Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a three-dimensional (3D) organ mesh for a low-contrast two-dimensional (2D) projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy.

The development of body sensor networks (BSNs) with rich multimodal signals has enabled highly accurate fine-grained action detection, which is the cornerstone of many humancomputer interaction applications. However, in the case of consecutive fine-grained actions, most existing wearable sensor-based detection methods are constrained by sliding windows because of their limited temporal receptive fields, and existing sequence-to-sequence detection methods cannot effectively leverage the potential of multimodal information of wearable sensors. Herein, to give multimodal signals full play in fine-grained action detection, we propose a novel temporal convolutional network by designing a channel attention-based multistream structure. We apply it to a promising application for correct and incorrect patient transfer nursing action detection. A dataset is collected from a BSN on a patient when nurses perform patient transfer. Extensive experiments on our dataset and public dataset (C-MHAD) demonstrate that the proposed method is superior to the state-of-the-art methods, because it can strengthen the utilization of prediction features from the more convincing modal stream at each time frame.

Respiratory motion and the associated deformations of abdominal organs and tumors are essential information in clinical applications. However, inter- and intra-patient multi-organ deformations are complex and have not been statistically formulated, whereas single organ deformations have been widely studied. In this paper, we introduce a multi-organ deformation library and its application to deformation reconstruction based on the shape features of multiple abdominal organs. Statistical multi-organ motion/deformation models of the stomach, liver, left and right kidneys, and duodenum were generated by shape matching their region labels defined on four-dimensional computed tomography images. A total of 250 volumes were measured from 25 pancreatic cancer patients. This paper also proposes a per-region-based deformation learning using the non-linear kernel model to predict the displacement of pancreatic cancer for adaptive radiotherapy. The experimental results show that the proposed concept estimates deformations better than general per-patient-based learning models and achieves a clinically acceptable estimation error with a mean distance of 1.2 ± 0.7 mm and a Hausdorff distance of 4.2 ± 2.3 mm throughout the respiratory motion.

This paper presents a nanomanipulation system for operation inside scanning electron microscopes (SEM). The system is small in size, capable of being mounted onto and demounted from an SEM through the specimen exchange chamber without breaking the high vacuum of the SEM. This advance eliminates frequent opening of the high-vacuum chamber, thus, incurs less contamination to the SEM, avoids lengthy pumping, and significantly eases the exchange of end-effectors (e.g., nano probes and grippers). The system consists of two independent 3-DOF Cartesian nanomanipulators based on piezo motors and piezo actuators. High-resolution optical encoders are integrated into the nanomanipulators to provide position feedback for closed-loop control. A look-then-move control system and a contact detection algorithm are implemented for horizontal and vertical nanopositioning. The system design, system characterization details, and system performance are described.

Indirect reciprocity, in which individuals help others with a good reputation but not those with a bad reputation, is a mechanism for cooperation in social dilemma situations when individuals do not repeatedly interact with the same partners. In a relatively large society where indirect reciprocity is relevant, individuals may not know each other's reputation even indirectly. Previous studies investigated the situations where individuals playing the game have to determine the action possibly without knowing others' reputations. Nevertheless, the possibility that observers of the game, who generate the reputation of the interacting players, assign reputations without complete information about them has been neglected. Because an individual acts as an interacting player and as an observer on different occasions if indirect reciprocity is endogenously sustained in a society, the incompleteness of information may affect either role. We examine the game of indirect reciprocity when the reputations of players are not necessarily known to observers and to interacting players. We find that the trustful discriminator, which cooperates with good and unknown players and defects against bad players, realizes cooperative societies under seven social norms. Among the seven social norms, three of the four suspicious norms under which cooperation (defection) to unknown players leads to a good (bad) reputation enable cooperation down to a relatively small observation probability. In contrast, the three trustful norms under which both cooperation and defection to unknown players lead to a good reputation are relatively efficient.

A talking and singing robot which adaptively learns the vocalization skill by an auditory feedback learning is being developed. In vocalization, the vibration of vocal cords generates a source sound, and then the sound wave is led to a vocal tract, which works as a resonance filter to determine the spectrum envelope. The robot consists of motor-controlled vocal organs such as vocal cords, a vocal tract and a nasal cavity to generate a natural voice imitating a human vocalization. The paper briefly introduces the construction of vocal cords and vocal tract for the realization of the talking robot, and then describes how the robot autonomously acquires the vocalization skill in the auditory feedback learning by listening to human talking and singing voices. The acquired voices were evaluated by listening experiments

A mechanical model of the human vocal system is being developed based on a mechatronics technology under a feedback control. While various ways of vocal sound production have been actively studied so far, a mechanical construction of the vocal system is considered to advantageously realize natural vocalization with its fluid dynamics. Motors are employed for the manipulation of the mechanical system. The system is able to adoptively learn the relations between motor control parameters and the produced vocal sounds using an auditory feedback with neural networks, by mimicking a human vocalization. This paper presents the construction of a talking robot and its singing performance, together with the adaptive control for the pitch learning. The talking robot generates vowel and consonant sounds of different pitches by dynamically controlling the vocal cords, vocal tract and nasal cavity.

Minimizing the cost of routing calculations is crucial for scaling up networks. This paper outlines a method for computing the shortest path in a hierarchical network by applying Dijkstra's (1959) algorithm to various subsets in an entire graph. In the proposed method, the routing calculations increase linearly with the number of layers, while they increase as a square of the number of layers in the existing method.