Hints against the cold and collisionless nature of dark matter from the galaxy velocity function

The observed number of dwarf galaxies as a function of rotation velocity is significantly smaller than predicted by the standard model of cosmology. This discrepancy cannot be simply solved by assuming strong baryonic feedback processes, since they would violate the observed relation between maximum circular velocity ($v_{\rm max}$) and baryon mass of galaxies. A speculative but tantalising possibility is that the mismatch between observation and theory points towards the existence of non-cold or non-collisionless dark matter (DM). In this paper, we investigate the effects of warm, mixed (i.e warm plus cold), and self-interacting DM scenarios on the abundance of dwarf galaxies and the relation between observed HI line-width and maximum circular velocity. Both effects have the potential to alleviate the apparent mismatch between the observed and theoretical abundance of galaxies as a function of $v_{\rm max}$. For the case of warm and mixed DM, we show that the discrepancy disappears, even for luke-warm models that evade stringent bounds from the Lyman-$\alpha$ forest. Self-interacting DM scenarios can also provide a solution as long as they lead to extended ($\gtrsim 1.5$ kpc) dark matter cores in the density profiles of dwarf galaxies. Only models with velocity-dependent cross sections can yield such cores without violating other observational constraints at larger scales.Comment: Matches published versio

Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning

This study aimed at evaluating the synergistic use of Sentinel-1 and Sentinel-2 data combined with the Support Vector Machines (SVMs) machine learning classifier for mapping land use and land cover (LULC) with emphasis on wetlands. In this context, the added value of spectral information derived from the Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) and Grey Level Co-occurrence Matrix (GLCM) to the classification accuracy was also evaluated. As a case study, the National Park of Koronia and Volvi Lakes (NPKV) located in Greece was selected. LULC accuracy assessment was based on the computation of the classification error statistics and kappa coefficient. Findings of our study exemplified the appropriateness of the spatial and spectral resolution of Sentinel data in obtaining a rapid and cost-effective LULC cartography, and for wetlands in particular. The most accurate classification results were obtained when the additional spectral information was included to assist the classification implementation, increasing overall accuracy from 90.83% to 93.85% and kappa from 0.894 to 0.928. A post-classification correction (PCC) using knowledge-based logic rules further improved the overall accuracy to 94.82% and kappa to 0.936. This study provides further supporting evidence on the suitability of the Sentinels 1 and 2 data for improving our ability to map a complex area containing wetland and non-wetland LULC classes

Matrix Factorization in Tropical and Mixed Tropical-Linear Algebras

Matrix Factorization (MF) has found numerous applications in Machine Learning and Data Mining, including collaborative filtering recommendation systems, dimensionality reduction, data visualization, and community detection. Motivated by the recent successes of tropical algebra and geometry in machine learning, we investigate two problems involving matrix factorization over the tropical algebra. For the first problem, Tropical Matrix Factorization (TMF), which has been studied already in the literature, we propose an improved algorithm that avoids many of the local optima. The second formulation considers the approximate decomposition of a given matrix into the product of three matrices where a usual matrix product is followed by a tropical product. This formulation has a very interesting interpretation in terms of the learning of the utility functions of multiple users. We also present numerical results illustrating the effectiveness of the proposed algorithms, as well as an application to recommendation systems with promising results

Efficient e-Marketing in Tourism through a Novel Customer Relationship Management Model

This paper proposes an efficient customerrelationship management model based on technologicalconvergence of emerging next generation networks, such asinteractive digital television and network multimedia systems.The proposed research approach is exploited in tourism sectorfor effective destination management, enabling for personalizede-marketing strategies and facilitating marketers to accomplishoptimum marketing data analysis. The proposed researchapproach is evaluated for its applicability and usefulness byinterviewing a sample of Destination Marketing Organizationmanagers. The findings of this research provide useful practicalimplications

System architecture and deployment scenarios for SESAME: small cEllS coordinAtion for Multi-tenancy and Edge services

The surge of the Internet traffic with exabytes of data flowing over operators’ mobile networks has created the need to rethink the paradigms behind the design of the mobile network architecture. The inadequacy of the 4G UMTS Long term Evolution (LTE) and even of its advanced version LTE-A is evident, considering that the traffic will be extremely heterogeneous in the near future and ranging from 4K resolution TV to machine-type communications. To keep up with these changes, academia, industries and EU institutions have now engaged in the quest for new 5G technology. In this paper we present the innovative system design, concepts and visions developed by the 5G PPP H2020 project SESAME (Small cEllS coordinAtion for Multi-tenancy and Edge services). The innovation of SESAME is manifold: i) combine the key 5G small cells with cloud technology, ii) promote and develop the concept of Small Cells-as-a-Service (SCaaS), iii) bring computing and storage power at the mobile network edge through the development of non-x86 ARM technology enabled micro-servers, and iv) address a large number of scenarios and use cases applying mobile edge computing