Deep Q-network algorithm is used to select economic span of bridge. Selection of bridge span has a significant impact on the total cost of bridge, and a reasonable selection of span can reduce engineering cost. Economic span of bridge is theoretically analyzed, and the theoretical solution formula of economic span is deduced. Construction process of bridge simulation environment is described in detail, including observation space, action space and reward function of the environment. Agent is constructed, convolutional neural network is used to approximate Q function,{\epsilon} greedy policy is used for action selection, and experience replay is used for training. The test verifies that the agent can successfully learn optimal policy and realize economic span selection of bridge. This study provides a potential decision-making tool for bridge design.

Many natural language processing tasks, e.g., coreference resolution and semantic role labeling, require selecting text spans and making decisions about them. A typical approach to such tasks is to score all possible spans and greedily select spans for task-specific downstream processing. This approach, however, does not incorporate any inductive bias about what sort of spans ought to be selected, e.g., that selected spans tend to be syntactic constituents. In this paper, we propose a novel grammar-based structured span selection model which learns to make use of the partial span-level annotation provided for such problems. Compared to previous approaches, our approach gets rid of the heuristic greedy span selection scheme, allowing us to model the downstream task on an optimal set of spans. We evaluate our model on two popular span prediction tasks: coreference resolution and semantic role labeling. We show empirical improvements on both.

To align advanced artificial intelligence (AI) with human values and promote safe AI, it is important for AI to predict the outcome of physical interactions. Even with the ongoing debates on how humans predict the outcomes of physical interactions among objects in the real world, there are works attempting to tackle this task via cognitive-inspired AI approaches. However, there is still a lack of AI approaches that mimic the mental imagery humans use to predict physical interactions in the real world. In this work, we propose a novel PIP scheme: Physical Interaction Prediction via Mental Imagery with Span Selection. PIP utilizes a deep generative model to output future frames of physical interactions among objects before extracting crucial information for predicting physical interactions by focusing on salient frames using span selection. To evaluate our model, we propose a large-scale SPACE+ dataset of synthetic video frames, including three physical interaction events in a 3D environment. Our experiments show that PIP outperforms baselines and human performance in physical interaction prediction for both seen and unseen objects. Furthermore, PIP's span selection scheme can effectively identify the frames where physical interactions among objects occur within the generated frames, allowing for added interpretability.

BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA). BERT is pre-trained on two auxiliary tasks: Masked Language Model and Next Sentence Prediction. In this paper we introduce a new pre-training task inspired by reading comprehension and an effort to avoid encoding general knowledge in the transformer network itself. We find significant and consistent improvements over both BERT-BASE and BERT-LARGE on multiple reading comprehension (MRC) and paraphrasing datasets. Specifically, our proposed model has strong empirical evidence as it obtains SOTA results on Natural Questions, a new benchmark MRC dataset, outperforming BERT-LARGE by 3 F1 points on short answer prediction. We also establish a new SOTA in HotpotQA, improving answer prediction F1 by 4 F1 points and supporting fact prediction by 1 F1 point. Moreover, we show that our pre-training approach is particularly effective when training data is limited, improving the learning curve by a large amount.