人类世界能够赋予的最高学历,一般被视为进入科研领域和学术圈的门槛。

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报告主题: 信息检索

报告摘要: 引入结构化的知识是目前辅助自然语言处理任务的重要方法之一。如何准确地从自由文本中获取结构化信息,以及进行有效的知识表示在近几年取得了广泛关注。在这次报告中,讲者将梳理知识表示与获取的发展脉络,分享相关领域的最新工作进展,报告人将会以他在知识表示与关系抽取上的若干代表工作为例子,对研究中遇到的具体问题进行深入探讨分析,并结合讲者个人的工作经验,讨论如何体系化地开展研究工作以及学术合作等问题,分享其在解决问题的过程中的一些心得体会。

邀请嘉宾: 韩旭 清华大学计算机系17级博士研究生,来自清华大学自然语言处理组,由刘知远副教授指导,主要研究方向为自然语言处理及信息抽取。目前已在人工智能、自然语言处理等领域的著名国际会议ACL,EMNLP,NAACL,COLING,AAAI发表相关论文多篇,在Github上维护开源工程多项。

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Lattice-based cryptography is one of the leading proposals for post-quantum cryptography. The Shortest Vector Problem (SVP) is arguably the most important problem for the cryptanalysis of lattice-based cryptography, and many lattice-based schemes have security claims based on its hardness. The best quantum algorithm for the SVP is due to Laarhoven [Laa16 PhD] and runs in (heuristic) time $2^{0.2653d + o(d)}$. In this article, we present an improvement over Laarhoven's result and present an algorithm that has a (heuristic) running time of $2^{0.2570 d + o(d)}$ where $d$ is the lattice dimension. We also present time-memory trade-offs where we quantify the amount of quantum memory and quantum random access memory of our algorithm. The core idea is to replace Grover's algorithm used in [Laa16 PhD] in a key part of the sieving algorithm by a quantum random walk in which we add a layer of local sensitive filtering.

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