Physically Based Animation of sea Anemones in Real-Time

This paper presents a technique for modeling and animating fiberlike objects such as sea anemones tentacles in real-time. Each fiber is described by a generalized cylinder defined around an articulated skeleton. The dynamics of each individual fiber is controlled by a physically based simulation that updates the position of the skeleton’s frames over time. We take into account the forces generated by the surrounding fluid as well as a stiffness function describing the bending behavior of the fiber. High level control of the animation is achieved through the use of four types of singularities to describe the three-dimensional continuous velocity field representing the fluid. We thus animate hundreds of fibers by key-framing only a small number of singularities. We apply this algorithm on a seascape composed of many sea anemones. We also show that our algorithm is more general and can be applied to other types of objects composed of fibers such as seagrasse

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author

Habitat Function In Alaska Nearshore Marine Ecosystems

Thesis (Ph.D.) University of Alaska Fairbanks, 2010This research demonstrates how habitat structures subtidal communities and supports individual species in Alaska nearshore marine ecosystems. This was accomplished through a case study of southeast Alaska coastal regions, and an in-depth investigation of red king crab Paralithodes camtschaticus early life stage ecology and nursery habitat. How subtidal communities reflect variation in the marine environment of southeast Alaska is poorly understood. The purpose of the first part of this body of research was to identify and compare patterns of community structure for macroalgae, invertebrate, and fish communities at shallow subtidal depths between inner coast and outer coast regions, and link patterns of community structure to environmental variability in southeast Alaska. The major hydrographic gradient of decreasing salinity and increasing temperature from the outer coast to the inner coast affected regional community structure, with greater species diversity at the outer coast. Species distribution for invertebrate communities was linked to variation in benthic habitat at local scales among sites within regions. This study improves understanding of processes that structure marine communities to better predict how environmental change will affect Alaska marine ecosystems. Many Alaska red king crab populations have collapsed and continue to experience little recovery, even for areas without a commercial fishery. Several aspects of red king crab early life stage ecology were investigated because reasons for the lack of recovery may be related to the early life history of this species. Field experiments were conducted in southeast Alaska. Settlement timing was consistent between study years (2008--09) and with historical data for this region. Local oceanographic processes that influence larval transport may be responsible for spatial variation in larval supply. In laboratory and field experiments, early juvenile crabs (age 0 and 1) demonstrated refuge response behavior to a predator threat that changed with crab ontogeny. When predators were absent, juvenile crabs preferred highly structured biogenic habitats due to foraging opportunities, and associated with any structural habitat to improve survival when predators were present. This research shows how availability of high quality nursery habitat affects red king crab early life stage success and potential for population recovery

Vector offset operators for deformable organic objects.

Many natural materials and most of living tissues exhibit complex deformable behaviours that may be characteriseda s organic. In computer animation, deformable organic material behaviour is needed for the development of characters and scenes based on living creatures and natural phenomena. This study addresses the problem of deformable organic material behaviour in computer animated objects. The focus of this study is concentrated on problems inherent in geometry based deformation techniques, such as non-intuitive interaction and difficulty in achieving realism. Further, the focus is concentrated on problems inherent in physically based deformation techniques, such as inefficiency and difficulty in enforcing spatial and temporal constraints. The main objective in this study is to find a general and efficient solution to interaction and animation of deformable 3D objects with natural organic material properties and constrainable behaviour. The solution must provide an interaction and animation framework suitable for the creation of animated deformable characters. An implementation of physical organic material properties such as plasticity, elasticity and iscoelasticity can provide the basis for an organic deformation model. An efficient approach to stress and strain control is introduced with a deformation tool named Vector Offset Operator. Stress / strain graphs control the elastoplastic behaviour of the model. Strain creep, stress relaxation and hysteresis graphs control the viscoelastic behaviour of the model. External forces may be applied using motion paths equipped with momentum / time graphs. Finally, spatial and temporal constraints are applied directly on vector operators. The suggested generic deformation tool introduces an intermediate layer between user interaction, deformation, elastoplastic and viscoelastic material behaviour and spatial and temporal constraints. This results in an efficient approach to deformation, frees object representation from deformation, facilitates the application of constraints and enables further development

Diversity of brain size in fishes: preliminary analysis of a database including 1174 species in 45 orders

Absolule and relative values of brain weight are now available for 1174 species of fishes, representing 45 taxonomic orders. The original FishBase "Brains" data was assembled by the research team of Bauchot and colleagues, to which the present report adds data for species representing several additional major taxonomic groups. This database is part of the FíshBase 97 package which provides researchers with a tool to explore lhe functional meaning of absolute and relative brain size díversily, in comparison with phylogenetic position, life history mode, locomotion, habitat, and other behavioral parameters. Several results are provided as an example of the use of these data. Galeomorph sharks and batoid rays possess the largest brains among fishes. and elongate forms with anguilliform locomotion (e.g.. hagfishes. lampreys, lrue eels, carapids, zoarcids) possess the smallest relative brain sizes. Among teleost fishes, Osteoglossomorphs possess the largest relative brain sizes. Brain size correlations with oxygen consumption suggest that larger brains consume proportionately more oxygen, or that active fish with higher metabolic rates have larger brain

11th International Coral Reef Symposium Abstracts

https://nsuworks.nova.edu/occ_icrs/1001/thumbnail.jp