Matching Items (35)

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Post-Optimization of Permutation Coverings

Description

Covering subsequences with sets of permutations arises in many applications, including event-sequence testing. Given a set of subsequences to cover, one is often interested in knowing the fewest number of

Covering subsequences with sets of permutations arises in many applications, including event-sequence testing. Given a set of subsequences to cover, one is often interested in knowing the fewest number of permutations required to cover each subsequence, and in finding an explicit construction of such a set of permutations that has size close to or equal to the minimum possible. The construction of such permutation coverings has proven to be computationally difficult. While many examples for permutations of small length have been found, and strong asymptotic behavior is known, there are few explicit constructions for permutations of intermediate lengths. Most of these are generated from scratch using greedy algorithms. We explore a different approach here. Starting with a set of permutations with the desired coverage properties, we compute local changes to individual permutations that retain the total coverage of the set. By choosing these local changes so as to make one permutation less "essential" in maintaining the coverage of the set, our method attempts to make a permutation completely non-essential, so it can be removed without sacrificing total coverage. We develop a post-optimization method to do this and present results on sequence covering arrays and other types of permutation covering problems demonstrating that it is surprisingly effective.

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  • 2014-12

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Mazes of Waverly Place: Interactive Algorithmic Art Generator

Description

This paper is a supplement to our interactive algorithmic art generator project which can be found at weiverlyroe.github.io/waverlyplace. For this thesis, we demonstrate how with certain input we can algorithmically

This paper is a supplement to our interactive algorithmic art generator project which can be found at weiverlyroe.github.io/waverlyplace. For this thesis, we demonstrate how with certain input we can algorithmically generate art, specifically a playable random maze with exactly one solution. We explore interactive and algorithmic art and how our mazes are a form of both. Through examining several maze generation algorithms, we show that an ideal representation of a single-solution maze, called a perfect maze, is a spanning tree of a planar graph. The final algorithm is a re-imagining of Kruskal's Minimum Spanning Tree Algorithm with these adjustments: (1) potential edges are ordered randomly rather than sorted and (2) certain edges are forced in the maze in order for the wall structure to display the player's text input. Lastly, we discuss improvements which could be made and features which we plan to add to the project in the future.

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Created

Date Created
  • 2016-12

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Collaborative Computation in Self-Organizing Particle Systems

Description

Many forms of programmable matter have been proposed for various tasks. We use an abstract model of self-organizing particle systems for programmable matter which could be used for a variety

Many forms of programmable matter have been proposed for various tasks. We use an abstract model of self-organizing particle systems for programmable matter which could be used for a variety of applications, including smart paint and coating materials for engineering or programmable cells for medical uses. Previous research using this model has focused on shape formation and other spatial configuration problems, including line formation, compression, and coating. In this work we study foundational computational tasks that exceed the capabilities of the individual constant memory particles described by the model. These tasks represent new ways to use these self-organizing systems, which, in conjunction with previous shape and configuration work, make the systems useful for a wider variety of tasks. We present an implementation of a counter using a line of particles, which makes it possible for the line of particles to count to and store values much larger than their individual capacities. We then present an algorithm that takes a matrix and a vector as input and then sets up and uses a rectangular block of particles to compute the matrix-vector multiplication. This setup also utilizes the counter implementation to store the resulting vector from the matrix-vector multiplication. Operations such as counting and matrix multiplication can leverage the distributed and dynamic nature of the self-organizing system to be more efficient and adaptable than on traditional linear computing hardware. Such computational tools also give the systems more power to make complex decisions when adapting to new situations or to analyze the data they collect, reducing reliance on a central controller for setup and output processing. Finally, we demonstrate an application of similar types of computations with self-organizing systems to image processing, with an implementation of an image edge detection algorithm.

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Date Created
  • 2016-12

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Gram-ART Applied to Music Recommendation Services

Description

In this paper we explore the design, implementation, and analysis of two different approaches for providing music recommendations to targeted users by implementing the Gram-ART unsupervised learning algorithm. We provide

In this paper we explore the design, implementation, and analysis of two different approaches for providing music recommendations to targeted users by implementing the Gram-ART unsupervised learning algorithm. We provide a content filtering approach using a dataset of one million songs which include various metadata tags and a collaborative filtering approach using the listening histories of over one million users. The two methods are evaluated by their results from Million Song Dataset Challenge. While both placed near the top third of the 150 challenge participants, the knowledge gained from the experiments will help further refine the process and likely produced much higher results in a system with the potential to scale several magnitudes.

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Date Created
  • 2015-05

Exploring the Range of Algorithmic Choreography

Description

The goal of this thesis is to explore and present a range of approaches to “algorithmic choreography.” In the context of this thesis, algorithmic choreography is defined as choreography with

The goal of this thesis is to explore and present a range of approaches to “algorithmic choreography.” In the context of this thesis, algorithmic choreography is defined as choreography with computational influence or elements. Traditionally, algorithmic choreography, despite containing works that use computation in a variety of ways, has been used as an umbrella term for all works that involve computation.
This thesis intends to show that the diversity of algorithmic choreography can be reduced into more specific categories. As algorithmic choreography is fundamentally intertwined with the concept of computation, it is natural to propose that algorithmic choreography works be separated based on a spectrum that is defined by the extent of the involvement of computation within each piece.
This thesis seeks to specifically outline three primary categories that algorithmic works can fall into: pieces that involve minimal computational influence, entirely computationally generated pieces, and pieces that lie in between. Three original works were created to reflect each of these categories. These works provide examples of the various methods by which computation can influence and enhance choreography.
The first piece, entitled Rαinwater, displays a minimal amount of computational influence. The use of space in the piece was limited to random, computationally generated paths. The dancers extracted a narrative element from the random paths. This iteration resulted in a piece that explores the dancers’ emotional interaction within the context of a rainy environment. The second piece, entitled Mymec, utilizes an intermediary amount of computation. The piece sees a dancer interact with a projected display of an Ant Colony Optimization (ACO) algorithm. The dancer is to take direct inspiration from the movement of the virtual ants and embody the visualization of the algorithm. The final piece, entitled nSkeleton, exhibited maximal computational influence. Kinect position data was manipulated using iterative methods from computational mathematics to create computer-generated movement to be performed by a dancer on-stage.
Each original piece was originally intended to be presented to the public as part of an evening-length show. However, due to the rise of the COVID-19 pandemic caused by the novel coronavirus, all public campus events have been canceled and the government has recommended that gatherings with more than 10 people be entirely avoided. Thus, the pieces will instead be presented in the form of a video published online. This video will encompass information about the creation of each piece as well as clips of choreography.

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Date Created
  • 2020-05

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complexMovement

Description

Computer Science and Dance are choice driven disciplines. The output of their processes are compositions of experience. Dancers are not computers and computers are not people but there are comparable

Computer Science and Dance are choice driven disciplines. The output of their processes are compositions of experience. Dancers are not computers and computers are not people but there are comparable traces of humanity in the way each interpret and interact with their respective inputs, outputs, and environments. These overlaps are perhaps not obvious, but in an increasingly specialized world it is important to discuss them. Dynamic Programming and improvisational movement exist within exclusive corners of their respective fields and are characterized by their inherent adaption to change. Inspired by the work of Ivar Hagendoorn, John Cage and other interdisciplinary artists, complexMovement is motivated by the need to create space for intersections between these two powerful groups and find overlaps in the questions they ask to achieve their goals. Dance and Computer Science are just one example of hidden partnerships between their respective fields. Their respective sides allow for ample side by side comparisons but for the purpose of this work, we will focus upon two smaller sectors of their studies: improvisational movement and the design of Dynamic Programming algorithms.

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Created

Date Created
  • 2016-05

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Target Detection Using Algorithmic Matter

Description

Over the years, advances in research have continued to decrease the size of computers from the size of<br/>a room to a small device that could fit in one’s palm. However,

Over the years, advances in research have continued to decrease the size of computers from the size of<br/>a room to a small device that could fit in one’s palm. However, if an application does not require extensive<br/>computation power nor accessories such as a screen, the corresponding machine could be microscopic,<br/>only a few nanometers big. Researchers at MIT have successfully created Syncells, which are micro-<br/>scale robots with limited computation power and memory that can communicate locally to achieve<br/>complex collective tasks. In order to control these Syncells for a desired outcome, they must each run a<br/>simple distributed algorithm. As they are only capable of local communication, Syncells cannot receive<br/>commands from a control center, so their algorithms cannot be centralized. In this work, we created a<br/>distributed algorithm that each Syncell can execute so that the system of Syncells is able to find and<br/>converge to a specific target within the environment. The most direct applications of this problem are in<br/>medicine. Such a system could be used as a safer alternative to invasive surgery or could be used to treat<br/>internal bleeding or tumors. We tested and analyzed our algorithm through simulation and visualization<br/>in Python. Overall, our algorithm successfully caused the system of particles to converge on a specific<br/>target present within the environment.

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Date Created
  • 2021-05

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A Research Review of The Trials and Errors of Predictive Policing

Description

The era of mass data collection is upon us and only recently have people begun to consider the value of their data. All of our clicks and likes have

The era of mass data collection is upon us and only recently have people begun to consider the value of their data. All of our clicks and likes have helped big tech companies build predictive models to tailor their product to the buying patterns of the consumer. Big data collection has its advantages in increasing profitability and efficiency, but many are concerned about the lack of transparency in these technologies (Dwyer). The dependency on algorithms to make and influence decisions has become a growing concern in law enforcement. The use of this technology is commonly referred to as data-driven decision making, which is also known as predictive policing. These technologies are thought to reduce the biases held in traditional policing by creating statistically sound evidence-based models. Although, many lawsuits have highlighted the fact that predictive technologies do more to reflect historical bias rather than to eradicate it. The clandestine measures behind the algorithms may be in conflict with the due process clause and the penumbra of privacy rights enumerated in the First, Third, Fourth, and Fifth Amendments. <br/> Predictive policing technology has come under fire for over policing historically black and latinx neighborhoods. GIS (Geographical Information Systems) is supposed to help officers identify where crime will likely happen over the next twelve hours. However, the LAPD’s own internal audit of their program concluded that the technology did not help officers solve crimes or reduce crime rate any better than traditional patrol methods (Puente). Similarly, other types of tools used to calculate recidivism risk for bond sentencing are disproportionately biased to calculate black people as having a higher risk to reoffend (Angwin). Lawsuits from civil liberties groups have been filed against the police departments that utilized these technologies. This paper will examine the constitutional pitfalls of predictive technology and propose ways that the system could work to ameliorate its practices.

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Date Created
  • 2021-05

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ANALYSIS UTILIZING EXPECTATIONS OF A FORAGER’S DECISION-MAKING HEURISTIC TO ENSURE AN OPTIMAL CALORIC STATE WITH MULTIPLE PREY

Description

Optimal foraging theory provides a suite of tools that model the best way that an animal will <br/>structure its searching and processing decisions in uncertain environments. It has been <br/>successful

Optimal foraging theory provides a suite of tools that model the best way that an animal will <br/>structure its searching and processing decisions in uncertain environments. It has been <br/>successful characterizing real patterns of animal decision making, thereby providing insights<br/>into why animals behave the way they do. However, it does not speak to how animals make<br/>decisions that tend to be adaptive. Using simulation studies, prior work has shown empirically<br/>that a simple decision-making heuristic tends to produce prey-choice behaviors that, on <br/>average, match the predicted behaviors of optimal foraging theory. That heuristic chooses<br/>to spend time processing an encountered prey item if that prey item's marginal rate of<br/>caloric gain (in calories per unit of processing time) is greater than the forager's<br/>current long-term rate of accumulated caloric gain (in calories per unit of total searching<br/>and processing time). Although this heuristic may seem intuitive, a rigorous mathematical<br/>argument for why it tends to produce the theorized optimal foraging theory behavior has<br/>not been developed. In this thesis, an analytical argument is given for why this<br/>simple decision-making heuristic is expected to realize the optimal performance<br/>predicted by optimal foraging theory. This theoretical guarantee not only provides support<br/>for why such a heuristic might be favored by natural selection, but it also provides<br/>support for why such a heuristic might a reliable tool for decision-making in autonomous<br/>engineered agents moving through theatres of uncertain rewards. Ultimately, this simple<br/>decision-making heuristic may provide a recipe for reinforcement learning in small robots<br/>with little computational capabilities.

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Date Created
  • 2021-05

Mapping the Implications of AI and Machine Learning in the Healthcare Market

Description

Within the last decade, there has been a lot of hype surrounding the potential medical applications of artificial intelligence (AI) and machine learning (ML) technologies. During the same timespan, big

Within the last decade, there has been a lot of hype surrounding the potential medical applications of artificial intelligence (AI) and machine learning (ML) technologies. During the same timespan, big tech companies such as Microsoft, Apple, Amazon, and Google have entered the healthcare market as developers of health-based AI and ML technologies. This project aims to create a comprehensive map of the existing health-AI market landscape for the standard biotech reader and to provide a critical commentary on the existing market structure.

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Date Created
  • 2021-05