The Dynamics of a Rigid Body in Potential Flow with Circulation

We consider the motion of a two-dimensional body of arbitrary shape in a planar irrotational, incompressible fluid with a given amount of circulation around the body. We derive the equations of motion for this system by performing symplectic reduction with respect to the group of volume-preserving diffeomorphisms and obtain the relevant Poisson structures after a further Poisson reduction with respect to the group of translations and rotations. In this way, we recover the equations of motion given for this system by Chaplygin and Lamb, and we give a geometric interpretation for the Kutta-Zhukowski force as a curvature-related effect. In addition, we show that the motion of a rigid body with circulation can be understood as a geodesic flow on a central extension of the special Euclidian group SE(2), and we relate the cocycle in the description of this central extension to a certain curvature tensor.Comment: 28 pages, 2 figures; v2: typos correcte

A Cloud-Based Framework for Machine Learning Workloads and Applications

[EN] In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing and publication. In such respect, the DEEP-Hybrid-DataCloud framework allows transparent access to existing e-Infrastructures, effectively exploiting distributed resources for the most compute-intensive tasks coming from the machine learning development cycle. Moreover, it provides scientists with a set of Cloud-oriented services to make their models publicly available, by adopting a serverless architecture and a DevOps approach, allowing an easy share, publish and deploy of the developed models.This work was supported by the project DEEP-Hybrid-DataCloud ``Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud'' that has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant 777435Lopez Garcia, A.; Marco De Lucas, J.; Antonacci, M.; Zu Castell, W.; David, M.; Hardt, M.; Lloret Iglesias, L.... (2020). A Cloud-Based Framework for Machine Learning Workloads and Applications. IEEE Access. 8:18681-18692. https://doi.org/10.1109/ACCESS.2020.2964386S1868118692

Relativistic Laser-Matter Interaction and Relativistic Laboratory Astrophysics

The paper is devoted to the prospects of using the laser radiation interaction with plasmas in the laboratory relativistic astrophysics context. We discuss the dimensionless parameters characterizing the processes in the laser and astrophysical plasmas and emphasize a similarity between the laser and astrophysical plasmas in the ultrarelativistic energy limit. In particular, we address basic mechanisms of the charged particle acceleration, the collisionless shock wave and magnetic reconnection and vortex dynamics properties relevant to the problem of ultrarelativistic particle acceleration.Comment: 58 pages, 19 figure

Model for Estimating the Heterogeneity of the Distribution of Globule Characteristics in Images of Skin Neoplasms

The problem of diagnosing skin melanoma by digital imaging of the tumor is considered. Clinical algorithms for detecting skin melanoma are briefly described. A review of works devoted to the automated assessment of distribution asymmetry of the shape, color, and area of globules – important signs of melanoma, is given. A model for assessing the distribution heterogeneity of globule characteristics on digital images in the diagnosis of skin neoplasms has been developed, and models of distribution heterogeneity indicators have been proposed. An experimental comparative assessment of indicator models was carried out using a software system developed in the C++ language. The most informative indicators of globule characteristics distribution heterogeneity have been determined. The maximum (93%) accuracy in assessing the distribution heterogeneity of globule characteristics was obtained for the indicator “reduced reciprocal of the highest frequency of occurrence of the measured areas of globules.” The results of the study can be useful in the development of medical decision support systems for the diagnosis of melanoma. © 2022, Springer Science+Business Media, LLC, part of Springer Nature

Model for Detecting Globules in Images of Skin Neoplasms

Abstract: This article is devoted to the digital processing of images of skin neoplasms to detect significant structural elements in the diagnosis of melanoma–globules (clumps, lumps). A new processing model is proposed, which makes it possible to stably select globules in images of different contrasts without the need to manually adjust the parameters. The results of the experiment confirming the adequacy of the model are presented. The globule recognition accuracy ranged from 81 to 89%, depending on the contrast of the original images. The experimental sample of images contained 2868 globules. © 2022, Pleiades Publishing, Ltd

A Model for Recognizing Structureless Hyperpigmented Areas in Dermato-Oncology

A model for recognizing structureless hyperpigmented areas in images of skin neoplasms has been developed. Recognition of hyperpigmented areas is important for the diagnosis of skin melanoma, a rapidly progressing skin cancer. A digital dermatoscope RDS-2 has been used to obtain images serving as the initial data for the model. Software for recognizing hyperpigmentation areas in images of skin neoplasms has been developed on the basis of the proposed model. Tests have shown the recognition accuracy to be 82%. The proposed model can be recommended for use in decision-making support systems for the diagnosis of melanoma. © 2022, Springer Science+Business Media, LLC, part of Springer Nature

Detection of Circles as Structural Elements in Dermatoscopic Images of Skin Neoplasms in the Diagnosis of Melanoma

A method for recognizing “circles”, significant structural elements of skin neoplasms, has been proposed. An RDS-2 dermatoscope has been used for imaging. Special software has been developed to implement the proposed method for circle recognition. The results of experimental detection of circles are presented. The developed method can be used in diagnostic systems for detecting skin melanoma, a dangerous form of cancer. © 2021, Springer Science+Business Media, LLC, part of Springer Nature

A Model for Recognition of Dermatoscopic Points in Images of Skin Neoplasms

The challenges of using computer diagnostics to seek structural elements of melanocytic neoplasms, including cutaneous melanomas at early stages of their development, are discussed. The characteristic features of the structural elements — dermatoscopic points — are also considered. A computer vision technique for recognizing these characteristic features is presented. The developed interdisciplinary approach can be used in the diagnosis of oncological diseases of the skin as a means of supporting decision making for the primary prevention of malignant neoplasms. © 2021, Springer Science+Business Media, LLC, part of Springer Nature