Brane-Localized Goldstone Fermions in Bulk Supergravity

We construct the action and transformation laws for bulk five-dimensional AdS supergravity coupled to one or two brane-localized Goldstone fermions. The resulting bulk-plus-brane system gives a model-independent description of brane-localized supersymmetry breaking in the Randall-Sundrum scenario. We explicitly reduce the action and transformation laws to spontaneously broken four-dimensional supergravity.Comment: 22 page

Simple d=4 supergravity with a boundary

To construct rigidly or locally supersymmetric bulk-plus-boundary actions, one needs an extension of the usual tensor calculus. Its key ingredients are the extended (F-, D-, etc.) density formulas and the rule for the decomposition of bulk multiplets into (co-dimension one) boundary multiplets. Working out these ingredients for d=4 N=1 Poincar\'e supergravity, we discover the special role played by R-symmetry (absent in the d=3 N=1 case we studied previously). The $U(1)_A$ R-symmetry has to be gauged which leads us to extend the old-minimal set of auxiliary fields S, P, A_\mu by a $U(1)_A$ compensator $a$. Our results include the ``F+A'' density formula, the ``Q+L+A'' formula for the induced supersymmetry transformations (closing into the standard d=3 N=1 algebra) and demonstration that the compensator $a$ is the first component of the extrinsic curvature multiplet. We rely on the superconformal approach which allows us to perform, in parallel, the same analysis for new-minimal supergravity.Comment: 26 pages. JHEP forma

Link Graph Analysis for Adult Images Classification

In order to protect an image search engine's users from undesirable results adult images' classifier should be built. The information about links from websites to images is employed to create such a classifier. These links are represented as a bipartite website-image graph. Each vertex is equipped with scores of adultness and decentness. The scores for image vertexes are initialized with zero, those for website vertexes are initialized according to a text-based website classifier. An iterative algorithm that propagates scores within a website-image graph is described. The scores obtained are used to classify images by choosing an appropriate threshold. The experiments on Internet-scale data have shown that the algorithm under consideration increases classification recall by 17% in comparison with a simple algorithm which classifies an image as adult if it is connected with at least one adult site (at the same precision level).Comment: 7 pages. Young Scientists Conference, 4th Russian Summer School in Information Retrieva

Rigid supersymmetry with boundaries

We construct rigidly supersymmetric bulk-plus-boundary actions, both in $x$-space and in superspace. For each standard supersymmetric bulk action a minimal supersymmetric bulk-plus-boundary action follows from an extended $F$- or $D$-term formula. Additional separately supersymmetric boundary actions can be systematically constructed using co-dimension one multiplets (boundary superfields). We also discuss the orbit of boundary conditions which follow from the Euler-Lagrange variational principle.Comment: 28 pages, JHEP clas

Vaizdo žaidimų filosofinės interpretacijos diskursai ir semantiniai tropai

The article explores one of the most remarkable and dynamic phenomena of modern technoculture – video games. It reconstructs the genesis of the philosophical discourse on video games, exposing the main difficulties arising in making the definitions. Special importance is attached to the critical comparative analysis of the major strategies for the philosophical explication of video games. With the aid of the method of comparative-historical reconstruction and a structuralist approach, the essential correlations between the essential definition of a video game and the ontological systems of Plato, the Gnostics, G. Berkeley, E. Kant, as well as post-modern philosophy was established. The research results in formulating a model-integrative definition of a video game.Straipsnyje tiriami vaizdo žaidimai – vieni išskirtiniausių ir dinamiškiausių moderniosios technokultūros fenomenų. Tekste rekonstruojama filosofinio diskurso apie vaizdo žaidimus genezė, atskleidžiamos esminės kliūtys, kylančios beieškant apibrėžimų. Ypatingas dėmesys skiriamas pagrindinėms vaizdo žaidimų filosofinio aiškinimo strategijoms ir jų kritinei lyginamajai analizei. Pasitelkę lyginamosios-istorinės rekonstrukcijos metodą ir struktūralistinę prieigą, nustatome esmines sąsajas tarp vaizdo žaidimo apibrėžimo ir ontologinių Platono, gnostikų, G. Berkeley’io, I. Kanto bei postmoderniosios filosofijos sistemų. Šio tyrimo rezultatas – suformuluotas integratyvųjį modelį atitinkantis vaizdo žaidimo apibrėžimas

Entropy-based machine learning model for diagnosis and monitoring of Parkinson's Disease in smart IoT environment

The study presents the concept of a computationally efficient machine learning (ML) model for diagnosing and monitoring Parkinson's disease (PD) in an Internet of Things (IoT) environment using rest-state EEG signals (rs-EEG). We computed different types of entropy from EEG signals and found that Fuzzy Entropy performed the best in diagnosing and monitoring PD using rs-EEG. We also investigated different combinations of signal frequency ranges and EEG channels to accurately diagnose PD. Finally, with a fewer number of features (11 features), we achieved a maximum classification accuracy (ARKF) of ~99.9%. The most prominent frequency range of EEG signals has been identified, and we have found that high classification accuracy depends on low-frequency signal components (0-4 Hz). Moreover, the most informative signals were mainly received from the right hemisphere of the head (F8, P8, T8, FC6). Furthermore, we assessed the accuracy of the diagnosis of PD using three different lengths of EEG data (150-1000 samples). Because the computational complexity is reduced by reducing the input data. As a result, we have achieved a maximum mean accuracy of 99.9% for a sample length (LEEG) of 1000 (~7.8 seconds), 98.2% with a LEEG of 800 (~6.2 seconds), and 79.3% for LEEG = 150 (~1.2 seconds). By reducing the number of features and segment lengths, the computational cost of classification can be reduced. Lower-performance smart ML sensors can be used in IoT environments for enhances human resilience to PD.Comment: 19 pages, 10 figures, 2 table