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

Molecular Characterization of the 14-3-3 Gene Family in Brachypodium distachyon L. Reveals High Evolutionary Conservation and Diverse Responses to Abiotic Stresses

The 14-3-3 gene family identified in all eukaryotic organisms is involved in a wide range of biological processes, particularly in resistance to various abiotic stresses. Here, we performed the first comprehensive study on the molecular characterisation, phylogenetics and responses to various abiotic stresses of the 14-3-3 gene family in Brachypodium distachyon L.. A total of seven 14-3-3 genes from B. distachyon and 120 from five main lineages among 12 species were identified, which were divided into five well-conserved subfamilies. The molecular structure analysis showed that the plant 14-3-3 gene family is highly evolutionarily conserved, although certain divergence had occurred in different subfamilies. The duplication event investigation revealed that segmental duplication seemed to be the predominant form by which the 14-3-3 gene family had expanded. Moreover, seven critical amino acids were detected, which may contribute to functional divergence. Expression profiling analysis showed that BdGF14 genes were abundantly expressed in the roots, but showed low expression in the meristems. All seven BdGF14 genes showed significant expression changes under various abiotic stresses, including heavy metal, phytohormone, osmotic, and temperature stresses, which might play important roles in responses to multiple abiotic stresses mainly through participating in ABA-dependent signalling and reactive oxygen species-mediated MAPK cascade signalling pathways. In particular, BdGF14 genes generally showed upregulated expression in response to multiple stresses of high temperature, heavy metal, abscisic acid (ABA), and salicylic acid (SA), but downregulated expression under H2O2, NaCl, and polyethylene glycol (PEG) stresses. Meanwhile, dynamic transcriptional expression analysis of BdGF14 genes under longer treatments with heavy metals (Cd2+, Cr3+, Cu2+, and Zn2+) and phytohormone (ABA) and recovery revealed two main expression trends in both roots and leaves: up-down and up-down-up expression from stress treatments to recovery. This study provides new insights into the structures and functions of plant 14-3-3 genes

GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer

We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods have shown great potential through bypassing the detection of repeatable keypoints which is difficult to do especially in low-overlap scenarios. They seek correspondences over downsampled superpoints, which are then propagated to dense points. Superpoints are matched based on whether their neighboring patches overlap. Such sparse and loose matching requires contextual features capturing the geometric structure of the point clouds. We propose Geometric Transformer, or GeoTransformer for short, to learn geometric feature for robust superpoint matching. It encodes pair-wise distances and triplet-wise angles, making it invariant to rigid transformation and robust in low-overlap cases. The simplistic design attains surprisingly high matching accuracy such that no RANSAC is required in the estimation of alignment transformation, leading to $100$ times acceleration. Extensive experiments on rich benchmarks encompassing indoor, outdoor, synthetic, multiway and non-rigid demonstrate the efficacy of GeoTransformer. Notably, our method improves the inlier ratio by $18{\sim}31$ percentage points and the registration recall by over $7$ points on the challenging 3DLoMatch benchmark. Our code and models are available at \url{https://github.com/qinzheng93/GeoTransformer}.Comment: Accepted by TPAMI. Extended version of our CVPR 2022 paper [arXiv:2202.06688

Short Paper: An Exploration of Code Diversity in the Cryptocurrency Landscape

Interest in cryptocurrencies has skyrocketed since their introduction a decade ago, with hundreds of billions of dollars now invested across a landscape of thousands of different cryptocurrencies. While there is significant diversity, there is also a significant number of scams as people seek to exploit the current popularity. In this paper, we seek to identify the extent of innovation in the cryptocurrency landscape using the open-source repositories associated with each one. Among other findings, we observe that while many cryptocurrencies are largely unchanged copies of Bitcoin, the use of Ethereum as a platform has enabled the deployment of cryptocurrencies with more diverse functionalities