Symmetry

Mori, Taisaku;Nojiri, Shin’ichi

doi: 10.3390/sym10090396pmid: N/A

Recently, we have proposed models of topological field theory including gravity in Mod. Phys. Lett. A 2016, 31, 1650213 and Phys. Rev. D 2017, 96, 024009, in order to solve the problem of the cosmological constant. The Lagrangian densities of the models are BRS (Becchi-Rouet-Stora) exact and therefore the models can be regarded as topological theories. In the models, the coupling constants, including the cosmological constant, look as if they run with the scale of the universe and its behavior is very similar to the renormalization group. Motivated by these models, we propose new models with an the infrared fixed point, which may correspond to the late time universe, and an ultraviolet fixed point, which may correspond to the early universe. In particular, we construct a model with the solutions corresponding to the de Sitter space-time both in the ultraviolet and the infrared fixed points.

Tanimoto, Yoh

doi: 10.3390/sym10090415pmid: N/A

We construct families of ground state representations of the U ( 1 ) -current net and of the Virasoro nets Vir c with central charge c ≥ 1 . We show that these representations are not covariant with respect to the original dilations, and those on the U ( 1 ) -current net are not solitonic. Furthermore, by going to the dual net with respect to the ground state representations of Vir c , one obtains possibly new family of Möbius covariant nets on S 1 .

He, Zhen;He, Hangen

doi: 10.3390/sym10090375pmid: N/A

Nowadays, video surveillance has become ubiquitous with the quick development of artificial intelligence. Multi-object detection (MOD) is a key step in video surveillance and has been widely studied for a long time. The majority of existing MOD algorithms follow the “divide and conquer” pipeline and utilize popular machine learning techniques to optimize algorithm parameters. However, this pipeline is usually suboptimal since it decomposes the MOD task into several sub-tasks and does not optimize them jointly. In addition, the frequently used supervised learning methods rely on the labeled data which are scarce and expensive to obtain. Thus, we propose an end-to-end Unsupervised Multi-Object Detection framework for video surveillance, where a neural model learns to detect objects from each video frame by minimizing the image reconstruction error. Moreover, we propose a Memory-Based Recurrent Attention Network to ease detection and training. The proposed model was evaluated on both synthetic and real datasets, exhibiting its potential.

José, Marco V.;Zamudio, Gabriel S.

doi: 10.3390/sym10090388pmid: N/A

It has long been claimed that the mitochondrial genetic code possesses more symmetries than the Standard Genetic Code (SGC). To test this claim, the symmetrical structure of the SGC is compared with noncanonical genetic codes. We analyzed the symmetries of the graphs of codons and their respective phenotypic graph representation spanned by the RNY (R purines, Y pyrimidines, and N any of them) code, two RNA Extended codes, the SGC, as well as three different mitochondrial genetic codes from yeast, invertebrates, and vertebrates. The symmetry groups of the SGC and their corresponding phenotypic graphs of amino acids expose the evolvability of the SGC. Indeed, the analyzed mitochondrial genetic codes are more symmetrical than the SGC.

Sarwar, Musavarah;Akram, Muhammad;Alshehri, Noura Omair

doi: 10.3390/sym10090404pmid: N/A

Hypergraph theory is the most developed tool for demonstrating various practical problems in different domains of science and technology. Sometimes, information in a network model is uncertain and vague in nature. In this paper, our main focus is to apply the powerful methodology of fuzziness to generalize the notion of competition hypergraphs and fuzzy competition graphs. We introduce various new concepts, including fuzzy column hypergraphs, fuzzy row hypergraphs, fuzzy competition hypergraphs, fuzzy k-competition hypergraphs and fuzzy neighbourhood hypergraphs, strong hyperedges, kth strength of competition and symmetric properties. We design certain algorithms for constructing different types of fuzzy competition hypergraphs. We also present applications of fuzzy competition hypergraphs in decision support systems, including predator–prey relations in ecological niche, social networks and business marketing.

Caramihale, Traian;Popescu, Dan;Ichim, Loretta

doi: 10.3390/sym10090414pmid: N/A

The detection of human emotions has applicability in various domains such as assisted living, health monitoring, domestic appliance control, crowd behavior tracking real time, and emotional security. The paper proposes a new system for emotion classification based on a generative adversarial network (GAN) classifier. The generative adversarial networks have been widely used for generating realistic images, but the classification capabilities have been vaguely exploited. One of the main advantages is that by using the generator, we can extend our testing dataset and add more variety to each of the seven emotion classes we try to identify. Thus, the novelty of our study consists in increasing the number of classes from N to 2N (in the learning phase) by considering real and fake emotions. Facial key points are obtained from real and generated facial images, and vectors connecting them with the facial center of gravity are used by the discriminator to classify the image as one of the 14 classes of interest (real and fake for seven emotions). As another contribution, real images from different emotional classes are used in the generation process unlike the classical GAN approach which generates images from simple noise arrays. By using the proposed method, our system can classify emotions in facial images regardless of gender, race, ethnicity, age and face rotation. An accuracy of 75.2% was obtained on 7000 real images (14,000, also considering the generated images) from multiple combined facial datasets.

Iglesias-Caamaño, Mario;Carballo-López, Javier;Álvarez-Yates, Tania;Cuba-Dorado, Alba;García-García, Oscar

doi: 10.3390/sym10090416pmid: N/A

The development of lateral asymmetries in athletes could have an influence on performance or injuries. The aim of this study was to determine the within-day reliability of the symmetry tests and the performance tests, and explore the relationship between them. Eighteen male volleyball players (18.1 ± 2.1 years) participated in this study. Seven lateral symmetry assessments were used, namely: lateral symmetry through tensiomyography (LS), active knee extension (AKE), Y-balance test (YBT), muscular electrical activity in attack jump (MEA-AJ), single-leg squat jump (SLSJ), triple hop test for distance (THTD), and bilateral maximum repetition in leg press (1RMSL); and three volleyball performance tests, namely: the T-test, counter-movement jump (CMJ), and attack jump (AJ). Three in-day measurements were taken from each volleyball player after the recovery was completed. The reliability was calculated through the intraclass correlation coefficient and the coefficient of variation, and the relationship was calculated through Pearson’s bivariate correlation coefficient (p < 0.05). The results indicate that AKE, YBT, and LS are the symmetry tests with increased reproducibility. THTD correlates positively with the AKE test and 1RMSL test, and a greater symmetry in the YBT correlates with a greater performance in the CMJ and AJ performance tests. In conclusion, AKE, LS, and YBT are the best tests to determine, with reliability, the asymmetries in volleyball players, and a greater symmetry in the YBT seems to influence the height of bilateral vertical jump.