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Robots are expected to perform smart services and to undertake various troublesome or difficult tasks in the place of humans. Since these human-scale tasks consist of a temporal sequence of events, robots need episodic memory to store and retrieve the sequences to perform the tasks autonomously in similar situations. As episodic memory, in this paper we propose a novel Deep adaptive resonance theory (ART) neural model and apply it to the task performance of the humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory at KAIST. Deep ART has a deep structure to learn events, episodes, and even more like daily episodes. Moreover, it can retrieve the correct episode from partial input cues robustly. To demonstrate the effectiveness and applicability of the proposed Deep ART, experiments are conducted with the humanoid robot, Mybot, for performing the three tasks of arranging toys, making cereal, and disposing of garbage.

Memory modeling has been a popular topic of research for improving the performance of autonomous agents in cognition related problems. Apart from learning distinct experiences correctly, significant or recurring experiences are expected to be learned better and be retrieved easier. In order to achieve this objective, this paper proposes a user preference-based dual-memory adaptive resonance theory network model, which makes use of a user preference to encode memories with various strengths and to learn and forget at various rates. Over a period of time, memories undergo a consolidation-like process at a rate proportional to the user preference at the time of encoding and the frequency of recall of a particular memory. Consolidated memories are easier to recall and are more stable. This dual-memory neural model generates distinct episodic memories and a flexible semantic-like memory component. This leads to an enhanced retrieval mechanism of experiences through two routes. The simulation results are presented to evaluate the proposed memory model based on various kinds of cues over a number of trials. The experimental results on Mybot are also presented. The results verify that not only are distinct experiences learned correctly but also that experiences associated with higher user preference and recall frequency are consolidated earlier. Thus, these experiences are recalled more easily relative to the unconsolidated experiences.

Gastric cancer is a complex disease that is affected by multiple genetic and environmental factors. For the precise diagnosis and effective treatment of gastric cancer, the heterogeneity of the disease must be simplified; one way to achieve this is by dividing the disease into subgroups. Toward this effort, recent advances in high-throughput sequencing technology have revealed four molecular subtypes of gastric cancer, which are classified as Epstein-Barr virus-positive, microsatellite instability, genomically stable, and chromosomal instability subtypes. We anticipate that this molecular subtyping will help to extend our knowledge for basic research purposes and will be valuable for clinical use. Here, we review the genomic and epigenomic heterogeneity of the four molecular subtypes of gastric cancer. We also describe a mutational meta-analysis and a reanalysis of DNA methylation that were performed using previously reported gastric cancer datasets.