EULAG (Eulerian/semi-Lagrangian fluid solver) is an established numerical model for simulating thermo-fluid flows across a wide range of scales and physical scenarios. The multidimensional positive definite advection ...
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ISBN:
(纸本)9783319219097;9783319219080
EULAG (Eulerian/semi-Lagrangian fluid solver) is an established numerical model for simulating thermo-fluid flows across a wide range of scales and physical scenarios. The multidimensional positive definite advection transport algorithm (MPDATA) is among the most time-consuming components of EULAG. In this study, we focus on adapting the 3D MPDATA computations to clusters with graphics processors. Our approach is based on a hierarchical decomposition including the level of cluster, as well as an optimized distribution of computations between GPU resources within each node. To implement the resulting computing scheme, the MPI standard is used across nodes, while CUDA is applied inside nodes. We present performance results for the 3D MPDATA code running on the NVIDIA GeForce GTX TITAN graphics card, as well as on the Piz Daint cluster equipped with NVIDIA Tesla K20x GPUs. In particular, the sustained performance of 138 Gflop/s is achieved for a single GPU, which scales up to more than 11 Tflop/s for 256 GPUs.
Regarding the spoken language understanding (SLU) pilot task of the Dialog State Tracking Challenge 5 (DSTC5), it is required to classify label sets of speech acts on human-to-human dialogues. In this paper, we propos...
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ISBN:
(纸本)9781509030156
Regarding the spoken language understanding (SLU) pilot task of the Dialog State Tracking Challenge 5 (DSTC5), it is required to classify label sets of speech acts on human-to-human dialogues. In this paper, we propose a multi-label classification model with the assistance of algorithm adaptation method. To be specific, a Convolutional Neural Network (CNN) model on top of pre-trained word vectors is adapted for the multi-label classification task by utilizing a threshold learning mechanism. In order to evaluate the performance of our proposed model, comparative experiments on the DSTC5 dialogue datasets are conducted. Experimental results show that the proposed model outperforms most of the submitted model in the DSTC5 in terms of F1-score. Without any manually designed features, our model has advantage of handling the multi-label SLU task, using only publicly available pre-trained word vectors.
Quantitative structure-retention relationship models (QSRR) have been utilized as an alternative to costly and time-consuming separation analyses and associated experiments for predicting retention time. However, achi...
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Quantitative structure-retention relationship models (QSRR) have been utilized as an alternative to costly and time-consuming separation analyses and associated experiments for predicting retention time. However, achieving 100 % accuracy in retention prediction is unrealistic despite the existence of various tools and approaches. The limitations of vast data availability and time complexity hinder the use of most algorithms for retention prediction. Therefore, in this study, we examined and compared two approaches for modelling retention time using a dataset of small molecules with retention times obtained at multiple conditions, referred to as multi-targets (five pH levels: 2.7, 3.5, 5, 6.5, and 8 at gradient times of 20 min of mobile phase). The first approach involved developing separate models for predicting retention time at each condition (single-target approach), while the second approach aimed to learn a single model for predicting retention across all conditions simultaneously (multi-target approach). Our findings highlight the advantages of the multi-target approach over the single-target modelling approach. The multi-target models are more efficient in terms of size and learning speed compared to the single-target models. These retention prediction models offer two-fold benefits. Firstly, they enhance knowledge and understanding of retention times, identifying molecular descriptors that contribute to changes in retention behaviour under different pH conditions. Secondly, these approaches can be extended to address other multi-target property prediction problems, such as multi-quantitative structure Property(X) relationship studies (mt-QS(X)R).
The goal of Graded Multi-label Classification (GMLC) is to assign a degree of membership or relevance of a class label to each data point. As opposed to multi-label classification tasks which can only predict whether ...
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The goal of Graded Multi-label Classification (GMLC) is to assign a degree of membership or relevance of a class label to each data point. As opposed to multi-label classification tasks which can only predict whether a class label is relevant or not. The graded multi-label setting generalizes the multi-label paradigm to allow a prediction on a gradual scale. This is in agreement with practical real-world applications where the labels differ in matter of level relevance. In this paper, we propose a novel decision tree classifier (GML_DT) that is adapted to the graded multi-label setting. It fully models the label dependencies, which sets it apart from the transformation-based approaches in the literature, and increases its performance. Furthermore, our approach yields comprehensive and interpretable rules that efficiently predict all the degrees of memberships of the class labels at once. To demonstrate the model's effectiveness, we tested it on real-world graded multi-label datasets and compared it against a baseline transformation-based decision tree classifier. To assess its predictive performance, we conducted an experimental study with different evaluation metrics from the literature. Analysis of the results shows that our approach has a clear advantage across the utilized performance measures.
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