Multimodal Differential Emission Measure in the Solar Corona

The Atmospheric Imaging Assembly (AIA) telescope on board the Solar Dynamics Observatory (SDO) provides coronal EUV imaging over a broader temperature sensitivity range than the previous generations of instruments (EUVI, EIT, and TRACE). Differential emission measure tomography (DEMT) of the solar corona based on AIA data is presented here for the first time. The main product of DEMT is the three-dimensional (3D) distribution of the local differential emission measure (LDEM). While in previous studies, based on EIT or EUVI data, there were 3 available EUV bands, with a sensitivity range $\sim 0.60 - 2.70$ MK, the present study is based on the 4 cooler AIA bands (aimed at studying the quiet sun), sensitive to the range $\sim 0.55 - 3.75$ MK. The AIA filters allow exploration of new parametric LDEM models. Since DEMT is better suited for lower activity periods, we use data from Carrington Rotation 2099, when the Sun was in its most quiescent state during the AIA mission. Also, we validate the parametric LDEM inversion technique by applying it to standard bi-dimensional (2D) differential emission measure (DEM) analysis on sets of simultaneous AIA images, and comparing the results with DEM curves obtained using other methods. Our study reveals a ubiquitous bimodal LDEM distribution in the quiet diffuse corona, which is stronger for denser regions. We argue that the nanoflare heating scenario is less likely to explain these results, and that alternative mechanisms, such as wave dissipation appear better supported by our results.Comment: 52 pages, 18 figure

Multimodal Attention Networks for Low-Level Vision-and-Language Navigation

Vision-and-Language Navigation (VLN) is a challenging task in which an agent needs to follow a language-specified path to reach a target destination. The goal gets even harder as the actions available to the agent get simpler and move towards low-level, atomic interactions with the environment. This setting takes the name of low-level VLN. In this paper, we strive for the creation of an agent able to tackle three key issues: multi-modality, long-term dependencies, and adaptability towards different locomotive settings. To that end, we devise "Perceive, Transform, and Act" (PTA): a fully-attentive VLN architecture that leaves the recurrent approach behind and the first Transformer-like architecture incorporating three different modalities -- natural language, images, and low-level actions for the agent control. In particular, we adopt an early fusion strategy to merge lingual and visual information efficiently in our encoder. We then propose to refine the decoding phase with a late fusion extension between the agent's history of actions and the perceptual modalities. We experimentally validate our model on two datasets: PTA achieves promising results in low-level VLN on R2R and achieves good performance in the recently proposed R4R benchmark. Our code is publicly available at https://github.com/aimagelab/perceive-transform-and-act

One Week of Motor Adaptation Induces Structural Changes in Primary Motor Cortex That Predict Long-Term Memory One Year Later

The neural bases of motor adaptation have been extensively explored in human and non-human primates. A network including the cerebellum, primary motor and the posterior parietal cortex appears to be crucial for this type of learning. Yet, to date, it is unclear whether these regions contribute directly or indirectly to the formation of motor memories. Here we trained subjects on a complex visuomotor rotation associated with long-term memory (in the order of months) to identify potential sites of structural plasticity induced by adaptation. One week of training led to i) an increment in local gray-matter concentration over the hand area of the contralateral primary motor cortex and ii) an increase in fractional anisotropy in an area underneath this region that correlated with the speed of learning. Moreover, the change in gray matter concentration measured immediately after training predicted improvements in the speed of learning during re-adaptation one year later. Our study suggests that motor adaptation induces structural plasticity in primary motor circuits. In addition, it provides the first piece of evidence indicating that early structural changes induced by motor learning may impact on behavior up to one year after training.Fil: Landi, Sofía Mariana. Universidad de Buenos Aires. Facultad de Medicina. Departamento de Ciencias Fisiológicas. Cátedra de Fisiologia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay; ArgentinaFil: Baguear, Miguel Federico. Universidad de Buenos Aires. Facultad de Medicina. Departamento de Ciencias Fisiológicas. Cátedra de Fisiologia; ArgentinaFil: Della Maggiore, Valeria Monica. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Departamento de Ciencias Fisiológicas. Cátedra de Fisiologia; Argentin

Spot the Difference: A Novel Task for Embodied Agents in Changing Environments

Embodied AI is a recent research area that aims at creating intelligent agents that can move and operate inside an environment. Existing approaches in this field demand the agents to act in completely new and unexplored scenes. However, this setting is far from realistic use cases that instead require executing multiple tasks in the same environment. Even if the environment changes over time, the agent could still count on its global knowledge about the scene while trying to adapt its internal representation to the current state of the environment. To make a step towards this setting, we propose Spot the Difference: a novel task for Embodied AI where the agent has access to an outdated map of the environment and needs to recover the correct layout in a fixed time budget. To this end, we collect a new dataset of occupancy maps starting from existing datasets of 3D spaces and generating a number of possible layouts for a single environment. This dataset can be employed in the popular Habitat simulator and is fully compliant with existing methods that employ reconstructed occupancy maps during navigation. Furthermore, we propose an exploration policy that can take advantage of previous knowledge of the environment and identify changes in the scene faster and more effectively than existing agents. Experimental results show that the proposed architecture outperforms existing state-of-the-art models for exploration on this new setting.Comment: Accepted by 26TH International Conference on Pattern Recognition (ICPR 2022

Out of the Box: Embodied Navigation in the Real World

The research field of Embodied AI has witnessed substantial progress in visual navigation and exploration thanks to powerful simulating platforms and the availability of 3D data of indoor and photorealistic environments. These two factors have opened the doors to a new generation of intelligent agents capable of achieving nearly perfect PointGoal Navigation. However, such architectures are commonly trained with millions, if not billions, of frames and tested in simulation. Together with great enthusiasm, these results yield a question: how many researchers will effectively benefit from these advances? In this work, we detail how to transfer the knowledge acquired in simulation into the real world. To that end, we describe the architectural discrepancies that damage the Sim2Real adaptation ability of models trained on the Habitat simulator and propose a novel solution tailored towards the deployment in real-world scenarios. We then deploy our models on a LoCoBot, a Low-Cost Robot equipped with a single Intel RealSense camera. Different from previous work, our testing scene is unavailable to the agent in simulation. The environment is also inaccessible to the agent beforehand, so it cannot count on scene-specific semantic priors. In this way, we reproduce a setting in which a research group (potentially from other fields) needs to employ the agent visual navigation capabilities as-a-Service. Our experiments indicate that it is possible to achieve satisfying results when deploying the obtained model in the real world

Dress Code: High-Resolution Multi-Category Virtual Try-On

Image-based virtual try-on strives to transfer the appearance of a clothing item onto the image of a target person. Prior work focuses mainly on upper-body clothes (e.g. t-shirts, shirts, and tops) and neglects full-body or lower-body items. This shortcoming arises from a main factor: current publicly available datasets for image-based virtual try-on do not account for this variety, thus limiting progress in the field. To address this deficiency, we introduce Dress Code, which contains images of multi-category clothes. Dress Code is more than 3x larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images (1024 x 768) with front-view, full-body reference models. To generate HD try-on images with high visual quality and rich in details, we propose to learn fine-grained discriminating features. Specifically, we leverage a semantic-aware discriminator that makes predictions at pixel-level instead of image- or patch-level. Extensive experimental evaluation demonstrates that the proposed approach surpasses the baselines and state-of-the-art competitors in terms of visual quality and quantitative results. The Dress Code dataset is publicly available at https://github.com/aimagelab/dress-code.Comment: Dress Code - Video Demo: https://www.youtube.com/watch?v=qr6TW3uTHG