Deductive synthesis of recursive plans in linear logic

Linear logic has previously been shown to be suitable for describing and deductively solving planning problems involving conjunction and disjunction. We introduce a recursively defined datatype and a corresponding induction rule, thereby allowing recursive plans to be synthesised. In order to make explicit the relationship between proofs and plans, we enhance the linear logic deduction rules to handle plans as a form of proof term

Evidence for ambient dark aqueous SOA formation in the Po Valley, Italy

Laboratory experiments suggest that water-soluble products from the gas-phase oxidation of volatile organic compounds can partition into atmospheric waters where they are further oxidized to form low volatility products, providing an alternative route for oxidation in addition to further oxidation in the gas phase. These products can remain in the particle phase after water evaporation, forming what is termed as aqueous secondary organic aerosol (aqSOA). However, few studies have attempted to observe ambient aqSOA. Therefore, a suite of measurements, including near-real-time WSOC (water-soluble organic carbon), inorganic anions/cations, organic acids, and gas-phase glyoxal, were made during the PEGASOS (Pan-European Gas-AeroSOls-climate interaction Study) 2012 campaign in the Po Valley, Italy, to search for evidence of aqSOA. Our analysis focused on four periods: Period A on 19–21 June, Period B on 30 June and 1–2 July, Period C on 3–5 July, and Period D on 6–7 July to represent the first (Period A) and second (Periods B, C, and D) halves of the study. These periods were picked to cover varying levels of WSOC and aerosol liquid water. In addition, back trajectory analysis suggested all sites sampled similar air masses on a given day. The data collected during both periods were divided into times of increasing relative humidity (RH) and decreasing RH, with the aim of diminishing the influence of dilution and mixing on SOA concentrations and other measured variables. Evidence for local aqSOA formation was only observed during Period A. When this occurred, there was a correlation of WSOC with organic aerosol (R2 = 0.84), aerosol liquid water (R2 = 0.65), RH (R2 = 0.39), and aerosol nitrate (R2 = 0.66). Additionally, this was only observed during times of increasing RH, which coincided with dark conditions. Comparisons of WSOC with oxygenated organic aerosol (OOA) factors, determined from application of positive matrix factorization analysis on the aerosol mass spectrometer observations of the submicron non-refractory organic particle composition, suggested that the WSOC differed in the two halves of the study (Period A WSOC vs. OOA-2 R2 = 0.83 and OOA-4 R2 = 0.04, whereas Period C WSOC vs. OOA-2 R2 = 0.03 and OOA-4 R2 = 0.64). OOA-2 had a high O ∕ C (oxygen ∕ carbon) ratio of 0.77, providing evidence that aqueous processing was occurring during Period A. Key factors of local aqSOA production during Period A appear to include air mass stagnation, which allows aqSOA precursors to accumulate in the region; the formation of substantial local particulate nitrate during the overnight hours, which enhances water uptake by the aerosol; and the presence of significant amounts of ammonia, which may contribute to ammonium nitrate formation and subsequent water uptake and/or play a more direct role in the aqSOA chemistry

Model Selection for Degree-corrected Block Models

The proliferation of models for networks raises challenging problems of model selection: the data are sparse and globally dependent, and models are typically high-dimensional and have large numbers of latent variables. Together, these issues mean that the usual model-selection criteria do not work properly for networks. We illustrate these challenges, and show one way to resolve them, by considering the key network-analysis problem of dividing a graph into communities or blocks of nodes with homogeneous patterns of links to the rest of the network. The standard tool for doing this is the stochastic block model, under which the probability of a link between two nodes is a function solely of the blocks to which they belong. This imposes a homogeneous degree distribution within each block; this can be unrealistic, so degree-corrected block models add a parameter for each node, modulating its over-all degree. The choice between ordinary and degree-corrected block models matters because they make very different inferences about communities. We present the first principled and tractable approach to model selection between standard and degree-corrected block models, based on new large-graph asymptotics for the distribution of log-likelihood ratios under the stochastic block model, finding substantial departures from classical results for sparse graphs. We also develop linear-time approximations for log-likelihoods under both the stochastic block model and the degree-corrected model, using belief propagation. Applications to simulated and real networks show excellent agreement with our approximations. Our results thus both solve the practical problem of deciding on degree correction, and point to a general approach to model selection in network analysis

Climate change litigation: a review of research on courts and litigants in climate government

Studies of climate change litigation have proliferated over the past two decades, as lawsuits across the world increasingly bring policy debates about climate change mitigation and adaptation, as well as climate change‐related loss and damage to the attention of courts. We systematically identify 130 articles on climate change litigation published in English in the law and social sciences between 2000 and 2018 to identify research trajectories. In addition to a budding interdisciplinarity in scholarly interest in climate change litigation we also document a growing understanding of the full spectrum of actors involved and implicated in climate lawsuits and the range of motivations and/or strategic imperatives underpinning their engagement with the law. Situating this within the broader academic literature on the topic we then highlight a number of cutting edge trends and opportunities for future research. Four emerging themes are explored in detail: the relationship between litigation and governance; how time and scale feature in climate litigation; the role of science; and what has been coined the “human rights turn” in climate change litigation. We highlight the limits of existing work and the need for future research—not limited to legal scholarship—to evaluate the impact of both regulatory and anti‐regulatory climate‐related lawsuits, and to explore a wider set of jurisdictions, actors and themes. Addressing these issues and questions will help to develop a deeper understanding of the conditions under which litigation will strengthen or undermine climate governance. This article is categorized under: Policy and Governance > Multilevel and Transnational Climate Change Governanc

Testing Propositions Derived from Twitter Studies: Generalization and Replication in Computational Social Science

Replication is an essential requirement for scientific discovery. The current study aims to generalize and replicate 10 propositions made in previous Twitter studies using a representative dataset. Our findings suggest 6 out of 10 propositions could not be replicated due to the variations of data collection, analytic strategies employed, and inconsistent measurements. The study’s contributions are twofold: First, it systematically summarized and assessed some important claims in the field, which can inform future studies. Second, it proposed a feasible approach to generating a random sample of Twitter users and its associated ego networks, which might serve as a solution for answering social-scientific questions at the individual level without accessing the complete data archive.published_or_final_versio

An efficient algorithm for the stochastic simulation of the hybridization of DNA to microarrays

<p>Abstract</p> <p>Background</p> <p>Although oligonucleotide microarray technology is ubiquitous in genomic research, reproducibility and standardization of expression measurements still concern many researchers. Cross-hybridization between microarray probes and non-target ssDNA has been implicated as a primary factor in sensitivity and selectivity loss. Since hybridization is a chemical process, it may be modeled at a population-level using a combination of material balance equations and thermodynamics. However, the hybridization reaction network may be exceptionally large for commercial arrays, which often possess at least one reporter per transcript. Quantification of the kinetics and equilibrium of exceptionally large chemical systems of this type is numerically infeasible with customary approaches.</p> <p>Results</p> <p>In this paper, we present a robust and computationally efficient algorithm for the simulation of hybridization processes underlying microarray assays. Our method may be utilized to identify the extent to which nucleic acid targets (e.g. cDNA) will cross-hybridize with probes, and by extension, characterize probe robustnessusing the information specified by MAGE-TAB. Using this algorithm, we characterize cross-hybridization in a modified commercial microarray assay.</p> <p>Conclusions</p> <p>By integrating stochastic simulation with thermodynamic prediction tools for DNA hybridization, one may robustly and rapidly characterize of the selectivity of a proposed microarray design at the probe and "system" levels. Our code is available at <url>http://www.laurenzi.net</url>.</p