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关键词： Robotics   Optics   Artificial intelligence   Computer science   Electrical engineering   Energy   Engineering   Mathematics   Physics  

摘要： In recent years, the use of integrated photonic systems for information processing has gathered significant interest as an alternative to conventional electronic computer architectures. The emerging field of neuromorphic photonics proposes to implement high-performance neural networks and related machine learning algorithms using electro-optic circuits, as these applications require high bandwidth, low latency, and low energy consumption. In this thesis, we present a novel photonic architecture for training neural networks on-chip using the direct feedback alignment algorithm. The architecture can operate at speeds of trillions of multiply-accumulate (MAC) operations per second while consuming less than one picojoule per MAC operation. We also introduce and simulate a photonic architecture for solving ordinary and partial differential equations using recurrent neural networks. Finally, we introduce a new computational method for self-consistent electrodynamics simulations of Lorentz oscillators and moving point charges, which can be integrated with neuromorphic photonics to harness potential improvements in speed and power when solving the simulation's governing differential equations. The high energy efficiency and operation speed of neuromorphic photonics could enable the development of innovative neural network applications that would be impossible to operate on current generation hardware.

关键词： SOLO分类理论   复习教学设计   Computer Science   行动研究  

摘要： 近年来,信息技术的迅猛发展对于社会生活产生了极大的影响,信息技术人才的需求也越来越大,而国际学校作为我国教育构成的重要部分对于信息技术人才的培养也做出了不小的贡献,鉴于Computer Science(即计算机科学)课程地位的提高,考纲的更新对于课程的复习带来了新的挑战,如何提高复习的效率和积极性,培养学生的学习信心,存在较大的困惑。SOLO分类理论是由教育心理学家Biggs和Collis提出的通过对学习结果的观察可以分析学生所处的认知水平,将其分为五种水平。本文以该理论为基础,结合Computer Science学科特点,分析考纲的变化,根据样卷的题型分布,选择合适的复习策略对复习课进行教学设计,增加学生对该课程的学习信心,提高学生学习的主观能动性。本研究选取上海某外国语学校的A1年级选择Computer Science课程的学生为实验班研究对象,用行动研究法进行三轮迭代,同时设置另一班级作为对照班,设计主观能动性、学习信心问卷以及对学生和家长进行了访谈,并基于SOLO分类理论设计了每轮教学实验前后测的试题,对数据进行描述性统计、T检验,分析数据,从提问策略、课堂互动、专题复习、考试题型复习以及作业布置等方面进行复习课的教学设计优化。经过三轮教学迭代,通过实验班和对照班的数据对比发现,基于SOLO分类理论的Computer Science复习课教学设计对于学生的成绩有显著的提高,且对处于不同水平结构的学生在不同水平的题目的得分率的提高有了可观的变化;另外,学生对于该课程的学习信心在三轮迭代研究后呈现显著性的提高,增加了学生学习的主观能动性。最后,本文从研究对象、课堂活动的诊断以及对往年试卷的解析分析了不足,并对教师在未来新授课程、研究对象和教学的信息化三方面方面进行了展望,希望本研究成果能给复习课的教学带来新的思考。

关键词： C (programming language)  

摘要： Information technology and artificial intelligence have promoted the informatization development of education and made many changes in the teaching mode. Especially in the era of artificial intelligence, the combination of human-machineassisted education teaching has become an optional and efficient teaching mode. As a teaching tool for the transformation of teaching methods empowered by artificial intelligence technology, educational robots have consistent influences on the learning effect of learners, which is the focus of educational technology. As the leading or auxiliary teaching, educational robots can meet the various educational needs of different groups of people and improve the learning performance of learners. This study conducts experimental research on learners majoring in computer science and explores the influences of educational robot-assisted teaching (the experimental group) and traditional classroom teaching (control group) on the learning performance of learners in C language programming. Results prove that no significance exists between different classes in pretest theory scores and pretest practice scores. A significant difference at 0.01 is observed between the pre-test and post-test theory scores in the experimental group (t=−3.891, p=0.001), and a significant difference at 0.01 exists between the pretest and post-test practice scores in the experimental group (t=−3.894, p=0.001). Findings of this study have important reference values for inspiring how educational robots and teachers collaborate to improve teaching effectiveness. © 2022 School of Science, IHU. All rights reserved

关键词： Artificial intelligence   Computer science   Deep learning   Natural language processing   Source code analysis  

摘要： Analyzing source code is becoming a helpful method as the amount of code and the number of coders increases. Developing software is a mentally strenuous activity because it involves understanding and formalizing solutions to complex problems. As solutions are adapted and the problem themselves change, the code changes with it. Traditionally, source code analysis (SCA) uses methods and tools that rely on the rigid structure of code to work, and while useful, are not always able to adapt to new information and code. By comparison, the natural language processing (NLP) field leverages naturally occurring patterns within natural language text to help people understand and interpret text robustly on a large scale. This works shows that methods from NLP can be applied to code to extract information automatically in broad and more adaptable ways. We show several examples of how techniques developed for NLP can be successfully applied to software to create source code tools that can help developers improve source code.

关键词： 大数据   计算机科学   教学方案  

摘要： 阐述大数据与计算机科学技术的教学方案，包括教学内容的改革、实践教学改革、教学方式的实践，通过翻转平台，学生可以对自己的项目作业进行系统化的展示。

关键词： Algebra   Polynomials   Phylogenetics   Algorithms   Geometry   Computer science   Genetics   Mathematics   Systematic biology  

摘要： Algebraic statistics uses tools from algebra, geometry, and combinatorics to study problems in statistics. This is often done by studying algebraic varieties which arise as the Zariski closure of a statistical model. In this thesis we mainly focus on statistical models and probability distributions which comes from phylogenetics. First we study the family of exchangeable and sampling consistent distributions on rooted binary trees with labelled leaves, which we call phylogenetic trees. We begin by introducing a notion of finite sampling consistency for phylogenetic trees and show that the set of finitely sampling consistent and exchangeable distributions on n leaf phylogenetic trees is a polytope. We then study the vertices of this polytope for trees with 4 and 5 leaves. We also introduce a new semialgebraic set of exchangeable and sampling consistent models we call the multinomial model and use it to characterize the set of exchangeable and sampling consistent distributions. Using this new model, we obtain a finite de Finetti-type theorem for rooted binary trees. Next, we discuss a new algorithm for proving that discrete parameters of parametric algebraic statistical models are identifiable. Identifiability is a particularly important property for statistical models to have since it ensures that the parameters can be reliably recovered from data. Our method uses algebraic matroids which are naturally associated to the model and by doing so avoids time consuming Gr¨obner basis calculations. We then use this method to solve some previously open identifiability problems from phylogenetics. Lastly, we study the vanishing ideal of the Cavendar-Farris-Neyman (CFN) model on level-1 phylogenetic networks. We show that these ideals are multigraded and use this multigrading to break up the ideal into homogeneous pieces called gloves. We then give an explicit description of the quadratic polynomials in each glove which we conjecture generate the entire ideal.

关键词： Probability   Algorithms   Computer science   Mathematics  

摘要： The primary focus of this thesis is on improving the operation of terrain relative navigation (TRN) in areas with incomplete map information. TRN is a navigation algorithm for vehicles operating in GPS denied environments. It operates by correlating measurements of the terrain to a prior terrain map in order to localize the vehicle. To address self-similarity between different sections of the terrain, TRN uses a non-parametric sample based representation of the belief distribution of vehicle locations such as a particle filter. The basic TRN measurement update requires map information for every particle in order to perform the TRN correlation step. When a subset of the particles has projected measurements that fall in an unmapped area, these off-map particles create an incomplete map problem. This thesis addresses the incomplete map problem through a new measurement update for TRN. This is called the Subcloud measurement update as it performs different updates for the on-map and off-map subsets of the particle cloud. At the core of the Subcloud measurement update is a new measurement probability for the off-map particles which is derived from the distribution of terrain heights in the off-map area. When this distribution is correct and other standard TRN assumptions are met, the Subcloud measurement update provably produces the correct posterior distribution in the on-map area. This means TRN will converge correctly when the vehicle is on-map and will correctly identify when the vehicle is off-map. For more realistic situations where the true distribution of terrain heights is not known, this thesis develops bounds on the safe operation and a modification to the Subcloud measurement update which guarantees the Subcloud measurement update will remain within these safe bounds. Finally, working with the Monterey Bay Aquarium Research Institute (MBARI), the Subcloud measurement update is demonstrated in the field for autonomous return-to-site missions with incomplete ma

关键词： Bay Area region   California State University   Female students   Minority students   Computer science   Engineering  

摘要： Over the last decade the Bay Area region has witnessed a lack of diversity in Silicon Valley technology professions, a subject of current political and popular discourse. The California State University (CSU) system provides accessible higher education; the CSU system approach to accessible higher education affirms the distinctness of regions, prepares graduates for professions in these distinct regions, and affirms the diversity in the state and the world. The CSU fall 2019–2021 enrollment dashboard data indicated there was a decrease in enrollment of underrepresented minority (URM) in graduate programs across three Bay Area campuses. Public fall 20119–2021 enrollment data disaggregated by degree program indicates URM participation in Computer and Information Sciences decreased to less than 10% of the graduate student body across the three Bay Area campuses; URM enrollment levels a decrease to approximately 10% at the graduate level in Engineering. Graduate education is a lever of social mobility, professional advancement, and provides opportunities to access knowledge economy professions. The Bay Area CSU campuses are situated at the nexus of the knowledge economy. The decrease in URM student participation in related disciplines at the graduate level is a problem of practice that raises questions of the role and mission of the CSU. Equity in opportunity in a regional economy dominated by technology professions intersects with CSU graduate education as a lever of social change and social mobility. Underrepresentation of minorities in technology professions and in graduate education is not without social consequences. A homogenous Bay Area knowledge economy workforce may result in new layers of digital infrastructures and applications that reinforce dominant cultural perspectives and biases. This qualitative research study explores the experiences, perceptions, and attitudes of URM graduate students in Computer Science and Engineering (CSE) disciplines at three publ

关键词： Heuristic algorithms   Predictive models   Feature extraction   Prediction algorithms   Network topology   Deep learning   Computer science   Named data networking   deep learning   graph neural network   content caching probability   cache replacement  

摘要： Named Data Networking (NDN) is a new architecture with in-network caching ability. NDN nodes can cache data packets in their cache store to satisfy further requests. Accurately caching popular content across the network is essential for NDN to reduce the traffic workload and improve network efficiency. However, traditional caching algorithms are not good at predicting future dynamic content popularity. In our paper, we propose a Graph Neural Network-based (GNN-based) caching strategy to optimize the caching performance in NDN. First, our paper utilizes a Convolutional Neural Network (CNN) to extract time-series features for each NDN node. Secondly, GNN is applied to make content caching probability predictions for each NDN node. Third, at each NDN node, a cache replacement decision is made based on its content caching probability ranking, and content with high caching probability replaces content with low probability. We compare our GNN-based caching strategy with three deep learning-based caching techniques, which are 1D-Convolutional Neural Network (1D-CNN), Long Short-Term Memory Encoder-Decoder (LSTM-ED), and Staked Auto Encoder (SAE), and three classical benchmark caching strategies, which are Least Frequently Used (LFU), Least Recently Used (LRU) and First-in-first-out (FIFO). All caching scenarios are simulated in the Mini-NDN platform and evaluated on the tree and arbitrary network topologies. Empirical results suggest that the GNN-based caching approach can achieve around a 50% higher cache hit ratio and a 30% lower latency in the best case than other deep learning-based caching strategies.

关键词： Computers   artificial intelligence   general   computers   computer science   games \u0026 activities   chess  

ISBN: (数字)9780691235141 ISBN: (纸本)9780691235134

摘要： The riveting quest to construct the machine that would take on the world’s greatest human chess player—told by the man who built itOn May 11, 1997, millions worldwide heard news of a stunning victory, as a machine defeated the defending world chess champion, Garry Kasparov. Behind Deep Blue tells the inside story of the quest to create the mother of all chess machines and what happened at the two historic Deep Blue vs. Kasparov matches. Feng-hsiung Hsu, the system architect of Deep Blue, reveals how a modest student project started at Carnegie Mellon in 1985 led to the production of a multimillion-dollar supercomputer. Hsu discusses the setbacks, tensions, and rivalries in the race to develop the ultimate chess machine, and the wild controversies that culminated in the final triumph over the world"s greatest human player. With a new foreword by Jon Kleinberg and a new preface from the author, Behind Deep Blue offers a remarkable look at one of the most famous advances in artificial intelligence, and the brilliant toolmaker who invented it.