Consequences of energy conservation violation: Late time solutions of Λ(T)CDM\Lambda(T) CDM subclass of f(R,T)f(R,T) gravity using dynamical system approach

Very recently, the authors of [PRL {\bf 118} (2017) 021102] have shown that violation of energy-momentum tensor (EMT) could result in an accelerated expansion state via appearing an effective cosmological constant, in the context of unimodular gravity. Inspired by this outcome, in this paper we investigate cosmological consequences of violation of the EMT conservation in a particular class of $f(R,T)$ gravity when only the pressure-less fluid is present. In this respect, we focus on the late time solutions of models of the type $f(R,T)=R+\beta \Lambda(-T)$. As the first task, we study the solutions when the conservation of EMT is respected and then we proceed with those in which violation occurs. We have found, provided that the EMT conservation is violated, there generally exist two accelerated expansion solutions which their stability properties depend on the underlying model. More exactly, we obtain a dark energy solution for which the effective equation of state (EoS) depend on model parameters and a de Sitter solution. We present a method to parametrize $\Lambda(-T)$ function which is useful in dynamical system approach and has been employed in the herein model. Also, we discuss the cosmological solutions for models with $\Lambda(-T)=8\pi G(-T)^{\alpha}$ in the presence of the ultra relativistic matter.Comment: 27 pages, 4 figure

Late-time cosmological evolution of a general class of f(R, T) gravity with minimal curvature-matter coupling

In this work, we study the late-time cosmological solutions of f(R,T)=g(R)+h(-T) models assuming that the conservation of the energy-momentum tensor (EMT) is violated. We perform our analysis through constructing an autonomous dynamical system for the equations of motion. We study the stability properties of solutions via considering linear perturbations about the related equilibrium points. These parameters which are constructed out of the functions g(R) and h(-T) play the main role in finding the late time behavior of the solutions.Comment: 28 pages, 4 figure

Stability of the Einstein static Universe in Einstein-Cartan-Brans-Dicke gravity

In the present work we consider the existence and stability of Einstein static {\sf ES} Universe in Brans-Dicke ({\sf BD}) theory with non-vanishing spacetime torsion. In this theory, torsion field can be generated by the {\sf BD} scalar field as well as the intrinsic angular momentum (spin) of matter. Assuming the matter content of the Universe to be a Weyssenhoff fluid, which is a generalization of perfect fluid in general relativity ({\sf GR}) in order to include the spin effects, we find that there exists a stable {\sf ES} state for a suitable choice of the model parameters. We analyze the stability of the solution by considering linear homogeneous perturbations and discuss the conditions under which the solution can be stable against these type of perturbations. Moreover, using dynamical system techniques and numerical analysis, the stability regions of the {\sf ES} Universe are parametrized by the {\sf BD} coupling parameter and first and second derivatives of the {\sf BD} scalar field potential, and it is explicitly shown that a large class of stable solutions exists within the respective parameter space. This allows for non-singular emergent cosmological scenarios where the Universe oscillates indefinitely about an initial {\sf ES} solution and is thus past eternal.Comment: 24 pages, 9 figures and 2 table

Bouncing cosmological solutions from f(R,T) gravity

In this work we study classical bouncing solutions in the context of $f({\sf R},{\sf T})={\sf R}+h({\sf T})$ gravity in a flat {\sf FLRW} background using a perfect fluid as the only matter content. Our investigation is based on introducing an effective fluid through defining effective energy density and pressure; we call this reformulation as the "effective picture". These definitions have been already introduced to study the energy conditions in $f({\sf R},{\sf T})$ gravity. We examine various models to which different effective equations of state, corresponding to different $h({\sf T})$ functions, can be attributed. It is also discussed that one can link between an assumed $f({\sf R},{\sf T})$ model in the effective picture and the theories with generalized equation of state ({\sf EoS}). We obtain cosmological scenarios exhibiting a nonsingular bounce before and after which the Universe lives within a de-Sitter phase. We then proceed to find general solutions for matter bounce and investigate their properties. We show that the properties of bouncing solution in the effective picture of $f({\sf R},{\sf T})$ gravity are as follows: for a specific form of the $f({\sf R,T})$ function, these solutions are without any future singularities. Moreover, stability analysis of the nonsingular solutions through matter density perturbations revealed that except two of the models, the parameters of scalar-type perturbations for the other ones have a slight transient fluctuation around the bounce point and damp to zero or a finite value at late times. Hence these bouncing solutions are stable against scalar-type perturbations. It is possible that all energy conditions be respected by the real perfect fluid, however, the null and the strong energy conditions can be violated by the effective fluid near the bounce event.Comment: 49 pages, 11 figures, one tabl

Improved Deep Convolutional Neural Network with Age Augmentation for Facial Emotion Recognition in Social Companion Robotics

Facial emotion recognition (FER) is a critical component for affective computing in social companion robotics. Current FER datasets are not sufficiently age-diversified as they are predominantly adults excluding seniors above fifty years of age which is the target group in long-term care facilities. Data collection from this age group is more challenging due to their privacy concerns and also restrictions under pandemic situations such as COVID-19. We address this issue by using age augmentation which could act as a regularizer and reduce the overfitting of the classifier as well. Our comprehensive experiments show that improving a typical Deep Convolutional Neural Network (CNN) architecture with facial age augmentation improves both the accuracy and standard deviation of the classifier when predicting emotions of diverse age groups including seniors. The proposed framework is a promising step towards improving a participant’s experience and interactions with social companion robots with affective computing

Investigating the Effect of Hydrophobic Additives in Moisture Damage Reduction of Asphalt Mixtures

In order to increase the life of the asphalt mixture and reduce the cost of the pavement life cycle, methods must be provided to improve the quality. Accordingly, the effects of aggregate surface coating with hydrophobic material in order to modify the aggregate mixture’s polar properties and reduce its hydrophilic properties are investigated. To this end, limestone and granite aggregates, 60-70 bitumen, and Two types of additives were used as the primary materials for the construction of asphalt mixtures. Thermodynamic concepts with cyclic loading have been used to evaluate the effects of these additives. The results obtained in this study indicate that the hydrophobic coating on the aggregate surface has increased the acidic components and decreased the alkaline components of the surface free energy for both types of aggregates. These changes will increase the bitumen-aggregate adhesion and make a better coating of bitumen on the aggregate surface. The results based on thermodynamic concepts suggest that the aggregate surface coating has reduced the system’s separation energy and the desire for stripping. The results of the dynamic modulus in wet to dry conditions also approve this outcome. The combination of thermodynamic concepts and the cyclic loading results show that the coating on the aggregate surface has reduced the aggregate’s stripping from bitumen. It is also obvious that the samples made with granite aggregates, which have acidic properties, are prone to moisture damage and have a higher tendency to strip

Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting

The performance of time series forecasting has recently been greatly improved by the introduction of transformers. In this paper, we propose a general multi-scale framework that can be applied to the state-of-the-art transformer-based time series forecasting models (FEDformer, Autoformer, etc.). By iteratively refining a forecasted time series at multiple scales with shared weights, introducing architecture adaptations, and a specially-designed normalization scheme, we are able to achieve significant performance improvements, from 5.5% to 38.5% across datasets and transformer architectures, with minimal additional computational overhead. Via detailed ablation studies, we demonstrate the effectiveness of each of our contributions across the architecture and methodology. Furthermore, our experiments on various public datasets demonstrate that the proposed improvements outperform their corresponding baseline counterparts. Our code is publicly available in https://github.com/BorealisAI/scaleformer

A New Optimization Algorithm Based on Search and Rescue Operations

[EN] In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems.This study was partially supported by the Spanish Research Project (nos. TIN2016-80856-R and TIN2015-65515-C4-1-R).Shabani, A.; Asgarian, B.; Gharebaghi, SA.; Salido Gregorio, MA.; Giret Boggino, AS. (2019). A New Optimization Algorithm Based on Search and Rescue Operations. Mathematical Problems in Engineering. 2019:1-23. https://doi.org/10.1155/2019/2482543S1232019Bianchi, L., Dorigo, M., Gambardella, L. M., & Gutjahr, W. J. (2008). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8(2), 239-287. doi:10.1007/s11047-008-9098-4Holland, J. H. (1992). Genetic Algorithms. Scientific American, 267(1), 66-72. doi:10.1038/scientificamerican0792-66Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), 29-41. doi:10.1109/3477.484436Manjarres, D., Landa-Torres, I., Gil-Lopez, S., Del Ser, J., Bilbao, M. N., Salcedo-Sanz, S., & Geem, Z. W. (2013). A survey on applications of the harmony search algorithm. Engineering Applications of Artificial Intelligence, 26(8), 1818-1831. doi:10.1016/j.engappai.2013.05.008Karaboga, D., Gorkemli, B., Ozturk, C., & Karaboga, N. (2012). A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42(1), 21-57. doi:10.1007/s10462-012-9328-0Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315. doi:10.1016/j.cad.2010.12.015Zhang, C., Lin, Q., Gao, L., & Li, X. (2015). Backtracking Search Algorithm with three constraint handling methods for constrained optimization problems. Expert Systems with Applications, 42(21), 7831-7845. doi:10.1016/j.eswa.2015.05.050Yang, X. S. (2010). Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation, 2(2), 78. doi:10.1504/ijbic.2010.032124Punnathanam, V., & Kotecha, P. (2016). Yin-Yang-pair Optimization: A novel lightweight optimization algorithm. Engineering Applications of Artificial Intelligence, 54, 62-79. doi:10.1016/j.engappai.2016.04.004Zhao, C., Wu, C., Chai, J., Wang, X., Yang, X., Lee, J.-M., & Kim, M. J. (2017). Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty. Applied Soft Computing, 55, 549-564. doi:10.1016/j.asoc.2017.02.009Zhao, C., Wu, C., Wang, X., Ling, B. W.-K., Teo, K. L., Lee, J.-M., & Jung, K.-H. (2017). Maximizing lifetime of a wireless sensor network via joint optimizing sink placement and sensor-to-sink routing. Applied Mathematical Modelling, 49, 319-337. doi:10.1016/j.apm.2017.05.001Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82. doi:10.1109/4235.585893Simon, D. (2008). Biogeography-Based Optimization. IEEE Transactions on Evolutionary Computation, 12(6), 702-713. doi:10.1109/tevc.2008.919004Garg, H. (2015). An efficient biogeography based optimization algorithm for solving reliability optimization problems. Swarm and Evolutionary Computation, 24, 1-10. doi:10.1016/j.swevo.2015.05.001Storn, R., & Price, K. (1997). Journal of Global Optimization, 11(4), 341-359. doi:10.1023/a:1008202821328Das, S., Mullick, S. S., & Suganthan, P. N. (2016). Recent advances in differential evolution – An updated survey. Swarm and Evolutionary Computation, 27, 1-30. doi:10.1016/j.swevo.2016.01.004Couzin, I. D., Krause, J., Franks, N. R., & Levin, S. A. (2005). Effective leadership and decision-making in animal groups on the move. Nature, 433(7025), 513-516. doi:10.1038/nature03236Gandomi, A. H., & Alavi, A. H. (2012). Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831-4845. doi:10.1016/j.cnsns.2012.05.010Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. doi:10.1016/j.advengsoft.2013.12.007Erol, O. K., & Eksin, I. (2006). A new optimization method: Big Bang–Big Crunch. Advances in Engineering Software, 37(2), 106-111. doi:10.1016/j.advengsoft.2005.04.005Kaveh, A., & Mahdavi, V. R. (2014). Colliding bodies optimization: A novel meta-heuristic method. Computers & Structures, 139, 18-27. doi:10.1016/j.compstruc.2014.04.005Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A Gravitational Search Algorithm. Information Sciences, 179(13), 2232-2248. doi:10.1016/j.ins.2009.03.004Zheng, Y.-J. (2015). Water wave optimization: A new nature-inspired metaheuristic. Computers & Operations Research, 55, 1-11. doi:10.1016/j.cor.2014.10.008Kaveh, A., & Khayatazad, M. (2012). A new meta-heuristic method: Ray Optimization. Computers & Structures, 112-113, 283-294. doi:10.1016/j.compstruc.2012.09.003Glover, F. (1989). Tabu Search—Part I. ORSA Journal on Computing, 1(3), 190-206. doi:10.1287/ijoc.1.3.190Chiang, H.-P., Chou, Y.-H., Chiu, C.-H., Kuo, S.-Y., & Huang, Y.-M. (2013). A quantum-inspired Tabu search algorithm for solving combinatorial optimization problems. Soft Computing, 18(9), 1771-1781. doi:10.1007/s00500-013-1203-7Mousavirad, S. J., & Ebrahimpour-Komleh, H. (2017). Human mental search: a new population-based metaheuristic optimization algorithm. Applied Intelligence, 47(3), 850-887. doi:10.1007/s10489-017-0903-6Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459-471. doi:10.1007/s10898-007-9149-xRao, R. V., Savsani, V. J., & Vakharia, D. P. (2012). Teaching–Learning-Based Optimization: An optimization method for continuous non-linear large scale problems. Information Sciences, 183(1), 1-15. doi:10.1016/j.ins.2011.08.006Digalakis, J. G., & Margaritis, K. G. (2001). On benchmarking functions for genetic algorithms. International Journal of Computer Mathematics, 77(4), 481-506. doi:10.1080/00207160108805080Karaboga, D., & Akay, B. (2009). A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation, 214(1), 108-132. doi:10.1016/j.amc.2009.03.090Lim, T. Y., Al-Betar, M. A., & Khader, A. T. (2015). Adaptive pair bonds in genetic algorithm: An application to real-parameter optimization. Applied Mathematics and Computation, 252, 503-519. doi:10.1016/j.amc.2014.12.030Fleury, C., & Braibant, V. (1986). Structural optimization: A new dual method using mixed variables. International Journal for Numerical Methods in Engineering, 23(3), 409-428. doi:10.1002/nme.1620230307Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3-18. doi:10.1016/j.swevo.2011.02.002Gandomi, A. H., Yang, X.-S., & Alavi, A. H. (2011). Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), 17-35. doi:10.1007/s00366-011-0241-yWang, G. G. (2003). Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points. Journal of Mechanical Design, 125(2), 210-220. doi:10.1115/1.1561044Cheng, M.-Y., & Prayogo, D. (2014). Symbiotic Organisms Search: A new metaheuristic optimization algorithm. Computers & Structures, 139, 98-112. doi:10.1016/j.compstruc.2014.03.007CHICKERMANE, H., & GEA, H. C. (1996). STRUCTURAL OPTIMIZATION USING A NEW LOCAL APPROXIMATION METHOD. International Journal for Numerical Methods in Engineering, 39(5), 829-846. doi:10.1002/(sici)1097-0207(19960315)39:53.0.co;2-uChou, J.-S., & Ngo, N.-T. (2016). Modified firefly algorithm for multidimensional optimization in structural design problems. Structural and Multidisciplinary Optimization, 55(6), 2013-2028. doi:10.1007/s00158-016-1624-xSonmez, M. (2011). Artificial Bee Colony algorithm for optimization of truss structures. Applied Soft Computing, 11(2), 2406-2418. doi:10.1016/j.asoc.2010.09.003Degertekin, S. O. (2012). Improved harmony search algorithms for sizing optimization of truss structures. Computers & Structures, 92-93, 229-241. doi:10.1016/j.compstruc.2011.10.022Degertekin, S. O., & Hayalioglu, M. S. (2013). Sizing truss structures using teaching-learning-based optimization. Computers & Structures, 119, 177-188. doi:10.1016/j.compstruc.2012.12.011Talatahari, S., Kheirollahi, M., Farahmandpour, C., & Gandomi, A. H. (2012). A multi-stage particle swarm for optimum design of truss structures. Neural Computing and Applications, 23(5), 1297-1309. doi:10.1007/s00521-012-1072-5Kaveh, A., Bakhshpoori, T., & Afshari, E. (2014). An efficient hybrid Particle Swarm and Swallow Swarm Optimization algorithm. Computers & Structures, 143, 40-59. doi:10.1016/j.compstruc.2014.07.012Kaveh, A., & Bakhshpoori, T. (2016). A new metaheuristic for continuous structural optimization: water evaporation optimization. Structural and Multidisciplinary Optimization, 54(1), 23-43. doi:10.1007/s00158-015-1396-8Jalili, S., & Hosseinzadeh, Y. (2015). A Cultural Algorithm for Optimal Design of Truss Structures. Latin American Journal of Solids and Structures, 12(9), 1721-1747. doi:10.1590/1679-7825154