This paper addresses the novel task of detecting chorus sections in English and Japanese lyrics text. Although chorus-section detection using audio signals has been studied, whether chorus sections can be detected from text-only lyrics is an open issue. Another open issue is whether patterns of repeating lyric lines such as those appearing in chorus sections depend on language. To investigate these issues, we propose a neural-network-based model for sequence labeling. It can learn phrase repetition and linguistic features to detect chorus sections in lyrics text. It is, however, difficult to train this model since there was no dataset of lyrics with chorus-section annotations as there was no prior work on this task. We therefore generate a large amount of training data with such annotations by leveraging pairs of musical audio signals and their corresponding manually time-aligned lyrics; we first automatically detect chorus sections from the audio signals and then use their temporal positions to transfer them to the line-level chorus-section annotations for the lyrics. Experimental results show that the proposed model with the generated data contributes to detecting the chorus sections, that the model trained on Japanese lyrics can detect chorus sections surprisingly well in English lyrics, and that patterns of repeating lyric lines are language-independent.

Tourist satisfaction plays a very important role in the development of local community tourism. For the development of tourist destinations in local communities, it is important to measure, maintain, and improve tourist destination royalties over the medium to long term. It has been proven that improving tourist satisfaction is a major factor in improving tourist destination royalties. Therefore, to improve tourist satisfaction in local communities, we identified multiple clusters of sightseeing spots and determined that the satisfaction of tourists can be increased based on these clusters of sightseeing spots. Our discovery flow can be summarized as follows. First, we extracted tourism keywords from guidebooks on sightseeing spots. We then constructed a complex network of tourists and sightseeing spots based on the data collected from experiments conducted in Kyoto. Next, we added the corresponding tourism keywords to each sightseeing spot. Finally, by analyzing network motifs, we successfully discovered multiple clusters of sightseeing spots that could be used to improve tourist satisfaction.

In this paper, we briefly review the concept of superconducting quantum computers and discuss their hardware architecture. We also describe the necessary technologies for the development of a medium-scale quantum computer with more than tens of thousands of quantum bits.

Superconducting nanowire single-photon detector(SNSPD) has been one of the important ingredients for photonic quantum information processing (QIP). In order to see the potential of SNSPDs, I briefly review recent progresses of the photonic QIP with SNSPDs implemented for various purposes and present a possible direction for the development of SNSPDs.

It is qualitatively considered that emergency information processing by using UTM grids is effective in generating COP (Common Operational Pictures). Here, we conducted a numerical evaluation based on emergency information-processing training to examine the efficiency of the use of UTM grid maps by staff at the Tagajo City Government office. The results of the demonstration experiment were as follows: 1) The time required for information propagation and mapping with UTM coordinates was less than that with address text consisting of area name and block number. 2) There was no measurable difference in subjective estimates of the training performance of participants with or without the use of UTM grids. 3) Fear of real emergency responses decreased among training participants using UTM grids. 4) Many of the negative free answers on a questionnaire evaluation of participants involved requests regarding the reliability and operability of UTM tools.

We approach nanophotonic computing on the basis of optical near-field interactions between quantum dots. A table lookup, or matrix-vector multiplication, architecture is proposed. As fundamental functionality, a data summation mechanism and digital-to-analog conversion are experimentally demonstrated using CuCl quantum dots. Owing to the diffraction-limit-free nature of nanophotonics, these architectures can achieve ultrahigh density integration compared to conventional bulky optical systems, as well as low power dissipation.

In nanophotonic device operations, characteristic features on a nanometer scale, such as locally excited states, dependence on the excitation number, and spatial symmetry of a system, play an important role. Using these features, selective excitation energy transfer via an optical near field is shown for a quantum-dot system with discrete energy levels. This selectivity strongly depends on a dipole-inactive state of an exciton, which cannot be excited by the far-field light. Operation principles of logic gates, photon storage, and quantum information processing device, which are based on the selectivity, are proposed, and the temporal dynamics are investigated analytically and numerically by using quantum theory. Nanophotonic devices, which are constructed from quantum mechanical and classical dissipative systems, are expected to become one of a key technologies in future device architecture.

In this paper, a new computing paradigm suitable for analog circuit systems is described in comparison to the digital circuit systems. The analog circuit systems have some disadvantages especially in terms of accuracy and stability, but there are some applications that don't require accuracy or stability in circuit component. The new computing concept for such applications, 'inaccurate' information processing, or 'rough' information processing, is proposed and described as well as some examples of such applications.

This paper analyzes the timing of eyeblink during visual identification of katakana characters on a display, which were presented under the constraint of a restricted visual field (R.V.F.). Blinks frequently occurred when the subject slowly brought the R.V.F. near a feature point (e.g., terminal point, crossing point).

Commercial breaks are often placed at the climax of stories in recent TV programs in Japan, which may cause some serious effects on audiences, especially children, since this practice disturbs the concentrations. The experiment measured the psycho-physiological state of four children before and after commercials. The results showed that the next peak of attention is delayed by distracting the attention.

Enhancement of resonance is shown by coupling and summing in sinusoidally driven chaotic neural networks. This resonance phenomenon has a peak at a drive frequency similar to noise-induced stochastic resonance (SR), however, the mechanism is different from noise-induced SR. We numerically study the properties of resonance in chaotic neural networks in the turbulent phase with summing and homogeneous coupling, with particular consideration of enhancement of the signal-to-noise ratio (SNR) by coupling and summing. Summing networks can enhance the SNR of a mean field based on the law of large numbers. Global coupling can enhance the SNR of a mean field and a neuron in the network. However, enhancement is not guaranteed and depends on the parameters. A combination of coupling and summing enhances the SNR, but summing to provide a mean field is more effective than coupling on a neuron level to promote the SNR. The global coupling network has a negative correlation between the SNR of the mean field and the Kolmogorov-Sinai (KS) entropy, and between the SNR of a neuron in the network and the KS entropy. This negative correlation is similar to the results of the driven single neuron model. The SNR is saturated as an increase in the drive amplitude, and further increases change the state into a nonchaotic one. The SNR is enhanced around a few frequencies and the dependence on frequency is clearer and smoother than the results of the driven single neuron model. Such dependence on the drive amplitude and frequency exhibits similarities to the results of the driven single neuron model. The nearest neighbor coupling network with a periodic or free boundary can also enhance the SNR of a neuron depending on the parameters. The network also has a negative correlation between the SNR of a neuron and the KS entropy whenever the boundary is periodic or free. The network with a free boundary does not have a significant effect on the SNR from both edges of the free boundaries.

We show by numerical calculations that a chaotic neuron model driven by a weak sinusoid has resonance. This resonance phenomenon has a peak at a drive frequency similar to that of noise-induced stochastic resonance (SR). This neuron model was proposed from biological studies and shows a chaotic response when a parameter is varied. SR is a noise induced effect in driven nonlinear dynamical systems. The basic SR mechanism can be understood through synchronization and resonance in a bistable system driven by a subthreshold sinusoid plus noise. Therefore, background noise can boost a weak signal using SR. This effect is found in biological sensory neurons and obviously has some useful sensory function. The signal-to-noise ratio (SNR) of the driven chaotic neuron model is improved depending on the drive frequency; especially at low frequencies, the SNR is remarkably promoted. The resonance mechanism in the model is different from the noise-induced SR mechanism. This paper considers the mechanism and proposes possible explanations. Also, the meaning of chaos in biological systems based on the resonance phenomenon is considered.

Computational sensor (smart sensor, vision chip in other words) is a very small integrated system, in which processing and sensing are unified on a single VLSI chip. It is designed for a specific targeted application. Research activities of computational sensor are described in this paper. There have been quite a few proposals and implementations in computational sensors. Firstly, their approaches are summarized from several points of view, such as advantage vs. disadvantage, neural vs. functional, architecture, analog vs. digital, local vs. global processing, imaging vs. processing, new processing paradigms. Then, several examples are introduced which are spatial processings, temporal processings, A/D conversions, programmable computational sensors. Finally, the paper is concluded.

A photonic/video hybrid system for optical information processing by synthesis of the coherence function is proposed. Optical coherence function can be synthesized to have delta-function-like shape or notch shape by using direct frequency modulation of a laser diode with an appropriate waveform. Therefore, by choosing only the interference component in the interferometer, information processing functions can be obtained. The photonic/video hybrid system proposed provides a novel way to choose the interference component, which can improve the spatial resolution compared with our previous system with holographic technique. Selective extraction two-dimensional (2-D) information from a three-dimensional (3-D) object is successfully performed in basic experiments.

Analysis of satellite images requires classificatio of image objects. Since different categories may have almost the same brightness or feature in high dimensional remote sensing data, many object categories overlap with each other. How to segment the object categories accurately is still an open question. It is widely recognized that the assumptions required by many classification methods (maximum likelihood estimation, etc.) are suspect for textural features based on image pixel brightness. We propose an image feature based neural network approach for the segmentation of AVHRR images. The learning algoriothm is a modified backpropagation with gain and weight decay, since feedforward networks using the backpropagation algorithm have been generally successful and enjoy wide popularity. Destructive algorithms that adapt the neural architecture during the training have been developed. The classification accuracy of 100% is reached for a validation data set. Classification result is compared with that of Kohonen's LVQ and basic backpropagation algorithm based pixel-by-pixel method. Visual investigation of the result images shows that our method can not only distinguish the categories with similar signatures very well, but also is robustic to noise.

We discuss possible new principles of information processing by utilizing microscopic, semi-microscopic and macroscopic phenomena occuring in nature. We first discuss quantum mechanical universal information processing in microscopic world governed by quantum mechanics, and then we discuss superconducting phenomena in a mesoscopic system, especially an information processing system using flux quantum. Finally, we discuss macroscopic self-organizing phenomena in biology and suggest possibility of self-organizing devices.

The newly developed GaAs-pin/SLM, that is structured with a GaAs-pin diode photodetector and a ferroelectric liquid crystal as the light phase modulator, shows the accumulative thresholding characteristic against the optical energy of the write-in pulse train. We experimentally investigate this characteristic and discuss its applications to optical parallel processings.

The VSTEP concept and its practical application in the form of an LED-type pnpn-VSTEP demonstrating low power consumption through electro-photonic operational modes are both shown. Further, with focus primarily on the new laser-mode VSTEP with high-intensity light output and narrow optical beam divergence, the design features such as threshold gain and optical absorptivity, device fabrication, and characteristics are explained. The possibility of ultimate performance based mainly on electrical to optical power conversion efficiency, important from the application viewpoint of optical interconnection, are also discussed. Also, as two examples of functional optical interconnection achieved by VSTEP, serial-to-parallel data conversion and optical self-routing switches are shown. Finally, future opto-electronic technologies to be developed for two-dimensionally integrable surface-type optical semiconductor devices, including the VSTEP, are discussed.

We review the role of optics in interconnects, analog processing, neural networks, and digital computing. The properties of low interference, massively parallel interconnections, and very high data rates promise extremely high performance for optical information processing systems.

We review the role of optics in interconnects, analog processing, neural networks, and digital computing. The properties of low interference, massively parallel interconnections, and very high data rates promise extremely high performance for optical information processing systems.