Characteristics of cloud fractions from satellite observations along the ship track of R/V Shirase

The Tenth Symposium on Polar Science/Ordinary sessions: [OM] Polar Meteorology and Glaciology, Wed. 4 Dec. / Entrance Hall (1st floor) , National Institute of Polar Researc

Experimental demonstration of random walk by probability chaos using single photons

In our former work (Sci. Rep. 4: 6039, 2014), we theoretically and numerically demonstrated that chaotic oscillation can be induced in a nanoscale system consisting of quantum dots between which energy transfer occurs via optical near-field interactions. Furthermore, in addition to the nanoscale implementation of oscillators, it is intriguing that the chaotic behavior is associated with probability derived via a density matrix formalism. Indeed, in our previous work (Sci. Rep. 6: 38634, 2016) we examined such oscillating probabilities via diffusivity analysis by constructing random walkers driven by chaotically driven bias. In this study, we experimentally implemented the concept of probability chaos using a single-photon source that was chaotically modulated by an external electro-optical modulator that directly yielded random walkers via single-photon observations after a polarization beam splitter. An evident signature was observed in the resulting ensemble average of the time-averaged mean square displacement. Although the experiment involved a scaled-up, proof-of-concept model of a genuine nanoscale oscillator, the experimental observations clearly validate the concept of oscillating probability, paving the way toward future ideal nanoscale systems

Learned spatial data partitioning

Due to the significant increase in the size of spatial data, it is essential to use distributed parallel processing systems to efficiently analyze spatial data. In this paper, we first study learned spatial data partitioning, which effectively assigns groups of big spatial data to computers based on locations of data by using machine learning techniques. We formalize spatial data partitioning in the context of reinforcement learning and develop a novel deep reinforcement learning algorithm. Our learning algorithm leverages features of spatial data partitioning and prunes ineffective learning processes to find optimal partitions efficiently. Our experimental study, which uses Apache Sedona and real-world spatial data, demonstrates that our method efficiently finds partitions for accelerating distance join queries and reduces the workload run time by up to 59.4%

Characteristics of cloud fraction from whole-sky camera and ceilometer observations onboard R/V Shirase during JARE 60

The Tenth Symposium on Polar Science/Ordinary sessions: [OM] Polar Meteorology and Glaciology, Wed. 4 Dec. / Entrance Hall (1st floor) , National Institute of Polar Researc

Entangled N-photon states for fair and optimal social decision making

Situations involving competition for resources among entities can be modeled by the competitive multi-armed bandit (CMAB) problem, which relates to social issues such as maximizing the total outcome and achieving the fairest resource repartition among individuals. In these respects, the intrinsic randomness and global properties of quantum states provide ideal tools for obtaining optimal solutions to this problem. Based on the previous study of the CMAB problem in the two-arm, two-player case, this paper presents the theoretical principles necessary to find polarization-entangled N-photon states that can optimize the total resource output while ensuring equality among players. These principles were applied to two-, three-, four-, and five-player cases by using numerical simulations to reproduce realistic configurations and find the best strategies to overcome potential misalignment between the polarization measurement systems of the players. Although a general formula for the N-player case is not presented here, general derivation rules and a verification algorithm are proposed. This report demonstrates the potential usability of quantum states in collective decision making with limited, probabilistic resources, which could serve as a first step toward quantum-based resource allocation systems.Comment: 22 pages and 7 figures, version 1.1 of July 27th 202