Object-Oriented Dynamics Learning through Multi-Level Abstraction

Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability. However, existing approaches suffer from structural limitations and optimization difficulties for common environments with multiple dynamic objects. In this paper, we present a novel self-supervised learning framework, called Multi-level Abstraction Object-oriented Predictor (MAOP), which employs a three-level learning architecture that enables efficient object-based dynamics learning from raw visual observations. We also design a spatial-temporal relational reasoning mechanism for MAOP to support instance-level dynamics learning and handle partial observability. Our results show that MAOP significantly outperforms previous methods in terms of sample efficiency and generalization over novel environments for learning environment models. We also demonstrate that learned dynamics models enable efficient planning in unseen environments, comparable to true environment models. In addition, MAOP learns semantically and visually interpretable disentangled representations.Comment: Accepted to the Thirthy-Fourth AAAI Conference On Artificial Intelligence (AAAI), 202

Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction

The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according to patients' history records. This paper develops deep learning techniques for clinical endpoint prediction, which are effective in many practical applications. However, the problem is very challenging since patients' history records contain multiple heterogeneous temporal events such as lab tests, diagnosis, and drug administrations. The visiting patterns of different types of events vary significantly, and there exist complex nonlinear relationships between different events. In this paper, we propose a novel model for learning the joint representation of heterogeneous temporal events. The model adds a new gate to control the visiting rates of different events which effectively models the irregular patterns of different events and their nonlinear correlations. Experiment results with real-world clinical data on the tasks of predicting death and abnormal lab tests prove the effectiveness of our proposed approach over competitive baselines.Comment: 8 pages, this paper has been accepted by AAAI 201

Autographa californica multiple nucleopolyhedrovirus ac66 is required for the efficient egress of nucleocapsids from the nucleus, general synthesis of preoccluded virions and occlusion body formation

AbstractAlthough orf66 (ac66) of Autographa californica multiple nucleopolyhedrovirus (AcMNPV) is conserved in all sequenced lepidopteran baculovirus genomes, its function is not known. This paper describes generation of an ac66 knockout AcMNPV bacmid mutant and analyses of the influence of ac66 deletion on the virus replication in Sf-9 cells so as to determine the role of ac66 in the viral life cycle. Results indicated that budded virus (BV) yields were reduced over 99% in ac66-null mutant infected cells in comparison to that in wild-type virus infected cells. Optical microscopy revealed that occlusion body synthesis was significantly reduced in the ac66 knockout bacmid-transfected cells. In addition, ac66 deletion interrupted preoccluded virion synthesis. The mutant phenotype was rescued by an ac66 repair bacmid. On the other hand, real-time PCR analysis indicated that ac66 deletion did not affect the levels of viral DNA replication. Electron microscopy revealed that ac66 is not essential for nucleocapsid assembly, but for the efficient transport of nucleocapsids from the nucleus to the cytoplasm. These results suggested that ac66 plays an important role for the efficient exit of nucleocapsids from the nucleus to the cytoplasm for BV synthesis as well as for preoccluded virion and occlusion synthesis

Joint Language Semantic and Structure Embedding for Knowledge Graph Completion

The task of completing knowledge triplets has broad downstream applications. Both structural and semantic information plays an important role in knowledge graph completion. Unlike previous approaches that rely on either the structures or semantics of the knowledge graphs, we propose to jointly embed the semantics in the natural language description of the knowledge triplets with their structure information. Our method embeds knowledge graphs for the completion task via fine-tuning pre-trained language models with respect to a probabilistic structured loss, where the forward pass of the language models captures semantics and the loss reconstructs structures. Our extensive experiments on a variety of knowledge graph benchmarks have demonstrated the state-of-the-art performance of our method. We also show that our method can significantly improve the performance in a low-resource regime, thanks to the better use of semantics. The code and datasets are available at https://github.com/pkusjh/LASS.Comment: COLING 202

Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding

Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers. This task is, however, time-consuming, subjective and labor-intensive. In this paper, we propose a novel road crack detection algorithm based on deep learning and adaptive image segmentation. Firstly, a deep convolutional neural network is trained to determine whether an image contains cracks or not. The images containing cracks are then smoothed using bilateral filtering, which greatly minimizes the number of noisy pixels. Finally, we utilize an adaptive thresholding method to extract the cracks from road surface. The experimental results illustrate that our network can classify images with an accuracy of 99.92%, and the cracks can be successfully extracted from the images using our proposed thresholding algorithm.Comment: 6 pages, 8 figures, 2019 IEEE Intelligent Vehicles Symposiu

Self-Organized Polynomial-Time Coordination Graphs

Coordination graph is a promising approach to model agent collaboration in multi-agent reinforcement learning. It conducts a graph-based value factorization and induces explicit coordination among agents to complete complicated tasks. However, one critical challenge in this paradigm is the complexity of greedy action selection with respect to the factorized values. It refers to the decentralized constraint optimization problem (DCOP), which and whose constant-ratio approximation are NP-hard problems. To bypass this systematic hardness, this paper proposes a novel method, named Self-Organized Polynomial-time Coordination Graphs (SOP-CG), which uses structured graph classes to guarantee the accuracy and the computational efficiency of collaborated action selection. SOP-CG employs dynamic graph topology to ensure sufficient value function expressiveness. The graph selection is unified into an end-to-end learning paradigm. In experiments, we show that our approach learns succinct and well-adapted graph topologies, induces effective coordination, and improves performance across a variety of cooperative multi-agent tasks

Intelligent Perception Control System of Railway Level Crossing Gate Based on TRIZ Theory

TRIZ theory is an innovative method to analyse problems and solve them, which is widely used in many fields. In this paper, TRIZ theory is used to improve the design of railway crossing guardrail system. The use of nine-screen analysis, functional analysis, cause-effect chain analysis and other tools to analyse the problem of poor manual control effect in the railway crossing guardrail system, the use of technical contradictions, physical contradictions and other tools to improve the system design, effectively reduce the possibility of danger when cars and pedestrians cross railway crossings, improve the traffic safety and traffic order of the railway level crossing, and reduce the work burden of railway crossing caretakers