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Get PDF奈良先端科学技術大学院大学修士(理学)master thesi
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Get PDF奈良先端科学技術大学院大学博士(理学)doctoral thesi
An Infrastructure for Collaborative Machine Learning on Resource-Constrained Heterogeneous Environments
Get PDF奈良先端科学技術大学院大学博士（工学）doctoral thesi
DAG-Pathwidth: Graph Algorithmic Analyses of DAG-Type Blockchain Networks
No full textThis paper analyzes a blockchain network forming a directed acyclic graph (DAG), called a DAG-type blockchain, from the viewpoint of graph algorithm theory. To use a DAG-type blockchain, NP-hard graph optimization problems on the DAG are required to be solved. Although various problems for undirected and directed graphs can be efficiently solved by using the notions of graph parameters, these currently known parameters are meaningless for DAGs, which implies that it is hopeless to design efficient algorithms based on the parameters for such problems. In this work, we propose a novel graph parameter for directed graphs called a DAG-pathwidth, which represents the closeness to a directed path. This is an extension of the pathwidth, a well-known graph parameter for undirected graphs. We analyze the features of the DAG-pathwidth and prove that computing the DAG-pathwidth of a DAG (directed graph in general) is NP-complete. Finally, we propose an efficient algorithm for a variant of the maximum k-independent set problem for the DAG-type blockchain when the DAG-pathwidth of the input graph is small.journal articl
Content Order-Controllable MR-to-Text
No full textContent order is critical in natural language generation (NLG) for emphasizing the focus of a generated text passage. In this paper, we propose a novel MR (meaning representation)-to-text method that controls the order of the MR values in a generated text passage based on the given order constraints. We use an MR-text dataset with additional value order annotations to train our order-controllable MR-to-text model. We also use it to train a text-to-MR model to check whether the generated text passage correctly reflects the original MR. Furthermore, we augment the dataset with synthetic MR-text pairs to mitigate the discrepancy in the number of non-empty attributes between the training and test conditions and use it to train another order-controllable MR-to-text model. Our proposed methods demonstrate better NLG performance than the baseline methods without order constraints in automatic and subjective evaluations. In particular, the augmented dataset effectively reduces the number of deletion, insertion, and substitution errors in the generated text passages.journal articl
Actin Filaments Couple the Protrusive Tips to the Nucleus through the I-BAR Domain Protein IRSp53 during the Migration of Cells on 1D Fibers
No full textThe cell migration cycle, well-established in 2D, proceeds with forming new protrusive structures at the cell membrane and subsequent redistribution of contractile machinery. Three-dimensional (3D) environments are complex and composed of 1D fibers, and 1D fibers are shown to recapitulate essential features of 3D migration. However, the establishment of protrusive activity at the cell membrane and contractility in 1D fibrous environments remains partially understood. Here the role of membrane curvature regulator IRSp53 is examined as a coupler between actin filaments and plasma membrane during cell migration on single, suspended 1D fibers. IRSp53 depletion reduced cell-length spanning actin stress fibers that originate from the cell periphery, protrusive activity, and contractility, leading to uncoupling of the nucleus from cellular movements. A theoretical model capable of predicting the observed transition of IRSp53-depleted cells from rapid stick-slip migration to smooth and slower migration due to reduced actin polymerization at the cell edges is developed, which is verified by direct measurements of retrograde actin flow using speckle microscopy. Overall, it is found that IRSp53 mediates actin recruitment at the cellular tips leading to the establishment of cell-length spanning fibers, thus demonstrating a unique role of IRSp53 in controlling cell migration in 3D.journal articl
Multi-step motion learning by combining learning-from-demonstration and policy-search
No full textIn this paper, we focus on tasks that require multi-step motions to achieve the goal (defined as a ‘multi-step task’), and we describe a method for a robot to automatically achieve the final goal of a multi-step task. We proposed a method based on reinforcement learning and ‘Teaching by Showing’ for multi-step tasks. A robot can learn how to complete a task automatically by referring to the motions of a human operator, even if the task consists of multi-step motions. Because a human operator is not required to operate the robot during the learning process, we believe that our proposed method can reduce the burden on the human operator. Finally, we conducted experiments to validate the effectiveness of the proposed method and compared it to a conventional reinforcement learning method.journal articl