XL-Sum induces competitive results compared to the ones obtained using similar monolingual datasets: we show higher than 11 ROUGE-2 scores on 10 languages we benchmark on, with some of them exceeding 15, as obtained by multilingual training.
In this work, we present, EDITH, a deep learning-based framework for ECG biometrics authentication system.
As a bi-product of the standard NLU benchmarks, we introduce a new downstream dataset on natural language inference (NLI) and show that BanglaBERT outperforms previous state-of-the-art results on all tasks by up to 3. 5%.
Background: The inception of next generations sequencing technologies have exponentially increased the volume of biological sequence data.
With the segmenter and the two methods combined, we compile a high-quality Bengali-English parallel corpus comprising of 2. 75 million sentence pairs, more than 2 million of which were not available before.
We are presenting COVID-19Base, a knowledgebase highlighting the biomedical entities related to COVID-19 disease based on literature mining.
This motivates us to develop a method to predict the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using photoplethysmogram (PPG) signals.
Several researches have been done to tackle this problem, most of which employed resampling, i. e. oversampling and undersampling techniques to bring the required balance in the data.
The evaluation metric accuracy and loss along with 5-fold cross validation was used to compare and select the best performing architecture.
We have compared our proposed architecture MultiResUNet with the classical U-Net on a vast repertoire of multimodal medical images.
The comparative results of the experiments conducted on three standard face image datasets show that the best performers for face image retrieval are Alexlayer7 with $K$-means and SSF, Alexlayer6 with $K$-SVD and SSF, and Alexlayer6 with $K$-means and SSF.
Ventricular Fibrillation (VF), one of the most dangerous arrhythmias, is responsible for sudden cardiac arrests.
The effectiveness of our method is tested on standard benchmark structures.
Graph partitioning, a well studied problem of parallel computing has many applications in diversified fields such as distributed computing, social network analysis, data mining and many other domains.
In this paper, we propose a new genetic algorithm for solving the Euclidean distance geometry problem for protein structure prediction given sparse NMR data.
Due to the advancements in technology number of entries in the structural database of proteins are increasing day by day.