FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers. We adapt the most recent state-of-the-art few-shot learning methods for relation classification and conduct a thorough evaluation of these methods. Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. We also show that a range of different reasoning skills are needed to solve our task. These results indicate that few-shot relation classification remains an open problem and still requires further research. Our detailed analysis points multiple directions for future research. All details and resources about the dataset and baselines are released on http://zhuhao.me/fewrel.Comment: EMNLP 2018. The first four authors contribute equally. The order is determined by dice rolling. Visit our website http://zhuhao.me/fewre

Application framework of urban morphology in planning practice : a case study of Beijing

Although it is generally recognized that a connection exists between urban form and urban planning practice, there is still a long way to go before the theories, concepts and methods of urban morphology can be widely applied to daily planning practice. Firstly, the paper makes a theoretical thinking. It is concluded that: 1. Urban form and urban planning practice are dynamic processes of space-time interaction; 2. Urban morphology research and urban planning are the relationship between knowledge and practice;3. Urban morphology research needs to be combined with urban planning business. Secondly, the paper puts forward an application framework of urban morphology in planning practice, including four aspects: knowledge base, analytical dimension, technical support and planning practice. The framework focuses on the space geometry, industrial economy, traffic organization, residential space, urban design, land use and policies in the research of urban morphology under different scales, more closely related to the main focuses in the discipline and practice of urban planning. Thirdly, this paper describes the application of the framework in Beijing planning practice in recent years and illustrates it plays a role and value in the actual planning business

Surface passivation for highly active, selective, stable, and scalable CO2 electroreduction

Electrochemical conversion of CO2 to formic acid using Bismuth catalysts is one the most promising pathways for industrialization. However, it is still difficult to achieve high formic acid production at wide voltage intervals and industrial current densities because the Bi catalysts are often poisoned by oxygenated species. Herein, we report a Bi3S2 nanowire-ascorbic acid hybrid catalyst that simultaneously improves formic acid selectivity, activity, and stability at high applied voltages. Specifically, a more than 95% faraday efficiency was achieved for the formate formation over a wide potential range above 1.0 V and at ampere-level current densities. The observed excellent catalytic performance was attributable to a unique reconstruction mechanism to form more defective sites while the ascorbic acid layer further stabilized the defective sites by trapping the poisoning hydroxyl groups. When used in an all-solid-state reactor system, the newly developed catalyst achieved efficient production of pure formic acid over 120 hours at 50 mA cm–2 (200 mA cell current)

Performance evaluation of deep feature learning for RGB-D image/video classification

Deep Neural Networks for image/video classification have obtained much success in various computer vision applications. Existing deep learning algorithms are widely used on RGB images or video data. Meanwhile, with the development of low-cost RGB-D sensors (such as Microsoft Kinect and Xtion Pro-Live), high-quality RGB-D data can be easily acquired and used to enhance computer vision algorithms [14]. It would be interesting to investigate how deep learning can be employed for extracting and fusing features from RGB-D data. In this paper, after briefly reviewing the basic concepts of RGB-D information and four prevalent deep learning models (i.e., Deep Belief Networks (DBNs), Stacked Denoising Auto-Encoders (SDAE), Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) Neural Networks), we conduct extensive experiments on five popular RGB-D datasets including three image datasets and two video datasets. We then present a detailed analysis about the comparison between the learned feature representations from the four deep learning models. In addition, a few suggestions on how to adjust hyper-parameters for learning deep neural networks are made in this paper. According to the extensive experimental results, we believe that this evaluation will provide insights and a deeper understanding of different deep learning algorithms for RGB-D feature extraction and fusion

Microdroplet operations in polymeric microtubes

Microsystem technologies allow a plethora of operations to be achieved for microemulsion- and microdroplet-based assays, providing miniaturised, yet large throughput capabilities to assist experimentation in analytical chemistry, biology and synthetic biology. Many of such approaches have been implemented on-chip, using microfluidic and lab-on-a-chip technologies. However, the microfabrication of such devices relies on expensive equipment and time-consuming methods, thus hindering their uptake and use by many research laboratories where microfabrication expertise is not available. Here, we demonstrate how fundamental water-in-oil microdroplet operations, such as droplet trapping, merging, diluting and splitting, can be obtained using straightforward, inexpensive and manually fabricated polymeric microtube modules. The modules are based on creating an angled tubing interface at the interconnection between two polymeric microtubes. We have characterised how the geometry and fluid dynamic conditions at this interface enabled different droplet operations to be achieved in a versatile and functional manner. We envisage this approach to be an alternative solution to expensive and laborious microfabrication protocols for droplet microfluidics