地震後の降雨における不飽和斜面の浸透・変形の評価

京都大学新制・課程博士博士(工学)甲第23485号工博第4897号新制||工||1765(附属図書館)京都大学大学院工学研究科社会基盤工学専攻(主査)教授 渦岡 良介, 教授 肥後 陽介, 准教授 PIPATPONGSA Thirapong学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDGA

Signature inversion for monotone paths

The aim of this article is to provide a simple sampling procedure to reconstruct any monotone path from its signature. For every N, we sample a lattice path of N steps with weights given by the coefficient of the corresponding word in the signature. We show that these weights on lattice paths satisfy the large deviations principle. In particular, this implies that the probability of picking up a "wrong" path is exponentially small in N. The argument relies on a probabilistic interpretation of the signature for monotone paths

A SURVEY OF LEAF VENATION IN NEW CALEDONIAN SYZYGIUM (MYRTACEAE)

New Caledonia is a globally recognized biodiversity ‘hotspot’ characterized by a rich flora and a large number of endemic species. Syzygium Gaertn., the largest genus of woody plants in the world, is common in New Caledonia with approximately 70 species. A comparative survey of leaf morphology and architectural traits among 27 species of New Caledonian Syzygium was conducted, focusing on leaf venation patterns. Separate and detailed descriptions of leaf morphology and leaf venation characters are provided for the species. In addition to morphological differences, this study found differences at the species level among characters such as the number of intramarginal veins, the number and angle of divergence of secondary veins, whether thinner intersecondary veins can be distinguished from normal secondary veins, patterns of tertiary venation, relative degree of areole development, and the level of ultimate tertiary branching within areoles A cluster analysis separated the sample species into three groups based primarily on differences in leaf morphology, not patterns of venation

Ontology-based annotation using naive Bayes and decision trees

The Cognitive Paradigm Ontology (CogPO) defines an ontological relationship between academic terms and experiments in the field of neuroscience. BrainMap (www.brainmap.org) is a database of literature describing these experiments, which are annotated by human experts based on the ontological framework defined in CogPO. We present a stochastic approach to automate this process. We begin with a gold standard corpus of abstracts annotated by experts, and model the annotations with a group of naive Bayes classifiers, then explore the inherent relationship among different components defined by the ontology using a probabilistic decision tree model. Our solution outperforms conventional text mining approaches by taking advantage of an ontology. We consider five essential ontological components (Stimulus Modality, Stimulus Type, Response Modality, Response Type, and Instructions) in CogPO, evaluate the probability of successfully categorizing a research paper on each component by training a basic multi-label naive Bayes classifier with a set of examples taken from the BrainMap database which are already manually annotated by human experts. According to the performance of the classifiers we create a decision tree to label the components sequentially on different levels. Each node of the decision tree is associated with a naive Bayes classifier built in different subspaces of the input universe. We first make decisions on those components whose labels are comparatively easy to predict, and then use these predetermined conditions to narrow down the input space along all tree paths, therefore boosting the performance of the naive Bayes classification upon components whose labels are difficult to predict. For annotating a new instance, we use the classifiers associated with the nodes to find labels for each component, starting from the root and then tracking down the tree perhaps on multiple paths. The annotation is completed when the bottom level is reached, where all labels produced along the paths are collected

Finding Optimal Modular Robots for Aerial Tasks

Traditional aerial vehicles have limitations in their capabilities due to actuator constraints, such as motor saturation. The hardware components and their arrangement are designed to satisfy specific requirements and are difficult to modify during operation. To address this problem, we introduce a versatile modular multi-rotor vehicle that can change its capabilities by reconfiguration. Our modular robot consists of homogeneous cuboid modules, propelled by quadrotors with tilted rotors. Depending on the number of modules and their configuration, the robot can expand its actuation capabilities. In this paper, we build a mathematical model for the actuation capability of a modular multi-rotor vehicle and develop methods to determine if a vehicle is capable of satisfying a task requirement. Based on this result, we find the optimal configurations for a given task. Our approach is validated in realistic 3D simulations, showing that our modular system can adapt to tasks with varying requirements

Arithmetic Phase Transitions For Mosaic Maryland Model

We give a precise description of spectral types of the Mosaic Maryland model with any irrational frequency, which provides a quasi-periodic unbounded model with non-monotone potential has arithmetic phase transition.Comment: arXiv admin note: substantial text overlap with arXiv:2205.04021 by other author