In this paper, we propose a new approach to the analysis and modeling of esophageal manometry (EGM) data to assist the diagnosis of esophageal motility disorders in humans. The proposed approach combines three techniques, namely, wavelet decomposition (WD), nonlinear pulse detection technique (NPDT), and statistical pulse modeling. Specifically, WD is applied to the filtering of the EGM data, which is contaminated with electrocardiography (ECG) artifacts. A new NPDT is applied to the denoised data leading to identification and extraction of diagnostically important information, i.e., esophageal pulses from the respiration artifacts. Such information is used to generate a statistical model that can classify the EGM patterns. The proposed approach is computationally effortless, thus making it suitable for real-time application. Experimental results using measured EGM data of 20 patients, including ten abnormal cases is presented. Comparison of our results with those from existing techniques illustrates the advantages of the proposed approach in terms of accuracy and efficiency.
User intent discovery is a key step in developing a Natural Language Understanding (NLU) module at the core of any modern Conversational AI system. Typically, human experts review a representative sample of user input data to discover new intents, which is subjective, costly, and error-prone. In this work, we aim to assist the NLU developers by presenting a novel method for discovering new intents at scale given a corpus of utterances. Our method utilizes supervised contrastive learning to leverage information from a domainrelevant, already labeled dataset and identifies new intents in the corpus at hand using unsupervised K-means clustering. Our method outperforms the state-of-the-art by a large margin up to 2% and 13% on two benchmark datasets, measured by clustering accuracy. Furthermore, we apply our method on a large dataset from the travel domain to demonstrate its effectiveness on a real-world use case.
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