18,838 research outputs found

    Deep Dynamic Cloud Lighting

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    Sky illumination is a core source of lighting in rendering, and a substantial amount of work has been developed to simulate lighting from clear skies. However, in reality, clouds substantially alter the appearance of the sky and subsequently change the scene's illumination. While there have been recent advances in developing sky models which include clouds, these all neglect cloud movement which is a crucial component of cloudy sky appearance. In any sort of video or interactive environment, it can be expected that clouds will move, sometimes quite substantially in a short period of time. Our work proposes a solution to this which enables whole-sky dynamic cloud synthesis for the first time. We achieve this by proposing a multi-timescale sky appearance model which learns to predict the sky illumination over various timescales, and can be used to add dynamism to previous static, cloudy sky lighting approaches.Comment: Project page: https://pinarsatilmis.github.io/DDC

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

    Millimeter-wave channel measurements and path loss characterization in a typical indoor office environment

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    In this paper, a path loss characterization at millimeter-wave (mmWave) frequencies is performed in a typical indoor office environment. Path loss results were derived from propagation channel measurements collected in the 25–40 GHz frequency band, in both line-of-sight (LOS) and obstructed-LOS (OLOS) propagation conditions. The channel measurements were performed using a frequency-domain channel sounder, which integrates an amplified radio over fiber (RoF) link to avoid the high losses at mmWave. The path loss was analyzed in the 26 GHz, 28 GHz, 33 GHz and 38 GHz frequency bands through the close-in free space reference distance (CI) and the floating-intercept (FI) models. These models take into account the distance dependence of the path loss for a single frequency. Nevertheless, to jointly study the distance and frequency dependence of the path loss, multi-frequency models were considered. The parameters of the ABG (A-alpha, B-beta and G-gamma) and the close-in free space reference distance with frequency path loss exponent (CIF) models were derived from the channel measurements in the whole 25–40 GHz band under the minimum mean square error (MMSE) approach. The results show that, in general, there is some relationship between the model parameters and the frequency. Path loss exponent (PLE) values smaller than the theoretical free space propagation were obtained, showing that there are a waveguide effect and a constructive interference of multipath components (MPCs). Since the measurements were obtained in the same environment and with the same configuration and measurement setup, it is possible to establish realistic comparisons between the model parameters and the propagation behavior at the different frequencies considered. The results provided here allow us to have a better knowledge of the propagation at mmWave frequencies and may be of interest to other researchers in the simulation and performance evaluation of future wireless communication systems in indoor hotspot environments.This work has been funded in part by the MCIN/AEI/10.13039/501100011033/ through the I+D+i Project under Grant PID2020-119173RB-C21 and Grant PID2020-119173RB-C22, and by COLCIENCIAS in Colombia

    UniverSeg: Universal Medical Image Segmentation

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    While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies, image modalities, or labels. Given a new segmentation task, researchers generally have to train or fine-tune models, which is time-consuming and poses a substantial barrier for clinical researchers, who often lack the resources and expertise to train neural networks. We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training. Given a query image and example set of image-label pairs that define a new segmentation task, UniverSeg employs a new Cross-Block mechanism to produce accurate segmentation maps without the need for additional training. To achieve generalization to new tasks, we have gathered and standardized a collection of 53 open-access medical segmentation datasets with over 22,000 scans, which we refer to as MegaMedical. We used this collection to train UniverSeg on a diverse set of anatomies and imaging modalities. We demonstrate that UniverSeg substantially outperforms several related methods on unseen tasks, and thoroughly analyze and draw insights about important aspects of the proposed system. The UniverSeg source code and model weights are freely available at https://universeg.csail.mit.eduComment: Victor and Jose Javier contributed equally to this work. Project Website: https://universeg.csail.mit.ed

    ARA-net: an attention-aware retinal atrophy segmentation network coping with fundus images

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    BackgroundAccurately detecting and segmenting areas of retinal atrophy are paramount for early medical intervention in pathological myopia (PM). However, segmenting retinal atrophic areas based on a two-dimensional (2D) fundus image poses several challenges, such as blurred boundaries, irregular shapes, and size variation. To overcome these challenges, we have proposed an attention-aware retinal atrophy segmentation network (ARA-Net) to segment retinal atrophy areas from the 2D fundus image.MethodsIn particular, the ARA-Net adopts a similar strategy as UNet to perform the area segmentation. Skip self-attention connection (SSA) block, comprising a shortcut and a parallel polarized self-attention (PPSA) block, has been proposed to deal with the challenges of blurred boundaries and irregular shapes of the retinal atrophic region. Further, we have proposed a multi-scale feature flow (MSFF) to challenge the size variation. We have added the flow between the SSA connection blocks, allowing for capturing considerable semantic information to detect retinal atrophy in various area sizes.ResultsThe proposed method has been validated on the Pathological Myopia (PALM) dataset. Experimental results demonstrate that our method yields a high dice coefficient (DICE) of 84.26%, Jaccard index (JAC) of 72.80%, and F1-score of 84.57%, which outperforms other methods significantly.ConclusionOur results have demonstrated that ARA-Net is an effective and efficient approach for retinal atrophic area segmentation in PM

    Évaluation de l'impact du changement climatique sur la défoliation de l'épinette noire par la tordeuse des bourgeons de l'épinette

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    Les modèles écologiques actuels prévoient de profonds effets des changements climatiques sur les régimes de perturbations naturelles des forêts. La tordeuse des bourgeons de l'épinette (Choristoneura fumiferana) (TBE) est le principal insecte défoliateur dans l'est de l'Amérique du Nord. Les épidémies de TBE ont un impact majeur sur la structure et la fonction de la forêt boréale canadienne puisque la défoliation entraîne une diminution de la croissance des arbres, une augmentation de la mortalité et une baisse de la productivité forestière. Les épidémies de TBE sont devenues plus sévères au cours du dernier siècle à cause des changements climatiques; cependant, nous savons peu de choses sur la manière dont l'effet intégré du climat et du TBE modifie la croissance des espèces hôtes. Nous évaluons ici comment l’interaction entre le climat et la gravité de l'épidémie affecte la croissance de l'épinette noire (Picea mariana) pendant l'épidémie de TBE qui a eu lieu entre 1968-1988 et 2006-2017. Nous avons compilé des séries dendrochronologiques (2271 arbres), des données de sévérité de l'épidémie (estimée par la défoliation aérienne observée) et des données climatiques pour 164 sites au Québec, Canada. Nous avons utilisé un modèle linéaire à effets mixtes pour déterminer l'impact des paramètres climatiques, de la défoliation cumulative (des cinq années précédentes) et de leur effet couplé sur la croissance en surface terrière. À la gravité maximale de l'épidémie, la croissance en surface terrière de l'épinette noire a été réduite de 14 à 18 % sur les cinq années en raison de l'effet TBE. Cette croissance a été affectée par le climat : des températures minimales estivales précédentes plus élevées et un indice d'humidité climatique estival plus élevé ont réduit la croissance de 11 % et 4 % respectivement. En revanche, l'effet négatif de la défoliation a été atténué de 9% pour une température minimale plus élevée au printemps précédent et de 7% pour une température maximale plus élevée l'été précédent. Cette étude améliore notre compréhension des effets combinés de la TBE et du climat et aide à prévoir les dommages futurs causés par cet insecte dans les peuplements forestiers afin de soutenir la gestion durable des forêts. Nous recommandons également que les projections des écosystèmes dans la forêt boréale incluent plusieurs classes de défoliation de la TBE et plusieurs scénarios climatiques

    Intra-night optical flux and polarization variability of BL~Lacertae during its 2020 - 2021 high state

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    In this work, we report the presence of rapid intra-night optical variations in both -- flux and polarization of the blazar BL Lacertae during its unprecedented 2020--2021 high state of brightness. The object showed significant flux variability and some color changes, but no firmly detectable time delays between the optical bands. The linear polarization was also highly variable in both -- polarization degree and angle (EVPA). The object was observed from several observatories throughout the world, covering in a total of almost 300 hours during 66 nights. Based on our results, we suggest, that the changing Doppler factor of an ensemble of independent emitting regions, travelling along a curved jet that at some point happens to be closely aligned with the line of sight can successfully reproduce our observations during this outburst. This is one of the most extensive variability studies of the optical polarization of a blazar on intra-night timescales.Comment: 23 pages,7 figures, 5 Tables (2 as appendix). Accepted for publication in MNRA

    A citizen science approach to the characterisation and modelling of urban pluvial flooding

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    Urban pluvial flooding (UPF), a growing challenge across cities worldwide that is expected to worsen due to climate change and urbanisation, requires comprehensive response strategies. However, the characterisation and simulation of UPF is more complex than traditional catchment hydrological modelling because UPF is driven by a complex set of interconnected factors and modelling constraints. Different integrated approaches have attempted to address UPF by coupling humans and environmental systems and reflecting on the possible outcomes from the interactions among varied disciplines. Nonetheless, it is argued that current integrated approaches are insufficient. To further improve the characterisation and modelling of UPF, this study advances a citizen science approach that integrates local knowledge with the understanding and interpretation of UPF. The proposed framework provides an avenue to couple quantitative and qualitative community-based observations with traditional sources of hydro-information. This approach allows researchers and practitioners to fill spatial and temporal data gaps in urban catchments and hydrologic/hydrodynamic models, thus yielding a more accurate characterisation of local catchment response and improving rainfall-runoff modelling of UPF. The results of applying this framework indicate how community-based practices provide a bi-directional learning context between experts and residents, which can contribute to resilience building by providing UPF knowledge necessary for risk reduction and response to extreme flooding events

    Similarity and variability of blocked weather-regime dynamics in the Atlantic–European region

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    Weather regimes govern an important part of the sub-seasonal variability of the mid-latitude circulation. Due to their role in weather extremes and atmospheric predictability, regimes that feature a blocking anticyclone are of particular interest. This study investigates the dynamics of these “blocked” regimes in the North Atlantic–European region from a year-round perspective. For a comprehensive diagnostic, wave activity concepts and a piecewise potential vorticity (PV) tendency framework are combined. The latter essentially quantifies the well-established PV perspective of mid-latitude dynamics. The four blocked regimes (namely Atlantic ridge, European blocking, Scandinavian blocking, and Greenland blocking) during the 1979–2021 period of ERA5 reanalysis are considered. Wave activity characteristics exhibit distinct differences between blocked regimes. After regime onset, Greenland blocking is associated with a suppression of wave activity flux, whereas Atlantic ridge and European blocking are associated with a northward deflection of the flux without a clear net change. During onset, the envelope of Rossby wave activity retracts upstream for Greenland blocking, whereas the envelope extends downstream for Atlantic ridge and European blocking. Scandinavian blocking exhibits intermediate wave activity characteristics. From the perspective of piecewise PV tendencies projected onto the respective regime pattern, the dynamics that govern regime onset exhibit a large degree of similarity: linear Rossby wave dynamics and nonlinear eddy PV fluxes dominate and are of approximately equal relative importance, whereas baroclinic coupling and divergent amplification make minor contributions. Most strikingly, all blocked regimes exhibit very similar (intra-regime) variability: a retrograde and an upstream pathway to regime onset. The retrograde pathway is dominated by nonlinear PV eddy fluxes, whereas the upstream pathway is dominated by linear Rossby wave dynamics. Importantly, there is a large degree of cancellation between the two pathways for some of the mechanisms before regime onset. The physical meaning of a regime-mean perspective before onset can thus be severely limited. Implications of our results for understanding predictability of blocked regimes are discussed. Further discussed are the limitations of projected tendencies in capturing the importance of moist-baroclinic growth, which tends to occur in regions where the amplitude of the regime pattern, and thus the projection onto it, is small. Finally, it is stressed that this study investigates the variability of the governing dynamics without prior empirical stratification of data by season or by type of regime transition. It is demonstrated, however, that our dynamics-centered approach does not merely reflect variability that is associated with these factors. The main modes of dynamical variability revealed herein and the large similarity of the blocked regimes in exhibiting this variability are thus significant results.</p

    Seer: Language Instructed Video Prediction with Latent Diffusion Models

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    Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video prediction (TVP) is an essential task to facilitate general robot policy learning, i.e., predicting future video frames with a given language instruction and reference frames. It is a highly challenging task to ground task-level goals specified by instructions and high-fidelity frames together, requiring large-scale data and computation. To tackle this task and empower robots with the ability to foresee the future, we propose a sample and computation-efficient model, named \textbf{Seer}, by inflating the pretrained text-to-image (T2I) stable diffusion models along the temporal axis. We inflate the denoising U-Net and language conditioning model with two novel techniques, Autoregressive Spatial-Temporal Attention and Frame Sequential Text Decomposer, to propagate the rich prior knowledge in the pretrained T2I models across the frames. With the well-designed architecture, Seer makes it possible to generate high-fidelity, coherent, and instruction-aligned video frames by fine-tuning a few layers on a small amount of data. The experimental results on Something Something V2 (SSv2) and Bridgedata datasets demonstrate our superior video prediction performance with around 210-hour training on 4 RTX 3090 GPUs: decreasing the FVD of the current SOTA model from 290 to 200 on SSv2 and achieving at least 70\% preference in the human evaluation.Comment: 17 pages, 15 figure
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