30 research outputs found
SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres
Event-centric structured prediction involves predicting structured outputs of
events. In most NLP cases, event structures are complex with manifold
dependency, and it is challenging to effectively represent these complicated
structured events. To address these issues, we propose Structured Prediction
with Energy-based Event-Centric Hyperspheres (SPEECH). SPEECH models complex
dependency among event structured components with energy-based modeling, and
represents event classes with simple but effective hyperspheres. Experiments on
two unified-annotated event datasets indicate that SPEECH is predominant in
event detection and event-relation extraction tasks.Comment: Accepted by ACL 2023 Main Conference. Code is released at
\url{https://github.com/zjunlp/SPEECH
Schema-aware Reference as Prompt Improves Data-Efficient Relational Triple and Event Extraction
Information Extraction, which aims to extract structural relational triple or
event from unstructured texts, often suffers from data scarcity issues. With
the development of pre-trained language models, many prompt-based approaches to
data-efficient information extraction have been proposed and achieved
impressive performance. However, existing prompt learning methods for
information extraction are still susceptible to several potential limitations:
(i) semantic gap between natural language and output structure knowledge with
pre-defined schema; (ii) representation learning with locally individual
instances limits the performance given the insufficient features. In this
paper, we propose a novel approach of schema-aware Reference As Prompt (RAP),
which dynamically leverage schema and knowledge inherited from global
(few-shot) training data for each sample. Specifically, we propose a
schema-aware reference store, which unifies symbolic schema and relevant
textual instances. Then, we employ a dynamic reference integration module to
retrieve pertinent knowledge from the datastore as prompts during training and
inference. Experimental results demonstrate that RAP can be plugged into
various existing models and outperforms baselines in low-resource settings on
four datasets of relational triple extraction and event extraction. In
addition, we provide comprehensive empirical ablations and case analysis
regarding different types and scales of knowledge in order to better understand
the mechanisms of RAP. Code is available in https://github.com/zjunlp/RAP.Comment: Work in progres
Caustic analysis of partially coherent self-accelerating beams: Investigating self-healing property
We employed caustic theory to analyze the propagation dynamics of partially
coherent self-accelerating beams such as self-healing of partially coherent
Airy beams. Our findings revealed that as the spatial coherence decreases, the
self-healing ability of beams increases. This result have been demonstrated
both in simulation and experiment. This is an innovative application of the
caustic theory to the field of partially coherent structured beams, and
provides a comprehensive understanding of self-healing property. Our results
have significant implications for practical applications of partially coherent
beams in fields such as optical communication, encryption, and imaging.Comment: 9 pages, 4 figure
An Attention-Based Method for Remaining Useful Life Prediction of Rotating Machinery
Data imbalance and large data probability distribution discrepancies are major factors that reduce the accuracy of remaining useful life (RUL) prediction of high-reliability rotating machinery. In feature extraction, most deep transfer learning models consider the overall features but rarely attend to the local target features that are useful for RUL prediction; insufficient attention paid to local features reduces the accuracy and reliability of prediction. By considering the contribution of input data to the modeling output, a deep learning model that incorporates the attention mechanism in feature selection and extraction is proposed in our work; an unsupervised clustering method for classification of rotating machinery performance state evolution is put forward, and a similarity function is used to calculate the expected attention of input data to build an input data extraction attention module; the module is then fused with a gated recurrent unit (GRU), a variant of a recurrent neural network, to construct an attention-GRU model that combines prediction calculation and weight calculation for RUL prediction. Tests on public datasets show that the attention-GRU model outperforms traditional GRU and LSTM in RUL prediction, achieves less prediction error, and improves the performance and stability of the model
3D Pose Recognition of Small Special-Shaped Sheet Metal with Multi-Objective Overlapping
This paper addresses the challenging task of determining the position and posture of small-scale thin metal parts with multi-objective overlapping. To tackle this problem, we propose a method that utilizes instance segmentation and a three-dimensional (3D) point cloud for recognizing the posture of thin special-shaped metal parts. We investigate the process of obtaining a single target point cloud by aligning the target mask with the depth map. Additionally, we explore a pose estimation method that involves registering the target point cloud with the model point cloud, designing a registration algorithm that combines the sample consensus initial alignment algorithm (SAC-IA) for coarse registration and the iterative closest point (ICP) algorithm for fine registration. The experimental results demonstrate the effectiveness of our approach. The average accuracy of the instance segmentation models, utilizing ResNet50 + FPN and ResNet101 + FPN as backbone networks, exceeds 97%. The time consumption of the ResNet50 + FPN model is reduced by 50%. Furthermore, the registration algorithm, which combines the SAC-IA and ICP, achieves a lower average consumption time while satisfying the requirements for the manufacturing of new energy batteries
Determination of the zirconium isotopic composition of the new isotopic standard NRC ZIRC-1 using MC-ICP-MS
International audienceFirst cross-calibration of all existing Zr isotopic standards and two certified reference materials to a new commercially available standard NRC ZIRC-1, and the first report of the Zr isotopic composition of Allende chondrite