Hampshire College

About: Hampshire College is a education organization based out in Amherst Center, Massachusetts, United States. It is known for research contribution in the topics: Genetic programming & Population. The organization has 461 authors who have published 998 publications receiving 40827 citations.

A feminist exploration of ‘populationism’: engaging contemporary forms of population control

Abstract: Following the International Conference on Population and Development in 1994 in Cairo, which prompted a discursive shift from population control to reproductive health and rights in international d...

Quantum circuits for OR and AND of ORs

Abstract: We give the first quantum circuit for computing f( 0)OR f( 1)more reliably than is classically possible with a single evaluation of the function. OR therefore joins XOR (i.e. parity, f( 0)f( 1)) to give the full set of logical connectives (up to relabelling of inputs and outputs) for which there is quantum speedup.

Beyond isohydricity: The role of environmental variability in determining plant drought responses

Abstract: Despite the appeal of the iso/anisohydric framework for classifying plant drought responses, recent studies have shown that such classifications can be strongly affected by a plant's environment. Here, we present measured in situ drought responses to demonstrate that apparent isohydricity can be conflated with environmental conditions that vary over space and time. In particular, we (a) use data from an oak species (Quercus douglasii) during the 2012-2015 extreme drought in California to demonstrate how temporal and spatial variability in the environment can influence plant water potential dynamics, masking the role of traits; (b) explain how these environmental variations might arise from climatic, topographic, and edaphic variability; (c) illustrate, through a "common garden" thought experiment, how existing trait-based or response-based isohydricity metrics can be confounded by these environmental variations, leading to Type-1 (false positive) and Type-2 (false negative) errors; and (d) advocate for the use of model-based approaches for formulating alternate classification schemes. Building on recent insights from greenhouse and vineyard studies, we offer additional evidence across multiple field sites to demonstrate the importance of spatial and temporal drivers of plants' apparent isohydricity. This evidence challenges the use of isohydricity indices, per se, to characterize plant water relations at the global scale.

High-performance, parallel, stack-based genetic programming

Abstract: HiGP is a new high-performance genetic programming system. This system combines techniques from string-based genetic algorithms, S-expression-based genetic programming systems, and high-performance parallel computing. The result is a fast, flexible, and easily portable genetic programming engine with a clear and efficient parallel implementation. HiGP manipulates and produces linear programs for a stack-based virtual machine, rather than the tree-structured S-expressions used in traditional genetic programming. In this paper we describe the HiGP virtual machine and genetic programming algorithms. We demonstrate the system's performance on a symbolic regression problem and show that HiGP can solve this problem with substantially less computational effort than can a traditional genetic programming system. We also show that HiGP's time performance is significantly better than that of a well-written S-expression-based system, also written in C. We further show that our parallel version of HiGP achieves a speedup that is nearly linear in the number of processors, without mandating the use of localized breeding strategies.