熱帯火山地域において気候と地球化学が土壌の二次鉱物分布と有機炭素プールに与える影響

京都大学新制・課程博士博士(地球環境学)甲第23352号地環博第210号新制||地環||40(附属図書館)京都大学大学院地球環境学舎地球環境学専攻(主査)教授 舟川 晋也, 教授 德地 直子, 准教授 渡邉 哲弘学位規則第4条第1項該当Doctor of Global Environmental StudiesKyoto UniversityDFA

Implications of Motion Planning: Optimality and k-survivability

We study motion planning problems, finding trajectories that connect two configurations of a system, from two different perspectives: optimality and survivability. For the problem of finding optimal trajectories, we provide a model in which the existence of optimal trajectories is guaranteed, and design an algorithm to find approximately optimal trajectories for a kinematic planar robot within this model. We also design an algorithm to build data structures to represent the configuration space, supporting optimal trajectory queries for any given pair of configurations in an obstructed environment. We are also interested in planning paths for expendable robots moving in a threat environment. Since robots are expendable, our goal is to ensure a certain number of robots reaching the goal. We consider a new motion planning problem, maximum k-survivability: given two points in a stochastic threat environment, find n paths connecting two given points while maximizing the probability that at least k paths reach the goal. Intuitively, a good solution should be diverse to avoid several paths being blocked simultaneously, and paths should be short so that robots can quickly pass through dangerous areas. Finding sets of paths with maximum k-survivability is NP-hard. We design two algorithms: an algorithm that is guaranteed to find an optimal list of paths, and a set of heuristic methods that finds paths with high k-survivability

Mining Entity Synonyms with Efficient Neural Set Generation

Mining entity synonym sets (i.e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications. Previous work either rank terms based on their similarity to a given query term, or treats the problem as a two-phase task (i.e., detecting synonymy pairs, followed by organizing these pairs into synonym sets). However, these approaches fail to model the holistic semantics of a set and suffer from the error propagation issue. Here we propose a new framework, named SynSetMine, that efficiently generates entity synonym sets from a given vocabulary, using example sets from external knowledge bases as distant supervision. SynSetMine consists of two novel modules: (1) a set-instance classifier that jointly learns how to represent a permutation invariant synonym set and whether to include a new instance (i.e., a term) into the set, and (2) a set generation algorithm that enumerates the vocabulary only once and applies the learned set-instance classifier to detect all entity synonym sets in it. Experiments on three real datasets from different domains demonstrate both effectiveness and efficiency of SynSetMine for mining entity synonym sets.Comment: AAAI 2019 camera-ready versio

Self-partitioning SlipChip for slip-induced droplet formation and human papillomavirus viral load quantification with digital LAMP

Human papillomavirus (HPV) is one of the most common sexually transmitted infections worldwide, and persistent HPV infection can cause warts and even cancer. Nucleic acid analysis of HPV viral DNA can be very informative for the diagnosis and monitoring of HPV. Digital nucleic acid analysis, such as digital PCR and digital isothermal amplification, can provide sensitive detection and precise quantification of target nucleic acids, and its utility has been demonstrated in many biological research and medical diagnostic applications. A variety of methods have been developed for the generation of a large number of individual reaction partitions, a key requirement for digital nucleic acid analysis. However, an easily assembled and operated device for robust droplet formation without preprocessing devices, auxiliary instrumentation or control systems is still highly desired. In this paper, we present a self-partitioning SlipChip (sp-SlipChip) microfluidic device for the slip-induced generation of droplets to perform digital loop-mediated isothermal amplification (LAMP) for the detection and quantification of HPV DNA. In contrast to traditional SlipChip methods, which require the precise alignment of microfeatures, this sp-SlipChip utilized a design of “chain-of-pearls” continuous microfluidic channel that is independent of the overlapping of microfeatures on different plates to establish the fluidic path for reagent loading. Initiated by a simple slipping step, the aqueous solution can robustly self-partition into individual droplets by capillary pressure-driven flow. This advantage makes the sp-SlipChip very appealing for the point-of-care quantitative analysis of viral load. As a proof of concept, we performed digital LAMP on an sp-SlipChip to quantify human papillomaviruses (HPVs) 16 and 18 and tested this method with fifteen anonymous clinical samples

Explicit Feature Interaction-aware Uplift Network for Online Marketing

As a key component in online marketing, uplift modeling aims to accurately capture the degree to which different treatments motivate different users, such as coupons or discounts, also known as the estimation of individual treatment effect (ITE). In an actual business scenario, the options for treatment may be numerous and complex, and there may be correlations between different treatments. In addition, each marketing instance may also have rich user and contextual features. However, existing methods still fall short in both fully exploiting treatment information and mining features that are sensitive to a particular treatment. In this paper, we propose an explicit feature interaction-aware uplift network (EFIN) to address these two problems. Our EFIN includes four customized modules: 1) a feature encoding module encodes not only the user and contextual features, but also the treatment features; 2) a self-interaction module aims to accurately model the user's natural response with all but the treatment features; 3) a treatment-aware interaction module accurately models the degree to which a particular treatment motivates a user through interactions between the treatment features and other features, i.e., ITE; and 4) an intervention constraint module is used to balance the ITE distribution of users between the control and treatment groups so that the model would still achieve a accurate uplift ranking on data collected from a non-random intervention marketing scenario. We conduct extensive experiments on two public datasets and one product dataset to verify the effectiveness of our EFIN. In addition, our EFIN has been deployed in a credit card bill payment scenario of a large online financial platform with a significant improvement.Comment: Accepted by SIGKDD 2023 Applied Data Science Trac