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Artificial neural networks (ANNs), a subset of Quantitative Structure-Property Relationship (QSPR) methods, offer a promising avenue for addressing challenges in materials science. In particular, ANNs can learn intricated patterns within the experimental data, enabling them to predict properties and recognize complex relationships with remarkable accuracy. However, the opacity of ANNs, normally acting as black boxes, raises concerns about their reliability and interpretability. To enhance their transparency and to uncover the underlying relationships between chemical features and material properties, we propose a novel approach that employs Gradient-weighted Class Activation Mapping (Grad-CAM) applied to Convolutional Neural Networks (CNNs). By analyzing these attention maps, we identify the crucial chemical features influencing the prediction of a polymer property, specifically the glass transition temperature (Tg). Our methodology is validated using a dataset of atactic acrylates, allowing us to not only predict Tg values for a control group of polymers but also to quantitatively assess the impact of individual monomer structural elements on these predictions. This work proposes a step towards transparent models in materials science, contributing to a deeper understanding of the intricate relationship between chemical structures and material properties.

Predicting binary mixtures’ glass transition temperature (Tg) is crucial in various fields, particularly for industrial materials affected by this property during production processes and in service-life. On the other hand, from the fundamental point of view, this predictive capability is relevant for understanding the chemical interactions between the two components and how this affects the Tg of the mixture. In this sense, some models provide different approaches for describing the Tg of the mixture. Among them, the Gordon-Taylor approach has been widely used since it only relies on the relationship between the Tg of the pure components, their weight fraction, and only one fitting parameter. Although simple, this approach still requires measurements of Tg of the pure components and at least some intermediated composition for the fitting procedure. In a previous work, our research has focused on neural networks methods for predicting Tg values directly from the chemical structure of monomers and molecules, but the scarcity of experimental data for binary mixtures limits the application of a similar approach. To address this problem, we propose to use in this work a transfer learning method that relays on the previous acquired knowledge of the chemical structure - Tg relationship, for the prediction of the Tg of the binary mixtures. Therefore, pure component characteristics are derived from chemical fingerprints originated in a pre-trained network, and enables a training process focused on their behavior within the mixtures. This approach successfully estimated K with very low deviations, even allowing for the exploration of the embedded chemical structure’s relation to previously unknown mixtures.

Kebutuhan air bersih semakin lama semakin meningkat tetapi persedian sumber daya alam berupa air bersih dan sehat mulai berkurang. Hal ini menbuat beberapa masyarakat tertarik untuk menggunakan jasa penyedia air bersih, sehat, dan layak dikonsumsi yang dikelola oleh salah satu penyelenggara pengelolaan air bersih dan sehat di wilayah Banyumas. Penelitian ini bertujuan untuk mengetahui efektivitas fungsi humas dalam pelayanan komplain pelanggan PDAM Tirta Satria, hambatan yang muncul dari pihak humas dalam memberikan pelayanan kepada pelanggan, dan upaya humas dalam mengatasi hambatan tersebut. Penelitian ini menggunakan metode penelitian kualitatif deskriptip. Dalam pengumpulan data penulis menggunakan metode observasi, wawancara dan dokumentasi. Sasaran dalam penelitian ini adalah pelanggan PDAM Tirta Satria. Teknik analisis data menggunakan Miles dan Huberman, melalui tiga tahap yaitu reduksi data, penyajian (display) data dan penarikan kesimpulan dan validasi. Teknik validitas dan reabilitas adalah dengan triangulasi. Hasil penelitian menunjukkan bahwa Kualitas pelayanan yang diberikan oleh PDAM belum dapat dikatakan baik. Laporan yang ada dapat diselesaikan pada hari yang sama. Hal tersebut dikarenakan masih banyak prosedur yang harus dilakukan oleh petugas PDAM. Biasanya pihak PDAM Tirta Satria baru merealisasikannya sehari kemudian setelah laporan pengaduan masuk. Upaya yang dilakukan oleh pihak humas di PDAM Tirta Satria untuk mengatasi hambatan-hambatan yaitu evaluasi dan Pelatihan atau training kehumasan dan studi banding.

The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15 000deg2 of the sky. Euclid is expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. In order to optimally exploit the expected very large dataset, appropriate methods and software tools need to be developed. Here we present a novel machine-learning-based methodology for the selection of quiescent galaxies using broadband Euclid IE, YE, JE, and HE photometry, in combination with multi-wavelength photometry from other large surveys (e.g. the Rubin LSST). The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods separately. The pipeline has been designed to have 'sparsity awareness', such that missing photometry values are informative for the classification. In addition, our pipeline is able to derive photometric redshifts for galaxies selected as quiescent, aided by the 'pseudo-labelling' semi-supervised method, and using an outlier detection algorithm to identify and reject likely catastrophic outliers. After the application of the outlier filter, our pipeline achieves a normalised mean absolute deviation of ≲0.03 and a fraction of catastrophic outliers of ≲0.02 when measured against the COSMOS2015 photometric redshifts. We apply our classification pipeline to mock galaxy photometry catalogues corresponding to three main scenarios: (i) Euclid Deep Survey photometry with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey photometry with ancillary ugriz, WISE, and radio data; and (iii) Euclid Wide Survey photometry only, with no foreknowledge of galaxy redshifts. In a like-for-like comparison, our classification pipeline outperforms UVJ selection, in add

Quantitative structure-property relationship (QSPR) is a powerful analytical method to find correlations between the structure of a molecule and its physicochemical properties. The glass transition temperature (Tg) is one of the most reported properties, and its characterisation is critical for tuning the physical properties of materials. In this work, we explore the use of machine learning in the field of QSPR by developing a recurrent neural network (RNN) that relates the chemical structure and the glass transition temperature of molecular glass formers. In addition, we performed a chemical embedding from the last hidden layer of the RNN architecture into an m-dimensional Tg-oriented space. Then, we test the model to predict the glass transition temperature of essential amino acids and peptides. The results are very promising and they can open the door for exploring and designing new materials.

Citizen science is gaining popularity as a valuable tool for labelling large collections of astronomical images by the general public. This is often achieved at the cost of poorer quality classifications made by amateur participants, which are usually verified by employing smaller data sets labelled by professional astronomers. Despite its success, citizen science alone will not be able to handle the classification of current and upcoming surveys. To alleviate this issue, citizen science projects have been coupled with machine learning techniques in pursuit of a more robust automated classification. However, existing approaches have neglected the fact that, apart from the data labelled by amateurs, (limited) expert knowledge of the problem is also available along with vast amounts of unlabelled data that have not yet been exploited within a unified learning framework. This paper presents an innovative learning methodology for citizen science capable of taking advantage of expert- and amateur-labelled data, featuring a transfer of labels between experts and amateurs. The proposed approach first learns from unlabelled data with a convolutional auto-encoder and then exploits amateur and expert labels via the pre-training and fine-tuning of a convolutional neural network, respectively. We focus on the classification of galaxy images from the Galaxy Zoo project, from which we test binary, multiclass, and imbalanced classification scenarios. The results demonstrate that our solution is able to improve classification performance compared to a set of baseline approaches, deploying a promising methodology for learning from different confidence levels in data labelling. © 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society.

[EN] Understanding the scientific and social structure of a discipline is a fundamental aspect for scientific evaluation processes, identifying trends and niches, and balancing the trade-off between exploitation and exploration in research. In the present contribution, the production of doctoral theses is used as a proxy to analyze the scientific structure of the knowledge area of business organization in Spain. To that end, a complex networks approach is selected, and two different networks are built: (i) the social network of co-participation in thesis examining committees and thesis supervision, and (ii) a bipartite network of theses and thesis descriptors. The former has a modular structure that is partially explained by thematic specialization in different subdisciplines. The latter serves to assess the interdisciplinary structure of the discipline, as it enables the characterization of affinity levels between fields, research poles and thematic clusters. Our results have implications for the scientific evaluation and formal definition of related fields.

A critical limitation of current methods based on Neural Radiance Fields (NeRF) is that they are unable to quantify the uncertainty associated with the learned appearance and geometry of the scene. This information is paramount in real applications such as medical diagnosis or autonomous driving where, to reduce potentially catastrophic failures, the confidence on the model outputs must be included into the decision-making process. In this context, we introduce Conditional-Flow NeRF (CF-NeRF), a novel probabilistic framework to incorporate uncertainty quantification into NeRF-based approaches. For this purpose, our method learns a distribution over all possible radiance fields modelling which is used to quantify the uncertainty associated with the modelled scene. In contrast to previous approaches enforcing strong constraints over the radiance field distribution, CF-NeRF learns it in a flexible and fully data-driven manner by coupling Latent Variable Modelling and Conditional Normalizing Flows. This strategy allows to obtain reliable uncertainty estimation while preserving model expressivity. Compared to previous state-of-the-art methods proposed for uncertainty quantification in NeRF, our experiments show that the proposed method achieves significantly lower prediction errors and more reliable uncertainty values for synthetic novel view and depth-map estimation.

The analysis of structural relaxation dynamics of polymers gives an insight into their mechanical properties, whose characterization is used to qualify a given material for its practical scope. The dynamics are usually expressed in terms of the temperature dependence of the relaxation time, which is only available through time-consuming experimental processes following polymer synthesis. However, it would be advantageous to estimate their dynamics before synthesizing them when designing new materials. In this work, we propose a combined approach of artificial neural networks and the elastically collective nonlinear Langevin equation (ECNLE) to estimate the temperature dependence of the main structural relaxation time of polymers based only on the knowledge of the chemical structure of the corresponding monomer.

Glass transition temperature and related dynamics play an essential role in amorphous materials research since many of their properties and functionalities depend on molecular mobility. However, the temperature dependence of the structural relaxation time for a given glass former is only experimentally accessible after synthesizing it, implying a time-consuming and costly process. In this work, we propose combining artificial neural networks and disordered systems theory to estimate the glass transition temperature and the temperature dependence of the main relaxation time based on the knowledge of the molecule's chemical structure. This approach provides a way to assess the dynamics of molecular glass formers, with reasonable accuracy, even before synthesizing them. We expect this methodology to boost industrial development, save time and resources, and accelerate the scientific understanding of structure-properties relationships.

[ES] En este trabajo de fin de grado se realizará un estudio basado en el análisis de la aplicación de algoritmos de aprendizaje por refuerzo para entornos mono-agente sobre entornos multi-agente basados en la plataforma MARLÖ. Todo esto con el objetivo de comparar la eficacia y eficiencia de dichos algoritmos en entornos para los cuales no han sido diseñados. Para esto será necesario tanto el diseño y creación de entornos personalizados como la modificación de las implementaciones de los algoritmos para adaptarlas a dichos entornos., [EN] In this final degree thesis, we perform a study based on the analysis of the application of reinforcement learning algorithms designed for single-agent environments on multi-agent environments based on the MARLÖ platform. All this in order to compare the effectiveness and efficiency of these algorithms in environments for which they have not been designed. This will require both the design and creation of custom environments and the modification of the algorithm implementations to adapt them to those environments., [CA] En aquest treball de fi de grau es realitzarà un estudi basat en l’anàlisi de l’aplicació d’algoritmes d’aprenentatge per reforç per a entorns mono-agent sobre entorns multiagent basats en la plataforma MARLÖ. Tot això amb l’objectiu de comparar l’eficàcia i eficiència d’aquests algorismes en entorns per als quals no han estat dissenyats. Per això serà necessari tant el disseny i creació d’entorns personalitzats com la modificació de les implementacions dels algoritmes per adaptar-les a aquests entorns.

The recognition of human activities captured by a wearable photo-camera is especially suited for understanding the behavior of a person. However, it has received comparatively little attention with respect to activity recognition from fixed cameras.In this work, we propose to use segmented events from photo-streams as temporal boundaries to improve the performance of activity recognition. Furthermore, we robustly measure its effectiveness when images of the evaluated person have been seen during training, and when the person is completely unknown during testing. Experimental results show that leveraging temporal boundary information on pictures of seen people improves all classification metrics, particularly it improves the classification accuracy up to 85.73%., Lecture Notes in Computer Science 12663

Background: Studies have previously shown evidence for presymptomatic cortical atrophy in genetic FTD. Whilst initial investigations have also identified early deep grey matter volume loss, little is known about the extent of subcortical involvement, particularly within subregions, and how this differs between genetic groups. Methods: 480 mutation carriers from the Genetic FTD Initiative (GENFI) were included (198 GRN, 202 C9orf72, 80 MAPT), together with 298 non-carrier cognitively normal controls. Cortical and subcortical volumes of interest were generated using automated parcellation methods on volumetric 3 T T1-weighted MRI scans. Mutation carriers were divided into three disease stages based on their global CDR® plus NACC FTLD score: asymptomatic (0), possibly or mildly symptomatic (0.5) and fully symptomatic (1 or more). Results: In all three groups, subcortical involvement was seen at the CDR 0.5 stage prior to phenoconversion, whereas in the C9orf72 and MAPT mutation carriers there was also involvement at the CDR 0 stage. In the C9orf72 expansion carriers the earliest volume changes were in thalamic subnuclei (particularly pulvinar and lateral geniculate, 9–10%) cerebellum (lobules VIIa-Crus II and VIIIb, 2–3%), hippocampus (particularly presubiculum and CA1, 2–3%), amygdala (all subregions, 2–6%) and hypothalamus (superior tuberal region, 1%). In MAPT mutation carriers changes were seen at CDR 0 in the hippocampus (subiculum, presubiculum and tail, 3–4%) and amygdala (accessory basal and superficial nuclei, 2–4%). GRN mutation carriers showed subcortical differences at CDR 0.5 in the presubiculum of the hippocampus (8%). Conclusions: C9orf72 expansion carriers show the earliest and most widespread changes including the thalamus, basal ganglia and medial temporal lobe. By investigating individual subregions, changes can also be seen at CDR 0 in MAPT mutation carriers within the limbic system. Our results suggest that subcortical brain volumes may be used as

In the last 2 decades, a great amount of work has been done on data mining and knowledge discovery using complex networks. These works have provided insightful information about the structure and evolution of scientific activity, as well as important biomedical discoveries. However, interdisciplinary knowledge discovery, including disciplines other than science, is more complicated to implement because most of the available knowledge is not indexed. Here, a new method is presented for mining Wikipedia to unveil implicit interdisciplinary knowledge to map and understand how different disciplines (art, science, literature) are related to and interact with each other. Furthermore, the formalism of complex networks allows us to characterise both individual and collective behaviour of the different elements (people, ideas, works) within each discipline and among them. The results obtained agree with well-established interdisciplinary knowledge and show the ability of this method to boost quantitative studies. Note that relevant elements in different disciplines that rarely directly refer to each other may nonetheless have many implicit connections that impart them and their relationship with new meaning. Owing to the large number of available works and to the absence of cross-references among different disciplines, tracking these connections can be challenging. This approach aims to bridge this gap between the large amount of reported knowledge and the limited human capacity to find subtle connections and make sense of them.

This is the peer reviewed version of the following article: del Mar Álvarez-Torres, M., Juan-Albarracín, J., Fuster-Garcia, E., Bellvís-Bataller, F., Lorente, D., Reynés, G., Font de Mora, J., Aparici-Robles, F., Botella, C., Muñoz-Langa, J., Faubel, R., Asensio-Cuesta, S., García-Ferrando, G.A., Chelebian, E., Auger, C., Pineda, J., Rovira, A., Oleaga, L., Mollà-Olmos, E., Revert, A.J., Tshibanda, L., Crisi, G., Emblem, K.E., Martin, D., Due-Tønnessen, P., Meling, T.R., Filice, S., Sáez, C. and García-Gómez, J.M. (2020), Robust association between vascular habitats and patient prognosis in glioblastoma: An international multicenter study. J Magn Reson Imaging, 51: 1478-1486, which has been published in final form at https://doi.org/10.1002/jmri.26958. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving., [EN] Background Glioblastoma (GBM) is the most aggressive primary brain tumor, characterized by a heterogeneous and abnormal vascularity. Subtypes of vascular habitats within the tumor and edema can be distinguished: high angiogenic tumor (HAT), low angiogenic tumor (LAT), infiltrated peripheral edema (IPE), and vasogenic peripheral edema (VPE). Purpose To validate the association between hemodynamic markers from vascular habitats and overall survival (OS) in glioblastoma patients, considering the intercenter variability of acquisition protocols. Study Type Multicenter retrospective study. Population In all, 184 glioblastoma patients from seven European centers participating in the NCT03439332 clinical study. Field Strength/Sequence 1.5T (for 54 patients) or 3.0T (for 130 patients). Pregadolinium and postgadolinium-based contrast agent-enhanced T-1-weighted MRI, T-2- and FLAIR T-2-weighted, and dynamic susceptibility contrast (DSC) T-2* perfusion. Assessment We analyzed preoperative MRIs to establish the association between the maximum relative cerebral blood volume (rCBV(max)) at each habitat with OS. Moreover, the stratification capabilities of the markers to divide patients into "vascular" groups were tested. The variability in the markers between individual centers was also assessed. Statistical Tests Uniparametric Cox regression; Kaplan-Meier test; Mann-Whitney test. Results The rCBV(max) derived from the HAT, LAT, and IPE habitats were significantly associated with patient OS (P < 0.05; hazard ratio [HR]: 1.05, 1.11, 1.28, respectively). Moreover, these markers can stratify patients into "moderate-" and "high-vascular" groups (P < 0.05). The Mann-Whitney test did not find significant differences among most of the centers in markers (HAT: P = 0.02-0.685; LAT: P = 0.010-0.769; IPE: P = 0.093-0.939; VPE: P = 0.016-1.000). Data Conclusion The rCBV(max) calculated in HAT, LAT, and IPE habitats have been validated as clinically relevant prognostic biomarkers for gli

This is the author's version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, Volume 378, 22 February 2020, DOI: 10.1016/j.neucom.2019.08.096, [EN] In this work, a methodology for applying semantic-based padding in Convolutional Neural Networks for Natural Language Processing tasks is proposed. Semantic-based padding takes advantage of the unused space required for having a fixed-size input matrix in a Convolutional Network effectively, using words present in the sentence. The methodology proposed has been evaluated intensively in Sentiment Analysis tasks using a variety of word embeddings. In all the experimentation carried out the proposed semantic-based padding improved the results achieved when no padding strategy is applied. Moreover, when the model used a pre-trained word embeddings, the performance of the state of the art has been surpassed.

[EN] Identification of reentrant activity driving atrial fibrillation (AF) is increasingly important to ablative therapies. The goal of this work is to study how the automatically-classified quality of the electrograms (EGMs) affects reentrant AF driver localization. EGMs from 259 AF episodes obtained from 29 AF patients were recorded using 64-poles basket catheters and were manually classified according to their quality. An algorithm capable of identifying signal quality was developed using time and spectral domain parameters. Electrical reentries were identified in 3D phase maps using phase transform and were compared with those obtained with a 2D activation-based method. Effect of EGM quality was studied by discarding 3D phase reentries detected in regions with low-quality EGMs. Removal of reentries identified by 3D phase analysis in regions with low-quality EGMs improved its performance, increasing the area under the ROC curve (AUC) from 0.69 to 0.80. The EGMs quality classification algorithm showed an accurate performance for EGM classification (AUC 0.94) and reentry detection (AUC 0.80). Automatic classification of EGM quality based on time and spectral signal parameters is feasible and accurate, avoiding the manual labelling. Discard of reentries identified in regions with automatically-detected poor-quality EGMs improved the specificity of the 3D phase-based method for AF driver identification.

[EN] Background: The Electrocardiographic Imaging (ECGI) technique, used to non-invasively reconstruct the epicardial electrical activity, requires an accurate model of the atria and torso anatomy. Here we evaluate a new automatic methodology able to locate the atrial anatomy within the torso based on an intrinsic electrical parameter of the ECGI solution. Methods: In 28 realistic simulations of the atrial electrical activity, we randomly displaced the atrial anatomy for +/- 2.5 cm and +/- 30 degrees on each axis. An automatic optimization method based on the L-curve curvature was used to estimate the original position using exclusively non-invasive data. Results: The automatic optimization algorithm located the atrial anatomy with a deviation of 0.5 +/- 0.5 cm in position and 16.0 +/- 10.7 degrees in orientation. With these approximate locations, the obtained electrophysiological maps reduced the average error in atrial rate measures from 1.1 +/- 1.1 Hz to 0.5 +/- 1.0 Hz and in the phase singularity position from 7.2 +/- 4.0 cm to 1.6 +/- 1.7 cm (p < 0.01). Conclusions: This proposed automatic optimization may help to solve spatial inaccuracies provoked by cardiac motion or respiration, as well as to use ECGI on torso and atrial anatomies from different medical image systems.

[EN] Affine registration of one or several brain image(s) onto a common reference space is a necessary prerequisite for many image processing tasks, such as brain segmentation or functional analysis. Manual assessment of registration quality is a tedious and time-consuming task, especially in studies comprising a large amount of data. Automated and reliable quality control (QC) becomes mandatory. Moreover, the computation time of the QC must be also compatible with the processing of massive datasets. Therefore, automated deep neural network approaches have emerged as a method of choice to automatically assess registration quality. In the current study, a compact 3D convolutional neural network, referred to as RegQCNET, is introduced to quantitatively predict the amplitude of an affine registration mismatch between a registered image and a reference template. This quantitative estimation of registration error is expressed using the metric unit system. Therefore, a meaningful task-specific threshold can be manually or automatically defined in order to distinguish between usable and non-usable images. The robustness of the proposed RegQCNET is first analyzed on lifespan brain images undergoing various simulated spatial transformations and intensity variations between training and testing. Secondly, the potential of RegQCNET to classify images as usable or non-usable is evaluated using both manual and automatic thresholds. During our experiments, automatic thresholds are estimated using several computer-assisted classification models (logistic regression, support vector machine, Naive Bayes and random forest) through cross-validation. To this end we use an expert's visual QC estimated on a lifespan cohort of 3953 brains. Finally, the RegQCNET accuracy is compared to usual image features such as image correlation coefficient and mutual information. The results show that the proposed deep learning QC is robust, fast and accurate at estimating affine registration error in

The consolidation of spatial memory depends on the reactivation (‘replay’) of hippocampal place cells that were active during recent behaviour. Such reactivation is observed during sharp-wave ripples (SWRs)—synchronous oscillatory electrical events that occur during non-rapid-eye-movement (non-REM) sleep and whose disruption impairs spatial memory. Although the hippocampus also encodes a wide range of non-spatial forms of declarative memory, it is not yet known whether SWRs are necessary for such memories. Moreover, although SWRs can arise from either the CA3 or the CA2 region of the hippocampus, the relative importance of SWRs from these regions for memory consolidation is unknown. Here we examine the role of SWRs during the consolidation of social memory—the ability of an animal to recognize and remember a member of the same species—focusing on CA2 because of its essential role in social memory. We find that ensembles of CA2 pyramidal neurons that are active during social exploration of previously unknown conspecifics are reactivated during SWRs. Notably, disruption or enhancement of CA2 SWRs suppresses or prolongs social memory, respectively. Thus, SWR-mediated reactivation of hippocampal firing related to recent experience appears to be a general mechanism for binding spatial, temporal and sensory information into high-order memory representations, including social memory.

Breast cancer is the most frequent cancer in women and the second most frequent overall after lung cancer. Although the 5-year survival rate of breast cancer is relatively high, recurrence is also common which often involves metastasis with its consequent threat for patients. DNA methylation-derived databases have become an interesting primary source for supervised knowledge extraction regarding breast cancer. Unfortunately, the study of DNA methylation involves the processing of hundreds of thousands of features for every patient. DNA methylation is featured by High Dimension Low Sample Size which has shown well-known issues regarding feature selection and generation. Autoencoders (AEs) appear as a specific technique for conducting nonlinear feature fusion. Our main objective in this work is to design a procedure to summarize DNA methylation by taking advantage of AEs. Our proposal is able to generate new features from the values of CpG sites of patients with and without recurrence. Then, a limited set of relevant genes to characterize breast cancer recurrence is proposed by the application of survival analysis and a pondered ranking of genes according to the distribution of their CpG sites. To test our proposal we have selected a dataset from The Cancer Genome Atlas data portal and an AE with a single-hidden layer. The literature and enrichment analysis (based on genomic context and functional annotation) conducted regarding the genes obtained with our experiment confirmed that all of these genes were related to breast cancer recurrence.

New treatments for diseases caused by antimicrobial-resistant microorganisms can be developed by identifying unexplored therapeutic targets and by designing efficient drug screening protocols. In this study, we have screened a library of compounds to find ligands for the flavin-adenine dinucleotide synthase (FADS) -a potential target for drug design against tuberculosis and pneumonia- by implementing a new and efficient virtual screening protocol. The protocol has been developed for the in silico search of ligands of unexplored therapeutic targets, for which limited information about ligands or ligand-receptor structures is available. It implements an integrative funnel-like strategy with filtering layers that increase in computational accuracy. The protocol starts with a pharmacophore-based virtual screening strategy that uses ligand-free receptor conformations from molecular dynamics (MD) simulations. Then, it performs a molecular docking stage using several docking programs and an exponential consensus ranking strategy. The last filter, samples the conformations of compounds bound to the target using MD simulations. The MD conformations are scored using several traditional scoring functions in combination with a newly-proposed score that takes into account the fluctuations of the molecule with a Morse-based potential. The protocol was optimized and validated using a compound library with known ligands of the Corynebacterium ammoniagenes FADS. Then, it was used to find new FADS ligands from a compound library of 14,000 molecules. A small set of 17 in silico filtered molecules were tested experimentally. We identified five inhibitors of the activity of the flavin adenylyl transferase module of the FADS, and some of them were able to inhibit growth of three bacterial species: C. ammoniagenes, Mycobacterium tuberculosis, and Streptococcus pneumoniae, where the last two are human pathogens. Overall, the results show that the integrative VS protocol is a cost-effective

[EN] The filtering task of noisy parallel corpora in WMT2019 aims to challenge participants to create filtering methods to be useful for training machine translation systems. In this work, we introduce a noisy parallel corpora filtering system based on generating hypotheses by means of a translation model. We train translation models in both language pairs: Nepali English and Sinhala English using provided parallel corpora. To create the best possible translation model, we first join all provided parallel corpora (Nepali, Sinhala and Hindi to English) and after that, we applied bilingual cross-entropy selection for both language pairs (Nepali English and Sinhala English). Once the translation models are trained, we translate the noisy corpora and generate a hypothesis for each sentence pair. We compute the smoothed BLEU score between the target sentence and generated hypothesis. In addition, we apply several rules to discard very noisy or inadequate sentences which can lower the translation score. These heuristics are based on sentence length, source and target similarity and source language detection. We compare our results with the baseline published on the shared task website, which uses the Zipporah model, over which we achieve significant improvements in one of the conditions in the shared task. The designed filtering system is domain independent and all experiments are conducted using neural machine translation.

Our interaction with the world is an inherently multi-modal experience. However, the understanding of human-to-object interactions has historically been addressed focusing on a single modality. In particular, a limited number of works have considered to integrate the visual and audio modalities for this purpose. In this work, we propose a multimodal approach for egocentric action recognition in a kitchen environment that relies on audio and visual information. Our model combines a sparse temporal sampling strategy with a late fusion of audio, spatial,and temporal streams. Experimental results on the EPIC-Kitchens dataset show that multimodal integration leads to better performance than unimodal approaches. In particular, we achieved a5.18%improvement over the state of the art on verb classification.

[EN] Glaucoma is a chronic eye disease and one of the major causes of permanent blindness. Since it does not show initial symptoms, early diagnosis is important to limit its progression. This paper presents an automatic optic nerve characterization algorithm for glaucoma diagnosis based only on retinal fundus images. For optic cup segmentation, we used a new method based on the stochastic watershed transformation applied on the YIQ colour space to extract clinical indicators such as the Cup/Disc ratio, the area Cup/Disc ratio and the ISNT rule. Afterwards, an assessment between normal and glaucomatous fundus images is performed. The proposed algorithm was evaluated on 6 different (private and public) databases containing 723 images (377 normal and 346 glaucomatous images) which achieved a specificity and sensitivity of 0.674 and 0.675, respectively. Moreover, an F-score of 0.770 was obtained when evaluating this method on the publicly available database Drishti-GS1. A comparison of the proposed work with other state-of-the-art methods demonstrates the robustness of the proposed algorithm; because it was tested using images from different databases with high variability, which is a common issue in this area. Additional comparisons with existing works for cup segmentation, that use the publicly available database Drishti-GS1, are also presented in this paper.

La capacidad de entender la materia y su interacción con el medio determinan en gran parte el desarrollo de las nuevas tecnologías. Hoy en día, las nanotecnologías son un campo emergente de investigación debido al gran impacto que tienen en la sociedad. Las simulaciones computacionales de fenómenos físicos en la nanoescala han contribuido a la aceleración de su desarrollo. En esta tesis doctoral nos hemos basado en simulaciones ab initio atomísticas para explicar el comportamiento de nanopartículas sometidas a estímulos externos. Con “atomístico” nos referimos a que la geometría del sistema se descibe mediante posiciones realístas de los átomos, es decir, que se tienen en cuenta las posiciones atómicas y la atracción Coulombiana generada por cada núcleo, en vez de reemplazarlas por un potencial efectivo suave que confina los electrónes en objetos de forma simple, como una esfera. “ab initio” significa que nos hemos basado en las leyes de la mecánica cuántica para modelar los electrones del sistema. De esta forma, en este trabajo hemos podido simular la interacción de centenas de electrones confinados dentro de nanopartículas, tanto entre ellos como con el medio. Este problema se conoce como el problema de muchos cuerpos ( many body problem en inglés). Desafortunadamente, hay que recurrir a aproximaciones para resolver el problema de muchos cuerpos. Las aproximaciones adoptadas en esta tesis son las integradas en la teoriá del funcional de la densidad (DFT, en inglés density functional theory) implementadas en los paquetes de SIESTA (Spanish Initiative for Electronic Simulations with Thousands of Atoms), así como su extensión a fenómenos dependientes del tiempo (TDDFT, en inglés timedependent DFT) implementadas en los paquetes de MBPT-LCAO (Many Body Perturbation Theory - Linear Combination of Atomic Orbitals) y PySCFNAO (Python-Based Simulations of Chemistry Framework - Numerical Atomic Orbitals). Por otra parte, en esta tesis doctoral hemos conseguido implementar c

Evolutionary topology optimization of three-dimensional continuum structures is a computationally demanding task in terms of memory consumption and processing time. This work aims to alleviate these constraints proposing a well-suited strategy for Graphics Processing Unit (GPU) computing. Such a proposal adopts a fine-grained GPU instance of matrix-free iterative solver for structural analysis and an efficient GPU implementation for isosurface extraction and volume fraction calculation. The performance of the solving stage is evaluated using two preconditioning techniques, including the comparison with the sparse-matrix CPU implementation. The proposal is evaluated using topology optimization problems for real-world applications.

This is the author’s version of a work that was accepted for publication in Computer Speech & Language. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Speech & Language 00 (2016) 1 20. DOI 10.1016/j.csl.2016.12.003., Despite the promising results achieved in last years by statistical machine translation, and more precisely, by the neural machine translation systems, this technology is still not error-free. The outputs of a machine translation system must be corrected by a human agent in a post-editing phase. Interactive protocols foster a human computer collaboration, in order to increase productivity. In this work, we integrate the neural machine translation into the interactive machine translation framework. Moreover, we propose new interactivity protocols, in order to provide the user an enhanced experience and a higher productivity. Results obtained over a simulated benchmark show that interactive neural systems can significantly improve the classical phrase-based approach in an interactive-predictive machine translation scenario. c 2016 Elsevier Ltd. All rights reserved.

We present a search, using the Murchison Widefield Array (MWA), for electromagnetic (EM) counterparts to two candidate high-energy neutrino events detected by the ANTARES neutrino telescope in 2013 November and 2014 March. These events were selected by ANTARES because they are consistent, within 0 degrees.4, with the locations of galaxies within 20 Mpc of Earth. Using MWA archival data at frequencies between 118 and 182 MHz, taken similar to 20. days prior to, at the same time as, and up to a year after the neutrino triggers, we look for transient or strongly variable radio sources that are consistent with the neutrino positions. No such counterparts are detected, and we set a 5 sigma upper limit for low-frequency radio emission of similar to 10(37) erg s(-1) for progenitors at 20 Mpc. If the neutrino sources are instead not in nearby galaxies, but originate in binary neutron star coalescences, our limits place the progenitors at z greater than or similar to 0.2. While it is possible, due to the high background from atmospheric neutrinos, that neither event is astrophysical, the MWA observations are nevertheless among the first to follow up neutrino candidates in the radio, and illustrate the promise of wide-field instruments like MWA for detecting EM counterparts to such events.

We combine experiments and simulations to study the acceleration of anisotropic etching of crystalline silicon at the mask-substrate interface, as a function of the coordination number of the substrate atoms located at the junction between obtuse-angled {1 1 1} facets and the mask layer. Atomistic simulations based on the use of the continuous cellular automaton (CCA) conclude that the interface atoms react faster with the etchant, thus initiating a step flow process that results in increased etch rates for the obtuse facets. By generating a wide range of complex cavities on high-index silicon wafers with a single-side, single-step etching, the comparison of the experimental and simulated results strongly indicates that the CCA method is suitable for accurately describing not only the development of micron-scaled structures but also, for the first time, the formation of submicron shapes. The study also describes the acceleration of obtuse facets formed through double-side etching, obtaining results in good agreement with previous experiments.

Manipulating textile objects with a robot is a challenging task, especially because the garment perception is difficult due to the endless configurations it can adopt, coupled with a large variety of colors and designs. Most current approaches follow a multiple re-grasp strategy, in which clothes are sequentially grasped from different points until one of them yields a recognizable configuration. In this work we propose a method that combines 3D and appearance information to directly select a suitable grasping point for the task at hand, which in our case consists of hanging a shirt or a polo shirt from a hook. Our method follows a coarse-to-fine approach in which, first, the collar of the garment is detected and, next, a grasping point on the lapel is chosen using a novel 3D descriptor. In contrast to current 3D descriptors, ours can run in real time, even when it needs to be densely computed over the input image. Our central idea is to take advantage of the structured nature of range images that most depth sensors provide and, by exploiting integral imaging, achieve speed-ups of two orders of magnitude with respect to competing approaches, while maintaining performance. This makes it especially adequate for robotic applications as we thoroughly demonstrate in the experimental section.

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., CUDA is a technology developed by NVIDIA which provides a parallel computing platform and programming model for NVIDIA GPUs and compatible ones. It takes benefit from the enormous parallel processing power of GPUs in order to accelerate a wide range of applications, thus reducing their execution time. rCUDA (remote CUDA) is a middleware which grants applications concurrent access to CUDA-compatible devices installed in other nodes of the cluster in a transparent way so that applications are not aware of accessing a remote device. In this paper we present a demo which shows, in real time, the overhead introduced by rCUDA in comparison to CUDA when running image filtering applications. The approach followed in this work is to develop a graphical demo which contains both an appealing design and technical contents.

Real-space grids are a powerful alternative for the simulation of electronic systems. One of the main advantages of the approach is the flexibility and simplicity of working directly in real space where the different fields are discretized on a grid, combined with competitive numerical performance and great potential for parallelization. These properties constitute a great advantage at the time of implementing and testing new physical models. Based on our experience with the Octopus code, in this article we discuss how the real-space approach has allowed for the recent development of new ideas for the simulation of electronic systems. Among these applications are approaches to calculate response properties, modeling of photoemission, optimal control of quantum systems, simulation of plasmonic systems, and the exact solution of the Schrödinger equation for low-dimensionality systems.