Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos

The task of video grounding, which temporally localizes a natural language description in a video, plays an important role in understanding videos. Existing studies have adopted strategies of sliding window over the entire video or exhaustively ranking all possible clip-sentence pairs in a pre-segmented video, which inevitably suffer from exhaustively enumerated candidates. To alleviate this problem, we formulate this task as a problem of sequential decision making by learning an agent which regulates the temporal grounding boundaries progressively based on its policy. Specifically, we propose a reinforcement learning based framework improved by multi-task learning and it shows steady performance gains by considering additional supervised boundary information during training. Our proposed framework achieves state-of-the-art performance on ActivityNet'18 DenseCaption dataset and Charades-STA dataset while observing only 10 or less clips per video.Comment: AAAI 201

The effect of Cr impurity to superconductivity in electron-doped BaFe2-xNixAs2

We use transport and magnetization measurements to study the effect of Cr-doping to the phase diagram of the electron-doped superconducting BaFe2-xNixAs2 iron pnictides. In principle, adding Cr to electron-doped BaFe2-xNixAs2 should be equivalent to the effect of hole-doping. However, we find that Cr doping suppresses superconductivity via impurity effect, while not affecting the normal state resistivity above 100 K. We establish the phase diagram of Cr-doped BaFe2-x-yNixCryAs2 iron pnictides, and demonstrate that Cr-doping near optimal superconductivity restore the long-range antiferromagnetic order suppressed by superconductivity.Comment: 10 pages, 5 figure

Fair Attribute Completion on Graph with Missing Attributes

Tackling unfairness in graph learning models is a challenging task, as the unfairness issues on graphs involve both attributes and topological structures. Existing work on fair graph learning simply assumes that attributes of all nodes are available for model training and then makes fair predictions. In practice, however, the attributes of some nodes might not be accessible due to missing data or privacy concerns, which makes fair graph learning even more challenging. In this paper, we propose FairAC, a fair attribute completion method, to complement missing information and learn fair node embeddings for graphs with missing attributes. FairAC adopts an attention mechanism to deal with the attribute missing problem and meanwhile, it mitigates two types of unfairness, i.e., feature unfairness from attributes and topological unfairness due to attribute completion. FairAC can work on various types of homogeneous graphs and generate fair embeddings for them and thus can be applied to most downstream tasks to improve their fairness performance. To our best knowledge, FairAC is the first method that jointly addresses the graph attribution completion and graph unfairness problems. Experimental results on benchmark datasets show that our method achieves better fairness performance with less sacrifice in accuracy, compared with the state-of-the-art methods of fair graph learning. Code is available at: https://github.com/donglgcn/FairAC

The effect of geographic distance on independent directors’ performance from the perspective of inefficient investment

Geoeconomics has attracted sustained attention in recent years, but the role of independent directors’ geographic distance in investment efficiency remains unexplored. We explore the governance effects of independent directors from a geographic location perspective. Specifically, the Great Circle Distance Formula is employed to calculate the geographic distance between the independent directors and the enterprise. Then, we measure the inefficient investment. Using a detailed sample in the Chinese market from 2009 to 2018, we find that geographic distance is not conducive to the functioning of independent directors and that there is a positive relationship between independent directors’ geographic distance and inefficient investment. The coefficients are robust to multiple robustness checks. In addition, the positive effect of independent directors’ geographic distance on inefficient investment will increase (become more positive) when there is no high-speed rail and the marketisation process is low in the enterprise’s location. Mechanism tests show that geographic distance does affect inefficient investment by inhibiting independent directors’ access to information as well as their reputation. Our results have important implications for investment policy and corporate governance