Sarah Chasins

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We developed Chlorophyll, a synthesis-aided programming model and compiler for the GreenArrays GA144, an extremely minimalist low-power spatial architecture that requires partitioning the program into fragments of no more than 256 instructions and 64 words of data. This processor is 100-times more energy efficient than its competitors, but currently can(More)
With increasing amounts of data available on the web and a diverse range of users interested in programmatically accessing that data, web automation must become easier. Automation helps users complete many tedious interactions, such as scraping data, completing forms, or transferring data between websites. However, writing web automation scripts typically(More)
The Plaid language introduces native support for state abstractions and state change. While efficient language implementation typically relies on stable object members, state change alters members at runtime. We built a JavaScript compilation target with a novel state representation, which enables fast member access. Cross-language performance comparisons(More)
Synthetic biology makes biology engineerable. One objective of this engineering is to modify the chemical reactions within the cell, i.e., the biochemistry, to produce non-native compounds of commercial interest. To do this at scale, ideas from language design, verification, and synthesis will be useful. In this talk, we present our lessons learnt, future(More)
Much work has been done in the area of monitoring on traditional systems, such as servers, workstations and laptops. User and application behavior has also been studied on a wide range of platforms. Recently, smartphones have seen a dramatic increase in availability and adoption. New monitoring tools are needed to handle the unique demands of these mobile(More)
In this paper, we examine the effect of fitness functions on the ability of a robot evolved with NEAT (NeuroEvolution of Augmenting Topologies) to find a light in a simple maze. By varying the fitness function used to determine a genotype’s likelihood of persisting in the next generation, we propose to look at how a robot’s solution to a task is influenced(More)
In this paper, we explore diverse methods of unsupervised morphemic segmentation. We test Successor and Predecessor Count algorithms, Entropy algorithms, and Affix Discovery algorithms. The paper examines word stemming based on these algorithms, and the influence of training corpus size on segmentation accuracy. We propose variations on these algorithms to(More)