2,362 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Canada\u27s Evergreen Playground: A History of Snow in Vancouver

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    The City of Vancouver is not as snowy as the rest of Canada; rain, not snow, is its defining weather feature. But snow is a common seasonal occurrence, having fallen there nearly every winter since the 1850s. This dissertation places snow at the centre of the City of Vancouver’s history. It demonstrates how cultural and natural factors influenced human experiences and relationships with snow on the coast between the 1850s and 2000s. Following Vancouver’s incorporation, commercial and civic boosters constructed – and settlers adopted – what I call an evergreen mentality. Snow was reconceptualized as a rare and infrequent phenomenon. The evergreen mentality was not completely false, but it was not entirely true, either. This mindset has framed human relationships with snow in Vancouver ever since. While this idea was consistent, how coastal residents experienced snow evolved in response to societal developments (such as the rise of the automobile and the adoption of new snow-clearing technologies) and regional climate change. I show that the history of snow in Vancouver cannot be fully understood without incorporating the southern Coast Mountains. Snow was a connecting force between the coastal metropolis and mountainous hinterland. Settlers drew snowmelt to the urban environment for its energy potential and life-sustaining properties; snow drew settlers to the mountains for recreation and economic opportunities. Mountain snow became a valuable resource for coastal residents throughout the twentieth century. Human relationships with snow in the mountains were shaped, as they were in the city, by seasonal expectations, societal circumstances, and shifting climate conditions. In charting a history of snow in Vancouver and the southern Coast Mountains, this dissertation clears a new path in Canadian environmental historiography by bringing snow to the historiographical forefront. It does so in an urban space not known for snow, broadening the existing geography of snow historiography. In uncovering snow’s impact on year-round activities, this work also expands the field’s temporal boundaries. Through this work, one sees how snow helped to make Canada’s Evergreen Playground

    Supporting Safety Analysis of Deep Neural Networks with Automated Debugging and Repair

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    Essays in the economics and econometrics of networks and peer effect

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    Defence date: 23 May 2023Examining Board: Prof. Andrea Ichino, (European University Institute, supervisor); Prof. Sule Alan, (European University Institute, co-supervisor); Prof. Eric Auerbach, (Northwestern University); Prof. Yann Bramoullé, (Aix-Marseille School of Economics)This thesis contributes to the understanding of peer effects, both methodologically and empirically. The endogeneity of network formation has been a major obstacle to the study of peer influence. The first and the second chapters of the thesis propose a causal identification solution in the potential outcome framework. Combining results from multiple causal inference and statistical network analysis, I show that confounding can be addressed by inferring propensity scores of network link formation from the adjacency matrix. This identification strategy imposes minimum restrictions on the data-generating process and, unlike existing econometric solutions, does not rely on any parametric modelling. As an application, I estimate the effect of high school friendships on bachelor’s degree attainment. While previous literature finds that exposure to more high-achieving boys makes girls less likely to obtain a bachelor’s degree, I show that if the girls consider the boys as friends, their interactions induce a positive impact instead. Since friendship endogeneity has been addressed, the estimated effect is causal. The third chapter looks at the peer effects generated by group competition. It focuses on the gender differences in preference for competition in a setting where the competition does not involve face-to-face confrontation, and effort is the only determinant of the final ranking. I first develop a model of group competition with heterogeneous preference for ranking. With empirical implications generated from the theoretical model, I then test the gender difference in the preference parameter using web-scraped data from Duolingo, a free online foreign-language learning platform with over 300 million users. Every week, language learners on Duolingo are randomly allocated to groups of 30 people to compete on the number of language lessons completed during that week. The empirical results suggest in this setting, females have a stronger preference for ranking than males.1. The linking effect: causal identification and estimation of the effect of peer relationship -- 2. Extensions, theoretical proofs, and additional results on the linking effect -- 3. Gender difference in preference for competition -- 4. Reference

    2017 GREAT Day Program

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    SUNY Geneseo’s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp

    Optimisation for Optical Data Centre Switching and Networking with Artificial Intelligence

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    Cloud and cluster computing platforms have become standard across almost every domain of business, and their scale quickly approaches O(106)\mathbf{O}(10^6) servers in a single warehouse. However, the tier-based opto-electronically packet switched network infrastructure that is standard across these systems gives way to several scalability bottlenecks including resource fragmentation and high energy requirements. Experimental results show that optical circuit switched networks pose a promising alternative that could avoid these. However, optimality challenges are encountered at realistic commercial scales. Where exhaustive optimisation techniques are not applicable for problems at the scale of Cloud-scale computer networks, and expert-designed heuristics are performance-limited and typically biased in their design, artificial intelligence can discover more scalable and better performing optimisation strategies. This thesis demonstrates these benefits through experimental and theoretical work spanning all of component, system and commercial optimisation problems which stand in the way of practical Cloud-scale computer network systems. Firstly, optical components are optimised to gate in ≈500ps\approx 500 ps and are demonstrated in a proof-of-concept switching architecture for optical data centres with better wavelength and component scalability than previous demonstrations. Secondly, network-aware resource allocation schemes for optically composable data centres are learnt end-to-end with deep reinforcement learning and graph neural networks, where 3×3\times less networking resources are required to achieve the same resource efficiency compared to conventional methods. Finally, a deep reinforcement learning based method for optimising PID-control parameters is presented which generates tailored parameters for unseen devices in O(10−3)s\mathbf{O}(10^{-3}) s. This method is demonstrated on a market leading optical switching product based on piezoelectric actuation, where switching speed is improved >20%>20\% with no compromise to optical loss and the manufacturing yield of actuators is improved. This method was licensed to and integrated within the manufacturing pipeline of this company. As such, crucial public and private infrastructure utilising these products will benefit from this work

    Reasoning about quantities and concepts: studies in social learning

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    We live and learn in a ‘society of mind’. This means that we form beliefs not just based on our own observations and prior expectations but also based on the communications from other people, such as our social network peers. Across seven experiments, I study how people combine their own private observations with other people’s communications to form and update beliefs about the environment. I will follow the tradition of rational analysis and benchmark human learning against optimal Bayesian inference at Marr’s computational level. To accommodate human resource constraints and cognitive biases, I will further contrast human learning with a variety of process level accounts. In Chapters 2–4, I examine how people reason about simple environmental quantities. I will focus on the effect of dependent information sources on the success of group and individual learning across a series of single-player and multi-player judgement tasks. Overall, the results from Chapters 2–4 highlight the nuances of real social network dynamics and provide insights into the conditions under which we can expect collective success versus failures such as the formation of inaccurate worldviews. In Chapter 5, I develop a more complex social learning task which goes beyond estimation of environmental quantities and focuses on inductive inference with symbolic concepts. Here, I investigate how people search compositional theory spaces to form and adapt their beliefs, and how symbolic belief adaptation interfaces with individual and social learning in a challenging active learning task. Results from Chapter 5 suggest that people might explore compositional theory spaces using local incremental search; and that it is difficult for people to use another person’s learning data to improve upon their hypothesis
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