Electro-Weak Dark Matter: non-perturbative effect confronting indirect detections

We update indirect constraints on Electro-Weak Dark Matter (EWDM) considering the Sommerfeld-Ramsauer-Townsend (SRT) effect for its annihilations into a pair of standard model gauge bosons assuming that EWDM accounts for the observed dark matter (DM) relic density for a given DM mass and mass gaps among the multiplet components. For the radiative or smaller mass splitting, the hypercharged triplet and higher multiplet EWDMs are ruled out up to the DM mass ~ 10 - 20 TeV by the combination of the most recent data from AMS-02 (antiproton), Fermi-LAT (gamma-ray), and HESS (gamma-line). The Majorana triplet (wino-like) EWDM can evade all the indirect constraints only around Ramsauer-Townsend dips which can occur for a tiny mass splitting of order 10 MeV or less. In the case of the doublet (Higgsino-like) EWDM, a wide range of its mass > 500 GeV is allowed except Sommerfeld peak regions. Such a stringent limit on the triplet DM can be evaded by employing a larger mass gap of the order of 10 GeV which allows its mass larger than about 1 TeV. However, the future CTA experiment will be able to cover most of the unconstrained parameter space.Comment: 17 pages, 4 figures; result for an O(10 GeV) mass gap, future sensitivity of CTA, and references adde

Adversarial Dropout for Supervised and Semi-supervised Learning

Recently, the training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has been proved to improve generalization performance of neural networks. In contrast to the individually biased inputs to enhance the generality, this paper introduces adversarial dropout, which is a minimal set of dropouts that maximize the divergence between the outputs from the network with the dropouts and the training supervisions. The identified adversarial dropout are used to reconfigure the neural network to train, and we demonstrated that training on the reconfigured sub-network improves the generalization performance of supervised and semi-supervised learning tasks on MNIST and CIFAR-10. We analyzed the trained model to reason the performance improvement, and we found that adversarial dropout increases the sparsity of neural networks more than the standard dropout does.Comment: submitted to AAAI-1

A Study on Virtual Reality Storytelling by Story Authoring Tool Algorithm

The objective of this study was to examine the storytelling principles of virtual reality contents, which are recently grabbing much attention, and the patterns of their generation rules and, based on the results, to analyze the elements and structure of a storytelling method suitable for virtual reality contents. In virtual reality environment, a story is usually being generated between choices made by a user who behaves autonomously under simulated environmental factors and the environmental constraints. This corresponds to a mutually complementary role of representation and simulation, which has been hotly discussed in the field of interactive storytelling. This study was conducted based on the assumption that such a mutually complementary realization is ideal for virtual reality storytelling. A simulation-based story authoring tool is a good example that shows this mutual complementation, in that it develops a story through various algorithms which involves the interaction of agents which occur within the strata of a virtual environment. Therefore, it can be a methodology for virtual reality storytelling. The structures and elements of narratives used in virtual reality storytelling which achieve balance of representation and simulation are much similar to an algorithm strategy of a simulation-based story authoring tool. The virtual reality contents released up to now can be classified into four categories based on the two axes of representation and simulation. The study focused on contents which are layered in higher strata of both representation and simulation. In the perspective of representation strata, these contents are actively using such elements as goal, event, action, perception, internal element, outcome, and setting element, which are constituents of ‘Fabula model’, to generate time relations and cause-effect relations. And in the perspective of simulation strata, the use of the ‘Late commitment’ strategy allowed users to understand the meanings of their actions taken during the process of experimenting with various dynamic principles within the environment

Fabrication and Evaluation of Mechanical Properties of CF/GNP Composites

AbstractCNT/CFRP (Carbon Nanotube/ Carbon Fiber Reinforced Plastic) composites and GNP/CFRP (Graphene Nano platelet/ Carbon Fiber Reinforced Plastic) have several excellent mechanical properties including, high strength, young's modulus, thermal conductivity, corrosion resistance, electronic shielding and so on. In this study, CNT/CFRP composites were manufactured by varying the CNT weight ratio as 2wt% and 3wt%, While GNP/CFRP composites were manufactured by varying the GNP weight ratio as 0.5wt% and 1wt%. The composites ware manufactured by mechanical method (3-roll-mill). Tensile, impact and wear tests were performed according to ASTM standards D638, D256 and D3181 respectively. It was observed that, increasing the CNT weight ratio improves the mechanical properties, e.g., tensile strength, impact and wear resistance