Cut elimination for propositional cyclic proof systems with fixed-point operators

Infinitary and cyclic proof systems are proof systems for logical formulas with fixed-point operators or inductive definitions. A cyclic proof system is a restriction of the corresponding infinitary proof system. Hence, these proof systems are generally not the same, as in the cyclic system may be weaker than the infinitary system. For several logics, the infinitary proof systems are shown to be cut-free complete. However, cyclic proof systems are characterized with many unknown problems on the (cut-free) completeness or the cut-elimination property. In this study, we show that the provability of infinitary and cyclic proof systems are the same for some propositional logics with fixed-point operators or inductive definitions and that the cyclic proof systems are cut-free complete

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