Less is more: energy-efficient mobile sensing with SenseLess

Workshop held as part of ACM SIGCOMM 2009We present SenseLess, a system that leverages the different energy consumption characteristics of sensors to maximise battery life in mobile-sensing applications. We use the less expensive sensors more often, thereby enabling us to use the more expensive sensors less frequently. In the context of location-aware services, experimental results indicate that for a typical indoor and outdoor walk, compared to a simple GPS-based system, our SenseLess system can reduce energy consumption by more than 58% when determining a user's location, while maintaining the fidelity of the sensed data. This extends the battery life of a typical handheld device from 9 hours to 22 hours.Postprin

Testing the efficiency of broadacre farms

A study of 93 broadacre farms has revealed that most farms display high levels of technical efficiency. On average, technical efficiency is improving, although a small proportion of farms remain relatively inefficient due to a number of factors

The diameter of the set of boundary slopes of a knot

Let K be a tame knot with irreducible exterior M(K) in a closed, connected, orientable 3--manifold Sigma such that pi_1(Sigma) is cyclic. If infinity is not a strict boundary slope, then the diameter of the set of strict boundary slopes of K, denoted d_K, is a numerical invariant of K. We show that either (i) d_K >= 2 or (ii) K is a generalized iterated torus knot. The proof combines results from Culler and Shalen [Comment. Math. Helv. 74 (1999) 530-547] with a result about the effect of cabling on boundary slopes.Comment: This is the version published by Algebraic & Geometric Topology on 29 August 200

Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification

The variety of complex algorithmic approaches for tackling time-series classification problems has grown considerably over the past decades, including the development of sophisticated but challenging-to-interpret deep-learning-based methods. But without comparison to simpler methods it can be difficult to determine when such complexity is required to obtain strong performance on a given problem. Here we evaluate the performance of an extremely simple classification approach -- a linear classifier in the space of two simple features that ignore the sequential ordering of the data: the mean and standard deviation of time-series values. Across a large repository of 128 univariate time-series classification problems, this simple distributional moment-based approach outperformed chance on 69 problems, and reached 100% accuracy on two problems. With a neuroimaging time-series case study, we find that a simple linear model based on the mean and standard deviation performs better at classifying individuals with schizophrenia than a model that additionally includes features of the time-series dynamics. Comparing the performance of simple distributional features of a time series provides important context for interpreting the performance of complex time-series classification models, which may not always be required to obtain high accuracy.Comment: 8 pages, 3 figure

Beyond Ads: Sequential Decision-Making Algorithms in Law and Public Policy

We explore the promises and challenges of employing sequential decision-making algorithms - such as bandits, reinforcement learning, and active learning - in law and public policy. While such algorithms have well-characterized performance in the private sector (e.g., online advertising), their potential in law and the public sector remains largely unexplored, due in part to distinct methodological challenges of the policy setting. Public law, for instance, can pose multiple objectives, necessitate batched and delayed feedback, and require systems to learn rational, causal decision-making policies, each of which presents novel questions at the research frontier. We highlight several applications of sequential decision-making algorithms in regulation and governance, and discuss areas for needed research to render such methods policy-compliant, more widely applicable, and effective in the public sector. We also note the potential risks of such deployments and describe how sequential decision systems can also facilitate the discovery of harms. We hope our work inspires more investigation of sequential decision making in law and public policy, which provide unique challenges for machine learning researchers with tremendous potential for social benefit.Comment: Version 1 presented at Causal Inference Challenges in Sequential Decision Making: Bridging Theory and Practice, a NeurIPS 2021 Worksho

Thermal stability of microstructural and optical modifications induced in sapphire by ultrafast laser filamentation

We report on the thermal stability of both structural and optical micromodifications created by ultrafast laser written filaments in sapphire crystals. By using the Cr3+ traces as optical probes, we have concluded that the filaments are constituted by both reversible and nonreversible defects with very different spatial locations. The strain field measured from the analysis of R lines has been found to be erased at the same time when the reversible centers are recombined (∼1100 °C). This fact seems to indicate that these defects act as pinning centers for the induced stress. Furthermore, we have found that the waveguide generated in the proximity of the filament disappear for annealing temperatures above 1100 °C. This clearly supports the assumption that waveguiding is produced by the strain stress induced refractive index increment based on the dominant electronic polarizability enhancement. © 2010 American Institute of Physics

Impact of co-resident health and living alone on risk of hospital admission for people with Parkinson’s disease

Acknowledgements This work was supported by Gatsby Charitable Foundation (GAT3676). YBS is partly funded by National Institute for Health and Care Research Applied Research Collaboration West (NIHR ARC West) and University of Bristol. This study is based on data from the Clinical Practice Research Datalink obtained under licence from the UK Medicines and Healthcare products Regulatory Agency. However, the interpretation and conclusions contained in this report are those of the author/s alone.Peer reviewe

Raven Eye: A Mobile Computing Solution for Site Exploitation

Site exploitation (SE) remains a critical mission for operators on the battlefield.  Since SE is a fairly new operation in the military, soldiers face specific challenges that hinder them from conducting a successful SE operation.  This paper details the design of a system, Raven Eye, which endeavors to improve the efficiency and effectiveness of SE.  Raven Eye is an Android based system that collects, stores, and sends SE data.  Raven Eye allows operators to collect exploited site data by capturing photos, videos, and biometrics.  Operators can annotate and tag recorded items.  Lastly, the operators transform data stored and collected via Raven Eye to a standardized report that accelerates follow-on analysis by intelligence personnel.