Dharani Punithan | Seoul National University (original) (raw)

Papers by Dharani Punithan

Research paper thumbnail of Scene Graph Parsing via Abstract Meaning Representation in Pre-trained Language Models

Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)

In this work, we propose the application of abstract meaning representation (AMR) based semantic ... more In this work, we propose the application of abstract meaning representation (AMR) based semantic parsing models to parse textual descriptions of a visual scene into scene graphs, which is the first work to the best of our knowledge. Previous works examined scene graph parsing from textual descriptions using dependency parsing and left the AMR parsing approach as future work since sophisticated methods are required to apply AMR. Hence, we use pre-trained AMR parsing models to parse the region descriptions of visual scenes (i.e. images) into AMR graphs and pre-trained language models (PLM), BART and T5, to parse AMR graphs into scene graphs. The experimental results show that our approach explicitly captures high-level semantics from textual descriptions of visual scenes, such as objects, attributes of objects, and relationships between objects. Our textual scene graph parsing approach outperforms the previous state-of-the-art results by 9.3% in the SPICE metric score.

Research paper thumbnail of Molecular Learning and Pattern Denoising using Markov Random Field Models

We propose an in silico molecular associative memory model for pattern learning, storage and deno... more We propose an in silico molecular associative memory model for pattern learning, storage and denoising using Pairwise Markov Random Field (PMRF) model. Our PMRF-based molecular associative memory model extracts locally distributed features from the exposed examples, learns and stores the patterns in the molecular associative memory and denoises the given noisy patterns via DNA computation based operations. Thus, our computational molecular model demonstrates the functionalities of content-addressability of human memory. Our molecular simulation results show that the averaged mean squared error between the learned and denoised patterns are low (< 0.014) up to 30% of noise.

Research paper thumbnail of Pattern Denoising in Molecular Associative Memory using Pairwise Markov Random Field Models

We propose an in silico molecular associative memory model for pattern learning, storage and deno... more We propose an in silico molecular associative memory model for pattern learning, storage and denoising using Pairwise Markov Random Field (PMRF) model. Our PMRF-based molecular associative memory model extracts locally distributed features from the exposed examples, learns and stores the patterns in the molecular associative memory and denoises the given noisy patterns via DNA computation based operations. Thus, our computational molecular model demonstrates the functionalities of content-addressability of human memory. Our molecular simulation results show that the averaged mean squared error between the learned and denoised patterns are low (< 0.014) up to 30% of noise.

Research paper thumbnail of Molecular Associative Memory with Spatial Auto-logistic Model for Pattern Recall

Procedia Computer Science

We propose a molecular associative memory model, by combining auto-logistic specifications, which... more We propose a molecular associative memory model, by combining auto-logistic specifications, which capture statistical dependencies within the local neighborhood systems of the exposed knowledge, with the bio-inspired DNA-based molecular operations, which store and evolve the memory. Our model, characterized by only the local dependencies of the spatial binary data, allows to capture only a fewer features. Our memory model stores the exposed patterns and recalls the stored patterns through bioinspired molecular operations. Our molecular memory simulation exemplifies the applications of associative memories in pattern storage and retrieval with high recall accuracy, even with lower order memory traces (pair-wise cliques) and thus exhibits brain-like content-addressing cognitive abilities.

Research paper thumbnail of Lifelong Learning with the Feedback-loop between Emotions and Actions via Internal Reward

Procedia Computer Science

We propose a cognitive model where an autonomous agent incorporates a cybernetic feedback-loop be... more We propose a cognitive model where an autonomous agent incorporates a cybernetic feedback-loop between emotional states and activity-selection mechanism via internal reward. In our model, emotions play a crucial role in providing motivation for agents to change activities and thereby promote continuous learning. We show that lifelong learning emerges as the emotions of the agent, decide the activity-selection via internal reward. We qualitatively analyze the emotional dynamics, exhibited by the proposed cognitive system in a minimalistic environment.

Research paper thumbnail of Ising Model based Molecular Associative Memory for Pattern Recall

We combine statistical-mechanical approach for probabilistic image processing with DNA based bio-... more We combine statistical-mechanical approach for probabilistic image processing with DNA based bio-molecular operations to construct associative memory for pattern recall. The statistical properties of the patterns are learned from the exposed examples, stored in memory and are recalled when presented with partial queries. The results show that our proposed memory model retrieves patterns with high recall accuracy.

Research paper thumbnail of Molecular Associative Memory for Pattern Recall with Local Neighborhood System

The 24th International Conference on DNA Computing and Molecular Programming (poster), 2018

Research paper thumbnail of Predicting the Progression of IgA Nephropathy using Machine Learning Methods

Proceedings of the 8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), 2015

Research paper thumbnail of Machine Learning Models and Statistical Measures for Predicting the Progression of IgA Nephropathy

International Journal of Software Engineering and Knowledge Engineering, 2015

Research paper thumbnail of Collective Dynamics and Homeostatic Emergence in Complex Adaptive Ecosystem

We investigate the behaviour of the daisyworld model on an adaptive network, comparing it to prev... more We investigate the behaviour of the daisyworld model on an adaptive network, comparing it to previous studies on a fixed topology grid, and a fixed small-world (Newman-Watts (NW)) network. The adaptive networks eventually generate topologies with small-world effect behaving similarly to the NW model -and radically different from the grid world. Under the same parameter settings, static but complex patterns emerge in the grid world. In the NW model, we see the emergence of completely coherent periodic dominance. In the adaptive-topology world, the systems may transit through varied behaviours, but can self-organise to a small-world network structure with similar cyclic behaviour to the NW model. Daisyworld (Watson and Lovelock, 1983) is an imaginary planet where only two types of species live -black and white daisies. These biotic components interact stigmergically via an abiotic component -temperature. The different colours of the daisies influence the albedo (reflectivity) of the planet. In the beginning, the atmosphere of the daisyworld is cooler and only black daisies thrive as they absorb all the energy. As the black daisy population expands, it warms the planet. When it is too warm for black daisies to survive, white daisies start to bloom since they reflect all the energy back into space. As the white daisy cover spreads, it cools the planet. When it is too cold for the survival of white daisies, again black daisies thrive. This endless cycle, owing to the bi-directional feedback loop between life and the environment, self-regulates the temperature and thereby allows life to persist.

Research paper thumbnail of Phase Transitions in Two-Dimensional Daisyworld with Small-World Effects - A Study of Local and Long-Range Couplings

Please cite this article as: D. Punithan, R.. McKay, Phase transitions in two-dimensional daisywo... more Please cite this article as: D. Punithan, R.. McKay, Phase transitions in two-dimensional daisyworld with small-world effects-A study of local and long-range couplings, Future Generation Computer Systems (2013), http://dx.

Research paper thumbnail of Spatio-Temporal Dynamics and Quantification of Daisyworld in Two-Dimensional Coupled Map Lattices

We spatially extend the daisyworld model on a two-dimensional toroidal coupled map lattice (CML -... more We spatially extend the daisyworld model on a two-dimensional toroidal coupled map lattice (CML -a generalisation of cellular automata). We investigated whether this tightly coupled system of local nonlinear dynamics with bi-directional life-environment feedback can generate a specific kind of behaviour, characterised by global stability coexisting with local instability. We introduce appropriate metrics to measure the spatio-temporal dynamics of the daisyworld system. Specifically, we evaluate spatial autocorrelation using Moran's I, and local and global temporal fluctuation through the permutation entropy and the temporal standard deviation. We categorise a range of different behaviours that can arise in such scenarios, and relate them through a parameter analysis. ß Please cite this article in press as: Punithan, D., et al., Spatio-temporal dynamics and quantification of daisyworld in two-dimensional coupled map lattices. Ecol. Complex. (2012), http://dx.

Research paper thumbnail of Sampling Bias in Estimation of Distribution Algorithms for Genetic Programming Using Prototype Trees

Probabilistic models are widely used in evolutionary and related algorithms. In Genetic Programmi... more Probabilistic models are widely used in evolutionary and related algorithms. In Genetic Programming (GP), the Probabilistic Prototype Tree (PPT) is often used as a model representation. Drift due to sampling bias is a widely recognised problem, and may be serious, particularly in dependent probability models. While this has been closely studied in independent probability models, and more recently in probabilistic dependency models, it has received little attention in systems with strict dependence between probabilistic variables such as arise in PPT representation. Here, we investigate this issue, and present results suggesting that the drift effect in such models may be particularly severe – so severe as to cast doubt on their scalability. We present a preliminary analysis through a factor representation of the joint probability distribution. We suggest future directions for research aiming to overcome this problem.

Research paper thumbnail of Evolutionary Dynamics and Ecosystems Feedback in Two Dimensional Daisyworld

Artificial Life, Jan 1, 2012

We introduce replicator-mutator mechanisms from evolutionary dynamics into a two-dimensional dais... more We introduce replicator-mutator mechanisms from evolutionary dynamics into a two-dimensional daisyworld model, thereby coupling evolutionary changes with daisyworld's bidirectional feedback between biota and environment. Daisyworld continues to self-regulate in the presence of these evolutionary forces. The most interesting behaviours, exhibiting a complex and dynamic dance through space and time in species' abundance, emerges through the introduction of additive spatio-temporal random perturbations in the form of thermal noise. The balance between ecosystem feedback and fluctuations in the ecosystem determines the spatial coexistence of domains of dominance between daisy species and their mutants or adaptants.

Research paper thumbnail of Daisyworld in Two Dimensional Small-World Networks

… Theory and Application, Bio-Science and …, Jan 1, 2011

Daisyworld was initially proposed as an abstract model of the selfregulation of planetary ecosyst... more Daisyworld was initially proposed as an abstract model of the selfregulation of planetary ecosystems. The original one-point model has also been extended to one-and two-dimensional worlds. The latter are especially interesting, in that they demonstrate the emergence of spatially-stablised homeostasis, in which individual locations in the world experience booms and busts, yet the overall behaviour is stabilised as patches of white and black daisies migrate around the world. We extend the model further, to small-world networks, more realistic for social interaction -and even for some forms of ecological interactionusing the Watts-Strogatz (WS) and Newman-Watts (NW) models. We find that spatially-stabilised homeostasis is able to persist in small-world networks. In the WS model, as the rewiring probabilities increase even far beyond normal smallworld limits, there is only a small loss of effectiveness. However as the average number of connections increases in the NW model, we see a gradual breakdown of spatial stabilisation, leading to less interesting -more homogeneous -worlds.

Research paper thumbnail of Cognitive Based Context Aware Reference History Management Tool

sc.snu.ac.kr

The aim of the research is to focus on the cognitive principles and to achieve humanlevel intelli... more The aim of the research is to focus on the cognitive principles and to achieve humanlevel intelligence in referring context based browser history and the Windows history. One of the major problems faced by today's computer users is insufficient and single exclusive context based reference of the browser history and the Windows history. Today we search for the browser history and Windows history in different places even though the context is the same. For e.g., When working on a research paper or preparing a business presentation, a user may require to refer many web sites on the internet and various documents on the local computer. The browser can provide only time based history. The windows document history is also time based and limited to list only few documents. Hence, we propose a tool "Cognitive Based Context Aware Reference History Management Tool" which helps to access the exclusive reference of context and time based history in one place. The tool also proposes to store image history with urls and classifies images of a specific topic accessed in different time, bookmarks management and cross browser history management. These features are very useful as we can access all related documents (doc, docx, ppt, pptx, pdf, txt, and html), web pages, images and bookmarks in one place. The tool uses the cognitive principles like classification and association to achieve the purpose. ↲

Research paper thumbnail of An XML format for sharing evolutionary algorithm output and analysis

Simulated Evolution and …, Jan 1, 2010

Analysis of artificial evolutionary systems uses post-processing to extract information from runs... more Analysis of artificial evolutionary systems uses post-processing to extract information from runs. Many effective methods have been developed, but format incompatibilities limit their adoption. We propose a solution combining XML and compression, which imposes modest overhead. We describe the steps to integrate our schema in existing systems and tools, demonstrating a realistic application. We measure the overhead relative to current methods, and discuss the extension of this approach into a community-wide standard representation.

Research paper thumbnail of Self-organizing spatio-temporal pattern formation in two-dimensional daisyworld

Self-Organizing Systems, Jan 1, 2012

Watson and Lovelock's daisyworld model was devised to demonstrate how the biota of a world could ... more Watson and Lovelock's daisyworld model was devised to demonstrate how the biota of a world could stabilise it, driving it to a temperature regime that favoured survival of the biota. The subsequent studies have focused on the behaviour of daisyworld in various fields. This study looks at the emergent patterns that arise in 2D daisyworlds at different parameter settings, demonstrating that a wide range of patterns can be observed. Selecting from an immense range of tested parameter settings, we present the emergence of complex patterns, Turinglike structures, cyclic patterns, random patterns and uniform dispersed patterns, corresponding to different kinds of possible worlds. The emergence of such complex behaviours from a simple, abstract model serve to illuminate the complex mosaic of patterns that we observe in real-world biosystems.

Research paper thumbnail of Scene Graph Parsing via Abstract Meaning Representation in Pre-trained Language Models

Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)

In this work, we propose the application of abstract meaning representation (AMR) based semantic ... more In this work, we propose the application of abstract meaning representation (AMR) based semantic parsing models to parse textual descriptions of a visual scene into scene graphs, which is the first work to the best of our knowledge. Previous works examined scene graph parsing from textual descriptions using dependency parsing and left the AMR parsing approach as future work since sophisticated methods are required to apply AMR. Hence, we use pre-trained AMR parsing models to parse the region descriptions of visual scenes (i.e. images) into AMR graphs and pre-trained language models (PLM), BART and T5, to parse AMR graphs into scene graphs. The experimental results show that our approach explicitly captures high-level semantics from textual descriptions of visual scenes, such as objects, attributes of objects, and relationships between objects. Our textual scene graph parsing approach outperforms the previous state-of-the-art results by 9.3% in the SPICE metric score.

Research paper thumbnail of Molecular Learning and Pattern Denoising using Markov Random Field Models

We propose an in silico molecular associative memory model for pattern learning, storage and deno... more We propose an in silico molecular associative memory model for pattern learning, storage and denoising using Pairwise Markov Random Field (PMRF) model. Our PMRF-based molecular associative memory model extracts locally distributed features from the exposed examples, learns and stores the patterns in the molecular associative memory and denoises the given noisy patterns via DNA computation based operations. Thus, our computational molecular model demonstrates the functionalities of content-addressability of human memory. Our molecular simulation results show that the averaged mean squared error between the learned and denoised patterns are low (< 0.014) up to 30% of noise.

Research paper thumbnail of Pattern Denoising in Molecular Associative Memory using Pairwise Markov Random Field Models

We propose an in silico molecular associative memory model for pattern learning, storage and deno... more We propose an in silico molecular associative memory model for pattern learning, storage and denoising using Pairwise Markov Random Field (PMRF) model. Our PMRF-based molecular associative memory model extracts locally distributed features from the exposed examples, learns and stores the patterns in the molecular associative memory and denoises the given noisy patterns via DNA computation based operations. Thus, our computational molecular model demonstrates the functionalities of content-addressability of human memory. Our molecular simulation results show that the averaged mean squared error between the learned and denoised patterns are low (< 0.014) up to 30% of noise.

Research paper thumbnail of Molecular Associative Memory with Spatial Auto-logistic Model for Pattern Recall

Procedia Computer Science

We propose a molecular associative memory model, by combining auto-logistic specifications, which... more We propose a molecular associative memory model, by combining auto-logistic specifications, which capture statistical dependencies within the local neighborhood systems of the exposed knowledge, with the bio-inspired DNA-based molecular operations, which store and evolve the memory. Our model, characterized by only the local dependencies of the spatial binary data, allows to capture only a fewer features. Our memory model stores the exposed patterns and recalls the stored patterns through bioinspired molecular operations. Our molecular memory simulation exemplifies the applications of associative memories in pattern storage and retrieval with high recall accuracy, even with lower order memory traces (pair-wise cliques) and thus exhibits brain-like content-addressing cognitive abilities.

Research paper thumbnail of Lifelong Learning with the Feedback-loop between Emotions and Actions via Internal Reward

Procedia Computer Science

We propose a cognitive model where an autonomous agent incorporates a cybernetic feedback-loop be... more We propose a cognitive model where an autonomous agent incorporates a cybernetic feedback-loop between emotional states and activity-selection mechanism via internal reward. In our model, emotions play a crucial role in providing motivation for agents to change activities and thereby promote continuous learning. We show that lifelong learning emerges as the emotions of the agent, decide the activity-selection via internal reward. We qualitatively analyze the emotional dynamics, exhibited by the proposed cognitive system in a minimalistic environment.

Research paper thumbnail of Ising Model based Molecular Associative Memory for Pattern Recall

We combine statistical-mechanical approach for probabilistic image processing with DNA based bio-... more We combine statistical-mechanical approach for probabilistic image processing with DNA based bio-molecular operations to construct associative memory for pattern recall. The statistical properties of the patterns are learned from the exposed examples, stored in memory and are recalled when presented with partial queries. The results show that our proposed memory model retrieves patterns with high recall accuracy.

Research paper thumbnail of Molecular Associative Memory for Pattern Recall with Local Neighborhood System

The 24th International Conference on DNA Computing and Molecular Programming (poster), 2018

Research paper thumbnail of Predicting the Progression of IgA Nephropathy using Machine Learning Methods

Proceedings of the 8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), 2015

Research paper thumbnail of Machine Learning Models and Statistical Measures for Predicting the Progression of IgA Nephropathy

International Journal of Software Engineering and Knowledge Engineering, 2015

Research paper thumbnail of Collective Dynamics and Homeostatic Emergence in Complex Adaptive Ecosystem

We investigate the behaviour of the daisyworld model on an adaptive network, comparing it to prev... more We investigate the behaviour of the daisyworld model on an adaptive network, comparing it to previous studies on a fixed topology grid, and a fixed small-world (Newman-Watts (NW)) network. The adaptive networks eventually generate topologies with small-world effect behaving similarly to the NW model -and radically different from the grid world. Under the same parameter settings, static but complex patterns emerge in the grid world. In the NW model, we see the emergence of completely coherent periodic dominance. In the adaptive-topology world, the systems may transit through varied behaviours, but can self-organise to a small-world network structure with similar cyclic behaviour to the NW model. Daisyworld (Watson and Lovelock, 1983) is an imaginary planet where only two types of species live -black and white daisies. These biotic components interact stigmergically via an abiotic component -temperature. The different colours of the daisies influence the albedo (reflectivity) of the planet. In the beginning, the atmosphere of the daisyworld is cooler and only black daisies thrive as they absorb all the energy. As the black daisy population expands, it warms the planet. When it is too warm for black daisies to survive, white daisies start to bloom since they reflect all the energy back into space. As the white daisy cover spreads, it cools the planet. When it is too cold for the survival of white daisies, again black daisies thrive. This endless cycle, owing to the bi-directional feedback loop between life and the environment, self-regulates the temperature and thereby allows life to persist.

Research paper thumbnail of Phase Transitions in Two-Dimensional Daisyworld with Small-World Effects - A Study of Local and Long-Range Couplings

Please cite this article as: D. Punithan, R.. McKay, Phase transitions in two-dimensional daisywo... more Please cite this article as: D. Punithan, R.. McKay, Phase transitions in two-dimensional daisyworld with small-world effects-A study of local and long-range couplings, Future Generation Computer Systems (2013), http://dx.

Research paper thumbnail of Spatio-Temporal Dynamics and Quantification of Daisyworld in Two-Dimensional Coupled Map Lattices

We spatially extend the daisyworld model on a two-dimensional toroidal coupled map lattice (CML -... more We spatially extend the daisyworld model on a two-dimensional toroidal coupled map lattice (CML -a generalisation of cellular automata). We investigated whether this tightly coupled system of local nonlinear dynamics with bi-directional life-environment feedback can generate a specific kind of behaviour, characterised by global stability coexisting with local instability. We introduce appropriate metrics to measure the spatio-temporal dynamics of the daisyworld system. Specifically, we evaluate spatial autocorrelation using Moran's I, and local and global temporal fluctuation through the permutation entropy and the temporal standard deviation. We categorise a range of different behaviours that can arise in such scenarios, and relate them through a parameter analysis. ß Please cite this article in press as: Punithan, D., et al., Spatio-temporal dynamics and quantification of daisyworld in two-dimensional coupled map lattices. Ecol. Complex. (2012), http://dx.

Research paper thumbnail of Sampling Bias in Estimation of Distribution Algorithms for Genetic Programming Using Prototype Trees

Probabilistic models are widely used in evolutionary and related algorithms. In Genetic Programmi... more Probabilistic models are widely used in evolutionary and related algorithms. In Genetic Programming (GP), the Probabilistic Prototype Tree (PPT) is often used as a model representation. Drift due to sampling bias is a widely recognised problem, and may be serious, particularly in dependent probability models. While this has been closely studied in independent probability models, and more recently in probabilistic dependency models, it has received little attention in systems with strict dependence between probabilistic variables such as arise in PPT representation. Here, we investigate this issue, and present results suggesting that the drift effect in such models may be particularly severe – so severe as to cast doubt on their scalability. We present a preliminary analysis through a factor representation of the joint probability distribution. We suggest future directions for research aiming to overcome this problem.

Research paper thumbnail of Evolutionary Dynamics and Ecosystems Feedback in Two Dimensional Daisyworld

Artificial Life, Jan 1, 2012

We introduce replicator-mutator mechanisms from evolutionary dynamics into a two-dimensional dais... more We introduce replicator-mutator mechanisms from evolutionary dynamics into a two-dimensional daisyworld model, thereby coupling evolutionary changes with daisyworld's bidirectional feedback between biota and environment. Daisyworld continues to self-regulate in the presence of these evolutionary forces. The most interesting behaviours, exhibiting a complex and dynamic dance through space and time in species' abundance, emerges through the introduction of additive spatio-temporal random perturbations in the form of thermal noise. The balance between ecosystem feedback and fluctuations in the ecosystem determines the spatial coexistence of domains of dominance between daisy species and their mutants or adaptants.

Research paper thumbnail of Daisyworld in Two Dimensional Small-World Networks

… Theory and Application, Bio-Science and …, Jan 1, 2011

Daisyworld was initially proposed as an abstract model of the selfregulation of planetary ecosyst... more Daisyworld was initially proposed as an abstract model of the selfregulation of planetary ecosystems. The original one-point model has also been extended to one-and two-dimensional worlds. The latter are especially interesting, in that they demonstrate the emergence of spatially-stablised homeostasis, in which individual locations in the world experience booms and busts, yet the overall behaviour is stabilised as patches of white and black daisies migrate around the world. We extend the model further, to small-world networks, more realistic for social interaction -and even for some forms of ecological interactionusing the Watts-Strogatz (WS) and Newman-Watts (NW) models. We find that spatially-stabilised homeostasis is able to persist in small-world networks. In the WS model, as the rewiring probabilities increase even far beyond normal smallworld limits, there is only a small loss of effectiveness. However as the average number of connections increases in the NW model, we see a gradual breakdown of spatial stabilisation, leading to less interesting -more homogeneous -worlds.

Research paper thumbnail of Cognitive Based Context Aware Reference History Management Tool

sc.snu.ac.kr

The aim of the research is to focus on the cognitive principles and to achieve humanlevel intelli... more The aim of the research is to focus on the cognitive principles and to achieve humanlevel intelligence in referring context based browser history and the Windows history. One of the major problems faced by today's computer users is insufficient and single exclusive context based reference of the browser history and the Windows history. Today we search for the browser history and Windows history in different places even though the context is the same. For e.g., When working on a research paper or preparing a business presentation, a user may require to refer many web sites on the internet and various documents on the local computer. The browser can provide only time based history. The windows document history is also time based and limited to list only few documents. Hence, we propose a tool "Cognitive Based Context Aware Reference History Management Tool" which helps to access the exclusive reference of context and time based history in one place. The tool also proposes to store image history with urls and classifies images of a specific topic accessed in different time, bookmarks management and cross browser history management. These features are very useful as we can access all related documents (doc, docx, ppt, pptx, pdf, txt, and html), web pages, images and bookmarks in one place. The tool uses the cognitive principles like classification and association to achieve the purpose. ↲

Research paper thumbnail of An XML format for sharing evolutionary algorithm output and analysis

Simulated Evolution and …, Jan 1, 2010

Analysis of artificial evolutionary systems uses post-processing to extract information from runs... more Analysis of artificial evolutionary systems uses post-processing to extract information from runs. Many effective methods have been developed, but format incompatibilities limit their adoption. We propose a solution combining XML and compression, which imposes modest overhead. We describe the steps to integrate our schema in existing systems and tools, demonstrating a realistic application. We measure the overhead relative to current methods, and discuss the extension of this approach into a community-wide standard representation.

Research paper thumbnail of Self-organizing spatio-temporal pattern formation in two-dimensional daisyworld

Self-Organizing Systems, Jan 1, 2012

Watson and Lovelock's daisyworld model was devised to demonstrate how the biota of a world could ... more Watson and Lovelock's daisyworld model was devised to demonstrate how the biota of a world could stabilise it, driving it to a temperature regime that favoured survival of the biota. The subsequent studies have focused on the behaviour of daisyworld in various fields. This study looks at the emergent patterns that arise in 2D daisyworlds at different parameter settings, demonstrating that a wide range of patterns can be observed. Selecting from an immense range of tested parameter settings, we present the emergence of complex patterns, Turinglike structures, cyclic patterns, random patterns and uniform dispersed patterns, corresponding to different kinds of possible worlds. The emergence of such complex behaviours from a simple, abstract model serve to illuminate the complex mosaic of patterns that we observe in real-world biosystems.