Artificial Fishes
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Fish Locomotion: Recent Advances and New Directions
MA07CH22-Lauder ARI 6 November 2014 13:40 Fish Locomotion: Recent Advances and New Directions George V. Lauder Museum of Comparative Zoology, Harvard University, Cambridge, Massachusetts 02138; email: [email protected] Annu. Rev. Mar. Sci. 2015. 7:521–45 Keywords First published online as a Review in Advance on swimming, kinematics, hydrodynamics, robotics September 19, 2014 The Annual Review of Marine Science is online at Abstract marine.annualreviews.org Access provided by Harvard University on 01/07/15. For personal use only. Research on fish locomotion has expanded greatly in recent years as new This article’s doi: approaches have been brought to bear on a classical field of study. Detailed Annu. Rev. Marine. Sci. 2015.7:521-545. Downloaded from www.annualreviews.org 10.1146/annurev-marine-010814-015614 analyses of patterns of body and fin motion and the effects of these move- Copyright c 2015 by Annual Reviews. ments on water flow patterns have helped scientists understand the causes All rights reserved and effects of hydrodynamic patterns produced by swimming fish. Recent developments include the study of the center-of-mass motion of swimming fish and the use of volumetric imaging systems that allow three-dimensional instantaneous snapshots of wake flow patterns. The large numbers of swim- ming fish in the oceans and the vorticity present in fin and body wakes sup- port the hypothesis that fish contribute significantly to the mixing of ocean waters. New developments in fish robotics have enhanced understanding of the physical principles underlying aquatic propulsion and allowed intriguing biological features, such as the structure of shark skin, to be studied in detail. -
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Index INDEX Note: page numbers in italics refer to fi gures, those in bold refer to tables and boxes. abducens nerve 55 activity cycles 499–522 inhibition 485 absorption effi ciency 72 annual patterns 515, 516, 517–22 interactions 485–6 abyssal zone 393 circadian rhythms 505 prey 445 Acanthaster planci (Crown-of-Thorns Starfi sh) diel patterns 499, 500, 501–2, 503–4, reduction 484 579 504–7 aggressive mimicry 428, 432–3 Acanthocybium (Wahoo) 15 light-induced 499, 500, 501–2, 503–4, aggressive resemblance 425–6 Acanthodii 178, 179 505 aglomerular 52 Acanthomorpha 284–8, 289 lunar patterns 507–9 agnathans Acanthopterygii 291–325 seasonal 509–15 gills 59, 60 Atherinomorpha 293–6 semilunar patterns 507–9 osmoregulation 101, 102 characteristics 291–2 supra-annual patterns 515, 516, 517–22 phylogeny 202 distribution 349, 350 tidal patterns 506–7 ventilation 59, 60 jaws 291 see also migration see also hagfi shes; lampreys Mugilomorpha 292–3, 294 adaptive response 106 agnathous fi shes see jawless fi shes pelagic 405 adaptive zones 534 agonistic interactions 83–4, 485–8 Percomorpha 296–325 adenohypophysis 91, 92 chemically mediated 484 pharyngeal jaws 291 adenosine triphosphate (ATP) 57 sound production 461–2 phylogeny 292, 293, 294 adipose fi n 35 visual 479 spines 449, 450 adrenocorticotropic hormone (ACTH) 92 agricultural chemicals 605 Acanthothoraciformes 177 adrianichthyids 295 air breathing 60, 61–2, 62–4 acanthurids 318–19 adult fi shes 153, 154, 155–7 ammonia production 64, 100–1 Acanthuroidei 12, 318–19 death 156–7 amphibious 60 Acanthurus bahianus -
Deep Learning of Neuromuscular Control for Biomechanical Human Animation
Deep Learning of Neuromuscular Control For Biomechanical Human Animation Masaki Nakada? and Demetri Terzopoulos Computer Science Department University of California, Los Angeles Abstract. Increasingly complex physics-based models enhance the real- ism of character animation in computer graphics, but they pose difficult motor control challenges. This is especially the case when controlling a biomechanically simulated virtual human with an anatomically real- istic structure that is actuated in a natural manner by a multitude of contractile muscles. Graphics researchers have pursued machine learn- ing approaches to neuromuscular control, but traditional neural network learning methods suffer limitations when applied to complex biomechan- ical models and their associated high-dimensional training datasets. We demonstrate that \deep learning" is a useful approach to training neu- romuscular controllers for biomechanical character animation. In par- ticular, we propose a deep neural network architecture that can effec- tively and efficiently control (online) a dynamic musculoskeletal model of the human neck-head-face complex after having learned (offline) a high-dimensional map relating head orientation changes to neck muscle activations. To our knowledge, this is the first application of deep learn- ing to biomechanical human animation with a muscle-driven model. 1 Introduction The modeling of graphical characters based on human anatomy is becoming increasingly important in the field of computer animation. Progressive fidelity in biomechanical modeling should, in principle, result in more realistic human animation. Given realistic biomechanical models, however, we must confront a variety of difficult motor control problems due to the complexity of human anatomy. In conjunction with the modeling of skeletal muscle [1,2], existing work in biomechanical human modeling has addressed the hand [3,4,5], torso [6,7,8], face [9,10,11], neck [12], etc. -
Natural Language Interaction with Explainable AI Models Arjun R Akula1, Sinisa Todorovic2, Joyce Y Chai3 and Song-Chun Zhu4
Natural Language Interaction with Explainable AI Models Arjun R Akula1, Sinisa Todorovic2, Joyce Y Chai3 and Song-Chun Zhu4 1,4University of California, Los Angeles 2Oregon State University 3Michigan State University [email protected] [email protected] [email protected] [email protected] Abstract This paper presents an explainable AI (XAI) system that provides explanations for its predictions. The system consists of two key components – namely, the predic- tion And-Or graph (AOG) model for rec- ognizing and localizing concepts of inter- est in input data, and the XAI model for providing explanations to the user about the AOG’s predictions. In this work, we focus on the XAI model specified to in- Figure 1: Two frames (scenes) of a video: (a) teract with the user in natural language, top-left image (scene1) shows two persons sitting whereas the AOG’s predictions are consid- at the reception and others entering the audito- ered given and represented by the corre- rium and (b) top-right (scene2) image people run- sponding parse graphs (pg’s) of the AOG. ning out of an auditorium. Bottom-left shows the Our XAI model takes pg’s as input and AOG parse graph (pg) for the top-left image and provides answers to the user’s questions Bottom-right shows the pg for the top-right image using the following types of reasoning: direct evidence (e.g., detection scores), medical diagnosis domains (Imran et al., 2018; part-based inference (e.g., detected parts Hatamizadeh et al., 2019)). provide evidence for the concept asked), Consider for example, two frames (scenes) of and other evidences from spatiotemporal a video shown in Figure1. -
Phd Thesis, Technical University of Denmark (DTU)
UCLA UCLA Electronic Theses and Dissertations Title An Online Collaborative Ecosystem for Educational Computer Graphics Permalink https://escholarship.org/uc/item/1h68m5hg Author Ridge, Garett Douglas Publication Date 2018 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA Los Angeles An Online Collaborative Ecosystem for Educational Computer Graphics A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Computer Science by Garett Douglas Ridge 2018 c Copyright by Garett Douglas Ridge 2018 ABSTRACT OF THE DISSERTATION An Online Collaborative Ecosystem for Educational Computer Graphics by Garett Douglas Ridge Doctor of Philosophy in Computer Science University of California, Los Angeles, 2018 Professor Demetri Terzopoulos, Chair This thesis builds upon existing introductory courses in the field of Computer Graphics, aiming to lower the excessive barrier of entry to graphics programming. We introduce tiny- graphics.js, a new software library for implementing educational WebGL projects in the classroom. To mitigate the difficulty of creating graphics-enabled websites and online games, we furthermore introduce the Encyclopedia of Code|a world wide web framework that encourages visitors to learn graphics, build educational graphical demos and articles, host them online, and organize them by topic. We provide our own examples that include custom educational games and tutorial articles, which are already being successfully employed to ease our undergraduate graphics students into the course material. Some of our modules expose students to new graphics techniques, while others are prototypes for new modes of online learning, collaboration, and computing. These include our \Active Textbooks" (educational 3D demos or games embedded in literate-programming-like articles). -
Three Gray Classics on the Biomechanics of Animal Movement
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Harvard University - DASH Three Gray Classics on the Biomechanics of Animal Movement The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Lauder, G. V., Eric Tytell. 2004. Three Gray Classics on the Biomechanics of Animal Movement. Journal of Experimental Biology 207, no. 10: 1597–1599. doi:10.1242/jeb.00921. Published Version doi:10.1242/jeb.00921 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:30510313 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA JEB Classics 1597 THREE GRAY CLASSICS locomotor kinematics, muscle dynamics, JEB Classics is an occasional ON THE BIOMECHANICS and computational fluid dynamic column, featuring historic analyses of animals moving through publications from The Journal of OF ANIMAL MOVEMENT water. Virtually every recent textbook in Experimental Biology. These the field either reproduces one of Gray’s articles, written by modern experts figures directly or includes illustrations in the field, discuss each classic that derive their inspiration from his paper’s impact on the field of figures (e.g. Alexander, 2003; Biewener, biology and their own work. A 2003). PDF of the original paper accompanies each article, and can be found on the journal’s In his 1933a paper, Gray aimed to website as supplemental data. -
Swimming Speed Performance in Coral Reef Fishes
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Springer - Publisher Connector Coral Reefs (2007) 26:217–228 DOI 10.1007/s00338-007-0195-0 REPORT Swimming speed performance in coral reef fishes: field validations reveal distinct functional groups C. J. Fulton Received: 11 October 2006 / Accepted: 6 January 2007 / Published online: 7 February 2007 Ó Springer-Verlag 2007 Abstract Central to our understanding of locomotion of efficiency, maneuverability and speed in one mode in fishes are the performance implications of using of propulsion, pectoral swimming appears to be a different modes of swimming. Employing a unique particularly versatile form of locomotion, well suited to combination of laboratory performance trials and field a demersal lifestyle on coral reefs. observations of swimming speed, this study investi- gated the comparative performance of pectoral and Keywords Gait Á Pectoral Á Body-caudal Á body-caudal fin swimming within an entire assemblage Habitat-use Á Energetic of coral reef fishes (117 species 10 families). Field observations of swimming behaviour identified three primary modes: labriform (pectoral 70 spp.), subca- Introduction rangiform (body-caudal 29 spp.) and chaetodontiform (augmented body-caudal 18 spp.). While representa- Swimming performance can be crucial for the survival tive taxa from all three modes were capable of speeds of fishes by affecting their ability to access habitats, exceeding 50 cm s–1 during laboratory trials, only avoid predators and acquire food. Such essential daily pectoral-swimmers maintained such high speeds under tasks often require precise movements, bursts of speed, field conditions. Direct comparisons revealed that or prolonged periods of swimming depending on the pectoral-swimming species maintained field speeds at a habitat or predator–prey system involved (Videler remarkable 70% of their maximum (lab-tested) re- 1993; Plaut 2001; Castro-Santos 2005). -
Snakes: Active Contour Models
International Journal of Computer Vision, 321-331 (1988) o 1987 KIuwer Academic Publishers, Boston, Manufactured in The Netherlands Snakes: Active Contour Models MICHAEL KASS, ANDREW WITKIN, and DEMETRI TERZOPOULOS Schlumberger Palo Alto Research, 3340 Hillview Ave., Palo Alto, CA 94304 Abstract A snake is an energy-minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Scale-space continuation can be used to enlarge the cap- ture region surrounding a feature. Snakes provide a unified account of a number of visual problems, in- cluding detection of edges, lines, and subjective contours; motion tracking; and stereo matching. We have used snakes successfully for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest. 1 Introduction organizations. By adding suitable energy terms to the minimization, it is possible for a user to push In recent computational vision research, low- the model out of a local minimum toward the level tasks such as edge or line detection, stereo desired solution. The result is an active model matching, and motion tracking have been widely that falls into the desired solution when placed regarded as autonomous bottom-up processes. near it. Marr and Nishihara [ 111, in a strong statement of Energy minimizing models have a rich history this view, say that up to the 2.5D sketch, no in vision going back at least to Sperling’s stereo “higher-level” information is yet brought to bear: model [16]. -
Lecture Notes in Computer Science 5535 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan Van Leeuwen
Lecture Notes in Computer Science 5535 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Alfred Kobsa University of California, Irvine, CA, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen University of Dortmund, Germany Madhu Sudan Massachusetts Institute of Technology, MA, USA Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max-Planck Institute of Computer Science, Saarbruecken, Germany Geert-Jan Houben Gord McCalla Fabio Pianesi Massimo Zancanaro (Eds.) User Modeling, Adaptation, and Personalization 17th International Conference, UMAP 2009 formerly UM and AH Trento, Italy, June 22-26, 2009 Proceedings 13 Volume Editors Geert-Jan Houben Delft University of Technology PO Box 5031, 2600 GA Delft, The Netherlands E-mail: [email protected] Gord McCalla University of Saskatchewan Saskatoon, Saskatchewan S7N 5E2, Canada E-mail: [email protected] Fabio Pianesi Massimo Zancanaro FBK-irst via Sommarive 18, 38050 Povo, Italy E-mail: {pianesi,zancana}@fbk.eu Library of Congress Control Number: 2009928433 CR Subject Classification (1998): H.5.2, I.2, H.4, I.6, J.4, K.4, K.6 LNCS Sublibrary: SL 3 – Information Systems and Application, incl. -
LOCOMOTION ENERGETICS of LEEDSICHTHYS PROBLEMATICUS (ACTINOPTERYGII, PACHYCORMIFORMES) by HUMBERTO G
[Palaeontology, Vol. 61, Part 5, 2018, pp. 775–783] ASSESSING METABOLIC CONSTRAINTS ON THE MAXIMUM BODY SIZE OF ACTINOPTERYGIANS: LOCOMOTION ENERGETICS OF LEEDSICHTHYS PROBLEMATICUS (ACTINOPTERYGII, PACHYCORMIFORMES) by HUMBERTO G. FERRON 1,* ,BORJAHOLGADO2,* , JEFFREY J. LISTON3,4,5,6,CARLOSMARTINEZ-PEREZ 1,6 and HECTOR BOTELLA1 1Institut Cavanilles de Biodiversitat i Biologia Evolutiva, Universitat de Valencia, C/Catedratic Jose Beltran Martınez 2, 46980, Paterna, Valencia Spain; [email protected], [email protected], [email protected] 2Laboratory of Systematics & Taphonomy of Fossil Vertebrates, Departamento de Geologia e Paleontologia, Museu Nacional/Universidade Federal do Riode Janeiro (UFRJ), Quinta da Boa Vista, s/n, S~ao Cristov ~ao, 20940-040, Rio de Janeiro, RJ Brazil; [email protected] 3Bayerische Staatssammlung fur€ Pal€aontologie und Geologie, Richard-Wagner-Straße 10, 80333, Munchen,€ Germany; [email protected], [email protected] 4National Museums Scotland, Chambers Street, Edinburgh, EH1 1JF, UK; [email protected] 5Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK; [email protected] 6School of Earth Sciences, University of Bristol, Bristol, BS8 1TQ, UK; [email protected], [email protected] *Corresponding authors Typescript received 21 October 2017; accepted in revised form 5 April 2018 Abstract: Maximum sizes attained by living actinoptery- weighing up to 44.9 tonnes would have been energetically gians are much smaller than those reached by chon- viable and suggests that similar body sizes could also be possi- drichthyans. Several factors, including the high metabolic ble among living taxa, discarding metabolic factors as likely requirements of bony fishes, have been proposed as possible body size constraints in actinopterygians. -
Energetics and Behavior of Coral Reef Fishes During Oscillatory Swimming in a Simulated Wave Surge Travis M
© 2019. Published by The Company of Biologists Ltd | Journal of Experimental Biology (2019) 222, jeb191791. doi:10.1242/jeb.191791 RESEARCH ARTICLE Energetics and behavior of coral reef fishes during oscillatory swimming in a simulated wave surge Travis M. Marcoux and Keith E. Korsmeyer* ABSTRACT exposure has been identified as an important factor affecting reef Oxygen consumption rates were measured for coral reef fishes fish distributions, with correlations with fish morphology and during swimming in a bidirectional, oscillatory pattern to simulate swimming performance (Bellwood and Wainwright, 2001; Fulton, station-holding in wave-induced, shallow-water flows. For all species 2010; Webb et al., 2010; Fulton et al., 2017). examined, increases in wave intensity, as simulated by increases in Coral reef fishes display a great diversity of body and fin shapes, frequency and amplitude of oscillation, yielded increased metabolic and also swimming gaits, or patterns of fin use for swimming (Price rates and net costs of swimming (NCOS; swimming metabolic rate et al., 2011; Fulton et al., 2013b; Pink and Fulton, 2014). Swimming minus standard metabolic rate). Comparing species with different mode in fishes can be divided into two broad functional groups: – swimming modes, the caudal fin swimming Kuhlia spp. (Kuhliidae) body caudal fin (BCF) swimming, using lateral undulations of – and simultaneous pectoral–caudal fin swimming Amphiprion ocellaris the body and caudal fin, and median paired fin (MPF) swimming, (Pomacentridae) turned around to face the direction of swimming involving movements of one or more median (dorsal and anal) or most of the time, whereas the median–paired fin (MPF) swimmers, paired (pectoral) fins, while the body is held rigid (Webb, 1998). -
Dispersal Patterns, Active Behaviour, and Flow Environment During Early Life History of Coastal Cold Water Fishes
Dispersal Patterns, Active Behaviour, and Flow Environment during Early Life History of Coastal Cold Water Fishes Ryan Stanley1*, Paul V. R. Snelgrove1,2, Brad deYoung3, Robert S. Gregory4 1 Ocean Sciences Centre and Biology Department, Memorial University of Newfoundland, St. John’s, Newfoundland, Canada, 2 Canada Research Chair in Boreal and Cold Ocean Systems, Memorial University of Newfoundland, St. John’s, Newfoundland, Canada, 3 Department of Physics and Physical Oceanography, Memorial University of Newfoundland, St. John’s, Newfoundland, Canada, 4 Ecological Sciences Section and Centre of Expertise for Aquatic Habitat Research, Fisheries and Oceans Canada, St. John’s, Newfoundland, Canada Abstract During the pelagic larval phase, fish dispersal may be influenced passively by surface currents or actively determined by swimming behaviour. In situ observations of larval swimming are few given the constraints of field sampling. Active behaviour is therefore often inferred from spatial patterns in the field, laboratory studies, or hydrodynamic theory, but rarely are these approaches considered in concert. Ichthyoplankton survey data collected during 2004 and 2006 from coastal Newfoundland show that changes in spatial heterogeneity for multiple species do not conform to predictions based on passive transport. We evaluated the interaction of individual larvae with their environment by calculating Reynolds number as a function of ontogeny. Typically, larvae hatch into a viscous environment in which swimming is inefficient, and later grow into more efficient intermediate and inertial swimming environments. Swimming is therefore closely related to length, not only because of swimming capacity but also in how larvae experience viscosity. Six of eight species sampled demonstrated consistent changes in spatial patchiness and concomitant increases in spatial heterogeneity as they transitioned into more favourable hydrodynamic swimming environments, suggesting an active behavioural element to dispersal.