Evolutionary games and spatial periodicity

We establish a theoretical framework to address evolutionary dynamics of spatial games under strong selection. As the selection intensity tends to infinity, strategy competition unfolds in the deterministic way of winners taking all. We rigorously prove that the evolutionary process soon or later either enters a cycle and from then on repeats the cycle periodically, or stabilizes at some state almost everywhere. This conclusion holds for any population graph and a large class of finite games. This framework suffices to reveal the underlying mathematical rationale for the kaleidoscopic cooperation of Nowak and May's pioneering work on spatial games: highly symmetric starting configuration causes a very long transient phase covering a large number of extremely beautiful spatial patterns. For all starting configurations, spatial patterns transit definitely over generations, so cooperators and defectors persist definitely. This framework can be extended to explore games including the snowdrift game, the public goods games (with or without loner, punishment), and repeated games on graphs. Aspiration dynamics can also be fully addressed when players deterministically switch strategy for unmet aspirations by virtue of our framework. Our results have potential implications for exploring the dynamics of a large variety of spatially extended systems in biology and physics.Comment: 35 pages, 10 figures, and supplementary informatio

Moving Away from Nasty Encounters Enhances Cooperation in Ecological Prisoner's Dilemma Game

We study the role of migration in the evolution of cooperation. Individuals spatially located on a square lattice play the prisoner's dilemma game. Dissatisfied players, who have been exploited by defectors, tend to terminate interaction with selfish partners by leaving the current habitats, and explore unknown physical niches available surrounding them. The time scale ratio of game interaction to natural selection governs how many game rounds occur before individuals experience strategy updating. Under local migration and strong selection, simulation results demonstrate that cooperation can be stabilized for a wide range of model parameters, and the slower the natural selection, the more favorable for the emergence of cooperation. Besides, how the selection intensity affects cooperators' evolutionary fate is also investigated. We find that increasing it weakens cooperators' viability at different speeds for different time scale ratios. However, cooperation is greatly improved provided that individuals are offered with enough chance to agglomerate, while cooperation can always establish under weak selection but vanishes under very strong selection whenever individuals have less odds to migrate. Whenever the migration range restriction is removed, the parameter area responsible for the emergence of cooperation is, albeit somewhat compressed, still remarkable, validating the effectiveness of collectively migrating in promoting cooperation

Global patterns of extinction risk and conservation needs for Rodentia and Eulipotyphla

AIM: To explore global patterns in spatial aggregations of species richness, vulnerability and data deficiency for Rodentia and Eulipotyphla. To evaluate the adequacy of existing protected area (PA) network for these areas. To provide a focus for local conservation initiatives. LOCATION: Global. METHODS: Total species, globally threatened (GT) species, and Data Deficient (DD) species richness were calculated for a 1° resolution grid. Correspondence analyses between global species richness against GT species richness were performed. To assess PA network adequacy, a correspondence analysis was conducted to identify areas of high richness and GT species richness that have poor protection. RESULTS: Six hotspots were identified for GT eulipotyphlans, encompassing 40% of GT species. Three of these contain higher numbers of GT species than would be expected based on their overall species richness. Ten priority regions were identified for GT rodents, which together contain 34% of all GT species. Six contain higher numbers of GT rodent species than would be expected based on their overall species richness. For DD species, 15% of DD eulipotyphlans were represented within three priority regions, whereas 18 were identified for rodents, capturing 53% of all DD species. Areas containing lower numbers of protected GT eulipotyphlan species than expected include Mexico; Cameroonian Highlands; Albertine Rift; Tanzania; Kenya; Ethiopia; western Asia; India; and Sri Lanka. Areas containing lower numbers of protected GT rodent species than expected are Borneo, Sumatra and Sulawesi. Five eulipotyphlans and 44 rodents have ranges which fall completely outside of PAs. MAIN CONCLUSION: Rodentia and Eulipotyphla priority regions should be considered separately to one another and to other mammals. This analysis approach allows us to pinpoint and delineate geographical areas which represent key regions at a global level for rodents and eulipotyphlans, in order to facilitate conservation, field research and capacity building at a local level

PGT-Net: Progressive Guided Multi-task Neural Network for Small-area Wet Fingerprint Denoising and Recognition

Fingerprint recognition on mobile devices is an important method for identity verification. However, real fingerprints usually contain sweat and moisture which leads to poor recognition performance. In addition, for rolling out slimmer and thinner phones, technology companies reduce the size of recognition sensors by embedding them with the power button. Therefore, the limited size of fingerprint data also increases the difficulty of recognition. Denoising the small-area wet fingerprint images to clean ones becomes crucial to improve recognition performance. In this paper, we propose an end-to-end trainable progressive guided multi-task neural network (PGT-Net). The PGT-Net includes a shared stage and specific multi-task stages, enabling the network to train binary and non-binary fingerprints sequentially. The binary information is regarded as guidance for output enhancement which is enriched with the ridge and valley details. Moreover, a novel residual scaling mechanism is introduced to stabilize the training process. Experiment results on the FW9395 and FT-lightnoised dataset provided by FocalTech shows that PGT-Net has promising performance on the wet-fingerprint denoising and significantly improves the fingerprint recognition rate (FRR). On the FT-lightnoised dataset, the FRR of fingerprint recognition can be declined from 17.75% to 4.47%. On the FW9395 dataset, the FRR of fingerprint recognition can be declined from 9.45% to 1.09%

Rotating machinery fault diagnosis for imbalanced data based on decision tree and fast clustering algorithm

To diagnose rotating machinery fault for imbalanced data, a kind of method based on fast clustering algorithm and decision tree is proposed. Combined with wavelet packet decomposition and isometric mapping (Isomap), sensitive features of different faults can be obtained so the imbalanced fault sample set is constituted. Then the fast clustering algorithm is applied to search core samples from the majority data of the imbalanced fault sample set. Consequently, the balanced fault sample set consisted of the clustered data and the minority data is built. After that, decision tree is trained with the balanced fault sample set to get the fault diagnosis model. Finally, gearbox fault data set and rolling bearing fault data set are used to test the fault diagnosis model. The experiment results show that proposed fault diagnosis model could accurately diagnose the rotating machinery fault for imbalanced data