412 research outputs found
Modeling Based on Elman Wavelet Neural Network for Class-D Power Amplifiers
In Class-D Power Amplifiers (CDPAs), the power supply noise can intermodulate
with the input signal, manifesting into power-supply induced intermodulation
distortion (PS-IMD) and due to the memory effects of the system, there exist
asymmetries in the PS-IMDs. In this paper, a new behavioral modeling based on
the Elman Wavelet Neural Network (EWNN) is proposed to study the nonlinear
distortion of the CDPAs. In EWNN model, the Morlet wavelet functions are
employed as the activation function and there is a normalized operation in the
hidden layer, the modification of the scale factor and translation factor in
the wavelet functions are ignored to avoid the fluctuations of the error
curves. When there are 30 neurons in the hidden layer, to achieve the same
square sum error (SSE) , EWNN needs 31 iteration steps,
while the basic Elman neural network (BENN) model needs 86 steps. The
Volterra-Laguerre model has 605 parameters to be estimated but still can't
achieve the same magnitude accuracy of EWNN. Simulation results show that the
proposed approach of EWNN model has fewer parameters and higher accuracy than
the Volterra-Laguerre model and its convergence rate is much faster than the
BENN model
Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning
BACKGROUND: Pharmacovigilance and drug-safety surveillance are crucial for monitoring adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug Administration Adverse Event Reporting System face challenges such as underreporting. Therefore, as complementary surveillance, data on ADEs are extracted from electronic health record (EHR) notes via natural language processing (NLP). As NLP develops, many up-to-date machine-learning techniques are introduced in this field, such as deep learning and multi-task learning (MTL). However, only a few studies have focused on employing such techniques to extract ADEs.
OBJECTIVE: We aimed to design a deep learning model for extracting ADEs and related information such as medications and indications. Since extraction of ADE-related information includes two steps-named entity recognition and relation extraction-our second objective was to improve the deep learning model using multi-task learning between the two steps.
METHODS: We employed the dataset from the Medication, Indication and Adverse Drug Events (MADE) 1.0 challenge to train and test our models. This dataset consists of 1089 EHR notes of cancer patients and includes 9 entity types such as Medication, Indication, and ADE and 7 types of relations between these entities. To extract information from the dataset, we proposed a deep-learning model that uses a bidirectional long short-term memory (BiLSTM) conditional random field network to recognize entities and a BiLSTM-Attention network to extract relations. To further improve the deep-learning model, we employed three typical MTL methods, namely, hard parameter sharing, parameter regularization, and task relation learning, to build three MTL models, called HardMTL, RegMTL, and LearnMTL, respectively.
RESULTS: Since extraction of ADE-related information is a two-step task, the result of the second step (ie, relation extraction) was used to compare all models. We used microaveraged precision, recall, and F1 as evaluation metrics. Our deep learning model achieved state-of-the-art results (F1=65.9%), which is significantly higher than that (F1=61.7%) of the best system in the MADE1.0 challenge. HardMTL further improved the F1 by 0.8%, boosting the F1 to 66.7%, whereas RegMTL and LearnMTL failed to boost the performance.
CONCLUSIONS: Deep learning models can significantly improve the performance of ADE-related information extraction. MTL may be effective for named entity recognition and relation extraction, but it depends on the methods, data, and other factors. Our results can facilitate research on ADE detection, NLP, and machine learning
Safety-quantifiable Line Feature-based Monocular Visual Localization with 3D Prior Map
Accurate and safety-quantifiable localization is of great significance for
safety-critical autonomous systems, such as unmanned ground vehicles (UGV) and
unmanned aerial vehicles (UAV). The visual odometry-based method can provide
accurate positioning in a short period but is subjected to drift over time.
Moreover, the quantification of the safety of the localization solution (the
error is bounded by a certain value) is still a challenge. To fill the gaps,
this paper proposes a safety-quantifiable line feature-based visual
localization method with a prior map. The visual-inertial odometry provides a
high-frequency local pose estimation which serves as the initial guess for the
visual localization. By obtaining a visual line feature pair association, a
foot point-based constraint is proposed to construct the cost function between
the 2D lines extracted from the real-time image and the 3D lines extracted from
the high-precision prior 3D point cloud map. Moreover, a global navigation
satellite systems (GNSS) receiver autonomous integrity monitoring (RAIM)
inspired method is employed to quantify the safety of the derived localization
solution. Among that, an outlier rejection (also well-known as fault detection
and exclusion) strategy is employed via the weighted sum of squares residual
with a Chi-squared probability distribution. A protection level (PL) scheme
considering multiple outliers is derived and utilized to quantify the potential
error bound of the localization solution in both position and rotation domains.
The effectiveness of the proposed safety-quantifiable localization system is
verified using the datasets collected in the UAV indoor and UGV outdoor
environments
Prompting Segmentation with Sound is Generalizable Audio-Visual Source Localizer
Never having seen an object and heard its sound simultaneously, can the model
still accurately localize its visual position from the input audio? In this
work, we concentrate on the Audio-Visual Localization and Segmentation tasks
but under the demanding zero-shot and few-shot scenarios. To achieve this goal,
different from existing approaches that mostly employ the
encoder-fusion-decoder paradigm to decode localization information from the
fused audio-visual feature, we introduce the encoder-prompt-decoder paradigm,
aiming to better fit the data scarcity and varying data distribution dilemmas
with the help of abundant knowledge from pre-trained models. Specifically, we
first propose to construct Semantic-aware Audio Prompt (SAP) to help the visual
foundation model focus on sounding objects, meanwhile, the semantic gap between
the visual and audio modalities is also encouraged to shrink. Then, we develop
a Correlation Adapter (ColA) to keep minimal training efforts as well as
maintain adequate knowledge of the visual foundation model. By equipping with
these means, extensive experiments demonstrate that this new paradigm
outperforms other fusion-based methods in both the unseen class and
cross-dataset settings. We hope that our work can further promote the
generalization study of Audio-Visual Localization and Segmentation in practical
application scenarios.Comment: 11 pages, 7 figures, modified the additional material
Effect of saline stress on the physiology and growth of maize hybrids and their related inbred lines
Salinity is one major abiotic stress that restrict plant growth and crop productivity. In maize (Zea mays L), salt stress causes significant yield loss each year. However, indices of maize response to salt stress are not completely explored and a desired method for maize salt tolerance evaluation is still not established. A Chinese leading maize variety Jingke968 showed various resistance to environmental factors, including salt stress. To compare its salt tolerance to other superior maize varieties, we examined the physiological and growth responses of three important maize hybrids and their related inbred lines under the control and salt stress conditions. By compar- ing the physiological parameters under control and salt treatment, we demonstrated that different salt tolerance mechanisms may be involved in different genotypes, such as the elevation of superoxide dismutase activity and/ or proline content. With Principal Component Analysis of all the growth indicators in both germination and seedling stages, along with the germination rate, superoxide dismutase activity, proline content, malondialdehyde content, relative electrolyte leakage, we were able to show that salt resistance levels of hybrids and their related inbred lines were Jingke968 > Zhengdan958 > X1132 and X1132M > Jing724 > Chang7-2 > Zheng58 > X1132F, respectively, which was consistent with the saline field observation. Our results not only contribute to a better understanding of salt stress response in three important hybrids and their related inbred lines, but also this evaluation system might be applied for an accurate assessment of salt resistance in other germplasms and breeding material
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