The document discusses various machine learning and artificial intelligence topics including Markov decision processes, decision tree algorithms like ID3, reinforcement learning using Q-learning, and search techniques. It provides examples of applying these concepts to problems like classifying weather to decide whether to play ball, controlling processes with Markov models, and building decision trees from training data.
Cat x x
A⊂G A′ ⊂ M Oak x x
Potato x x
Formal concept analysis studies how objects can be grouped hierarchically based on their common attributes. It models concepts as units consisting of an extension (objects belonging to the concept) and an intension (attributes common to those objects). Formal contexts represent relationships between objects and attributes, and derivation operators identify the attributes common to a group of objects or the objects sharing a group of attributes.
09 genetic algorithms by Priyesh Marvipriyeshmarvi
Genetic algorithms (GAs) are adaptive heuristic search algorithms based on Darwinian evolution. GAs represent an intelligent exploitation of random search used to solve optimization problems. In GAs, competition among potential solutions for limited resources results in fitter solutions dominating over weaker ones. Unlike older AI systems, GAs are more robust and perform well in large, complex search spaces where optimal solutions may not be found through other techniques. GAs navigate huge search spaces, looking for optimal combinations one may not otherwise discover in a lifetime.
Genetic algorithms are adaptive heuristic search algorithms based on Darwinian principles of natural selection and genetics. They represent an intelligent exploitation of random search used to solve optimization problems. The document discusses the biological background of genetic algorithms, including chromosomes, genes, alleles, and evolution. It also covers the basic concepts of genetic algorithms such as representation of solutions, fitness functions, selection, crossover and mutation operators.
cvpr2011: human activity recognition - part 2: overviewzukun
The document discusses approaches for human activity analysis from videos. It describes activity classification, detection, and recognition processes that analyze videos to identify human activities. It presents a taxonomy that categorizes recognition approaches as single-layered or hierarchical. Single-layered approaches recognize actions directly from videos, while hierarchical approaches model activities as combinations of sub-events. Hierarchical approaches are suitable for recognizing complex activities and interactions between humans or humans and objects.
Large-scale long-term networks to monitor and understand the changing ecology...CIFOR-ICRAF
This presentation examines the changing ecology of tropical forests and the effect that this has on maintaining data quality when it comes to monitoring large-scale sites over time. Some lessons learned are also outlined.
This presentation formed part of the CRP6 Sentinel Landscape planning workshop held on 30 September – 1 October 2011 at CIFOR’s headquarters in Bogor, Indonesia. Further information on CRP6 and Sentinel Landscapes can be accessed from http://www.cifor.org/crp6/ and http://www.cifor.org/fileadmin/subsites/crp/CRP6-Sentinel-Landscape-workplan_2011-2014.pdf respectively.
The document contains notifications acknowledging the insertion of several Partner Search inquiries into the ideal-ist website. The inquiries are seeking partners for projects related to topics such as in-car entertainment systems, e-learning, 3D television, intelligent transportation systems, mobile platform security, virtual presence technologies, accessibility of online information, and nanostructured materials. The notifications provide details about the project proposals such as keywords, deadlines, and links for further information.
This document describes a study that used machine learning to analyze online knowledge sharing conversations between students collaboratively solving problems. The researchers used Hidden Markov Models to classify knowledge sharing episodes as either effective or ineffective based on features of the conversation. They were able to accurately classify episodes 93% of the time, significantly better than random chance. The study provides insights into how to better understand and assess how students share and assimilate new knowledge in collaborative learning groups.
This document provides information about a proposed workshop on knowledge acquisition from distributed, autonomous, and semantically heterogeneous data sources to be held at the 2005 IEEE International Conference on Data Mining. The workshop aims to bring together researchers from areas like machine learning, data mining, knowledge representation, databases, and selected application domains to address challenges in performing knowledge discovery from multiple distributed data sources that may have semantic differences. Topics of interest include learning from distributed data, making data sources self-describing through ontologies, learning ontologies and mappings between schemas, and handling semantic heterogeneity. The workshop will include invited talks and presentations of contributed papers, and targets researchers, students, and practitioners interested in knowledge acquisition from distributed data.
Cat x x
A⊂G A′ ⊂ M Oak x x
Potato x x
Formal concept analysis studies how objects can be grouped hierarchically based on their common attributes. It models concepts as units consisting of an extension (objects belonging to the concept) and an intension (attributes common to those objects). Formal contexts represent relationships between objects and attributes, and derivation operators identify the attributes common to a group of objects or the objects sharing a group of attributes.
09 genetic algorithms by Priyesh Marvipriyeshmarvi
Genetic algorithms (GAs) are adaptive heuristic search algorithms based on Darwinian evolution. GAs represent an intelligent exploitation of random search used to solve optimization problems. In GAs, competition among potential solutions for limited resources results in fitter solutions dominating over weaker ones. Unlike older AI systems, GAs are more robust and perform well in large, complex search spaces where optimal solutions may not be found through other techniques. GAs navigate huge search spaces, looking for optimal combinations one may not otherwise discover in a lifetime.
Genetic algorithms are adaptive heuristic search algorithms based on Darwinian principles of natural selection and genetics. They represent an intelligent exploitation of random search used to solve optimization problems. The document discusses the biological background of genetic algorithms, including chromosomes, genes, alleles, and evolution. It also covers the basic concepts of genetic algorithms such as representation of solutions, fitness functions, selection, crossover and mutation operators.
cvpr2011: human activity recognition - part 2: overviewzukun
The document discusses approaches for human activity analysis from videos. It describes activity classification, detection, and recognition processes that analyze videos to identify human activities. It presents a taxonomy that categorizes recognition approaches as single-layered or hierarchical. Single-layered approaches recognize actions directly from videos, while hierarchical approaches model activities as combinations of sub-events. Hierarchical approaches are suitable for recognizing complex activities and interactions between humans or humans and objects.
Large-scale long-term networks to monitor and understand the changing ecology...CIFOR-ICRAF
This presentation examines the changing ecology of tropical forests and the effect that this has on maintaining data quality when it comes to monitoring large-scale sites over time. Some lessons learned are also outlined.
This presentation formed part of the CRP6 Sentinel Landscape planning workshop held on 30 September – 1 October 2011 at CIFOR’s headquarters in Bogor, Indonesia. Further information on CRP6 and Sentinel Landscapes can be accessed from http://www.cifor.org/crp6/ and http://www.cifor.org/fileadmin/subsites/crp/CRP6-Sentinel-Landscape-workplan_2011-2014.pdf respectively.
The document contains notifications acknowledging the insertion of several Partner Search inquiries into the ideal-ist website. The inquiries are seeking partners for projects related to topics such as in-car entertainment systems, e-learning, 3D television, intelligent transportation systems, mobile platform security, virtual presence technologies, accessibility of online information, and nanostructured materials. The notifications provide details about the project proposals such as keywords, deadlines, and links for further information.
This document describes a study that used machine learning to analyze online knowledge sharing conversations between students collaboratively solving problems. The researchers used Hidden Markov Models to classify knowledge sharing episodes as either effective or ineffective based on features of the conversation. They were able to accurately classify episodes 93% of the time, significantly better than random chance. The study provides insights into how to better understand and assess how students share and assimilate new knowledge in collaborative learning groups.
This document provides information about a proposed workshop on knowledge acquisition from distributed, autonomous, and semantically heterogeneous data sources to be held at the 2005 IEEE International Conference on Data Mining. The workshop aims to bring together researchers from areas like machine learning, data mining, knowledge representation, databases, and selected application domains to address challenges in performing knowledge discovery from multiple distributed data sources that may have semantic differences. Topics of interest include learning from distributed data, making data sources self-describing through ontologies, learning ontologies and mappings between schemas, and handling semantic heterogeneity. The workshop will include invited talks and presentations of contributed papers, and targets researchers, students, and practitioners interested in knowledge acquisition from distributed data.
This document discusses machine learning concepts including what learning is, different types of learning tasks like classification and problem solving/planning, measuring performance, reasons to study machine learning, related disciplines, defining learning tasks, designing learning systems, sample learning problems, and lessons learned about learning. It uses the example of learning to play checkers to illustrate many of these concepts such as representing the target function, obtaining training data, choosing a learning algorithm, and discussing specific algorithms like least mean squares regression.
This document provides a summary of selected publications by Ivan Bratko. It lists 7 books he has authored or edited on topics including Prolog programming, machine learning, and qualitative knowledge for expert systems. It also lists over 50 papers and book chapters on artificial intelligence, machine learning, qualitative reasoning, and applications in biology, medicine, and other domains. A more detailed bibliography is available online at the provided URL.
Alcantara Stone ha contribuido a la rehabilitación y remodelación de varias áreas del Observatorio Astronómico Nacional en Madrid, proporcionando uno de sus exclusivos materiales, el Alcantara Iridium®, para la creación de caminos y áreas en los jardines así como en la zona de la escalera principal y mirador.
This proposal seeks funding to develop technology for detecting insider threats through analyzing email data and application event traces. The proposal involves extending existing email tracking and mining technologies called MET and EMT to incorporate additional data sources like host-based sensors. The work will result in an integrated email security appliance called the EmailWall that can detect anomalous insider behavior, model groups of insiders, and quarantine potentially malicious emails. The work will be conducted by researchers at Columbia University and implemented by System Detection, Inc. over an 18 month period involving milestones like integrating additional data sources, testing on simulated data, and deploying the system for evaluation.
High-Performance Computing Needs Machine Learning... And Vice Versa (NIPS 201...npinto
This document discusses using high-performance computing for machine learning tasks like analyzing large convolutional neural networks for visual object recognition. It proposes running hundreds of thousands of large neural network models in parallel on GPUs to more efficiently search the parameter space, beyond what is normally possible with a single graduate student and model. This high-throughput screening approach aims to identify better performing network architectures through exploring a vast number of possible combinations in the available parameter space.
08.10.12 Artificial Intelligence and Cognition - Natural CognitionLESIS More UoB
Natural Cognition and Artificial Intelligence: What can biologists learn from AI?
Dr. Jackie Chappell discusses how artificial intelligence can help biologists better understand natural cognition. [1] AI tools like biomimetic robots and cognitive models can be used to test hypotheses about animal behavior and mechanisms. [2] Modeling problems animals face in their environments using AI techniques helps define the problems for experimentation. [3] While robots don't prove animal mechanisms, using AI for perception-action models and problem decomposition can provide important insights for biologists.
1) The document discusses using data mining techniques to build process models from full-scale plant data in order to optimize water and wastewater treatment processes.
2) Building accurate models is challenging due to issues scaling up from pilots, nonlinear and chaotic behaviors, and sensitivity to initial conditions. Data mining full-scale data can help address these challenges.
3) Case studies demonstrate using neural networks to model relationships between inputs like turbidity, temperature and outputs like disinfection byproducts. This allows predicting impacts of changes to optimize chemical use and meet regulations.
The document discusses business rules and production rules. It defines business rules as requirements or definitions of business behavior. Production rules use a condition-action format to define system behavior. The RETE algorithm is described as an efficient way to match patterns in production rule conditions against working memory. OWL2 ontologies are also discussed as a way to ground business rules in rich logical knowledge descriptions using classes, properties, and axioms.
Learning Moving Cast Shadows for Foreground Detection (VS 2008)Jia-Bin Huang
The document summarizes a research paper about learning moving cast shadows for foreground detection. It presents a proposed algorithm that uses a confidence-rated Gaussian mixture learning approach and Bayesian framework with Markov random fields to model local and global shadow features. This exploits the complementary nature of local and global features to improve shadow detection. The algorithm is evaluated on outdoor and indoor video sequences, showing improved accuracy over previous methods especially in adaptability to different lighting conditions. Future work could incorporate additional features and more powerful models.
Genetic algorithms are adaptive heuristic search algorithms based on Darwinian principles of natural selection and genetics. They represent an intelligent exploitation of random search used to solve optimization problems. The document discusses the biological background of genetic algorithms, including chromosomes, genes, alleles, and evolution. It also covers the basic concepts of genetic algorithms such as representation of solutions, fitness functions, selection, crossover and mutation operators.
Neuronal structures are intricately related to their functions. Study of the neuronal structures reveals healthy and pathologic conditions, crucial to understanding how the Brain works. Current advances in microscopy techniques produce huge volume of data where manual reconstruction and analysis may take several years. Moreover, most of this data is sparse; hence digital reconstructions capturing the essential structural information of the neuronal networks provide ease of archiving, exchanging and analysing. The lack of powerful computational tools to automatically reconstruct neuronal arbors has emerged as a major technical bottleneck in neuroscience research. This work extends the Marked Point Process methodology, which has been proved to be an efficient framework for network extraction in 2D, to 3D neuronal network extraction from microscopy image stacks. The optimization process considers a multiple birth and death dynamics embedded in a simulated annealing scheme. To speed up the convergence a birth map based on the projection of the neuronal processes is considered.
The MOST project developed ontologies and reasoning technologies to support model-based software engineering, including tools for transforming between ontologies and UML models, scalable ontology reasoning, and feature-based configurable reasoning services. The project made available device, process, requirement, and other models along with task, artefact, and domain ontologies to enable ontology-assisted guidance for software processes. Contact information is provided for Jeff Pan and details on demonstrations of the TrOWL ontology reasoning system at ESWC.
Computer Vision, Computation, and GeometryJason Miller
Jason Miller is an associate professor of mathematics who studies visual perception and computation using techniques from geometry and topology. He gave a talk outlining his work using medial axes and relative critical sets to analyze medical images and segment objects. This involves translating assumptions about images into mathematical models and comparing implications to real data. His subsequent work has applied these methods to projects in biology and medical imaging.
This document summarizes a master's thesis on developing a library for organizing image recognition systems. It outlines the thesis, which proposes a modular plug-in based system called AMORS that standardizes interfaces between acquisition, processing, and display modules. It then discusses applications of AMORS to automatic recognition of micro-objects, human brain cells (using segmentation, features and self-organizing maps), and counting bacteria. Finally, it describes the library developed containing classes to handle images, objects and configurations to support portability.
The penetration of mobile devices equipped with various embedded sensors also make it possible to capture the physical and virtual context of the user and surrounding environment. Further, the modeling of human behaviors based on those data becomes very important due to the increasing popularity of context-aware computing and people-centric applications, which utilize users' behavior pattern to improve the existing services or enable new services. In many natural settings, however, their broader applications are hindered by three main challenges: rarity of labels, uncertainty of activity granularities, and the difficulty of multi-dimensional sensor fusion.
Thank you for the summary. Discrete event simulation is a useful technique for modeling complex multi-robot systems with large state spaces and uncertainties.
This document provides an introduction to deep learning using Intel Nervana. It discusses machine learning basics like supervised and unsupervised learning. It then introduces deep learning concepts like neural network layers and architectures. An example of classifying handwritten digits with a multi-layer perceptron is presented to demonstrate basic deep learning concepts like forward propagation, backpropagation, and updating weights. The document also discusses model ingredients like data, parameters, optimization techniques, and frameworks like Neon that can be used for deep learning.
Elettronica: Multimedia Information Processing in Smart Environments by Aless...Codemotion
The document discusses research into multimedia information processing for smart environments. It covers topics such as feature extraction, object recognition, distributed video coding for multiple sources, and new imaging techniques. The overall goal is to develop technologies that integrate sensors, distributed computing systems, and communications to create environments that can adapt to conditions, respond to users, and improve quality of life.
1) Machine learning algorithms aim to learn patterns in labeled data to predict labels for new data, while data mining describes patterns without guaranteed generalization.
2) Running machine learning on Hadoop has issues with iterations and data sparsity causing many small, empty files.
3) Techniques like speculation, grouping rare values, and sampling can improve performance by reducing iterations and sparsity when learning decision trees on Hadoop.
This document discusses machine learning concepts including what learning is, different types of learning tasks like classification and problem solving/planning, measuring performance, reasons to study machine learning, related disciplines, defining learning tasks, designing learning systems, sample learning problems, and lessons learned about learning. It uses the example of learning to play checkers to illustrate many of these concepts such as representing the target function, obtaining training data, choosing a learning algorithm, and discussing specific algorithms like least mean squares regression.
This document provides a summary of selected publications by Ivan Bratko. It lists 7 books he has authored or edited on topics including Prolog programming, machine learning, and qualitative knowledge for expert systems. It also lists over 50 papers and book chapters on artificial intelligence, machine learning, qualitative reasoning, and applications in biology, medicine, and other domains. A more detailed bibliography is available online at the provided URL.
Alcantara Stone ha contribuido a la rehabilitación y remodelación de varias áreas del Observatorio Astronómico Nacional en Madrid, proporcionando uno de sus exclusivos materiales, el Alcantara Iridium®, para la creación de caminos y áreas en los jardines así como en la zona de la escalera principal y mirador.
This proposal seeks funding to develop technology for detecting insider threats through analyzing email data and application event traces. The proposal involves extending existing email tracking and mining technologies called MET and EMT to incorporate additional data sources like host-based sensors. The work will result in an integrated email security appliance called the EmailWall that can detect anomalous insider behavior, model groups of insiders, and quarantine potentially malicious emails. The work will be conducted by researchers at Columbia University and implemented by System Detection, Inc. over an 18 month period involving milestones like integrating additional data sources, testing on simulated data, and deploying the system for evaluation.
High-Performance Computing Needs Machine Learning... And Vice Versa (NIPS 201...npinto
This document discusses using high-performance computing for machine learning tasks like analyzing large convolutional neural networks for visual object recognition. It proposes running hundreds of thousands of large neural network models in parallel on GPUs to more efficiently search the parameter space, beyond what is normally possible with a single graduate student and model. This high-throughput screening approach aims to identify better performing network architectures through exploring a vast number of possible combinations in the available parameter space.
08.10.12 Artificial Intelligence and Cognition - Natural CognitionLESIS More UoB
Natural Cognition and Artificial Intelligence: What can biologists learn from AI?
Dr. Jackie Chappell discusses how artificial intelligence can help biologists better understand natural cognition. [1] AI tools like biomimetic robots and cognitive models can be used to test hypotheses about animal behavior and mechanisms. [2] Modeling problems animals face in their environments using AI techniques helps define the problems for experimentation. [3] While robots don't prove animal mechanisms, using AI for perception-action models and problem decomposition can provide important insights for biologists.
1) The document discusses using data mining techniques to build process models from full-scale plant data in order to optimize water and wastewater treatment processes.
2) Building accurate models is challenging due to issues scaling up from pilots, nonlinear and chaotic behaviors, and sensitivity to initial conditions. Data mining full-scale data can help address these challenges.
3) Case studies demonstrate using neural networks to model relationships between inputs like turbidity, temperature and outputs like disinfection byproducts. This allows predicting impacts of changes to optimize chemical use and meet regulations.
The document discusses business rules and production rules. It defines business rules as requirements or definitions of business behavior. Production rules use a condition-action format to define system behavior. The RETE algorithm is described as an efficient way to match patterns in production rule conditions against working memory. OWL2 ontologies are also discussed as a way to ground business rules in rich logical knowledge descriptions using classes, properties, and axioms.
Learning Moving Cast Shadows for Foreground Detection (VS 2008)Jia-Bin Huang
The document summarizes a research paper about learning moving cast shadows for foreground detection. It presents a proposed algorithm that uses a confidence-rated Gaussian mixture learning approach and Bayesian framework with Markov random fields to model local and global shadow features. This exploits the complementary nature of local and global features to improve shadow detection. The algorithm is evaluated on outdoor and indoor video sequences, showing improved accuracy over previous methods especially in adaptability to different lighting conditions. Future work could incorporate additional features and more powerful models.
Genetic algorithms are adaptive heuristic search algorithms based on Darwinian principles of natural selection and genetics. They represent an intelligent exploitation of random search used to solve optimization problems. The document discusses the biological background of genetic algorithms, including chromosomes, genes, alleles, and evolution. It also covers the basic concepts of genetic algorithms such as representation of solutions, fitness functions, selection, crossover and mutation operators.
Neuronal structures are intricately related to their functions. Study of the neuronal structures reveals healthy and pathologic conditions, crucial to understanding how the Brain works. Current advances in microscopy techniques produce huge volume of data where manual reconstruction and analysis may take several years. Moreover, most of this data is sparse; hence digital reconstructions capturing the essential structural information of the neuronal networks provide ease of archiving, exchanging and analysing. The lack of powerful computational tools to automatically reconstruct neuronal arbors has emerged as a major technical bottleneck in neuroscience research. This work extends the Marked Point Process methodology, which has been proved to be an efficient framework for network extraction in 2D, to 3D neuronal network extraction from microscopy image stacks. The optimization process considers a multiple birth and death dynamics embedded in a simulated annealing scheme. To speed up the convergence a birth map based on the projection of the neuronal processes is considered.
The MOST project developed ontologies and reasoning technologies to support model-based software engineering, including tools for transforming between ontologies and UML models, scalable ontology reasoning, and feature-based configurable reasoning services. The project made available device, process, requirement, and other models along with task, artefact, and domain ontologies to enable ontology-assisted guidance for software processes. Contact information is provided for Jeff Pan and details on demonstrations of the TrOWL ontology reasoning system at ESWC.
Computer Vision, Computation, and GeometryJason Miller
Jason Miller is an associate professor of mathematics who studies visual perception and computation using techniques from geometry and topology. He gave a talk outlining his work using medial axes and relative critical sets to analyze medical images and segment objects. This involves translating assumptions about images into mathematical models and comparing implications to real data. His subsequent work has applied these methods to projects in biology and medical imaging.
This document summarizes a master's thesis on developing a library for organizing image recognition systems. It outlines the thesis, which proposes a modular plug-in based system called AMORS that standardizes interfaces between acquisition, processing, and display modules. It then discusses applications of AMORS to automatic recognition of micro-objects, human brain cells (using segmentation, features and self-organizing maps), and counting bacteria. Finally, it describes the library developed containing classes to handle images, objects and configurations to support portability.
The penetration of mobile devices equipped with various embedded sensors also make it possible to capture the physical and virtual context of the user and surrounding environment. Further, the modeling of human behaviors based on those data becomes very important due to the increasing popularity of context-aware computing and people-centric applications, which utilize users' behavior pattern to improve the existing services or enable new services. In many natural settings, however, their broader applications are hindered by three main challenges: rarity of labels, uncertainty of activity granularities, and the difficulty of multi-dimensional sensor fusion.
Thank you for the summary. Discrete event simulation is a useful technique for modeling complex multi-robot systems with large state spaces and uncertainties.
This document provides an introduction to deep learning using Intel Nervana. It discusses machine learning basics like supervised and unsupervised learning. It then introduces deep learning concepts like neural network layers and architectures. An example of classifying handwritten digits with a multi-layer perceptron is presented to demonstrate basic deep learning concepts like forward propagation, backpropagation, and updating weights. The document also discusses model ingredients like data, parameters, optimization techniques, and frameworks like Neon that can be used for deep learning.
Elettronica: Multimedia Information Processing in Smart Environments by Aless...Codemotion
The document discusses research into multimedia information processing for smart environments. It covers topics such as feature extraction, object recognition, distributed video coding for multiple sources, and new imaging techniques. The overall goal is to develop technologies that integrate sensors, distributed computing systems, and communications to create environments that can adapt to conditions, respond to users, and improve quality of life.
1) Machine learning algorithms aim to learn patterns in labeled data to predict labels for new data, while data mining describes patterns without guaranteed generalization.
2) Running machine learning on Hadoop has issues with iterations and data sparsity causing many small, empty files.
3) Techniques like speculation, grouping rare values, and sampling can improve performance by reducing iterations and sparsity when learning decision trees on Hadoop.
Dynamic Synthesis of Mediators to Support Interoperability in Autonomic SystemsAmel Bennaceur
The document discusses dynamic synthesis of mediators to support interoperability in autonomic systems. It proposes using ontologies to dynamically synthesize and deploy mediators that can seamlessly overcome heterogeneities between systems and enable their interactions. The approach involves discovering system models, learning their semantics from ontologies, synthesizing a mediator model, concretizing it for the target platforms, and monitoring interactions to ensure correct mediation.
Integrative
analyses of large scale spatio-temporal datasets play increasingly important roles in many areas of science and engineering. Our recent work in this area is motivated by application scenarios involving complementary digital microscopy, Radiology and "omic"
analyses in cancer research. In these scenarios, our objective is to use a coordinated set of image analysis, feature extraction and machine learning methods to predict disease progression and to aid in targeting new therapies.
We describe methods
we have developed for extraction, management and analysis of features along with the systems software methods for optimizing execution on high end CPU/GPU platforms. We will also describe biomedical results obtained from these studies and extensions of the
computational methods to broader application areas.
This document discusses methods for calculating mesh saliency maps to guide large-scale mesh decimation for artist workflows. It proposes using the entropy of tropical angle of curvature at multiple scales as a robust saliency measure. An adaptive subsampling approach allows it to be calculated quickly while maintaining accuracy. Testing on models up to 150k vertices showed it outperforms previous methods in both speed and reliability. Feedback from game studios indicated it provides artists sufficient control over decimation results through adjustible saliency maps.
Este documento analiza el modelo de negocio de YouTube. Explica que YouTube y otros sitios de video online representan un nuevo modelo de negocio para contenidos audiovisuales debido al cambio en los hábitos de consumo causado por las nuevas tecnologías. Describe cómo YouTube aprovecha la participación de los usuarios para mejorar continuamente y atraer una audiencia diferente a la de los medios tradicionales.
The defense was successful in portraying Michael Jackson favorably to the jury in several ways:
1) They dressed Jackson in ornate costumes that conveyed images of purity, innocence, and humility.
2) Jackson was shown entering the courtroom as if on a red carpet, emphasizing his celebrity status.
3) Jackson appeared vulnerable, childlike, and in declining health during the trial, eliciting sympathy from jurors.
4) Defense attorney Tom Mesereau effectively presented a coherent narrative of Jackson as a victim and portrayed Neverland as a place of refuge, undermining the prosecution's arguments.
Michael Jackson was born in 1958 in Gary, Indiana and rose to fame in the 1960s as the lead singer of The Jackson 5, topping music charts in the 1970s. As a solo artist in the 1980s, his album Thriller broke music records. In the 1990s and 2000s, Jackson faced several legal issues related to child abuse allegations while continuing to release music. He married Lisa Marie Presley and Debbie Rowe and had two children before his death in 2009.
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...butest
This document appears to be a list of popular books from various authors. It includes over 150 book titles across many genres such as fiction, non-fiction, memoirs, and novels. The books cover a wide range of topics from politics to cooking to autobiographies.
The prosecution lost the Michael Jackson trial due to several key mistakes and weaknesses in their case:
1) The lead prosecutor, Thomas Sneddon, was too personally invested in the case against Jackson, having pursued him for over a decade without success.
2) Sneddon's opening statement was disorganized and weak, failing to effectively outline the prosecution's case.
3) The accuser's mother was not credible and damaged the prosecution's case through her erratic testimony, history of lies and con artist behavior.
4) Many prosecution witnesses were not credible due to prior lawsuits against Jackson, debts owed to him, or having been fired by him. Several witnesses even took the Fifth Amendment.
Here are three examples of public relations from around the world:
1. The UK government's "Be Clear on Cancer" campaign which aims to raise awareness of cancer symptoms and encourage early diagnosis.
2. Samsung's global brand marketing and sponsorship activities which aim to increase brand awareness and favorability of Samsung products worldwide.
3. The Brazilian government's efforts to improve its international image and relations with other countries through strategic communication and diplomacy.
The three most important functions of public relations are:
1. Media relations because the media is how most organizations reach their key audiences. Strong media relationships are crucial.
2. Writing, because written communication is at the core of public relations and how most information is
Michael Jackson Please Wait... provides biographical information about Michael Jackson including his birthdate, birthplace, parents, height, interests, idols, favorite foods, films, and more. It discusses his background, career highlights including influential albums like Thriller, and films he appeared in such as The Wiz and Moonwalker. The document contains photos and details about Jackson's life and illustrious music career.
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazzbutest
The document discusses the process of manufacturing celebrity and its negative byproducts. It argues that celebrities are rarely the best in their individual pursuits like singing, dancing, etc. but become famous due to being products of a system controlled by wealthy elites. This system stifles opportunities for worthy artists and creates feudalism. The document also asserts that manufactured celebrities should not be viewed as role models due to behaviors like drug abuse and narcissism that result from the celebrity-making process.
Michael Jackson was a child star who rose to fame with the Jackson 5 in the late 1960s and early 1970s. As a solo artist in the 1970s and 1980s, he had immense commercial success with albums like Off the Wall, Thriller, and Bad, which featured hit singles and groundbreaking music videos. However, his career and public image were plagued by controversies related to allegations of child sexual abuse in the 1990s and 2000s. He continued recording and performing but faced ongoing media scrutiny into his private life until his death in 2009.
Social Networks: Twitter Facebook SL - Slide 1butest
The document discusses using social networking tools like Twitter and Facebook in K-12 education. Twitter allows students and teachers to share short updates and can be used to give parents a window into classroom activities. Facebook allows targeted advertising that could be used to promote educational activities. Both tools could help facilitate communication between schools and communities if used properly while managing privacy and security concerns.
Facebook has over 300 million active users who log on daily, and allows brands to create public profile pages to interact with users. Pages are for brands and organizations only, while groups can be made by any user about any topic. Pages do not show admin names and have no limits on fans, while groups display admin names and are limited to 5,000 members. Content on pages should aim to provoke action from subscribers and establish a regular posting schedule using a conversational tone.
Executive Summary Hare Chevrolet is a General Motors dealership ...butest
Hare Chevrolet is a car dealership located in Noblesville, Indiana that has successfully used social media platforms like Twitter, Facebook, and YouTube to create a positive brand image. They invest significant time interacting directly with customers online to foster a sense of community rather than overtly advertising. As a result, Hare Chevrolet has built a large, engaged audience on social media and serves as a model for how brands can use online presences strategically.
Welcome to the Dougherty County Public Library's Facebook and ...butest
This document provides instructions for signing up for Facebook and Twitter accounts. It outlines the sign up process for both platforms, including filling out forms with name, email, password and other details. It describes how the platforms will then search for friends and suggest people to connect with. It also explains how to search for and follow the Dougherty County Public Library page on both Facebook and Twitter once signed up. The document concludes by thanking participants and providing a contact for any additional questions.
Paragon Software announces the release of Paragon NTFS for Mac OS X 8.0, which provides full read and write access to NTFS partitions on Macs. It is the fastest NTFS driver on the market, achieving speeds comparable to native Mac file systems. Paragon NTFS for Mac 8.0 fully supports the latest Mac OS X Snow Leopard operating system in 64-bit mode and allows easy transfer of files between Windows and Mac partitions without additional hardware or software.
This document provides compatibility information for Olympus digital products used with Macintosh OS X. It lists various digital cameras, photo printers, voice recorders, and accessories along with their connection type and any notes on compatibility. Some products require booting into OS 9.1 for software compatibility or do not support devices that need a serial port. Drivers and software are available for download from Olympus and other websites for many products to enable use with OS X.
To use printers managed by the university's Information Technology Services (ITS), students and faculty must install the ITS Remote Printing software on their Mac OS X computer. This allows them to add network printers, log in with their ITS account credentials, and print documents while being charged per page to funds in their pre-paid ITS account. The document provides step-by-step instructions for installing the software, adding a network printer, and printing to that printer from any internet connection on or off campus. It also explains the pay-in-advance printing payment system and how to check printing charges.
The document provides an overview of the Mac OS X user interface for beginners, including descriptions of the desktop, login screen, desktop elements like the dock and hard disk, and how to perform common tasks like opening files and folders. It also addresses frequently asked questions for Windows users switching to Mac OS X, such as where documents are stored, how to save or find documents, and what the equivalent of the C: drive is in Mac OS X. The document concludes with sections on file management tasks like creating and deleting folders, organizing files within applications, using Spotlight search, and an overview of the Dashboard feature.
This document provides a checklist for securing Mac OS X version 10.5, focusing on hardening the operating system, securing user accounts and administrator accounts, enabling file encryption and permissions, implementing intrusion detection, and maintaining password security. It describes the Unix infrastructure and security framework that Mac OS X is built on, leveraging open source software and following the Common Data Security Architecture model. The checklist can be used to audit a system or harden it against security threats.
This document summarizes a course on web design that was piloted in the summer of 2003. The course was a 3 credit course that met 4 times a week for lectures and labs. It covered topics such as XHTML, CSS, JavaScript, Photoshop, and building a basic website. 18 students from various majors enrolled. Student and instructor evaluations found the course to be very successful overall, though some improvements were suggested like ensuring proper software and pairing programming/non-programming students. The document also discusses implications of incorporating web design material into existing computer science curriculums.
1. Machine Learning Proposed Term Paper Topics
Robert Stengel
Robotics and Intelligent Systems MAE 345,
Princeton University, 2009
MAE 345, Fall 2009
! Multistep NN with Memory
• Markov Decision Processes ! Maze-Navigating Robot
– Optimal and near-optimal control ! Robotic Prosthetic Device
• Finding Decision Rules in Data ! Optimal Control of an Ambiguous Robot
– ID3 algorithm ! Game-Playing NN
• Search ! NN for Object Recognition
! Robotic Cloth Folder
! SAGA Simulated Creature
! NN to Optimize Problem Set Solution
! Blob-Tracking NN
! Dust-Collecting Robot that Learns
! NN for Stock Return Prediction
Copyright 2009 by Robert Stengel. All rights reserved. For educational use only.
http://www.princeton.edu/~stengel/MAE345.html
Finding Decision Example of On-Line
Rules in Data Code Modification
• Identification of key attributes and • Execute a decision tree
outcomes – Get wrong answer
• Add logic to distinguish between right and wrong
• Taxonomies developed by experts cases
• First principles of science and – If Comfort Zone = Water,
• then Animal = Hippo,
mathematics • else Animal = Rhino
• Trial and error – True, but Animal is Dinosaur, not Hippo
– Ask user for right answer
• Probability theory and fuzzy logic – Ask user for a rule that distinguishes between right and
wrong answer: If Animal is extinct, …
• Simulation and empirical results
2. Maximizing the Utility Function
Markov Decision Process of a Markov Process
• Model for decision making under uncertainty "
Utility function: J = # ! (t)Ra(t ) [ x(t), x(t + 1)]
! S, A, Pam ( x k , x ') , Ram ( x k , x ') #
t =0
" $ ! (t) : discount rate, 0<! (t)<1
where
S : finite set of states, x1 , x 2 ,…, x K "
A : finite set of actions, a1 , a2 ,…, aM Utility function to go = Value function: V = # ! (t)Ra(t ) [ x(t), x(t + 1)]
Pam ( x k , x ') = Pr ! x k ( ti +1 ) = x ' | x k ( ti ) = x k , a ( ti ) = am #
t =t current
" $
Ram ( x k , x ') = Expected immediate reward for transition from x k to x ' • Optimal control at t
$
& " (
&
• Optimal decision maximizes expected total reward (or u opt ( t ) = arg max % Ra(t ) [ x(t), x(t + 1)] + ! (t) # Pa(t ) [ x(t), x(t + 1)]V [ x(t + 1)])
minimizes expected total cost) by choosing best set of a &
' t =t current &
*
actions (or control policy) • Optimized value function
– Linear-quadratic-Gaussian (LQG) control "
– Dynamic programming -> HJB equation ~> A* search V * ( t ) = Ruopt (t ) [ x * (t)] + ! (t) # Puopt (t ) [ x * (t), x est * (t + 1)]V [ x est * (t + 1)]
t =t current
– Reinforcement learning ~> Heuristic search
Reinforcement (“Q”) Learning Q Learning Control of a Markov
Control of a Markov Process Process is Analogous to LQG
• Q: quality of a state-action function Control in the LTI Case
• Heuristic value function
• One-step philosophy for heuristic optimization $ {
Q [ x(t + 1), u(t + 1)] = Q [ x(t), u(t)] + ! (t) # Ru(t ) [ x(t)] + " (t)max Q [ x(t + 1), u ]% ' Q [ x(t), u(t)]
u & }
! (t) : learning rate, 0<! (t)<1
$ { u & }
Q [ x(t + 1), u(t + 1)] = Q [ x(t), u(t)] + ! (t) # Ru(t ) [ x(t)] + " (t)max Q [ x(t + 1), u ]% ' Q [ x(t), u(t)]
Controller
! (t) : learning rate, 0<! (t)<1
x k +1 = !x k + "C ( x k # x k *)
ˆ
• Various algorithms for computing best control value
Estimator
ubest ( t ) = arg max Q [ x(t + 1), u ]
u
x k = !x k "1 " #C ( x k "1 " x k "1 *) + K ( z k " H x x k "1 )
ˆ ˆ ˆ ˆ
Q-Learning Snail Q-Learning, Ball on Plate
3. LQG Control Optimizes Discrete- Structuring an Efficient
Time LTI Markov Process Decision Tree (Off-Line)
• Choose most important attributes first
• Recognize when no result can be
deduced
• Exclude irrelevant factors
! S, A, Pam ( x k , x ') , Ram ( x k , x ') #
" $
where
• Iterative Dichotomizer*: the ID3 Algorithm
S : infinite set of states, x1 , x 2 ,…, x K – Build an efficient decision tree from a fixed
A : infinite set of actions, a1 , a2 ,…, aM set of examples (supervised learning)
Pam ( x k , x ') = Pr ! x k ( ti +1 ) = x ' | x k ( ti ) = x k , a ( ti ) = am #
" $
Ram ( x k , x ') = Expected immediate reward for transition from x k to x ' *Dichotomy: Division into two (usually contradictory)
parts or opinions
Fuzzy Ball-Game Training Set Parameters of the ID3 Algorithm
Attributes Decisions
Case # Forecast Temperature Humidity Wind Play Ball?
1 Sunny Hot High Weak No
2 Sunny Hot High Strong No
3 Overcast Hot High Weak Yes
4 Rain Mild High Weak Yes
5 Rain Cool Low Weak Yes
6 Rain Cool Low Strong No
7 Overcast Cool Low Strong Yes
8 Sunny Mild High Weak No
9 Sunny Cool Low Weak Yes • Decisions, e.g., Play ball or
10 Rain Mild Low Weak Yes
11 Sunny Mild Low Strong Yes don!t play ball
12 Overcast Mild High Strong Yes
13 Overcast Hot Low Weak Yes – D = Number of possible decisions
14 Rain Mild High Strong No • Decision: Yes, no
4. Parameters of Parameters of
the ID3 Algorithm the ID3 Algorithm
• Attributes, e.g., Temperature, humidity, • Training trials, e.g., all the
wind, weather forecast
– M = Number of attributes to be considered in games played last month
making a decision – N = Number of training trials
– Im = Number of values that the ith attribute can
take – n(i) = Number of examples with
• Temperature: Hot, mild, cool ith attribute
• Humidity: High, low
• Wind: Strong, weak
• Forecast: Sunny, overcast, rain
Example: Probability Spaces for Example: Decision, given
Three Attributes Values of Three Attributes
• Probability of an attribute value
represented by area in diagram
Attribute #1 Attribute #2 Attribute #3 Attribute #1 Attribute #2 Attribute #3
2 possible values 6 possible values 4 possible values 2 possible values 6 possible values 4 possible values
5. Accurate Detection of Events Depends
Accurate Detection of Events Depends
on Their Probability of Occurence
on Their Probability of Occurence
! noise = 0.1
! noise = 0.2
! noise = 0.4
Entropy Measures Information Entropy of Two Events with Various
Content of a Signal Frequencies of Occurrence
• Pr(i) log2Pr(i) represents the channel capacity
(i.e., average number of bits) required to portray
• S = Entropy of a signal encoding I distinct events the ith event
I • Frequencies of occurrence estimate
S = ! " Pr(i) log 2 Pr(i) 0 " Pr(.) " 1
log2 Pr(.) " 0 probabilities of each event (#1 and #2)
i =1 – Pr(#1) = n(#1)/N
log2 Pr(#1 or #2) " 0
– Pr(#2) = n(#2)/N = 1 – n(#1)/N
• i = Index identifying an event encoded by
a signal
• Pr(i) = Probability of ith event
S = S# 1 + S# 2
• log2Pr(i) = Number of bits required to = ! Pr(#1) log 2 Pr(#1) ! Pr(# 2) log 2 Pr(# 2)
characterize the probability that the ith
event occurs
6. Best Decision is Related to Entropy
Entropy of Two Events with Various and the Probability of Occurrence
Frequencies of Occurrence • High entropy
Entropies for 128 Trials – Signal provides high coding I
S = !" Pr(i) log 2 Pr(i)
Pr(#1) - # of Bits(#1) Pr(#2) - # of Bits(#2) Entropy
precision of distinct events
n n/N log2(n/N) 1 - n/N log2(1 - n/N) S
1 0.008 -7 0.992 -0.011 0.066 – Differences coded with few bits
2 0.016 -6 0.984 -0.023 0.116 i=1
4 0.031 -5 0.969 -0.046 0.201 • Low entropy
8 0.063 -4 0.938 -0.093 0.337
16 0.125 -3 0.875 -0.193 0.544 – Lack of distinction between
32 0.25 -2 0.75 -0.415 0.811 signal values
64 0.50 -1 0.50 -1 1
96 0.75 -0.415 0.25 -2 0.811 – Detecting differences requires
112 0.875 -0.193 0.125 -3 0.544
120 0.938 -0.093 0.063 -4 0.337 many bits
124 0.969 -0.046 0.031 -5 0.201
126 0.984 -0.023 0.016 -6 0.116 • Best classification of events
127 0.992 -0.011 0.008 -7 0.066 when S = 1...
– but that may not be achievable
Case # Forecast Temperature Humidity Wind Play Ball?
1
2
3
4
Sunny
Sunny
Overcast
Rain
Hot
Hot
Hot
Mild
High
High
High
High
Weak
Strong
Weak
Weak
No
No
Yes
Yes
Decision-Making Decision Tree Produced by
5 Rain Cool Low Weak Yes
6
7
Rain
Overcast
Cool
Cool
Low
Low
Strong
Strong
No
Yes
ID3 Algorithm
Parameters for ID3
8 Sunny Mild High Weak No
9 Sunny Cool Low Weak Yes
10 Rain Mild Low Weak Yes
11 Sunny Mild Low Strong Yes
12
13
Overcast
Overcast
Mild
Hot
High
Low
Strong
Weak
Yes
Yes
• Root Attribute gains, Gi
14 Rain Mild High Strong No
– Forecast: 0.246
– Temperature: 0.029
• SD = Entropy of all possible decisions –
–
Humidity: 0.151
Wind: 0.048
D
SD = !" Pr(d) log 2 Pr(d)
d =1
• Gi = Information gain of ith attribute
Im D
Gi = SD + ! Pr(i) ! Pr(id ) log 2 Pr(id )
i=1 d =1
• Pr(id) = n(id)/ N(d) = Probability that ith • Temperature is inconsequential and
attribute correlates with dth decision is not included in the decision tree
7. Decision Tree Produced by Search
ID3 Algorithm
• Typical AI textbook problems
– Prove a theorem
• Sunny Branch
– Solve a puzzle (e.g., Tower of
Attribute gains, Gi Hanoi)
– Temperature: 0.57 – Find a sequence of moves that
– Humidity: 0.97 wins a game (e.g., chess)
– Wind: 0.019 – Find the shortest path
connecting a set of points (e.g.,
Traveling salesman problem)
– Find a sequence of symbolic
transformations that solve a
calculus problem (e.g.,
Mathematica)
• The common thread: search
– Structures for search
– Strategies for search
Curse of Structures for Search
Dimensionality
• Feasible search paths may • Trees
grow without bound
– Possible combinatorial – Single path between root and any node
explosion
– Checkers: 5 x 1020 possible
– Path between adjacent nodes = arc
moves – Root node
– Chess: 10120 moves
– Protein folding: ? • no precursors
• Limiting search complexity – Leaf node
– Redefine search space
– Employ heuristic (i.e., pragmatic)
• no successors
rules • possible terminator
– Establish restricted search range
– Invoke decision models that
have worked in the past
8. Structures for Search Directions of Search
• Forward chaining
• Graphs –Reason from premises to actions
–Multiple paths –Data-driven: draw conclusions
between root from facts
and some • Backward chaining
nodes
–Reason from actions to premises
–Trees are
subsets of –Goal-driven: find facts that
graphs support hypotheses
Strategies for Search Blind Search
• Search forward from opening?
• Node expansion
• Search backward from end game? – Find all successors to that node
• Realistic assessment • Both?
• Depth-first forward search
– Not necessary to consider all 10120 possible moves – Expand nodes descended from most recently
to play good chess expanded node
– Playing excellent chess may require much forward – Consider other paths only after reaching a node
and backward chaining, but not 10120 evaluations with no successors
– Most applications are more procedural
• Breadth-first forward search
• Search categories – Expand nodes in order of proximity to the start node
– Blind search – Consider all sequences of arc number n (from root
– Heuristic search node) before considering any of number (n + 1)
– Probabilistic search – Exhaustive, but guaranteed to find the shortest path
– Optimization to a terminator
9. AND/OR Graph Search
Blind Search
• Bidirectional search
– Search forward from root node and
backward from one or more leaf nodes
– Terminate when search nodes coincide • A node is “solved” if
• Minimal-cost forward search – It is a leaf node with a satisfactory goal
– Each arc is assigned a cost state
– Expand nodes in order of minimum cost – It has solved AND nodes as successors
– It has OR nodes as successors, at least
one of which is solved.
• Goal: Solve the root node
Heuristic Search Heuristic Optimal Search
• For large problems, blind search typically
leads to combinatorial explosion
• Employ heuristic knowledge about the
quality of possible paths
– Decide which node to expand next
– Discard (or prune) nodes that are unlikely to
be fruitful
• Search for feasible (approximately
optimal) rather than optimal solutions
• Ordered or best-first search
– Always expand “most promising” node
10. Mechanical Control System
Heuristic Dynamic
Programming: A* Search
k kf
Jk f = ! Ji +
ˆ ! J (arc )
ˆ
i i
i=1 i= k +1
• Each arc bears an incremental cost
• Cost, J, estimated at kth instant =
– Cost accrued to k
– Remaining cost to reach final point, kf
• Goal: minimize estimated cost by choice of
remaining arcs
• Choose arck+1, arck+2 accordingly
• Use heuristics to estimate remaining cost
Inferential Fault Analyzer for
Helicopter Control System Local Failure Analysis
• Local failure analysis • Frames store facts and facilitate search and inference
– Set of hypothetical models of specific failure – Components and up-/downstream linkages of control system
• Global failure analysis – Failure model parameters
– Forward reasoning assesses failure impact – Rule base for failure analysis (LISP)
– Backward reasoning deduces possible causes
Aft Rotor
Forward Rotor
Cockpit Controls
11. Heuristic Search Global Failure Analysis
• Global failure analysis
– Determination based on aggregate of
local models
• Heuristic score based on
– Criticality of failure
– Reliability of component
– Extensiveness of failure
– Implicated devices
– Level of backtracking
– Severity of failure
– Net probability of failure model
Shortest Path Problems
• Find the shortest (or • Simulated annealing solution
Next Time:
least costly) path that
• Genetic algorithm solution
visits all selected cities
just once • Neural network solution
Knowledge
– Traveling Saleman
– MapQuest/GPS/GIS
Representation
Modified Dijkstra
Algorithm