23 papers · ranked by Valyu relevance
Yi Qiu, Yu Lin, Yun Lin, Davide Chicco
Music emotion regression (MER) is a vital field that bridges psychology and music information retrieval. Music has the powerful ability to evoke a wide range of human emotions, from joy and sadness to anger and calmness. Understanding how music influences emotional states is essential for grasping its psychological…
Yue Zhang, Qi Liu, Linfeng Song
Bi-directional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer various limitations due to their sequential nature. We investigate an alternative LSTM structure for encoding text, which consists of a parallel state for each word. Recurrent steps are used to perform…
Manuel Campos-Taberner, Francisco Javier García-Haro, Beatriz Martínez, Emma Izquierdo-Verdiguier + 3 more
'Beatriz Martínez' 'Emma Izquierdo-Verdiguier' 'Clement Atzberger' 'Gustau Camps-Valls' 'María Amparo Gilabert'] The use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is rapidly increasing. DL techniques have provided excellent results in applications ranging from parameter estimation to…
Qian Cao, Junling Zheng, Yunyue Liu, Zongjin Li + 4 more
Since the end of 2019, a novel coronavirus known as COVID-19 has caused a severe outbreak worldwide. Due to the complexity of epidemic data, traditional algorithms have struggled to accurately predict the development of the pandemic. The Autoregressive Integrated Moving Average (ARIMA) model is capable of capturing…
Hiren Mewada, Jawad F. Al-Asad, Faris A. Almalki, Adil H. Khan + 4 more
'Nouf Abdullah Almujally' 'Samir El-Nakla' 'Qamar Naith' 'Kit Yan Chan'] Voice-controlled devices are in demand due to their hands-free controls. However, using voice-controlled devices in sensitive scenarios like smartphone applications and financial transactions requires protection against fraudulent attacks referred…
Ilias Chalkidis, Ion Androutsopoulos, Achilleas Michos
We consider the task of detecting contractual obligations and prohibitions. We show that a self-attention mechanism improves the performance of a BILSTM classifier, the previous state of the art for this task, by allowing it to focus on indicative tokens. We also introduce a hierarchical BILSTM, which converts each…
Yifei Chen
Background Automatic extraction of biomedical events from literature is an important task in the understanding biological systems, allowing for faster update of the latest discoveries automatically. Detecting trigger words which indicate events is a critical step in the process of event extraction, because following…
Cheng Huang, Peng Xie, Chunming Wu, Xiaojuan Liu + 2 more
'José Alberto Benítez-Andrades'] In order to improve the recognition rate of the tone classification of doctors in online medical services scenarios, we propose a model that integrates a one-dimensional convolutional neural network (1DCNN) with a bidirectional long short-term memory network (BiLSTM). Firstly…
John M. Giorgi, Gary D. Bader
Automatic biomedical named entity recognition (BioNER) is a key task in biomedical information extraction (IE). For some time, state-of-the-art BioNER has been dominated by machine learning methods, particularly conditional random fields (CRFs), with a recent focus on deep learning. However, recent work has suggested…
Wenhao Dai, Rongxiu Lu, Pengzhan Chen, Hui Yang + 1 more
Long short-term memory (LSTM) networks demonstrate superior time-series feature extraction capabilities and have exhibited significant advantages in the soft sensing of key indicators in complex industrial processes. However, conventional LSTM networks rely solely on the output information from forward propagation…
Xuan Wang, Yu Zhang, Xiang Ren, Yuhao Zhang + 4 more
State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network models for BioNER to free experts from manual feature engineering, the performance remains…
Yejian Wu, Lujing Cao, Zhipeng Wu, Xinyi Wu + 2 more
Human major histocompatibility complex (MHC) proteins are encoded by the human leukocyte antigen (HLA) gene complex. When exogenous peptide fragments form peptide-HLA (pHLA) complexes with HLA molecules on the outer surface of cells, they can be recognized by T cells and trigger an immune response. Therefore…
Siddhartha Brahma
Recurrent neural networks have become ubiquitous in computing representations of sequential data, especially textual data in natural language processing. In particular, Bidirectional LSTMs are at the heart of several neural models achieving state-of-the-art performance in a wide variety of tasks in NLP. However…
Ling Luo, Zhihao Yang, Pei Yang, Yin Zhang + 3 more
'Hongfei Lin'] In biomedical research, patents contain the significant amount of information, and biomedical text mining has received much attention in patents recently. To accelerate the development of biomedical text mining for patents, the BioCreative V.5 challenge organized three tracks, i.e., chemical entity…
Ziang Yan, Satoshi Omori, Kazunori D Yamada, Hafumi Nishi + 1 more
The biological functions of proteins are traditionally thought to depend on well-defined three-dimensional structures, but many experimental studies have shown that disordered regions lacking fixed three-dimensional structures also have crucial biological roles. In some of these regions, disorder–order transitions are…
Bharat Bohara, Raymond Fernandez, Vysali Gollapudi, Xingpeng Li
—Higher penetration of renewable and smart home technologies at the residential level challenges grid stability as utility-customer interactions add complexity to power system operations. In response, short-term residential load forecasting has become an increasing area of focus. However, forecasting at the residential…
Yang Yue, Lin Yuxiang, Zhang Ying, Su + 8 more
Prediction of post-loan default is an important task in credit risk management, and can be addressed by detection of financial anomalies using machine learning. This study introduces a ResE-BiLSTM model, using a sliding window technique, and is evaluated on 44 independent cohorts from the extensive Freddie Mac US…
Ahmadreza Keihani, Francesco L. Donati, Simone Russo, Sara Parmigiani + 9 more
Transcranial Magnetic Stimulation (TMS) with simultaneous Electroencephalogram (TMS-EEG) allows assessing the neurophysiological properties of cortical neurons. However, TMS-evoked EEG potentials (TEPs) can be affected by components unrelated to TMS direct neuronal activation. Accurate, automatic tools are therefore…
Majid Ghorbani Eftekhar
Identifying subcellular localization of protein is significant for understanding its molecular function. It provides valuable insights that can be of huge help to protein’s function research and the detection of potential cell surface/secreted drug targets. The prediction of protein subcellular localization using…
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…
Richard Apodaca
Despite its widespread use, Simplified Molecular Input Line Entry System (SMILES) remains underspecified. The lack of a detailed specification encourages improvisation by software developers, complicates data standardization efforts, and undermines extension development. Balsa, a reformulation of SMILES, addresses…
Sherif Tawfik, Olexandr Isayev, Catherine Stampfl, Joseph Shapter + 2 more
There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work however, even the most…
Sherif Tawfik, Olexandr Isayev, Catherine Stampfl, Joseph Shapter + 2 more
There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work however, even the most…