Developing miniature robots that can carry out versatile clinical procedures inside the body under the remote instructions of medical professionals has been a long time challenge. In this paper, we present origami-based robots that can be ingested into the stomach, locomote to a desired location, patch a wound, remove a foreign body, deliver drugs, and biodegrade. We designed and fabricated composite material sheets for a biocompatible and biodegradable robot that can be encapsulated in ice for delivery through the esophagus, embed a drug layer that is passively released to a wounded area, and be remotely controlled to carry out underwater maneuvers specific to the tasks using magnetic fields. The performances of the robots are demonstrated in a simulated physical environment consisting of an esophagus and stomach with properties similar to the biological organs.

The Passive Aggressive framework [1] is a principled approach to online linear classification that advocates minimal weight updates i.e., the least required so that the current training instance is correctly classified. While the PA framework allows integration with different loss functions, it is yet to be combined with a multiclass loss function that penalizes every class with a score higher than the true class. We call the method of training the classifier with this loss function the Support Class Passive Aggressive Algorithm. In order to obtain a weight update formula, we solve a quadratic optimization problem by using multiple constraints and arrive at a closed-form solution. This lets us obtain a simple but effective algorithm that updates the classifier against multiple classes for which an instance is likely to be mistaken. We call them the support classes. Experiments demonstrated that our method improves the traditional PA algorithms.

Currently, relation extraction (RE) and event extraction (EE) are the two main streams of biological information extraction. In 2009, the majority of these RE and EE research efforts were centered around the BioCreative II.5 Protein-Protein Interaction (PPI) challenge and the "BioNLP event extraction shared task." Although these challenges took somewhat different approaches, they share the same ultimate goal of extracting bio-knowledge from the literature. This paper compares the two challenge task definitions, and presents a unified system that was successfully applied in both these and several other PPI extraction task settings. The AkaneRE system has three parts: A core engine for RE, a pool of modules for specific solutions, and a configuration language to adapt the system to different tasks. The core engine is based on machine learning, using either Support Vector Machines or Statistical Classifiers and features extracted from given training data. The specific modules solve tasks like sentence boundary detection, tokenization, stemming, part-of-speech tagging, parsing, named entity recognition, generation of potential relations, generation of machine learning features for each relation, and finally, assignment of confidence scores and ranking of candidate relations. With these components, the AkaneRE system produces state-of-the-art results, and the system is freely available for academic purposes at http://www-tsujii.is.s.u-tokyo.ac.jp/satre/akane/.

This paper introduces a novel framework for the accurate retrieval of relational concepts from huge texts. Prior to retrieval, all sentences are annotated with predicate argument structures and ontological identifiers by applying a deep parser and a term recognizer. During the run time, user requests are converted into queries of region algebra on these annotations. Structural matching with pre-computed semantic annotations establishes the accurate and efficient retrieval of relational concepts. This framework was applied to a text retrieval system for MEDLINE. Experiments on the retrieval of biomedical correlations revealed that the cost is sufficiently small for real-time applications and that the retrieval precision is significantly improved.

We present a practical HPSG parser for English, an intelligent search engine to retrieve MEDLINE abstracts that represent biomedical events and an efficient MED-LINE search tool helping users to find information about biomedical entities such as genes, proteins, and the interactions between them.

To realize practical micromachines using fluid power with high power density, a micro fluid control system using homogeneous ER fluid is proposed and developed. Firstly, a new micro ER (electrorheological) valve using homogeneous ER fluid is proposed and fabricated. Its diameter is 9 mm. Static and dynamic characteristics are investigated. With supply pressure of 25 kPa and electric field strength change of 1.0 kV/mm, it is ascertained that the controllable pressure range is 10 kPa and the bandwidth is 0.5 Hz. Secondly, output characteristics of a resonantly-driven piezoelectric micropump 9 mm in diameter and 10 mm long are experimentally investigated by using homogeneous ER fluid. Maximum flow rate of 15 mm/sup 3//s and maximum pumping pressure of 0.2 MPa are obtained. Finally, an open-loop micro position control system is fabricated, which consists of the micropump, the micro ER valve and a bellows microactuator. Through basic experiments, it is confirmed that the output displacement of 23 /spl mu/m and the bandwidth of 0.3 Hz are realized.

To realize autonomous manipulators which work outdoors, 3D vision sensors having capability of real-time processing, robustness against sunlight, and compactness are required to measure objects and environments. In this paper a simple real-time motion stereo vision is proposed, which utilizes direct calculation of the integrated gradient constraint equation for sequential images taken by a motion camera. The vision sensor can measure 3D coordinates at every pixel without huge computation. The basic characteristics are investigated through indoor measuring experiments. The measuring accuracy of /spl plusmn/10% in a range of 0.7-1.5 m and the estimated measuring time of 0.7 s for 512 pixels /spl times/100 scanning lines are obtained. Then the performance of the proposed vision sensor are experimentally investigated under natural conditions including outdoors.

Real-time stereo vision using a multiple arrayed camera system, named MAC vision, is proposed. MAC vision processes the correspondence problem, one of the basic problems in stereo vision, very rapidly by making use of the simple geometric relationship of images on the same-height scanning lines. Several problems involved in MAC vision are discussed. The results of experiments performed on a newly constructed five-camera prototype are presented. The experimental results showed that a speed of 16 (frames/s), where each frame included 1800 points.>