An effect of consumer\u27s earlier decision to purchase a discount ticket

In this article, we consider how effect consumer\u27s earlier decision to purchase a discount ticket will have on the competition, price and timetable, between airlines. We focus on a relationship between consumer\u27s purchasing behavior and a competition between airlines. We consider that a consumer can purchase a ticket two times, i.e. ex-ante and ex-post, corresponding to this timing, airlines also can set their prices of tickets. The main conclusion highlighted by this article is that, in a subgame perfect equilibrium, each airline\u27s expected profit is unique and timetable is socially optimal regardless to a consumer\u27s purchasing behavior

Efficient development of high performance data analytics in Python

Our society is generating an increasing amount of data at an unprecedented scale, variety, and speed. This also applies to numerous research areas, such as genomics, high energy physics, and astronomy, for which large-scale data processing has become crucial. However, there is still a gap between the traditional scientific computing ecosystem and big data analytics tools and frameworks. On the one hand, high performance computing (HPC) programming models lack productivity, and do not provide means for processing large amounts of data in a simple manner. On the other hand, existing big data processing tools have performance issues in HPC environments, and are not general-purpose. In this paper, we propose and evaluate PyCOMPSs, a task-based programming model for Python, as an excellent solution for distributed big data processing in HPC infrastructures. Among other useful features, PyCOMPSs offers a highly productive general-purpose programming model, is infrastructure-agnostic, and provides transparent data management with support for distributed storage systems. We show how two machine learning algorithms (Cascade SVM and K-means) can be developed with PyCOMPSs, and evaluate PyCOMPSs’ productivity based on these algorithms. Additionally, we evaluate PyCOMPSs performance on an HPC cluster using up to 1,536 cores and 320 million input vectors. Our results show that PyCOMPSs achieves similar performance and scalability to MPI in HPC infrastructures, while providing a much more productive interface that allows the easy development of data analytics algorithms.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND2016-754433. This work has been supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), by Generalitat de Catalunya, Spain (contract 2014-SGR-1051). The research leading to these results has also received funding from the collaboration between Fujitsu and BSC (Script Language Platform).Peer Reviewe

The First Beam Recirculation and Beam Tuning in the Compact ERL at KEK

Superconducting(SC)-linac-based light sources, which can produce ultra-brilliant photon beams in CW operation, are attracting worldwide attention. In KEK, we have been conducting R&D; efforts towards the energy-recovery-linac(ERL)-based light source* since 2006. To demonstrate the key technologies for the ERL, we constructed the Compact ERL (cERL)** from 2009 to 2013. In the cERL, high-brightness CW electron beams are produced using a 500-kV photocathode DC gun. The beams are accelerated using SC cavities, transported through a recirculation loop, decelerated in the SC cavities, and dumped. In the February of 2014, we succeeded in accelerating and recirculating the CW beams of 4.5 micro-amperes in the cERL; the beams were successfully transported from the gun to the beam dump under energy recovery operation in the main linac. Then, precise tuning of beam optics and diagnostics of beam properties are under way. We report our experience on the beam commissioning, as well as the results of initial measurements of beam properties

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

non present