University of Electronic Science and Technology of China

About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.

Efficient Electrochemical N2 Reduction to NH3 on MoN Nanosheets Array under Ambient Conditions

Abstract: Electrochemical N2 reduction reaction (NRR) under ambient conditions offers us an environmentally friendly route for artificial synthesis of NH3. However, up to now, few noble-metal-free electrocatalysts with satisfactory catalytic activities have been explored. In this Letter, we demonstrate that MoN nanosheets array on carbon cloth (MoN NA/CC) acts as a high-performance NRR electrocatalyst toward NH3 electrosynthesis in 0.1 M HCl under ambient conditions. This catalyst achieves a large NH3 yield of 3.01 × 10–10 mo1 s–1 cm–2 and a Faradaic efficiency of 1.15% at −0.3 V vs reversible hydrogen electrode with strong electrochemical durability and selectivity. Density functional theory calculations reveal that MoN NA/CC catalyzes NRR via the Mars–van Krevelen mechanism.

Hierarchical LSTMs with Adaptive Attention for Visual Captioning

Abstract: Recent progress has been made in using attention based encoder-decoder framework for image and video captioning. Most existing decoders apply the attention mechanism to every generated word including both visual words (e.g., “gun” and “shooting”) and non-visual words (e.g., “the”, “a”). However, these non-visual words can be easily predicted using natural language model without considering visual signals or attention. Imposing attention mechanism on non-visual words could mislead and decrease the overall performance of visual captioning. Furthermore, the hierarchy of LSTMs enables more complex representation of visual data, capturing information at different scales. Considering these issues, we propose a hierarchical LSTM with adaptive attention (hLSTMat) approach for image and video captioning. Specifically, the proposed framework utilizes the spatial or temporal attention for selecting specific regions or frames to predict the related words, while the adaptive attention is for deciding whether to depend on the visual information or the language context information. Also, a hierarchical LSTMs is designed to simultaneously consider both low-level visual information and high-level language context information to support the caption generation. We design the hLSTMat model as a general framework, and we first instantiate it for the task of video captioning. Then, we further instantiate our hLSTMarefine it and apply it to the imioning task. To demonstrate the effectiveness of our proposed framework, we test our method on both video and image captioning tasks. Experimental results show that our approach achieves the state-of-the-art performance for most of the evaluation metrics on both tasks. The effect of important components is also well exploited in the ablation study.