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

Editorial: Modulating Prokaryotic Lifestyle by DNA-Binding Proteins: Learning from (Apparently) Simple Systems

While this Editorial was written, authors were funded by Spanish MINECO, grants CSD2008-00013, BIO2013-49148-C2-2-R, BIO2013-49148-C2-1-R, BIO2015-69085-REDC, BIO2016-76412-C2-2-R-AEI/FEDER, UE, and BIO2016-76412-C2-1-R-AEI/FEDER, UE.Peer reviewedPeer Reviewe

Thermal Conductivity of Partially Graphitized Biocarbon Obtained by Carbonization of Medium-Density Fiberboard in the Presence of a Ni-Based Catalyst

The thermal conductivity k and resistivity ρ of biocarbon matrices, prepared by carbonizing medium-density fiberboard at Tcarb = 850 and 1500°C in the presence of a Ni-based catalyst (samples MDFC( Ni)) and without a catalyst (samples MDF-C), have been measured for the first time in the temperature range of 5–300 K. X-ray diffraction analysis has revealed that the bulk graphite phase arises only at Tcarb = 1500°C. It has been shown that the temperature dependences of the thermal conductivity of samples MDFC- 850 and MDF-C-850(Ni) in the range of 80–300 K are to each other and follow the law of k(T) ~ T1.65, but the use of the Ni-catalyst leads to an increase in the thermal conductivity by a factor of approximately 1.5, due to the formation of a greater fraction of the nanocrystalline phase in the presence of the Ni-catalyst at Tcarb = 850°C. In biocarbon MDF-C-1500 prepared without a catalyst, the dependence is k(T) ~ T1.65, and it is controlled by the nanocrystalline phase. In MDF-C-1500(Ni), the bulk graphite phase formed increases the thermal conductivity by a factor of 1.5–2 compared to the thermal conductivity of MDF-C-1500 in the entire temperature range of 5–300 K; k(T = 300 K) reaches the values of ~10 W m–1 K–1, characteristic of biocarbon obtained without a catalyst only at high temperatures of Tcarb = 2400°C. It has been shown that MDF-C-1500(Ni) in the temperature range of 40‒300 K is characterized by the dependence, k(T) ~ T1.3, which can be described in terms of the model of partially graphitized biocarbon as a composite of an amorphous matrix with spherical inclusions of the graphite phaseRussian Foundation for Basic Research 14-03- 0049

Self-Supervision for 3D Real-World Challenges

We consider several possible scenarios involving synthetic and real-world point clouds where supervised learning fails due to data scarcity and large domain gaps. We propose to enrich standard feature representations by leveraging self-supervision through a multi-task model that can solve a 3D puzzle while learning the main task of shape classification or part segmentation

Joint Supervised and Self-Supervised Learning for 3D Real-World Challenges

Point cloud processing and 3D shape understanding are very challenging tasks for which deep learning techniques have demonstrated great potentials. Still further progresses are essential to allow artificial intelligent agents to interact with the real world, where the amount of annotated data may be limited and integrating new sources of knowledge becomes crucial to support autonomous learning. Here we consider several possible scenarios involving synthetic and real-world point clouds where supervised learning fails due to data scarcity and large domain gaps. We propose to enrich standard feature representations by leveraging self-supervision through a multi-task model that can solve a 3D puzzle while learning the main task of shape classification or part segmentation. An extensive analysis investigating few-shot, transfer learning and cross-domain settings shows the effectiveness of our approach with state-of-the-art results for 3D shape classification and part segmentation

Selection of sensors by a new methodology coupling a classification technique and entropy criteria

Complex industrial processes invest a lot of money in sensors and automation devices to monitor and supervise the process in order to guarantee the production quality and the plant and operators safety. Fault detection is one of the multiple tasks of process monitoring and it critically depends on the sensors that measure the significant process variables. Nevertheless, most of the works on fault detection and diagnosis found in literature emphasis more on developing procedures to perform diagnosis given a set of sensors, and less on determining the actual location of sensors for efficient identification of faults. A methodology based on learning and classification techniques and on the information quantity measured by the Entropy concept, is proposed in order to address the problem of sensor location for fault identification. The proposed methodology has been applied to a continuous intensified reactor, the "Open Plate Reactor (OPR)", developed by Alfa Laval and studied at the Laboratory of Chemical Engineering of Toulouse. The different steps of the methodology are explained through its application to the carrying out of an exothermic reaction

Modulating prokaryotic lifestyle by DNA-binding proteins: Learning from (apparently) simple systems

Within the research in Molecular Biology, one important field along the years has been the analyses on how prokaryotes regulate the expression of their genes and what the consequences of these activities are. Prokaryotes have attracted the interests of researchers not only because the processes taking place in their world are important to cells, but also because many of the effects often can be readily measured, both at the single cell level and in large populations. Contributing to the interest of the present topic is the fact that modulation of gene activity involves the sensing of intra- and inter-cellular conditions, DNA binding and DNA dynamics, and interaction with the replication/transcription machinery of the cell. All of these processes are fundamental to the operation of a biological entity and they condition its lifestyle. Further, the discoveries achieved in the bacterial world have been of ample use in eukaryotes. In addition to the fundamental interest of understanding modulation of prokaryotic lifestyle by DNA-binding proteins, there is an added interest from the healthcare point of view. As it is well-known the antibiotic-resistance strains of pathogenic bacteria are a major world problem, so that there is an urgent need of innovative approaches to tackle it. Human and animal infectious diseases impose staggering costs worldwide in terms of loss of human life and livestock, diminished productivity, and the heavy economic burden of disease. The global dimension of international trade, personal travel, and population migration expands at an ever-accelerating rate. This increasing mobility results in broader and quicker dissemination of bacterial pathogens and in rapid spread of antibiotic resistance. The majority of the newly acquired resistances are horizontally spread among bacteria of the same or different species by processes of lateral (horizontal) gene transfer, so that discovery of new antibiotics is not the definitive solution to fighting infectious diseases. There is an absolute need of finding novel alternatives to the "classical" approach to treat infections by bacterial pathogens, and these new ways must include the exploration and introduction of novel antibacterials, the development of alternative strategies, and the finding of novel bacterial targets. However, all these approaches will result in a stalemate if we, researchers, are not able to achieve a better understanding of the mechanistic processes underlying bacterial gene expression. It is, then, imperative to continue gaining insight into the basic mechanisms by which bacterial cells regulate the expression of their genes. That is why our Research Topic hosted by Frontiers in Molecular Biosciences was timely, and the output of it offers novel and up-to-date points of view to the "simple" bacterial world