203 research outputs found

    PifPaf: Composite Fields for Human Pose Estimation

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    We propose a new bottom-up method for multi-person 2D human pose estimation that is particularly well suited for urban mobility such as self-driving cars and delivery robots. The new method, PifPaf, uses a Part Intensity Field (PIF) to localize body parts and a Part Association Field (PAF) to associate body parts with each other to form full human poses. Our method outperforms previous methods at low resolution and in crowded, cluttered and occluded scenes thanks to (i) our new composite field PAF encoding fine-grained information and (ii) the choice of Laplace loss for regressions which incorporates a notion of uncertainty. Our architecture is based on a fully convolutional, single-shot, box-free design. We perform on par with the existing state-of-the-art bottom-up method on the standard COCO keypoint task and produce state-of-the-art results on a modified COCO keypoint task for the transportation domain.Comment: CVPR 201

    Litho-sedimentological and morphodynamic characterization of the Pisa Province coastal area (northern Tuscany, Italy)

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    In this paper litho-sedimentological and morphodynamic maps of the coastal sector belonging to the Pisa Province are presented as an example of how increasing the accessibility to data on lithology, sedimentology, and morphodynamics may lead to a better approach to coastal management. The database used to build the maps includes an original rendering of remote sensing data (aerial imagery) and new field data (geologic survey), as well as data retrieved from the scientific literature (grain-size and past coastline positions). The maps show that the geometry of beach ridges is an indication of the evolution of the Arno River delta in the last 3000 years, highlighting the relationships between geological aspects and morphodynamic features. The maps represent the synthesis of different data available in the database, and they may be a useful support to coastal management as they are more easily understandable and straightforward than the database from which are created

    PifPaf: Composite Fields for Human Pose Estimation

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    We propose a new bottom-up method for multi-person 2D human pose estimation that is particularly well suited for urban mobility such as self-driving cars and delivery robots. The new method, PifPaf, uses a Part Intensity Field (PIF) to localize body parts and a Part Association Field (PAF) to associate body parts with each other to form full human poses. Our method outperforms previous methods at low resolution and in crowded, cluttered and occluded scenes thanks to (i) our new composite field PAF encoding fine-grained information and (ii) the choice of Laplace loss for regressions which incorporates a notion of uncertainty. Our architecture is based on a fully convolutional, single shot, box-free design. We perform on par with the existing state-of-the-art bottom-up method on the standard COCO keypoint task and produce state-of-the-art results on a modified COCO keypoint task for the transportation domain

    MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation

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    We tackle the fundamentally ill-posed problem of 3D human localization from monocular RGB images. Driven by the limitation of neural networks outputting point estimates, we address the ambiguity in the task by predicting confidence intervals through a loss function based on the Laplace distribution. Our architecture is a light-weight feed-forward neural network that predicts 3D locations and corresponding confidence intervals given 2D human poses. The design is particularly well suited for small training data, cross-dataset generalization, and real-time applications. Our experiments show that we (i) outperform state-of-the-art results on KITTI and nuScenes datasets, (ii) even outperform a stereo-based method for far-away pedestrians, and (iii) estimate meaningful confidence intervals. We further share insights on our model of uncertainty in cases of limited observations and out-of-distribution samples

    Recruitment of RNA polymerase is a rate-limiting step for the activation of the σ54 promoter Pu of Pseudomonas putida

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    The activity of the \u3c354-promoter Pu of Pseudomonas putida was examined in vitro with a DNA template lacking upstream activating sequences, such that RNA polymerase can be activated by the enhancer-binding protein XylR only from solution. Although the transcription activation pathway in this system lacked the step of integration host factor (IHF)-mediated looping of the XylR\ub7DNA complex toward the prebound RNA polymerase, IHF still stimulated promoter activity. The positive effect of IHF became evident not only with XylR from solution, but also with other \u3c354-dependent activators such as NtrC and NifA. Furthermore, an equivalent outcome was shown for the nonspecific DNA-binding protein HU. This stimulation of transcription in the absence of the enhancer was traced to the recruitment of RNA polymerase (i.e. increased efficiency of formation of closed complexes) brought about by IHF or HU binding. Thus, under limiting concentrations of the polymerase, the factor-mediated binding of the enzyme to Pu seems to enter a kinetic checkpoint in the system that prevents the XylR-mediated formation of an open complex

    Simulations of Optical Emissions for Attacking AES and Masked AES

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    In this paper we present a novel attack based on photonic emission analysis targeting software implementations of AES. We focus on the particular case in which the attacker can collect the photonic emission of a limited number of sense amplifiers (e.g. only one) of the SRAM storing the S-Box. The attack consists in doing hypothesis on the secret key based on the knowledge of the partial output of the SubBytes operation. We also consider the possibility to attack a masked implementation of AES using the photonic emission analysis. In the case of masking, the attacker needs 2 leakages of the same encryption to overcome the randomization of the masks. For our analysis, we assume the same physical setup described in other previous works. Reported results are based on simulations with some hypothesis on the probability of photonic emission of a single transistor
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