Academic literature on the topic 'Global consistency error (GCE)'

Recently, hashing is widely used in approximate nearest neighbor search for its storage and computational efficiency. Most of the unsupervised hashing methods learn to map images into semantic similarity-preserving hash codes by constructing local semantic similarity structure from the pre-trained model as the guiding information, i.e., treating each point pair similar if their distance is small in feature space. However, due to the inefficient representation ability of the pre-trained model, many false positives and negatives in local semantic similarity will be introduced and lead to error propagation during the hash code learning. Moreover, few of the methods consider the robustness of models, which will cause instability of hash codes to disturbance. In this paper, we propose a new method named Comprehensive sImilarity Mining and cOnsistency learNing (CIMON). First, we use global refinement and similarity statistical distribution to obtain reliable and smooth guidance. Second, both semantic and contrastive consistency learning are introduced to derive both disturb-invariant and discriminative hash codes. Extensive experiments on several benchmark datasets show that the proposed method outperforms a wide range of state-of-the-art methods in both retrieval performance and robustness.

Learning to order events at discourse-level is a crucial text understanding task. Despite many efforts for this task, the current state-of-the-art methods rely heavily on manually designed features, which are costly to produce and are often specific to tasks/domains/datasets. In this paper, we propose a new graph perspective on the task, which does not require complex feature engineering but can assimilate global features and learn inter-dependencies effectively. Specifically, in our approach, each document is considered as a temporal graph, in which the nodes and edges represent events and event-event relations respectively. In this sense, the temporal ordering task corresponds to constructing edges for an empty graph. To train our model, we design a graph mask pre-training mechanism, which can learn inter-dependencies of temporal relations by learning to recover a masked edge following graph topology. In the testing stage, we design an certain-first strategy based on model uncertainty, which can decide the prediction orders and reduce the risk of error propagation. The experimental results demonstrate that our approach outperforms previous methods consistently and can meanwhile maintain good global consistency.

This paper discusses key principles for the development of materials property information management software systems. There are growing needs for automated materials information management in various organizations. In part these are fuelled by the demands for higher efficiency in material testing, product design and engineering analysis. But equally important, organizations are being driven by the need for consistency, quality and traceability of data, as well as control of access to sensitive information such as proprietary data. Further, the use of increasingly sophisticated nonlinear, anisotropic and multi-scale engineering analyses requires both processing of large volumes of test data for development of constitutive models and complex materials data input for Computer-Aided Engineering (CAE) software. And finally, the globalization of economy often generates great needs for sharing a single “gold source” of materials information between members of global engineering teams in extended supply-chains. Fortunately, material property management systems have kept pace with the growing user demands and evolved to versatile data management systems that can be customized to specific user needs. The more sophisticated of these provide facilities for: (i) data management functions such as access, version, and quality controls; (ii) a wide range of data import, export and analysis capabilities; (iii) data “pedigree” traceability mechanisms; (iv) data searching, reporting and viewing tools; and (v) access to the information via a wide range of interfaces. In this paper the important requirements for advanced material data management systems, future challenges and opportunities such as automated error checking, data quality characterization, identification of gaps in datasets, as well as functionalities and business models to fuel database growth and maintenance are discussed.

Intensive global competition requires the automotive manufacturers to launch new vehicle models with a shorter launch time, better quality, lower cost and more customization. One of the key enablers for achieving these objectives is to have an efficient & error-free manufacturing automation system which is typically controlled by Programmable Logic Controllers (PLC). The current PLC logic code testing process in automotive industry is usually performed manually by individual engineer, and the overall testing quality highly depends on the engineer’s expertise and experience. The PLC logic code testing normally consists of two parts, testing requirements (specification) and testing methods. One of the major hurdles for applying rigorous testing methods on PLC logic verification in industry is the lack of formal testing specifications. The current PLC testing specifications and requirements are documented in various formats, such as sequence of operation document, process instrument diagram, wiring diagrams, and time motion diagrams, etc. These varied documents cannot be directly used for control logic testing and require a better alternative. Formal methods, the latest technology widely used in software testing, is an approach selected for generating comprehensive test cases to ensure the correctness and consistency of PLC programs. This paper presents a novel approach for specifying the expected behavior of the already implemented industrial PLC code. The generated formal specification models can work with math-based logic verification tool to facilitate the usage of formal verification in manufacturing automation control. The specification modeling methodology contains all the required information from different logic design and testing phases, such as process sequence, wiring information, logic pattern, code comments, and domain knowledge, etc. And the statecharts is used as model formalism, because of its capability to model states hierarchically and provide a better visual representation.