–100 Brought to you by | University of Groningen Authenticated Download Date | 11/2/15 1:27 PM
Oui! Outlier Interpretation on Multi‐dimensional Data via Visual Analytics

Outliers, the data instances that do not conform with normal patterns in a dataset, are widely studied in various domains, such as cybersecurity, social analysis, and public health. By detecting and analyzing outliers, users can either gain insights into abnormal patterns or purge the data of errors. However, different domains usually have different considerations with respect to outliers. Understanding the defining characteristics of outliers is essential for users to select and filter appropriate outliers based on their domain requirements. Unfortunately, most existing work focuses on the efficiency and accuracy of outlier detection, neglecting the importance of outlier interpretation. To address these issues, we propose Oui, a visual analytic system that helps users understand, interpret, and select the outliers detected by various algorithms. We also present a usage scenario on a real dataset and a qualitative user study to demonstrate the effectiveness and usefulness of our system.
Formalising Luck Improved Probabilistic Semantics for Property-Based Generators

Property-Based Testing consists in running a program on many randomly generated inputs to invalidate an executable property. Popularized by QuickCheck (Claessen and Hughes, 2000), this technique is now widely used, both in programming languages (Arts et al., 2008; Lindblad, 2007; Hughes, 2007; Pacheco and Ernst, 2007) and in proof assistants (Chamarthi et al., 2011; Bulwahn, 2012; Owre, 2006; Dybjer et al., 2003; Paraskevopoulou et al., 2015). Frequently, the tested property takes the form of an implication pñ q, where p is a precondition over the input: for example, we may assume a list to be a sorted, or a program to be well-typed if we are testing a compiler. The test is then a trivial success for any input not satisfying p, leading to inefficient testing if p is sparse (verified by a small proportion of the possible inputs), as in the examples above. To address this issue, QuickCheck allows the user to write a propertybased generator, returning exclusively data satisfying property p. However, generators can easily be biased toward values sharing some specific structure; and worse, they can be incomplete, sometimes covering a very small part of the values satisfying p. In these cases, testing becomes quite hopeless, as potential counterexamples involving a structure that is not covered by the generator may remain undetected. Writing complete, balanced and efficient generators for complex properties is difficult and error prone. Luck (Lampropoulos et al., 2016a) (§1) is a language aimed at addressing this challenging problem.
Mine Drainage Management – Replicating Dump Physiogeochemical Conditions in Laboratory Columns

A number of key issues have been identified when using standardised kinetic tests to predict the physiogeochemical evolution of waste rock dumps (WRDs) due to the uncertainty of how to apply scaling factors to laboratory data. O’Kane Consultants has devised the advanced customisable leach column (ACLC) to better replicate actual field conditions while an automated monitoring system provides flexibility in controlling key geochemical variables. The ACLC method allows control of air flow rates, ambient temperatures, water application and drying with the ability to customise each column. Economic coal seams within the Buller coalfield are associated with the Brunner Coal Measures (BCM). The coal measure sequences contain carbonaceous mudstone and siltstone with elevated pyrite indicative of estuarine conditions. Exposure of these pyritic materials during mining on the West Coast leads to significant acid and metalliferous drainage (AMD) management issues. Consequences of inadequate water quality management can be seen at historic mines where downstream contaminant loads are high. The ACLC method has been used to evaluate the intrinsic oxidation rate (IOR) of BCM by directly measuring oxygen consumption, to understand the geochemical differences that result from a WRD built to minimise oxygen ingress. Management of AMD by prevention of oxygen ingress, through improved construction methods of WRDs using shorter lift heights, compaction between lifts and the placement of cover systems, reduces oxygen ingress. Two ACLCs have been set up using field parameters to model: 1) a poorly constructed waste rock dump with free exchange of atmospheric gases, and; 2) a well-constructed dump where, at the centre of the dump, oxygen content and flux is limited. Initial results from the ACLCs will be presented and compared with a concurrently running AMIRA column, that uses the sulfate release rates, and the stoichiometric relationship between sulfate release to the quantity of pyrite oxidised, to calculate an IOR. Improved data provides better estimates of potential contaminant loads, essential for AMD management, and an improved understanding of risk.
Heads in the Cloud: An Example of Practice-Based Information and Communication Technology in Child Welfare

ABSTRACT Changes in information and communications technology (ICT) in human services have accelerated rapidly and have elicited academic concern about the impact on practice, changes in approaches to implementation, and a shift to user-led development. This article describes the development of ICT by a nongovernment child welfare agency over the past two decades. It is guided by a social constructionist view of technology in which technology is shaped by social factors, but in turn shapes the “social” implementation as requiring the integration of two distinct technologies: a standardized framework for practice and the computer. Both technologies had to be carefully aligned to workplace culture, but the interests of workers have not been allowed to dominate those of and children and families. The organization of work has had to change. This article explores the agency’s experience drawing on theories of implementation of ICT in human services.
Sensor Locations for Reliable Travel Time Prediction and Dynamic Management of Traffic Networks

State estimation and prediction models rely on the quality and the dissemination of the available traffic data. In complex networks, such as large urban areas, the position of traffic counts (i.e. loop detectors) is critical for such models. Nevertheless, sensor locations are typically chosen for other objectives (e.g., monitoring bottleneck sections, estimating origin–destination flows). As a result, link state estimations and predictions are often found to be inaccurate. A new approach to the sensor location problem was proposed; the objective was optimizing the position of traffic counts for reliable state estimation and prediction at complex networks. To do so, the maximum possible relative error, used in past approaches, was reformulated to minimize the error in link traffic states. By doing so, more reliable predictions of partial as well as whole network travel times can be obtained. Computing the new maximum possible relative error (MPRE) requires two extra inputs—the specification of a state estimation model and the specification of the network traffic variability—to set the boundaries of the solution space. For large complex networks, the latter information might be very difficult to obtain. As an alternative, a simple solution algorithm to the problem was proposed that uses link flow and travel time correlations between links to select, in sequence, the most representative locations in the network. The algorithm was tested on a toy network, which showed that it succeeds in catching a substantially larger percentage of link flows with respect to a classical approach.
Laser-GMA Hybrid Welding : Process Monitoring and Thermal Modeling

Laser-Gas Metal Arc (GMA) hybrid welding is an increasingly accepted technology for a variety of commercial applications, from industries as diverse as shipbuilding to automobile manufacture. As applications become more widespread, there is a growing need to understand the relationship between the numerous process parameters and the process results, including weld quality and distortion. To build upon the body of knowledge supporting this, two separate experiments are performed. In the first, hybrid welds are performed with a 2.6 kW Nd:YAG laser and sensors are used to monitor GMA voltage and current, as well as the arc-plasma electromagnetic emissions in both the ultraviolet and infrared regions. Process perturbations, such as fluctuations in GMAW voltage and wire speed, laser angle of incidence, and laser/GMAW torch head separation distance, are introduced to study their effect on sensor output. Finally, thermal finite element models are developed and used to quantify the varying heat input per unit length when compared with conventional GMAW and laser welding processes, particularly as applied to joining of thin steel structures. The onset of buckling during weld fabrication has been shown to be strongly dependent upon the heat input used to produce the weld. A thermal model of the laser-GMA hybrid welding process is developed to serve as a representation of this complex process.
Understanding Methodology for Efficient CNN Architectures in Profiling Attacks

The use of deep learning in side-channel analysis has been more and more prominent recently. In particular, Convolution Neural Networks (CNN) are very eﬃcient tools to extract the secret information from side-channel traces. Previous work regarding the use of CNN in side-channel has been mostly proposed through practical results. Zaid et al. have proposed a theoretical methodology in order to better understand the convolutional part of CNN and to understand how to construct an eﬃcient CNN in the side-channel context [ZBHV19]. The proposal of Zaid et al. has been recently questioned by [WAGP20]. However this revisit is based on wrong assumptions and misinterpretations. Hence, many of the claims of [WAGP20] are unfounded regarding [ZBHV19]. In this paper, we clear out the potential misunderstandings brought by [WAGP20] and explain more thoroughly the contributions of [ZBHV19].
Semi-Supervised Robust Deep Neural Networks for Multi-Label Classification

In this paper, we propose a robust method for semisupervised training of deep neural networks for multi-label image classification. To this end, we use ramp loss, which is more robust against noisy and incomplete image labels compared to the classical hinge loss. The proposed method allows for learning from both labeled and unlabeled data in a semi-supervised learning setting. This is achieved by propagating labels from the labeled images to their unlabeled neighbors. Using a robust loss function becomes crucial here, as the initial label propagations may include many errors, which degrades the performance of non-robust loss functions. In contrast, the proposed robust ramp loss restricts extreme penalties for the samples with incorrect labels, and the label assignment improves in each iteration and contributes to the learning process. The proposed method achieves state-of-the-art results in semisupervised learning experiments on the CIFAR-10 and STL10 datasets, and comparable results to the state-of the-art in supervised learning experiments on the NUS-WIDE and MS-COCO datasets.
Applications of a simplified protocol of RoboCup 2 D Soccer Simulation

The RoboCup 2D Soccer Simulation (RCSS) is a sophisticated soccer simulation environment introduced in [1] and implemeted in [2], [3]. For educational and other sport science purposes, we have simplified the protocol of the RCSS. With raw phrasing we would say that we have eliminated the main AI part of the AI-based RCSS simulation model in order that it can be used easily in sport science and in education of programming. In compliance with this aim, we must modify the RCSS environment. In our terminology, the suitable modified rcssserver is referred to as ”lighter RCSS” [4], [5]. In this paper, the experiences of our first ”lighter teams” will be presented.
Quality of Institutions, Technological Progress, and Pollution Havens in Latin America. An Analysis of the Environmental Kuznets Curve Hypothesis

A set of 17-year panel data (1996–2013) across a representative sample from eighteen Latin American countries is used to respond four research questions: Did Latin American Greenhouse Gas (GHG) emissions prove the Environmental Kuznets Curve (EKC) hypothesis? Did the quality of institutions play a compensating role for income on environmental stress? Did technological progress help decouple income from environmental stress? Has the Pollution Haven Hypothesis (PHH) been proven? In order to answer the research questions, the paper expands the traditional EKC approach by including an exclusive quality analysis of institutions, technological progress, and PHH as part of the model. This innovation is developed considering the most recent literature about EKC as a starting point. Major findings show that the relationship between income and GHG emissions is adjusted to the traditional EKC hypothesis for the analyzed period. They also show that the quality of institutions and technological progress improve environmental sustainability. However, the variables, Foreign Direct Investment and International Trade, provide a negative answer to the fourth question. The main methodological contribution of this paper is to use a threefold extended classic EKC model to conduct the feasible generalized least squares method. The paper also contributes to the growing body of PHH literature.
A Note on the Mechanics of Ancient Gear Systems

This paper deals with the mechanical behavior of the gearwheels of the antiquity, which were generally characterized by triangular shaping of the toothing. The engagement of the conjugate profiles is analyzed in detail, calculating the temporal variation of the speed ratio due to the back and forth shifting of the relative instant center, searching for the admissibility conditions of the points of the theoretical contact path, estimating the rattling level because of the small successive tooth collision and ascertaining the energy losses connected with the particular nature of the coupling. A very interesting result is that only one couple of teeth turns out to be active at each time instant and the entire real path may belong to the only access region entirely or to the only recess one entirely or may be split into two separate sub-phases, the one in access and the other in recess, or may straddle both regions. The occurrence of each of these situations depends on the average speed ratio (tooth ratio) and on the assigned allowance between the two wheel axes. It is also found that the speed oscillation is roughly contained in a ±10% range and the efficiency may reach rather high values, despite the presumable crude finishing of the ancient gearwheels due to the rudimentary technology used in the construction of the tooth profiles.
Anomaly-Based Manipulation Detection in Satellite Images

Satellite overhead imagery can be easily acquired and shared. The integrity of these type of images cannot longer be assumed, due to availability of sophisticated classical and machine learning based image manipulation tools. In this paper we proposed a deep learning based method for detecting and localizing splicing manipulations in overhead images. Our method uses recent advances in anomaly detection and does not require any prior knowledge of the type of manipulations that an adversary could insert in the satellite imagery. We compare our method against robust satellite-based manipulation detection approaches. We show that our proposed technique outperforms all previous methods, especially in detecting small-sized manipulations.
Adoption Level and Constraints Faced by Backyard Poultry Farmers in Bihar

A study was conducted on adoption and constraints of backyard poultry rearing practices in rural areas of Katihar and Banka district of Bihar during the year 2011 to 2014. The data were collected from 125 respondents on the basis of interview schedule with the help of questionnaire. Majority of the poultry farmers showed medium level of adoption (65.43%) of recommended technology after training. The overall adoption index of different backyard poultry rearing practices were recorded 67.11 percent. All the constraints obtained positive significant correlation with poultry rearing practices. Highly significant correlation values were recorded for high cost of inputs (2.76), difficulty in supply inputs (2.61), lack of awareness of prompt veterinary aids (2.32) and difficulty in finance (2.10). Low egg production in summers showed negative non significant correlation. Other variables recorded positive significant correlation for all constraints. The study also pointed some suggestions for solving the constraints regarding backyard poultry rearing practices in state Bihar.