Quivers with relations for symmetrizable Cartan matrices and algebraic Lie theory

We give an overview of our effort to introduce (dual) semicanonical bases in the setting of symmetrizable Cartan matrices

Cluster structures on quantum coordinate rings

We show that the quantum coordinate ring of the unipotent subgroup N(w) of a symmetric Kac-Moody group G associated with a Weyl group element w has the structure of a quantum cluster algebra. This quantum cluster structure arises naturally from a subcategory C_w of the module category of the corresponding preprojective algebra. An important ingredient of the proof is a system of quantum determinantal identities which can be viewed as a q-analogue of a T-system. In case G is a simple algebraic group of type A, D, E, we deduce from these results that the quantum coordinate ring of an open cell of a partial flag variety attached to G also has a cluster structure.Comment: v2: minor corrections. v3: references updated, final version to appear in Selecta Mathematic

Categorification of skew-symmetrizable cluster algebras

We propose a new framework for categorifying skew-symmetrizable cluster algebras. Starting from an exact stably 2-Calabi-Yau category C endowed with the action of a finite group G, we construct a G-equivariant mutation on the set of maximal rigid G-invariant objects of C. Using an appropriate cluster character, we can then attach to these data an explicit skew-symmetrizable cluster algebra. As an application we prove the linear independence of the cluster monomials in this setting. Finally, we illustrate our construction with examples associated with partial flag varieties and unipotent subgroups of Kac-Moody groups, generalizing to the non simply-laced case several results of Gei\ss-Leclerc-Schr\"oer.Comment: 64 page

Image super-resolution with dense-sampling residual channel-spatial attention networks for multi-temporal remote sensing image classification

Image super-resolution (SR) techniques can benefit a wide range of applications in the remote sensing (RS) community, including image classification. This issue is particularly relevant for image classification on time series data, considering RS datasets that feature long temporal coverage generally have a limited spatial resolution. Recent advances in deep learning brought new opportunities for enhancing the spatial resolution of historic RS data. Numerous convolutional neural network (CNN)-based methods showed superior performance in terms of developing efficient end-to-end SR models for natural images. However, such models were rarely exploited for promoting image classification based on multispectral RS data. This paper proposes a novel CNNbased framework to enhance the spatial resolution of time series multispectral RS images. Thereby, the proposed SR model employs Residual Channel Attention Networks (RCAN) as a backbone structure, whereas based on this structure the proposed models uniquely integrate tailored channel-spatial attention and dense-sampling mechanisms for performance improvement. Subsequently, state-of-the-art CNN-based classifiers are incorporated to produce classification maps based on the enhanced time series data. The experiments proved that the proposed SR model can enable unambiguously better performance compared to RCAN and other (deep learning-based) SR techniques, especially in a domain adaptation context, i.e., leveraging Sentinel-2 images for generating SR Landsat images. Furthermore, the experimental results confirmed that the enhanced multi-temporal RS images can bring substantial improvement on fine-grained multi-temporal land use classification

Using deep neural networks for predictive modelling of informal settlements in the context of flood risk

Abstract Global climate change has substantially increased the risks of cities being adversely affected by natural hazards such as floods. Among the inhabitants of cities at risk, residents dwelling in informal settlements are the most vulnerable group. To identify the future exposure of informal settlements, we adopt a data-driven model from the machine learning domain to anticipate the growth patterns of formal and informal settlements in flood-prone areas. The potential emergence of informal settlements in Shenzhen, China, is predicted by the proposed method. Then, through an analysis of the flood susceptibility of the predicted informal settlement areas, the emerging vulnerability of Shenzhen towards flooding is revealed.</jats:p

Using InSAR stacking techniques to predict bridge collapse due to scour

Failure of bridges due to scour is of great concern to bridge asset owners, and is currently very difficult to predict and monitor regularly using conventional assessment methods. This paper presents evidence of how InSAR techniques can be used to monitor bridges at risk of scour, using Tadcaster Bridge, England, as a case study. Tadcaster Bridge suffered a partial collapse due to river scour on the evening of December 29th, 2015 following a period of severe rainfall and flooding. SAR scenes over the bridge from the two-year period prior to the collapse are analysed using SBAS interferometry methods, highlighting a distinct movement in the region of the bridge where the collapse occurred prior to the actual event. This precursor to failure observed in the data suggests the possible use of InSAR in structural health monitoring of bridges at risk of scour, as a means of an early warning system

Variation of ionic conductivity in a plastic-crystalline mixture

Ionically-conducting plastic crystals are possible candidates for solid-state electrolytes in energy-storage devices. Interestingly, the admixture of larger molecules to the most prominent molecular PC electrolyte, succinonitrile, was shown to drastically enhance its ionic conductivity. Therefore, binary mixtures seem to be a promising way to tune the conductivity of such solid-state electrolytes. However, to elucidate the general mechanisms of ionic charge transport in plastic crystals and the influence of mixing, a much broader data base is needed. In the present work, we investigate mixtures of two well-known plastic-crystalline systems, cyclohexanol and cyclooctanol, to which 1 mol% of Li ions were added. Applying differential scanning calorimetry and dielectric spectroscopy, we present a thorough investigation of the phase behavior and the ionic and dipolar dynamics of this system. All mixtures reveal plastic-crystalline phases with corresponding orientational glass-transitions. Moreover, their conductivity seems to be dominated by the "revolving-door" mechanism, implying a close coupling between the ionic translational and the molecular reorientational dynamics of the surrounding plastic-crystalline matrix. In contrast to succinonitrile-based mixtures, there is no strong variation of this coupling with the mixing ratio.Comment: 8 pages, 6 figures, final version as accepted for publicatio

A remark on Leclerc's Frobenius categories

Leclerc recently studied certain Frobenius categories in connection with cluster algebra structures on coordinate rings of intersections of opposite Schubert cells. We show that these categories admit a description as Gorenstein projective modules over an Iwanaga-Gorenstein ring of virtual dimension at most two. This is based on a Morita type result for Frobenius categories.Comment: 5 pages, extended abstract for a talk at the Workshop Homological Bonds between Commutative Algebra and Representation Theory at CRM Barcelona, February 2015, comments welcom

Mixing layer height as an indicator for urban air quality?

The mixing layer height (MLH) is a measure for the vertical turbulent exchange within the boundary layer, which is one of the controlling factors for the dilution of pollutants emitted near the ground. Based on continuous MLH measurements with a Vaisala CL51 ceilometer and measurements from an air quality network, the relationship between MLH and near-surface pollutant concentrations has been investigated. In this context the uncertainty of the MLH retrievals and the representativeness of ground-based in situ measurements are crucial. We have investigated this topic by using data from the BAERLIN2014 campaign in Berlin, Germany, conducted from June to August 2014. To derive the MLH, three versions of the proprietary software BL-VIEW and a novel approach COBOLT were compared. It was found that the overall agreement is reasonable if mean diurnal cycles are considered. The main advantage of COBOLT is the continuous detection of the MLH with a temporal resolution of 10 min and a lower number of cases when the residual layer is misinterpreted as mixing layer. We have calculated correlations between MLH as derived from the different retrievals and concentrations of pollutants (PM10, O-3 and NOx) for different locations in the metropolitan area of Berlin. It was found that the correlations with PM10 are quite different for different sites without showing a clear pattern, whereas the correlation with NOx seems to depend on the vicinity of emission sources in main roads. In the case of ozone as a secondary pollutant, a clear correlation was found. We conclude that the effects of the heterogeneity of the emission sources, chemical processing and mixing during transport exceed the differences due to different MLH retrievals. Moreover, it seems to be unrealistic to find correlations between MLH and near-surface pollutant concentrations representative for a city like Berlin (flat terrain), in particular when traffic emissions are dominant. Nevertheless it is worthwhile to use advanced MLH retrievals for ceilometer data, for example as input to dispersion models and for the validation of chemical transport models