1,575 research outputs found
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 () 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 .
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-
forest. Self-interacting DM scenarios can also provide a solution as long as
they lead to extended ( 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
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