Miracle-dz/UniFAR-FTUs huggingface dataset:
Phonological awareness and pronunciation in a second language

The objective of this dissertation is to increase knowledge about L2 phonological awareness through three research agendas: to investigate the nature of L2 phonological awareness in adult language learners and its relation to some individual differences, to examine the relationship between L2 phonological awareness and L2 pronunciation, and to create novel language-specific instruments to measure L2 phonological awareness reliably. Research on phonological awareness has focused on L1 literacy acquisition, where it has been understood as the ability to manipulate speech segments. In SLA, phonological awareness has been examined in its explicit dimension. Nevertheless, due to the special nature of L2 speech acquisition, L2 learners are rarely able to elaborate explicitly on aspects of pronunciation. Consequently, the present study advocates that L2 phonological awareness mainly consists of proceduralized knowledge. L1 Brazilian Portuguese learners of English (n=71) were tested on their awareness about the L2 phonological system through three domain-specific (segmental, suprasegmental and phonotactic) tasks. Performance in the L2 phonological awareness tasks was related to the participants’ L2 pronunciation (measured with a Foreign Accent Rating Task) and to individual differences in the amount of L2 experience, L2 use and L2 proficiency. Additionally, 19 L1 American English speakers performed the same phonological awareness tasks, enabling comparison between L1 and L2 phonological awareness. The results revealed that L2 learners manifested significantly lower degrees of phonological awareness than L1 speakers. Moreover, L2 phonological awareness explained 32.8% of the variance in L2 pronunciation. As for the individual differences, L2 proficiency explained unique variance in L2 phonological awareness, whereas the role of L2 experience and use remained unsettled. Apart from contributing to our understanding of the nature of L2 phonological awareness, the findings of the present study have important pedagogical implications. Knowing the gaps in a language learner’s L2 phonological awareness enables the instructor to bring them to the learner’s attention, which in turn could be positively reflected in improved L2 pronunciation. Finally, the instruments developed for the present study are expected to guide further studies on L2 phonological awareness. Keywords: phonological awareness, L2 speech learning, second language acquisition
Is tuberculin skin testing useful to diagnose latent tuberculosis in BCG-vaccinated children?

BACKGROUND The tuberculin skin test (TST) is the most commonly used tool to detect infection with Mycobacterium tuberculosis. We sought to determine whether tuberculin skin testing is useful to detect latent infection by M. tuberculosis in a population that was vaccinated with the Bacille Calmette Guérin (BCG) vaccine. METHODS We performed a cross-sectional study during October 2000-February 2001, enrolling first and sixth graders from a random, stratified sample of public elementary schools in Orizaba, Veracruz, Mexico. We assessed the relationship between sociodemographic and epidemiological information, BCG scars, and TST reactivity. RESULTS There were 858 children enrolled in the study with a completed questionnaire and TST result. The prevalence of a positive TST result (> or =10 mm) was 12.4%. Controlling for BCG scar, age, and other characteristics, close contact with pulmonary tuberculosis patients (odds ratio 6.56, 95% confidence interval 2.05-21.07, P = 0.001) was independently associated with TST reactivity. CONCLUSIONS TST results helped identify children in a BCG-vaccinated population who had recent exposure to persons with pulmonary tuberculosis, were probably infected with M. tuberculosis, and could benefit from treatment for their latent tuberculosis infection.
A search scheme resulting in mixing compressed text files

The direct search of text files is a very useful technique. That not only reduces the amount of storage space required for a text file, but it also speeds up the search time. Furthermore, protecting secret documents is a basic and important requirement of computer systems. In this paper, we present a more secure compression and decompression technique for large natural language texts. The merits of our method are: (1) The word-based approximate matching process can be performed directly on the secure compressed text directly; (2) updating a word directly into the compressed text is supported; (3) the decompression process can be started at the position of the search results; (4) the search process does not require complex encryption computation. The scheme we present is simple. The complexity of the search phase is only O(n), which makes our scheme very practical. We believe that this technique has a great potential for its applications to be extended to solve other problems, for example, the private information retrieval problem, encrypted databases and so on.
Video denoising using spatio-temporal filtering

Video denoising is very important to be used as a pre-processing stage, which should be considered both spatial and temporal dimensions. In this paper, video denoising scheme based on spatio-temporal filtering is researched, which contains spatially filtering in the two dimensional wavelet domain and temporal filtering in the time domain. Spatial filtering is firstly performed on individual video frames by wavelet shrinkage with an adaptive threshold selection. We use motion detection and recursive time weighted averaging to perform temporal filtering. It should be noted that both previous and following frames are considered to get the final filtered frame during the temporal filtering. Experimental results show that the proposed scheme yields better results in terms of both objective and subjective quality of the reconstructed video sequence.
Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes

Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) entities interacting locally with their environment cause coherent functional global patterns to emerge. SI provides a basis with which it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model. To tackle the formation of a coherent social collective intelligence from individual behaviors, we discuss several concepts related to self-organization, stigmergy and social foraging in animals. Then, in a more abstract level we suggest and stress the role played not only by the environmental media as a driving force for societal learning, as well as by positive and negative feedbacks produced by the many interactions among agents. Finally, presenting a simple model based on the above features, we will address the collective adaptation of a social community to a cultural (environmental, contextual) or media informational dynamical landscape, represented here – for the purpose of different experiments – by several threedimensional mathematical functions that suddenly change over time. Results indicate that the collective intelligence is able to cope and quickly adapt to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes.
Flatten a Curved Space by Kernel : From Einstein to Euclid

Einstein’s general theory of relativity fundamentally changed our view about the physical world. Different from Newton’s theory, Einstein’s space and time are not flat but can be warped by matter. For a curved space such as Einstein’s space, Euclidean geometry is no longer suitable, and Riemannian geometry is usually used instead. In parallel with physics, due to an explosion of data from all fields of science, there is an increasing need for pattern analysis tools, which are capable of analyzing patterns of data in a non-Euclidean (curved) space. To handle data in a curved space, linear approaches are not directly applicable, and instead nonlinear approaches are the right weapon. However, early-day nonlinear approaches were usually based on gradient descent or greedy heuristics, and suffered from local minima and overfitting [1]. In contrast, kernel methods provide a powerful means for transforming data in a non-Euclidean curved space (such as Einstein space) into points in a highdimensional Euclidean flat space, so that linear approaches can be applied to the transformed points in the high-dimensional Euclidean space. With this flattening capability, kernel methods combine the best features of linear approaches and nonlinear approaches, i.e., kernel methods are capable of dealing with nonlinear structures while enjoying a low computational complexity like linear approaches. In this column, we provide important insights into kernel methods and illustrate the power of kernel methods in two important pattern analysis problems: feature extraction and clustering.
Strong Lensing Analysis of A1689 from Deep ACS Images

ACS observations of massive lensing clusters permit an order of magnitude increase in the numbers of multiply-lensed background galaxies identified behind a given cluster. We have developed a code to take the pixels belonging to any given image and generate counter-images with full resolution, so that multiple systems are convincingly and exhaustively identified. Over 130 images of 35 multiply lensed galaxies are found behind A1689, including many radial arcs and also tiny counter-images projected on the center of mass. The derived mass profile is found to flatten steadily towards the center, like and NFW profile, with a mean slope $d{\log{\Sigma}}/d{\log{r}}\approx-0.55\pm0.1$, over the range $r<250$ kpc/h, which is somewhat steeper than predicted for such a massive halo. We also clearly see the expected geometric increase of bend angles with redshift, however, given the low redshift of A1689, $z=0.18$, the dependence on cosmological parameters is weak, but using higher redshift clusters from our GTO program we may derive a more competitive constraint.To search for other articles by the author(s) go to: http://adsabs.harvard. edu/abstract_service. html
Anti-Swinging Input Shaping Control of an Automatic Construction Crane

This paper describes the control of an automatic overhead crane for assembly of modular building elements. The automatic system was developed using a commercial full-size crane, which was modified by adding adequate sensors, servomotors, and control strategy. The crane, which maintained its original cables, was converted to a robotic system, and is controlled via a computer-based multiaxes control board. The implemented algorithms solve two main problems of module assembly: 1) precision positioning of large and heavy modules in the order of a few centimeters, and 2) anti-swinging transportation of modules, even in difficult weather conditions (not extreme). The implementation of the anti-swinging control is achieved using the on-line two-dimensional inclinometer measurements and an on-line calculation of input impulse trains. The developed input-shaping control is divided into two phases: straight line motion and external perturbances cancellation. Experimental results of the developed algorithms which demonstrate the effectiveness of the new process are presented.
Use of Photovoice in Processing Race-Based Topics in a Multicultural Counseling Course

ABSTRACT This phenomenological study assessed master-level students’ experiences of using photovoice, a creative learning activity, in a race-based multicultural counseling course. Students were asked to engage in weekly written reflections regarding a course-related photographic image, with the goal of more deeply processing cognitive and affective reactions to the course. Data analyzed included focus groups, student written responses, and photos. Findings indicated use of photovoice as a means for processing cognitions, which allowed students to deepen learning by extending a racialized lens outside of the classroom. Students also indicated use of the tool to process emotional reactions at various stages of the course, allowing learners to creatively and visually express feelings as they surfaced.
A new kinetic lattice Monte Carlo modeling framework for the source-drain selective epitaxial growth process

We have developed a new kinetic lattice Monte Carlo modeling framework for Si/Ge selective epitaxial growth based on neighbor binding interactions within the third-nearest-neighbor range of the diamond lattice. We find that first- and second-nearest-neighbor interactions contribute significantly to the faceting between {100} and {111}, while the third-nearest-neighbor interaction is the cause of {311} facet formation. The second-nearest-neighbor interaction also facilitates lateral growth and island formation within a plane. The simulated growth kinetics and shapes are in good agreement with experimental data.
Feature extraction and analysis of prostate cancer proteomic mass spectra for biomarker discovery

Early detection of cancer is a critical issue for improving patient survival rates. Recent progress in mass spectrometry has shown the promising potential of biomarker discovery in the diagnosis of diseases especially in early stages. In the present study, an alternative approach to feature extraction from mass spectrometry data of prostate cancer is proposed that results in the definition of different biomarkers. The latter provide information rich features that improve the performance of an MLP classifier in differentiating among datasets with different PSA levels of prostate cancer and with no evidence of disease. Prostate cancer dataset was collected from the National Cancer Institute Clinical Proteomics Database. The overall accuracy, in correctly classifying 63 spectra with no evidence of disease (PSA<1) and 69 spectra with prostate cancer (PSA≥4), was 95%. Furthermore 93% was the classification overall accuracy in discriminating 26 spectra of prostate cancer with (4 PSA<10) from 43 spectra of prostate cancer with (PSA>10). The high accuracies obtained by the proposed method might lead to informative biomarkers for early stage of prostate cancer diagnosis. ≤
An improved AC-amplifier for electrophysiology

We present the design, simulation and test results of a new AC amplifier for electrophysiological measurements based on a three op-amp instrumentation amplifier (IA). The design target was to increase the common mode rejection ratio (CMRR), thereby improving the quality of the recorded physiological signals in a noisy environment. The new amplifier actively suppresses the DC component of the differential signal and actively reduces the common mode signal in the first stage of the IA. These functions increase the dynamic range of the amplifier's first stage of the differential signal. The next step was the realization of the amplifier in a single chip technology. The design and tests of the new AC amplifier with a differential gain of 79.2 dB, a CMRR of 130 dB at 50 Hz, a high-pass cutoff frequency at 0.01 Hz and common mode reduction in the first stage of the 49.8 dB are presented in this paper.
Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications

Tracking gliomas dynamics on MRI has became more and more important for therapeutic management. Powerful computational tools have been recently developed in this context enabling in silico growth on a virtual brain that can be matched with real 3D segmented evolution through registration between atlases and patient brain MRI data. In this paper, we provide an extensive review of existing algorithms for the three computational tasks involved in patient-specific tumor modeling: image segmentation, image registration, and in silico growth modelling (with special emphasis on the proliferation-diffusion model). Accuracy and limits of the reviewed algorithms are systematically discussed. Finally applications of these methods for both clinical practice and fundamental research are also discussed.
How Sensitive Is Foreign Investment in China to Wage Differences? Skill Intensity, Product Market Competition, and Networks*

Deeper international integration through an inflow of foreign direct investment alters domestic labor markets, whether by shifting the labor demand curve or by increasing the elasticity of demand for domestic workers. This paper uses the location choices of multinational firms investing in China as a window into the relationship between foreign investment and hostcountry labor demand. With data on 2884 manufacturing equity joint venture projects in China during 1993-1996, we investigate the extent to which an investor’s sensitivity to wages depends on the skill intensity of the activity, product market competition, and source country development level. Applying conditional logit techniques developed in a series of papers by Petrin and Train (2005, 2006), we find a significant, elastic response of capital to wages; ceteris paribus, investors are attracted to locations with low wages. Moreover, investors involved in the least skill intensive activities exhibit the most wage sensitivity. Using the Broda-Weinstein (2006) U.S. import demand elasticity estimates for Chinese exports to measure pass-through ability, we find that investors in those industries where China faces the most elastic import demand are the most sensitive to wages differences, even when we control for the skill-intensity of the manufacturing activity. We also find that while OECD investors are more responsive to wage differences overall, they are less likely to choose a location that has received a large share of prior foreign investment. Simulations of regional wage subsidies indicate that policies to shift investment to inland regions alter the composition as well as the location of investment.
The Bible and the Reader's Response

Reader response theory, the broad range of literary perspectives which place emphasis upon the role of readers and their responses to texts, has contributed important insights to biblical hermeneutics and to pedagogy in literature education. Yet reader response theory does not appear, as yet, to have had as significant an influence as it might upon the way we teach individuals to read and respond to that most important of texts, the Bible. It is proposed in this article that Rosenblatt's transactional theory of the literary work offers valuable insights that can be applied to both the reading of the Bible and also how it can be taught in a range of contexts, in Christian and state schools, as well as in churches. Consequently, pedagogy informed by Rosenblatt's reader response theory may offer us a biblical use of the Bible as it can foster the spiritual development of readers by enabling them to engage with Scripture at a deeply personal level. It is suggested that Bible teaching must be responsive to the individual and to society but must, most of all, be responsive to the Holy Spirit.
A Review of Picture Exchange Communication Interventions Implemented by Parents and Practitioners

ABSTRACT The picture exchange communication system (PECS) is widely used with individuals with Autism Spectrum Disorders (ASD) and other developmental disabilities to improve communication skills. Several studies indicate the positive outcomes of PECS on communication skills of children with ASD. We reviewed 13 studies that included training parents and practitioners to implement PECS with individuals with ASD. Results indicate that the parents and practitioners in these studies mastered the PECS stages in which they were trained and implemented the intervention. In addition, in many of the studies individuals with ASD acquired communication skills. The results are discussed and directions for future research are provided.
Reasoning with Temporal Context in News Analysis

One aspect of implicit, contextual information is its temporal component. Explicating this component in a formal model makes it possible to disambiguate some context-dependent expressions and discover connections between expressions. We have implemented and extended Allen’s algebra of temporal intervals in a reasoner that takes into account the linear nature of time and the granularity of temporal expressions (days/weeks/.). If this algebra is used to model the temporal extension of events, the reasoner can track and connect the reference of indexical expressions about them. We intend to use the reasoner for analysing news streams, to help discover connections between news items.
Towards Recognizing Feature Points using Classification Trees

In earlier work \cite{Lepetit04b}, we proposed to treat wide baseline matching of feature points as a classification problem and proposed an implementation based on K-means and nearest neighbor classification. We showed that this method is both reliable and faster than competing methods, but still too slow for real-time implementations. Here we show that using decision trees instead speeds up the computation greatly, while increasing the robustness. This allows point matching under large viewpoint and illumination changes that is suitable for accurate object pose estimation at 25 Hz on a standard Pentium IV PC. Most of the previous methods rely either on using {\em ad hoc} local descriptors or on estimating local affine deformations. By contrast, we treat wide baseline matching of keypoints as a classification problem, in which each class corresponds to the set of all possible views of such a point. Given one or more images of a target object, we train the system by synthesizing a large number of views of individual keypoints and by using statistical classification tools to produce a compact description of this {\it view set}. At run-time, we rely on this description to decide to which class, if any, an observed feature belongs. This formulation allows us to use decision trees to reduce matching error rates, and to move some of the computational burden from matching to training, which can be performed beforehand. We will show that our method is both reliable and fast enough to detect and estimate in real-time the 3D pose of an object in the presence of occlusions, illumination changes, and cluttered backgrounds.
Topology-Aware Rank Reordering for MPI Collectives

As we move toward the Exascale era, HPC systems are becoming more complex, introducing increasing levels of heterogeneity in communication channels. This leads to variations in communication performance at different levels of hierarchy within modern HPC systems. Consequently, communicating peers such as MPI processes should be mapped onto the target cores in a topology-aware fashion so as to avoid message transmissions over slower channels. This is especially true for collective communications due to the global nature of their communication patterns and their vast use in many of parallel applications. In this paper, we exploit the rank reordering mechanism of MPI to realize run-time topology awareness for collective communications and in particular MPI_Allgather. To this end, we propose four fine-tuned mapping heuristics for various communication patterns and algorithms commonly used in MPI_Allgather. The heuristics provide a better match between the collective communication pattern and the topology of the target system. Our experimental results with 4096 processes show that MPI rank reordering using the proposed fine-tuned mapping heuristics can provide up to 78% reduction in MPI_Allgather latency at the micro-benchmark level. At the application level, we can achieve up to 34% reduction in execution time. The results also show that the proposed heuristics significantly outperform the Scotch library which provides a general-purpose graph mapping library.