Classical Planning in Deep Latent Space

Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We evaluate Latplan using image-based versions of 6 planning domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of LightsOut.Comment: Under review at Journal of Artificial Intelligence Research (JAIR

プライバシ保護クラウドソーシング

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 山西 健司, 東京大学教授 中川 裕志, 東京大学准教授 國廣 昇, 京都大学教授 鹿島 久嗣, 筑波大学准教授 佐久間 淳University of Tokyo(東京大学

Improving Molecular Properties Prediction Through Latent Space Fusion

Pre-trained Language Models have emerged as promising tools for predicting molecular properties, yet their development is in its early stages, necessitating further research to enhance their efficacy and address challenges such as generalization and sample efficiency. In this paper, we present a multi-view approach that combines latent spaces derived from state-of-the-art chemical models. Our approach relies on two pivotal elements: the embeddings derived from MHG-GNN, which represent molecular structures as graphs, and MoLFormer embeddings rooted in chemical language. The attention mechanism of MoLFormer is able to identify relations between two atoms even when their distance is far apart, while the GNN of MHG-GNN can more precisely capture relations among multiple atoms closely located. In this work, we demonstrate the superior performance of our proposed multi-view approach compared to existing state-of-the-art methods, including MoLFormer-XL, which was trained on 1.1 billion molecules, particularly in intricate tasks such as predicting clinical trial drug toxicity and inhibiting HIV replication. We assessed our approach using six benchmark datasets from MoleculeNet, where it outperformed competitors in five of them. Our study highlights the potential of latent space fusion and feature integration for advancing molecular property prediction. In this work, we use small versions of MHG-GNN and MoLFormer, which opens up an opportunity for further improvement when our approach uses a larger-scale dataset.Comment: 8 Pages, 4 Figures - Submited to the AI4Science Workshop - Neurips 202

MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network

Property prediction plays an important role in material discovery. As an initial step to eventually develop a foundation model for material science, we introduce a new autoencoder called the MHG-GNN, which combines graph neural network (GNN) with Molecular Hypergraph Grammar (MHG). Results on a variety of property prediction tasks with diverse materials show that MHG-GNN is promising.Comment: 8 pages, 1 figur

Effectiveness of prehospital Magill forceps use for out-of-hospital cardiac arrest due to foreign body airway obstruction in Osaka City

Background: Although foreign body airway obstruction (FBAO) accounts for many preventable unintentional accidents, little is known about the epidemiology of FBAO patients and the effect of forceps use on those patients. This study aimed to assess characteristics of FBAO patients transported to hospitals by emergency medical service (EMS) personnel, and to verify the relationship between prehospital Magill forceps use and outcomes among out-of-hospital cardiac arrests (OHCA) patients with FBAO. Methods: We retrospectively reviewed ambulance records of all patients who suffered FBAO, and were treated by EMS in Osaka City from 2000 through 2007, and assessed the characteristics of those patients. We also performed a multivariate logistic-regression analysis to assess factors associated with neurologically favorable survival among bystander-witnessed OHCA patients with FBAO in larynx or pharynx. Results: A total of 2,354 patients suffered from FBAO during the study period. There was a bimodal distribution by age among infants and old adults. Among them, 466 (19.8%) had an OHCA when EMS arrived at the scene, and 344 were witnessed by bystanders. In the multivariate analysis, Magill forceps use for OHCA with FBAO in larynx or pharynx was an independent predictor of neurologically favorable survival (16.4% [24/146] in the Magill forceps use group versus 4.3% [4/94] in the non-use group; adjusted odds ratio, 3.96 [95% confidence interval, 1.21-13.00], p = 0.023).Conclusions: From this large registry in Osaka, we revealed that prehospital Magill forceps use was associated with the improved outcome of bystander-witnessed OHCA patients with FBAO

Development of an atmospheric Cherenkov imaging camera for the CANGAROO-III experiment

A Cherenkov imaging camera for the CANGAROO-III experiment has been developed for observations of gamma-ray induced air-showers at energies from 10$^{11}$ to 10$^{14}$ eV. The camera consists of 427 pixels, arranged in a hexagonal shape at 0.17$^\circ$ intervals, each of which is a 3/4-inch diameter photomultiplier module with a Winston-cone--shaped light guide. The camera was designed to have a large dynamic range of signal linearity, a wider field of view, and an improvement in photon collection efficiency compared with the CANGAROO-II camera. The camera, and a number of the calibration experiments made to test its performance, are described in detail in this paper.Comment: 25 pages, 29 figures, elsart.cls, to appear in NIM-

Research and Design of a Routing Protocol in Large-Scale Wireless Sensor Networks

无线传感器网络，作为全球未来十大技术之一，集成了传感器技术、嵌入式计算技术、分布式信息处理和自组织网技术，可实时感知、采集、处理、传输网络分布区域内的各种信息数据，在军事国防、生物医疗、环境监测、抢险救灾、防恐反恐、危险区域远程控制等领域具有十分广阔的应用前景。 本文研究分析了无线传感器网络的已有路由协议，并针对大规模的无线传感器网络设计了一种树状路由协议，它根据节点地址信息来形成路由，从而简化了复杂繁冗的路由表查找和维护，节省了不必要的开销，提高了路由效率，实现了快速有效的数据传输。 为支持此路由协议本文提出了一种自适应动态地址分配算——ADAR（AdaptiveDynamicAddre...As one of the ten high technologies in the future, wireless sensor network, which is the integration of micro-sensors, embedded computing, modern network and Ad Hoc technologies, can apperceive, collect, process and transmit various information data within the region. It can be used in military defense, biomedical, environmental monitoring, disaster relief, counter-terrorism, remote control of haz...学位：工学硕士院系专业：信息科学与技术学院通信工程系_通信与信息系统学号：2332007115216