Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinants, measurement and promotion of physical activity in 10-14 year-old Bedfordshire children: a multidisciplinary approach

Regular moderate-vigorous physical activity (MVPA) is associated with significant physiological and psychological health benefits (Department of Health, DoH, 2004). However, many children are not undertaking recommended levels of physical activity (PA) (DoH, 2009). This research examined relationships between physiological health parameters, psychological determinants and PA levels in 10-14 year old schoolchildren (N = 249) and assessed the influence of three school-based PA interventions on these constructs in the context of the Health And Physical activity Promotion in Youth (HAPPY) study. Study 1 revealed that sedentary behaviours, moderate PA (MPA), vigorous PA (VPA) and MVPA levels were higher on weekdays than weekend days (p < .001). However, schoolchildren’s PA is often difficult to measure accurately. The selfreport measure utilised in study 2 underestimated total MVPA versus accelerometry for both sexes on weekdays and girls on weekend days (p < .01). However, study 3 highlighted a lack of agreement between two RT3® triaxial accelerometer cut-offs for all activity categories. The importance of VPA for promoting health was highlighted in the updated PA guidelines (DoH, 2011). Study 4 reported that cardiorespiratory fitness (CRF) was positively associated with VPA but not MPA (β = .27, p < .01) and inversely correlated with measures of body composition (% body fat; body mass index; waist circumference) (r = -.74, r = -.60, r = -.39, p < .001).
Astrocyte-Neuron Metabolic Pathways

Mounting evidence from in vitro experiments indicates that lactate is an efficient energy substrate for neurons and that it may significantly contribute to maintain synaptic transmission, particularly during periods of intense activity. Since lactate does not cross the blood-brain barrier easily, blood-borne lactate cannot be a significant source. In vitro studies by several laboratories indicate that astrocytes release large amounts of lactate. In 1994, we proposed a mechanism whereby lactate could be produced by astrocytes in an activitydependent, glutamate-mediated manner. Over the last 2 years we have obtained further evidence supporting the notion that a transfer of lactate from astrocytes to neurons might indeed take place. In this article, we first review data showing the presence of mRNA encoding for two monocarboxylate transporters, MCT1 and MCT2, in the adult mouse brain. Second, by using monoclonal antibodies selectively directed against the two distinct lactate dehydrogenase isoforms, LDH1 and LDH5, a specific cellular distribution between neurons and astrocytes is revealed which suggests that a population of astrocytes is a lactate ‘source’ while neurons may be a lactate ‘sink’. Third, we provide biochemical evidence that lactate is interchangeable with glucose to support oxidative metabolism in cortical neurons. This set of data is consistent with the existence of an activity-dependent astrocyte-neuron lactate shuttle for the supply of energy substrates to neurons. OOOOOOOOOOOOOOOOOOOOOO Received: February 11, 1998 Accepted: March 4, 1998 Luc Pellerin, PhD Institut de Physiologie Rue du Bugnon 7 CH-1005 Lausanne (Switzerland) Tel. +41 21 692 5547, Fax +41 21 692 5595, E-Mail luc.pellerin@iphysiol.unil. ch ABC Fax + 41 61 306 12 34 E-Mail karger@karger.ch www.karger. com © 1998 S. Karger AG, Basel 0378–5866/98/0205–0291$15.00/0 Accessible online at: http://BioMedNet.com/karger Introduction A fundamental principle in the physiology of brain energy metabolism is that neuronal activity is tightly coupled to blood flow and energy metabolism. Since in normal conditions, glucose is the exclusive energy substrate for the brain, from the metabolic point of view, this coupling implies that spatially localized and temporally restricted increases over basal glucose utilization occur in register with neuronal activity in brain areas subserving specific modalities. In order to elucidate the physiological bases of this coupling it is crucial to better understand the cellular and molecular bases of brain energy metabolism and in particular the nature of the signals produced by neuronal activity which lead to the localized increases in glucose utilization.
The influence of participation and regularity of participation in the breast cancer screening program on tumour characteristics

Background: To improve the prognosis for women diagnosed with breast cancer, a national screening program started in 1989. However, the effectiveness of screening is unclear. This is especially the case for the elderly women that are invited to participate in screening. Objective: To determine whether participation and regularity of participation in the screening program influences the tumour characteristics in women aged between 70 and 75 years and women aged 75 years and older, diagnosed with breast cancer between 1999 and 2012 in the northern region of the Netherlands. Method: Data from the Netherlands Cancer Registry and Bevolkingsonderzoek Noord were matched. Using Pearson’s Chi-square tests, tumour characteristics were compared between women who have participated in the screening program and who have never participated, and between women who have participated on a regular and irregular basis. For both analyses a distinction was made between women aged between 70 and 75 years and women aged older than 75 years. Conclusion: Participation in breast cancer screening by elderly women appears to influence tumour characteristics in a positive way compared to no participation. However, it does not seem to have any influence whether these women participate regularly or irregularly.
The Molecular Mechanism of Cholestatic Pruritus

Pruritus is a frequent symptom in patients with cholestatic liver diseases. Pruritus can be excruciating and, in rare cases, become a primary indication for liver transplantation. The molecular mechanism of itch signal transduction is largely unclear. It was our hypothesis that compounds which accumulate in the circulation during cholestasis act as direct or indirect pruritogens by affecting signaling in itch fibers. To test this, we screened plasma samples of a large group of patients with various cholestatic conditions for their capacity to activate neuroblastoma cells. Quite strikingly, we found that samples from itchy cholestatic patients caused a significantly higher activation than samples from non-itchy cholestatic patients and healthy controls. Purification revealed lysophosphatidic acid (LPA) as the active compound. LPA is a very potent signaling lipid that can activate cells through various LPA receptors. Subsequently, we could demonstrate that cholestatic patients with pruritus have highly elevated levels of serum autotaxin (ATX), the enzyme that converts lysophosphatidylcholine into LPA. This is a striking finding as ATX has never been connected to itch perception thus far. We have also shown that LPA, when injected intradermally, causes itching in mice. On the basis of our results, we hypothesize that during cholestasis, expression of ATX is induced and gives rise to increased local formation of LPA near unmyelinated nerve endings of itch fibers. LPA then activates these neurons through one of the LPA receptors, which in turn potentiates action potentials along itch fibers.
Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers

In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more artificially intelligent. Deep learning is remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, health, medicine, sports, robotics, drones, etc. In deep learning, Convolutional Neural Network (CNN) is at the center of spectacular advances that mixes Artificial Neural Network (ANN) and up to date deep learning strategies. It has been used broadly in pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document analysis, scene, and handwritten digit recognition. The goal of this paper is to observe the variation of accuracies of CNN to classify handwritten digits using various numbers of hidden layers and epochs and to make the comparison between the accuracies. For this performance evaluation of CNN, we performed our experiment using Modified National Institute of Standards and Technology (MNIST) dataset. Further, the network is trained using stochastic gradient descent and the back-propagation algorithm.
Neutron diffraction and magnetic properties of 
Co2Cr1−xTixAl
 Heusler alloys

We report the structural, magnetic, and magnetocaloric properties of Co$_2$Cr$_{1-x}$Ti$_x$Al ($x=$ 0--0.5) Heusler alloys for spintronic and magnetic refrigerator applications. Room temperature X-ray diffraction and neutron diffraction patterns along with Rietveld refinements confirm that the samples are of single phase and possess a cubic structure. Interestingly, magnetic susceptibly measurements indicate a second order phase transition from paramagnetic to ferromagnetic where the Curie temperature (T$_{\rm C}$) of Co$_2$CrAl increases from 330~K to 445~K with Ti substitution. Neutron powder diffraction data of the $x=$ 0 sample across the magnetic phase transition taken in a large temperature range confirm the structural stability and exclude the possibility of antiferromagnetic ordering. The saturation magnetization of the $x=$ 0 sample is found to be 8000~emu/mol (1.45~$\mu_{\rm B}$/{\it f.u.}) at 5~K, which is in good agreement with the value (1.35$\pm$0.05~$\mu_{\rm B}$/{\it f.u.}) obtained from the Rietveld analysis of the neutron powder diffraction pattern measured at temperature of 4~K. By analysing the temperature dependence of the neutron data of the $x=$ 0 sample, we find that the change in the intensity of the most intense Bragg peak (220) is consistent with the magnetization behavior with temperature. Furthermore, an enhancement of change in the magnetic entropy and relative cooling power values has been observed for the $x=$ 0.25 sample. Interestingly, the critical behavior analysis across the second order magnetic phase transition and extracted exponents ($\beta\approx$ 0.496, $\gamma\approx$ 1.348, and $\delta\approx$ 3.71 for the $x=$ 0.25 sample) suggest the presence of long-range ordering, which deviates towards 3D Heisenberg type interactions above T$_{\rm C}$, consistent with the interaction range value $\sigma$.
Bias In, Bias Out? Evaluating the Folk Wisdom

We evaluate the folk wisdom that algorithmic decision rules trained on data produced by biased human decision-makers necessarily reflect this bias. We consider a setting where training labels are only generated if a biased decision-maker takes a particular action, and so "biased" training data arise due to discriminatory selection into the training data. In our baseline model, the more biased the decision-maker is against a group, the more the algorithmic decision rule favors that group. We refer to this phenomenon as "bias reversal. " We then clarify the conditions that give rise to bias reversal. Whether a prediction algorithm reverses or inherits bias depends critically on how the decision-maker affects the training data as well as the label used in training. We illustrate our main theoretical results in a simulation study applied to the New York City Stop, Question and Frisk dataset.
Asymptotic Analysis of a Coupled System of Nonlocal Equations with Oscillatory Coefficients

In this paper we study the asymptotic behavior of solutions to systems of strongly coupled integral equations with oscillatory coefficients. The system of equations is motivated by a peridynamic model of the deformation of heterogeneous media that additionally accounts for short-range forces. We consider the vanishing nonlocality limit on the same length scale as the heterogeneity and show that the system's effective behavior is characterized by a coupled system of local equations that are elliptic in the sense of Legendre-Hadamard. This effective system is characterized by a fourth-order tensor that shares properties with Cauchy elasticity tensors that appear in the classical equilibrium equations for linearized elasticity.
An efficient numerical method for a long-term simulation of heat and mass transfer: the case of an insulated rammed earth wall

Innovative numerical scheme studied in this work enables to overcome two main limitations of Building Performance Simulation (BPS) programs as high computational cost and the choice of a very fine numerical grid. The method, called Super-Time-Stepping (STS), is novel to the state-of-the-art of building simulations, but has already proved to be sufficiently efficient in recent studies from anisotropic heat conduction in astrophysics (Meyer et al. 2014). The given research is focused on employment of this adopted numerical method to model drying of a rammed earth wall with an additional insulation layer. The results show considerable advantage of the STS method compared to standard Euler explicit scheme. It is possible to choose at least 100 times bigger time-steps to maintain high accuracy and to cut computational cost by more than 92% in the same time.
Red and dead CANDELS: massive passive galaxies at the dawn of the Universe

We search the five CANDELS fields (COSMOS, EGS, GOODS-North, GOODS-South, and UDS) for passively evolving a.k. a. ‘red and dead’ massive galaxies in the first 2 Gyr after the big bang, integrating and updating the work on GOODS-South presented in a previous paper. We perform SED-fitting on photometric data, with top-hat star-formation histories to model an early and abrupt quenching, and using a probabilistic approach to select only robust candidates. Using libraries without (with) spectral lines emission, starting from a total of more than 20 000 z > 3 sources we end up with 102 (40) candidates, including one at z = 6.7. This implies a minimal number density of 1.73 ± 0.17 × 10−5 (6.69 ± 1.08 × 10−6) Mpc−3 for 3 < z < 5; applying a correction factor to account for incompleteness yields 2.30 ± 0.20 × 10−5. We compare these values with those from five recent hydrodynamical cosmological simulations, finding a reasonable agreement at z < 4; tensions arise at earlier epochs. Finally, we use the star-formation histories from the best-fitting models to estimate the contribution of the high-redshift passive galaxies to the global star formation rate density during their phase of activity, finding that they account for ∼5–10 per cent of the total star formation at 3 < z < 8, despite being only $\sim 0.5{{\ \rm per\ cent}}$ of the total in number. The resulting picture is that early and strong star formation activity, building massive galaxies on short time-scales and followed by a quick and abrupt quenching, is a rare but crucial phenomenon in the early Universe: the evolution of the cosmos must be heavily influenced by the short but powerful activity of these pristine monsters.
Free Quantum Groups and Related Topics

The unitary group U_N has a free analogue U_N^+, and the study of the closed subgroups G\subset U_N^+ is a problem of general interest. We review here the general theory of U_N^+ and its subgroups, with all the needed preliminaries included. We discuss as well a number of more advanced topics, selected for their beauty, and potential importance.
Distributed Machine Learning on Mobile Devices: A Survey

In recent years, mobile devices have gained increasingly development with stronger computation capability and larger storage. Some of the computation-intensive machine learning and deep learning tasks can now be run on mobile devices. To take advantage of the resources available on mobile devices and preserve users' privacy, the idea of mobile distributed machine learning is proposed. It uses local hardware resources and local data to solve machine learning sub-problems on mobile devices, and only uploads computation results instead of original data to contribute to the optimization of the global model. This architecture can not only relieve computation and storage burden on servers, but also protect the users' sensitive information. Another benefit is the bandwidth reduction, as various kinds of local data can now participate in the training process without being uploaded to the server. In this paper, we provide a comprehensive survey on recent studies of mobile distributed machine learning. We survey a number of widely-used mobile distributed machine learning methods. We also present an in-depth discussion on the challenges and future directions in this area. We believe that this survey can demonstrate a clear overview of mobile distributed machine learning and provide guidelines on applying mobile distributed machine learning to real applications.
CAMAL: Context-Aware Multi-scale Attention framework for Lightweight Visual Place Recognition

In the last few years, Deep Convolutional Neural Networks (D-CNNs) have shown state-of-the-art performances for Visual Place Recognition (VPR). Their prestigious generalization power has played a vital role in identifying persistent image regions under changing conditions and viewpoints. However, against the computation intensive D-CNNs based VPR algorithms, lightweight VPR techniques are preferred for resource-constraints mobile robots. This paper presents a lightweight CNN-based VPR technique that captures multi-layer context-aware attentions robust under changing environment and viewpoints. Evaluation of challenging benchmark datasets reveals better performance at low memory and resources utilization over state-of-the-art contemporary VPR methodologies.
IR-NAS: Neural Architecture Search for Image Restoration

Recently, neural architecture search (NAS) methods have attracted much attention and outperformed manually designed architectures on a few high-level vision tasks. In this paper, we propose IR-NAS, an effort towards employing NAS to automatically design effective neural network architectures for low-level image restoration tasks, and apply to two such tasks: image denoising and image de-raining. IR-NAS adopts an flexible hierarchical search space, including inner cell structures and outer layer widths. The proposed IR-NAS is both memory and computationally efficient, which takes only 6 hours for searching using a single GPU and saves memory by sharing cell weights across different feature levels. We evaluate the effectiveness of our proposed IR-NAS on three different datasets, including an additive white Gaussian noise dataset BSD500, a realistic noise dataset SIM1800 and a challenging de-raining dataset Rain800. Results show that the architectures found by IR-NAS have fewer parameters and enjoy a faster inference speed, while achieving highly competitive performance compared with state-of-the-art methods. We also present analysis on the architectures found by NAS.
Optimizing Through Learned Errors for Accurate Sports Field Registration

We propose an optimization-based framework to register sports field templates onto broadcast videos. For accurate registration we go beyond the prevalent feed-forward paradigm. Instead, we propose to train a deep network that regresses the registration error, and then register images by finding the registration parameters that minimize the regressed error. We demonstrate the effectiveness of our method by applying it to real-world sports broadcast videos, outperforming the state of the art. We further apply our method on a synthetic toy example and demonstrate that our method brings significant gains even when the problem is simplified and unlimited training data is available.1
What matters, context or sentiment?: Analysing the influence of news in U.S. elections using Natural Language Processing

A key question in the analysis of collective social behaviour is related to know if and how mass media can influence public opinion. In this paper, we explore quantitatively the relation between a specific manifestation of public opinion and the intention to vote a given candidate, with the information related to the candidates in the Mass Media. We analyse the political news articles related to the US presidential campaign during the year 2016, using techniques of natural language processing. We applied recursive deep models for semantic composition over sentiment treebank to be able to detect the sentiment of sentences, and topic detection methods in order to characterise how media outlets get involved in the election coverage. The results of the analysis were compared to the outcomes of political polls in order to know which of these two aspects of the information have more influence and if there exists any causal relationship between them. Our results suggest that the sentiment content of the news by itself is not enough to understand the differences in the polls between both candidates (validating the popular quote"There is no such thing as bad publicity") but the topic coverage distribution does and the sentiment content discriminated by topic is consistent with these results. In particular, we found that one topic (news related to Clinton's email scandal) is the more relevant in this analysis because it correlates negatively and shows a significant causal relation with the difference between Clinton and Trump in the polls and also explain the negative values of sentiment analysis in Fox News coverage.
Worst-case complexity bounds of directional direct-search methods for multiobjective derivative-free optimization.

Direct Multisearch (DMS) is a well-established class of algorithms, suited for multiobjective derivative-free optimization. In this work, we analyze the worst-case complexity of this class of methods in its most general formulation for unconstrained optimization. Considering nonconvex smooth functions, we show that the DMS algorithm takes at most $\mathcal{O}(|L(\epsilon)|\epsilon^{-2m})$ iterations for driving a criticality measure below $\epsilon>0$ (here $m$ represents the number of components of the objective function and $|L(\epsilon)|$ the cardinality of the approximation to the Pareto front). We then focus on a particular instance of DMS, which considers a more strict criterion for accepting new nondominated points. In this case, we can establish a better worst-case complexity bound of $\mathcal{O}(\epsilon^{-2})$ for driving the same criticality measure below $\epsilon>0$.
Impact of Electron–Phonon Scattering on Optical Properties of CH3NH3PbI3 Hybrid Perovskite Material

We numerically investigate the impact of electron–phonon scattering on the optical properties of a perovskite material (CH3NH3PbI3). Using nonequilibrium Green function formalism, we calculate the local density of states for several values of the electron–phonon scattering strength. We report an Urbach-like penetration of the density of states in the band gap due to scattering. A physical analytical model allows us to attribute this behavior to a multiphonon process. Values of Urbach energy up to 9.5 meV are obtained, meaning that scattering contribution to the total experimental Urbach energy of 15 meV is quite important. We also show that the open-circuit voltage Voc, for a solar cell assuming such a material as an absorber, depends on the scattering strength. Voc loss increases with the scattering strength, up to 41 mV. Finally, an unexpected result of this study, is that the impact of electron–phonon scattering on Urbach tail and Voc increases with the phonon energy. This low value in perovskite (8 meV) is therefore an advantage for photovoltaic applications.