The Gremlin Graph Traversal Machine and Language

Gremlin is a graph traversal machine and language designed, developed, and distributed by the Apache TinkerPop project. Gremlin, as a graph traversal machine, is composed of three interacting components: a graph $G$, a traversal $\Psi$, and a set of traversers $T$. The traversers move about the graph according to the instructions specified in the traversal, where the result of the computation is the ultimate locations of all halted traversers. A Gremlin machine can be executed over any supporting graph computing system such as an OLTP graph database and/or an OLAP graph processor. Gremlin, as a graph traversal language, is a functional language implemented in the user's native programming language and is used to define the $\Psi$ of a Gremlin machine. This article provides a mathematical description of Gremlin and details its automaton and functional properties. These properties enable Gremlin to naturally support imperative and declarative querying, host language agnosticism, user-defined domain specific languages, an extensible compiler/optimizer, single- and multi-machine execution models, hybrid depth- and breadth-first evaluation, as well as the existence of a Universal Gremlin Machine and its respective entailments.Comment: To appear in the Proceedings of the 2015 ACM Database Programming Languages Conferenc

Muon Spin Relaxation Studies of Superconductivity in a Crystalline Array of Weakly Coupled Metal Nanoparticles

We report Muon Spin Relaxation studies in weak transverse fields of the superconductivity in the metal cluster compound, Ga$\_{84}$[N(SiMe$\_{3}$)$\_{2}$]$\_{20}$-Li$\_{6}$Br$\_{2}$(thf)$\_{20}\cdot$2toluene. The temperature and field dependence of the muon spin relaxation rate and Knight shift clearly evidence type II bulk superconductivity below $T\_{\text{c}}\approx7.8$ K, with $B\_{\text{c1}}\approx 0.06$ T, $B\_{\text{c2}}\approx 0.26$ T, $\kappa\sim 2$ and weak flux pinning. The data are well described by the s-wave BCS model with weak electron-phonon coupling in the clean limit. A qualitative explanation for the conduction mechanism in this novel type of narrow band superconductor is presented.Comment: 4 figures, 5 page

Leadership around the clock: Balancing caregiving and chairing

Are you reading this abstract while texting to make sure your kids got off the bus ok or your elders took their medications today? If yes, this session is for you! We will discuss finding balance in a “lean in” culture, the effect of role strain and depletion fatigue, and how to generate self-compassion while juggling it all-or at least some of it. We will share strategies to make peace with your individual career trajectory, embrace the multiple purposes in your life, and survive the chaos

Wide-Field Multi-Parameter FLIM: Long-Term Minimal Invasive Observation of Proteins in Living Cells.

Time-domain Fluorescence Lifetime Imaging Microscopy (FLIM) is a remarkable tool to monitor the dynamics of fluorophore-tagged protein domains inside living cells. We propose a Wide-Field Multi-Parameter FLIM method (WFMP-FLIM) aimed to monitor continuously living cells under minimum light intensity at a given illumination energy dose. A powerful data analysis technique applied to the WFMP-FLIM data sets allows to optimize the estimation accuracy of physical parameters at very low fluorescence signal levels approaching the lower bound theoretical limit. We demonstrate the efficiency of WFMP-FLIM by presenting two independent and relevant long-term experiments in cell biology: 1) FRET analysis of simultaneously recorded donor and acceptor fluorescence in living HeLa cells and 2) tracking of mitochondrial transport combined with fluorescence lifetime analysis in neuronal processes

Topophilia and the Quality of Life

With this research I tested the hypothesis that individual preferences for specific ecosystem components and restorative environments are significantly associated with quality of life (QOL). A total of 379 human subjects responded to a structured 18-item questionnaire on topophilia and to the 26-item World Health Organization’s Quality of Life (WHOQOL-Bref) instrument. Confirmatory factor analyses revealed four domains of topophilia (ecodiversity, synesthetic tendency, cognitive challenge, and familiarity) and four domains of QOL (physical, psychological, social, and environmental). Synesthetic tendency was the strongest domain of topophilia, whereas the psychological aspect of QOL was the strongest. Structural equation modeling was used to explore the adequacy of a theoretical model linking topophilia and QOL. The model fit the data extremely well: χ(2) = 5.02, p = 0.414; correlation = 0.12 (p = 0.047). All four domains of topophilia were significantly correlated with the level of restoration experienced by respondents at their current domicile [for cognitive challenge: r = 0.19; p < 0.01; familiarity: r = 0.12; p < 0.05; synesthetic tendency: r = 0.18; p < 0.01; ecodiversity (the highest value): r = 0.28; p < 0.01]. Within ecodiversity, preferences for water and flowers were associated with high overall QOL (r = 0.162 and 0.105, respectively; p < 0.01 and 0.05, respectively). Within the familiarity domain, identifiability was associated with the environmental domain of QOL (r = 0.115; p < 0.05), but not with overall QOL. These results provide a new methodologic framework for linking environmental quality and human health and for implementing evidence-based provision of restorative environments through targeted design of built environments to enhance human QOL

Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks

Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait‐matching for determining species interactions, however, vary significantly among different types of ecological networks. Here, we show that ambiguity among empirical trait‐matching studies may have arisen at least in parts from using overly simple statistical models. Using simulated and real data, we contrast conventional generalized linear models (GLM) with more flexible Machine Learning (ML) models (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Neural Networks, Support Vector Machines, naïve Bayes, and k‐Nearest‐Neighbor), testing their ability to predict species interactions based on traits, and infer trait combinations causally responsible for species interactions. We found that the best ML models can successfully predict species interactions in plant–pollinator networks, outperforming GLMs by a substantial margin. Our results also demonstrate that ML models can better identify the causally responsible trait‐matching combinations than GLMs. In two case studies, the best ML models successfully predicted species interactions in a global plant–pollinator database and inferred ecologically plausible trait‐matching rules for a plant–hummingbird network from Costa Rica, without any prior assumptions about the system. We conclude that flexible ML models offer many advantages over traditional regression models for understanding interaction networks. We anticipate that these results extrapolate to other ecological network types. More generally, our results highlight the potential of machine learning and artificial intelligence for inference in ecology, beyond standard tasks such as image or pattern recognition

Modelling trade offs between public and private conservation policies

To reduce global biodiversity loss, there is an urgent need to determine the most efficient allocation of conservation resources. Recently, there has been a growing trend for many governments to supplement public ownership and management of reserves with incentive programs for conservation on private land. At the same time, policies to promote conservation on private land are rarely evaluated in terms of their ecological consequences. This raises important questions, such as the extent to which private land conservation can improve conservation outcomes, and how it should be mixed with more traditional public land conservation. We address these questions, using a general framework for modelling environmental policies and a case study examining the conservation of endangered native grasslands to the west of Melbourne, Australia. Specifically, we examine three policies that involve: i) spending all resources on creating public conservation areas; ii) spending all resources on an ongoing incentive program where private landholders are paid to manage vegetation on their property with 5-year contracts; and iii) splitting resources between these two approaches. The performance of each strategy is quantified with a vegetation condition change model that predicts future changes in grassland quality. Of the policies tested, no one policy was always best and policy performance depended on the objectives of those enacting the policy. This work demonstrates a general method for evaluating environmental policies and highlights the utility of a model which combines ecological and socioeconomic processes.Comment: 20 pages, 5 figure