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- All Subjects: Coverage
Description
News outlets frequently portray people with disabilities as either helpless victims or objects of motivation. Portrayal of people with disabilities has improved over the years, but there is still room to grow. News outlets tend to make disability the center of the story. A story about a disabled person is primarily about their disability, with their other accomplishments framed by it.
As one example of the victimhood narrative, ABC News used to run a special called My Extreme Affliction as part of 20/20 until 2012. As the name implies, the specials covered people with disabilities, specifically extreme versions. One 2008 episode on Tourette’s syndrome described Tourette’s like it was some sort of demonic possession. The narrator talked about children who were “prisoners in their own bodies” and a family that was at risk of being “torn apart by Tourette’s.” I have Tourette’s syndrome myself, which made ABC’s special especially uncomfortable to watch. When not wringing their metaphorical hands over the “victims” of disability, many news outlets fall into the “supercrip” narrative. They refer to people as “heroes” who “overcome” their disabilities to achieve something that ranges from impressive to utterly mundane. The main emphasis is on the disability rather than the person who has it. These articles then exploit that disability to make readers feel good. As a person with a disability, I am aware that it impacts my life, but it is not the center of my life. The tics from my Tourette’s syndrome made it difficult to speak to people when I was younger, but even then they did not rule me.
Disability coverage, however, is still incredibly important for promoting acceptance and giving people with disabilities a voice. A little over a fifth of adults in the United States have a disability (CDC: 53 million adults in the US live with a disability), so poor coverage means marginalizing or even excluding a large amount of people. Journalists should try to reach their entire audience. The news helps shape public opinion with the stories it features. Therefore, it should provide visibility for people with disabilities in order to increase acceptance. This is a matter of civil rights. People with disabilities deserve fair and accurate representation.
My personal experience with ABC’s Tourette’s special leads me to believe that the media, especially the news, needs to be more responsible in their reporting. Even the name “My Extreme Affliction” paints a poor picture of what to expect. A show that focuses on sensationalist portrayals in pursuit of views further ostracizes people with disabilities. The emphasis should be on a person and not their condition. The National Center for Disability Journalism tells reporters to “Focus on the person you are interviewing, not the disability” (Tips for interviewing people with disabilities). This people-first approach is the way to improve disability coverage: Treat people with disabilities with the same respect as any other minority group.
As one example of the victimhood narrative, ABC News used to run a special called My Extreme Affliction as part of 20/20 until 2012. As the name implies, the specials covered people with disabilities, specifically extreme versions. One 2008 episode on Tourette’s syndrome described Tourette’s like it was some sort of demonic possession. The narrator talked about children who were “prisoners in their own bodies” and a family that was at risk of being “torn apart by Tourette’s.” I have Tourette’s syndrome myself, which made ABC’s special especially uncomfortable to watch. When not wringing their metaphorical hands over the “victims” of disability, many news outlets fall into the “supercrip” narrative. They refer to people as “heroes” who “overcome” their disabilities to achieve something that ranges from impressive to utterly mundane. The main emphasis is on the disability rather than the person who has it. These articles then exploit that disability to make readers feel good. As a person with a disability, I am aware that it impacts my life, but it is not the center of my life. The tics from my Tourette’s syndrome made it difficult to speak to people when I was younger, but even then they did not rule me.
Disability coverage, however, is still incredibly important for promoting acceptance and giving people with disabilities a voice. A little over a fifth of adults in the United States have a disability (CDC: 53 million adults in the US live with a disability), so poor coverage means marginalizing or even excluding a large amount of people. Journalists should try to reach their entire audience. The news helps shape public opinion with the stories it features. Therefore, it should provide visibility for people with disabilities in order to increase acceptance. This is a matter of civil rights. People with disabilities deserve fair and accurate representation.
My personal experience with ABC’s Tourette’s special leads me to believe that the media, especially the news, needs to be more responsible in their reporting. Even the name “My Extreme Affliction” paints a poor picture of what to expect. A show that focuses on sensationalist portrayals in pursuit of views further ostracizes people with disabilities. The emphasis should be on a person and not their condition. The National Center for Disability Journalism tells reporters to “Focus on the person you are interviewing, not the disability” (Tips for interviewing people with disabilities). This people-first approach is the way to improve disability coverage: Treat people with disabilities with the same respect as any other minority group.
ContributorsMackrell, Marguerite (Author) / Gilger, Kristin (Thesis director) / Doig, Steve (Committee member) / Walter Cronkite School of Journalism & Mass Comm (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Drylands, though one of the largest biomes, are also one of the most understudied biomes on the planet. This leaves scientists with limited understanding of unique life forms that have adapted to live in these arid environments. One such life form is the hypolithic microbial community; these are autotrophic cyanobacteria colonies that can be found on the underside of translucent rocks in deserts. With the light that filters through the rock above them, the microbes can photosynthesize and fix carbon from the atmosphere into the soil. In this study I looked at hypolith-like rock distribution in the Namib Desert by using image recognition software. I trained a Mask R-CNN network to detect quartz rock in images from the Gobabeb site. When the method was analyzed using the entire data set, the distribution of rock sizes between the manual annotations and the network predictions was not similar. When evaluating rock sizes smaller than 0.56 cm2 the method showed statistical significance in support of being a promising data collection method. With more training and corrective effort on the network, this method shows promise to be an accurate and novel way to collect data efficiently in dryland research.
ContributorsCollins, Catherine (Author) / Throop, Heather (Thesis director) / Das, Jnaneshwar (Committee member) / Aparecido, Luiza (Committee member) / School of Earth and Space Exploration (Contributor) / School of Art (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Despite the rapid adoption of robotics and machine learning in industry, their application to scientific studies remains under-explored. Combining industry-driven advances with scientific exploration provides new perspectives and a greater understanding of the planet and its environmental processes. Focusing on rock detection, mapping, and dynamics analysis, I present technical approaches and scientific results of developing robotics and machine learning technologies for geomorphology and seismic hazard analysis. I demonstrate an interdisciplinary research direction to push the frontiers of both robotics and geosciences, with potential translational contributions to commercial applications for hazard monitoring and prospecting. To understand the effects of rocky fault scarp development on rock trait distributions, I present a data-processing pipeline that utilizes unpiloted aerial vehicles (UAVs) and deep learning to segment densely distributed rocks in several orders of magnitude. Quantification and correlation analysis of rock trait distributions demonstrate a statistical approach for geomorphology studies. Fragile geological features such as precariously balanced rocks (PBRs) provide upper-bound ground motion constraints for hazard analysis. I develop an offboard method and onboard method as complementary to each other for PBR searching and mapping. Using deep learning, the offboard method segments PBRs in point clouds reconstructed from UAV surveys. The onboard method equips a UAV with edge-computing devices and stereo cameras, enabling onboard machine learning for real-time PBR search, detection, and mapping during surveillance. The offboard method provides an efficient solution to find PBR candidates in existing point clouds, which is useful for field reconnaissance. The onboard method emphasizes mapping individual PBRs for their complete visible surface features, such as basal contacts with pedestals–critical geometry to analyze fragility. After PBRs are mapped, I investigate PBR dynamics by building a virtual shake robot (VSR) that simulates ground motions to test PBR overturning. The VSR demonstrates that ground motion directions and niches are important factors determining PBR fragility, which were rarely considered in previous studies. The VSR also enables PBR large-displacement studies by tracking a toppled-PBR trajectory, presenting novel methods of rockfall hazard zoning. I build a real mini shake robot providing a reverse method to validate simulation experiments in the VSR.
ContributorsChen, Zhiang (Author) / Arrowsmith, Ramon (Thesis advisor) / Das, Jnaneshwar (Thesis advisor) / Bell, James (Committee member) / Berman, Spring (Committee member) / Christensen, Philip (Committee member) / Whipple, Kelin (Committee member) / Arizona State University (Publisher)
Created2022
Description
This work has improved the quality of the solution to the sparse rewards problemby combining reinforcement learning (RL) with knowledge-rich planning. Classical
methods for coping with sparse rewards during reinforcement learning modify the
reward landscape so as to better guide the learner. In contrast, this work combines
RL with a planner in order to utilize other information about the environment. As
the scope for representing environmental information is limited in RL, this work has
conflated a model-free learning algorithm – temporal difference (TD) learning – with
a Hierarchical Task Network (HTN) planner to accommodate rich environmental
information in the algorithm. In the perpetual sparse rewards problem, rewards
reemerge after being collected within a fixed interval of time, culminating in a lack of a
well-defined goal state as an exit condition to the problem. Incorporating planning in
the learning algorithm not only improves the quality of the solution, but the algorithm
also avoids the ambiguity of incorporating a goal of maximizing profit while using
only a planning algorithm to solve this problem. Upon occasionally using the HTN
planner, this algorithm provides the necessary tweak toward the optimal solution. In
this work, I have demonstrated an on-policy algorithm that has improved the quality
of the solution over vanilla reinforcement learning. The objective of this work has
been to observe the capacity of the synthesized algorithm in finding optimal policies to
maximize rewards, awareness of the environment, and the awareness of the presence
of other agents in the vicinity.
ContributorsNandan, Swastik (Author) / Pavlic, Theodore (Thesis advisor) / Das, Jnaneshwar (Thesis advisor) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2022
Description
This work describes an approach for distance computation between agents in a
multi-agent swarm. Unlike other approaches, this work relies solely on signal Angleof-
Arrival (AoA) data and local trajectory data. Each agent in the swarm is able
to discretely determine distance and bearing to every other neighbor agent in the
swarm. From this information, I propose a lightweight method for sensor coverage
of an unknown area based on the work of Sameera Poduri. I also show that this
technique performs well with limited calibration distances.
multi-agent swarm. Unlike other approaches, this work relies solely on signal Angleof-
Arrival (AoA) data and local trajectory data. Each agent in the swarm is able
to discretely determine distance and bearing to every other neighbor agent in the
swarm. From this information, I propose a lightweight method for sensor coverage
of an unknown area based on the work of Sameera Poduri. I also show that this
technique performs well with limited calibration distances.
ContributorsMulford, Philip (Author) / Das, Jnaneshwar (Thesis advisor) / Takahashi, Timothy (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2020
Description
The need for incorporating game engines into robotics tools becomes increasingly crucial as their graphics continue to become more photorealistic. This thesis presents a simulation framework, referred to as OpenUAV, that addresses cloud simulation and photorealism challenges in academic and research goals. In this work, OpenUAV is used to create a simulation of an autonomous underwater vehicle (AUV) closely following a moving autonomous surface vehicle (ASV) in an underwater coral reef environment. It incorporates the Unity3D game engine and the robotics software Gazebo to take advantage of Unity3D's perception and Gazebo's physics simulation. The software is developed as a containerized solution that is deployable on cloud and on-premise systems.
This method of utilizing Gazebo's physics and Unity3D perception is evaluated for a team of marine vehicles (an AUV and an ASV) in a coral reef environment. A coordinated navigation and localization module is presented that allows the AUV to follow the path of the ASV. A fiducial marker underneath the ASV facilitates pose estimation of the AUV, and the pose estimates are filtered using the known dynamical system model of both vehicles for better localization. This thesis also investigates different fiducial markers and their detection rates in this Unity3D underwater environment. The limitations and capabilities of this Unity3D perception and Gazebo physics approach are examined.
This method of utilizing Gazebo's physics and Unity3D perception is evaluated for a team of marine vehicles (an AUV and an ASV) in a coral reef environment. A coordinated navigation and localization module is presented that allows the AUV to follow the path of the ASV. A fiducial marker underneath the ASV facilitates pose estimation of the AUV, and the pose estimates are filtered using the known dynamical system model of both vehicles for better localization. This thesis also investigates different fiducial markers and their detection rates in this Unity3D underwater environment. The limitations and capabilities of this Unity3D perception and Gazebo physics approach are examined.
ContributorsAnand, Harish (Author) / Das, Jnaneshwar (Thesis advisor) / Yang, Yezhou (Committee member) / Berman, Spring M (Committee member) / Arizona State University (Publisher)
Created2020
Description
Scientific research encompasses a variety of objectives, including measurement, making predictions, identifying laws, and more. The advent of advanced measurement technologies and computational methods has largely automated the processes of big data collection and prediction. However, the discovery of laws, particularly universal ones, still heavily relies on human intellect. Even with human intelligence, complex systems present a unique challenge in discerning the laws that govern them. Even the preliminary step, system description, poses a substantial challenge. Numerous metrics have been developed, but universally applicable laws remain elusive. Due to the cognitive limitations of human comprehension, a direct understanding of big data derived from complex systems is impractical. Therefore, simplification becomes essential for identifying hidden regularities, enabling scientists to abstract observations or draw connections with existing knowledge. As a result, the concept of macrostates -- simplified, lower-dimensional representations of high-dimensional systems -- proves to be indispensable. Macrostates serve a role beyond simplification. They are integral in deciphering reusable laws for complex systems. In physics, macrostates form the foundation for constructing laws and provide building blocks for studying relationships between quantities, rather than pursuing case-by-case analysis. Therefore, the concept of macrostates facilitates the discovery of regularities across various systems. Recognizing the importance of macrostates, I propose the relational macrostate theory and a machine learning framework, MacroNet, to identify macrostates and design microstates. The relational macrostate theory defines a macrostate based on the relationships between observations, enabling the abstraction from microscopic details. In MacroNet, I propose an architecture to encode microstates into macrostates, allowing for the sampling of microstates associated with a specific macrostate. My experiments on simulated systems demonstrate the effectiveness of this theory and method in identifying macrostates such as energy. Furthermore, I apply this theory and method to a complex chemical system, analyzing oil droplets with intricate movement patterns in a Petri dish, to answer the question, ``which combinations of parameters control which behavior?'' The macrostate theory allows me to identify a two-dimensional macrostate, establish a mapping between the chemical compound and the macrostate, and decipher the relationship between oil droplet patterns and the macrostate.
ContributorsZhang, Yanbo (Author) / Walker, Sara I (Thesis advisor) / Anbar, Ariel (Committee member) / Daniels, Bryan (Committee member) / Das, Jnaneshwar (Committee member) / Davies, Paul (Committee member) / Arizona State University (Publisher)
Created2023