Ryosuke Kojima 교수

Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GC

This paper addresses bird song analysis based on semi-automatic annotation. Research in animal behavior, especially with birds, would be aided by automated (or semiautomated) systems that can localize sounds, measure their timing, and identify their source. This is difficult to achieve in real environments where several birds may be singing from different locations and at the same time. Analysis of recordings from the wild has in the past typically required manual annotation. Such annotation is

Molecular generation is crucial for advancing drug discovery, materials science, and chemical exploration. It expedites the search for new drug candidates, facilitates tailored material creation, and enhances our understanding of molecular diversity. By employing artificial intelligence techniques such as molecular generative models based on molecular graphs, researchers have tackled the challenge of identifying efficient molecules with desired properties. Here, we propose a new molecular genera

This paper addresses real-time bird song scene analysis. Observation of animal behavior such as communication of wild birds would be aided by a portable device implementing a real-time system that can localize sound sources, measure their timing, classify their sources, and visualize these factors of sources. The difficulty of such a system is an integration of these functions considering the real-time requirement. To realize such a system, we propose a cascaded approach, cascading sound source

[abstFig src='/00290001/22.jpg' width='300' text='Spatial-cue-based probabilistic model' ] This paper addresses bird song scene analysis based on semi-automatic annotation. Research in animal behavior, especially in birds, would be aided by automated or semi-automated systems that can localize sounds, measure their timing, and identify their sources. This is difficult to achieve in real environments, in which several birds at different locations may be singing at the same time. Analysis of recor

<div>Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the

This paper addresses multimodal “scene understanding” for a robot using audio-visual and text information. Scene understanding is defined by extracting six-W information such as What, When, Where, Who, Why, and hoW on the surrounding environment. Although scene understanding for a robot has been studied in the fields of robot vision and audition, only the first four Ws except for why and how information were considered. We, thus, focus on extracting how information, in particular, on cooking sce

This paper addresses bird song scene analysis focusing on location of birds and acoustic features of bird songs. Such a research area usually requires manual annotation related to positions and/or vocalization types of the target animals for a large amount of observed data. However, this manual annotation has two problems. One is that it is tough to annotate data observed in real environments because environmental noise exist and sound is reflected by trees and the ground, and also several birds