Learned Garbage Collection

Several programming languages use garbage collectors (GCs) to automatically manage memory for the programmer. Such collectors must decide when to look for unreachable objects to free, which can have a large performance impact on some applications. In this preliminary work, we propose a design for a learned garbage collector that autonomously learns over time when to perform collections. By using reinforcement learning, our design can incorporate user-defined reward functions, allowing an autonomous garbage collector to learn to optimize the exact metric the user desires (e.g., request latency or queries per second). We conduct an initial experimental study on a prototype, demonstrating that an approach based on tabular Q learning may be promising

Reshaping Buffalo\u27s Recycling Initiatives

The city of Buffalo recycles approximately eight percent of its curbside waste per year. This is far below the national average of 27% and pales by comparison with cities such as San Francisco, which recycles at a rate of 72%. Within Western New York, there is also great disparity in regard to recycling. The Town of Tonawanda, to give one example, currently recycles 13.5% of its curbside waste

The Economics of Residential Solid Waste Management

This paper provides a broad overview of recent trends in solid waste and recycling, related public policy issues, and the economics literature devoted to these topics. Public attention to solid waste and recycling has increased dramatically over the past decade both in the United States and in Europe. In response, economists have developed models to help policy makers choose the efficient mix of policy levers to regulate solid waste and recycling activities. Economists have also employed different kinds of data to estimate the factors that contribute to the generation of residential solid waste and recycling and to estimate the effectiveness of many of the policy options employed.

The Dawn of Fully Automated Contract Drafting: Machine Learning Breathes New Life Into a Decades-Old Promise

Technological advances within contract drafting software have seemingly plateaued. Despite the decades-long hopes and promises of many commentators, critics doubt this technology will ever fully automate the drafting process. But, while there has been a lack of innovation in contract drafting software, technological advances have continued to improve contract review and analysis programs. “Machine learning,” the leading innovative force in these areas, has proven incredibly efficient, performing in mere minutes tasks that would otherwise take a team of lawyers tens of hours. Some contract drafting programs have already experimented with machine learning capabilities, and this technology may pave the way for the full automation of contract drafting. Although intellectual property, data access, and ethical obstacles may delay complete integration of machine learning into contract drafting, full automation is likely still viable

Picturing impact of the PEDIGREA program: a case study from Indramayu, Indonesia

Over the last twelve years, FIELD Indonesia staff has been using various participatory approaches towards measuring impact of its interventions, mainly in the framework of its involvement under FAO Community Integrated Pest Management (IPM) in Asia program. Since 2002, FIELD is one of the partners in the PEDIGREA program, focusing on participatory crop and farm animal improvement. PEDIGREA is a regional program on farmer’s management of genetic resources, i.e. rice, local vegetables and poultry, which is implemented by three NGOs in Philippines, Cambodia and Indonesia, and supported by Wageningen UR, FAO, and IPGRI APO. The first attempt in 1991 (the development of three IPM Village Profiles) involved having farmers draw and discuss the benefits of participation in a Farmer Field School (FFS). Other approaches are relying on aerial planning and interactive participation techniques, iterative appraisal approaches, and socio-economic impacts. Comparing these approaches reveals that a wide scope of options for monitoring impact is available. Here we report on the results of an impact assessment method that appeared highly practical as a participatory tool: a participatory and interactive perception measuring technique for which farmers were asked to analyze the impact of the PEDIGREA program activities in their villages by making a photograph series of the project results and discussing the photographs in the community. The process distinguishes three steps: a) a three days workshop with farmer representatives from each group/village to discuss the concept of project results and impacts, to learn how to take useful photographs, and to make a work plan of objects and situations for each village to be photographed; b) a two week period of activities in each village to take photos, to select the interesting pictures, and to write the explanatory notes for each of the photos; c) a three days workshop to finalize the notes for each picture, to reflect on the program impacts and farmer’s benefits, to evaluate the impact study process, and to develop follow-up plan for each group/village. Some of the major results as visualized in the impact monitoring approach include: other farmers in the villages started to learn the breeding process from the farmer participants in the FFS; other farmers started to ask for and plant the local vegetable seeds, e.g. luffa and bitter gourd, which resulted from breeding activities in the village; better prices in local market for luffa produce by the farmer participants were realized; and some village authorities provided resources to the groups to conduct local field studies