Miracle-dz/UniFAR-FTUs huggingface dataset:
Trastorno por déficit de atención/hiperactividad: estado de la cuestión y futuras líneas de investigación

(‘inatento’, ‘hiperactivo/impulsivo’, ‘combinado’) arroja mas confusion, especialmente el subtipo inatento, que a menudo no concuerda con el conocimiento general que tienen los profesionales sobre el TDAH. Existe, por tanto, una gran controversia en la misma comunidad cientifica sobre las dimensiones reales de este trastorno, que aun hoy en dia continua sin ninguna prueba diagnostica patognomonica y cuyos criterios definitorios se siguen debatiendo.
A Case Study in Chronic Obstructive Pulmonary Disease ( COPD )

Phase II dose finding studies in clinical drug development are typically conducted to adequately characterize the dose response relationship of a new drug. An important decision is then on the choice of a suitable dose response function to support dose selection for the subsequent Phase III studies. In this paper we compare different approaches for model selection and model averaging using mathematical properties as well as simulations. Accordingly, we review and illustrate asymptotic properties of model selection criteria and investigate their behavior when changing the sample size but keeping the effect size constant. In a large scale simulation study we investigate how the various approaches perform in realistically chosen settings. Finally, the different methods are illustrated with a recently conducted Phase II dosefinding study in patients with chronic obstructive pulmonary disease.
Disinformation Detection: A review of linguistic feature selection and classification models in news veracity assessments

Over the past couple of years, the topic of "fake news" and its influence over people's opinions has become a growing cause for concern. Although the spread of disinformation on the Internet is not a new phenomenon, the widespread use of social media has exacerbated its effects, providing more channels for dissemination and the potential to "go viral. " Nowhere was this more evident than during the 2016 United States Presidential Election. Although the current of disinformation spread via trolls, bots, and hyperpartisan media outlets likely reinforced existing biases rather than sway undecided voters, the effects of this deluge of disinformation are by no means trivial. The consequences range in severity from an overall distrust in news media, to an ill-informed citizenry, and in extreme cases, provocation of violent action. It is clear that human ability to discern lies from truth is flawed at best. As such, greater attention has been given towards applying machine learning approaches to detect deliberately deceptive news articles. This paper looks at the work that has already been done in this area.
Task-Informed Fidelity Management for Speeding Up Robotics Simulation

Simulators are an important tool in robotics that is used to develop robot software and generate synthetic data for machine learning algorithms. Faster simulation can result in better software validation and larger amounts of data. Previous efforts for speeding up simulators have been performed at the level of simulator building blocks, and robot systems. Our key insight, motivating this work, is that further speedups can be obtained at the level of the robot task. Building on the observation that not all parts of a scene need to be simulated in high fidelity at all times, our approach is to toggle between high- and low-fidelity states for scene objects in a task-informed manner. Our contribution is a framework for speeding up robot simulation by exploiting task knowledge. The framework is agnostic to the underlying simulator, and preserves simulation fidelity. As a case study, we consider a complex material-handling task. For the associated simulation, which contains many of the characteristics that make robot simulation slow, we achieve a speedup that can be up to three times faster than high fidelity without compromising on the quality of the results. We also demonstrate that faster simulation allows us to train better policies for performing the task at hand in a short period of time. A video summarizing our contributions can be found at https://youtu.be/PEzypDyqc3o .
Swarm Behaviour Evolution via Rule Sharing and Novelty Search

We present in this paper an exertion of our previous work by increasing the robustness and coverage of the evolution search via hybridisation with a state-of-the-art novelty search and accelerate the individual agent behaviour searches via a novel behaviour-component sharing technique. Via these improvements, we present Swarm Learning Classifier System 2.0 (SLCS2), a behaviour evolving algorithm which is robust to complex environments, and seen to out-perform a human behaviour designer in challenging cases of the data-transfer task in a range of environmental conditions. Additionally, we examine the impact of tailoring the SLCS2 rule generator for specific environmental conditions. We find this leads to over-fitting, as might be expected, and thus conclude that for greatest environment flexibility a general rule generator should be utilised.
SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction

Recent years have witnessed growing interest in machine learning-based models and techniques for low-dose X-ray CT (LDCT) imaging tasks. The methods can typically be categorized into supervised learning methods and unsupervised or model-based learning methods. Supervised learning methods have recently shown success in image restoration tasks. However, they often rely on large training sets. Model-based learning methods such as dictionary or transform learning do not require large or paired training sets and often have good generalization properties, since they learn general properties of CT image sets. Recent works have shown the promising reconstruction performance of methods such as PWLS-ULTRA that rely on clustering the underlying (reconstructed) image patches into a learned union of transforms. In this paper, we propose a new Supervised-UnsuPERvised (SUPER) reconstruction framework for LDCT image reconstruction that combines the benefits of supervised learning methods and (unsupervised) transform learning-based methods such as PWLS-ULTRA that involve highly image-adaptive clustering. The SUPER model consists of several layers, each of which includes a deep network learned in a supervised manner and an unsupervised iterative method that involves image-adaptive components. The SUPER reconstruction algorithms are learned in a greedy manner from training data. The proposed SUPER learning methods dramatically outperform both the constituent supervised learning-based networks and iterative algorithms for LDCT, and use much fewer iterations in the iterative reconstruction modules.
Approximate Bayesian Computation with the Sliced-Wasserstein Distance

Approximate Bayesian Computation (ABC) is a popular method for approximate inference in generative models with intractable but easy-to-sample likelihood. It constructs an approximate posterior distribution by finding parameters for which the simulated data are close to the observations in terms of summary statistics. These statistics are defined beforehand and might induce a loss of information, which has been shown to deteriorate the quality of the approximation. To overcome this problem, Wasserstein-ABC has been recently proposed, and compares the datasets via the Wasserstein distance between their empirical distributions, but does not scale well to the dimension or the number of samples. We propose a new ABC technique, called Sliced-Wasserstein ABC and based on the Sliced-Wasserstein distance, which has better computational and statistical properties. We derive two theoretical results showing the asymptotical consistency of our approach, and we illustrate its advantages on synthetic data and an image denoising task.
L O ] 22 S ep 2 01 1 From X to π Representing the Classical Sequent Calculus in the π-calculus Extended

We study theπ-calculus, enriched with pairing and non-blocking input, and define a notion of type assignment that uses the type c onstructor→. We encode the circuits of the calculus X into this variant ofπ, and show that all reduction (cut-elimination) and assignable types are pres erv d. SinceX enjoys the Curry-Howard isomorphism for Gentzen’s calculus LK , this implies that all proofs inLK have a representation in π.
Asynchronous Methods for Model-Based Reinforcement Learning

Significant progress has been made in the area of model-based reinforcement learning. State-of-the-art algorithms are now able to match the asymptotic performance of model-free methods while being significantly more data efficient. However, this success has come at a price: state-of-the-art model-based methods require significant computation interleaved with data collection, resulting in run times that take days, even if the amount of agent interaction might be just hours or even minutes. When considering the goal of learning in real-time on real robots, this means these state-of-the-art model-based algorithms still remain impractical. In this work, we propose an asynchronous framework for model-based reinforcement learning methods that brings down the run time of these algorithms to be just the data collection time. We evaluate our asynchronous framework on a range of standard MuJoCo benchmarks. We also evaluate our asynchronous framework on three real-world robotic manipulation tasks. We show how asynchronous learning not only speeds up learning w.r. t wall-clock time through parallelization, but also further reduces the sample complexity of model-based approaches by means of improving the exploration and by means of effectively avoiding the policy overfitting to the deficiencies of learned dynamics models.
Teaching Elementary Physical Education: A Handbook for the Classroom Teacher

Designed and written especially for teachers of physical education, this book, and its three lesson plan books, together provide the material an elementary teacher needs to design a curriculum for students' individual needs. The book provides both content and guidelines for instructional methodology, and can be used as a resource to supplement the lesson plan. Every area of the curriculum is covered, starting with an introduction to the teaching of physical education, and the need for students to experience physical activity every day. The book focuses on methods of teaching motor skills to children, implementing a sound physical education lesson, using discipline strategies, and dealing effectively with children with disabilities. It also covers legal liabilities, offers advice on caring properly for students, and demonstrates how to introduce physical activity and fitness to children in a positive way.
Unraveling the Effect of Spatial Resolution and Scan Acceleration on 3D Image-Based Navigators for Respiratory Motion Tracking in Coronary MR Angiography.

Purpose: To study the accuracy of motion information extracted from beat-to-beat 3D image-based navigators (3D iNAVs) collected using a variable-density cones trajectory with different combinations of spatial resolutions and scan acceleration factors. Methods: Fully sampled, breath-held 4.4 mm 3D iNAV datasets for six respiratory phases are acquired in a volunteer. Ground truth translational and nonrigid motion information is derived from these datasets. Subsequently, the motion estimates from synthesized undersampled 3D iNAVs with isotropic spatial resolutions of 4.4 mm (acceleration factor = 10.9), 5.4 mm (acceleration factor = 7.2), 6.4 mm (acceleration factor = 4.2), and 7.8 mm (acceleration factor = 2.9) are assessed against the ground truth information. The undersampled 3D iNAV configuration with the highest accuracy motion estimates in simulation is then compared with the originally proposed 4.4 mm undersampled 3D iNAV in six volunteer studies. Results: The simulations indicate that for navigators beyond certain scan acceleration factors, the accuracy of motion estimates is compromised due to errors from residual aliasing and blurring/smoothening effects following compressed sensing reconstruction. The 6.4 mm 3D iNAV achieves an acceptable spatial resolution with a small acceleration factor, resulting in the highest accuracy motion information among all assessed undersampled 3D iNAVs. Reader scores for six volunteer studies demonstrate superior coronary vessel sharpness when applying an autofocusing nonrigid correction technique using the 6.4 mm 3D iNAVs in place of 4.4 mm 3D iNAVs. Conclusion: Undersampled 6.4 mm 3D iNAVs enable motion tracking with improved accuracy relative to previously proposed undersampled 4.4 mm 3D iNAVs.
LUTNet: Learning FPGA Configurations for Highly Efficient Neural Network Inference

Research has shown that deep neural networks contain significant redundancy, and thus that high classification accuracy can be achieved even when weights and activations are quantized down to binary values. Network binarization on FPGAs greatly increases area efficiency by replacing resource-hungry multipliers with lightweight XNOR gates. However, an FPGA's fundamental building block, the <inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq1-2978817.gif"/></alternatives></inline-formula>-LUT, is capable of implementing far more than an XNOR: it can perform any <inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq2-2978817.gif"/></alternatives></inline-formula>-input Boolean operation. Inspired by this observation, we propose LUTNet, an end-to-end hardware-software framework for the construction of area-efficient FPGA-based neural network accelerators using the native LUTs as inference operators. We describe the realization of both unrolled and tiled LUTNet architectures, with the latter facilitating smaller, less power-hungry deployment over the former while sacrificing area and energy efficiency along with throughput. For both varieties, we demonstrate that the exploitation of LUT flexibility allows for far heavier pruning than possible in prior works, resulting in significant area savings while achieving comparable accuracy. Against the state-of-the-art binarized neural network implementation, we achieve up to twice the area efficiency for several standard network models when inferencing popular datasets. We also demonstrate that even greater energy efficiency improvements are obtainable.
The utility of surface parasternal intercostal electromyography in the assessment of paediatric respiratory disease

4 TABLE OF CONTENTS . 6 INDEX OF TABLES . 12 INDEX OF TABLES . 12 INDEX OF FIGURES . 14 LIST OF ABBREVIATIONS . 17 1. CHAPTER ONE: INTRODUCTION . 20 1.1 RESPIRATORY DISEASE IN CHILDHOOD . 20 1.1.1 CHILDHOOD ASTHMA . 21 1.1.2 PRE-SCHOOL WHEEZE . 22 1.1.3 CYSTIC FIBROSIS . 23 1.1. 4 PATHOPHYSIOLOGICAL CHANGES IN OBSTRUCTIVE LUNG DISEASE . 24 1.2 STRUCTURAL ANATOMY OF THE RESPIRATORY SYSTEM . 25 1.2.1 DEVELOPMENT AND MATURATION OF THE RESPIRATORY SYSTEM. 26 1.2.2 THE RESPIRATORY MUSCLES . 28 1.2.2.1 Respiratory muscles: the diaphragm . 28 1.2.2. 2 Respiratory muscles: intercostal muscles . 30 1.2.2.3 Respiratory muscles: the parasternal intercostal muscles . 31 1.2.2.4 Respiratory muscles: sternocleidomastoid . 33 1.2.2.5 Respiratory muscles: the scalenes . 35 1.2.2.6 Respiratory muscles: the abdominal muscles . 36 1.2.3 RESPIRATORY MUSCLE STRUCTURE AND FUNCTION . 38 1.2.3.1 Physiology of muscle contraction . 39 1.2.3.2 Factors affecting muscle contraction . 40 1.3 ASSESSMENT OF RESPIRATORY FUNCTION . 42 1.3.1 SPIROMETRY . 43
SN Ia Explosions from Hybrid Carbon–Oxygen–Neon White Dwarf Progenitors that Have Mixed during Cooling

The creation of “hybrid” white dwarfs, made of a C–O core within an O–Ne shell has been proposed, and studies indicate that ignition in the C-rich central region makes these viable progenitors for thermonuclear (type Ia) supernovae. Recent work found that the C–O core is mixed with the surrounding O–Ne as the white dwarf cools prior to accretion, which results in lower central C fractions in the massive progenitor than previously assumed. To further investigate the efficacy of hybrid white dwarfs as progenitors of thermonuclear supernovae, we performed simulations of thermonuclear supernovae from a new series of hybrid progenitors that include the effects of mixing during cooling. The progenitor white dwarf model was constructed with the one-dimensional stellar evolution code Modules for Experiments in Stellar Astrophysics (MESA) and represented a star evolved through the phase of unstable interior mixing followed by accretion until it reached conditions for the ignition of carbon burning. This MESA model was then mapped to a two-dimensional initial condition for explosions simulated with FLASH. For comparison, similar simulations were performed for a traditional C–O progenitor white dwarf. By comparing the yields of the explosions, we find that, as with earlier studies, the lower C abundance in the hybrid progenitor compared to the traditional C–O progenitor leads to a lower average yield of . Although the unmixed hybrid white dwarf showed a similar decrement also in total iron-group yield, the mixed case does not and produces a smaller fraction of iron-group elements in the form of . We attribute this to the higher central density required for ignition and the location, center or off-center, of deflagration ignition.
Chemotherapy for Large Cell Neuroendocrine Carcinoma of the Lung: Should It Be Treated with the Same Strategy as Small Cell Lung Carcinoma?

Lung cancer is leading cause of cancer death in many advanced countries and one of the challenging malignancies because of poor prognosis. Lung cancer is traditionally divided into two major categories, so called small cell lung carcinoma (SCLC) and non-small cell lung carcinoma (NSCLC) because of distinctive prognostic and treatment strategies between them. On the other hand, there is a spectrum of tumors called pulmonary neuroendocrine (NE) tumors that are thought to originate from neuroendocrine cells in the pulmonary and bronchial epithelium. Until recently, pulmonary NE tumors were classified into three categories, i.e., typical carcinoid (TC), atypical carcinoid (AC), and SCLC. Large cell neuroendocrine carcinoma (LCNEC) of the lung was officially identified by Travis et al. in 1991 as a fourth category, a unique higher grade NSCLC existing between TC and SCLC (Travis et al., 1991). It is often difficult to diagnose LCNEC with small biopsy specimens because accurate diagnosis needs morphological and immunohistochemical information. Although earlier reports mainly focused on prognosis after surgical procedures, several recent studies reported on the efficacy of chemotherapy for advanced LCNEC. Because of the limited numbers of cases (in surgical series, LCNEC represents ~3% of lung cancers), large scale prospective studies have not been reported. Standard treatment for LCNEC, especially if advanced, is not established although LCNEC is included in NSCLC in the treatment algorithm in many guidelines. However, accumulating data including recent retrospective studies have suggested that there is similarity in the prognosis and treatment response between LCNEC and SCLC. In this review, we will focus on the treatment of advanced LCNEC for the better selection of chemotherapeutic regimens for the patients with this relatively rare lung cancer.
Real-Time, Environmentally-Robust 3D LiDAR Localization

Localization, or position fixing, is an important problem in robotics research. In this paper, we propose a novel approach for long-term localization in a changing environment using 3D LiDAR. We first create the map of a real environment using GPS and LiDAR. Then, we divide the map into several small parts as the targets for cloud registration, which can not only improve the robustness but also reduce the registration time. We proposed a localization method called PointLocalization. PointLocalization allows us to fuse different kinds of odometers, which can optimize the accuracy and frequency of localization results. We evaluate our algorithm on an unmanned ground vehicle (UGV) using LiDAR and a wheel encoder, and obtain the localization results at more than 20 Hz after fusion. The algorithm can also localize the UGV in a 180-degree field of view (FOV). Using an outdated map captured six months ago, this algorithm shows great robustness, and the test results show that it can achieve an accuracy of 10 cm. PointLocalization has been tested for a period of more than six months in a crowded factory and has operated successfully over a distance of more than 2000 km.
Ensemble asteroseismology of pulsating B-type stars in NGC 6910

Asteroseismology offers the possibility of probing stellar interiors and testing evolutionary and seismic models. Precise photometry and spectroscopy obtained during multi-site campaigns on young open clusters allows discovering rich samples of pulsating stars and using them in a simultaneous seismic modelling called ensemble asteroseismology. The aim of this study is to obtain the age of the open cluster NGC 6910 by means of ensemble asteroseismology of the early-type pulsating members, to derive their stellar parameters, and to classify the excited modes. We used time-series analysis, performed photometric and spectroscopic mode identification, and calculated grids of evolutionary and seismic models to apply the procedure of ensemble asteroseismology for nine pulsating members of NGC 6910. With two iterations of the procedure of ensemble asteroseismology applied to nine pulsating stars we derived an age of 10.6−0.8+0.9 Myr for NGC 6910. We also identified the degree l for 8 of 37 modes detected in these stars and classified all modes in terms of p, g, and mixed-mode pulsations. Of the nine pulsating stars examined in the paper, eight are β Cep stars, including three that are hybrid β Cep and slowly pulsating B-type (SPB) pulsators, and one is an SPB star. Interestingly, the least massive β Cep star, NGC 6910-38, has a mass of about 5.6 M⊙. The present theory does not predict unstable p modes in B-type stars with such a low mass. The g modes with relatively high frequencies (> 3.5 d−1), observed in three members of the cluster, are also stable according to seismic modelling. Both findings pose a challenge for theoretical calculations and prompt a revision of the opacities. The procedure of ensemble asteroseismology was found to be successful for NGC 6910 and χ Per on the basis of pulsating B-type stars and can therefore be applied to other young open clusters that are rich in such stars.
Introduction to local certification

A distributed graph algorithm is basically an algorithm where every node of a graph can look at its neighborhood at some distance in the graph and chose its output. As distributed environment are subject to faults, an important issue is to be able to check that the output is correct, or in general that the network is in proper configuration with respect to some predicate. One would like this checking to be very local, to avoid using too much resources. Unfortunately most predicates cannot be checked this way, and that is where certification comes into play. Local certification (also known as proof-labeling schemes, locally checkable proofs or distributed verification) consists in assigning labels to the nodes, that certify that the configuration is correct. There are several point of view on this topic: it can be seen as a part of self-stabilizing algorithms, as labeling problem, or as a non-deterministic distributed decision. This paper is an introduction to the domain of local certification, giving an overview of the history, the techniques and the current research directions.