Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of small disjuncts and class distribution on decision tree learning

The main goal of classifier learning is to generate a model that makes few misclassification errors. Given this emphasis on error minimization, it makes sense to try to understand how the induction process gives rise to classifiers that make errors and whether we can identify those parts of the classifier that generate most of the errors. In this thesis we provide the first comprehensive studies of two major sources of classification errors. The first study concerns small disjuncts, which are those disjuncts within a classifier that cover only a few training examples. An analysis of classifiers induced from thirty data sets shows that these small disjuncts are extremely error prone and often account for the majority of all classification errors. Because small disjuncts largely determine classifier performance, we use them as a “lens” through which to study classifier induction. Factors such as pruning, training-set size, noise and class imbalance are each analyzed to determine how they affect small disjuncts and, more generally, classifier learning. The second study analyzes the effect that rare classes and class distribution have on learning. Those examples belonging to rare classes are shown to be misclassified much more often than common classes. The thesis then goes on to analyze the impact that varying the class distribution of the training data has on classifier performance. The experimental results indicate that the naturally occurring class distribution is not always best for learning and that a balanced class distribution should be chosen to generate a classifier robust to different misclassification costs. It is often necessary to limit the amount of training data used for learning, due to the costs associated with obtaining and learning from this data. This thesis presents a budget-sensitive progressive-sampling algorithm for selecting training examples in this situation. This algorithm is shown to produce a class distribution that performs quite well for learning (i.e., is near optimal).
Putting Muscle Into Sports Analytics: Strength, Conditioning, and Ice Hockey Performance

Abstract Kniffin, KM, Howley, T, and Bardreau, C. Putting muscle into sports analytics: strength, conditioning, and ice hockey performance. J Strength Cond Res 31(12): 3253–3259, 2017—Sports analytics is best known as the field of research that focuses on discovering slight but significant improvements within competitions; however, broader sets of athlete- and team-level data from outside competitions (e.g., strength and conditioning metrics) have been typically left out from such analyses. Given that strength and conditioning programs are perhaps the most common avenue through which people expect extra-competition progress to translate into within-competition performance, it is clear that strength and conditioning metrics warrant closer analytic attention. To illustrate this approach, we present a study of National Collegiate Athletic Association (NCAA) Division 1 Men's Ice Hockey players that integrates both (a) strength and conditioning metrics and (b) in-game performance measurements. Bivariate analyses show a significant positive correlation between bench press performance and points scored (r = 0.15), although multivariate analyses point to positive relationships between strength and conditioning measures and playing time as the more important finding. Although within-competition data are increasingly accessible for analytics research, the basic approach that we develop highlights the importance of considering extra-competition variables such as strength and conditioning metrics for understanding both coaching decisions regarding playing time and within-competition performance. We also discuss ways in which the integrated approach that we present offers potential applications for strength and conditioning professionals as well as players, coaches, and team managers.
New voltage balancing strategy for flying capacitor multilevel converters

Multilevel converters topologies present important benefits when they are used on medium and high power applications. However, it is necessary to design new alternating methods to control easily the internal dynamics of the converter. This paper presents a new voltage balancing strategy for a flying capacitor multilevel converter in conjunction with a space vector modulation technique. The advantages of this strategy with respect to others control methods are, the equilibrated use of the switches in the converter, the independence from the modulation method and the easy implementation. The proposed strategy is based on the redundancy of switching states and it is designed to accomplish three objectives: a) ensure the demanded voltage level by the pulse width modulation strategy, b) balance the capacitor voltages, and c) ensure the equilibrated use of the converter switches. The performance of the proposed technique is validated by simulation and experimental results in a 5-level inverter.
MuseumScrabble: Design of a Mobile Game for Children's Interaction with a Digitally Augmented Cultural Space

Mobile technology has created new possibilities for location-based playful learning experiences. This article describes the MuseumScrabble mobile game, aimed at children visiting a historical museum. The game requires that the players explore the museum and link abstract concepts with physical artefacts using a mobile device. The focus of this article is on the interaction design process and the subsequent observations made during field evaluation of the game. Design principles that guide the development of such a game are presented and concern playfulness, learning, social interaction, physical aspects of the game and flow between physical and digital space. This article explores how these design principles are reflected in the study and how problem-solving strategies and collaboration and competition patterns are developed by children in this multi-player educational game.
Neurologic Improvement in a Type 3 Gaucher Disease Patient Treated with Imiglucerase/Miglustat Combination

Summary: Purpose: Gaucher disease (GD) is an autosomal recessive lysosomal disorder caused by a deficiency of glucocerebrosidase. The neurologic manifestations of GD patients have to date been refractory to any treatment approach. We present a report of a neuronopathic GD patient whose myoclonic epilepsy improved after combination therapy with imiglucerase and miglustat.
Determinants of the rate and extent of spermatogenic suppression during hormonal male contraception: an integrated analysis.

CONTEXT Male hormonal contraceptive methods require effective suppression of sperm output. OBJECTIVE The objective of the study was to define the covariables that influence the rate and extent of suppression of spermatogenesis to a level shown in previous World Health Organization-sponsored studies to be sufficient for contraceptive purposes (< or =1 million/ml). DESIGN This was an integrated analysis of all published male hormonal contraceptive studies of at least 3 months' treatment duration. SETTING Deidentified individual subject data were provided by investigators of 30 studies published between 1990 and 2006. PARTICIPANTS A total of 1756 healthy men (by physical, blood, and semen exam) aged 18-51 yr of predominantly Caucasian (two thirds) or Asian (one third) descent were studied. This represents about 85% of all the published data. INTERVENTION(S) Men were treated with different preparations of testosterone, with or without various progestins. MAIN OUTCOME MEASURE Semen analysis was the main measure. RESULTS Progestin coadministration increased both the rate and extent of suppression. Caucasian men suppressed sperm output faster initially but ultimately to a less complete extent than did non-Caucasians. Younger age and lower initial blood testosterone or sperm concentration were also associated with faster suppression, but the independent effect sizes for age and baseline testicular function were relatively small. CONCLUSION Male hormonal contraceptives can be practically applied to a wide range of men but require coadministration of an androgen with a second agent (i.e. progestin) for earlier and more complete suppression of sperm output. Whereas considerable progress has been made toward defining clinically effective combinations, further optimization of androgen-progestin treatment regimens is still required.
Growth rate control of adherent bacterial populations

We report a novel in vitro method which, through application of appropriate nutrient limitations, enables growth rate control of adherent bacterial populations. Exponentially growing cells are collected by pressure filtration onto cellulose acetate membranes. Following inversion into the bases of modified fermentors, membranes and bacteria are perfused with fresh medium. Newly formed and loosely attached cells are eluted with spent medium. Steady-state conditions (dependent upon the medium flow rate) at which the adherent bacterial biomass is constant and proportional to the limiting nutrient concentrations are rapidly achieved, and within limits, the growth rate is proportional to the medium flow rate. Scanning electron microscopic studies showed that such populations consist of individual cells embedded within an extracellular polymer matrix.
RFTrack: TVWS Spectrum Measurements using Android Phones

Despite successful trials in several Developing Countries, TVWS has not gained the amount of attention that it deserves. One of the reasons is the lack of awareness by both the general public and the spectrum regulators about the abundance of idle spectrum. Traditionally, spectrum measurements required expensive instruments and considerable operator's expertise. This has changed with the emergence of low cost spectrum analyzers that are also easier to operate. In this paper we present an Android application called RFTrack that measures spectrum using a low-cost spectrum analyzer and geo-tags the data using the phone's GPS. Data is stored in the internal memory. Once an Internet connection is available, the program sends data to a server that presents the results in an easy-to-understand way. After having performed some measurement campaigns with this set up, we believe that this is a useful tool to demonstrate the existence of significant swats of of underutilized spectrum, especially in rural areas of developing countries.
Lipid nanoparticles as novel delivery systems for cosmetics and dermal pharmaceuticals

Introduction: Lipid nanoparticles are innovative carrier systems developed as an alternative to traditional vehicles such as emulsions, liposomes and polymeric nanoparticles. Solid lipid nanoparticles (SLN) and the newest nanostructured lipid carriers (NLC) show important advantages for dermal application of cosmetics and pharmaceuticals. Area covered: This article focuses on the main features of lipid nanoparticles, in terms of their preparation and recent advancements. A detailed review of the literature is presented, introducing the importance of these systems in the topical delivery of drugs and active substances. Expert opinion: Lipid nanoparticles are able to enhance drug penetration into the skin, allowing increased targeting to the epidermis and consequently increasing treatment efficiency and reducing the systemic absorption of drugs and cosmetic actives. The complete biodegradation of lipid nanoparticles and their biocompatible chemical nature have secured them the title of ‘nanosafe carriers. ' SLN and NLC represent a new technological era, which has been taken over by the cosmetic and pharmaceutical industry, which will open new channels for effective topical delivery of substances.
Effects of the Calcium Release Inhibitor Dantrolene and the Ca2+-ATPase Inhibitor Thapsigargin on Spinal Nociception in Rats

The effects produced by the intrathecal administration of dantrolene and thapsigargin, measured in several analgesic tests in the rat are described. Dantrolene decreases the release of calcium from intracellular stores and thapsigargin is able to inhibit the reticular Ca2+-ATPase, avoiding intracellular calcium storage. Dantrolene (30–300 nmol/rat) and thapsigargin (3–30 nmol/rat) reduced the nociceptive behavior (biting, scratching, licking; BSL) produced by the NK1 receptor agonist septide (0.5 µg), without affecting the BSL induced by AMPA (2 µg) or NMDA (4 µg). Also, both drugs elicited analgesia in the tail-flick test but not in the formalin test. The antinociceptive effects induced by thapsigargin were more intense and long-lasting than those produced by dantrolene. These results seem to indicate that the intracellular modulation of calcium homeostasis could be an interesting target in order to induce spinal analgesia.
An association rule mining-based methodology for automated detection of ischemic ECG beats

Currently, an automated methodology based on association rules is presented for the detection of ischemic beats in long duration electrocardiographic (ECG) recordings. The proposed approach consists of three stages. 1) Preprocessing: Noise is removed and all the necessary ECG features are extracted. 2) Discretization: The continuous valued features are transformed to categorical. 3) Classification: An association rule extraction algorithm is utilized and a rule-based classification model is created. According to the proposed methodology, electrocardiogram (ECG) features extracted from the ST segment and the T-wave, as well as the patient's age, were used as inputs. The output was the classification of the beat as ischemic or not. Various algorithms were tested both for discretization and for classification using association rules. To evaluate the methodology, a cardiac beat dataset was constructed using several recordings of the European Society of Cardiology ST-T database. The obtained sensitivity (Se) and specificity (Sp) was 87% and 93%, respectively. The proposed methodology combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules
1 A Framework for Peer-to-Peer Service Interaction

Although structured/unstructured peer-to-peer (P2P) algorithms proliferate in recent years, building P2P application is still complex and time-consuming due to inadequate infrastructure support. On the other hand, service-oriented architecture significantly reduces developing complexity by further decoupling dependent components. Based on the idea of using services as fundamental elements for developing P2P applications, we propose a P2P service framework to facilitate P2P application development. The framework chooses eQuus as the DHT implementation for the reason of robustness and integrates with OSGi to maximize its usability. Group communication helps the framework to achieve replication with almost free cost. Pull and push based service discovery mechanism enables applications unaware of the underlying remote distribution. Through relaying mechanism, our framework is able to traverse NATs. Based on our framework, we also implemented QChat a pure P2P chat application.
Closed formulas for evaluating radar monopulse accuracy

Monopulse is a widely used algorithm to estimate the target angular co-ordinates in radar application. Formulas exist that give approximate values for the accuracy achievable with monopulse. This paper derives simple but accurate closed formulas to estimate the monopulse accuracy by taking into account the radar detection threshold (and then PD and Pfa) and the target fluctuation models.
Telomere length and incident atrial fibrillation – data of the PREVEND cohort

Background The incidence of atrial fibrillation (AF) increases with age. Telomere length is considered a marker of biological ageing. We investigated the association between leukocyte telomere length and incident AF in the Dutch Prevention of Renal and Vascular End-stage Disease (PREVEND) study. Methods We included 7775 individuals without prevalent AF, and with leukocyte telomere length measured. Mean telomere length was determined by a monochrome multiplex quantitative polymerase chain reaction-based assay. Results Mean age of our cohort was 49±13 years, and 50% were men. During a mean follow-up of 11.4±2.9 years incident AF was detected in 367 (4.7%) individuals. Telomere length was shorter in individuals developing incident AF compared to those without AF (p = 0.013). Incident AF was inversely related to the telomere length. In the quartile with the longest telomere length 68 (3.5%) individuals developed AF, in the shortest telomere length quartile 100 (5.1%) individuals (p = 0.032). Telomere length was associated with incident AF in the second shortest telomere length quartile using the longest telomere length quartile as reference (hazard ratio 1.64; 95% CI 1.02–2.66; p = 0.043). After including age or AF risk factors, the relation between telomere length and incident AF was no longer significant. We found a significant interaction of age, male sex, systolic blood pressure, BMI, heart failure, and myocardial infarction with telomere length for the association with incident AF. Conclusions We found that shorter leukocyte telomere length is not independently associated with incident AF in a community-based cohort.
The Solution of Combinatorial Optimization Problems Based on Reinforcement Learning

Combinatorial optimization problems are known as important ones which can be applied to various fields. However, there is a problem that it is difficult to find an optimal solution, especially for a large-scale combinatorial optimization problem. Meanwhile, in the field of machine learning, reinforcement learning has attracted attention because learning is achieved rapidly and optimally. In this paper, we propose a method in which the basic framework of the reinforcement learning is introduced, for solving the combinatorial optimization problems.
An AOP-RBPNN approach to infer user interests and mine contents on social media

Usersengaginginonlinesocialnetworksprovidesparsedataaboutthemselves,e.g.byparticipatingin groupstodiscuss some topics, linking to each other, etc. Such sparse data can be carefully used to build both user and group profiles, automatically. We put forward a multi-agent system that collects and analyses data scattered on an online social network. The analysis aims at characterisingbothusers,byinsertingthemintocategories,andgroups,withasetofkeywords.Theuserclassificationtechnology is an especially devised neural network that extracts relevant characteristics from raw data characterising user behaviour, and then provides for unknown users the most likely category. Thanks to the said classification tool, some online activities performed by a given user that are unusual for such a user are automatically detected. Moreover, according to the user interests, contents inserted on public pages, which the user is unaware of, can be automatically found and suggested.
RecognizingP3-Structure: A Switching Approach

AP3is a set of three vertices of a graph that induces a chordless path; theP3-structure of a graph is the set of allP3's. Chvatal asked if there is a polynomial time algorithm to determine whether an arbitrary three-uniform hypergraph is theP3-structure of some graph. The answer is yes: we describe such an algorithm. Our algorithm is based on switching (as defined by Seidel), a characterization of graphs with the sameP3-structure, a sufficient condition forP3-structure uniqueness, and a reduction of a certain partitioning problem to two-satisfiability.
Hundred forty eight more days with depression: the association between marital conflict and depression‐free days

Although collaborative care programs are effective in improving late‐life depression, only about half of treated patients achieve clinically meaningful improvement. Thus, we need to examine what characteristics may predict poor late‐life depression course. Despite the robust evidence for the negative association between the quality of couple relationships and depression outcomes, few studies have examined these associations in the context of long‐term late‐life depression course.
GOALS: generator of adaptive learning scenarios

The problem of generating personalised learning activities for learners is a difficult task. The difficulty is compounded if the learning activity is mediated or presented through a serious game. In this paper, we present a system, called generator of adaptive learning scenarios GOALS that is capable of generating learning scenarios taking into account the learners' interaction traces, pedagogical objectives and the specificities of serious games. The generator we propose aims to be generic, i.e., independent of the application domain and serious games. To achieve this, we propose an architecture that organises the knowledge in three layers: domain concepts, pedagogical resources and serious game resources. This work has been conducted in the context of Cognitive Linguistic Elements Stimulations Project CLES. This project targets the development of an online serious game dedicated to persons with cognitive disabilities. In order to validate our approaches, we conducted experiments in the context of CLES project. These experiments are based on comparative method that compares the results generated by our system with that of an expert. The results of the valuations, conducted with a domain expert and end users, are also presented.
Metagenomics reveals impact of geography and acute diarrheal disease on the Central Indian human gut microbiome

ABSTRACT Background The Central Indian gut microbiome remains grossly understudied. Herein, we sought to investigate the burden of antimicrobial resistance and diarrheal diseases, particularly Clostridioides difficile, in rural-agricultural and urban populations in Central India, where there is widespread unregulated antibiotic use. We utilized shotgun metagenomics to comprehensively characterize the bacterial and viral fractions of the gut microbiome and their encoded functions in 105 participants. Results We observed distinct rural-urban differences in bacterial and viral populations, with geography exhibiting a greater influence than diarrheal status. Clostridioides difficile disease was more commonly observed in urban subjects, and their microbiomes were enriched in metabolic pathways relating to the metabolism of industrial compounds and genes encoding resistance to 3rd generation cephalosporins and carbapenems. By linking phages present in the microbiome to their bacterial hosts through CRISPR spacers, phage variation could be directly related to shifts in bacterial populations, with the auxiliary metabolic potential of rural-associated phages enriched for carbon and amino acid energy metabolism. Conclusions We report distinct differences in antimicrobial resistance gene profiles, enrichment of metabolic pathways and phage composition between rural and urban populations, as well as a higher burden of Clostridioides difficile disease in the urban population. Our results reveal that geography is the key driver of variation in urban and rural Indian microbiomes, with acute diarrheal disease, including C. difficile disease exerting a lesser impact. Future studies will be required to understand the potential role of dietary, cultural, and genetic factors in contributing to microbiome differences between rural and urban populations.
The largest synthetic structure with molecular precision: towards a molecular object.

Pushing the limits: A 200A - 10 Da structurally defined, linear macromolecule (PG5) has a molar mass, cross-section dimension, and cylindrical shape that are comparable to some naturally occurring objects, such as amyloid fibrils or certain plant viruses. The macromolecule is resistant against flattening out on a surface; the picture shows PG5 embracing the tobacco mosaic virus (TMV).