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Abstract:The class of closed patterns is a well known condensed representations of frequent patterns, and have recently attracted considerable interest. In this paper, we propose an efficient algorithm LCM (Linear time Closed pattern Miner) for mining frequent closed patterns from large transaction databases. The main theoretical contribution is our proposed prefix-preserving closure extension of closed patterns, which enables us to search all frequent closed patterns in a depth-first manner, in linear time for the number of frequent closed patterns. Our algorithm do not need any storage space for the previously obtained patterns, while the existing algorithms needs it. Performance comparisons of LCM with straightforward algorithms demonstrate the advantages of our prefix-preserving closure extension.

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In contrast to AI hardware, neuromorphic hardware is based on neuroscience, wherein constructing both spiking neurons and their dense and complex networks is essential to obtain intelligent abilities. However, the integration density of present neuromorphic devices is much less than that of human brains. In this report, we present molecular neuromorphic devices, composed of a dynamic and extremely dense network of single-walled carbon nanotubes (SWNTs) complexed with polyoxometalate (POM). We show experimentally that the SWNT/POM network generates spontaneous spikes and noise. We propose electron-cascading models of the network consisting of heterogeneous molecular junctions that yields results in good agreement with the experimental results. Rudimentary learning ability of the network is illustrated by introducing reservoir computing, which utilises spiking dynamics and a certain degree of network complexity. These results indicate the possibility that complex functional networks can be constructed using molecular devices, and contribute to the development of neuromorphic devices.

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We describe a majority-logic gate device suitable for use in developing single-electron integrated circuits. The device consists of a capacitor array for input summation and an irreversible single-electron box for threshold operation. It accepts three binary inputs and produces a corresponding output, a complementary majority-logic output, by using the change in its tunneling threshold caused by the input signals; it produces a logical 1 output if two or three of the inputs are logical 0 and a logical 0 output if two or three of the inputs are logical 1. We combined several of these gate devices to form subsystems, a shift register and a full adder, and confirmed their operation by computer simulation. The gate device is simple in structure and powerful in terms of implementing digital functions with a small number of devices. These superior features will enable the device to contribute to the development of single-electron integrated circuits.

Investigation of stochastic resonance in GaAs-based nanowire field-effect transistors (FETs) controlled by Schottky wrap gate and their networks is described. When a weak pulse train is given to the gate of the FET operating in a subthreshold region, the correlation between the input-pulse and source-drain current increases by adding input noise. Enhancement of the correlation is observed in a summing network of the FETs. Measured correlation coefficient of the present system can be larger than that in a linear system in the wide range of noise. An analytical model based on the electron motion over a gate-induced potential barrier quantitatively explains the experimental behaviors.