Data clustering using a model granular magnet

We present a new approach to clustering, based on the physical properties of an inhomogeneous ferromagnet. No assumption is made regarding the underlying distribution of the data. We assign a Potts spin to each data point and introduce an interaction between neighboring points, whose strength is a decreasing function of the distance between the neighbors. This magnetic system exhibits three phases. At very low temperatures it is completely ordered; all spins are aligned. At very high temperatures the system does not exhibit any ordering and in an intermediate regime clusters of relatively strongly coupled spins become ordered, whereas different clusters remain uncorrelated. This intermediate phase is identified by a jump in the order parameters. The spin-spin correlation function is used to partition the spins and the corresponding data points into clusters. We demonstrate on three synthetic and three real data sets how the method works. Detailed comparison to the performance of other techniques clearly indicates the relative success of our method.Comment: 46 pages, postscript, 15 ps figures include

Learning when to skim and when to read

Many recent advances in deep learning for natural language processing have come at increasing computational cost, but the power of these state-of-the-art models is not needed for every example in a dataset. We demonstrate two approaches to reducing unnecessary computation in cases where a fast but weak baseline classier and a stronger, slower model are both available. Applying an AUC-based metric to the task of sentiment classification, we find significant efficiency gains with both a probability-threshold method for reducing computational cost and one that uses a secondary decision network.Comment: 8 pages (4 article, 1 references, 3 appendix), 11 figures, 3 tables, published at ACL2017 workshop Repl4NL

Chemical variation in a dominant tree species: population divergence, selection and genetic stability across environments

Understanding among and within population genetic variation of ecologically important plant traits provides insight into the potential evolutionary processes affecting those traits. The strength and consistency of selection driving variability in traits would be affected by plasticity in differences among genotypes across environments (G×E). We investigated population divergence, selection and environmental plasticity of foliar plant secondary metabolites (PSMs) in a dominant tree species, Eucalyptus globulus. Using two common garden trials we examined variation in PSMs at multiple genetic scales; among 12 populations covering the full geographic range of the species and among up to 60 families within populations. Significant genetic variation in the expression of many PSMs resides both among and within populations of E. globulus with moderate (e.g., sideroxylonal A h2op = 0.24) to high (e.g., macrocarpal G h2op = 0.48) narrow sense heritabilities and high coefficients of additive genetic variation estimated for some compounds. A comparison of Qst and Fst estimates suggest that variability in some of these traits may be due to selection. Importantly, there was no genetic by environment interaction in the expression of any of the quantitative chemical traits despite often significant site effects. These results provide evidence that natural selection has contributed to population divergence in PSMs in E. globulus, and identifies the formylated phloroglucinol compounds (particularly sideroxylonal) and a dominant oil, 1,8-cineole, as candidates for traits whose genetic architecture has been shaped by divergent selection. Additionally, as the genetic differences in these PSMs that influence community phenotypes is stable across environments, the role of plant genotype in structuring communities is strengthened and these genotypic differences may be relatively stable under global environmental changes

Digitizing mass spectrometry data to explore the chemical diversity and distribution of marine cyanobacteria and algae.

Natural product screening programs have uncovered molecules from diverse natural sources with various biological activities and unique structures. However, much is yet underexplored and additional information is hidden in these exceptional collections. We applied untargeted mass spectrometry approaches to capture the chemical space and dispersal patterns of metabolites from an in-house library of marine cyanobacterial and algal collections. Remarkably, 86% of the metabolomics signals detected were not found in other available datasets of similar nature, supporting the hypothesis that marine cyanobacteria and algae possess distinctive metabolomes. The data were plotted onto a world map representing eight major sampling sites, and revealed potential geographic locations with high chemical diversity. We demonstrate the use of these inventories as a tool to explore the diversity and distribution of natural products. Finally, we utilized this tool to guide the isolation of a new cyclic lipopeptide, yuvalamide A, from a marine cyanobacterium

Coinductive Formal Reasoning in Exact Real Arithmetic

In this article we present a method for formally proving the correctness of the lazy algorithms for computing homographic and quadratic transformations -- of which field operations are special cases-- on a representation of real numbers by coinductive streams. The algorithms work on coinductive stream of M\"{o}bius maps and form the basis of the Edalat--Potts exact real arithmetic. We use the machinery of the Coq proof assistant for the coinductive types to present the formalisation. The formalised algorithms are only partially productive, i.e., they do not output provably infinite streams for all possible inputs. We show how to deal with this partiality in the presence of syntactic restrictions posed by the constructive type theory of Coq. Furthermore we show that the type theoretic techniques that we develop are compatible with the semantics of the algorithms as continuous maps on real numbers. The resulting Coq formalisation is available for public download.Comment: 40 page

Textual indicators of deliberative dialogue: a systematic review of methods for studying the quality of online dialogues

High-quality online dialogues help sustain democracy. Deliberative theory, which predates the internet, provides the primary model for assessing the quality of online dialogues. It conceptualizes high-quality online dialogue as civil, rational, constructive, equal, interactive, and for the common good. More recently, advances in computation have driven an upsurge of empirical studies using automated methods for operationalizing online dialogue and measuring its quality. While related in their aims, deliberative theory and the wider empirical literature generally operate independently. To bridge the gap between the two literatures, we introduce Textual Indicators of Deliberative Dialogue (TIDDs). TIDDs are defined as text-based measures of online dialogue quality under a deliberative model (e.g., disagreement, incivility, justifications). In this study, we identified 123 TIDDs by systematically reviewing 67 empirical studies of online dialogue. We found them to have mid-low reliability, low criterion validity, and high construct validity for measuring two deliberative dimensions (civility and rationality). Our results highlight the limitations of deliberative theory for conceptualizing the variety of ways online dialogues can be operationalized. We report the most promising TIDDs for measuring the quality of online dialogue and suggest deliberative theory would benefit from altering its models in line with the broader empirical literature

It Won\u27t Happen To Me! : Self-Disclosure in Online Social Networks

Despite the considerable amount of self-disclosure in Online Social Networks (OSN), the motivation behind this phenomenon is still little understood. Building on the Privacy Calculus theory, this study fills this gap by taking a closer look at the factors behind individual self-disclosure decisions. In a Structural Equation Model with 237 subjects we find Perceived Enjoyment and Privacy Concerns to be significant determinants of information revelation. We confirm that the privacy concerns of OSN users are primarily determined by the perceived likelihood of a privacy violation and much less by the expected damage. These insights provide a solid basis for OSN providers and policy-makers in their effort to ensure healthy disclosure levels that are based on objective rationale rather than subjective misconceptions