258 research outputs found
Leveraging Key Information Modeling to Improve Less-Data Constrained News Headline Generation via Duality Fine-Tuning
Recent language generative models are mostly trained on large-scale datasets,
while in some real scenarios, the training datasets are often expensive to
obtain and would be small-scale. In this paper we investigate the challenging
task of less-data constrained generation, especially when the generated news
headlines are short yet expected by readers to keep readable and informative
simultaneously. We highlight the key information modeling task and propose a
novel duality fine-tuning method by formally defining the probabilistic duality
constraints between key information prediction and headline generation tasks.
The proposed method can capture more information from limited data, build
connections between separate tasks, and is suitable for less-data constrained
generation tasks. Furthermore, the method can leverage various pre-trained
generative regimes, e.g., autoregressive and encoder-decoder models. We conduct
extensive experiments to demonstrate that our method is effective and efficient
to achieve improved performance in terms of language modeling metric and
informativeness correctness metric on two public datasets.Comment: Accepted by AACL-IJCNLP 2022 main conferenc
Effect of geometric factors on the energy performance of high-rise office towers in Tianjin, China
To improve energy efficiency of office buildings in Tianjin, we select a prototypical high-rise office tower as an example and focus on the effect of geometric factors on building energy performance. These factors include the orientation, plane shape, floor area, plane shape factor (the ratio of the plane length to the plane width, only as regards to a rectangle-shaped plane), floor height, floor number and window-to-wall ratio. The simulation is performed in DesignBuilder, which integrates artificial lighting with instantaneous daylight during the energy simulation process. The geometric factors of the defined prototype are examined in both single-parameter and multi-parameter evaluations. As to the multi-parameter results, the energy saving rate can vary by up to 18.9%, and reducing the floor height is observed to be the most effective means of reducing annual total end-use energy consumption, followed by increasing the plane shape factor and reducing the floor area. The results can serve as a reference for passive design strategies related to geometric factors in the early design stage
A quantitative approach for risk assessment of a ship stuck in ice in Arctic waters
International audienc
Automated detection and growth tracking of 3D bio-printed organoid clusters using optical coherence tomography with deep convolutional neural networks
Organoids are advancing the development of accurate prediction of drug efficacy and toxicity in vitro. These advancements are attributed to the ability of organoids to recapitulate key structural and functional features of organs and parent tumor. Specifically, organoids are self-organized assembly with a multi-scale structure of 30–800 μm, which exacerbates the difficulty of non-destructive three-dimensional (3D) imaging, tracking and classification analysis for organoid clusters by traditional microscopy techniques. Here, we devise a 3D imaging, segmentation and analysis method based on Optical coherence tomography (OCT) technology and deep convolutional neural networks (CNNs) for printed organoid clusters (Organoid Printing and optical coherence tomography-based analysis, OPO). The results demonstrate that the organoid scale influences the segmentation effect of the neural network. The multi-scale information-guided optimized EGO-Net we designed achieves the best results, especially showing better recognition workout for the biologically significant organoid with diameter ≥50 μm than other neural networks. Moreover, OPO achieves to reconstruct the multiscale structure of organoid clusters within printed microbeads and calibrate the printing errors by segmenting the printed microbeads edges. Overall, the classification, tracking and quantitative analysis based on image reveal that the growth process of organoid undergoes morphological changes such as volume growth, cavity creation and fusion, and quantitative calculation of the volume demonstrates that the growth rate of organoid is associated with the initial scale. The new method we proposed enable the study of growth, structural evolution and heterogeneity for the organoid cluster, which is valuable for drug screening and tumor drug sensitivity detection based on organoids
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