This document summarizes several papers on ant-based clustering algorithms. Key points include:
- Ant clustering algorithms are inspired by how ant colonies self-organize through decentralized control and stigmergy (indirect communication via pheromones).
- Early work applied this approach to problems like the traveling salesman problem. Later work explored using ants for data clustering.
- Typical ant clustering algorithms involve ants randomly placing objects in a workspace and probabilistically picking up and dropping objects based on similarity to neighbors.
- Researchers have explored ways to improve ant clustering, such as using pheromones to guide ant movement, cooling schedules, and progressive vision ranges for ants.
- Other work has applied genetic algorithms and agent
Particle swarm optimization is a metaheuristic algorithm inspired by the social behavior of bird flocking. It works by having a population of candidate solutions, called particles, that fly through the problem space, adjusting their positions based on their own experience and the experience of neighboring particles. Each particle keeps track of its best position and the best position of its neighbors. The algorithm iteratively updates the velocity and position of each particle to move it closer to better solutions.
The document discusses Particle Swarm Optimization (PSO), which is an optimization technique inspired by swarm intelligence and the social behavior of bird flocking. PSO initializes a population of random solutions and searches for optima by updating generations of candidate solutions. Each candidate, or particle, updates its position based on its own experience and the experience of neighboring highly-ranked particles. The algorithm is simple to implement and converges quickly to produce approximate solutions to difficult optimization problems.
The document discusses particle swarm optimization (PSO), which is a population-based optimization technique where multiple candidate solutions called particles fly through the problem search space looking for the optimal position. Each particle adjusts its position based on its own experience and the experience of neighboring particles. The procedure for implementing PSO involves initializing particles with random positions and velocities, evaluating each particle, updating particles' velocities and positions based on personal and global best experiences, and repeating until a stopping criterion is met. The document also discusses modifications to basic PSO such as limiting maximum velocity, adding an inertia weight, using a constriction factor, features of PSO, and strategies for selecting PSO parameters.
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
This document discusses particle swarm optimization (PSO), which is an optimization technique inspired by swarm intelligence and the social behavior of bird flocking or fish schooling. PSO uses a population of candidate solutions called particles that fly through the problem hyperspace, with each particle adjusting its position based on its own experience and the experience of neighboring particles. The algorithm iteratively improves the particles' positions to locate the best solution based on fitness evaluations.
This document presents an extended Kalman filter method for object tracking. It discusses using polynomials to model extended targets observed from imagery sensors to enable tracking of moving objects. The extended Kalman filter framework allows tracking extended targets using state-space models. Simulation results show the estimated position of an object tracked over time using the extended Kalman filter matches closely with the true position, demonstrating the effectiveness of this method for target tracking applications like radar signal processing.
This document analyzes node uptime data from the PlanetLab testbed over a year to characterize typical uptime behaviors. It identifies six distinct clusters of nodes based on their uptime patterns, which provide a more accurate model than standard distributions. Understanding the actual uptime behaviors is important for designing distributed applications and services to properly handle failures.
Particle swarm optimization is a metaheuristic algorithm inspired by the social behavior of bird flocking. It works by having a population of candidate solutions, called particles, that fly through the problem space, adjusting their positions based on their own experience and the experience of neighboring particles. Each particle keeps track of its best position and the best position of its neighbors. The algorithm iteratively updates the velocity and position of each particle to move it closer to better solutions.
The document discusses Particle Swarm Optimization (PSO), which is an optimization technique inspired by swarm intelligence and the social behavior of bird flocking. PSO initializes a population of random solutions and searches for optima by updating generations of candidate solutions. Each candidate, or particle, updates its position based on its own experience and the experience of neighboring highly-ranked particles. The algorithm is simple to implement and converges quickly to produce approximate solutions to difficult optimization problems.
The document discusses particle swarm optimization (PSO), which is a population-based optimization technique where multiple candidate solutions called particles fly through the problem search space looking for the optimal position. Each particle adjusts its position based on its own experience and the experience of neighboring particles. The procedure for implementing PSO involves initializing particles with random positions and velocities, evaluating each particle, updating particles' velocities and positions based on personal and global best experiences, and repeating until a stopping criterion is met. The document also discusses modifications to basic PSO such as limiting maximum velocity, adding an inertia weight, using a constriction factor, features of PSO, and strategies for selecting PSO parameters.
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
This document discusses particle swarm optimization (PSO), which is an optimization technique inspired by swarm intelligence and the social behavior of bird flocking or fish schooling. PSO uses a population of candidate solutions called particles that fly through the problem hyperspace, with each particle adjusting its position based on its own experience and the experience of neighboring particles. The algorithm iteratively improves the particles' positions to locate the best solution based on fitness evaluations.
This document presents an extended Kalman filter method for object tracking. It discusses using polynomials to model extended targets observed from imagery sensors to enable tracking of moving objects. The extended Kalman filter framework allows tracking extended targets using state-space models. Simulation results show the estimated position of an object tracked over time using the extended Kalman filter matches closely with the true position, demonstrating the effectiveness of this method for target tracking applications like radar signal processing.
This document analyzes node uptime data from the PlanetLab testbed over a year to characterize typical uptime behaviors. It identifies six distinct clusters of nodes based on their uptime patterns, which provide a more accurate model than standard distributions. Understanding the actual uptime behaviors is important for designing distributed applications and services to properly handle failures.
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
Firefly Algorithm: Recent Advances and ApplicationsXin-She Yang
This document summarizes a research paper on the firefly algorithm, a nature-inspired metaheuristic optimization algorithm. It briefly reviews the fundamentals and development of the firefly algorithm, discussing how it balances exploration and exploitation. The firefly algorithm is shown to be more efficient than intermittent search strategies through numerical experiments. Its automatic subdivision ability and ability to handle multimodality make it well-suited for complex optimization problems.
Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. The algorithm is widely used and rapidly developed for its easy implementation and few particles required to be tuned. The main idea of the principle of PSO is presented; the advantages and the shortcomings are summarized. At last this paper presents some kinds of improved versions of PSO and research situation, and the future research issues are also given.
The document describes the firefly algorithm, a metaheuristic optimization algorithm inspired by the flashing behaviors of fireflies. The firefly algorithm works by simulating the flashing and attractiveness of fireflies, where the brightness of a firefly represents the quality of a solution. Fireflies move towards more bright fireflies and flash in synchrony in order to find near-optimal solutions to optimization problems. The document outlines the assumptions, formulas, pseudo-code, applications, and comparisons of the firefly algorithm to other algorithms like particle swarm optimization.
Hyperoptimized Machine Learning and Deep Learning Methods For Geospatial and ...Neelabha Pant
Neelabh Pant successfully defended his PhD thesis titled "Hyper-optimized Machine Learning and Deep Learning Methods for Geo-Spatial and Temporal Function Estimation" at the University of Texas at Arlington. His research focused on developing recurrent neural networks, long short-term memory models, and genetic optimization techniques to predict locations, stock prices, and currency exchanges based on spatio-temporal data. Pant's dissertation committee included Dr. Ramez Elmasri as his advisor along with four other professors who evaluated his work.
Firefly Algorithm, Stochastic Test Functions and Design OptimisationXin-She Yang
This document describes the Firefly Algorithm, a metaheuristic optimization algorithm inspired by the flashing behavior of fireflies. It summarizes the main concepts of the algorithm, including how firefly attractiveness varies with distance, and provides pseudocode for the algorithm. It also introduces some new test functions with singularities or stochastic components that can be used to validate optimization algorithms. As an example application, the Firefly Algorithm is used to find the optimal solution to a pressure vessel design problem.
Optimization and particle swarm optimization (O & PSO) Engr Nosheen Memon
The document discusses particle swarm optimization (PSO) which is a population-based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. It summarizes PSO as follows: PSO initializes a population of random solutions and searches for optima by updating generations of candidate solutions. Each candidate is adjusted based on the best candidates in the local neighborhood and overall population. This process is repeated until a termination criterion is met.
This document discusses machine learning tools and particle swarm optimization for content-based search in large multimedia databases. It begins with an outline and then covers topics like big data sources and characteristics, descriptive and prescriptive analytics using tools like particle swarm optimization, and methods for exploring big data including content-based image retrieval. It also discusses challenges like optimization of non-convex problems and proposes methods like multi-dimensional particle swarm optimization to address issues like premature convergence.
Proposing a New Job Scheduling Algorithm in Grid Environment Using a Combinat...Editor IJCATR
Scheduling jobs to resources in grid computing is complicated due to the distributed and heterogeneous nature of the resources.
The purpose of job scheduling in grid environment is to achieve high system throughput and minimize the execution time of applications.
The complexity of scheduling problem increases with the size of the grid and becomes highly difficult to solve effectively.
To obtain a good and efficient method to solve scheduling problems in grid, a new area of research is implemented. In this paper, a job
scheduling algorithm is proposed to assign jobs to available resources in grid environment. The proposed algorithm is based on Ant
Colony Optimization (ACO) algorithm. This algorithm is combined with one of the best scheduling algorithm, Suffrage. This paper uses
the result of Suffrage in proposed ACO algorithm. The main contribution of this work is to minimize the makespan of a given set of
jobs. The experimental results show that the proposed algorithm can lead to significant performance in grid environment.
A Study of Firefly Algorithm and its Application in Non-Linear Dynamic Systemsijtsrd
Firefly Algorithm (FA) is a newly proposed computation technique with inherent parallelism, capable for local as well as global search, meta-heuristic and robust in computing process. In this paper, Firefly Algorithm for Dynamic System (FADS) is a proposed system to find instantaneous behavior of the dynamic system within a single framework based on the idealized behavior of the flashing characteristics of fireflies. Dynamic system where flows of mass and / or energy is cause of dynamicity is generally represented as a set of differential equations and Fourth Order Runge-Kutta (RK4) method is one of used tool for numerical measurement of instantaneous behaviours of dynamic system. In FADS, experimental results are demonstrating the existence of more accurate and effective RK4 technique for the study of dynamic system. Gautam Mahapatra | Srijita Mahapatra | Soumya Banerjee"A Study of Firefly Algorithm and its Application in Non-Linear Dynamic Systems" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd8393.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/8393/a-study-of-firefly-algorithm-and-its-application-in-non-linear-dynamic-systems/gautam-mahapatra
The document summarizes three algorithms for multi-robot path planning: Bacteria Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). BFO is inspired by how bacteria like E. coli search for food by swimming and tumbling. ACO is based on how ants deposit and follow pheromone trails to find food sources. PSO mimics the movement of bird flocking and fish schooling. The document provides details on the mechanisms and equations used in each algorithm's approach to finding optimal paths for multiple robots.
TALP-UPC at MediaEval 2014 Placing Task: Combining Geographical Knowledge Bas...multimediaeval
This paper describes our Georeferencing approaches, experiments, and results at the MediaEval 2014 Placing Task evaluation. The task consists of predicting the most probable geographical coordinates of Flickr images and videos using its visual, audio and metadata associated features. Our approaches used only Flickr users textual metadata annotations and tagsets. We used four approaches for this task: 1) an approach based on Geographical Knowledge Bases (GeoKB), 2) the Hiemstra Language Model (HLM) approach with Re-Ranking, 3) a combination of the GeoKB and the HLM (GeoFusion). 4) a combination of the GeoFusion with a HLM model derived from the English Wikipedia georeferenced pages. The HLM approach with Re-Ranking showed the best performance within 10m to 1km distances. The GeoFusion approaches achieved the best results within the margin of errors from 10km to 5000km.
http://ceur-ws.org/Vol-1263/mediaeval2014_submission_77.pdf
Solving travelling salesman problem using firefly algorithmishmecse13
The document describes adapting the firefly algorithm to solve the travelling salesman problem (TSP). Key points:
- The firefly algorithm is inspired by the flashing behavior of fireflies to find optimal solutions. It is adapted for TSP by representing fireflies as permutations and using inversion mutation for movement between cities.
- Distance between fireflies is calculated using Hamming or swap distance on their city orderings. Brighter fireflies attract nearby fireflies to move toward better solutions.
- The algorithm is implemented in MATLAB to test on standard TSP datasets. Results show the firefly algorithm finds better solutions than ant colony optimization, genetic algorithm, and simulated annealing on most problem instances.
Bat Algorithm: Literature Review and ApplicationsXin-She Yang
This document provides a review of the bat algorithm, which is a bio-inspired optimization algorithm developed in 2010 based on the echolocation behavior of microbats. The paper summarizes the basic behavior and formulation of the bat algorithm, reviews variants that have been developed, and highlights diverse applications that have been studied. It also discusses the essence of algorithms and links between algorithms and self-organization, noting that optimization algorithms can be viewed as complex dynamical systems that self-organize to select optimal solutions.
Evolution of Coordination and Communication in Groups of Embodied AgentsOlaf Witkowski
A PhD Thesis Defense by Olaf Witkowski. January 2015.
-- This presentation was given at the University of Tokyo, Hongo Campus, on 19 January 2015, at an Examination for the Degree of Doctor of Philosophy in Computer Science.
Combinatorial optimization and deep reinforcement learning민재 정
The document discusses using deep learning approaches for solving combinatorial optimization problems like task allocation. It reviews different reinforcement learning methods that have been applied to problems like the vehicle routing problem using pointer networks, transformers, and graph neural networks. Future work opportunities are identified in applying these deep learning techniques to multi-vehicle routing problems and using them to solve specific task allocation scenarios.
This document describes an implementation of Q-learning using an off-the-shelf rover to navigate a "rover-in-a-box" environment. The rover's camera feed is processed to reduce the state space before Q-learning is applied using a simulated environment. The Q-learning model is trained in simulation and then used by the physical rover. The goal is to learn an optimal policy to navigate from its starting position to the reward state in as few moves as possible. Image processing techniques like morphological operations and comparing across frames are used to filter noise from the camera feed and identify the relevant colors on the box walls.
B.sc biochem i bobi u 3.2 algorithm + blastRai University
The Needleman-Wunsch algorithm is a dynamic programming algorithm used to align biological sequences like protein or DNA. It was developed in 1970 by Needleman and Wunsch and is still widely used for optimal global sequence alignment. The algorithm divides a sequence alignment problem into smaller subproblems and combines their solutions to find the optimal global alignment.
This document summarizes a research paper that proposes enhancing classification schemes for spatial data mining using bio-inspired optimization approaches. The paper aims to compare the performance of a hybrid K-means and Ward's clustering method optimized with honeybee optimization and firefly optimization algorithms. Spatial data mining involves discovering patterns in spatial databases, which can be more difficult than other data types due to complex spatial relationships. The paper outlines spatial data mining and clustering techniques. It then proposes a hybrid clustering algorithm combined with honeybee optimization and firefly optimization to enhance classification performance measured by precision, recall, and other metrics.
This talk is developed to address a refresher course at Yanam for one full day. I have introduced the audience to clustering, both hierarchical and non-hierarchical. Clustering methods such as K-Means, K-Mediods, etc all introduced with live demonstrations.
The document discusses Received Signal Strength Indicator (RSSI) in wireless sensor networks. RSSI is a measurement of the power of a received radio signal. It can be used to estimate node distance and connectivity. Some cautions of using RSSI in TinyOS include that RSSI values are platform-specific and may not be in dBm units. An example Java application called RSSIDEMO outputs RSSI measurements from a sending node to a RSSI base station node. Advantages of RSSI for localization include not requiring directional sensors and working indoors or outdoors, while difficulties include effects of multipath propagation, fading, and bad frontend devices.
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
Firefly Algorithm: Recent Advances and ApplicationsXin-She Yang
This document summarizes a research paper on the firefly algorithm, a nature-inspired metaheuristic optimization algorithm. It briefly reviews the fundamentals and development of the firefly algorithm, discussing how it balances exploration and exploitation. The firefly algorithm is shown to be more efficient than intermittent search strategies through numerical experiments. Its automatic subdivision ability and ability to handle multimodality make it well-suited for complex optimization problems.
Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. The algorithm is widely used and rapidly developed for its easy implementation and few particles required to be tuned. The main idea of the principle of PSO is presented; the advantages and the shortcomings are summarized. At last this paper presents some kinds of improved versions of PSO and research situation, and the future research issues are also given.
The document describes the firefly algorithm, a metaheuristic optimization algorithm inspired by the flashing behaviors of fireflies. The firefly algorithm works by simulating the flashing and attractiveness of fireflies, where the brightness of a firefly represents the quality of a solution. Fireflies move towards more bright fireflies and flash in synchrony in order to find near-optimal solutions to optimization problems. The document outlines the assumptions, formulas, pseudo-code, applications, and comparisons of the firefly algorithm to other algorithms like particle swarm optimization.
Hyperoptimized Machine Learning and Deep Learning Methods For Geospatial and ...Neelabha Pant
Neelabh Pant successfully defended his PhD thesis titled "Hyper-optimized Machine Learning and Deep Learning Methods for Geo-Spatial and Temporal Function Estimation" at the University of Texas at Arlington. His research focused on developing recurrent neural networks, long short-term memory models, and genetic optimization techniques to predict locations, stock prices, and currency exchanges based on spatio-temporal data. Pant's dissertation committee included Dr. Ramez Elmasri as his advisor along with four other professors who evaluated his work.
Firefly Algorithm, Stochastic Test Functions and Design OptimisationXin-She Yang
This document describes the Firefly Algorithm, a metaheuristic optimization algorithm inspired by the flashing behavior of fireflies. It summarizes the main concepts of the algorithm, including how firefly attractiveness varies with distance, and provides pseudocode for the algorithm. It also introduces some new test functions with singularities or stochastic components that can be used to validate optimization algorithms. As an example application, the Firefly Algorithm is used to find the optimal solution to a pressure vessel design problem.
Optimization and particle swarm optimization (O & PSO) Engr Nosheen Memon
The document discusses particle swarm optimization (PSO) which is a population-based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. It summarizes PSO as follows: PSO initializes a population of random solutions and searches for optima by updating generations of candidate solutions. Each candidate is adjusted based on the best candidates in the local neighborhood and overall population. This process is repeated until a termination criterion is met.
This document discusses machine learning tools and particle swarm optimization for content-based search in large multimedia databases. It begins with an outline and then covers topics like big data sources and characteristics, descriptive and prescriptive analytics using tools like particle swarm optimization, and methods for exploring big data including content-based image retrieval. It also discusses challenges like optimization of non-convex problems and proposes methods like multi-dimensional particle swarm optimization to address issues like premature convergence.
Proposing a New Job Scheduling Algorithm in Grid Environment Using a Combinat...Editor IJCATR
Scheduling jobs to resources in grid computing is complicated due to the distributed and heterogeneous nature of the resources.
The purpose of job scheduling in grid environment is to achieve high system throughput and minimize the execution time of applications.
The complexity of scheduling problem increases with the size of the grid and becomes highly difficult to solve effectively.
To obtain a good and efficient method to solve scheduling problems in grid, a new area of research is implemented. In this paper, a job
scheduling algorithm is proposed to assign jobs to available resources in grid environment. The proposed algorithm is based on Ant
Colony Optimization (ACO) algorithm. This algorithm is combined with one of the best scheduling algorithm, Suffrage. This paper uses
the result of Suffrage in proposed ACO algorithm. The main contribution of this work is to minimize the makespan of a given set of
jobs. The experimental results show that the proposed algorithm can lead to significant performance in grid environment.
A Study of Firefly Algorithm and its Application in Non-Linear Dynamic Systemsijtsrd
Firefly Algorithm (FA) is a newly proposed computation technique with inherent parallelism, capable for local as well as global search, meta-heuristic and robust in computing process. In this paper, Firefly Algorithm for Dynamic System (FADS) is a proposed system to find instantaneous behavior of the dynamic system within a single framework based on the idealized behavior of the flashing characteristics of fireflies. Dynamic system where flows of mass and / or energy is cause of dynamicity is generally represented as a set of differential equations and Fourth Order Runge-Kutta (RK4) method is one of used tool for numerical measurement of instantaneous behaviours of dynamic system. In FADS, experimental results are demonstrating the existence of more accurate and effective RK4 technique for the study of dynamic system. Gautam Mahapatra | Srijita Mahapatra | Soumya Banerjee"A Study of Firefly Algorithm and its Application in Non-Linear Dynamic Systems" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd8393.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/8393/a-study-of-firefly-algorithm-and-its-application-in-non-linear-dynamic-systems/gautam-mahapatra
The document summarizes three algorithms for multi-robot path planning: Bacteria Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). BFO is inspired by how bacteria like E. coli search for food by swimming and tumbling. ACO is based on how ants deposit and follow pheromone trails to find food sources. PSO mimics the movement of bird flocking and fish schooling. The document provides details on the mechanisms and equations used in each algorithm's approach to finding optimal paths for multiple robots.
TALP-UPC at MediaEval 2014 Placing Task: Combining Geographical Knowledge Bas...multimediaeval
This paper describes our Georeferencing approaches, experiments, and results at the MediaEval 2014 Placing Task evaluation. The task consists of predicting the most probable geographical coordinates of Flickr images and videos using its visual, audio and metadata associated features. Our approaches used only Flickr users textual metadata annotations and tagsets. We used four approaches for this task: 1) an approach based on Geographical Knowledge Bases (GeoKB), 2) the Hiemstra Language Model (HLM) approach with Re-Ranking, 3) a combination of the GeoKB and the HLM (GeoFusion). 4) a combination of the GeoFusion with a HLM model derived from the English Wikipedia georeferenced pages. The HLM approach with Re-Ranking showed the best performance within 10m to 1km distances. The GeoFusion approaches achieved the best results within the margin of errors from 10km to 5000km.
http://ceur-ws.org/Vol-1263/mediaeval2014_submission_77.pdf
Solving travelling salesman problem using firefly algorithmishmecse13
The document describes adapting the firefly algorithm to solve the travelling salesman problem (TSP). Key points:
- The firefly algorithm is inspired by the flashing behavior of fireflies to find optimal solutions. It is adapted for TSP by representing fireflies as permutations and using inversion mutation for movement between cities.
- Distance between fireflies is calculated using Hamming or swap distance on their city orderings. Brighter fireflies attract nearby fireflies to move toward better solutions.
- The algorithm is implemented in MATLAB to test on standard TSP datasets. Results show the firefly algorithm finds better solutions than ant colony optimization, genetic algorithm, and simulated annealing on most problem instances.
Bat Algorithm: Literature Review and ApplicationsXin-She Yang
This document provides a review of the bat algorithm, which is a bio-inspired optimization algorithm developed in 2010 based on the echolocation behavior of microbats. The paper summarizes the basic behavior and formulation of the bat algorithm, reviews variants that have been developed, and highlights diverse applications that have been studied. It also discusses the essence of algorithms and links between algorithms and self-organization, noting that optimization algorithms can be viewed as complex dynamical systems that self-organize to select optimal solutions.
Evolution of Coordination and Communication in Groups of Embodied AgentsOlaf Witkowski
A PhD Thesis Defense by Olaf Witkowski. January 2015.
-- This presentation was given at the University of Tokyo, Hongo Campus, on 19 January 2015, at an Examination for the Degree of Doctor of Philosophy in Computer Science.
Combinatorial optimization and deep reinforcement learning민재 정
The document discusses using deep learning approaches for solving combinatorial optimization problems like task allocation. It reviews different reinforcement learning methods that have been applied to problems like the vehicle routing problem using pointer networks, transformers, and graph neural networks. Future work opportunities are identified in applying these deep learning techniques to multi-vehicle routing problems and using them to solve specific task allocation scenarios.
This document describes an implementation of Q-learning using an off-the-shelf rover to navigate a "rover-in-a-box" environment. The rover's camera feed is processed to reduce the state space before Q-learning is applied using a simulated environment. The Q-learning model is trained in simulation and then used by the physical rover. The goal is to learn an optimal policy to navigate from its starting position to the reward state in as few moves as possible. Image processing techniques like morphological operations and comparing across frames are used to filter noise from the camera feed and identify the relevant colors on the box walls.
B.sc biochem i bobi u 3.2 algorithm + blastRai University
The Needleman-Wunsch algorithm is a dynamic programming algorithm used to align biological sequences like protein or DNA. It was developed in 1970 by Needleman and Wunsch and is still widely used for optimal global sequence alignment. The algorithm divides a sequence alignment problem into smaller subproblems and combines their solutions to find the optimal global alignment.
This document summarizes a research paper that proposes enhancing classification schemes for spatial data mining using bio-inspired optimization approaches. The paper aims to compare the performance of a hybrid K-means and Ward's clustering method optimized with honeybee optimization and firefly optimization algorithms. Spatial data mining involves discovering patterns in spatial databases, which can be more difficult than other data types due to complex spatial relationships. The paper outlines spatial data mining and clustering techniques. It then proposes a hybrid clustering algorithm combined with honeybee optimization and firefly optimization to enhance classification performance measured by precision, recall, and other metrics.
This talk is developed to address a refresher course at Yanam for one full day. I have introduced the audience to clustering, both hierarchical and non-hierarchical. Clustering methods such as K-Means, K-Mediods, etc all introduced with live demonstrations.
The document discusses Received Signal Strength Indicator (RSSI) in wireless sensor networks. RSSI is a measurement of the power of a received radio signal. It can be used to estimate node distance and connectivity. Some cautions of using RSSI in TinyOS include that RSSI values are platform-specific and may not be in dBm units. An example Java application called RSSIDEMO outputs RSSI measurements from a sending node to a RSSI base station node. Advantages of RSSI for localization include not requiring directional sensors and working indoors or outdoors, while difficulties include effects of multipath propagation, fading, and bad frontend devices.
1. Self-organizing maps (SOM) are an unsupervised learning algorithm that transform high-dimensional data into lower dimensions for visualization while preserving topological properties.
2. The SOM network has an input layer fully connected to an output layer arranged in a grid, with each node containing a weight vector of the same dimension as inputs.
3. During training, the best matching unit (BMU) and its neighbors on the grid have their weight vectors adjusted to better match the input based on their distance from the BMU, with learning rates decreasing over time.
Bluetooth is a wireless technology standard that was created to provide wireless connections between various digital devices like computers, phones, and other electronics. It was named after the Danish king Harald Bluetooth who united warring tribes in Denmark and Norway. The Bluetooth Special Interest Group was formed in 1998 by five companies - Ericsson, IBM, Intel, Nokia, and Toshiba - to develop the Bluetooth standard. Version 1.0 of the Bluetooth specification was released in 1999, allowing the first Bluetooth products to arrive on the market that year.
This document discusses various penalty function methods for handling constraints in genetic algorithms. It describes 4 categories of constraint handling methods:
1. Methods based on penalty functions, including death penalty, static penalties, dynamic penalties, annealing penalties, adaptive penalties, segregated genetic algorithms, and co-evolutionary penalties.
2. Methods based on searching the feasible solution space directly, including repairing unfeasible solutions, preferring feasible solutions, and using behavioral memory.
3. Methods aimed at preserving feasibility, such as the GENOCOP system, boundary searching, and homomorphous mapping.
4. Hybrid methods that combine multiple constraint handling approaches.
It then provides more details on several specific penalty function
The document discusses cluster analysis and various clustering methods. It begins with defining what cluster analysis is and some key concepts. It then discusses different types of applications of cluster analysis. Next, it covers different data types and how to calculate distances between data points for different attribute types. Finally, it provides an overview of major clustering methods including partitioning methods, hierarchical methods, density-based methods, grid-based methods, and model-based methods.
Cerebellar Model Articulation ControllerZahra Sadeghi
The document provides an overview of the Cerebellar Model Articulation Controller (CMAC) neural network model. Some key points:
- CMAC is a 3-layer feedforward neural network that mimics the functionality of the mammalian cerebellum. It uses coarse coding to store weights in a localized associative memory.
- The input layer uses threshold units to activate a fixed number of neurons. The second layer performs logic AND operations. The third layer computes the weighted sum to produce the output.
- Learning involves comparing the actual output to the desired output and adjusting weights using methods like least mean square. Generalization occurs due to overlapping receptive fields between neurons.
- Applications include robot control,
The document discusses several density-based and grid-based clustering algorithms. DBSCAN is described as a density-based method that forms clusters as maximal sets of density-connected points. OPTICS extends DBSCAN to produce a special ordering of the database with respect to density-based clustering structure. DENCLUE uses density functions to allow mathematically describing arbitrarily shaped clusters. Grid-based methods like STING, WaveCluster, and CLIQUE partition space into a grid structure to perform fast clustering.
This is very simple introduction to Clustering with some real world example. At the end of lecture I use stackOverflow API to test some clustering. I also wants to try facebook but it has some problem with it's API
This document summarizes the DBSCAN clustering algorithm. DBSCAN finds clusters based on density, requiring only two parameters: Eps, which defines the neighborhood distance, and MinPts, the minimum number of points required to form a cluster. It can discover clusters of arbitrary shape. The algorithm works by expanding clusters from core points, which have at least MinPts points within their Eps-neighborhood. Points that are not part of any cluster are classified as noise. Applications include spatial data analysis, image segmentation, and automatic border detection in medical images.
The document discusses various model-based clustering techniques for handling high-dimensional data, including expectation-maximization, conceptual clustering using COBWEB, self-organizing maps, subspace clustering with CLIQUE and PROCLUS, and frequent pattern-based clustering. It provides details on the methodology and assumptions of each technique.
This document provides a short review of clustering techniques for students. It defines clustering and different types of grouping methods such as hard vs soft clustering. It discusses popular clustering algorithms like hierarchical clustering, k-means clustering, and density-based clustering. It also covers cluster validity, usability, preprocessing techniques, meta methods, and visual clustering. Open problems in clustering mentioned include how to identify outlier objects and accelerate classification.
Cluster analysis involves grouping data objects into clusters so that objects within the same cluster are more similar to each other than objects in other clusters. There are several major clustering approaches including partitioning methods that iteratively construct partitions, hierarchical methods that create hierarchical decompositions, density-based methods based on connectivity and density, grid-based methods using a multi-level granularity structure, and model-based methods that find the best fit of a model to the clusters. Partitioning methods like k-means and k-medoids aim to optimize a partitioning criterion by iteratively updating cluster centroids or medoids.
An introduction to Autonomous mobile robotsZahra Sadeghi
This document provides an introduction and overview of autonomous mobile robots and various techniques used in their development, including:
- Simulation studies allow researchers to test robot behaviors without building physical robots.
- The Khepera robot is a small, low-cost platform that has been used widely in research due its modularity and accessibility.
- Fuzzy logic, neuro-fuzzy systems, evolutionary robotics, and genetic programming are some methods explored for developing autonomous robot control systems without explicit programming. Co-evolution and complex environments can generate more advanced robot behaviors.
Cluster analysis is a descriptive technique that groups similar objects into clusters. It finds natural groupings within data according to characteristics in the data. Cluster analysis is used for taxonomy development, data simplification, and relationship identification. Some applications of cluster analysis include market segmentation in marketing, grouping users on social networks, and reducing markers on maps. It requires representative data and assumes groups will be sufficiently sized and not distorted by outliers.
Clustering is an unsupervised learning technique used to group unlabeled data points together based on similarities. It aims to maximize similarity within clusters and minimize similarity between clusters. There are several clustering methods including partitioning, hierarchical, density-based, grid-based, and model-based. Clustering has many applications such as pattern recognition, image processing, market research, and bioinformatics. It is useful for extracting hidden patterns from large, complex datasets.
Types of clustering and different types of clustering algorithmsPrashanth Guntal
The document discusses different types of clustering algorithms:
1. Hard clustering assigns each data point to one cluster, while soft clustering allows points to belong to multiple clusters.
2. Hierarchical clustering builds clusters hierarchically in a top-down or bottom-up approach, while flat clustering does not have a hierarchy.
3. Model-based clustering models data using statistical distributions to find the best fitting model.
It then provides examples of specific clustering algorithms like K-Means, Fuzzy K-Means, Streaming K-Means, Spectral clustering, and Dirichlet clustering.
Comparative Study of Ant Colony Optimization And Gang SchedulingIJTET Journal
Abstract— Ant Colony Optimization (ACO) is a well known and rapidly evolving meta-heuristic technique. All optimization problems have already taken advantage of the ACO technique while countless others are on their way. Ant Colony Optimization (ACO) has been used as an effective algorithm in solving the scheduling problem in grid computing. Whereas gang scheduling is a scheduling algorithm that is used to schedule the parallel systems and schedules related threads or processes to run simultaneously on different processors. The threads that are scheduled are belonging to the same process, but they from different processes in some cases, for example when the processes have a producer-consumer relationship, when all processes come from the same MPI program.
1) The document summarizes a research paper that proposes a honey bees mating optimization algorithm to solve the Euclidean traveling salesman problem.
2) The proposed algorithm uses multiple phase neighborhood search, expanding neighborhood search, and an adaptive memory crossover operator to increase the efficiency over the basic honey bees mating optimization algorithm.
3) Computational results showed that the algorithm performed well, ranking 3rd in optimally solving sample problems from a standard implementation challenge for traveling salesman problem algorithms.
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.
A Randomized Load Balancing Algorithm In Grid using Max Min PSO Algorithm IJORCS
Grid computing is a new paradigm for next generation computing, it enables the sharing and selection of geographically distributed heterogeneous resources for solving large scale problems in science and engineering. Grid computing does require special software that is unique to the computing project for which the grid is being used. In this paper the proposed algorithm namely dynamic load balancing algorithm is created for job scheduling in Grid computing. Particle Swarm Intelligence (PSO) is the latest evolutionary optimization techniques in Swarm Intelligence. It has the better performance of global searching and has been successfully applied to many areas. The performance measure used for scheduling is done by Quality of service (QoS) such as makespan, cost and deadline. Max PSO and Min PSO algorithm has been partially integrated with PSO and finally load on the resources has been balanced.
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.
This document discusses swarm intelligence algorithms including particle swarm optimization (PSO) and ant colony optimization (ACO). It begins with an overview of swarm intelligence as the collective behavior of decentralized and self-organized systems, with characteristics like simple local rules and no centralized control. It then covers metaheuristics, PSO, ACO, and a case study applying PSO to data clustering. PSO is presented as optimizing a problem by updating particles based on personal and global best positions. ACO draws inspiration from how ants find food via pheromone trails, with the algorithm constructing solutions probabilistically based on pheromone levels. The case study shows PSO clustering outperforming K-means by avoiding local optima
A Multi-Objective Ant Colony System Algorithm for Virtual Machine PlacementIJERA Editor
This document proposes a multi-objective ant colony system algorithm for virtual machine placement to minimize total resource wastage and power consumption. It introduces the virtual machine placement problem and reviews existing literature on ant colony optimization algorithms. The proposed algorithm is described in detail, including initialization, iterative construction of solutions by ants, evaluation and updating of pheromone trails. The algorithm is tested on examples from literature and shown to perform better than existing approaches in efficiently finding non-dominated solutions for the multi-objective virtual machine placement problem.
This document describes improvements made to an ant colony optimization algorithm called Ant-Miner for generating classification rules from data. The improved algorithm is called Ant-Miner3. It uses a different pheromone updating strategy and state transition rule compared to the original Ant-Miner algorithm. The goal is to improve accuracy by increasing diversity in the rules generated by the ants. Ant-Miner3 was tested on two standard problems and performed better than the original Ant-Miner algorithm in terms of accuracy.
The document discusses the challenges of analyzing large remote sensing datasets that have high volume, velocity, and variety of data. The authors present the K-Tree, a data structure and clustering algorithm that can gracefully scale to large numbers of objects and clusters, handle streaming data, and handle data with high variety. They applied the K-Tree to satellite image data and extended it to a multicore system. Experiments showed the K-Tree was much more efficient than baseline approaches and the multicore extension further increased efficiency.
Bio-inspired Artificial Intelligence for Collective SystemsAchini_Adikari
Artificial Intelligence is a constantly growing field of study. Today, there is an emerging interest to bind concepts natural systems to computing to develop self-organized machines
Classification with ant colony optimizationkamalikanath89
This document describes using an Ant Colony Optimization (ACO) algorithm for classification rule discovery in databases. ACO is inspired by how real ants find the shortest path between food sources and their nest. The ACO algorithm allows artificial ants to incrementally build classification rules by moving on a weighted graph representing the problem, biased by pheromone levels that are updated based on rule quality. This approach can discover more flexible and robust rules than traditional methods for applications like data mining and decision making.
The document summarizes two nature-inspired metaheuristic algorithms: the Cuckoo Search algorithm and the Firefly algorithm.
The Cuckoo Search algorithm is based on the brood parasitism of some cuckoo species. It lays its eggs in the nests of other host birds. The algorithm uses Lévy flights for generating new solutions and considers the best solutions for the next generation.
The Firefly algorithm is based on the flashing patterns of fireflies to attract mates. It considers attractiveness that decreases with distance and movement of fireflies towards more attractive ones. The pseudo codes of both algorithms are provided along with some example applications.
Bat Algorithm is Better Than Intermittent Search StrategyXin-She Yang
This document compares the bat algorithm to the intermittent search strategy for balancing exploration and exploitation in metaheuristic optimization algorithms. It reviews several metaheuristic algorithms and analyzes the theoretical basis for optimal balancing of exploration and exploitation phases. Equations are presented for the optimal ratio of exploration and exploitation phases in 2D problems based on the intermittent search strategy. The bat algorithm is described and its ability to achieve near-optimal balancing is demonstrated through numerical experiments on test functions. The document concludes higher dimensional problems require more exploration effort to find global optima with limited computations.
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
An Updated Survey on Niching Methods and Their ApplicationsSajib Sen
This document provides an overview of niching methods and their applications in multi-modal optimization problems. It discusses how niching techniques like fitness sharing, crowding, and clearing promote population diversity and allow evolutionary algorithms to find multiple optimal solutions. Recent developments include applying niching to particle swarm optimization and differential evolution. Niching methods have real-world applications in areas like truss optimization, drug design, job scheduling, and image segmentation. Maintaining found solutions and scalability remain ongoing challenges for niching approaches.
Performance Evaluation of Different Network Topologies Based On Ant Colony Op...ijwmn
All networks tend to become more and more complicated. They can be wired, with lots of routers, or wireless, with lots of mobile node. The problem remains the same, in order to get the best from the network; there is a need to find the shortest path. The more complicated the network is, the more difficult it is to manage the routes and indicate which one is the best. The Nature gives us a solution to find the shortest path. The ants, in their necessity to find food and brings it back to the nest, manage not only to explore a vast area, but also to indicate to their peers the location of the food while bringing it back to the nest. Most of the time, they will find the shortest path and adapt to ground changes, hence proving their great efficiency toward this difficult task. The purpose of this paper is to evaluate the performance of different network topologies based on Ant Colony Optimization Algorithm. Simulation is done in NS-2.
Comparison of different Ant based techniques for identification of shortest p...IOSR Journals
This document compares different ant colony optimization (ACO) techniques for identifying the shortest path in a distributed network. ACO is based on the behavior of ants finding food sources and uses pheromone trails to probabilistically determine paths. The document reviews several ACO algorithms and techniques, including Max-Min, rank-based, and fuzzy rule-based approaches. It then implements an efficient ACO algorithm that performs better at finding the shortest path compared to other existing ACO techniques.
Optimal Data Collection from a Network using Probability Collectives (Swarm B...IJRES Journal
This paper contains the implementation of the BeeAdhoc algorithm for data routing in mobile Ad Hoc Network (MANet). The algorithm was inspired by the foraging behaviour of honey bees and its implementation mimics this behaviour. The integration was done on Network Simulator version 2 (NS-2.34) where different scenarios were considered in comparison with other existing state-of-the-art routing algorithms that have been implemented in the chosen simulator. The comparison was carried out between DSR, DSDV, AOMDV which are all multipath routing algorithms as the BeeAdhoc; this gave a better insight to the different behaviour of the algorithms on a common application environment. Throughput, end-to-end delay and routing overhead constitute the indices used for the performance evaluation. Experimental results showed the best performance of BeeAdhoc over, DSDV and AOMDV algorithms.
Signal & Image Processing: An International Journal (SIPIJ) sipij
This document summarizes an article that proposes an efficient nearest neighbor method based on partial distance to improve the performance of flocking simulations. Flocking behavior involves objects moving together in groups according to rules of cohesion, alignment and separation. Conventional nearest neighbor methods have complexity of O(n^2) for n objects. The proposed partial distance method allows early termination of distance calculations, improving efficiency. It was tested on a simulation of flocking fish and showed better performance than conventional methods, especially for larger flock sizes.
Efficient Method to find Nearest Neighbours in Flocking Behaviourssipij
Flocking is a behaviour in which objects move or work together as a group. This behaviour is very common in nature think of a flock of flying geese or a school of fish in the sea. Flocking behaviours have been simulated in different areas such as computer animation, graphics and games. However, the simulation of the flocking behaviours of large number of objects in real time is computationally intensive task. This intensity is due to the n-squared complexity of the nearest neighbour (NN) algorithm used to separate objects, where n is the number of objects. This paper proposes an efficient NN method based on the partial distance approach to enhance the performance of the flocking algorithm and its application to flocking behaviour. The proposed method was implemented and the experimental results showed that the proposed method outperformed conventional NN methods when applied to flocking fish.
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Quality Assurance in Modern Software DevelopmentZahra Sadeghi
This document discusses quality assurance in modern software development. It begins by providing resources on the topic and outlining the agenda. It then reviews basic concepts of software, quality, and the differences between quality assurance and quality control. It introduces several quality models including McCall's quality model and discusses important factors in software quality. Finally, it covers quality assurance methodology using PDCA, quality management tools including Ishikawa diagrams and Pareto charts, and software quality testing. The document provides a comprehensive overview of key aspects of quality assurance in software development.
Attention mechanism in brain and deep neural networkZahra Sadeghi
Attention implements an information-processing bottleneck that allows only a small part of the incoming sensory information to reach short-term memory and visual awareness.
Perception, representation, structure, and recognitionZahra Sadeghi
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- Different studies are mentioned that examine emerging conceptual categories at different layers of deep neural networks trained on visual datasets, as well as investigations into semantic representations derived from object co-occurrence in scenes.
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This document discusses semantic search using the semantic web. It begins by describing limitations of current keyword-based search engines. It then introduces the semantic web, which aims to represent information in a way that is understandable by machines through standards like XML, RDF, RDFS, and OWL. This will allow semantic search engines to better understand the meaning of web pages to improve search results. Examples are provided of representing information about a conference using these semantic web standards to illustrate how machines could infer new facts not explicitly stated.
When using mathematical programming methods to solve practical problem, it is usually not so easy for decision makers to determine the proper values of model parameters; on the contrary, such uncertainty can be roughly represented as an interval of confidence.
This document discusses the 16-bit 68000 microprocessor architecture. It describes the 68000's 16-bit external data bus, 32-bit registers including 8 data registers and 7 address registers. It covers the register organization, 24-bit address space of 16MB, and functions of registers like the program counter, stack pointer, and status register.
The document explains the instruction word format and different instruction types. It details the addressing modes like direct, register indirect, autoincrement, autodecrement, and absolute. Assembly language syntax and directives like ORG, EQU, and DS are outlined. Logic instructions, condition codes, conditional and unconditional branching, subroutines, and the stack are summarized. The document also provides
The document describes several tools available in Electronic WorkBench (EWB) used for designing and simulating digital circuits, including:
1) A drawing area used to assemble circuits and a description window to add text notes.
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3) A word generator used to input 16-bit digital words or patterns into a circuit in parallel.
4) A logic analyzer that displays the signal levels of up to 16 lines in a circuit over time.
5) Other tools include a multimeter to measure voltage, current and resistance
The document describes the MS-DOS boot process. It begins with the CPU initialization and BIOS checks like the POST. The BIOS then looks to the MBR and loads the boot code. This boot code looks for the IO.SYS and MSDOS.SYS files to load the kernel. Next CONFIG.SYS is read to configure devices. COMMAND.COM is then loaded, which looks for the AUTOEXEC.BAT file. Finally, the command prompt is displayed.
This document provides an introduction to threads. It discusses the history of threads, key terminology like processes and threads, and how threads allow programs to perform multiple tasks concurrently. The document also covers benefits of threads like improving responsiveness, but notes costs like reduced processor time per thread. It provides examples of how threads work and challenges like race conditions that can occur with shared memory access across threads.
Multimedia involves using multiple media types like text, graphics, sound, and animation together. It is often used to refer to including audio, video, and animation on web pages. There are three main categories of multimedia applications: streaming stored audio and video, streaming live audio/video, and real-time interactive audio/video. Streaming stored media involves compressing and storing files on a web or streaming server. Users can pause, resume, and skip around files as they download. Streaming live media is similar to radio and TV broadcasts over the internet. Real-time interactive media allows people to communicate with audio and video in real-time, like internet telephony. Common compression techniques compress audio by encoding differences between samples and compress video
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
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The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
5. • The high number of individuals and the decen-
tralized approach to task coordination means
that
▫ ant colonies show high degrees of parallelism, self-
organization and fault tolerance.
▫ All of which are desired characteristics in modern
computer systems.
6. • The work on ant-based techniques is considered to have
started by the Ant Colony Optimization my Dorigo et. al.,
[6].
• In this work, a group of ant- agents is used to solve the
TSP problem.
• While each ant walks on the graph, it leaves a
pheromone signal through the path it used.
• Shorter paths will leave stronger signals.
• The next ants, when deciding which path to take, tend to
choose paths with stronger signals with a higher
probability, so as shorter paths are found, more ants try
to explore these paths, by a positive reinforcement cycle.
7. • One important characteristic of the ant-inspired
technologies is shown on the work on network
routing by Ant Colony Optimization, ANT NET
[4].
8. stigmergy
• The root of this adaptation is the stigmergic
nature of the ant-system.
• Stigmergy is a central idea of all ant-based
algorithms.
• Here, it happens, as described before, by the
leaving of “pheromone" trails.
9. • The pheromone value of the path that composes
the optimal solution is higher than that of the
non- optimal solutions after the end of the
algorithm.
• If there is any change in the topology, like a
route that fails or a new route, the system can
use the existing values of the pheromone trails to
adapt to the changes while online.
10.
11. • we will call this bi-dimensional grid the
workspace, and the n-dimensional space the
feature space.
12. • The first step in a canonical" ant-clustering
system is to distribute the data (objects)
randomly into the workspace.
• Each object is projected onto one grid of the
workspace
13. • Then a number na of ants is put on random
positions of the workspace.
• It is defined that only one ant and only one
object can be put into one grid of the work space.
• Each ant is also able to carry one data object
with itself.
14. • At each time step, each ant will, if loaded, try to
unload its object onto its current position or, if
unloaded, try to pick an object in the same grid
as itself.
• The probability of picking or dropping an object
is based on the disparity (or distance in feature
space) between that object and other objects in
its neighborhood.
15.
16. • Ant-based clustering usually came first or in a
close second (as shown in the data selected for
this work).
• When observing the run-time, it was observed
that for low dimensionality data, ant-based
clustering would be slightly slower than the
other techniques, but its runtime scales linearly,
so that it becomes the fastest algorithm for high-
dimensionality data.
17. • ant-based clustering techniques are an
appropriate alternative to traditional clustering
algorithms.
• it has the ability of automatically discovering the
number of clusters.
• Also, it linearly scales against the dimensionality
of data.
• It automatically generates a representation of
the formed clusters that can be intuitively
understood by humans.
18. Towards Improving Clustering Ants:
An Adaptive Ant Clustering Algorithm
André L. Vizine, Leandro N. de Castro, Eduardo
R. Hruschka, Ricardo R. Gudwin
2005
19. • Among the many bio-inspired techniques, ant-
based clustering algorithms have received special
attention from the community over the past few
years for two main reasons.
• First, they are particularly suitable to perform
exploratory data analysis and,
• second, they still require much investigation to
improve performance, stability, convergence, and
other key features that would make such
algorithms mature tools for diverse applications.
20. Cooling Schedule for kp
• a cooling schedule for the parameter that drives
the picking probability kp is employed.
• The adopted scheme is simple: after one cycle
(10,000 ant steps) has passed, the value of the
parameter kp starts being geometrically
decreased, at each cycle, until it reaches a
minimal allowed value, kpmin, which
corresponds to the stopping criterion for the
algorithm.
21. • In the current implementation, kp is cooled
based on a geometric scheme presented in Eq.
(4).
22. Progressive Vision
• The definition of a fixed value for s^2 may
sometimes cause inappropriate behaviors,
because a fixed perceptual area does not allow
distinguishing between clusters of different
sizes.
• A small area of vision implies a small perception
of the cluster at a global level.
• Thus, small clusters and large clusters are all the
same in this sense, for the agent only perceives a
limited area of the environment.
23. Progressive Vision
• On the other hand, a large vision field may be
inefficient in the initial iterations, when the data
elements are scattered at random on the grid,
because analyzing a broad area may imply in
analyzing a large number of small clusters
simultaneously.
25. ‘How can an ant agent detect the size of a cluster
so as to control the size of its vision field?’
• There is a relationship between the size of a
cluster and its density dependent function: the
average value of f(i) increases as the clustering
proceeds, and this happens because larger
clusters tend to be formed.
• When f(i) achieves a value greater than a pre-
specified threshold θ, the parameter s2 is
incremented by ns units until it reaches its
maximum value.
26. Pheromone Heuristics
• Sherafat et al. (2004a,b) introduced a pheromone
function, Phe( φmax, φmin,P, φ(i)), given by Eq.
(6), that influences the probability of picking up and
dropping off objects from and on the grid.
• The proposed pheromone function varies linearly
with the pheromone level at each grid position, φ(i),
and depends on a number of userdefined
parameters, such as
the φmax and φmin values of pheromone perceived by
the agent, and
the maximal influence of pheromone allowed, P.
27. Pheromone Heuristics
• To accommodate the addition of pheromone on
the grid, some variations on the picking and
dropping probability functions of SACA were
proposed in (Sherafat et al.,2004a,b), as
described in Eqs. (7) and (8), respectively:
28. Pheromone Heuristics
• where φmax represents the current largest
amount of pheromone perceived by this agent;
• φmin corresponds to the current smallest
amount of pheromone perceived by this agent;
• P is the maximum influence of the pheromone in
changing the probability of picking and dropping
data elements;
• and φ(i) is the quantity of pheromone in the
current position i.
29. • the probability that an ant picks up an item from
the grid is inversely proportional to the amount
of pheromone at that position and also to the
density of objects around i.
30. • The rate at which pheromone evaporates is
preset
32. The effect of using evolutionary
algorithms on Ant Clustering
Techniques
Claus Aranha1 and Hitoshi Iba
33. • Ant-based clustering algorithms can be
considered non-hierarchical, hard,
agglomerative clustering methods.
▫ Non-hierarchical means that there is no parent-
child relationship between the objects or the
clusters formed by the technique.
▫ Hard means that each object is assigned to only
one cluster.
▫ Agglomerative means that the clusters are formed
bottom-up - in other words, isolated objects are
progressively put together to form bigger clusters.
34. • Ramos et al. [15]. applied ant-based clustering to
the classification of stone images.
• In their works, they noticed that the normal LF
algorithm would generate a large quantity of
small clusters, and that many actions were
wasted when the ants moved through empty
space.
• To address this concerns they used pheromones
to guide the ant movement.
35. • Handl et al. [9] changed the ants’ movement
policy so that the ants, after dropping an object,
would “teleport” to the next isolated object, and
pick it automatically.
• In this way, an ant would never give a step while
not carrying an object, which did not add
anything to the clustering effort.
• They also added limited local memory to each
ant, which would give them “hints” to the best
place to drop the carried object.
36. • Hartmann [11] proposes the use of Neural
Networks to replace the pick and drop functions.
37. neighborhood disparity function
• xs is an object within the neighborhood radius of i,
• Md is the maximum distance between any two
objects,
• and St is the total number of objects in the
neighborhood of I
• The neighborhood of an object is given by all the
objects within Manhattan distance sight of the
object
39. Use of Genetic Algorithms
• it is commented that the sensitivity of the many
parameters in ant-clustering is a topic worthy of
study
• To improve the ant clustering algorithm, we’ll
try to optimize its parameters (presented in table
1) by using Genetic algorithms.
40. each individual
• each individual is represented by the set of
configuration parameters in table 1.
• For each generation, we run the program once
with each set of parameters, and take the fitness
from each run.
41. Elite selection
• We use the Elite selection strategy for GA,
where, for each generation, the best elite size
individuals are directly copied into the next
generation, and the remaining individuals of the
population are deleted and replaced by crossover
between this elite.
42. • For the crossover operator, we randomly choose two
parents from the elite, and create a new individual
by choosing one parameter value from each parent
(equal probability for both parents).
• After that we run the mutation operator (with a
probability equal to the mutation parameter for each
individual).
• The mutation operator can either change the value
of one parameter by 10%, or generate a new random
value for that parameter.
43. • The key in a successful application of GA to a
problem is an appropriate choice of the fitness
function.
• One of the strong points of ant clustering is the
ability to auto-detect the number of clusters.
44. To extract the clusters
• To extract the clusters from the workspace, we
define a cluster as a group of objects within 2
units of Manhattan distance from any member
of the group.
• In this way, in figure 2, we can see 2 such
different clusters.
45. • However, the number of clusters alone does not
tell us how good the clustering is, so we must
also account for the quality of the clusters.
• We Average Local Linkage, to measure the
quality of one cluster.
46. Local Linkage
• First, we take the neighborhood disparity
function f(i) to determine the local Linkage of
one object.
• From this value, we calculate the ALL for the
cluster as:
• Where Csize is the number of objects in the
cluster, and each is an object belonging to
the cluster.
47. fitness of one individual
• To calculate the fitness of one individual, then,
we identify the clusters by using the definition in
figure 2,
• and then averaging ALL(C) for all clusters where
Csize > 1.
48. • There is, however, one extra thing that must be
taken care with when calculating the fitness of
ant clustering algorithms.
• As reported in [18], LF does not reach an stable
configuration - since the pick and drop
probabilities are not deterministic, the ants may
pick some pieces from established clusters,
lowering the fitness, just to put them back a few
turns later.
49.
50. • Therefore, if we just pick any one time step, and
measure the fitness at that moment, we can get a
lucky high or low unstable state
• In order to avoid that, after a given turn t, we
start measuring the fitness for the next fit turns,
and take the average fitness of this period as the
individual’s fitness.
51. Experiments
• Running the experiment, we found out that in
fact the evolved solutions could generate a
smaller number of clusters with the passing of
the generations
52.
53. Effects of Inter-agent
Communication in Ant-Based
Clustering Algorithms: A case
Study on Communication Policies
in Swarm Systems
Marco A. Montes de Oca,
Leonardo Garrido, and Jos´e L.
Aguirre
Springer-Verlag Berlin Heidelberg
2005
54. • In natural settings, stigmergy [6] plays a key
role as it provides the means for indirect
communication among insects through the
environment.
• we need to consider the question of whether
agents should/could communicate in other ways
to achieve organization or better solutions to
problems.
• we need to study the effects of letting agents use
different communication policies.
55. pheromone
• In ACO, we can see stigmergy in action whenever an
artificial ant deposits a pheromone trail on a
problem solution space.
• If an artificial ant come across a pheromone trail, it
is attracted to it, very much like termites are
attracted by clusters of soil pellets.
• By means of this indirect communication channel,
ants share knowledge and the pheromone trail is a
“blue print” to build a good solution to the problem
at hand.
56. • The similarity measure used in all the experiments was
the cosine metric
• S is the steepness of the response curve and D serves as
a displacement factor.
• S was fixed to 5 because it provides a similarity value
close to 0 when the cosine measure is minimum,
• when the cosine measure gives a value of −1, and D to 1
because this allows us to better distinguish vectors with
separation angles between 0 and π/2.
57. • All algorithms were tested 30 times with every database
for 1,000,000 simulation cycles.
• We tried with populations of 10 and 30 agents within an
environment of 100 × 100 locations in all the
experiments.
58. Direct Information Exchange
• direct information exchange occurs only when
two or more agents meet at a location on the
grid.
• Hence, the probability of an encounter between
two agents moving randomly raises as the
number of agents is increased
59. Indirect Information Exchange
• agents lay packets which contain information about
data distribution on the environment for others to
pick and use.
• Direct communication among agents in ant-based
clustering has two disadvantages:
• (i) even when the number of exchanges increases,
we cannot expect many of them to happen since the
number of agents must be kept small (for
performance reasons),
• and (ii) many exchanges do not have any effect since
agents walk in a randomly fashion, i.e., two agents
coincide many times, over and over again, before
they follow different trajectories.
60. • So the idea is that if we let agents lay information on
their environment, it could be possible to increase
dramatically the number of exchanges without even
increasing the number of agents.
• Two information laying policies were studied:
▫ a periodic laying policy and
With the periodic laying policy, an agent drops
information packets every given number of simulation
cycles.
▫ an adaptive laying policy.
With the adaptive laying policy, an agent drops
information after it has modified the environment and a
given number of simulation cycles have passed.
61. AN ANT COLONY CLUSTERING
ALGORITHM
BAO-JIANG ZHAO
2007
62. • The algorithm considers R agents, namely
artificial ants, to build solutions.
• An agent starts with an empty solution string S
of length N where each element of string
corresponds to one of the test samples.
• The value assigned to an element of solution
string S represents the cluster number to which
the test sample is assigned in S.
63. • To construct a solution, the agent uses the
pheromone trail information to allocate each
element of string S to an appropriate cluster
label.
• At the start of the algorithm, the pheromone
matrix, τ is initialized to a value τ0.
• The trail value, τij at location (i, j) represents
the pheromone concentration of sample i
associated to the cluster j.
64. • For the problem of separating N samples into K clusters
the pheromone matrix is of size.
• The pheromone trail matrix evolves as we iterate.
• At any iteration level, each one of the agents will develop
such trial solutions using the process of pheromone-
mediated communication with a view to obtain a near-
optimal partition of the given N test samples into K
groups satisfying the defined objective.
• After generating a population of R trial solutions,
crossover operator is performed to further improve
fitness of these solutions.
• The pheromone matrix is then updated depending on
the quality of solutions produced by the agents.
• the above steps are repeated for certain number of
iterations.
65. • the agent select cluster number for each element of
string S by the following way:
is a parameter which determines the relative
influence of the heuristic information.
J is a random variable selected according to the
probability distribution given by
• probability for element i belongs to cluster j:
66.
67. Cluster Analysis Based on
Artificial Immune System and
Ant Algorithm
Chui-Yu Chiu and Chia-Hao Lin
IEEE 2007
68. Immunity-based Ant Clustering
Algorithm(IACA)
• immune system utilizes problem-specific
heuristic to conduct local search and fine-tuning
in the solution space.
• using artificial immune system to fine-tune the
objects between two different clusters is the
most important characteristic of IACA