We address the question of aggregating the preferences of voters in the context of participatory budgeting. We scrutinize the voting method currently used in practice, underline its drawbacks, and introduce a novel scheme tailored to this setting, which we call "Knapsack Voting". We study its strategic properties-we show that it is strategy-proof under a natural model of utility (a disutility given by the 1 distance between the outcome and the true preference of the voter), and "partially" strategy-proof under general additive utilities. We extend Knapsack Voting to more general settings with revenues, deficits or surpluses, and prove a similar strategy-proofness result. To further demonstrate the applicability of our scheme, we discuss its implementation on the digital voting platform that we have deployed in partnership with the local government bodies in many cities across the nation. From voting data thus collected, we present empirical evidence that Knapsack Voting works well in practice.

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In large scale collective decision making, social choice is a normative study of how one ought to design a protocol for reaching consensus. However, in instances where the underlying decision space is too large or complex for ordinal voting, standard voting methods of social choice may be impractical. How then can we design a mechanism -preferably decentralized, simple, scalable, and not requiring any special knowledge of the decision space -to reach consensus? We propose sequential deliberation as a natural solution to this problem. In this iterative method, successive pairs of agents bargain over the decision space using the previous decision as a disagreement alternative. We describe the general method and analyze the quality of its outcome when the space of preferences define a median graph. We show that sequential deliberation finds a 1.208-approximation to the optimal social cost on such graphs, coming very close to this value with only a small constant number of agents sampled from the population. We also show lower bounds on simpler classes of mechanisms to justify our design choices. We further show that sequential deliberation is ex-post Pareto efficient and has truthful reporting as an equilibrium of the induced extensive form game. We finally show that for general metric spaces, the second moment of of the distribution of social cost of the outcomes produced by sequential deliberation is also bounded.

BackgroundEstimates of an individual’s cumulative ultraviolet (UV) radiation exposure can be useful since ultraviolet radiation exposure increases skin cancer risk, but a comprehensive tool that is practical for use in the clinic does not currently exist.The objective of this study is to develop a geographically-adjusted tool to systematically estimate an individual’s self-reported cumulative UV radiation exposure, investigate the association of these estimates with skin cancer diagnosis, and assess test reliability.MethodsA 12-item online questionnaire from validated survey items for UV exposure and skin cancer was administered to online volunteers across the United States and results cross-referenced with UV radiation indices. Cumulative UV exposure scores (CUES) were calculated and correlated with personal history of skin cancer in a case–control design. Reliability was assessed in a separate convenience sample.Results1,118 responses were included in the overall sample; the mean age of respondents was 46 (standard deviation 15, range 18 – 81) and 150 (13 %) reported a history of skin cancer. In bivariate analysis of 1:2 age-matched cases (n = 149) and controls (n = 298), skin cancer cases were associated with (1) greater CUES prior to first skin cancer diagnosis than controls without skin cancer history (242,074 vs. 205,379, p = 0.003) and (2) less engagement in UV protective behaviors (p < 0.01). In a multivariate analysis of age-matched data, individuals with CUES in the lowest quartile were less likely to develop skin cancer compared to those in the highest quartile. In reliability testing among 19 volunteers, the 2-week intra-class correlation coefficient for CUES was 0.94. We have provided the programming code for this tool as well as the tool itself via open access.ConclusionsCUES is a useable and comprehensive tool to better estimate lifetime ultraviolet exposure, so that individuals with higher levels of exposure may be identified for counseling on photo-protective measures.Electronic supplementary materialThe online version of this article (doi:10.1186/s12895-016-0038-1) contains supplementary material, which is available to authorized users.

This article examines the impact of augmented reality (AR) visualizations on users’ sense of physical presence, knowledge gain, and perceptions of the authenticity of journalistic visuals. In a mixed experimental design, 79 participants were randomly assigned to view three The New York Times articles on a mobile phone featuring one of three viewing modalities: (1) AR visualizations, (2) interactive (non-AR) visualizations, or (3) non-interactive, static visualizations. AR induced a greater sense of physical presence compared to the other modalities. The findings suggest that immersive properties of AR can contribute to journalism’s goal of engaging the audience. However, AR was not a superior medium for informing the participants, and the viewing modality did not have an effect on the perceived authenticity of the visuals. The findings indicate a need for more efficient ways to relay information through journalistic AR visualizations while keeping the user engaged in an immersive experience.

Visualizing 3D molecular structures is crucial to understanding and predicting their chemical behavior. However, static 2D hand-drawn skeletal structures remain the preferred method of chemical communication. Here, we combine cutting-edge technologies in augmented reality (AR), machine learning, and computational chemistry to develop MolAR, an open-source mobile application for visualizing molecules in AR directly from their hand-drawn chemical structures. Users can also visualize any molecule or protein directly from its name or PDB ID, and compute chemical properties in real time via quantum chemistry cloud computing. MolAR provides an easily accessible platform for the scientific community to visualize and interact with 3D molecular structures in an immersive and engaging way.

We present SWAN, a statistical framework for robust detection of genomic structural variants in next-generation sequencing data and an analysis of mid-range size insertion and deletions (<10 Kb) for whole genome analysis and DNA mixtures. To identify these mid-range size events, SWAN collectively uses information from read-pair, read-depth and one end mapped reads through statistical likelihoods based on Poisson field models. SWAN also uses soft-clip/split read remapping to supplement the likelihood analysis and determine variant boundaries. The accuracy of SWAN is demonstrated by in silico spike-ins and by identification of known variants in the NA12878 genome. We used SWAN to identify a series of novel set of mid-range insertion/deletion detection that were confirmed by targeted deep re-sequencing. An R package implementation of SWAN is open source and freely available.

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