Miracle-dz/UniFAR-FTUs huggingface dataset:
Making space for assessment: dance teachers’ experiences of learning and teaching prerequisites

ABSTRACT The aim of this study is to illuminate and discuss assessment within dance education in Swedish upper secondary schools through teachers’ reflections. The study investigates how teachers reflect upon the range of possibilities explored and difficulties encountered in their assessment practice. In order to be able to comprehend the phenomenon of teachers’ reflections regarding their assessment practice, both written and verbal teacher reflections were gathered by means of seven interviews with four teachers, and one trilateral talk. Four teachers participated in the study. In the analytical process, the phenomenon was seen, broadened, varied, and then condensed into two themes: Conditions for assessment for learning and Making space for assessment. Both themes have aspects, which are intertwined: both include conditions for assessment as seen through various modalities, methods and tools.
The Impact of Visual Context on the Content of Referring Expressions

Traditional approaches to referring expression generation (REG) have taken as a fundamental requirement the need to distinguish the intended referent from other entities in the context. It seems obvious that this should be a necessary condition for successful reference; but we suggest that a number of recent investigations cast doubt on the significance of this aspect of reference. In the present paper, we look at the role of visual context in determining the content of a referring expression, and come to the conclusion that, at least in the referential scenarios underlying our data, visual context appears not to be a major factor in content determination for reference. We discuss the implications of this surprising finding.
Investigation of the spatial spectrum of cortical contours on cross-sections of the brain

The human brain possesses a highly structured surface. A quantitative examination of the cortical surface is of particular interest for many questions, e.g. the investigation the relation between the spatial frequencies of cortical surfaces and spatial frequencies within Electroencephalogram and the analysis of changes of the structure of the human brain caused by diseases.
Modeling Generic Markov Processes using a Symmetrical Binomial Lattice Approach

Propósito Central do Trabalho: The mathematical complexity associated with derivatives and real options theory derives from the need for a probabilistic solution for the optimal investment decision throughout the life to an option. The solution to this dynamic optimization problem is to model the uncertainty of the underlying asset as a stochastic process where the optimum decision value of investment is obtained by solving a differential equation with the appropriate boundary conditions. In many cases, however, this differential equation has no analytical solution. In these cases, a discrete approximation to the underlying stochastic process can be used. One of these alternatives is the binomial lattice developed by Cox, Ross and Rubinstein (1979), which is a robust, precise and intuitively appealing tool for option valuation models. It is widely accepted as an efficient approximation to the Black, Scholes and Merton’s (1973) model. Furthermore this approach provides the solution to the early exercise of American type options. Often, however, the uncertainty to be modeled does not behave as a GBM type of stochastic diffusion process. This occurs when the value of a variable is a function of a longterm equilibrium level or Mean Reversion (MR) model, as is usually the case of non-financial commodities or interest rates, but unfortunately the Cox, et al. (1979) approach only applies to uncertainties that can be modeled through a GBM process. In this paper we develop a Symmetrical Binomial Lattice that is equivalent to the Cox, et al. (1979) approach for GBM modeling, but is also more generic and able to model Mean Reverting (MR) stochastic dynamics as well as other types of Markov processes.
Off-line Odia handwritten numeral recognition using neural network: A comparative analysis

Character recognition is one of the most interesting and challenging research areas in the field of image processing. The recognition rate of handwritten character is still limited due to the presence of large variety of shape, scale and format in hand written characters. A sophisticated handwritten character recognition system demands a better feature extraction technique that would take care of such variety of handwriting. The work proposed in this paper is an attempt to develop recognizer for Odia handwritten numeral digits based on Binarization and discrete cosine transform (DCT) scheme. The recognizer puts emphasis on exploiting the inherit characteristics of Odia numeral images. The system first employs the techniques like thinning, foreground and background noise removal, cropping and size normalization etc., to preprocess the character images. Binarization and DCT technique is employed separately to extract the features from the images. Subsequently, these feature vectors are sent to the neural network classifier. Extensive simulations show that the results are very promising over a standard dataset and the recognition rate for Binarization and DCT are 80.2% and 90%, respectively.
Protein complex prediction via cost-based clustering

MOTIVATION Understanding principles of cellular organization and function can be enhanced if we detect known and predict still undiscovered protein complexes within the cell's protein-protein interaction (PPI) network. Such predictions may be used as an inexpensive tool to direct biological experiments. The increasing amount of available PPI data necessitates an accurate and scalable approach to protein complex identification. RESULTS We have developed the Restricted Neighborhood Search Clustering Algorithm (RNSC) to efficiently partition networks into clusters using a cost function. We applied this cost-based clustering algorithm to PPI networks of Saccharomyces cerevisiae, Drosophila melanogaster and Caenorhabditis elegans to identify and predict protein complexes. We have determined functional and graph-theoretic properties of true protein complexes from the MIPS database. Based on these properties, we defined filters to distinguish between identified network clusters and true protein complexes. CONCLUSIONS Our application of the cost-based clustering algorithm provides an accurate and scalable method of detecting and predicting protein complexes within a PPI network.
Multi-species biofilm of Candida albicans and non-Candida albicans Candida species on acrylic substrate

Objective In polymicrobial biofilms bacteria extensively interact with Candida species, but the interaction among the different species of the Candida is yet to be completely evaluated. In the present study, the difference in biofilm formation ability of clinical isolates of four species of Candida in both single-species and multi-species combinations on the surface of dental acrylic resin strips was evaluated. Material and Methods The species of Candida, isolated from multiple species oral candidiasis of the neutropenic patients, were used for the experiment. Organisms were cultured on Sabouraud dextrose broth with 8% glucose (SDB). Biofilm production on the acrylic resins strips was determined by crystal violet assay. Student's t-test and ANOVA were used to compare in vitro biofilm formation for the individual species of Candida and its different multi-species combinations. Results In the present study, differences between the mean values of the biofilm-forming ability of individual species (C. glabrata>C. krusei>C. tropicalis>C. albicans) and in its multi-species' combinations (the highest for C. albicans with C. glabrata and the lowest for all the four species combination) were reported. Conclusions The findings of this study showed that biofilm-forming ability was found greater for non-Candida albicans Candida species (NCAC) than for C. albicans species with intra-species variation. Presence of C. albicans in multi-species biofilms increased, whereas; C. tropicalis decreased the biofilm production with all other NCAC species.
MIML: A Framework for Learning with Ambiguous Objects

In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework for learning with ambiguous objects, where an example is described by multiple instances and associated with multiple class labels. Comparing with traditional learning frameworks, the MIML framework is more convenient and natural for representing ambiguous objects. To learn MIML examples, we propose the MimlBoost and MimlSvm algorithms based on a simple degeneration strategy, and experiments show that solving problems involving ambiguous objects in the MIML framework can lead to good performances. Considering that the degeneration process may lose information, we propose the D-MimlSvm algorithm which tackles MIML problems directly in a regularization framework. Moreover, we show that even when we do not have access to the raw objects and thus cannot capture more information from raw objects by using the MIML representation, MIML is still useful. We propose the InsDif and SubCod algorithms. InsDif works by transforming single-instances into the MIML representation for learning, while SubCod works by transforming single-label examples into the MIML representation for learning. Experiments show that in some tasks they are able to achieve better performances than learning the single-instances or single-label examples directly.
Centering Subjectivities

Postcolonial feminist and African diaspora theories provide lenses through which to consider the impacts of forced migration on the internal and relational lives of women—aspects of experience less visible in policy, practice, and scholarship. Policy, practice, and research contribute to the framing of “refugees” as a static category of people irrespective of complex histories, geopolitical origins, and fluid identities impacted by structural forces. They can thus deny the subjective possibilities of women through the construction of identities that informs who refugees are and who they are expected to become. These overarching trends reflected in policy and practice have particular implications for women whose internal and relational processes remain to a large extent invisible. Drawing from postcolonial feminist and African diaspora theories, this article suggests that a practice of centering the subjectivities of women in forced migration may enhance the work of researchers and practitioners.
Statistical analysis of exacerbation rates in COPD: TRISTAN and ISOLDE revisited

Various statistical methods have been used to measure the impact of treatment on chronic obstructive pulmonary disease (COPD) exacerbations. Poisson regression has recently been recommended as the appropriate method but the model does not satisfactorily account for variability between patients. In contrast, use of a negative binomial model, which corresponds to assuming a separate Poisson parameter for each patient, offers a more appealing approach. The present paper reviews analysis methods, with particular focus on the negative binomial model. To illustrate the differences that arise from using different analysis methods, we have reanalysed data from two large studies which, among other objectives, investigated the effectiveness of inhaled corticosteroids in reducing COPD exacerbation rates. Using the negative binomial model to reanalyse data from the TRISTAN and ISOLDE studies, the overall estimates of exacerbation rates on each treatment arm are higher and the confidence intervals for comparisons between treatments are wider, but the overall conclusions of TRISTAN and ISOLDE regarding reduction of exacerbations remain unchanged. The negative binomial approach appears to provide a better fit to the distribution of the data than earlier methods and is currently the method of choice. Research needs to continue on further methods to improve the analysis of exacerbation data.
Script Generation Following Frontal and Parietal Lesions

ABSTRACT The purpose of this investigation was to distinguish putative effects of parietal lobe lesions on script generation, in distinction from the better known and established effects of frontal lobe lesions. Nine patients, most with excised parietal lesions, were compared to nine age, gender and education matched normal participants. Eleven patients with excised tumors of the frontal lobe were compared to twelve age, gender and education matched normal subjects. Participants were requested to generate, out loud, scripts corresponding to everyday activities. Half the scripts were relatively more demanding with respect to temporal representation (understanding the time line of events) and the other half with respect to spatial representation (understanding the layout of the actions in space). These two conditions were further broken down into conditions of high and low demands on working memory (reciting the scripts backwards versus forward). The frontal lobe patients enunciated significantly fewer actions overall. They were also significantly more impaired than the normal participants on all tasks with high demands on working memory, and more often, high temporal demands (sequencing and perseverative errors). The parietal lobe patients had significant difficulty in sequencing in all conditions, and manifested no perseveration. Though script generation tasks have been primarily associated with frontal lobe function until now, consideration should be given to the type of activity being scripted as a function of relative demands on spatial or temporal representation, as well as working memory, and the contributions of other lobes ought to be taken into consideration.
High Performance Implementation of an Econometrics and Financial Application on GPUs

In this paper, we describe a GPU based implementation for an estimator based on an indirect likelihood inference method. This method relies on simulations from a model and on nonparametric density or regression function computations. The estimation application arises in various domains such as econometrics and finance, when the model is fully specified, but too complex for estimation by maximum likelihood. We implemented the estimator on a machine with two 2.67GHz Intel Xeon X5650 processors and four NVIDIA M2090 GPU devices. We optimized the GPU code by efficient use of shared memory and registers available on the GPU devices. We compared the optimized GPU code performance with a C based sequential version of the code that was executed on the host machine. We observed a speed up factor of up to 242 with four GPU devices.
Remarkable Anti-Trichomonas vaginalis Activity of Plants Traditionally Used by the Mbyá-Guarani Indigenous Group in Brazil

Trichomonas vaginalis, a flagellate protozoan, is the causative agent of trichomonosis, the most common nonviral sexually transmitted disease worldwide. Taking into account the increased prevalence of metronidazole-resistant isolates, alternative drugs are essential for the successful treatment. Natural products are the source of most new drugs, and popular wisdom about the use of medicinal plants is a powerful tool in this search. In this study, the activity of 10 medicinal plants extensively used in daily life by Mbyá-Guarani indigenous group was evaluated against seven different T. vaginalis isolates. Among the aqueous extracts tested, Verbena sp. (Guachu ka'a in Mbyá-Guarani language) and Campomanesia xanthocarpa (Guavira in Mbyá-Guarani language) showed the highest activity against T. vaginalis with MIC value of 4.0 mg/mL reaching 100% of efficacy against the parasite. The kinetic growth assays showed that the extracts promoted complete growth abolishment after 4 h of incubation. In addition, the extracts tested did not promote a significant hemolytic activity against human erythrocytes. Our results show for the first time the potential activity of Verbena sp. and C. xanthocarpa against T. vaginalis. In addition, this study demonstrates that indigenous knowledge is an important source of new prototype antiprotozoal agents.
PARALLELIZING THE FAST MULTIPOLE METHOD USING A TASK-BASED RUNTIME FOR HETEROGENEOUS ARCHITECTURES

Emmanuel Agullo, Bérenger Bramas, Olivier Coulaud, Eric Darve , Matthias Messner, Toru Takahashi 1 Inria, Hiepacs Project, Talence, France, Olivier.Coulaud@inria.fr 2 Mechanical Engineering Department and Institute for Computational and Mathematical Engineering, Stanford University, USA, darve@stanford.edu 3 Department of Mechanical Science and Engineering, Nagoya University, Japan, ttaka@nuem.nagoya-u.ac. jp
Prevention and treatment of lymphatic metastasis by antilymphangiogenic therapy.

Cancer cells escape a tumor by two primary routes—blood vessels and lymphatic vessels—to establish distant metastases. Thus, it seems reasonable to hypothesize that blocking the growth of new blood vessels (angiogenesis) and lymphatic vessels (lymphangiogenesis) will inhibit hematogenic and lymphogenic metastases, respectively. An impressive array of preclinical studies has demonstrated prevention and suppression of hematogenic metastases by antiangiogenic and antivascular approaches. Whether antilymphangiogenic and antilymphatic approaches will yield similar results for lymphogenic metastases remains to be seen. Both vascular endothelial growth factor
Hyperlink-extended pseudo relevance feedback for improved microblog retrieval

Microblog retrieval has received much attention in recent years due to the wide spread of social microblogging platforms such as Twitter. Many research studies investigated different approaches for microblog retrieval. Query expansion is one of the approaches that showed stable performance for improving microblog retrieval effectiveness. In this paper we comprehensively investigate query expansion for microblog retrieval using pseudo relevance feedback (PRF) with different configurations. In addition, we propose a hyperlink-extended PRF by expanding queries using embedded hyperlinks in retrieved microblogs. Our experimental results on TREC microblog data showed that PRF alone could outperform many retrieval approaches if configured properly. Results also showed that utilizing hyperlinked documents in tweets for query expansion improves results over traditional PRF. Our best results achieved using hyperlink-extended PRF outperformed most of the reported results using other approaches.
One Process to Reap Them All: Garbage Collection as-a-Service

Ubiquitous mobile platforms such as Android rely on managed language run-time environments, also known as language virtual machines (VMs), to run a diverse range of user applications (apps). Each app runs in its own private VM instance, and each VM makes its own private local decisions in managing its use of processor and memory resources. Moreover, the operating system and the hardware do not communicate their low-level decisions regarding power management with the high-level app environment. This lack of coordination across layers and across apps restricts more effective global use of resources on the device. We address this problem by devising and implementing a global memory manager service for Android that optimizes memory usage, run-time performance, and power consumption globally across all apps running on the device. The service focuses on the impact of garbage collection (GC) along these dimensions, since GC poses a significant overhead within managed run-time environments. Our prototype collects system-wide statistics from all running VMs, makes centralized decisions about memory management across apps and across software layers, and also collects garbage centrally. Furthermore, the global memory manager coordinates with the power manager to tune collector scheduling. In our evaluation, we illustrate the impact of such a central memory management service in reducing total energy consumption (up to 18%) and increasing throughput (up to 12%), and improving memory utilization and adaptability to user activities.
Performance evaluation of OFDM transmission scheme in vehicular environment

In this modern age of high spectral efficiency, Orthogonal Frequency Division Multiplexing (OFDM) has gained a great importance in the field of wireless communication. In this paper, first we have shown a useful comparison for different modulation order in vehicular environment. Next, we have proposed a modified scheme for fast moving environment and on the basis of this scheme we have compared the performance of various transmission schemes in terms of error performance evaluation parameters. Finally, through simulation result we have concluded that our proposed scheme performs quite better than previous OFDM schemes in vehicular environment.
Result representation of Rao-Blackwellized particle filtering for SLAM

Recently, particle filters have been applying to many robotic problems including the simultaneous localization and mapping (SLAM). Specifically, SLAM approaches employing Rao-Blackwellized particle filter (RBPF) have shown good results. However, no research is conducted to analyze representation of the results of particle filtering. After finishing the particle filtering, the results such as a map and a path are stored in the separate particles. In most cases, the result of the particle that has the highest importance weight is represented as the result. However, this approach does not give the best result all the time. Thus, We provide the analysis of final representation of particle filtering. In this paper, we compares several methods to derive the final representation of the result after finishing RBPF-SLAM. According to the result, combining data of each particle provides the better result with high probability than using just data of a particle such as the highest weighted particle representation.
Representing Documents and Queries as Sets of Word Embedded Vectors for Information Retrieval

A major difficulty in applying word vector embeddings in IR is in devising an effective and efficient strategy for obtaining representations of compound units of text, such as whole documents, (in comparison to the atomic words), for the purpose of indexing and scoring documents. Instead of striving for a suitable method for obtaining a single vector representation of a large document of text, we rather aim for developing a similarity metric that makes use of the similarities between the individual embedded word vectors in a document and a query. More specifically, we represent a document and a query as sets of word vectors, and use a standard notion of similarity measure between these sets, computed as a function of the similarities between each constituent word pair from these sets. We then make use of this similarity measure in combination with standard IR based similarities for document ranking. The results of our initial experimental investigations shows that our proposed method improves MAP by up to $5.77\%$, in comparison to standard text-based language model similarity, on the TREC ad-hoc dataset.
Deferred Enrollment: Chicano/Latino Males, Social Mobility and Military Enlistment

Chicanos/Latinos comprise the largest racial and ethnic population in the U.S. public K-12 schools, yet hold some of the largest rates of academic underperformance in high school and college completion. This study aims at understanding the academic trajectories of five Chicano/Latino males and their decision to enlist in the military after high school graduation. Using in-depth semistructured interviews, participants discussed the role that high school structures, family, social capital, and sociocultural forces played in their career decision-making process. Results showed that students viewed the military as a way of upward social mobility and their most viable career option after completing high school. The study also highlights the lack of resources available to students who may not be viewed as college material by school personnel.