Form, performance and trade-offs in swimming and stability of armed larvae

Diverse larval forms swim and feed with ciliary bands on arms or analogous structures. Armed morphologies are varied: numbers, lengths, and orientations of arms differ among species, change through development, and can be plastic in response to physiological or environmental conditions. A hydromechanical model of idealized equal-armed larvae was used to examine functional consequences of these varied arm arrangements for larval swimming performance. With effects of overall size, ciliary tip speed, and viscosity factored out, the model suggested trade-offs between morphological traits conferring high swimming speed and weight-carrying ability in still water (generally few arms and low arm elevations), and morphologies conferring high stability to external disturbances such as shear flows (generally many arms and high arm elevations). In vertical shear, larvae that were passively stabilized by a center of buoyancy anterior to the center of gravity tilted toward and consequently swam into downwelling flows. Thus, paradoxically, upward swimming by passively stable swimmers in vertical shear resulted in enhanced downward transport. This shear-dependent vertical transport could affect diverse passively stable swimmers, not just armed larvae. Published descriptions of larvae and metamorphosis of 13 ophiuroids suggest that most ophioplutei fall into two groups: those approximating modeled forms with two arms at low elevations, predicted to enhance speed and weight capacity, and those approximating modeled forms with more numerous arms of equal length at high elevations, predicted to enhance stability in shear

Causal modelling and validation based on observational data and domain knowledge

By assessing the effect of hypothetical actions without the need to directly interact with the real world, causal inference offers valuable tools for data science and artificial intelligence. However, a consensus on how to combine different causal algorithms into a holistic analysis workflow, as well as a universally agreed-upon validation strategy for causal models are yet to be established. In this thesis, a causal end-to-end analysis is proposed as a combination of multiple methods of graph-based causal inference from observational data and domain knowledge. Quantitative probing is introduced as a model-agnostic causal validation strategy in accordance with Popper’s falsificationist view on scientific discovery. The effectiveness of the strategy is evidenced by a thorough simulation study that includes a discussion of its current limits at the example of malfunctioning validation runs. In order to provide application scenarios for the methodological contributions, selected use cases from the domain of manufacturing light-emitting diodes are presented. Open-source Python packages for executing the causal end-to-end analysis and benchmarking the quantitative probing validation strategy are provided

Quantitative probing: Validating causal models using quantitative domain knowledge

We present quantitative probing as a model-agnostic framework for validating causal models in the presence of quantitative domain knowledge. The method is constructed as an analogue of the train/test split in correlation-based machine learning and as an enhancement of current causal validation strategies that are consistent with the logic of scientific discovery. The effectiveness of the method is illustrated using Pearl's sprinkler example, before a thorough simulation-based investigation is conducted. Limits of the technique are identified by studying exemplary failing scenarios, which are furthermore used to propose a list of topics for future research and improvements of the presented version of quantitative probing. The code for integrating quantitative probing into causal analysis, as well as the code for the presented simulation-based studies of the effectiveness of quantitative probing is provided in two separate open-source Python packages.Comment: submitted to the Journal of Causal Inferenc

Effects of Low Oxygen Levels on Copepod Size Distribution with Depth in Hood Canal

Deoxygenation and hypoxia are affecting marine trophic webs throughout the world’s oceans. Hood Canal, Puget Sound is a basin that experiences seasonal hypoxia due to its restricted circulation and high primary production. The region supports a large secondary trophic level dominated by mesozooplankton, and particularly copepods. Studies suggest that zooplankton exhibit changes in their vertical distribution when faced with low oxygen concentrations and even face mortality when concentrations fall below ~1 mg/l. Because zooplankton are an important food resource for many aquatic animals, a shift in their distribution could have major implications for the food chain. This study examines how low oxygen levels affect the distribution of copepods relative to the oxygen minimum to explore how predator/prey interactions may be impacted by changing water conditions. Samples were collected in summer and fall 2017 and 2018 across normoxic and hypoxic conditions in Hood Canal. Zooplankton pumps and depth-stratified net tows were deployed during the day and night to observe diel vertical migration (DVM), and a camera equipped with a telecentric lens was mounted on a profiling mooring, which collected a continuous stream of photos during multiple profiles of the water column each day. A convolutional neural network, ResNet-18, was used to process the thousands of images collected in situ. Preliminary results suggest oxygen levels significantly impact species composition and distribution in the water column. In-situ imaging data are currently being analyzed to substantiate these findings and expand them across a broad range of oxygen conditions

Using collision cones to assess biological deconfliction methods

Biological systems consistently outperform autonomous systems governed by engineered algorithms in their ability to reactively avoid collisions. To better understand this discrepancy, a collision avoidance algorithm was applied to frames of digitized video trajectory data from bats, swallows and fish (Myotis velifer, Petrochelidon pyrrhonota and Danio aequipinnatus). Information available from visual cues, specifically relative position and velocity, was provided to the algorithm which used this information to define collision cones that allowed the algorithm to find a safe velocity requiring minimal deviation from the original velocity. The subset of obstacles provided to the algorithm was determined by the animal's sensing range in terms of metric and topological distance. The algorithmic calculated velocities showed good agreement with observed biological velocities, indicating that the algorithm was an informative basis for comparison with the three species and could potentially be improved for engineered applications with further study

State Transitions and the Continuum Limit for a 2D Interacting, Self-Propelled Particle System

We study a class of swarming problems wherein particles evolve dynamically via pairwise interaction potentials and a velocity selection mechanism. We find that the swarming system undergoes various changes of state as a function of the self-propulsion and interaction potential parameters. In this paper, we utilize a procedure which, in a definitive way, connects a class of individual-based models to their continuum formulations and determine criteria for the validity of the latter. H-stability of the interaction potential plays a fundamental role in determining both the validity of the continuum approximation and the nature of the aggregation state transitions. We perform a linear stability analysis of the continuum model and compare the results to the simulations of the individual-based one

Narrowing the gap between combinatorial and hyperbolic knot invariants via deep learning

In this paper, we present a statistical approach for the discovery of relationships between mathematical entities that is based on linear regression and deep learning with fully connected artificial neural networks. The strategy is applied to computational knot data and empirical connections between combinatorial and hyperbolic knot invariants are revealed