Physical Sciences and Mathematics Commons^™

The Silencing Power Of Algorithms: How The Facebook News Feed Algorithm Manipulates Users' Perceptions Of Opinion Climates, Callie Jessica Morgan

University Honors Theses

This extended literature review investigates how the architecture and features of the Facebook Newsfeed algorithm, EdgeRank, can inhibit and facilitate the expression of political opinions. This paper will investigate how Elisabeth Noelle-Neumann's theory on public opinion, Spiral of Silence, can be used to assess the Facebook news feed as a political opinion source that actively shapes users' perceptions of minority and majority opinion climates. The feedback loops created by the algorithm's criteria influences users' decisions to self-censor or express their political opinions with interpersonal connections and unfamiliar connections on the site.

Bounding Box Improvement With Reinforcement Learning, Andrew Lewis Cleland

Dissertations and Theses

In this thesis, I explore a reinforcement learning technique for improving bounding box localizations of objects in images. The model takes as input a bounding box already known to overlap an object and aims to improve the fit of the box through a series of transformations that shift the location of the box by translation, or change its size or aspect ratio. Over the course of these actions, the model adapts to new information extracted from the image. This active localization approach contrasts with existing bounding-box regression methods, which extract information from the image only once. I implement, train, and …

An Exploration Of Linear Classifiers For Unsupervised Spiking Neural Networks With Event-Driven Data, Wesley Chavez

Dissertations and Theses

Object recognition in video has seen giant strides in accuracy improvements in the last few years, a testament to the computational capacity of deep convolutional neural networks. However, this computational capacity of software-based neural networks coincides with high power consumption compared to that of some spiking neural networks (SNNs), up to 300,000 times more energy per synaptic event in IBM's TrueNorth chip, for example. SNNs are also well-suited to exploit the precise timing of event-driven image sensors, which transmit asynchronous "events" only when the luminance of a pixel changes above or below a threshold value. The combination of event-based imagers …

Cox Processes For Counting By Detection, Purnima Rajan, Yongming Ma, Bruno Jedynak

Portland Institute for Computational Science Publications

In this work, doubly stochastic Poisson (Cox) processes and convolutional neural net (CNN) classifiers are used to estimate the number of instances of an object in an image. Poisson processes are well suited to model events that occur randomly in space, such as the location of objects in an image or the enumeration of objects in a scene. The proposed algorithm selects a subset of bounding boxes in the image domain, then queries them for the presence of the object of interest by running a pre-trained CNN classifier. The resulting observations are then aggregated, and a posterior distribution over the …

Gaussian Processes With Context-Supported Priors For Active Object Localization, Bruno Jedynak

Portland Institute for Computational Science Publications

We devise an algorithm using a Bayesian optimization framework in conjunction with contextual visual data for the efficient localization of objects in still images. Recent research has demonstrated substantial progress in object localization and related tasks for computer vision. However, many current state-of-the-art object localization procedures still suffer from inaccuracy and inefficiency, in addition to failing to provide a principled and interpretable system amenable to high-level vision tasks. We address these issues with the current research.

Our method encompasses an active search procedure that uses contextual data to generate initial bounding-box proposals for a target object. We train a convolutional …

Opportunity Identification For New Product Planning: Ontological Semantic Patent Classification, Farshad Madani

Dissertations and Theses

Intelligence tools have been developed and applied widely in many different areas in engineering, business and management. Many commercialized tools for business intelligence are available in the market. However, no practically useful tools for technology intelligence are available at this time, and very little academic research in technology intelligence methods has been conducted to date.

Patent databases are the most important data source for technology intelligence tools, but patents inherently contain unstructured data. Consequently, extracting text data from patent databases, converting that data to meaningful information and generating useful knowledge from this information become complex tasks. These tasks are currently …

A Machine Learning Algorithm For Identifying And Tracking Bacteria In Three Dimensions Using Digital Holographic Microscopy, Manuel Bedrossian, Marwan El-Kholy, Daniel Neamati, Jay Nadeau

Physics Faculty Publications and Presentations

Digital Holographic Microscopy (DHM) is an emerging technique for three-dimensional imaging of microorganisms due to its high throughput and large depth of field relative to traditional microscopy techniques. While it has shown substantial success for use with eukaryotes, it has proven challenging for bacterial imaging because of low contrast and sources of noise intrinsic to the method (e.g. laser speckle). This paper describes a custom written MATLAB routine using machine-learning algorithms to obtain three-dimensional trajectories of live, lab-grown bacteria as they move within an essentially unrestrained environment with more than 90% precision. A fully annotated version of the software used …