Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 83-87). / The need and desire to create objects is built into human civilization throughout history. The immense diversity of cultures in the world has led to the development of tremendously diverse design and making traditions. Each of these reflects unique imagination threads in our collective thought process and carries immense value in the growth of it. These days, we are witnessing a rise in the digital maker movement, propelled by digital fabrication machines like 3D printers. These are democratizing manufacturing, however, a point to note is that all of the CAD (computer aided design) software tools to design objects for digital fabrication have been developed in an industrial context. This inherently means that these tools support operations like extrude, revolve etc., but not traditional operations like weaving techniques (plaiting, twining etc.) for example. Digital design language therefore lacks a representation of the diverse making traditions and as such these are not accessible to people to design with. This projects to a subsequent decline in usage of these traditional methods resulting in them slowly fading out. Instead, modem design tools should celebrate the diversity of making traditions and harness the strength of digital means combining it with traditional operations and aesthetics to create objects that were not possible with either of them individually. Through this thesis, I explore questions around how traditional making practices can be incorporated in CAD tools. How can one approach the design of such a tool and what is the variety of design possibilities this opens up, once the traditional and modern techniques inter-weave. I present InterWoven, a CAD tool that aids people in creating designs using traditional basket-weaving techniques. / by Harshit Agrawal. / S.M.

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 69-74). / This thesis presents BurstMapper, a system for detecting and characterizing bursts of tweets generated by multiple sources in order to understand interactions between Twitter users and the role of exogenous events (not directly observable on Twitter) in driving tweets. The first stage of the system finds temporal clusters, or bursts of tweets. The second stage characterizes bursts along two dimensions, semantic coherence and causal influence. Semantic coherence measures the semantic relatedness of the tweets in a burst to each other based on a deep neural network derived embedding of tweet contents. Causal influence measures the potential causal interaction between Twitter users using the Hawkes process model. We introduce an annotated corpus of 7,220 tweets produced by five leading candidates in the 2016 U.S. presidential election. Evaluating the system on the annotated corpus shows that with a precision of 75%, tweets caused clearly by specific exogenous events (or responsive tweets hereafter) are detected by the burst detector components of our system. Furthermore, experiments show that the linear combination of semantic coherence and causal influence are predictive of the presence of responsive tweets in a burst, with the Fl-score of 0.76. Examining bursts along the two dimensions reveals that (i) the measures are positively correlated with each other (corr=0.33, p<0.001), (ii) the measures allow us to understand how candidates tend to respond differently to exogenous events, e.g., by attacking opponents or making plan announcements, and (iii) the measures can be used to describe the influence dynamics between candidates over time. Plotting the bursts from a corpus of 1,470 Twitter accounts (the five leading candidates and the users followed by them) shows visual evidence that some user groups (e.g., campaign staffs, journalists, etc.) have a higher levels of semantic coherence and causal interactions. These experiments suggest that the bursts detected by our system provide a useful level of abstraction that summarizes tweet content, providing a solution for coping with massive amount of data on Twitter. / by Pau Perng-Hwa Kung. / S.M.

Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 141-149). / Submitted to the Program in Media Arts and Sciences, School of Architecture and Planning, on December 8, 2017 in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Media, Arts and Sciences at the Massachusetts Institute of Technology Throughout history, Nature has always been part of the discourse in Design theory and practice. The Digital Age in Design brings about new computational tools, redefining the role of Nature in Design. In this thesis, I aim to expand the role of Nature in Design and digital fabrication by investigating distributed fabrication strategies for the production of constructs that are, at once, large in scale and materially tunable towards swarm-based design. Digital fabrication approaches can be classified with respect to two basic attributes: (1) the degree of material tailorability, and (2) the level of collaboration between fabrication units. Conventional manufacturing is typically confined to only one of these attribute axes, with certain approaches utilizing complex tunable materials but virtually no collaboration, and others assembling pre-fabricated building blocks with high levels of intercommunication between fabrication units. A similar pattern is mirrored in biological systems: silkworms, for example, deposit a multifunctional tunable material with minimal communication between organisms; while ants, bees and termites operate as multi-agent communicative entities assembling larger constructs out of simple, unifunctional, 'generic' materials. The purpose of this thesis is to depart from these uniaxial manufacturing approaches and develop a novel swarm-inspired distributed digital fabrication method capable of producing tunable multifunctional materials that is also collaborative. This research merges fiber-based digital fabrication and swarm-based logic to produce a system capable of digitally fabricating complex objects and large-scale architectural components through a novel multi-robotic fabrication paradigm. I hypothesize that this design approach-its theoretical foundations, methodological set up and related tools and technologies-will ultimately enable the design of large-scale structures with high spatial resolution in manufacturing that, like biological swarms, can tune their material make-up relative to their environment during the process of construction. Building on the insights derived from case study projects, fabricating with silkworms, ants, and bees, I demonstrate the design and deployment of a multi-robotic system erecting a 4.5-meter tall structure from fiber composites This thesis addresses the current limitations of digital fabrication, namely: (a) the material limitation, through automated digital fabrication of structural multi-functional materials; (b) the gantry limitation, through the construction of large components from a swarm of cooperative small scale robots; and (c) the method limitation, through digital construction methods that are not limited to layered manufacturing, but also support free-form printing (i.e. 3D-printing without support materials), CNC woven constructions and digitally aggregated constructions. / by Markus Kayser. / Ph. D.

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 75-77). / In this thesis we present ShowMe++, an immersive mobile collaboration system that allows a remote user to communicate with a peer using video, audio and hand gestures. We explore the use of a Head Mounted Display (HMD), depth camera and wearable haptic devices to create a system that (1) enables a remote user to be immersed in another first-person's point of view, (2) offers a new way for the remote expert to provide guidance through three dimensional, real-time hand gestures and voice, (3) allows natural interactions with interfaces of Internet of Things (IoT) devices and (4) provides haptic feedback when interacting with remote or virtual interfaces. Using our system, both users feel present in the same physical environment and can perceive realtime communication from one another in the form of 2-handed gestures and voice. We discuss the design and implementation of the system as well as applications scenarios such as remote maintenance, 3D exploration and remote ghost presence. The user study demonstrates that hand transmission, first person point of view and immersion improve the feeling of co-presence and make remote teaching more effective. / by Xavier Benavides Palos. / S.M.

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 94-98). / Plants are photosynthetic eukaryotes with a billion years of evolutionary history. While primarily sessile, they have developed distinctive abilities to adapt to the environment. They are self-powered, self-fabricating, self-regenerating and active signal networks. They carry highly advanced systems to sense and respond to the environment. We strive for such sensing and responses in our electronics; self growing or self repairing abilities in our architecture; and being sustainable at scale in general. The industrial and technological thought process has mostly been devising artificial means or replicating natural systems synthetically. However, I propose a convergent view of technological evolution with our ecology where techno-plant hybrids are created. The approach is to formulate symbiotic associations and to place the technology in conjunction with the plant function(s). In this thesis, I go from the outside to inside the plants in conceiving such synergetic processes and present case studies of their implementation and analysis. I begin with a robot-plant hybrid where the robotic device adds mobility and is triggered with the plant's own signals. Next, lead (II) detection nanosensors are presented which reside inside the leaf of a plant and continuously sample through plant hydraulics. This is followed with a design study for plants with new conductive channels grown inside them and their subsequent use as inconspicuous motion sensors. I conclude with a symbiotic robot that lives on a sunflower plant and automatically trains or directs its growth with onboard lighting. The end result is an augmented-plant society where technology adds non-native functions or redirects the natural processes.. / by Harpreet Sareen. / S.M.

Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 156-160). / State of the art technology has made it possible to monitor various physiological signals for prolonged periods. Using wearable sensors, individuals can be monitored; sensor data can be collected and stored in digital format, transmitted to remote locations, and analyzed at later times. This technology may open the door to a multitude of exciting and innovative applications. We could learn the effects of the environment and of our day-to-day choices on our physiology. Does the number of hours we sleep affect our mood during the following day? Is our performance impacted by the times we schedule our recreational activities? Does physical activity affect our quality of sleep? Do these choices have an impact on chronic conditions? This proliferation of smart phones and wearable sensors is creating very large data sets that may contain useful information. Gartner claims that the Internet of Things Install Base Will Grow to 26 Billion Units By 2020. However, the magnitude of generated data creates new challenges as well. Processing and analyzing these large data sets in an efficient manner requires advanced computational tools. The challenge is that as more data are collected, it becomes more computationally expensive to process requiring novel algorithmic techniques and parallel architectures. Traditional analysis techniques do not scale adequately and in many cases researchers are required to create customized environments. This thesis explores and extends the affordances of warehouse scale computing for interactivity and pliability of large-scale time series data sets. In the first part of the thesis, I describe a theoretical framework for distributed processing of time-series data that is implementation invariant and may be implemented on an existing distributed computation infrastructure. Next, I present a detailed architecture and implementation of the theoretical framework, which was deployed on several clusters, as well as indepth analysis of the user-interface design considerations and the user experience design process. In the second part of the thesis, I present a system evaluation that consists of two parts. The first part is a quantitative characterization of the system performance in a variety of scenarios that included different dataset and cluster sizes. The second part contains the results of a qualitative user study: researchers were asked to use the system to analyze data that they had collected in their own studies and to participate in an ethnographic study on their experience. This study reveals that distributed computing holds great potential for accelerating scientific research utilizing large scale sensor data sets, providing new ways to see patterns in large sets of data, and much speedier analyses. / by Yadid Ayzenberg. / Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 122-131). / This thesis introduces computer vision algorithms that harness street-level imagery to conduct automated surveys of the built environment and populations at an unprecedented resolution and scale. We introduce new tools for computing quantitative measures of urban appearance and urban change. First, we describe Streetscore, an algorithm that quantifies how safe a street block looks to a human observer, using computer vision and crowdsourcing. We extend this work with an efficient convolutional neural network-based method that is capable of computing several perceptual attributes of the built environment from thousands of cities from all six inhabited continents. Second, we introduce a computer vision algorithm to compute Streetchange-a metric for change in the built environment-from time-series street-level imagery. A positive Streetchange is indicative of urban growth; while negative Streetchange is indicative of decay. We use these tools to introduce new datasets. We use the Streetscore algorithm to generate the largest dataset of urban appearance to date, which covers more than 1 million street blocks from 21 American cities. We use the Streetchange algorithm to also generate a dataset for urban change containing more than 1.5 million street blocks from five large American cities. These datasets have enabled research studies across fields such as economics, sociology, architecture, urban planning, and public health. We utilize these datasets to provide new insights on important research questions. With the dataset on urban appearance, we show that criminal activity has a robust positive correlation with the spatial variation in architecture within neighborhoods. With the dataset on urban change, we show that positive urban change occurs in geographically and physically attractive areas with dense, highly-educated populations. Taken together, the tools, datasets, and insights described in this thesis demonstrate that computer vision-driven surveys of people and places have the potential to massively scale up studies in social science, to change the way cities are built, and to improve the design, execution, and evaluation of policy and aid interventions. / by Nikhil Naik. / Ph. D.

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 69-77). / Artificial intelligence algorithms are becoming an increasingly important part of human life with many chat bots and digital personal assistants now interacting directly with us through natural language. Such human-computer interaction can be made more useful by enriching the underlying algorithms with a detailed sense of emotion. In my thesis I propose new ways to detect, encode and modify emotional content in text. First, I show how we can leverage the vast amount of texts on social media with emojis to train a classifier that can accurately detect various kinds of emotional content in text. Secondly, I introduce a state-of-the-art domain adaptation method that is explicitly designed to tackle issues occurring in the messy real-world text data that existing NLP methods struggle with. Lastly, I propose a new algorithm that could be used to decompose text inputs into disentangled representations and then manipulate these representations in a controlled manner to obtain a modified version of the input. / by Bjarke Felbo. / S.M.

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 26-28). / Many behaviors spread through social contact. However, different behaviors seem to require different degrees of social reinforcement to spread within a network. Some behaviors spread via simple contagion, where a single contact with an "activated node" is sufficient for transmission, while others require complex contagion, with reinforcement from multiple nodes to adopt the behavior. But why do some behaviors require more social reinforcement to spread than others? Here we hypothesize that learning more difficult behaviors requires more social reinforcement. We test this hypothesis by analyzing the programming language adoption of hundreds of thousands of programmers on the social coding platform Github. We show that adopting more difficult programming languages requires more reinforcement from the collaboration network. This research sheds light on the role of collaboration networks in programming language acquisition. / by Sanjay Guruprasad. / S.M.

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references. / Training Transhumanism is a psycho-physical training regimen for evolving the future of the human, developed in collaboration with the choreographer luciana achugar. The regimen seeks to develop within the human new sensitivities and capacities for a world marked by everincreasing ecological and technological change, based on the model of the cephalopod. We focus on three main traits: (1) Embodied and tactile awareness and cognition; (2) The resiliency of camouflage, defined as a hyper-awareness of one's local environment and the flexibility to respond swiftly by morphing one's perceived identity; and (3) a distributed intelligence (positing that the future of the human may include more than one "body" - how to then develop our abilities to push past negotiation, collaboration and into the forming of a single intention and/or organism with one or more people). Training Transhumanism is rooted in a body of research aimed towards developing an ecological, embodied and ethical approach towards the "the future". Using the cephalopod rather than the machine as the model for the future of the human, the work embraces the capacities residing in the biological human body and the pleasures rooted in bodily labors; explores the possibilities for mythological, embodied and indigenous knowledge for the project of innovation; posits the "model species" as a role model for a kind of humanity rather than only an instrument for science; and embraces training as a technology is rooted in practice, development of internal abilities, and equity in access. / by Miriam Simun. / S.M.