The Partial Information Decomposition of Generative Neural Network Models

In this work we study the distributed representations learnt by generative neural network models. In particular, we investigate the properties of redundant and synergistic information that groups of hidden neurons contain about the target variable. To this end, we use an emerging branch of information theory called partial information decomposition (PID) and track the informational properties of the neurons through training. We find two differentiated phases during the training process: a first short phase in which the neurons learn redundant information about the target, and a second phase in which neurons start specialising and each of them learns unique information about the target. We also find that in smaller networks individual neurons learn more specific information about certain features of the input, suggesting that learning pressure can encourage disentangled representations

Linear advancing actions followed by deceleration and turn are the most common movements preceding goals in male professional soccer

Data were collected through time-motion analysis from soccer players participating in the English Premier League using a modified version of the Bloomfield Movement Classification with differences analysed through chi-square. The most common individual movement preceding a goal was a linear advancing motion (32.4 ± 1%), followed by deceleration (20.2 ± 0.9%) and turn (19.8 ± 0.9%). Actions also involved were change in angle run (cut and arc run), ball blocking, lateral advancing motion (crossover and shuffle) and jumps. Although players followed similar trends there were dissimilarities based on the role, with attackers (assistant and scorer) performing more linear actions, subtle turns and cuts and defenders (defender of assistant and defender of scorer) more ball blockings, lateral movements and arc runs. In 82.9 ± 1.5% of player involvements there was at least 1 high intensity (HI) movement with assistant showing the lowest percentage and defender of scorer the highest. This study shows the multidirectional nature and context specificity of soccer during goal scoring situations, with linear actions such as sprints being the most common movements, followed by decelerations and turns. Moreover, it highlights the recurrent application of these at HI, and so, training strategies should prioritize the development of player’s explosiveness

Regeneration performance of metal–organic frameworks

Notions about the reception of print fiction as well as new media texts have a strong tendency to fall back upon the dichotomy between naïve and critical reading. It is presupposed that reception will be characterized by either the one or the other. We will try to critique this dichotomy on the basis of the hypothesis that media cultural change brings with it new and hybrid textual forms, ways of reading, and patterns of reception which not lend themselves to description in simple terms of naïve or critical. We make a case for the necessity of transgressing the dominant assumptions of transactional reception theory within literary studies and instead move in the direction of what we call creative reading and media-reflexivity

Benzophénanthridines isolées de Zanthoxylum psammophilum

Dix-huit composés dont deux nouvelles benzophénanthridines la 8-méthoxy-7,8-dihydrofagaridine 1 et la 8-acétonyl-7,8-dihydrofagaridine 2 ont été isolés des racines de Zanthoxylum psammophilum (Rutaceae). La structure de ces composés (1-18) a été déterminée principalement par l’utilisation de la spectroscopie de RMN 1D (1H et 13C) et 2D (COSY, NOESY, HSQC, HMBC). Le composé 1 a montré une activité antimicrobienne sur S. Aureus.Mots clés: Rutaceae, alcaloïdes, 8-méthoxy-7,8-dihydrofagaridine, 8- acétonyl-7,8-dihydrofagaridin

THE HIGH CADENCE TRANSIENT SURVEY (HITS). I. SURVEY DESIGN AND SUPERNOVA SHOCK BREAKOUT CONSTRAINTS

Indexación: Web of Science; Scopus.We present the first results of the High Cadence Transient Survey (HiTS), a survey for which the objective is to detect and follow-up optical transients with characteristic timescales from hours to days, especially the earliest hours of supernova (SN) explosions. HiTS uses the Dark Energy Camera and a custom pipeline for image subtraction, candidate filtering and candidate visualization, which runs in real-time to be able to react rapidly to the new transients. We discuss the survey design, the technical challenges associated with the real-time analysis of these large volumes of data and our first results. In our 2013, 2014, and 2015 campaigns, we detected more than 120 young SN candidates, but we did not find a clear signature from the short-lived SN shock breakouts (SBOs) originating after the core collapse of red supergiant stars, which was the initial science aim of this survey. Using the empirical distribution of limiting magnitudes from our observational campaigns, we measured the expected recovery fraction of randomly injected SN light curves, which included SBO optical peaks produced with models from Tominaga et al. (2011) and Nakar & Sari (2010). From this analysis, we cannot rule out the models from Tominaga et al. (2011) under any reasonable distributions of progenitor masses, but we can marginally rule out the brighter and longer-lived SBO models from Nakar & Sari (2010) under our best-guess distribution of progenitor masses. Finally, we highlight the implications of this work for future massive data sets produced by astronomical observatories, such as LSST.http://iopscience.iop.org/article/10.3847/0004-637X/832/2/155/meta;jsessionid=76BDFFFE378003616F6DBA56A9225673.c4.iopscience.cld.iop.or

A Neural Approach to Ordinal Regression for the Preventive Assessment of Developmental Dyslexia

Developmental Dyslexia (DD) is a learning disability related to the acquisition of reading skills that affects about 5% of the population. DD can have an enormous impact on the intellectual and personal development of affected children, so early detection is key to implementing preventive strategies for teaching language. Research has shown that there may be biological underpinnings to DD that affect phoneme processing, and hence these symptoms may be identifiable before reading ability is acquired, allowing for early intervention. In this paper we propose a new methodology to assess the risk of DD before students learn to read. For this purpose, we propose a mixed neural model that calculates risk levels of dyslexia from tests that can be completed at the age of 5 years. Our method first trains an auto-encoder, and then combines the trained encoder with an optimized ordinal regression neural network devised to ensure consistency of predictions. Our experiments show that the system is able to detect unaffected subjects two years before it can assess the risk of DD based mainly on phonological processing, giving a specificity of 0.969 and a correct rate of more than 0.92. In addition, the trained encoder can be used to transform test results into an interpretable subject spatial distribution that facilitates risk assessment and validates methodology.Comment: 12 pages, 4 figure

Obesity and contraceptive use: impact on cardiovascular risk.

Obesity and oestrogen containing contraceptive products are well-known independent cardiovascular risk factors. However, a significant number of obese women continue to receive prescriptions of hormonal products that contain oestrogens for their contraception. We have conducted a narrative review to discuss the latest evidence, ongoing research, and controversial issues on the synergistic effect of obesity and contraceptive use, in terms of cardiovascular risk. There is compelling evidence of an interplay between obesity and contraception in increasing cardiovascular risk. Women who present both obesity and use of combined oral contraceptives (COCs) have a greater risk (between 12 and 24 times) to develop venous thromboembolism than non-obese non-COC users. Data here discussed offer new insights to increase clinicians' awareness on the cardiovascular risk in the clinical management of obese women. The synergistic effect of obesity and COCs on deep venous thrombosis risk must be considered when prescribing hormonal contraception. Progestin-only products are a safer alternative to COCs in patients with overweight or obesity. Obese women taking contraceptives should be viewed as an 'at risk' population, and as such, they should receive advice to change their lifestyle, avoiding other cardiovascular risk factors, as a form of primary prevention. This indication should be extended to young women, as data show that COCs should be avoided in obese women of any age

Identification of disease-causing genes using microarray data mining and gene ontology

Background: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes. Methods: We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results. Results: The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth. Conclusions: The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers

Explicit de Sitter Flux Vacua for Global String Models with Chiral Matter

We address the open question of performing an explicit stabilisation of all closed string moduli (including dilaton, complex structure and Kaehler moduli) in fluxed type IIB Calabi-Yau compactifications with chiral matter. Using toric geometry we construct Calabi-Yau manifolds with del Pezzo singularities. D-branes located at such singularities can support the Standard Model gauge group and matter content. In order to control complex structure moduli stabilisation we consider Calabi-Yau manifolds which exhibit a discrete symmetry that reduces the effective number of complex structure moduli. We calculate the corresponding periods in the symplectic basis of invariant three-cycles and find explicit flux vacua for concrete examples. We compute the values of the flux superpotential and the string coupling at these vacua. Starting from these explicit complex structure solutions, we obtain AdS and dS minima where the Kaehler moduli are stabilised by a mixture of D-terms, non-perturbative and perturbative alpha'-corrections as in the LARGE Volume Scenario. In the considered example the visible sector lives at a dP_6 singularity which can be higgsed to the phenomenologically interesting class of models at the dP_3 singularity.Comment: 49 pages, 5 figures; v2: references adde

DEEP MOVEMENT: Deep learning of movie files for management of endovascular thrombectomy

Objectives: Treatment and outcomes of acute stroke have been revolutionised by mechanical thrombectomy. Deep learning has shown great promise in diagnostics but applications in video and interventional radiology lag behind. We aimed to develop a model that takes as input digital subtraction angiography (DSA) videos and classifies the video according to (1) the presence of large vessel occlusion (LVO), (2) the location of the occlusion, and (3) the efficacy of reperfusion. / Methods: All patients who underwent DSA for anterior circulation acute ischaemic stroke between 2012 and 2019 were included. Consecutive normal studies were included to balance classes. An external validation (EV) dataset was collected from another institution. The trained model was also used on DSA videos post mechanical thrombectomy to assess thrombectomy efficacy. / Results: In total, 1024 videos comprising 287 patients were included (44 for EV). Occlusion identification was achieved with 100% sensitivity and 91.67% specificity (EV 91.30% and 81.82%). Accuracy of location classification was 71% for ICA, 84% for M1, and 78% for M2 occlusions (EV 73, 25, and 50%). For post-thrombectomy DSA (n = 194), the model identified successful reperfusion with 100%, 88%, and 35% for ICA, M1, and M2 occlusion (EV 89, 88, and 60%). The model could also perform classification of post-intervention videos as mTICI < 3 with an AUC of 0.71. / Conclusions: Our model can successfully identify normal DSA studies from those with LVO and classify thrombectomy outcome and solve a clinical radiology problem with two temporal elements (dynamic video and pre and post intervention). / Key Points: • DEEP MOVEMENT represents a novel application of a model applied to acute stroke imaging to handle two types of temporal complexity, dynamic video and pre and post intervention. • The model takes as an input digital subtraction angiograms of the anterior cerebral circulation and classifies according to (1) the presence or absence of large vessel occlusion, (2) the location of the occlusion, and (3) the efficacy of thrombectomy. • Potential clinical utility lies in providing decision support via rapid interpretation (pre thrombectomy) and automated objective gradation of thrombectomy outcomes (post thrombectomy)