Reinforced Mnemonic Reader for Machine Reading Comprehension

In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects. First, a reattention mechanism is proposed to refine current attentions by directly accessing to past attentions that are temporally memorized in a multi-round alignment architecture, so as to avoid the problems of attention redundancy and attention deficiency. Second, a new optimization approach, called dynamic-critical reinforcement learning, is introduced to extend the standard supervised method. It always encourages to predict a more acceptable answer so as to address the convergence suppression problem occurred in traditional reinforcement learning algorithms. Extensive experiments on the Stanford Question Answering Dataset (SQuAD) show that our model achieves state-of-the-art results. Meanwhile, our model outperforms previous systems by over 6% in terms of both Exact Match and F1 metrics on two adversarial SQuAD datasets.Comment: Published in 27th International Joint Conference on Artificial Intelligence (IJCAI), 201

Relationship between Crude Oil Prices and Stock Prices of Alternative Energy Companies with Recent Evidence

This paper examines the recent interactive relationships between crude oil prices and stock performances of alternative energy companies. Oil prices and stock index of alternative energy sector are found independent from each other before late 2006. Contrary to existing studies, however, we find significant interdependence between oil prices and stock index of alternative energy industry in the recent years. Since late 2006, oil prices become significantly responsible for the stock performances of alternative energy companies. This finding suggests that the stock market investors of alternative energy sector incorporate oil price shocks into their trading decisions only recently.Crude oil price; Alternative energy; Oil stock index

6,14-Dibromo-2,11-dithia­[3.3]paracyclo­phane

In the title compound, C16H14Br2S2 [systematic name: 12,52-dibromo-2,7-dithia-1,5(1,4)-dibenzenaocta­phane], the cen­troids of the two benzene rings are separated by 3.313 (5) Å. The crystal packing exhibits weak inter­molecular S⋯S contacts of 3.538 (2) Å

NASA-CNSA Collaboration

Due to the Public Law 112-55, Sec. 539 passed by the 112th United States Congress in April 2011, the National Aeronautics and Space Administration (NASA) is prohibited from using funds to host Chinese visitors at NASA facilities. This law restricts NASA scientists from engaging with China’s recent developments in aerospace technology, resulting in the loss of a potential ally and a mutually beneficial partnership. This law stems from a multitude of reasons, including concern for the security of research centers and distrust that the Chinese will only steal American information. NASA and CNSA (China National Space Administration) have both expressed interest in working together despite US law constraining bilateral cooperation. Thus, in order to encourage collaboration to further advance the US’s space program while protecting national security and information, we propose modifying the Public Law 112-55, Sec. 539 to explicitly allow Chinese visitors to attend conferences in the US without the need for Congress to certify each collaboration incident individually. NASA scientists and national security experts, instead of Congress, should evaluate the benefits of the knowledge that may be exchanged. The US economy will not be impacted and national security will remain strong

Explicit Topology Optimization of Conforming Voronoi Foams

Topology optimization is able to maximally leverage the high DOFs and mechanical potentiality of porous foams but faces three fundamental challenges: conforming to free-form outer shapes, maintaining geometric connectivity between adjacent cells, and achieving high simulation accuracy. To resolve the issues, borrowing the concept from Voronoi tessellation, we propose to use the site (or seed) positions and radii of the beams as the DOFs for open-cell foam design. Such DOFs cover extensive design space and have clear geometrical meaning, which makes it easy to provide explicit controls (e.g. granularity). During the gradient-based optimization, the foam topology can change freely, and some seeds may even be pushed out of the shape, which greatly alleviates the challenges of prescribing a fixed underlying grid. The mechanical property of our foam is computed from its highly heterogeneous density field counterpart discretized on a background mesh, with a much improved accuracy via a new material-aware numerical coarsening method. We also explore the differentiability of the open-cell Voronoi foams w.r.t. its seed locations, and propose a local finite difference method to estimate the derivatives efficiently. We do not only show the improved foam performance of our Voronoi foam in comparison with classical topology optimization approaches, but also demonstrate its advantages in various settings, especially when the target volume fraction is extremely low