高夜温に対するダイズの収量，生育および作物生理反応に関する研究

京都大学新制・課程博士博士(農学)甲第23252号農博第2459号新制||農||1085(附属図書館)学位論文||R3||N5342京都大学大学院農学研究科農学専攻(主査)教授 白岩 立彦, 教授 土井 元章, 教授 中﨑 鉄也学位規則第4条第1項該当Doctor of Agricultural ScienceKyoto UniversityDGA

An Implementation of Fully Convolutional Network for Surface Mesh Segmentation

This thesis presents an implementation of a 3-Dimensional triangular surface mesh segmentation architecture named Shape Fully Convolutional Network, which is proposed by Pengyu Wang and Yuan Gan in 2018. They designed a deep neural network that has a similar architecture as the Fully Convolutional Network, which provides a good segmentation result for 2D images, on 3D triangular surface meshes. In their implementation, 3D surface meshes are represented as graph structures to feed the network. There are three main barriers when applying the Fully Convolutional Network to graph-based data structures. • First, the pooling operation is much harder to apply to general graphs. • Second, the convolution order on a graph structure is unstable. • Third, the raw data of surface meshes cannot be directly applied to the network. To solve these problems, first, all the nodes inside the graph are re-ordered into a 1-dimensional list based on a multi-level graph coarsening algorithm, which allows the pooling operation to be applied as easily as a 1D pooling. Second, a self-defined generating layer is added before each convolution layer in the network to generate the neighbors of each node on the graph, and at the same time, sort all neighbors based on the L2 similarity to keep the convolution operation in a stable manner. Finally, three translation and rotation free low-level geometric features are pre-processed and used as input to train the network. This Shape Fully Convolution Network can effectively learn and predict triangular face-wise labels. In the end, to achieve a better result, the final labeling is optimized by the multi-label graph cut algorithm, which gives punishment to the predicted result based on the smoothness of the surface. The experiments show that the model can effectively learn and predict triangle-wise labels on surface meshes and yields good segmentation results

Sums of almost equal squares of primes

We study the representations of large integers $n$ as sums $p_1^2 + ... + p_s^2$, where $p_1,..., p_s$ are primes with $| p_i - (n/s)^{1/2} | \le n^{\theta/2}$, for some fixed $\theta < 1$. When $s = 5$ we use a sieve method to show that all sufficiently large integers $n \equiv 5 \pmod {24}$ can be represented in the above form for $\theta > 8/9$. This improves on earlier work by Liu, L\"{u} and Zhan, who established a similar result for $\theta > 9/10$. We also obtain estimates for the number of integers $n$ satisfying the necessary local conditions but lacking representations of the above form with $s = 3, 4$. When $s = 4$ our estimates improve and generalize recent results by L\"{u} and Zhai, and when $s = 3$ they appear to be first of their kind

Offline Deep Reinforcement Learning and Off-Policy Evaluation for Personalized Basal Insulin Control in Type 1 Diabetes

Recent advancements in hybrid closed-loop systems, also known as the artificial pancreas (AP), have been shown to optimize glucose control and reduce the self-management burdens for people living with type 1 diabetes (T1D). AP systems can adjust the basal infusion rates of insulin pumps, facilitated by real-time communication with continuous glucose monitoring. Empowered by deep neural networks, deep reinforcement learning (DRL) has introduced new paradigms of basal insulin control algorithms. However, all the existing DRL-based AP controllers require a large number of random online interactions between the agent and environment. While this can be validated in T1D simulators, it becomes impractical in real-world clinical settings. To this end, we propose an offline DRL framework that can develop and validate models for basal insulin control entirely offline. It comprises a DRL model based on the twin delayed deep deterministic policy gradient and behavior cloning, as well as off-policy evaluation (OPE) using fitted Q evaluation. We evaluated the proposed framework on an in silico dataset containing 10 virtual adults and 10 virtual adolescents, generated by the UVA/Padova T1D simulator, and the OhioT1DM dataset, a clinical dataset with 12 real T1D subjects. The performance on the in silico dataset shows that the offline DRL algorithm significantly increased time in range while reducing time below range and time above range for both adult and adolescent groups. The high Spearman's rank correlation coefficients between actual and estimated policy values indicate the accurate estimation made by the OPE. Then, we used the OPE to estimate model performance on the clinical dataset, where a notable increase in policy values was observed for each subject. The results demonstrate that the proposed framework is a viable and safe method for improving personalized basal insulin control in T1D

Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation

People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although the artificial pancreas and continuous glucose monitoring have been proven to be effective in achieving closed-loop control, significant challenges still remain due to the high complexity of glucose dynamics and limitations in the technology. In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In particular, the delivery strategies are developed by double Q-learning with dilated recurrent neural networks. For designing and testing purposes, the FDA-accepted UVA/Padova Type 1 simulator was employed. First, we performed long-term generalized training to obtain a population model. Then, this model was personalized with a small data-set of subject-specific data. In silico results show that the single and dual-hormone delivery strategies achieve good glucose control when compared to a standard basal-bolus therapy with low-glucose insulin suspension. Specifically, in the adult cohort (n=10), percentage time in target range [70, 180] mg/dL improved from 77.6% to 80.9% with single-hormone control, and to $85.6\%$ with dual-hormone control. In the adolescent cohort (n=10), percentage time in target range improved from 55.5% to 65.9% with single-hormone control, and to 78.8% with dual-hormone control. In all scenarios, a significant decrease in hypoglycemia was observed. These results show that the use of deep reinforcement learning is a viable approach for closed-loop glucose control in T1D

Weakly supervised learning from referring expression: Challenge and directions

We explore methods of weakly supervised learning from referring expression. Unlike traditional fully supervised semantic segmentation of object recognition tasks, in which a a small set of discrete class bases is provided, the referring expression task is performed associated with a sentence phrase, e.g. “the dude on the dolphin”. Previous approaches use LSTM and fully convolutional network and have fairly good results under fully supervised setting. However, the fully supervised setting is limited by manual labeling of segmentation masks, which requires a significant amount of human labor. Therefore, we work on an approach to perform segmentation with only image level language descriptions. Under our weakly supervised setting, we are only provided with input images and the corresponding sentence descriptions, without the pixel level labeling for each image as ground truth. In order to get supervision only from language description, we utilize the multiple instance learning loss. We first develop an end-to-end model to localize the image content corresponding to the language expressions. In this model, we use GloVe and ELMo sentence embeddings to get a vector representation for each sentence and combined with image features from a fully convolutional network. However, the sentence level model is hard to interpret hence we also study a more fundamental problem of weakly supervised object localization from referring expressions. We compare the performance of the sentence level model on this task to an alternative word-level model. Our investigation suggests that breaking the referring expressions localization problem into smaller more manageable components is promising

Bayesian Networks for Whole Building Level Fault Diagnosis and Isolation

Buildings consume more than 40% of primary energy in the U.S. and 57% of the energy usage in commercial and residential buildings are consumed by the heating, ventilation and air conditioning (HVAC) system.Malfunctioning sensors, components, and control systems, as well as degrading systems in HVAC and lighting systems are main reasons for energy waste and unsatisfactory indoor environment. In HVAC systems, faults occur in one component or equipmentcan also cause abnormality in other subsystems because of the coupling among different subsystems. Therefore, whole building level fault diagnosis methods is critical to locate fault root cause and isolate the fault. Bayesian network (BN) is a prevalent toolin fault diagnosis which can deal withprobabilistic reasoning of uncertainty. In this paper, a two-layer Bayesian network which consists of fault layer and fault symptom layer is developed to diagnose whole building HVAC system fault. Weather information based Pattern Matching (WPM) method which was employed in fault detection was also used to create baseline data and generate LEAK probability. BAS data from a campus building are collected to evaluate the effectiveness of the proposed method

GluGAN: Generating Personalized Glucose Time Series Using Generative Adversarial Networks

Time series data generated by continuous glucose monitoring sensors offer unparalleled opportunities for developing data-driven approaches, especially deep learning-based models, in diabetes management. Although these approaches have achieved state-of-the-art performance in various fields such as glucose prediction in type 1 diabetes (T1D), challenges remain in the acquisition of large-scale individual data for personalized modeling due to the elevated cost of clinical trials and data privacy regulations. In this work, we introduce GluGAN, a framework specifically designed for generating personalized glucose time series based on generative adversarial networks (GANs). Employing recurrent neural network (RNN) modules, the proposed framework uses a combination of unsupervised and supervised training to learn temporal dynamics in latent spaces. Aiming to assess the quality of synthetic data, we apply clinical metrics, distance scores, and discriminative and predictive scores computed by post-hoc RNNs in evaluation. Across three clinical datasets with 47 T1D subjects (including one publicly available and two proprietary datasets), GluGAN achieved better performance for all the considered metrics when compared with four baseline GAN models. The performance of data augmentation is evaluated by three machine learning-based glucose predictors. Using the training sets augmented by GluGAN significantly reduced the root mean square error for the predictors over 30 and 60-minute horizons. The results suggest that GluGAN is an effective method in generating high-quality synthetic glucose time series and has the potential to be used for evaluating the effectiveness of automated insulin delivery algorithms and as a digital twin to substitute for pre-clinical trials