Geometry meets semantics for semi-supervised monocular depth estimation

Depth estimation from a single image represents a very exciting challenge in computer vision. While other image-based depth sensing techniques leverage on the geometry between different viewpoints (e.g., stereo or structure from motion), the lack of these cues within a single image renders ill-posed the monocular depth estimation task. For inference, state-of-the-art encoder-decoder architectures for monocular depth estimation rely on effective feature representations learned at training time. For unsupervised training of these models, geometry has been effectively exploited by suitable images warping losses computed from views acquired by a stereo rig or a moving camera. In this paper, we make a further step forward showing that learning semantic information from images enables to improve effectively monocular depth estimation as well. In particular, by leveraging on semantically labeled images together with unsupervised signals gained by geometry through an image warping loss, we propose a deep learning approach aimed at joint semantic segmentation and depth estimation. Our overall learning framework is semi-supervised, as we deploy groundtruth data only in the semantic domain. At training time, our network learns a common feature representation for both tasks and a novel cross-task loss function is proposed. The experimental findings show how, jointly tackling depth prediction and semantic segmentation, allows to improve depth estimation accuracy. In particular, on the KITTI dataset our network outperforms state-of-the-art methods for monocular depth estimation.Comment: 16 pages, Accepted to ACCV 201

ContextVP: Fully Context-Aware Video Prediction

Video prediction models based on convolutional networks, recurrent networks, and their combinations often result in blurry predictions. We identify an important contributing factor for imprecise predictions that has not been studied adequately in the literature: blind spots, i.e., lack of access to all relevant past information for accurately predicting the future. To address this issue, we introduce a fully context-aware architecture that captures the entire available past context for each pixel using Parallel Multi-Dimensional LSTM units and aggregates it using blending units. Our model outperforms a strong baseline network of 20 recurrent convolutional layers and yields state-of-the-art performance for next step prediction on three challenging real-world video datasets: Human 3.6M, Caltech Pedestrian, and UCF-101. Moreover, it does so with fewer parameters than several recently proposed models, and does not rely on deep convolutional networks, multi-scale architectures, separation of background and foreground modeling, motion flow learning, or adversarial training. These results highlight that full awareness of past context is of crucial importance for video prediction.Comment: 19 pages. ECCV 2018 oral presentation. Project webpage is at https://wonmin-byeon.github.io/publication/2018-ecc

Parton Branching in Color Mutation Model

The soft production problem in hadronic collisions as described in the eikonal color mutation branching model is improved in the way that the initial parton distribution is treated. Furry branching of the partons is considered as a means of describing the nonperturbative process of parton reproduction in soft interaction. The values of all the moments, and $C_q$, for q=2,...,5, as well as their energy dependences can be correctly determined by the use of only two parameters.Comment: 8 pages (LaTeX) + 2 figures (ps files), submitted to Phys. Rev.

Quantifying and Controlling Prethermal Nonergodicity in Interacting Floquet Matter

The use of periodic driving for synthesizing many-body quantum states depends crucially on the existence of a prethermal regime, which exhibits drive-tunable properties while forestalling the effects of heating. This dependence motivates the search for direct experimental probes of the underlying localized nonergodic nature of the wave function in this metastable regime. We report experiments on a many-body Floquet system consisting of atoms in an optical lattice subjected to ultrastrong sign-changing amplitude modulation. Using a double-quench protocol, we measure an inverse participation ratio quantifying the degree of prethermal localization as a function of tunable drive parameters and interactions. We obtain a complete prethermal map of the drive-dependent properties of Floquet matter spanning four square decades of parameter space. Following the full time evolution, we observe sequential formation of two prethermal plateaux, interaction-driven ergodicity, and strongly frequency-dependent dynamics of long-time thermalization. The quantitative characterization of the prethermal Floquet matter realized in these experiments, along with the demonstration of control of its properties by variation of drive parameters and interactions, opens a new frontier for probing far-from-equilibrium quantum statistical mechanics and new possibilities for dynamical quantum engineering

PlanT: Explainable Planning Transformers via Object-Level Representations

Planning an optimal route in a complex environment requires efficientreasoning about the surrounding scene. While human drivers prioritize importantobjects and ignore details not relevant to the decision, learning-basedplanners typically extract features from dense, high-dimensional gridrepresentations containing all vehicle and road context information. In thispaper, we propose PlanT, a novel approach for planning in the context ofself-driving that uses a standard transformer architecture. PlanT is based onimitation learning with a compact object-level input representation. On theLongest6 benchmark for CARLA, PlanT outperforms all prior methods (matching thedriving score of the expert) while being 5.3x faster than equivalentpixel-based planning baselines during inference. Combining PlanT with anoff-the-shelf perception module provides a sensor-based driving system that ismore than 10 points better in terms of driving score than the existing state ofthe art. Furthermore, we propose an evaluation protocol to quantify the abilityof planners to identify relevant objects, providing insights regarding theirdecision-making. Our results indicate that PlanT can focus on the most relevantobject in the scene, even when this object is geometrically distant.<br

Genetic diversity of Verticillium dahliae isolates from olive trees in Algeria

Verticillium wilt of olive trees (Olea europaea L.), a wilt caused by the soil-borne fungus Verticillium dahliae (Kleb), is one of the most serious diseases in Algerian olive groves. To assess the pathogenic and genetic diversity of olive-infecting V. dahliae populations in Algeria, orchards from the two main olive-producing regions (north-western Algeria and Kabylia) were sampled and 27 V. dahliae isolates were recovered. For purposes of comparison, V. dahliae strains from France and Syria were added to the analysis. By means of PCR primers that specifically discriminate between defoliating (D) and non-defoliating (ND) V. dahliae pathotypes it was shown that all V. dahliae isolates belonged to the ND pathotype. The amount of genetic variation between the 43 isolates was assessed by random amplification of polymorphic DNA (RAPD). A total of 16 RAPD haplotypes were found on the basis of the presence or absence of 25 polymorphic DNA fragments. Genotypic diversity between the 27 Algerian isolates was low, with two RAPD haplotypes accounting for 70% of all isolates. Genotypic diversity was however greater between isolates from Kabylia than between isolates from north-western Algeria. Cluster analysis showed that most of the Algerian V. dahliae isolates grouped together with the French and Syrian isolates. On the basis of their ability to form heterokaryons with each other, a subset of 25 olive-pathogenic isolates was grouped into a single vegetative compatibility group (VCG). These results suggest that the olive-infecting V. dahliae populations in Algeria show limited diversity and that caution should be taken to prevent introduction of the D pathotype

Numerical modelling of Bose-Einstein correlations

We propose extension of the algorithm for numerical modelling of Bose-Einstein correlations (BEC), which was presented some time ago in the literature. It is formulated on quantum statistical level for a single event and uses the fact that identical particles subjected to Bose statistics do bunch themselves, in a maximal possible way, in the same cells in phase-space. The bunching effect is in our case obtained in novel way allowing for broad applications and fast numerical calculations. First comparison with $e^+e^-$ annihilations data performed by using simple cascade hadronization model is very encouraging.Comment: LaTeX file and 5 eps file with figures, 9 pages altogethe

Multi-spectral Material Classification in Landscape Scenes Using Commodity Hardware

We investigate the advantages of a stereo, multi-spectral acquisition system for material classication in ground-level landscape images. Our novel system allows us to acquire high-resolution, multi- spectral stereo pairs using commodity photographic equipment. Given additional spectral information we obtain better classication of vege- tation classes than the standard RGB case. We test the system in two modes: splitting the visible spectrum into six bands; and extending the recorded spectrum to near infra-red. Our six-band design is more prac- tical than standard multi-spectral techniques and foliage classication using acquired images compares favourably to simply using a standard camera

Challenges of ”a new hybrid ecosystem”: celebrities, fake news and Covid-19

The complex intertwining of mainstream and social media has resulted in the creation of a “new hybrid ecosystem” in which consumers are primarily engaged with ideas and news posted on social media, that are then transmitted as news in mainstream media (Wheeler 2018). In this new “hyper-connected environment” (Pepper 2018), “fake news” occupies a specific position. The concept of “fake news” is very complex, contradictory and ambivalent because it appears as an umbrella term covering various phenomena and different practices of which some are already known, while others are fairly new (Molina et al. 2021). The new communication environment and the role of fake news as part of it, may also be analysed through the celebrity phenomenon. This paper uses the method of discourse analysis to examine texts on various statements by celebrities about COVID-19, published on two web portals in Croatia (index.hr, 24sata.hr). It becomes clear that celebrities function as very potent sharers of fake news, since consumers of online content give great weight to their actions and statements. On the other hand, mainstream media often act as a corrective to social media, in their efforts to convincingly deny fake news and the celebrities that share them on social media

Event by Event Analysis and Entropy of Multiparticle Systems

The coincidence method of measuring the entropy of a system, proposed some time ago by Ma, is generalized to include systems out of equilibrium. It is suggested that the method can be adapted to analyze multiparticle states produced in high-energy collisions.Comment: 13 pages, 2 figure