Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning

We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only. This is achieved by extending recent ideas from learning of unsupervised image denoisers to unstructured 3D point clouds. Unsupervised image denoisers operate under the assumption that a noisy pixel observation is a random realization of a distribution around a clean pixel value, which allows appropriate learning on this distribution to eventually converge to the correct value. Regrettably, this assumption is not valid for unstructured points: 3D point clouds are subject to total noise, i.e. deviations in all coordinates, with no reliable pixel grid. Thus, an observation can be the realization of an entire manifold of clean 3D points, which makes the quality of a naive extension of unsupervised image denoisers to 3D point clouds unfortunately only little better than mean filtering. To overcome this, and to enable effective and unsupervised 3D point cloud denoising, we introduce a spatial prior term, that steers converges to the unique closest out of the many possible modes on the manifold. Our results demonstrate unsupervised denoising performance similar to that of supervised learning with clean data when given enough training examples - whereby we do not need any pairs of noisy and clean training data

Deep-learning the Latent Space of Light Transport

We suggest a method to directly deep‐learn light transport, i. e., the mapping from a 3D geometry‐illumination‐material configuration to a shaded 2D image. While many previous learning methods have employed 2D convolutional neural networks applied to images, we show for the first time that light transport can be learned directly in 3D. The benefit of 3D over 2D is, that the former can also correctly capture illumination effects related to occluded and/or semi‐transparent geometry. To learn 3D light transport, we represent the 3D scene as an unstructured 3D point cloud, which is later, during rendering, projected to the 2D output image. Thus, we suggest a two‐stage operator comprising a 3D network that first transforms the point cloud into a latent representation, which is later on projected to the 2D output image using a dedicated 3D‐2D network in a second step. We will show that our approach results in improved quality in terms of temporal coherence while retaining most of the computational efficiency of common 2D methods. As a consequence, the proposed two stage‐operator serves as a valuable extension to modern deferred shading approaches

Building a taxonomy of eco-innovation types in firms: A quantitative perspective

Eco-innovations, or innovations that reduce the environmental impacts of production and consumption activities, are considered crucial for sustainability transitions and a key element of a Circular Economy. Although previous contributions have acknowledged the existence of different types of eco-innovations (e.g., product vs. service or incremental vs. radical), a precise conceptualization of eco-innovation types, which takes into account its multifaceted character, is missing. Yet such a conceptualization is crucial in order to understand how eco-innovations contribute to a sustainable transition, how policy makers can promote different eco-innovation types, and how business practitioners can develop eco-innovations. This article covers this gap in the literature. Its aim is twofold: 1) to develop a quantitative method to categorise different eco-innovation types in a particular setting, taking into account their distinct features and dimensions; 2) to apply this method in a given sector and country, building a taxonomy of eco-innovation types. It draws on a survey of 197 Spanish industrial small and medium size enterprises (SMEs) which developed or adopted an eco-innovation between 2012 and 2013. The statistical analyses reveal the existence of a taxonomy of five eco-innovation types: systemic, externally driven, continuous improvement, radical (technology-push initiated) and eco-efficient. They differ in their techno-economic configurations, contribution to environmental sustainability and corporate goals and required changes in the firms. Specific policy and managerial implications are deducted

An easy synthetic way to exfoliate and stabilize MWCNTs in a thermoplastic pyrrole-containing matrix assisted by hydrogen bonds

This work focuses on the design of an engineered thermoplastic polymer containing pyrrole units in the main chain and hydroxyl pendant groups (A-PPy-OH), which help in achieving nanocomposites containing well-distributed, exfoliated and undamaged MWCNTs. The thermal annealing at 100 °C of the pristine nanocomposite promotes the redistribution of the nanotubes in terms of a percolative network, thus converting the insulating material in a conducting soft matrix (60 μΩ m). This network remains unaltered after cooling to r.t. and successive heating cycles up to 100 °C thanks to the effective stabilization of MWCNTs provided by the functional polymer matrix. Notably, the resistivity-temperature profile is very reproducible and with a negative temperature coefficient of -0.002 K-1, which suggests the potential application of the composite as a temperature sensor. Overall, the industrial scale by which A-PPy-OH can be produced offers a straightforward alternative for the scale-up production of suitable polymers to generate multifunctional nanocomposites

Noticias falsas y creencias infundadas en la era de la posverdad

The dissemination of fake news embodies a pressing problem for democracy that is exacerbated by theubiquity of information available on the Internet and by the exploitation of those who, appealing to theemotionality of audiences, have capitalized on the injection of falsehoods into the social fabric. In thisstudy, through a cross-sectional, correlational and non-experimental design, the relationship betweencredibility in the face of fake news and some types of dysfunctional thoughts was explored in a sampleof Chilean university students. The results reveal that greater credibility in fake news is associated withhigher scores of magical, esoteric and naively optimistic thinking, beliefs that would be the meetingpoint for a series of cognitive biases that operate in the processing of information. The highest correlationis found with the paranormal beliefs facet and, particularly, with ideas about the laws of mentalattraction, telepathy and clairvoyance. Significant differences were also found in credibility in fake newsas a function of the gender of the participants, with the female gender scoring higher on average thanthe male gender. These findings highlight the need to promote critical thinking, skepticism and scientificattitude in all segments of society.La difusión de noticias falsas encarna un apremiante problema para la democracia que se ve agudizadopor la ubicuidad de la información disponible en el internet y por el aprovechamiento de quienes, apelandoa la emocionalidad de las audiencias, han capitalizado a su favor la inyección de falsedades en elentramado social. En este estudio a través de un diseño transversal, correlacional y no experimental seexploró la relación entre credibilidad frente a las noticias falsas y algunos tipos de pensamientos disfuncionales en una muestra de estudiantes universitarios chilenos. Los resultados develan que una mayorcredibilidad en noticias falsas va aparejada con mayores puntajes de pensamiento mágico, esotérico eingenuamente optimista, creencias que serían el punto de encuentro para una serie de sesgos cognitivosque operan en el procesamiento de la información. La correlación más alta se encuentra con la faceta decreencias paranormales y, particularmente, con las ideas acerca de las leyes de atracción mental, la telepatíay la clarividencia. También se hallaron diferencias significativas en la credibilidad en noticias falsasen función del género de los participantes, encontrando que el género femenino puntúa una media másalta que el género masculino. Estos hallazgos ponen en relieve la necesidad de promover el pensamientocrítico, el escepticismo y la actitud científica en todos los segmentos de la sociedad

Photocatalytic degradation of contaminants of concern with composite NF-TiO2 films under visible and solar light

This study reports the synthesis and characterization of composite nitrogen and fluorine co-doped titanium dioxide (NF-TiO2) for the removal of contaminants of concern (COCs) in wastewater under visible and solar light. Monodisperse anatase TiO2 nanoparticles of different sizes and Evonik P25 were assembled to immobilized NF-TiO2 by direct incorporation into the sol-gel or by the layer-by-layer technique. The composite films were characterized with X-ray diffraction, high resolution-transmission electron microscopy, environmental scanning electron microscopy, and porosimetry analysis. The photocatalytic degradation of atrazine, carbamazepine, and caffeine was evaluated in a synthetic water solution and in an effluent from a hybrid biological concentrator reactor (BCR). Minor aggregation and improved distribution of monodisperse titania particles was obtained with NF-TiO2-monodisperse (10 and 50 nm) from the layer-by-layer technique than with NF-TiO2 + monodisperse TiO2 (300 nm) directly incorporated into the sol. The photocatalysts synthesized with the layer-by-layer method achieved significantly higher degradation rates in contrast with NF-TiO2-monodisperse titania (300 nm) and slightly faster values when compared with NF-TiO2-P25. Using NF-TiO2 layer-by-layer with monodisperse TiO2 (50 nm) under the solar light irradiation, the respective degradation rates in synthetic water and BCR effluent were 14.6 and 9.5·10-3 min-1 for caffeine, 12.5 and 9.0·10-3 min-1 for carbamazepine, and 10.9 and 5.8·10-3 min-1 for atrazine. These results suggest that the layer-by-layer technique is a promising method for the synthesis of composite TiO2-based films compared to the direct addition of nanoparticles into the sol

Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds

Deep learning systems extensively use convolution operations to process input data. Though convolution is clearly defined for structured data such as 2D images or 3D volumes, this is not true for other data types such as sparse point clouds. Previous techniques have developed approximations to convolutions for restricted conditions. Unfortunately, their applicability is limited and cannot be used for general point clouds. We propose an efficient and effective method to learn convolutions for non-uniformly sampled point clouds, as they are obtained with modern acquisition techniques. Learning is enabled by four key novelties: first, representing the convolution kernel itself as a multilayer perceptron; second, phrasing convolution as a Monte Carlo integration problem, third, using this notion to combine information from multiple samplings at different levels; and fourth using Poisson disk sampling as a scalable means of hierarchical point cloud learning. The key idea across all these contributions is to guarantee adequate consideration of the underlying non-uniform sample distribution function from a Monte Carlo perspective. To make the proposed concepts applicable to real-world tasks, we furthermore propose an efficient implementation which significantly reduces the GPU memory required during the training process. By employing our method in hierarchical network architectures we can outperform most of the state-of-the-art networks on established point cloud segmentation, classification and normal estimation benchmarks. Furthermore, in contrast to most existing approaches, we also demonstrate the robustness of our method with respect to sampling variations, even when training with uniformly sampled data only. To support the direct application of these concepts, we provide a ready-to-use TensorFlow implementation of these layers at https://github.com/viscom-ulm/MCCNN

A newly described strain of Eimeria arloingi (strain A) belongs to the phylogenetic group of ruminant-infecting pathogenic species, which replicate in host endothelial cells in vivo

Coccidiosis caused by Eimeria species is an important disease worldwide, particularly in ruminants and poultry. Eimeria infection can result in significant economic losses due to costs associated with treatment and slower growth rates, or even with mortality of heavily infected individuals. In goat production, a growing industry due to increasing demand for caprine products worldwide, coccidiosis is caused by several Eimeria species with E. arloingi and E. ninakohlyakimovae the most pathogenic. The aims of this study were genetic characterization of a newly isolated European E. arloingi strain (A) and determination of phylogenetic relationships with Eimeria species from other ruminants. Therefore, a DNA sequence of E. arloingi strain (A) containing 2290 consensus nucleotides (the majority of 18S rDNA, complete ITS-1 and 5.8S sequences, and the partial ITS-2) was amplified and phylogenetic relationship determined with the most similar sequences available on GenBank. The phylogenetic tree presented a branch constituted by bovine Eimeria species plus E. arloingi, and another one exclusively populated by ovine Eimeria species. Moreover, E. arloingi, E. bovis and E. zuernii, which all replicate in host intestinal endothelial cells of the lacteals, were found within the same cluster. This study gives new insights into the evolutionary phylogenetic relationships of this newly described caprine Eimeria strain and confirmed its close relationship to other highly pathogenic ruminant Eimeria species characterized by macromeront formation in host endothelial cells of the central lymph capillaries of the small intestine

Comparison of the information provided by electronic health records data and a population health survey to estimate prevalence of selected health conditions and multimorbidity

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.Background Health surveys (HS) are a well-established methodology for measuring the health status of a population. The relative merit of using information based on HS versus electronic health records (EHR) to measure multimorbidity has not been established. Our study had two objectives: 1) to measure and compare the prevalence and distribution of multimorbidity in HS and EHR data, and 2) to test specific hypotheses about potential differences between HS and EHR reporting of diseases with a symptoms-based diagnosis and those requiring diagnostic testing. Methods Cross-sectional study using data from a periodic HS conducted by the Catalan government and from EHR covering 80% of the Catalan population aged 15 years and older. We determined the prevalence of 27 selected health conditions in both data sources, calculated the prevalence and distribution of multimorbidity (defined as the presence of ≥2 of the selected conditions), and determined multimorbidity patterns. We tested two hypotheses: a) health conditions requiring diagnostic tests for their diagnosis and management would be more prevalent in the EHR; and b) symptoms-based health problems would be more prevalent in the HS data. Results We analysed 15,926 HS interviews and 1,597,258 EHRs. The profile of the EHR sample was 52% women, average age 47 years (standard deviation: 18.8), and 68% having at least one of the selected health conditions, the 3 most prevalent being hypertension (20%), depression or anxiety (16%) and mental disorders (15%). Multimorbidity was higher in HS than in EHR data (60% vs. 43%, respectively, for ages 15-75+, P <0.001, and 91% vs. 83% in participants aged ≥65 years, P <0.001). The most prevalent multimorbidity cluster was cardiovascular. Circulation disorders (other than varicose veins), chronic allergies, neck pain, haemorrhoids, migraine or frequent headaches and chronic constipation were more prevalent in the HS. Most symptomatic conditions (71%) had a higher prevalence in the HS, while less than a third of conditions requiring diagnostic tests were more prevalent in EHR. Conclusions Prevalence of multimorbidity varies depending on age and the source of information. The prevalence of self-reported multimorbidity was significantly higher in HS data among younger patients; prevalence was similar in both data sources for elderly patients. Self-report appears to be more sensitive to identifying symptoms-based conditions. A comprehensive approach to the study of multimorbidity should take into account the patient perspective.Ministry of Science and Innovation through the Instituto Carlos IIIISCiii-RETICSInstitut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol