441 research outputs found
Optimal Contract Length for Voluntary Land Conservation Programs
In many parts of the world, deteriorating environmental conditions have led policy makers to develop policies and programs aimed at promoting conservation practices on lands devoted to agriculture. Such programs have been studied by environmental economists, but little research has been done on the usefulness of strategically varying the conservation contract's length. This paper uses theory and simulation to investigate the optimal contract length of land conservation programs when a policy maker tries to maximize the present discounted value of the stream of environmental benefits from the program. We find that contract length should vary with characteristics of the ecological processes that yield benefits from land retirement. Optimal contracts are longer when the environmental benefits in question things like woodland biodiversity take time to develop. However, it is not typically optimal to have the indefinitelylived contracts favored by some conservation groups, or even to offer contracts as long as the maturation period for the environmental services in question. In general, the optimal contract length depends on the trade off between an ecological effect (increasing the environmental benefits from one farmer) and an enrollment effect (increasing the number of farmers enrolled). Our findings also suggest that non-ecological regional characteristics (such as turnover rate and average farm income) could play an important role in the design of conservation programs.Environmental Economics and Policy,
IONet: Learning to Cure the Curse of Drift in Inertial Odometry
Inertial sensors play a pivotal role in indoor localization, which in turn
lays the foundation for pervasive personal applications. However, low-cost
inertial sensors, as commonly found in smartphones, are plagued by bias and
noise, which leads to unbounded growth in error when accelerations are double
integrated to obtain displacement. Small errors in state estimation propagate
to make odometry virtually unusable in a matter of seconds. We propose to break
the cycle of continuous integration, and instead segment inertial data into
independent windows. The challenge becomes estimating the latent states of each
window, such as velocity and orientation, as these are not directly observable
from sensor data. We demonstrate how to formulate this as an optimization
problem, and show how deep recurrent neural networks can yield highly accurate
trajectories, outperforming state-of-the-art shallow techniques, on a wide
range of tests and attachments. In particular, we demonstrate that IONet can
generalize to estimate odometry for non-periodic motion, such as a shopping
trolley or baby-stroller, an extremely challenging task for existing
techniques.Comment: To appear in AAAI18 (Oral
Autonomous learning for face recognition in the wild via ambient wireless cues
Facial recognition is a key enabling component for emerging Internet of Things (IoT) services such as smart homes or responsive offices. Through the use of deep neural networks, facial recognition has achieved excellent performance. However, this is only possibly when trained with hundreds of images of each user in different viewing and lighting conditions. Clearly, this level of effort in enrolment and labelling is impossible for wide-spread deployment and adoption. Inspired by the fact that most people carry smart wireless devices with them, e.g. smartphones, we propose to use this wireless identifier as a supervisory label. This allows us to curate a dataset of facial images that are unique to a certain domain e.g. a set of people in a particular office. This custom corpus can then be used to finetune existing pre-trained models e.g. FaceNet. However, due to the vagaries of wireless propagation in buildings, the supervisory labels are noisy and weak. We propose a novel technique, AutoTune, which learns and refines the association between a face and wireless identifier over time, by increasing the inter-cluster separation and minimizing the intra-cluster distance. Through extensive experiments with multiple users on two sites, we demonstrate the ability of AutoTune to design an environment-specific, continually evolving facial recognition system with entirely no user effort
Biologically Plausible Learning on Neuromorphic Hardware Architectures
With an ever-growing number of parameters defining increasingly complex
networks, Deep Learning has led to several breakthroughs surpassing human
performance. As a result, data movement for these millions of model parameters
causes a growing imbalance known as the memory wall. Neuromorphic computing is
an emerging paradigm that confronts this imbalance by performing computations
directly in analog memories. On the software side, the sequential
Backpropagation algorithm prevents efficient parallelization and thus fast
convergence. A novel method, Direct Feedback Alignment, resolves inherent layer
dependencies by directly passing the error from the output to each layer. At
the intersection of hardware/software co-design, there is a demand for
developing algorithms that are tolerable to hardware nonidealities. Therefore,
this work explores the interrelationship of implementing bio-plausible learning
in-situ on neuromorphic hardware, emphasizing energy, area, and latency
constraints. Using the benchmarking framework DNN+NeuroSim, we investigate the
impact of hardware nonidealities and quantization on algorithm performance, as
well as how network topologies and algorithm-level design choices can scale
latency, energy and area consumption of a chip. To the best of our knowledge,
this work is the first to compare the impact of different learning algorithms
on Compute-In-Memory-based hardware and vice versa. The best results achieved
for accuracy remain Backpropagation-based, notably when facing hardware
imperfections. Direct Feedback Alignment, on the other hand, allows for
significant speedup due to parallelization, reducing training time by a factor
approaching N for N-layered networks
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