Attention-Based Deep Learning System for Negation and Assertion Detection in Clinical Notes

Natural language processing (NLP) has been recently used to extract clinical information from free text in Electronic Health Record (EHR). In clinical NLP one challenge is that the meaning of clinical entities is heavily affected by assertion modifiers such as negation, uncertain, hypothetical, experiencer and so on. Incorrect assertion assignment could cause inaccurate diagnosis of patients’ condition or negatively influence following study like disease modelling. Thus, high-performance clinical NLP systems which can automatically detect negation and other assertion status of given target medical findings (e.g. disease, symptom) in clinical context are highly demanded. Here in this work, we propose a deep-learning system based on word embedding and Attention-based Bidirectional Long Short-Term Memory networks (AttBiLSTM) for assertion detection in clinical notes. Unlike previous state-of-art methods which require knowledge input, our system is a knowledge poor machine learning system and can be easily extended or transferred to other domains. The evaluation of our system on public benchmarking corpora demonstrates that a knowledge poor deep-learning system can also achieve high performance for detecting negation and assertions comparing to state-of-the-art system

Deep Learning based Underwater Object Detection

Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs) equipped with an intelligent object detection system play a vital role in various underwater applications such as marine resource exploitation, marine environment monitoring, and marine cable protection. Deep learning based object detection methods have presented great performance advantages over traditional machine learning based methods. However, these deep learning based methods lack sufficient capabilities to handle underwater object detection (UOD) due to these challenges: (1) underwater images acquired in complicated environments suffer fromsevere distortion which dramatically degrades image visibility, objects in the underwater datasets and real applications are usually small whilst accompanying severe noise that greatly degrade the detection accuracy of UOD tasks. (2) well-annotated underwater data is not sufficient in terms of diversity and amount which highly influences the performance of deep learning models. (3) severely imbalanced data distribution and label noise distribtuion occur in underwater datasets, driving a deep learning model to be more biased towards the majority class. In this thesis, we aim to address all these challenges, and develop robust deep learning systems to enhance and detect objects in complex underwater images. To achieve this goal, we firstly propose novel perceptual enhancement models to enhance the quality of underwater images. Secondly, we propose a novel Sample-WeIghted hyPEr Network (SWIPENET), and a robust training paradigm named Curriculum Multi-Class Adaboost (CMA), to address the noise and small object detection problems at the same time. Finally, to address the class imbalance problem, we propose a factor-agnostic gradient re-weighting algorithm (FAGR) that can adaptively fine tune the gradients of individual classes according to the distributions of their detection precision. We have evaluated the proposed methods by conducting extensive experiments on public datasets. Experimental results show the effectiveness of our methods for underwater image synthsis, image enhancement and object detection.</p

When work support does not work: investigating the joint moderating effect of challenge stressors and hindrance stressors on safety compliance

This study explores the within-person relationship between work support and safety compliance, as this has not been addressed by previous scholars. Drawing from the job demands–resources model, we argue for the positive impact of daily work support on daily safety compliance. We examined this hypothesis by collecting 221 daily diary data from 50 medical care personnel. The results show that the relationship between daily work support and daily safety compliance is positive and jointly moderated by challenge and hindrance stressors. Specifically, the positive relationship between daily work support and daily safety compliance is enhanced when there are high levels of challenge and hindrance stressors, and when there are high levels of challenge stressor and low levels of hindrance stressor. In addition, this positive relationship is not significant when there are low levels of challenge stressor and high levels of hindrance stressor.</p

Flow velocity contours and streamlines at peak inspiration.

(a) Healthy, (b) NSIP, and (c) IPF.</p

Stress contours of tissue at peak inspiration.

(a) Healthy, (b) NSIP, and (c) IPF. (d) Stress over time at several positions.</p

Temporal variation in contour of pressure drop at T = 0.5 s, T = 1.0 s, T = 1.5 s, T = 2.0 s, T = 2.5 s, T = 3.0 s, T = 3.5 s.

(a) Healthy, (b) NSIP, and (c)IPF.</p

Cross-section and open boundary surface of computational meshes for fluid domain and tissue domain.

(a) Healthy, (b) NSIP, and (c) IPF.</p

Temporal variation in contour of wall displacement at T = 0.5 s, T = 1.0 s, T = 1.5 s.

(a) Healthy, (b) NSIP, and (c) IPF.</p

Geometric models of pulmonary acinus.

(a) Human airway model. (b) Two generations of alveolar ducts within the pulmonary acinus and alveolar sac composed of 14-hedrons. (c) Cross-section of alveolar sac for healthy, NSIP, and IPF models.</p

Data sets for " The nature of mean-field generation in three classes of optimal dynamos"

The tar archive Optimal_Dynamos.tar contains and index.html file with links to the run directories for each figure and the two tables of the paper "The nature of mean-field generation in three classes of optimal dynamos" by Axel Brandenburg (Nordita) and Long Chen (Durham University) with the temporary URL http://norlx51.nordita.org/~brandenb/tmp/long_chen. Corrections and updates are available on the active URL to this tar archive: https://www.nordita.org/~brandenb/projects/Optimal_Dynamos/</pre