Domain Generalization by Solving Jigsaw Puzzles

Human adaptability relies crucially on the ability to learn and merge knowledge both from supervised and unsupervised learning: the parents point out few important concepts, but then the children fill in the gaps on their own. This is particularly effective, because supervised learning can never be exhaustive and thus learning autonomously allows to discover invariances and regularities that help to generalize. In this paper we propose to apply a similar approach to the task of object recognition across domains: our model learns the semantic labels in a supervised fashion, and broadens its understanding of the data by learning from self-supervised signals how to solve a jigsaw puzzle on the same images. This secondary task helps the network to learn the concepts of spatial correlation while acting as a regularizer for the classification task. Multiple experiments on the PACS, VLCS, Office-Home and digits datasets confirm our intuition and show that this simple method outperforms previous domain generalization and adaptation solutions. An ablation study further illustrates the inner workings of our approach.Comment: Accepted at CVPR 2019 (oral

Multi-Modal RGB-D Scene Recognition Across Domains

Scene recognition is one of the basic problems in computer vision research with extensive applications in robotics. When available, depth images provide helpful geometric cues that complement the RGB texture information and help to identify discriminative scene image features. Depth sensing technology developed fast in the last years and a great variety of 3D cameras have been introduced, each with different acquisition properties. However, those properties are often neglected when targeting big data collections, so multi-modal images are gathered disregarding their original nature. In this work, we put under the spotlight the existence of a possibly severe domain shift issue within multi-modality scene recognition datasets. As a consequence, a scene classification model trained on one camera may not generalize on data from a different camera, only providing a low recognition performance. Starting from the well-known SUN RGB-D dataset, we designed an experimental testbed to study this problem and we use it to benchmark the performance of existing methods. Finally, we introduce a novel adaptive scene recognition approach that leverages self-supervised translation between modalities. Indeed, learning to go from RGB to depth and vice-versa is an unsupervised procedure that can be trained jointly on data of multiple cameras and may help to bridge the gap among the extracted feature distributions. Our experimental results confirm the effectiveness of the proposed approach

A model based in the radius of vesicles to predict the number of unilamellar liposomes

In particulate systems such as liposomes, concentration units are not enough to describe the drug distribution, as suspensions are not homogeneous. In certain in vitro assays, exposure to different number of particles introduces an extra variable regarding to contact phenomena. the aim is to achieve a rapid estimation of the number of unilamellar liposomes in a suspension. A simple mathematical method was developed; variables were the area and molecular weight of lipids, and the mean size of the liposomes. Unilamellar liposomes were prepared. Size was determined by dynamic light scattering, and then the number of particles were determined by tunable resistive pulse sensing. there was about a 90% coincidence between the theoretical results and the number of counted liposomes. This model could be useful for interpretation of in vitro experiments, when results could depend on the distribution of actives into different quantities of liposomes.Fil: Martinetti Montanari, Jorge Anibal. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Multidisciplinario de Biología Celular. Grupo Vinculado al IMBICE - Grupo de Biología Estructural y Biotecnología - Universidad Nacional de Quilmes - GBEyB | Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Multidisciplinario de Biología Celular. Grupo Vinculado al IMBICE - Grupo de Biología Estructural y Biotecnología - Universidad Nacional de Quilmes - GBEyB | Universidad Nacional de La Plata. Instituto Multidisciplinario de Biología Celular. Grupo Vinculado al IMBICE - Grupo de Biología Estructural y Biotecnología - Universidad Nacional de Quilmes - GBEyB; ArgentinaFil: Bucci, Paula Lorena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Multidisciplinario de Biología Celular. Grupo Vinculado al IMBICE - Grupo de Biología Estructural y Biotecnología - Universidad Nacional de Quilmes - GBEyB | Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Multidisciplinario de Biología Celular. Grupo Vinculado al IMBICE - Grupo de Biología Estructural y Biotecnología - Universidad Nacional de Quilmes - GBEyB | Universidad Nacional de La Plata. Instituto Multidisciplinario de Biología Celular. Grupo Vinculado al IMBICE - Grupo de Biología Estructural y Biotecnología - Universidad Nacional de Quilmes - GBEyB; ArgentinaFil: Alonso, Silvia del Valle. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Multidisciplinario de Biología Celular. Grupo Vinculado al IMBICE - Grupo de Biología Estructural y Biotecnología - Universidad Nacional de Quilmes - GBEyB | Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Multidisciplinario de Biología Celular. Grupo Vinculado al IMBICE - Grupo de Biología Estructural y Biotecnología - Universidad Nacional de Quilmes - GBEyB | Universidad Nacional de La Plata. Instituto Multidisciplinario de Biología Celular. Grupo Vinculado al IMBICE - Grupo de Biología Estructural y Biotecnología - Universidad Nacional de Quilmes - GBEyB; Argentin

Microcystin Contamination in Sea Mussel Farms from the Italian Southern Adriatic Coast following Cyanobacterial Blooms in an Artificial Reservoir

An experimental study was performed in 2009-2010 to investigate the polluting effect of eutrophic inland waters communicating with the sea coast. The study was planned after a heavy and long-lastingPlanktothrix rubescensbloom occurred in the Lake Occhito, an artificial reservoir. The waters of the reservoir flow into the southern Adriatic Sea, near several marine breeding ofMytilus galloprovincialismussels, a typical seafood from the Apulia region (Southern Italy). A monitoring study of water and mussels from the sea coast of northern Apulia region and on the Occhito reservoir was carried out over twelve months, to get more information regarding the contamination by cyanobacteria and related cyanotoxins. Elisa immunoassay analyses estimated total microcystin amounts from 1.73 to 256 ng/g in mussels, up to 0.61 μg/L in sea water and up to 298.7 μg/L in lake water. Analyses of some samples of free-living marine clams as well as of marine and freshwater fish proved microcystin contamination. Selective confirmatory analyses by LC/ESI-Q-ToF-MS/MS on some mussel samples identified the microcystin desMe-MC-RR as the major toxin; this compound has been reported in the literature as a specific marker toxin ofPlanktothrix rubescensblooms. Our study describes for the first time the direct relationship between environmental pollution and food safety, caused by seafood contamination from freshwater toxic blooms

Shape matters: long-range transport of microplastic fibers in the atmosphere

Deposition of giant microplastic particles from the atmosphere has been observed in the most remote places on Earth. However, their deposition patterns are difficult to reproduce using current atmospheric transport models. These models usually treat particles as perfect spheres, whereas the real shapes of microplastic particles are often far from spherical. Such particles experience lower settling velocities compared to volume-equivalent spheres, leading to longer atmospheric transport. Here, we present novel laboratory experiments on the gravitational settling of microplastic fibers in air and find that their settling velocities are reduced by up to 76% compared to spheres of the same volume. An atmospheric transport model constrained with the experimental data shows that shape-corrected settling velocities significantly increase the horizontal and vertical transport of particles. Our model results show that microplastic fibers of about 1 mm length emitted in populated areas can reach extremely remote regions of the globe, including the High Arctic, which is not the case for spheres. We also calculate that fibers with lengths of up to 100 {\mu}m settle slowly enough to be lifted high into the stratosphere, where degradation by ultraviolet radiation may release chlorine and bromine, thus potentially damaging the stratospheric ozone layer. These findings suggest that the growing environmental burden and still increasing emissions of plastics pose multiple threats to life on Earth