Miracle-dz/UniFAR-FTUs huggingface dataset:
Los docentes en los programas de educación bilingüe en Cantabria

espanolLa implementacion de programas AICLE en Espana va en aumento. Este estudio analiza los datos obtenidos a traves de ocho entrevistas semiestructuradas asi como de cuestionarios completados por ochenta docentes, tanto bilingues como no bilingues, que trabajan en Colegios Publicos de Educacion Infantil y Primaria con programas de educacion bilingue en Cantabria (Espana). Los resultados muestran que no hay consenso entre los docentes sobre algunos de los retos que se deben afrontar en un programa bilingue como la formacion y la mayor coordinacion que requiere la imparticion de DNL en ingles. No obstante, puede concluirse que los docentes pueden realizar contribuciones muy reveladoras que pueden mejorar el diseno y la regulacion de los programas bilingues. EnglishCLIL programmes are being increasingly implemented in Spain. This study analyses the data obtained by means of 8 semi-structured interviews as well as questionnaires on the perceptions of 80 teachers, either bilingual and non bilingual, who teach at an Infant or Primary level in half of the state schools running a bilingual program in Cantabria (Spain). Results show that there is no consensus among CLIL and mainstream teachers on perceptions about some of the challenges to be faced in a bilingual program such as training and coordination. Nevertheless, it can be concluded that teachers can make very illuminating contributions that can improve design and regulation of bilingual programmes.
Subgroups and Quotients of Fundamental Groups

topological spaces have been studied for over a century. A standard tool for studying path connected spaces is the fundamental group. There are questions about fundamental groups of Polish (separable, completely metrizable) spaces that can be answered using descriptive set theory. If one further restricts attention to the fundamental group of a Peano continuum (a connected, locally connected compact metrizable space) then there are interesting dichotomies. In [Sh] Shelah demonstrated the following dichotomy: The fundamental group of a Peano continuum is either finitely generated or of cardinality continuum. Using a theorem by Cannon and Conner [CC] one can replace the word “generated” in the conclusion of Shelah’s theorem with the word “presented”. Pawlikowski later gave a simplified proof of Shelah’s result [P]. Using similar methods Conner and the author obtain the same result for first homology [CoCo]. In this thesis we are going to present improvements on these techniques and numerous applications, which give a great deal of information about fundamental groups of path connected Polish spaces. In Chapter 2 we provide some preliminary definitions and key ideas, as well as some motivation for the results in the succeeding sections. Chapter 3 provides some useful general theorems which provide dichotomies on quotients of the fundamental group. In Chapter 4 we give some examples of topologically defined subgroups of the fundamental group and exhibit upper bounds on their topological complexity. An alternative characterization of the shape kernel is provided for locally path connected spaces. In Chapter 5 we define comonster groups and show an interesting dichotomy on the fundamental group of a Peano continuum. In Chapter 6 we show that subgroups of the fundamental group of a covering space are not more complex than their images under the covering map. Chapter 7 provides
Resolving Power of Asteroseismic Inversion of the Kepler Legacy Sample

Abstract The Kepler Asteroseismic Legacy Project provided frequencies, separation ratios, error estimates, and covariance matrices for 66 Kepler main sequence targets. Most of the previous analysis of these data was focused on fitting standard stellar models. We present results of direct asteroseismic inversions using the method of optimally localized averages (OLA), which effectively eliminates the surface effects and attempts to resolve the stellar core structure. The inversions are presented for various structure properties, including the density stratification and sound speed. The results show that the mixed modes observed in post-main sequence F-type stars allow us to resolve the stellar core structure and reveal significant deviations from the evolutionary models obtained by the grid-fitting procedure to match the observed oscillation frequencies.
TFApprox: Towards a Fast Emulation of DNN Approximate Hardware Accelerators on GPU

Energy efficiency of hardware accelerators of deep neural networks (DNN) can be improved by introducing approximate arithmetic circuits. In order to quantify the error introduced by using these circuits and avoid the expensive hardware prototyping, a software emulator of the DNN accelerator is usually executed on CPU or GPU. However, this emulation is typically two or three orders of magnitude slower than a software DNN implementation running on CPU or GPU and operating with standard floating point arithmetic instructions and common DNN libraries. The reason is that there is no hardware support for approximate arithmetic operations on common CPUs and GPUs and these operations have to be expensively emulated. In order to address this issue, we propose an efficient emulation method for approximate circuits utilized in a given DNN accelerator which is emulated on GPU. All relevant approximate circuits are implemented as look-up tables and accessed through a texture memory mechanism of CUDA capable GPUs. We exploit the fact that the texture memory is optimized for irregular read-only access and in some GPU architectures is even implemented as a dedicated cache. This technique allowed us to reduce the inference time of the emulated DNN accelerator approximately 200 times with respect to an optimized CPU version on complex DNNs such as ResNet. The proposed approach extends the TensorFlow library and is available online at github.com/ehw-fit/tf-approximate.
Incorporating Effective Global Information via Adaptive Gate Attention for Text Classification

The dominant text classification studies focus on training classifiers using textual instances only or introducing external knowledge (e.g., hand-craft features and domain expert knowledge). In contrast, some corpus-level statistical features, like word frequency and distribution, are not well exploited. Our work shows that such simple statistical information can enhance classification performance both efficiently and significantly compared with several baseline models. In this paper, we propose a classifier with gate mechanism named Adaptive Gate Attention model with Global Information (AGA+GI), in which the adaptive gate mechanism incorporates global statistical features into latent semantic features and the attention layer captures dependency relationship within the sentence. To alleviate the overfitting issue, we propose a novel Leaky Dropout mechanism to improve generalization ability and performance stability. Our experiments show that the proposed method can achieve better accuracy than CNN-based and RNN-based approaches without global information on several benchmarks.
The Importance of Home Versus 24-Hour Ambulatory Blood Pressure Monitoring and Assessment of Blood Pressure Variability in Hypertension

Abstract Background: A number of studies reveal that home blood pressure variability is associated with cardiovascular risk factors. However, we do not have a consensus regarding the variability index and the frequency of measurements. Objective: The aim of the study was to assess home blood pressure variability for a period of 7 consecutive days and 24-hour ambulatory blood pressure variability using the average real variability index and to test whether home blood pressure variability represents a suitable parameter for long-term monitoring of the hypertensive patients. Material and methods: A number of 31 hypertensive patients were included in the study, 8 male, 23 female, mean age 60.19±7.35 years. At the inclusion ambulatory blood pressure monitoring was performed, home blood pressure monitoring was carried out for 7 consecutive days with 2 measurements daily. We compared ambulatory blood pressure values, variability using paired t-test. We were looking for correlations between HBP values and cardiovascular risk factors. Results: Ambulatory versus home blood pressure derived mean blood pressure was 131.38±15.2 versus 131.93±8.25, p=0.81. Ambulatory derived variability was 10.65±2.05 versus home variability 10.56±4.83, p=0.91. Home versus ambulatory pulse pressure was 51.8± 9.06 mmHg vs. 54.9±11.9 mmHg, p=0.046. We found positive correlation between HBPV and home BP values, p=0.027, r2=0.1577, (CI: 0.04967 to 0.6588). Home, as well as ambulatory derived variability were positively correlated to age p=0.043, r2=0.1377 (CI: 0.01234 to 0.6451) versus p<0.0001, CI: 0.3870 to 0.8220, r2=0.4302. Conclusion: Assessment of home blood pressure monitoring and variability could represent a well-tolerated alternative for long-term follow-up of hypertension management.
Predictive Sampling with Forecasting Autoregressive Models

Autoregressive models (ARMs) currently hold state-of-the-art performance in likelihood-based modeling of image and audio data. Generally, neural network based ARMs are designed to allow fast inference, but sampling from these models is impractically slow. In this paper, we introduce the predictive sampling algorithm: a procedure that exploits the fast inference property of ARMs in order to speed up sampling, while keeping the model intact. We propose two variations of predictive sampling, namely sampling with ARM fixed-point iteration and learned forecasting modules. Their effectiveness is demonstrated in two settings: i) explicit likelihood modeling on binary MNIST, SVHN and CIFAR10, and ii) discrete latent modeling in an autoencoder trained on SVHN, CIFAR10 and Imagenet32. Empirically, we show considerable improvements over baselines in number of ARM inference calls and sampling speed.
Citations Systematically Misrepresent the Quality and Impact of Research Articles: Survey and Experimental Evidence from Thousands of Citers

Citations are ubiquitous in evaluating research, but how exactly they relate to what they are thought to measure (quality and intellectual impact) is unclear. We investigate the relationships between citations, quality, and impact using a survey with an embedded experiment in which 12,670 authors in 15 academic fields describe about 25K specific referencing decisions. Results suggest that citation counts, when equated with quality and impact, are biased in opposite directions. First, experimentally exposing papers' actual citation counts during the survey causes respondents to perceive all but the top 10% cited papers as of lower quality. Because perceptions of quality are a key factor in citing decisions, citation counts are likely to endogenously cause more citing of top papers and equating them with quality overestimates the actual quality of those papers. Conversely, 54% of references had either zero or minor influence on authors who cite them, but references to highly cited papers were about 200% more likely to denote substantial impact. Equating citations with impact thus underestimates the impact of highly cited papers. Real citation practices thus reveal that citations are biased measures of quality and impact.
Learning From Strategic Agents: Accuracy, Improvement, and Causality

In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model (predicting some outcome) that accounts for agents' incentives to "game" their features in order to receive better decisions. Whereas the strategic classification literature generally assumes that agents' outcomes are not causally dependent on their features (and thus strategic behavior is a form of lying), we join concurrent work in modeling agents' outcomes as a function of their changeable attributes. Our formulation is the first to incorporate a crucial phenomenon: when agents act to change observable features, they may as a side effect perturb hidden features that causally affect their true outcomes. We consider three distinct desiderata for a decision-maker's model: accurately predicting agents' post-gaming outcomes (accuracy), incentivizing agents to improve these outcomes (improvement), and, in the linear setting, estimating the visible coefficients of the true causal model (causal precision). As our main contribution, we provide the first algorithms for learning accuracy-optimizing, improvement-optimizing, and causal-precision-optimizing linear regression models directly from data, without prior knowledge of agents' possible actions. These algorithms circumvent the hardness result of Miller et al. (2019) by allowing the decision maker to observe agents' responses to a sequence of decision rules, in effect inducing agents to perform causal interventions for free.
MCG+07-20-052: Interacting Dwarf Pair in a Group Environment

We present an observational study of the interacting pair of dwarf galaxies, MCG+07-20-052, in the vicinity of Milky Way mass spiral galaxy NGC 2998. MCG+07-20-052 is located at a sky-projected distance of 105 kpc from NGC 2998 and the two have a relative line-of-sight velocity of 60 km s−1. We observed tidal tail-like extensions on both members (D1 and D2) of the interacting pair MCG+07-20-052. The interacting dwarf galaxies, D1 and D2, have B-band absolute magnitudes of −17.17 and −17.14 mag, respectively, and D2 is significantly bluer than D1. We obtained H i 21 cm line data of the NGC 2998 system using the Giant Metrewave Radio Telescope to get a more detailed view of the neutral hydrogen (H i) emission in the interacting dwarf galaxies and in the galaxy members of the NGC 2998 group. Evidence of a merger between the dwarf galaxies in the MCG+07-20-052 is also present in the H i kinematics and morphology where we find that H i is mostly concentrated around D2, which also shows a higher level of star-forming activity and a bluer g–r color index compared to D1. In addition, we detect extended tenuous H i emission around another member galaxy, NGC 3006, located close to the MCG+07-20-052 pair at a sky-projected distance of 41 kpc. We compare here our results from the MCG+07-20-052 pair NGC 2998 system with other known Large Magellanic Cloud/Small Magellanic Cloud/Milky Way-type systems and discuss the possible origin of the dwarf–dwarf interaction.
Reducing urban traffic congestion due to localized routing decisions

Balancing traffic flow by influencing drivers' route choices to alleviate congestion is becoming increasingly more appealing in urban traffic planning. Here, we introduce a discrete dynamical model comprising users who make their own routing choices on the basis of local information and those who consider routing advice based on localized inducement. We identify the formation of traffic patterns, develop a scalable optimization method for identifying control values used for user guidance, and test the effectiveness of these measures on synthetic and real-world road networks.
An Efficient Secure Dynamic Skyline Query Model

It is now cost-effective to outsource large dataset and perform query over the cloud. However, in this scenario, there exist serious security and privacy issues that sensitive information contained in the dataset can be leaked. The most effective way to address that is to encrypt the data before outsourcing. Nevertheless, it remains a grand challenge to process queries in ciphertext efficiently. In this work, we shall focus on solving one representative query task, namely dynamic skyline query, in a secure manner over the cloud. However, it is difficult to be performed on encrypted data as its dynamic domination criteria require both subtraction and comparison, which cannot be directly supported by a single encryption scheme efficiently. To this end, we present a novel framework called SCALE. It works by transforming traditional dynamic skyline domination into pure comparisons. The whole process can be completed in single-round interaction between user and the cloud. We theoretically prove that the outsourced database, query requests, and returned results are all kept secret under our model. Moreover, we also present an efficient strategy for dynamic insertion and deletion of stored records. Empirical study over a series of datasets demonstrates that our framework improves the efficiency of query processing by nearly three orders of magnitude compared to the state-of-the-art.
Empirical Study on Airline Delay Analysis and Prediction

The Big Data analytics are a logical analysis of very large scale datasets. The data analysis enhances an organization and improve the decision making process. In this article, we present Airline Delay Analysis and Prediction to analyze airline datasets with the combination of weather dataset. In this research work, we consider various attributes to analyze flight delay, for example, day-wise, airline-wise, cloud cover, temperature, etc. Moreover, we present rigorous experiments on various machine learning model to predict correctly the delay of a flight, namely, logistic regression with L2 regularization, Gaussian Naive Bayes, K-Nearest Neighbors, Decision Tree classifier and Random forest model. The accuracy of the Random Forest model is 82% with a delay threshold of 15 minutes of flight delay. The analysis is carried out using dataset from 1987 to 2008, the training is conducted with dataset from 2000 to 2007 and validated prediction result using 2008 data. Moreover, we have got recall 99% in the Random Forest model.
Assessment of approaches for dispersive forces employing graphone as a case study

We have studied two interchange layer systems, (i) free standing partly hydrogenated graphene (graphone), and (ii) graphone on the Nickel (111) surface, to assess various density functional theory based computational schemes incorporating van der Waals forces. The various van der Waals methods have been employed ranging from the semiempirical force-field-like correction of Grimme, through non-local van der Waals density functionals, up to the functionals involving exact exchange and the random phase approximation for the correlation. Generally, all computational schemes lead to a similar qualitative picture of hydrogen layer physisorption and chemisorption to graphene. The largest discrepancies between the approaches emerge for the energetics of the investigated systems. Our studies shed light on the physical mechanisms of graphene hydrogenation both in vacuum and in the proximity of metallic surface. In particular, it is revealed that the adsorption of hydrogen atoms affects the nature of the bonding between graphene and the Ni(111) surface, from the weak to strong semi-covalent bonding. On the other hand, it turns out that the adsorption of hydrogen layer to graphene is stronger in the presence of the metallic surface.
Positive Fuss-Catalan numbers and Simple-minded systems in negative Calabi-Yau categories

We establish a bijection between $d$-simple-minded systems ($d$-SMSs) of $(-d)$-Calabi-Yau cluster category $\cal C_{-d}(kQ)$ and silting objects of $\cal D^{\rm b}(kQ)$ contained in $\cal D^{\le 0}\cap \cal D^{\ge 1-d}$ for Dynkin quiver $Q$ and $d\ge 1$. We show that the number of $d$-SMSs in $\cal C_{-d}(kQ)$ is the positive Fuss-Catalan number $C_{d}^{+}(W)$ of the corresponding Weyl group $W$, by applying this bijection and Buan-Reiten-Thomas' and Zhu's results on Fomin-Reading's generalized cluster complexes. Our results are based on a refined version of silting-$t$-structure correspondence.
Learning differentially reorganizes brain activity and connectivity

Human learning is a complex process in which future behavior is altered via the reorganization of brain activity and connectivity. It remains unknown whether activity and connectivity differentially reorganize during learning, and, if so, how that differential reorganization tracks stages of learning across distinct brain areas. Here, we address this gap in knowledge by measuring brain activity and functional connectivity in a longitudinal fMRI experiment in which healthy adult human participants learn the values of novel objects over the course of four days. An increasing similarity in activity or functional connectivity across subjects during learning reflects reorganization toward a common functional architecture. We assessed the presence of reorganization in activity and connectivity both during value learning and during the resting-state, allowing us to differentiate common elicited processes from intrinsic processes. We found a complex and dynamic reorganization of brain connectivity and activity—as a function of time, space, and performance—that occurs while subjects learn. Spatially localized brain activity reorganizes across the brain to a common functional architecture early in learning, and this reorganization tracks early learning performance. In contrast, spatially distributed connectivity reorganizes across the brain to a common functional architecture as training progresses, and this reorganization tracks later learning performance. Particularly good performance is associated with a sticky connectivity, that persists into the resting state. Broadly, our work uncovers distinct principles of reorganization in activity and connectivity at different phases of value learning, which inform the ongoing study of learning processes more generally.
Testing Goodness of Fit of Conditional Density Models with Kernels

We propose two nonparametric statistical tests of goodness of fit for conditional distributions: given a conditional probability density function $p(y|x)$ and a joint sample, decide whether the sample is drawn from $p(y|x)r_x(x)$ for some density $r_x$. Our tests, formulated with a Stein operator, can be applied to any differentiable conditional density model, and require no knowledge of the normalizing constant. We show that 1) our tests are consistent against any fixed alternative conditional model; 2) the statistics can be estimated easily, requiring no density estimation as an intermediate step; and 3) our second test offers an interpretable test result providing insight on where the conditional model does not fit well in the domain of the covariate. We demonstrate the interpretability of our test on a task of modeling the distribution of New York City's taxi drop-off location given a pick-up point. To our knowledge, our work is the first to propose such conditional goodness-of-fit tests that simultaneously have all these desirable properties.
Convex Shape Representation with Binary Labels for Image Segmentation: Models and Fast Algorithms

We present a novel and effective binary representation for convex shapes. We show the equivalence between the shape convexity and some properties of the associated indicator function. The proposed method has two advantages. Firstly, the representation is based on a simple inequality constraint on the binary function rather than the definition of convex shapes, which allows us to obtain efficient algorithms for various applications with convexity prior. Secondly, this method is independent of the dimension of the concerned shape. In order to show the effectiveness of the proposed representation approach, we incorporate it with a probability based model for object segmentation with convexity prior. Efficient algorithms are given to solve the proposed models using Lagrange multiplier methods and linear approximations. Various experiments are given to show the superiority of the proposed methods.