Humans have a tendency to invent new problems rather than solve old ones. As we build larger, more complex systems, we unearth global challenges around networks, compute resources and data. Have we neglected to see more elegant examples which existed all along?
It is possible for even the most complex systems to be organized and simplified in ways that may not occur to us. In situations where we still search for the right algorithms, by turning to complex natural systems around us we can find the problem was solved long ago. What we think is a new protocol may in fact be one that has been tested and evolving over hundreds or millions of years. One invented for the early internet is incredibly similar to a strategy evolved by desert ants millions of years ago. And this is why it works.
This talk will address these questions with examples of self-organization, decentralization and diversification from emergent phenomena found in nature.
Disorder And Tolerance In Distributed Systems At ScaleHelena Edelson
Rethinking intelligent resilient systems. Re-framing problems changes how we see and solve them. The intersection of scientific thought and principles parallels much of what we solve as engineers of information (e.g. uncertainty, time, distribution) and need. This talk is an interdisciplinary look at complex adaptive systems and how they innately solve things like resource distribution, growth and rebalancing. From the context of intelligence and systems, this talk will look at ideas around entropy and time, ensemble forecasting, self-organization theory, the butterfly effect, virus-human co-evolution and adaption, natural feedback loops, self-balancing, and adaptation.
Can we leverage these principles, behaviors and strategies to design intelligent systems at scale?
Can seeing things in an interdisciplinary way benefit solving common problems and speed innovation?
Swarm intelligence is an artificial intelligence technique based on the collective behavior of decentralized and self-organized systems. Ant colony optimization is an algorithm that was developed based on the behavior of ants in nature. In ant colony optimization, artificial ants probabilistically build solutions to optimization problems and modify pheromone trails that influence the behavior of other ants. This process results in the emergence of shortest paths through positive feedback as more ants follow promising trails.
Jyotishkar dey roll 36.(swarm intelligence)Jyotishkar Dey
Swarm intelligence is inspired by collective behavior of social insects like ants and bees. It involves developing algorithms for problem solving using decentralized and self-organized agents. Some examples of swarm intelligence include ant colony optimization and bee algorithms. Ant colony optimization works by simulating the pheromone trails left by ants to find shortest paths. The bee algorithm is based on how bees communicate through waggle dances to efficiently locate pollen sources. Swarm intelligence has applications in robotics, communication networks, and mobile ad-hoc networks. While it offers advantages like scalability and robustness, it also has disadvantages such as unknown convergence times and potential for stagnation.
This document discusses various approaches to natural computing, including artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, molecular computing, and quantum computing. It also discusses several nature-inspired models of computation such as cellular automata, neural networks, evolutionary computation, swarm intelligence, artificial immune systems, and amorphous computing. Specific examples discussed include the Game of Life cellular automaton, ant colony optimization, artificial immune systems, amorphous computing, and artificial life simulations.
Swarm intelligence is an artificial intelligence technique inspired by the collective behavior of decentralized and self-organized systems found in nature, such as ant colonies, bird flocks, and bee swarms. It involves agents interacting locally with each other and their environment. Notable swarm intelligence algorithms include ant colony optimization, which is used to solve optimization problems, and particle swarm optimization.
This presentation proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole.
This presentation is based on https://link.springer.com/article/10.1007%2Fs00521-015-1870-7
Linnaeus developed the first hierarchical system for classifying organisms in the 18th century based on observable traits. While useful, it did not reflect evolutionary relationships. Modern systematics aims to classify organisms based on their evolutionary history and common ancestry, as revealed by anatomical similarities, genetic data, and the fossil record. Phylogenetic trees illustrate evolutionary relationships and shared ancestors between modern species and are constantly refined as new evidence emerges.
The tweet spread widely within 48 hours, invading other spaces on Twitter. Some accounts resisted sharing it while others helped disseminate it by retweeting. The original tweet and active retweeters acted as bridges to spread the idea more widely. Hubs helped the tweet reach more people and networks, confirming theories about how information spreads through social networks.
Disorder And Tolerance In Distributed Systems At ScaleHelena Edelson
Rethinking intelligent resilient systems. Re-framing problems changes how we see and solve them. The intersection of scientific thought and principles parallels much of what we solve as engineers of information (e.g. uncertainty, time, distribution) and need. This talk is an interdisciplinary look at complex adaptive systems and how they innately solve things like resource distribution, growth and rebalancing. From the context of intelligence and systems, this talk will look at ideas around entropy and time, ensemble forecasting, self-organization theory, the butterfly effect, virus-human co-evolution and adaption, natural feedback loops, self-balancing, and adaptation.
Can we leverage these principles, behaviors and strategies to design intelligent systems at scale?
Can seeing things in an interdisciplinary way benefit solving common problems and speed innovation?
Swarm intelligence is an artificial intelligence technique based on the collective behavior of decentralized and self-organized systems. Ant colony optimization is an algorithm that was developed based on the behavior of ants in nature. In ant colony optimization, artificial ants probabilistically build solutions to optimization problems and modify pheromone trails that influence the behavior of other ants. This process results in the emergence of shortest paths through positive feedback as more ants follow promising trails.
Jyotishkar dey roll 36.(swarm intelligence)Jyotishkar Dey
Swarm intelligence is inspired by collective behavior of social insects like ants and bees. It involves developing algorithms for problem solving using decentralized and self-organized agents. Some examples of swarm intelligence include ant colony optimization and bee algorithms. Ant colony optimization works by simulating the pheromone trails left by ants to find shortest paths. The bee algorithm is based on how bees communicate through waggle dances to efficiently locate pollen sources. Swarm intelligence has applications in robotics, communication networks, and mobile ad-hoc networks. While it offers advantages like scalability and robustness, it also has disadvantages such as unknown convergence times and potential for stagnation.
This document discusses various approaches to natural computing, including artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, molecular computing, and quantum computing. It also discusses several nature-inspired models of computation such as cellular automata, neural networks, evolutionary computation, swarm intelligence, artificial immune systems, and amorphous computing. Specific examples discussed include the Game of Life cellular automaton, ant colony optimization, artificial immune systems, amorphous computing, and artificial life simulations.
Swarm intelligence is an artificial intelligence technique inspired by the collective behavior of decentralized and self-organized systems found in nature, such as ant colonies, bird flocks, and bee swarms. It involves agents interacting locally with each other and their environment. Notable swarm intelligence algorithms include ant colony optimization, which is used to solve optimization problems, and particle swarm optimization.
This presentation proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole.
This presentation is based on https://link.springer.com/article/10.1007%2Fs00521-015-1870-7
Linnaeus developed the first hierarchical system for classifying organisms in the 18th century based on observable traits. While useful, it did not reflect evolutionary relationships. Modern systematics aims to classify organisms based on their evolutionary history and common ancestry, as revealed by anatomical similarities, genetic data, and the fossil record. Phylogenetic trees illustrate evolutionary relationships and shared ancestors between modern species and are constantly refined as new evidence emerges.
The tweet spread widely within 48 hours, invading other spaces on Twitter. Some accounts resisted sharing it while others helped disseminate it by retweeting. The original tweet and active retweeters acted as bridges to spread the idea more widely. Hubs helped the tweet reach more people and networks, confirming theories about how information spreads through social networks.
Swarm intelligence is an artificial intelligence technique inspired by the collective behavior of decentralized and self-organized systems found in nature, such as ant colonies and bird flocks. Two common swarm intelligence algorithms are ant colony optimization and particle swarm optimization. Ant colony optimization is based on the behavior of real ant colonies and can be used to find approximate solutions to difficult optimization problems. Particle swarm optimization is a population-based stochastic optimization technique inspired by swarming behavior in nature, such as bird flocking. It searches for optimal solutions within a problem space by updating the movement of individual particles based on their own experiences and those of neighboring particles.
Temporal profiles of avalanches on networksJames Gleeson
My talk at the Workshop on Advances on Epidemics in Complex Networks, Delft University of Technology, The Netherlands, 31 Aug 2017 www.nas.ewi.tudelft.nl/aecn/
Ch1-Introduction to computation Intelligence.pptxAbhijeet Gole
Here are 3 assignments related to the document:
1. Analyze Turing's test for computer intelligence and his prediction that computers could pass a 5-minute version with 70% probability given 109 bits of storage.
2. Evaluate the 1996 IEEE Neural Networks Council's definition of artificial intelligence in relation to computational intelligence paradigms.
3. Demonstrate how each computational intelligence paradigm—artificial neural networks, evolutionary computation, swarm intelligence, artificial immune systems, and fuzzy systems—satisfies the provided definition of computational intelligence.
Emergent Behavior and SCM Introduction In this exercise, the .docxSALU18
Emergent Behavior and SCM
Introduction:
In this exercise, the student will analyze emergent behavior as it applies to SCM.
Tasks:
Read "Executive Insight in Hugos": Essentials of Supply Chain Management, answer the following questions:
• Explain how negative feedback improves the performance of a supply chain.
• Describe the steps that managers can take to encourage positive emergent behavior in their supply chains.
• Why is emergent behavior important to continued success?
2-3 pages. APA citations.
Emergent behavior is what happens when an interconnected system of relatively simple elements begins to self-organize to form a more intelligent and more adaptive higher-level system. Steven Johnson in his book, Emergence: The Connected Lives of Ants, Brains, Cities, and Software, explores the conditions that bring about this phenomenon.
In an interview with Steven Johnson I posed six questions and asked him to share his insights on a range of topics. These topics range from what gives a system emergent characteristics to how could companies organize their supply chains so as to encourage and benefit from emergent behavior.
· What is an “emergent system”? How is an emergent system different from an assembly line? The catchphrase that I sometimes use is that an emergent system is “smarter” than the sum of its parts. They tend to be systems made up of many interacting agents, each of which is following relatively simple rules governing its encounters with other agents. Somehow, out of all these local interactions, a higher-level, global intelligence “emerges.” The extraordinary thing about these systems is that there's no master planner or executive branch—the overall group creates the intelligence and adaptability; it's not something passed down from the leadership. An ant colony is a great example of this: colonies manage to pull off extraordinary feats of resource management and engineering and task allocation, all by following remarkably simple rules of interaction, using a simple chemical language to communicate. There's a queen ant in the colony, but she's only called that because she's the chief reproductive engine for the colony—she doesn't have any actually command authority. The ordinary ants just do the thinking collectively, without a leader. A key difference between an emergent system and an assembly line lies in the fluidity of the emergent system: randomness is a key component of the way an ant colony will explore a given environment—take the random element out, and the colony gets much less interesting, much less capable of stumbling across new ideas. Assembly lines are all about setting fixed patterns, and eliminating randomness; emergence is all about stumbling across new patterns that work better than the old ones.
· You say that such systems are “bottom up systems, not top-down.” These systems solve problems by drawing on masses of simple elements instead of relying on a single, intelligent “executive branch.” What ...
This document discusses swarm intelligence, which is an artificial intelligence technique inspired by the collective behavior of decentralized, self-organized systems found in nature, such as bird flocking, ant colonies, bee swarms, and fish schooling. The key principles of swarm intelligence are that there is no central control, agents follow simple rules, and emergent intelligence arises from the interactions between agents. Two commonly used swarm intelligence algorithms are ant colony optimization, inspired by how ants find food sources, and particle swarm optimization, inspired by the flocking behavior of birds. Swarm intelligence techniques have various applications in areas like robotics, engineering, telecommunications, and more.
Swarm intelligence is a biologically inspired field that studies how social behaviors emerge from the interactions between individuals in a decentralized system. It draws inspiration from natural systems like bird flocking and ant colonies. Particle swarm optimization and ant colony optimization are two popular swarm intelligence algorithms. PSO mimics bird flocking by having particles update their velocities based on their own experience and the swarm's experience. ACO mimics ant foraging behavior by having artificial ants deposit and follow pheromone trails to iteratively find optimal solutions. Both algorithms have been applied to problems like optimization and routing.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
The document discusses swarm intelligence and several algorithms inspired by it, including ant colony optimization, particle swarm optimization, and stochastic diffusion search. It provides examples of how each algorithm works, modeling the decentralized and self-organized behavior of swarms in nature. It also mentions related metaheuristic optimization techniques like genetic algorithms, simulated annealing, and tabu search.
Adaptive Collective Systems - Herding black sheepFoCAS Initiative
This book is about understanding, designing, controlling, and governing adaptive collective systems. It is intended for readers from master's students to Ph.D. students, from engineers to decision makers, and anyone else who is interested in understanding how technologies are changing the way we think and live.
The authors are academics working in various areas of a new rising field: adaptive collective systems.
Stuart Anderson (The University of Edinburgh, United Kingdom)
Nicolas Bredeche (Université Pierre et Marie Curie, France)
A.E. Eiben (VU University Amsterdam, Netherlands)
George Kampis (DFKI, Germany)
Maarten van Steen (VU University Amsterdam, Netherlands)
Book Sprint collaborative writing session facilitator: Adam Hyde
Editor: Sandra Sarala
Designer: Henrik van Leeuwen
Cell junctions , cell adhesion and extra cellular matrixMinali Singh
Cell junctions connect cells to each other and to the extracellular matrix through four main types: anchoring junctions, occluding junctions, channel-forming junctions, and signal-relaying junctions. Anchoring junctions include cadherins and integrins, which link cells together and attach cells to the extracellular matrix. Tight junctions and desmosomes form barriers and anchor cells via intermediate filaments. Gap junctions connect cell cytoplasm to allow communication through molecule and ion transfer. The extracellular matrix surrounds cells and is composed of collagen fibers and proteoglycans.
Interlude (2): Life and knowledge at higher levels of organization - Meetup s...William Hall
The document discusses different levels of organization in living systems and the emergence of autopoiesis and knowledge at each level. It covers:
1) The emergence of autopoietic cognition at the molecular level through self-regulation and feedback control embodied in molecular structure.
2) The codification of self-regulatory knowledge in self-replicating macromolecules like DNA, RNA, and proteins.
3) Higher levels of autopoiesis and knowledge at the cellular level in prokaryotes and eukaryotes, and then at the multicellular level in tissues, organs and organ systems.
Chaos theory deals with nonlinear and complex systems that are highly sensitive to initial conditions. These systems, while deterministic, are largely unpredictable due to this sensitivity. Lorenz discovered this "butterfly effect" through modeling atmospheric convection. Chaotic systems evolve toward attractors, which can be fixed points, limit cycles, or strange attractors exhibiting fractal geometry. This geometry is seen throughout nature. While chaotic systems cannot be precisely predicted, control methods like Ott-Grebogi-Yorke can influence their behavior. Chaos theory has applications across many domains.
This document provides an overview of molecular phylogenetics and computational methods for reconstructing evolutionary relationships between genetic sequences. It discusses key topics like molecular evolution, calculating genetic distances, clustering algorithms like UPGMA and neighbor joining, and cladistic methods like parsimony. The document also explains important concepts in phylogenetics including orthologs and paralogs, phenetic versus cladistic approaches, and maximum likelihood methods.
Swarm intelligence is inspired by the collective behavior of social insects like ants and bees. It involves the decentralized control of groups of simple agents interacting locally with each other and their environment. Some key points covered:
- Particle swarm optimization and ant colony optimization are two popular swarm intelligence algorithms. They are inspired by bird flocking and ant foraging behavior respectively.
- PSO mimics social interaction to optimize problems. Each agent updates its position based on its own experience and neighboring agents. ACO uses simulated ants depositing pheromones to probabilistically construct solutions.
- Swarm intelligence systems are robust, relatively simple, and can achieve complex functions through self-organization and emergence. They have applications in routing
Swarm intelligence is inspired by the collective behavior of social insects like ants and bees. It involves the decentralized control of groups of simple agents interacting locally with each other and their environment. Some key points covered:
- Particle swarm optimization and ant colony optimization are two popular swarm intelligence algorithms. They are inspired by bird flocking and ant foraging behavior respectively.
- PSO mimics social interaction to optimize problems. Each agent updates its position based on its own experience and neighboring agents. ACO uses artificial ants depositing pheromones to probabilistically construct solutions.
- Swarm intelligence systems are robust, relatively simple, and can achieve complex functions through self-organization and emergence. They have applications in routing
Swarm intelligence systems often comprise a population of essential agents interacting locally with one another and their surroundings. Again, nature, particularly biological systems, is a frequent source of inspiration. Although no centralized control structure dictates how individual agents should behave, local and, to some extent, random interactions between such agents create "intelligent" global behaviour unknown to the respective agents.
Web mining involves discovering useful information from web data through three main types: web structure mining analyzes hyperlink structures between pages, web content mining extracts information from page contents, and web usage mining analyzes patterns from user interactions on websites. Swarm intelligence is used to model collective behavior of decentralized self-organized systems and has been applied to problems like routing, robotics, and optimization through approaches like ant colony optimization and particle swarm optimization that are inspired by behaviors of social insects like ants and birds.
This document provides an introduction to genetic algorithms and genetic programming. It discusses how genetic algorithms are inspired by natural selection and genetics, using operations like crossover and mutation to evolve solutions to problems. It also outlines the basic steps of a genetic programming framework, including generating an initial population randomly, evaluating fitness, selecting parents, performing crossover and mutation to create offspring, and iterating until a solution is found. Representation using syntax trees and example genetic operators like single point crossover are described.
Data stream processing platforms and microservices platform infrastructure and strategies are converging. As we edge towards larger, more complex and decoupled systems, combined with the continual growth of the global information graph, our frontiers of unsolved challenges grow equally as fast. Central challenges for distributed systems include persistence strategies across DCs, zones or regions, network partitions, data optimization, system stability in all phases.
How does leveraging CRDTs and Event Sourcing address several core distributed systems challenges? What are useful strategies and patterns involved in the design, deployment, and running of stateful and stateless applications for the cloud, for example with Kubernetes. Combined with code samples, we will see how Akka Cluster, Multi-DC Persistence, Split Brain, Sharding and Distributed Data can help solve these problems.
Building Reactive Distributed Systems For Streaming Big Data, Analytics & Mac...Helena Edelson
Building Self Healing, Intelligent Platforms, systems that learn, multi-datacenter, removing human intervention with ML. Reactive Summit 2016 @helenaedelson
Swarm intelligence is an artificial intelligence technique inspired by the collective behavior of decentralized and self-organized systems found in nature, such as ant colonies and bird flocks. Two common swarm intelligence algorithms are ant colony optimization and particle swarm optimization. Ant colony optimization is based on the behavior of real ant colonies and can be used to find approximate solutions to difficult optimization problems. Particle swarm optimization is a population-based stochastic optimization technique inspired by swarming behavior in nature, such as bird flocking. It searches for optimal solutions within a problem space by updating the movement of individual particles based on their own experiences and those of neighboring particles.
Temporal profiles of avalanches on networksJames Gleeson
My talk at the Workshop on Advances on Epidemics in Complex Networks, Delft University of Technology, The Netherlands, 31 Aug 2017 www.nas.ewi.tudelft.nl/aecn/
Ch1-Introduction to computation Intelligence.pptxAbhijeet Gole
Here are 3 assignments related to the document:
1. Analyze Turing's test for computer intelligence and his prediction that computers could pass a 5-minute version with 70% probability given 109 bits of storage.
2. Evaluate the 1996 IEEE Neural Networks Council's definition of artificial intelligence in relation to computational intelligence paradigms.
3. Demonstrate how each computational intelligence paradigm—artificial neural networks, evolutionary computation, swarm intelligence, artificial immune systems, and fuzzy systems—satisfies the provided definition of computational intelligence.
Emergent Behavior and SCM Introduction In this exercise, the .docxSALU18
Emergent Behavior and SCM
Introduction:
In this exercise, the student will analyze emergent behavior as it applies to SCM.
Tasks:
Read "Executive Insight in Hugos": Essentials of Supply Chain Management, answer the following questions:
• Explain how negative feedback improves the performance of a supply chain.
• Describe the steps that managers can take to encourage positive emergent behavior in their supply chains.
• Why is emergent behavior important to continued success?
2-3 pages. APA citations.
Emergent behavior is what happens when an interconnected system of relatively simple elements begins to self-organize to form a more intelligent and more adaptive higher-level system. Steven Johnson in his book, Emergence: The Connected Lives of Ants, Brains, Cities, and Software, explores the conditions that bring about this phenomenon.
In an interview with Steven Johnson I posed six questions and asked him to share his insights on a range of topics. These topics range from what gives a system emergent characteristics to how could companies organize their supply chains so as to encourage and benefit from emergent behavior.
· What is an “emergent system”? How is an emergent system different from an assembly line? The catchphrase that I sometimes use is that an emergent system is “smarter” than the sum of its parts. They tend to be systems made up of many interacting agents, each of which is following relatively simple rules governing its encounters with other agents. Somehow, out of all these local interactions, a higher-level, global intelligence “emerges.” The extraordinary thing about these systems is that there's no master planner or executive branch—the overall group creates the intelligence and adaptability; it's not something passed down from the leadership. An ant colony is a great example of this: colonies manage to pull off extraordinary feats of resource management and engineering and task allocation, all by following remarkably simple rules of interaction, using a simple chemical language to communicate. There's a queen ant in the colony, but she's only called that because she's the chief reproductive engine for the colony—she doesn't have any actually command authority. The ordinary ants just do the thinking collectively, without a leader. A key difference between an emergent system and an assembly line lies in the fluidity of the emergent system: randomness is a key component of the way an ant colony will explore a given environment—take the random element out, and the colony gets much less interesting, much less capable of stumbling across new ideas. Assembly lines are all about setting fixed patterns, and eliminating randomness; emergence is all about stumbling across new patterns that work better than the old ones.
· You say that such systems are “bottom up systems, not top-down.” These systems solve problems by drawing on masses of simple elements instead of relying on a single, intelligent “executive branch.” What ...
This document discusses swarm intelligence, which is an artificial intelligence technique inspired by the collective behavior of decentralized, self-organized systems found in nature, such as bird flocking, ant colonies, bee swarms, and fish schooling. The key principles of swarm intelligence are that there is no central control, agents follow simple rules, and emergent intelligence arises from the interactions between agents. Two commonly used swarm intelligence algorithms are ant colony optimization, inspired by how ants find food sources, and particle swarm optimization, inspired by the flocking behavior of birds. Swarm intelligence techniques have various applications in areas like robotics, engineering, telecommunications, and more.
Swarm intelligence is a biologically inspired field that studies how social behaviors emerge from the interactions between individuals in a decentralized system. It draws inspiration from natural systems like bird flocking and ant colonies. Particle swarm optimization and ant colony optimization are two popular swarm intelligence algorithms. PSO mimics bird flocking by having particles update their velocities based on their own experience and the swarm's experience. ACO mimics ant foraging behavior by having artificial ants deposit and follow pheromone trails to iteratively find optimal solutions. Both algorithms have been applied to problems like optimization and routing.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
The document discusses swarm intelligence and several algorithms inspired by it, including ant colony optimization, particle swarm optimization, and stochastic diffusion search. It provides examples of how each algorithm works, modeling the decentralized and self-organized behavior of swarms in nature. It also mentions related metaheuristic optimization techniques like genetic algorithms, simulated annealing, and tabu search.
Adaptive Collective Systems - Herding black sheepFoCAS Initiative
This book is about understanding, designing, controlling, and governing adaptive collective systems. It is intended for readers from master's students to Ph.D. students, from engineers to decision makers, and anyone else who is interested in understanding how technologies are changing the way we think and live.
The authors are academics working in various areas of a new rising field: adaptive collective systems.
Stuart Anderson (The University of Edinburgh, United Kingdom)
Nicolas Bredeche (Université Pierre et Marie Curie, France)
A.E. Eiben (VU University Amsterdam, Netherlands)
George Kampis (DFKI, Germany)
Maarten van Steen (VU University Amsterdam, Netherlands)
Book Sprint collaborative writing session facilitator: Adam Hyde
Editor: Sandra Sarala
Designer: Henrik van Leeuwen
Cell junctions , cell adhesion and extra cellular matrixMinali Singh
Cell junctions connect cells to each other and to the extracellular matrix through four main types: anchoring junctions, occluding junctions, channel-forming junctions, and signal-relaying junctions. Anchoring junctions include cadherins and integrins, which link cells together and attach cells to the extracellular matrix. Tight junctions and desmosomes form barriers and anchor cells via intermediate filaments. Gap junctions connect cell cytoplasm to allow communication through molecule and ion transfer. The extracellular matrix surrounds cells and is composed of collagen fibers and proteoglycans.
Interlude (2): Life and knowledge at higher levels of organization - Meetup s...William Hall
The document discusses different levels of organization in living systems and the emergence of autopoiesis and knowledge at each level. It covers:
1) The emergence of autopoietic cognition at the molecular level through self-regulation and feedback control embodied in molecular structure.
2) The codification of self-regulatory knowledge in self-replicating macromolecules like DNA, RNA, and proteins.
3) Higher levels of autopoiesis and knowledge at the cellular level in prokaryotes and eukaryotes, and then at the multicellular level in tissues, organs and organ systems.
Chaos theory deals with nonlinear and complex systems that are highly sensitive to initial conditions. These systems, while deterministic, are largely unpredictable due to this sensitivity. Lorenz discovered this "butterfly effect" through modeling atmospheric convection. Chaotic systems evolve toward attractors, which can be fixed points, limit cycles, or strange attractors exhibiting fractal geometry. This geometry is seen throughout nature. While chaotic systems cannot be precisely predicted, control methods like Ott-Grebogi-Yorke can influence their behavior. Chaos theory has applications across many domains.
This document provides an overview of molecular phylogenetics and computational methods for reconstructing evolutionary relationships between genetic sequences. It discusses key topics like molecular evolution, calculating genetic distances, clustering algorithms like UPGMA and neighbor joining, and cladistic methods like parsimony. The document also explains important concepts in phylogenetics including orthologs and paralogs, phenetic versus cladistic approaches, and maximum likelihood methods.
Swarm intelligence is inspired by the collective behavior of social insects like ants and bees. It involves the decentralized control of groups of simple agents interacting locally with each other and their environment. Some key points covered:
- Particle swarm optimization and ant colony optimization are two popular swarm intelligence algorithms. They are inspired by bird flocking and ant foraging behavior respectively.
- PSO mimics social interaction to optimize problems. Each agent updates its position based on its own experience and neighboring agents. ACO uses simulated ants depositing pheromones to probabilistically construct solutions.
- Swarm intelligence systems are robust, relatively simple, and can achieve complex functions through self-organization and emergence. They have applications in routing
Swarm intelligence is inspired by the collective behavior of social insects like ants and bees. It involves the decentralized control of groups of simple agents interacting locally with each other and their environment. Some key points covered:
- Particle swarm optimization and ant colony optimization are two popular swarm intelligence algorithms. They are inspired by bird flocking and ant foraging behavior respectively.
- PSO mimics social interaction to optimize problems. Each agent updates its position based on its own experience and neighboring agents. ACO uses artificial ants depositing pheromones to probabilistically construct solutions.
- Swarm intelligence systems are robust, relatively simple, and can achieve complex functions through self-organization and emergence. They have applications in routing
Swarm intelligence systems often comprise a population of essential agents interacting locally with one another and their surroundings. Again, nature, particularly biological systems, is a frequent source of inspiration. Although no centralized control structure dictates how individual agents should behave, local and, to some extent, random interactions between such agents create "intelligent" global behaviour unknown to the respective agents.
Web mining involves discovering useful information from web data through three main types: web structure mining analyzes hyperlink structures between pages, web content mining extracts information from page contents, and web usage mining analyzes patterns from user interactions on websites. Swarm intelligence is used to model collective behavior of decentralized self-organized systems and has been applied to problems like routing, robotics, and optimization through approaches like ant colony optimization and particle swarm optimization that are inspired by behaviors of social insects like ants and birds.
This document provides an introduction to genetic algorithms and genetic programming. It discusses how genetic algorithms are inspired by natural selection and genetics, using operations like crossover and mutation to evolve solutions to problems. It also outlines the basic steps of a genetic programming framework, including generating an initial population randomly, evaluating fitness, selecting parents, performing crossover and mutation to create offspring, and iterating until a solution is found. Representation using syntax trees and example genetic operators like single point crossover are described.
Data stream processing platforms and microservices platform infrastructure and strategies are converging. As we edge towards larger, more complex and decoupled systems, combined with the continual growth of the global information graph, our frontiers of unsolved challenges grow equally as fast. Central challenges for distributed systems include persistence strategies across DCs, zones or regions, network partitions, data optimization, system stability in all phases.
How does leveraging CRDTs and Event Sourcing address several core distributed systems challenges? What are useful strategies and patterns involved in the design, deployment, and running of stateful and stateless applications for the cloud, for example with Kubernetes. Combined with code samples, we will see how Akka Cluster, Multi-DC Persistence, Split Brain, Sharding and Distributed Data can help solve these problems.
Building Reactive Distributed Systems For Streaming Big Data, Analytics & Mac...Helena Edelson
Building Self Healing, Intelligent Platforms, systems that learn, multi-datacenter, removing human intervention with ML. Reactive Summit 2016 @helenaedelson
NoLambda: Combining Streaming, Ad-Hoc, Machine Learning and Batch AnalysisHelena Edelson
Slides from my talk with Evan Chan at Strata San Jose: NoLambda: Combining Streaming, Ad-Hoc, Machine Learning and Batch Analysis. Streaming analytics architecture in big data for fast streaming, ad hoc and batch, with Kafka, Spark Streaming, Akka, Mesos, Cassandra and FiloDB. Simplifying to a unified architecture.
Fast and Simplified Streaming, Ad-Hoc and Batch Analytics with FiloDB and Spa...Helena Edelson
O'Reilly Webcast with Myself and Evan Chan on the new SNACK Stack (playoff of SMACK) with FIloDB: Scala, Spark Streaming, Akka, Cassandra, FiloDB and Kafka.
This talk will address new architectures emerging for large scale streaming analytics. Some based on Spark, Mesos, Akka, Cassandra and Kafka (SMACK) and other newer streaming analytics platforms and frameworks using Apache Flink or GearPump. Popular architecture like Lambda separate layers of computation and delivery and require many technologies which have overlapping functionality. Some of this results in duplicated code, untyped processes, or high operational overhead, let alone the cost (e.g. ETL).
I will discuss the problem domain and what is needed in terms of strategies, architecture and application design and code to begin leveraging simpler data flows. We will cover how the particular set of technologies addresses common requirements and how collaboratively they work together to enrich and reinforce each other.
Streaming Analytics with Spark, Kafka, Cassandra and AkkaHelena Edelson
This document discusses a new approach to building scalable data processing systems using streaming analytics with Spark, Kafka, Cassandra, and Akka. It proposes moving away from architectures like Lambda and ETL that require duplicating data and logic. The new approach leverages Spark Streaming for a unified batch and stream processing runtime, Apache Kafka for scalable messaging, Apache Cassandra for distributed storage, and Akka for building fault tolerant distributed applications. This allows building real-time streaming applications that can join streaming and historical data with simplified architectures that remove the need for duplicating data extraction and loading.
Lambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, ScalaHelena Edelson
Scala Days, Amsterdam, 2015: Lambda Architecture - Batch and Streaming with Spark, Cassandra, Kafka, Akka and Scala; Fault Tolerance, Data Pipelines, Data Flows, Data Locality, Akka Actors, Spark, Spark Cassandra Connector, Big Data, Asynchronous data flows. Time series data, KillrWeather, Scalable Infrastructure, Partition For Scale, Replicate For Resiliency, Parallelism
Isolation, Data Locality, Location Transparency
Lambda Architecture with Spark, Spark Streaming, Kafka, Cassandra, Akka and S...Helena Edelson
Regardless of the meaning we are searching for over our vast amounts of data, whether we are in science, finance, technology, energy, health care…, we all share the same problems that must be solved: How do we achieve that? What technologies best support the requirements? This talk is about how to leverage fast access to historical data with real time streaming data for predictive modeling for lambda architecture with Spark Streaming, Kafka, Cassandra, Akka and Scala. Efficient Stream Computation, Composable Data Pipelines, Data Locality, Cassandra data model and low latency, Kafka producers and HTTP endpoints as akka actors...
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This document provides an overview of streaming big data with Spark, Kafka, Cassandra, Akka, and Scala. It discusses delivering meaning in near-real time at high velocity and an overview of Spark Streaming, Kafka and Akka. It also covers Cassandra and the Spark Cassandra Connector as well as integration in big data applications. The presentation is given by Helena Edelson, a Spark Cassandra Connector committer and Akka contributor who is a Scala and big data conference speaker working as a senior software engineer at DataStax.
Delivering Meaning In Near-Real Time At High Velocity In Massive Scale with A...Helena Edelson
Streaming Big Data: Delivering Meaning In Near-Real Time At High Velocity At Massive Scale with Apache Spark, Apache Kafka, Apache Cassandra, Akka and the Spark Cassandra Connector. Why this pairing of technologies and How easy it is to implement. Example application: https://github.com/killrweather/killrweather
Project Management Semester Long Project - Acuityjpupo2018
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The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
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Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
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The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
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5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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This video focuses on integration of Salesforce with Bonterra Impact Management.
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Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
10. @helenaedelson
“Any living cell carries with it the
experience of a billion years of
experimentation. ”
- Max Delbrück
Protein molecules within a cell (in green) self-organize
in response to stress: www.pierce-antibodies.com
Data Lineage
patterns evolved to solve problems
11. @helenaedelson
– Margaret Wheatley “Three Images” Noetic Science Review (Spring 96)
“We live in a world which in constantly exploring what’s possible,
finding new combinations – not struggling to survive,
but playing, tinkering, to find what’s possible.”
21. @helenaedelson
Our Brains Are
The Ultimate Collective
• Every decision is the outcome of a neural collective
computation
• Nothing in the brain tells the rest of it to think or remember
Neurons fire signals that only collectively create intelligence
22. @helenaedelson
Patterns In Collective
Computation
This pattern of information accumulation and consensus is
seen in neurons, ants and bees, monkey societies, and many
other systems.
• Neurons go out and semi-independently collect information
about the noisy input, like neural crowdsourcing
• Then come together and reach a consensus on what the
decision should be
23. @helenaedelson
Swarm Intelligence
Computational systems inspired by emergent amplification of
collective intelligence, through the cooperation of hundreds to
millions of homogeneous agents in a system.
Applicable anywhere there is collective decision-making, e.g.
search optimization, network routing, image analysis, data
mining, training neural networks, democratic elections and
fluctuating markets.
24. @helenaedelson
Swarm Intelligence
• Autonomy: many agents networked together, interacting locally
• Decentralization: no leader, supervisor or global coordination
• Order: spontaneous self-assembly into emergent patterns
• System-level patterns are unpredictable from behavior of its members
• Group intelligence and capabilities far exceed the individual
complex adaptive systems that behave in unpredictable ways, wholly
different than the behavior of its parts
26. @helenaedelson
No One In Charge
• Autonomy: many agents networked together, interacting
locally
• No leader, supervisor or global coordination
• No leader election or follower
• No single unit in the network knows what’s going on overall
• Nothing tracks or knows all events and change
convergence between neurons, bees and ants
27. @helenaedelson
Autonomous Agent
• Simple instructions and feedback loops
• Subject to common laws (gravity, aerodynamics)
• Common processing environment and perception systems
• Common goals
• Influence and limit each other's actions
autonomous agents don’t exist in pure chaos,
shared principles bind them together
28. @helenaedelson
Network Diffusion and Contagion
How something spreads over hundreds to
thousands of unique nodes (not clones)
• Regular continual interactions and
computation
• Amplification: through the node to node
feedback loop
• Eventual synchronization
amplification across the noisy collective
29. @helenaedelson
Swarm Algorithms
Swarm intelligence algorithms and strategies are distributed,
decentralized, adaptive, scalable and incorporate randomness for
performance.
• Particle Swarm Optimization (PSO)
• Ant Colony Optimization (ACO)
• Artificial Bee Colony (ABC)
• Stochastic Vehicle Routing problem (VRP)
• Traveling Salesman Problem (TSP)
• Bee Nest-Site Selection Scheme (BNSSS)
30. @helenaedelson
Computational Agent
• Limited capabilities and intelligence
• Governed by a set of very simple rules
• Rules provide criteria to make decisions
• Shares information with proximal peers
• Communicates often through brief interactions
individuals behave like neurons in a human brain
32. @helenaedelson
– Adrian Dyer, Researcher, Royal Melbourne Institute of Technology
“Our computers are electricity-guzzling machines.”
The bee, however, “is doing fairly high-level cognitive tasks
with a tiny drop of nectar.
Their brains are probably processing information in a very clever way.”
33. @helenaedelson
Bees
The colony’s collective behavior enables solving complex
tasks like:
• Maintaining a constant temperature in the hive
• Keeping track of changing foraging conditions
• Selecting the best possible nest site
34. @helenaedelson
House Hunting Algorithm
How does a colony solve the life or death problem of finding a new
home? They hold a democratic debate.
• Search: highly distributed searching by scouts
• Assessment: evaluation of potential sites, based on criteria
• Advertise: locally communicate information about a resource
• Consensus: each evaluates for quorum threshold
• Relocation: transport to the new home
colonies and consensus
36. @helenaedelson
– Deborah Gordon, Biologist, Stanford
“Ant algorithms have to be simple, distributed and
scalable – the very qualities that we need in large
engineered distributed systems"
37. @helenaedelson
Chaos Or Pattern?
The world's largest ant colony stretches over 2.7 km2 / 670 acres, contains
approximately 306 million workers and 1 million queens across 45,000
interconnected nests.
38. @helenaedelson
Hundreds of thousands of travelers speed along densely packed highways,
transporting huge loads, without congestion.
Congestion Avoidance Optimization
Inbound
Outbound
Outbound
Army ants have evolved a three-lane traffic system
39. @helenaedelson
Exchanging Information
Ants interact via smell with their antennae, or if it encounters a short-
lived patch of pheromone deposited by another.
With one quick touch, an ant can identify
• A nest-mate - established trust
• What task the other has been doing
identity, trust and task
40. @helenaedelson
One algorithm we work with was invented in the early stages
of the internet because operating costs were high. Its goal
was managing data congestion by gauging bandwidth
availability.
It is incredibly similar to one evolved by desert ants to gauge
resource scarcity, many millions of years ago.
41. @helenaedelson
• When an ant forages in the sun it loses water
• It gets water back from seeds it eats
• Would-be foragers wait at a narrow tunnel
entrance to the nest
• As returning food-bearing foragers pass, they
drop their load to briefly touch antennae with
those waiting (the positive feedback loop)
Foraging Strategy
Harvester ants evolved an algorithm for conserving water in the desert. They have
to spend it to get it.
42. @helenaedelson
The rate of interactions drive decisions of
individuals.
• It doesn't matter which ant it meets
• Only the rate at which it meets other ants
Foraging Optimization
rate of interactions over content
Additionally they had to solve searching for resources that are scattered (by
wind and flooding), with unpredictable spatial dispersal versus predictably
clustered.
43. @helenaedelson
Acks that trigger transmission
of the next data packet and
indicates available bandwidth.
A forager leaves the nest in
response to the rate it meets
returning foragers with food.
TCP Three-Way Handshake
congestion avoidance and determining availability
Just as the rate of packet transmission increases/decreases with the rate of
returned Acks, the rate of outbound foragers increases/decreases with the rate
of successfully returning foragers.
44. @helenaedelson
A source sends out a large
wave of packets at the
beginning of a transmission to
gauge bandwidth
Foraging harvester ants send
out scout foragers to gauge
food availability before auto-
scaling the rate of outgoing
foragers
TCP slow start
gauging bandwidth & elastic scaling
45. @helenaedelson
Timeout when a data transfer
link breaks or is disrupted, and
the source stops sending
packets
When foragers are prevented
from returning to the nest for
more than 20 minutes foragers
stop going out.
TCP Timeout
system stays stopped unless a positive event occurs
49. @helenaedelson
- Radhika Nagpal, Professor of Computer Science,
Harvard University Wyss Institute for Biologically Inspired Engineering
“The beauty of biological systems is that they are elegantly simple,
and yet in large numbers, accomplish the seemingly impossible.
At some level, you no longer even see the individuals;
you just see the collective as an entity to itself.”
50. @helenaedelson
Distributed
Robotics
• A single simple robot has many
limitations, and can only do a few
simple things
• Yet, at scale, the smart algorithm
overcomes its physical and
mathematical limitations
51. @helenaedelson
AI Algorithms At Scale
• Schools of autonomous underwater vehicles coordinating with no
central leadership to
• gather data on ocean currents and ecology
• monitor or clean up pollution
• Hundreds of robots cooperating for quick disaster response
• Millions of self-driving cars on our highways
53. @helenaedelson
Algorithms Tuned By Evolution
• Flexible roles
• Decentralization, No leader
• No reporting to one particular unit
• Distributed consensus
Unus pro omnibus, omnes pro uno
• Simple rules and instructions
• Local interactions and feedback loops
• Self-organizing
• Super-coordinators
With the right organization, a group can solve cognitive problems with an
ability that far exceeds that of its members.
Resilience and Reduced complexity:
54. @helenaedelson
Reliability and Resilience
Resilience is a measure of a system’s ability to survive and persist within
a variable environment.
• Societies like monkeys, swarms or proteins in a cell have evolved
strategies to survive shock
• Swarm networks respond efficiently to attack and disruption through
simple interactions
• These networks are easy to repair and can grow or shrink because
they evolved to tolerate randomness
how to thrive in a random world
55. @helenaedelson
Adaptation and Rapid Exploitation
The capacity of collectives to quickly learn, adapt and invent new patterns is
much higher than top-down command/control.
• In a flood of possibly conflicting neural signals, our brains have to quickly
compute what we perceive and decide how respond
• If ants or bees encounter a roadblock they quickly experiment with options
and rapidly exploit a viable solution (like ensemble forecasting)
a single visual neuron is like a single bee or ant scout
56. @helenaedelson
– Buckminster Fuller
“You never change things by fighting the existing reality.
To change something, build a new model that makes the
existing model obsolete.”
@helenaedelson
58. @helenaedelson
Resources
• Pattern Discovery over Pattern Recognition: A New Way for Computers to See
• The 1000 robot swarm
• Swarm intelligence and neural network for data classification
• Smart swarms of robots seek better algorithms
• Neural Underpinnings of Decision Strategy Selection: A Review and a Theoretical Model
• Collective Computation
• A Markov Chain Algorithm for Compression in Self-Organizing Particle Systems
• The effect of individual variation on the structure and function of interaction networks in harvester ants
• The Remarkable Self-Organization of Ants
• The ants go marching, and manage to avoid traffic jams, Princeton Weekly Bulletin
• The Regulation of Ant Colony Foraging Activity without Spatial Information
• A Survey On Bee Colony Algorithms
• Artificial Bee Colony Optimization: A New Selection Scheme and Its Performance Andrej Aderhold, Konrad Diwold, Alexander
Scheidler, and Martin Middendorf
• How and why trees talk to eachother
• What Is Spacetime
• Chaos Theory, The Butterfly Effect, And The Computer Glitch That Started It All
• Scott Camazine, “Self Organization in Biological Systems”
• Protein aggregation after heat shock is an organized, reversible cellular response
• Chaos, Meaning, and Rabbits: Remembering Walter J. Freeman
• Distributed House-Hunting in Ant Colonies Mohsen Ghaffari Cameron Musco Tsvetomira Radeva Nancy Lynch {ghaffari,
cnmusco, radeva, lynch}@csail.mit.edu, MIT
• Phototactic Supersmarticles
• How Nature Solves Problems Through Computation
• How Ants Use Quorum Sensing To Estimate The Average Quality Of A Fluctuating Resource