4,005 research outputs found
Characterizing the Dynamic Response of a Chassis Frame in a Heavy-Duty Dump Vehicle based on an Improved Stochastic System Identification
This paper presents an online method for the assessment of the dynamic performance of the chassis frame in a heavy-duty dump truck based on a novel stochastic subspace identification (SSI) method. It introduces the use of an average correlation signal as the input data to conventional SSI methods in order to reduce the noisy and nonstationary contents in the vibration signals from the frame, allowing accurate modal properties to be attained for realistically assessing the dynamic behaviour of the frame when the vehicle travels on both bumped and unpaved roads under different operating conditions. The modal results show that the modal properties obtained online are significantly different from the offline ones in that the identifiable modes are less because of the integration of different vehicle systems onto the frame. Moreover, the modal shapes between 7Hz and 40Hz clearly indicate the weak section of the structure where earlier fatigues and unsafe operations may occur due to the high relative changes in the modal shapes. In addition, the loaded operations show more modes which cause high deformation on the weak section. These results have verified the performance of the proposed SSI method and provide reliable references for optimizing the construction of the frame
Light vector meson and heavy baryon strong interaction
We calculate the coupling constants between the light vector mesons and heavy
baryons within the framework of the light-cone QCD sum rule in the leading
order of heavy quark effective theory. Most resulting sum rules are stable with
the variations of the Borel parameter and the continuum threshold. The
extracted couplings will be useful in the study of the possible heavy baryon
molecular states
A Comparative Study on Regularization Strategies for Embedding-based Neural Networks
This paper aims to compare different regularization strategies to address a
common phenomenon, severe overfitting, in embedding-based neural networks for
NLP. We chose two widely studied neural models and tasks as our testbed. We
tried several frequently applied or newly proposed regularization strategies,
including penalizing weights (embeddings excluded), penalizing embeddings,
re-embedding words, and dropout. We also emphasized on incremental
hyperparameter tuning, and combining different regularizations. The results
provide a picture on tuning hyperparameters for neural NLP models.Comment: EMNLP '1
Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path
Relation classification is an important research arena in the field of
natural language processing (NLP). In this paper, we present SDP-LSTM, a novel
neural network to classify the relation of two entities in a sentence. Our
neural architecture leverages the shortest dependency path (SDP) between two
entities; multichannel recurrent neural networks, with long short term memory
(LSTM) units, pick up heterogeneous information along the SDP. Our proposed
model has several distinct features: (1) The shortest dependency paths retain
most relevant information (to relation classification), while eliminating
irrelevant words in the sentence. (2) The multichannel LSTM networks allow
effective information integration from heterogeneous sources over the
dependency paths. (3) A customized dropout strategy regularizes the neural
network to alleviate overfitting. We test our model on the SemEval 2010
relation classification task, and achieve an -score of 83.7\%, higher than
competing methods in the literature.Comment: EMNLP '1
Microbiome and Cancer Immunotherapies
Here, we present 3 different perspectives on how the microbiome has impacted cancer patients, treatment, and clinical studies. We hear about the challenges of implementing microbiome analyses into the clinics, the impact these analyses might have on patients\u27 care, and treatment in the future, specifically for gastric cancer treatment. These are a few of the many voices that are highlighting the role of the microbiome in cancer development, treatment, and clinical outcomes
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