Miracle-dz/UniFAR-FTUs huggingface dataset:
IMPACT ANALYSIS OF THE EDUCATIONAL MATERIAL IN HIGHER EDUCATION

In the last decade, the universities have evolved for suiting the demands raised by the new generation of students and the current changing society. Learning Management Systems (LMS) have been implemented in higher education but much of the effort has been focused on training the teaching staff on the use of such applications. Unfortunately, there is not a substantial improvement in the academic results since these tools basically, deliver the necessary documentation for lectures in digital form rather than printed. However, teachers are not aware of the predilection of the students for printed documentation, who finally proceed to print such digital content. Many studies try to assess the satisfaction of the students, which is certainly positive with regard to the online platform, but do not delve into the issue in detail. Some universities consider sufficient the implementation of a LMS that allow teachers to interact with the students in an online basis. However, the use that teachers make of those platforms is overlooked. An extensive survey was conducted among some students and teachers of the Engineering Faculty of Bilbao. This study reveals the tendency to share mainly lectures in text and presentation formats. At the same time, the skills of the teachers to develop more engaging educational material such as, 3D models, interactive documents, etc., are analysed. Finally, the opinion of both population samples is gathered in qualitative terms.
Current status of management of ectopic pregnancy

Objective To evaluate the relative safety and efficacy of conservative surgical, medical and expectant management. Design Relevant studies were identified through a computer Medline search. Results Laparoscopy is not mandatory for diagnosis of ectopic pregnancy, which can be diagnosed by the use of transvaginal scan and estimation of quantitative βhCG. Laparoscopic salpingectomy or salpingostomy is preferred to laparotomy in a haemodynamically stable patient and should be used more frequently. The role of expectant management in ectopic pregnancy is very limited. Spontaneous resolution may occur only in a selected group of unruptured ectopic pregnancies with an initial βhCG of <200 mIU/ml and declining hCG level. Systemic methotrexate administration is a promising treatment in patients with early and unruptured ectopic pregnancy. Several prospective studies have shown success rates of 85–94% following single-dose systemic methotrexate treatment. Single-dose methotrexate is associated with fewer side-effects but is as effective as multiple-dose regimes. Recurrent ectopic pregnancy and intrauterine pregnancy rates following systemic methotrexate are comparable to those following laparoscopic salpingostomy. Conclusion Systemic methotrexate is a viable alternative to laparoscopic salpingostomy for women wishing to preserve their fertility. A large prospective randomized trial is needed to establish whether laparoscopic surgery or methotrexate should be the first-line treatment.
Online object tracking via bag-of-local-patches

As one of the most important tasks in computer vision, online object tracking plays a critical role in numerous lines of research, which has drawn a lot of researchers’ attention and be of many realistic applications. This paper develops a novel tracking algorithm based on the bag-of-local-patches representation with the discriminative learning scheme. In the first frame, a codebook is learned by applying the Kmeans algorithm to a set of densely sampled local patches of the tracked object, and then used to represent the template and candidate samples. During the tracking process, the similarities between the coding coefficients of the candidates and template are chosen as the likelihood values of these candidates. In addition, we propose effective model updating and discriminative learning schemes to capture the appearance change of the tracked object and incorporate the discriminative information to achieve a robust matching. Both qualitative and quantitative evaluations on some challenging image sequences demonstrate that the proposed tracker performs better than other state-of-the-art tracking methods.
Studies in relational grammar

This collection of nine original syntactic studies carried out within the framework for syntactic theory and description known as Relational Grammar provides a state-of-the-art survey of this and allied fields. In relational theory, grammatical relations such as subject, direct object, and predicate are taken to be theoretical primitives which permit the definition of formal objects called Arcs, the fundamental building blocks of syntactic structures. Edited by Paul M. Postal and Brian D. Joseph, this volume is the third in a series highlighting work in Relational Grammar. It extends the foundational studies of the first two volumes to refine and modify the insights, analyses, and theoretical devices developed in earlier connections, while at the same time providing support for some of the earlier constructs and claims. Of the nine papers, four treat various aspects of advancements to and demotions from indirect object; three deal with raising and clause union constructions, in which initial immediate constituents of one structure are nonimmediate constituents of another; and two are concerned with problems in the description and formalization of verbal agreement systems. The nine articles cover languages ranging from Chamorro to English, French, Georgian, Greek, Japanese, Kek'chi, Korean, Southern Tiwa, Spanish, and Tzotzil.
Quantum Error Correction with Biased Noise

Quantum computing offers powerful new techniques for speeding up the calculation of many classically intractable problems. Quantum algorithms can allow for the efficient simulation of physical systems, with applications to basic research, chemical modeling, and drug discovery; other algorithms have important implications for cryptography and internet security. At the same time, building a quantum computer is a daunting task, requiring the coherent manipulation of systems with many quantum degrees of freedom while preventing environmental noise from interacting too strongly with the system. Fortunately, we know that, under reasonable assumptions, we can use the techniques of quantum error correction and fault tolerance to achieve an arbitrary reduction in the noise level. In this thesis, we look at how additional information about the structure of noise, or "noise bias," can improve or alter the performance of techniques in quantum error correction and fault tolerance. In Chapter 2, we explore the possibility of designing certain quantum gates to be extremely robust with respect to errors in their operation. This naturally leads to structured noise where certain gates can be implemented in a protected manner, allowing the user to focus their protection on the noisier unprotected operations. In Chapter 3, we examine how to tailor error-correcting codes and fault-tolerant quantum circuits in the presence of dephasing biased noise, where dephasing errors are far more common than bit-flip errors. By using an appropriately asymmetric code, we demonstrate the ability to improve the amount of error reduction and decrease the physical resources required for error correction. In Chapter 4, we analyze a variety of protocols for distilling magic states, which enable universal quantum computation, in the presence of faulty Clifford operations. Here again there is a hierarchy of noise levels, with a fixed error rate for faulty gates, and a second rate for errors in the distilled states which decreases as the states are distilled to better quality. The interplay of of these different rates sets limits on the achievable distillation and how quickly states converge to that limit.
Novel extensions of label propagation for biomarker discovery in genomic data

One primary goal of analyzing genomic data is the identification of biomarkers which may be causative of, correlated with, or otherwise biologically relevant to disease phenotypes. In this work, I implement and extend a multivariate feature ranking algorithm called label propagation (LP) for biomarker discovery in genome-wide single-nucleotide polymorphism (SNP) data. This graph-based algorithm utilizes an iterative propagation method to efficiently compute the strength of association between a SNP and a phenotype. I developed three extensions to the LP algorithm, with the goal of tailoring it to genomic data. The first extension is a modification to the LP score which yields a variable-level score for each SNP, rather than a score for each SNP genotype. The second extension incorporates prior biological knowledge that is encoded as a prior value for each SNP. The third extension enables the combination of rankings produced by LP and another feature ranking algorithm. The LP algorithm, its extensions, and two control algorithms (chi squared and sparse logistic regression) were applied to 11 genomic datasets, including a synthetic dataset, a semi-synthetic dataset, and nine genome-wide association study (GWAS) datasets covering eight diseases. The quality of each feature ranking algorithm was evaluated by using a subset of top-ranked SNPs to construct a classifier, whose predictive power was evaluated in terms of the area under the Receiver Operating Characteristic curve. Top-ranked SNPs were also evaluated for prior evidence of being associated with disease using evidence from the literature. The LP algorithm was found to be effective at identifying predictive and biologically meaningful SNPs. The single-score extension performed significantly better than the original algorithm on the GWAS datasets. The prior knowledge extension did not improve on the feature ranking results, and in some cases it reduced the predictive power of top-ranked variants. The ranking combination method was effective for some pairs of algorithms, but not for others. Overall, this work’s main results are the formulation and evaluation of several algorithmic extensions of LP for use in the analysis of genomic data, as well as the identification of several disease-associated SNPs.
A scheduling algorithm for an out-tree DAG

An effective scheduling algorithm is key to achieving high performance. This paper presents an algorithm based on task duplication to schedule an out-tree task graph which represents a number of divide-and-conquer algorithms. The scheduling algorithm has the shortest possible scheduling length and economizes the processors with O(|e|/spl middot/|/spl nu/|/sup 2/) complexity. Moreover, it also outperforms the CPFD (critical-path fast duplication) and TDS (task duplication-based scheduling) algorithms.
The effect of target stimulus properties on saccadic parameters: Do size and contrast of a target stimulus affect latency and peak velocity of saccades?

3 Ágrip . 4 Acknowledgement . 5 Table of contents . 6 Introduction . 7 Method . 12 Participants . 12 Research design . 12 Stimuli . 12 Equipment. 12 Procedure . 13 Statistical analysis . 14 Results . 15 Latency . 15 Peak velocity . 17 Discussion . 19 References . 21
A probabilistic framework for learning task relationships in multi-task learning

For many real-world machine learning applications, labeled data is costly because the data labeling process is laborious and time consuming. As a consequence, only limited labeled data is available for model training, leading to the so-called labeled data deficiency problem. In the machine learning research community, several directions have been pursued to address this problem. Among these efforts, a promising direction is multi-task learning which is a learning paradigm that seeks to boost the generalization performance of a model on a learning task with the help of some other related tasks. This learning paradigm has been inspired by human learning activities in that people often apply the knowledge gained from previous learning tasks to help learn a new task more efficiently and effectively. Of the several approaches proposed in previous research for multi-task learning, a relatively less studied yet very promising approach is based on automatically learning the relationships among tasks from data. In this thesis, we first propose a powerful probabilistic framework for multi-task learning based on the task relationship learning approach. The main novelty of our framework lies in the use of a matrix variate prior with parameters that model task relationships. Based on this general multi-task learning framework, we then propose four specific methods, namely, multi-task relationship learning (MTRL), multi-task generalized t process (MTGTP), multi-task high-order task relationship learning (MTHOL), and probabilistic multi-task feature selection (PMTFS). By utilizing a matrix variate normal distribution as a prior on the model parameters of all tasks, MTRL can be formulated efficiently as a convex optimization problem. On the other hand, MTGTP is a Bayesian method that models the task covariance matrix as a random matrix with an inverse-Wishart prior and integrates it out to achieve Bayesian model averaging to improve the generalization performance. With MTRL as a base, MTHOL provides a generalization that learns high-order task relationships and model parameters. Unlike MTRL, MTGTP and MTHOL which are for standard multi-task classification or regression problems, PMTFS addresses the feature selection problem under the multi-task setting by incorporating the learning of task relationships. Besides conducting experimental validation of the proposed methods on several data sets for multi-task learning, we also investigate in detail a collaborative filtering application under the multi-task setting. Through both theoretical and empirical studies on the several methods proposed, we show that task relationship learning is a very promising approach for multi-task learning and related learning problems.
Melodic Paradoxes in the Music of the Thai Pi-Phat and Javanese Gamelan

The two musical cultures of Thailand and Java, especially as conveyed by the leading percussion ensembles (the Thai pi-phat and the Javanese gamelan), share several important concepts, as well as obvious physical similarities between the instruments themselves. Studies by Becker (1968, 1980) draw attention to the “musical family” of Southeast Asia, especially to similarities within “bronze gong cultures” in that region (Becker 1980: 453). One of her papers is a comparison of Thai and Javanese music (Becker 1980). Another feature that paper shares with the present one is that the stimulus for it came from working with a graduate student whose research lay in Thai music, in the latter case an expert Thai performing artist. The present paper investigates what is perhaps the most striking, and paradoxical, similarity between Thai and Javanese music: the processes of improvisation and elaboration turn out to be governed by a melody which is not actually performed. In both traditions the art of playing individual instruments is, broadly, the art of realising this elusive melody according to the idiomatic patterns associated with a particular instrument. How it is done varies greatly from one tradition to the other, and within each ensemble. Theoretical constraints must also be considered. In both traditions rules must be observed, the purpose of which is to create structures which are shaped and balanced in ways not only conveying aesthetic appeal but also suggesting more universal ideas about melodic organisation and logic. Thai musicians tend to talk about what they do in ways which relate to improvisation (kan phrae tamnong, literally “changing the melody”). A similar process may be observed in Javanese music, even if the notion of improvisation is not as prevalent. The comparable term here is gam-pan (literally “working on”). There is no need to give detailed examples of how kan phrae tamnong and garapan work in specific pieces, as this aspect has been thoroughly documented in the literature of the two traditions—for example Brinner (1995), Martopangrawit (1984), Ketukaenchan (1989), Sorrell (2000), Sumrongthong (1997), and Sutton (1979). The main purpose here is to compare Thai and Javanese concepts of unheard melody and their musical realizations.
Prevalência e caracterização da anemia em idosos do Programa de Saúde da Família

Anemia is a serious public health problem worldwide that mainly affects children and women of childbearing age. However, data about anemia in elderly individuals are still scarce in developing countries. This study aimed at assessing the prevalence and characteristics of anemia among an elderly population attending the Health Family Program in Camaragibe, northeast Brazil. Following a systematic sampling procedure, a cross-sectional study was carried out in November and December 2003 involving 284 subjects of both genders with ages grater than or equal to 60 years old. Anemia was estimated by the measurement of hemoglobin, mean cell hemoglobin concentration, mean cell volume and red cell distribution width. The prevalence of anemia was around 11.0%. Red cell distribution width and mean cell volume were not correlated to hemoglobin count. The major characteristics of anemia were normochromia with normocytosis without anisocytosis suggesting anemia due to chronic diseases. The very low prevalence of microcytosis, macrocytosis and anisocytosis seems to be more likely due to causes other than iron, vitamin B12 or folic acid deficiencies. The use of indicators that reflect the degree of anisocytosis, as well as parameters to assess iron status should be strongly recommended in the screening of anemia in the elderly population.
The Repair of the Athena Parthenos: A Story of Five Dowels

IN 1910, while drawing a plan of the Parthenon in connection with Dr. Hill's impending publication of the Older Parthenon,' I was obliged to pay due attention to the pedestal of the gold-and-ivory Athena Parthenos, the masterpiece of Pheidias dedicated at the Panathenaic festival of 438 B.C. Before 1910, just as in more recent years, there have been several discussions of the pedestal;2 but, since no thorough examination of its site has yet been made, it seems desirable to retrieve some of the observations buried in my old notebooks. The well-known traces of the statue pedestal consist, primarily, of a portion of the floor area (about 2.62 x 6.53 m.)3 wherein, for reasons of economy, thirty blocks of poros limestone were substituted for eight marble pavement slabs. At the exact center of the poros area is a rectangular hole (0.549 x 0.755 m. and 0.372 m. deep) for the central mast of the statue's skeleton.And all around the poros area are dimly perceptible traces, sometimes engraved lines, and sometimes merely variations of weathering, outlining an area 4.096 x 8.043 m.5 On the marble surface, between the engraved outline and the poros area, and also within the poros area itself, are dowel holes and pry cuttings marking joints of the lowest course of the pedestal, showing that the blocks were uniformly about 1.03 x 1.30 m. Six of these blocks, of marble 0.295 m. high, have recently been identified by means of their dimensions and by the fact that one of them reproduces the socket for the central mast; they have been replaced, therefore, approximately in their original positions (Fig. 1), but, unfortunately, all have been so mutilated that none retains any trace of the original external finish of the pedestal. With regard to the jointing of the pedestal, Lehmann-Hartleben argues that the lowest course was composed of twenty-four blocks, each 1.03 x 1.31 m. , and thus obtains his total dimensions of 4.12 x 7.86 m. These dimensions conflict with the actual outline of the pedestal engraved on the marble floor (4.096 x 8.043 m.), and also with the jointing. It is clear, from seventeen pry cuttings and two dowel holes (one of the latter a T-dowel) on the poros area, that the core of the structure was
An R package for fitting age, period and cohort models

In this paper we present the R implementation of a GLIM macro which fits age-period-cohort model following Osmond and Gardner. In addition to the estimates of the corresponding model, owing to the programming capability of R as an object oriented language, methods for printing, plotting and summarizing the results are provided. Furthermore, the researcher has fully access to the output of the main function (apc) which returns all the models fitted within the function. It is so possible to critically evaluate the goodness of fit of the resulting model
Identity and Career Choice among Mexican American and Euro-American Preservice Bilingual Teachers

This article discusses an investigation that explored the relationship between the ethnic identity and career choice of Mexican American and Euro-American Spanish bilingual teaching candidates. In-depth interviews with 6 Mexican American and Euro-American preservice bilingual teaching candidates respectively were analyzed to determine their reference group orientation, affiliative identity, and rationale for becoming a bilingual teacher. Mexican American bilingual teaching candidates indicated a strong reference group orientation and affiliative identity toward Mexican culture, whereas their Euro-American counterparts indicated a diffused reference group orientation toward Mexican culture. Mexican American bilingual teaching candidates also indicated biculturalism and serving as role models for Latino children as being important reasons for becoming bilingual teachers. Euro-American teaching candidates did not see themselves as role models for Latino children. Instead, they indicated various other reasons for becoming a bilingual teacher.
Automatic recognition of healthcare worker hand hygiene

Hand hygiene is an important part of preventing disease transmission in the hospital. Due to this importance, electronic systems have been proposed for automatically monitoring healthcare worker adherence to hand hygiene guidelines. However, these systems can miss certain hand hygiene events and do not include quality metrics such as duration or technique. We propose that hand hygiene duration and technique can be automatically inferred using the motion of the wrist. This work presents a system utilizing wrist-based 3-dimensional accelerometers and orientation sensors, signal processing (including novel features), and machine learning to detect healthcare worker hand hygiene and report quality metrics such as duration and whether the healthcare worker used recommended rubbing technique. We validated the system using several different types of data sets with up to 116 healthcare workers and activities ranging from synthetically generated hand hygiene movements to observation of healthcare worker hand hygiene on the hospital floor. In these experiments our system detects up to 98.4% of hand hygiene events, detects hand hygiene technique with up to 92.1% accuracy, and accurately estimates hand hygiene duration.
Trends in Transportation Research

Proceedings of journal and conference papers are good sources of big textual data to examine research trends in various branches of science. The contents, usually unstructured in nature, require fast machine-learning algorithms to be deciphered. Exploratory analysis through text mining usually provides the descriptive nature of the contents but lacks quantification of the topics and their correlations. Topic models are algorithms designed to discover the main theme or trend in massive collections of unstructured documents. Through the use of a structural topic model, an extension of latent Dirichlet allocation, this study introduced distinct topic models on the basis of the relative frequencies of the words used in the abstracts of 15,357 TRB compendium papers. With data from 7 years (2008 through 2014) of TRB annual meeting compendium papers, the 20 most dominant topics emerged from a bag of 4 million words. The findings of this study contributed to the understanding of topical trends in the complex and evolving field of transportation engineering research.