Linear Logic Programming for Narrative Generation

Abstract. In this paper, we explore the use of Linear Logic programming for story generation. We use the language Celf to represent narrative knowledge, and its own querying mechanism to generate story instances, through a number of proof terms. Each proof term obtained is used, through a resource-flow analysis, to build a directed graph where nodes are narrative actions and edges represent inferred causality relationships. Such graphs represent narrative plots structured by narrative causality. Building on previous work evidencing the suitability of Linear Logic as a conceptual model of action and change for narratives, we explore the conditions under which these representations can be operationalized through Linear Logic Programming techniques. This approach is a candidate technique for narrative generation which unifies declarative representations and generation via query and deduction mechanisms

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

A study on the friendship paradox – quantitative analysis and relationship with assortative mixing

The friendship paradox is the observation that friends of individuals tend to have more friends or be more popular than the individuals themselves. In this work, we first study local metrics to capture the strength of the paradox and the direction of the paradox from the perspective of individual nodes, i.e., an indication of whether the individual is more or less popular than its friends. These local metrics are aggregated, and global metrics are proposed to express the phenomenon on a network-wide level. Theoretical results show that the defined metrics are well-behaved enough to capture the friendship paradox. We also theoretically analyze the behavior of the friendship paradox for popular network models in order to understand regimes where friendship paradox occurs. These theoretical findings are complemented by experimental results on both network models and real-world networks. By conducting a correlation study between the proposed metrics and degree assortativity, we experimentally demonstrate that the phenomenon of the friendship paradox is related to the well-known phenomenon of assortative mixing

Aerosol Liquid Water Driven by Anthropogenic Nitrate: Implications for Lifetimes of Water-Soluble Organic Gases and Potential for Secondary Organic Aerosol Formation

Aerosol liquid water (ALW) influences aerosol radiative properties and the partitioning of gas-phase water-soluble organic compounds (WSOC_g) to the condensed phase. A recent modeling study drew attention to the anthropogenic nature of ALW in the southeastern United States, where predicted ALW is driven by regional sulfate. Herein, we demonstrate that ALW in the Po Valley, Italy, is also anthropogenic but is driven by locally formed nitrate, illustrating regional differences in the aerosol components responsible for ALW. We present field evidence for the influence of controllable ALW on the lifetimes and atmospheric budgets of reactive organic gases and note the role of ALW in the formation of secondary organic aerosol (SOA). Nitrate is expected to increase in importance due to increased emissions of nitrate precursors, as well as policies aimed at reducing sulfur emissions. We argue that the impacts of increased particulate nitrate in future climate and air quality scenarios may be under predicted because they do not account for the increased potential for SOA formation in nitrate-derived ALW, nor do they account for the impacts of this ALW on reactive gas budgets and gas-phase photochemistry

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

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