45,786 research outputs found
Extensions to behavioral genetic programming
No full textThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (page 55).In this work I introduce genetic programming [5] as a general technique to produce programs with arbitrary behavior. I discuss genetic programming and its application the task of symbolic regression. I introduce behavioral genetic programming [6] as an extension to genetic programming and explore various extensions to it. The codebase that I build is made sufficiently flexible to easily accommodate future adaptions to the behavioral genetic programming methodology. I test the performance of the implementation of behavioral genetic programming along with several extensions.by Steven B. Fine.M. Eng
Open science in behavioral medicine: Multiple perspectives and provocative questions
No full textUnderstanding and embracing the tenets of “open science” is garnering traction among researchers in behavioral medicine, including SBM’s “Task Force on Open Science”. Open science is seen as a scientific movement aimed at increasing openness, integrity, and reproducibility of scholarly research. It calls upon behavioral medicine researchers to consider complex, and often provocative, questions related to data sharing, registration of research plans and methods, and transparency in publishing. At the same time, many researchers are unsure about how to adopt these practices, and are concerned about how to “do” open science across commonly used behavioral medicine research designs, including longitudinal observational studies and experimental research. This panel, organized by the International Journal of Behavioral Medicine, aims to provide both practical knowledge about open science practice as well as useful discussion from multiple perspectives. Panelists will include behavioral medicine principal investigators, a graduate student, a publisher (from Springer Publications), an NIH Representative (National Cancer Institute), journal editors/associate editors, as well as a representative from the Center for Open Science. We will use “Slido”, a smartphone platform that allows the audience to ask and rate questions. The most highly rated questions will be asked of the panel. The moderator will also maintain a list of questions. Examples include: • Will open science change how I do research? • How will open science affect publishing for me? • Is the evidence base strong enough to begin to shift incentive structures toward rewarding and acknowledging open science practices? • How can we guard against public humiliation when studies are not replicated? • How can the time required for pre-registration and other open science practices be built into student research programs? • When communicating our research to lay people and the media, how can we convey the difference between a pre-registered and entirely transparent study, and studies that have not followed these practices? • How can we balance openly sharing resources and with protecting our ideas and intellectual property given that not all researchers engage in open science practices? • What are the special considerations of open science for observational research? Does open science mean I cannot conduct secondary analyses? • What does it mean to register a study beforehand with a journal or study registry? • What is a badge? • How long should I wait before sharing data? How do I even share my data? • How can an open science approach in medicine be cultivated to include behavioral outcomes as an indispensable part of systemic change? • What are some of the “big solutions” related to open science for persistent problems in behavioral medicine that SBM could encourage?No Full Tex
Design of the gas-puff imaging diagnostic for Wendelstein 7-X
No full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: S.B., Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2019Cataloged from PDF version of thesis.Includes bibliographical references (page 97).Stellarators, being not as well-studied as tokamaks, have plenty of interesting physics to examine, as investigations of stellarators as a viable configuration for future power plants continue. One of these aspects is boundary turbulence in the plasma, as the magnetic configuration in stellarators is different from that in tokamaks and thus provides different plasma behavior. To study this turbulence, we are designing a "gas-puff imaging" diagnostic to install onto the Max Planck Institute of Plasma Physics's Wendelstein 7-X (W7-X), which is currently the world's most advanced and largest stellarator. This diagnostic employs a fast-camera to observe a localized puff of gas as it interacts with the boundary plasma near the last closed flux surface of the plasma. The diagnostic consists of a fast-camera component, a light-collection component, a "gas-puff" component with valves to inject controlled amounts of gas, and a component for valve control and data collection purposes. This thesis documents some of the aspects of the design of the diagnostic and its components for W7-X.by Kevin Tang.S.B.S.B. Massachusetts Institute of Technology, Department of Nuclear Science and Engineerin
A posture-based Markov analysis of behavioral states in Caenorhabditis elegans
No full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF of thesis.Includes bibliographical references (pages 63-66).Organisms of varying degrees of complexity coordinate their diverse behavioral outputs over time, yet the internal neural dynamics underlying such behavioral organization is not completely understood. Behavior coordination can be captured through the quantitative description and subsequent analysis of behavioral states. Here, we develop an analytical method for the characterization of behavioral states in C. elegans. We observe posture sequences in wild type C. elegans and utilize a hidden Markov model to detect the behavioral states giving rise to these posture sequences. We then demonstrate that this method is generalizable to different C. elegans strains by applying this posture-based Markov analysis to C. elegans mutants and survey how these mutants differentially exhibit key behaviors within these behavioral states. This methodology provides a framework by which behavioral states can be quantified for further study of the neural dynamics underlying behavior coordination.by Rebekah I. Clark.M. Eng.M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc
Behavioral data collection and simulation
No full textThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (page 45).On-demand ridesharing services, such as Uber and Lyft, and autonomous vehicles are significantly changing the landscape of transportation and mobility. In light of these disruptions, we aim to determine consumer preferences with regards to transportation and use this data to simulate and analyze the urban effects of smart mobility solutions. We collect behavioral data using Future Mobility Sensing (FMS), a smartphone and prompted-recall-based integrated activity-travel survey, and create simulations using the data with SimMobility, a simulation platform that integrates various mobility-sensitive behavioral models with state-of-the-art scalable simulators to predict the impact of mobility demands on transportation networks, intelligent transportation services, and vehicular emissions. Enhancing these projects with on-demand preferences, individual patterns, and incentives as inputs, we aim to simulate and analyze a wide range of viable smart mobility solutions.by Akshay Padmanabha.M. Eng
The landscape of open science in behavioral addiction research: Current practices and future directions
No full textOpen science refers to a set of practices that aim to make scientific research more transparent, accessible, and reproducible, including pre-registration of study protocols, sharing of data and materials, the use of transparent research methods, and open access publishing. In this commentary, we describe and evaluate the current state of open science practices in behavioral addiction research. We highlight the specific value of open science practices for the field; discuss recent field-specific meta-scientific reviews that show the adoption of such practices remains in its infancy; address the challenges to engaging with open science; and make recommendations for how researchers, journals, and scientific institutions can work to overcome these challenges and promote high-quality, transparently reported behavioral addiction research. By collaboratively promoting open science practices, the field can create a more sustainable and productive research environment that benefits both the scientific community and society as a whole.
Although behavioral addiction research emerged at the end of the last century (Holden, 2001; Marks, 1990), the nosological status of a wide range of behavioral addictions (with the exception of Gambling and Gaming Disorders) remains debated (Billieux, Schimmenti, Khazaal, Maurage, & Heeren, 2015; Mihordin, 2012; Starcevic, Billieux, & Schimmenti, 2018). Globally, the field is still often considered as an “emerging” or “new” one. We decided to write this commentary to describe and evaluate the open science practices in the field of behavioral addictions to promote awareness and to encourage the field to adopt these practices to further improve research quality in this field. Our objective was to specify what we mean when talking about open science and identify the issues pertaining to the (perceived) status quo in the field of behavioral addictions regarding open science. It is worth acknowledging that we are not the first to call for more open and transparent research in this field, and therefore the current paper is oriented towards avenues and solutions to further integrate and promote open science
A Connectionist and Multivariate Approach to Science Maps: Som, Clustering and Mds Applied to Library & Information Science Research.
No full textThe visualization of scientific field structures is a classic of scientometric studies. This paper presents a domain analysis of the library and information science discipline based on author co-citation analysis (ACA) and journal cocitation analysis (JCA). The techniques used for map construction are the self-organizing map (SOM) neural
algorithm, Ward’s clustering method and multidimensional
scaling (MDS). The results of this study are compared with
similar research developed by Howard White and Katherine
McCain [1]. The methodologies used allow us to confirm that
the subject domains identified in this paper are, as well,
present in our study for the corresponding period. The appearance of studies pertaining to library science reveals
the relationship of this realm with information science.
Especially significant is the presence of the management on the journal maps. From a methodological standpoint, meanwhile, we would agree with those authors who consider
MDS, the SOM and clustering as complementary methods
that provide representations of the same reality from different analytical points of view. Even so, the MDS representation is the one offering greater possibilities for the structural representation of the clusters in a set of variables
A connectionist and multivariate approach to science maps: the SOM, clustering and MDS applied to library and information science research
No full textThe visualization of scientific field structures is a classic of scientometric studies. This paper presents a domain analysis of the library and information science discipline based on author co-citation analysis (ACA) and journal cocitation analysis (JCA). The techniques used for map construction are the self-organizing map (SOM) neural algorithm, Ward’s clustering method and multidimensional scaling (MDS). The results of this study are compared with similar research developed by Howard White and Katherine McCain [1]. The methodologies used allow us to confirm that the subject domains identified in this paper are, as well,
present in our study for the corresponding period. The appearance of studies pertaining to library science reveals the relationship of this realm with information science. Especially significant is the presence of the management on the journal maps. From a methodological standpoint, meanwhile, we would agree with those authors who consider MDS, the SOM and clustering as complementary methods that provide representations of the same reality from different analytical points of view. Even so, the MDS representation is the one offering greater possibilities for the structural representation of the clusters in a set of variables
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Presentation: God, Darwin, and Design: Lessons from the Dover Monkey Trail
No full textIt has been 80 years since the Scopes Monkey Trial, but the debate between science and religion has never been as heated as it is now. Recent efforts to introduce “intelligent design” into science classes will likely lead to a major Supreme Court ruling on the issue. Kenneth R. Miller, a professor of biology at Brown University, is a preeminent evolutionary scientist—and the author of the most widely used high school biology textbook in America. He is perfectly suited to address this controversial topic on many fronts.Environmental Science Institut
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