1,015 research outputs found

    University of Maine presents 3D-printed Dirigo Star to the Maine Bicentennial Commission for bicentennial time capsule

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    The University of Maine Advanced Structures and Composites Center presented a 3D-printed Dirigo Star to the Maine Bicentennial Commission to be the core component of the Maine State Bicentennial Time Capsule

    Delyte Morris\u27 furniture- a collection of mid-century modern ideas

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    In the 1970s, gone were the days of a rapidly increasing student population and major building projects and gone were the days of an only 3,006 student body. Instead, there was a world-class academic university with an environment rooted in innovation and advancement. The pieces from Delyte Morris’ presidency show a dedication to a very specific vision he had: to create a university that is both supporting of the students and pushing towards advancement. He and C. D. May, the associate architect for the university, supported this idea by buying furnishings that were manufactured in Illinois and sticking to a strict budget. By choosing post-WWII, mid-century modern furniture, the interior design of the university appealed to many of the new students at the school; middle-class suburbanites who wanted sophisticated, clean designs without luxury materials. Herman Miller Inc and Knoll Inc provided a large amount of furniture to the university, much of which is now housed in the Delyte Morris Furniture Collection in the University Museum Archives. Even after the end of his presidency, Delyte Morris’ legacy drew on, in part through the Knoll Inc furniture that was acquired because of its connection to handmade fabric and wooden bases. The pieces in this collection directly mirror the progression of Southern Illinois University, and its dedication to being a campus that is constantly expanding, yet with strong Illinois roots

    Pond IDE: Machine level program development environment and register transfer level simulator for a massively parallel computer architecture

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    As computing architectures are being implemented in late and post silicon technologies, fault tolerance and concurrent operation are becoming increasingly important. It is already common knowledge that manufacturers are putting two, four or even more cores on a single silicon die to improve computing performance. The proposed architecture far exceeds this number by grouping thousands or even millions of simple reduced instruction set computing (RISC) processors, each of which is capable of a single operation at a time, and to communicate with its eight nearest neighbors. In this architecture, if a single core or cluster of cores have defects at the time of manufacture, or later in the life of the system, it is possible to test and disable them as necessary. A fine-grained architecture of this kind calls for a parallel programming style. One approach to this problem is the use of a parallelizing compiler. Another approach may be to use one of the several application programming interfaces (APIs) available for standard text based programming languages, with some built-in features for parallel programming. This work has generated a solution for creating machine level parallel programs for the massively parallel computer architecture described above using text and graphical means. To support this programming method, an integrated development environment (IDE) and a zero communication latency, register transfer level (RTL) simulator have been developed. Experimental results include the implementation of fundamental data processing algorithms and complex functions

    The role of reward signal in deep reinforcement learning

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    The goal of the thesis is to study the role of the reward signal in deep reinforcement learning. The reward signal is a scalar quantity received by the agent, and it has a big impact on both the training process of a reinforcement learning algorithm and its resulting behaviour. Firstly, we study the behaviour of an agent that is learning with different reward signals in the same environment with the same learning algorithm. We introduce and measure agents’ happiness as a relation between agents’ actual reward obtained from the environment, as compared to the possible maximum and minimum rewards in a given setting. The experiments show that the rewards intended to result in a given behaviour during training do not result in the same behaviour when agents interact with each other. Secondly, we use these observations to investigate the role of the reward signal further. Namely, we explore the space of all possible reward signals in a given environment through an evolutionary algorithm. Through experiments, we demonstrate that it is possible to learn complex behaviours of winning, losing, and cooperating through reward signal evolution. Some of the solutions found by the algorithm are surprising, in the sense that they would probably not have been chosen by a person trying to hand-code a given behaviour through a specific reward signal. The results presented in the thesis indicate that the role of the reward signal in reinforcement learning is likely bigger than indicated by its current coverage in the literature and is worth investigating in greater detail. Not only can it lead to programmes with less overfitting, but it can also improve our understanding of what reinforcement learning algorithms are really learning. This in turn will give us more robust, explainable, and overall safer systems

    Same/Different Concept Learning and Category Discrimination in Honeybees.

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    Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018

    Analysis of Hardened Concrete for Admixture Content : Interim Report

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