Blackness & Utopia

Pilot for a modular course in-person, hybrid, or online format. Texts and other materials available in digital and streaming formats

Mike Brown's Body: New Materialism and Black Form

A contribution to the Editors' Forum on Queer Form, edited by Kadji Amin, Roy Pérez, and Amber Musser, for ASAP/Journal

Kernel Methods and Measures for Classification with Transparency, Interpretability and Accuracy in Health Care

Support vector machines are a popular method in machine learning. They learn from data about a subject, for example, lung tumors in a set of patients, to classify new data, such as, a new patient’s tumor. The new tumor is classified as either cancerous or benign, depending on how similar it is to the tumors of other patients in those two classes—where similarity is judged by a kernel. The adoption and use of support vector machines in health care, however, is inhibited by a perceived and actual lack of rationale, understanding and transparency for how they work and how to interpret information and results from them. For example, a user must select the kernel, or similarity function, to be used, and there are many kernels to choose from but little to no useful guidance on choosing one. The primary goal of this thesis is to create accurate, transparent and interpretable kernels with rationale to select them for classification in health care using SVM—and to do so within a theoretical framework that advances rationale, understanding and transparency for kernel/model selection with atomic data types. The kernels and framework necessarily co-exist. The secondary goal of this thesis is to quantitatively measure model interpretability for kernel/model selection and identify the types of interpretable information which are available from different models for interpretation. Testing my framework and transparent kernels with empirical data I achieve classification accuracy that is better than or equivalent to the Gaussian RBF kernels. I also validate some of the model interpretability measures I propose

"Notes Towards a Feminist Futurist Manifesto"

Curatorial note from Digital Pedagogy in the Humanities: Sarah Kember marks the moment for the launch of Ada: A Journal of Gender, New Media, and Technology by unsettling readers who might think existing scholarship has exhausted “the complex relation between technology, politics[,] and the social.” Taking gender as one axis in a multidimensional rendering of “a future that isn’t (and never was) all about technology,” the article returns issues of desire, intelligence, consciousness, and objecthood to the agenda of humanistic inquiry. It raises questions that would occasion lively debate during any course or module on gender, knowledge production, or subjectivity: To what ends do we submit ourselves to emergent surveillance practices? How are the priorities of academic journals shaped by attitudes toward technology and the mediation of scholarly persona? Kember also models the manifesto as a form amenable to presenting a synopsis of the movement from previous to current intellectual colloquies and outlining distinct points for elaboration through further inquiry, a task that can be uniquely instructive for graduate students learning how to articulate the main concerns within a field of scholarship

Mapping the Futures of Higher Education

Curatorial note from Digital Pedagogy in the Humanities: The Mapping the Futures of Higher Education (MFHE) course at the City University of New York (CUNY) supplements the cultural and political geography of the metropolis with student-centered timelines and genealogies. The course “emphasized student-centered pedagogies and digital innovation and its practical application within diverse undergraduate classrooms at CUNY.” Measures meant to empower undergraduates and graduate student instructors were crucial to its success and integral to its potential implications for future projects that use digital tools to enhance and represent spatial knowledge. Evaluators noted that workshops on mapping software and data management would have enhanced the course by enabling students to produce more technically sophisticated maps; for courses of study that have the objective of imparting or refining those technical skills, mapping their institutions in accordance with local, community-sourced concerns like those of the MFHE students is an ideal summative assessment. Instructors and students interested in mapping their settings can employ the insights of the final report in order to outline what student-centered, technology-assisted learning means in their courses, for example by reporting the balance of anxiety and motivation ascribed to their respective responsibilities, maintaining an inventory of their skills and needs, and collaborating to design future courses that use the maps they create and the information represented therein as resources

#FutureED

Curatorial note from Digital Pedagogy in the Humanities: Participants in the Humanities, Arts, Science, and Technology Alliance and Collaboratory (HASTAC) gather in person and virtually on a regular basis to share ideas for the advancement of humanistic education in the digital era. The #FutureED initiative translates efforts that began with questions about the place of the humanities in the contemporary university into questions about the place of the humanities in the future (Davidson and Goldberg B7). As described on the #FutureED page on the HASTAC Web site, #FutureED builds on “courses, workshops, events, and reading groups, in different locations and online, all open to the public . . . created by members of the HASTAC alliance on ‘The History and Future of Higher Education.’” The initiative’s online presence includes a crowdsourced bibliography annotated with keywords (funding, assessment, peer learning), reflections on ongoing changes to institutional structures, and innovations in teaching, all of which are open to further contributions in wiki format

Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation

Optimal performance is critical for decision-making tasks from medicine to autonomous driving, however common performance measures may be too general or too specific. For binary classifiers, diagnostic tests or prognosis at a timepoint, measures such as the area under the receiver operating characteristic curve, or the area under the precision recall curve, are too general because they include unrealistic decision thresholds. On the other hand, measures such as accuracy, sensitivity or the F1 score are measures at a single threshold that reflect an individual single probability or predicted risk, rather than a range of individuals or risk. We propose a method in between, deep ROC analysis, that examines groups of probabilities or predicted risks for more insightful analysis. We translate esoteric measures into familiar terms: AUC and the normalized concordant partial AUC are balanced average accuracy (a new finding); the normalized partial AUC is average sensitivity; and the normalized horizontal partial AUC is average specificity. Along with post-test measures, we provide a method that can improve model selection in some cases and provide interpretation and assurance for patients in each risk group. We demonstrate deep ROC analysis in two case studies and provide a toolkit in Python.Comment: 14 pages, 6 Figures, submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), currently under revie

Wild by Design: Workshop on Designing Ubiquitous Health Monitoring Technologies for Challenging Environments

Recent years have shown an emergence of ubiquitous technologies that aim to monitor a person’s health in their day to day. However, albeit focused at a real world setting and technically able, most research is still limited in its real-world coverage, suitability, and adoption. In this workshop, we will focus on the challenges of real world health monitoring deployments to produce forward-looking insights that can shape the way researchers and practitioners think about health monitoring, in platforms and systems that account for the complex environments where they are bound to be used

Organelle trafficking of chimeric ribozymes and genetic manipulation of mitochondria

With the expansion of the RNA world, antisense strategies have become widespread to manipulate nuclear gene expression but organelle genetic systems have remained aside. The present work opens the field to mitochondria. We demonstrate that customized RNAs expressed from a nuclear transgene and driven by a transfer RNA-like (tRNA-like) moiety are taken up by mitochondria in plant cells. The process appears to follow the natural tRNA import specificity, suggesting that translocation indeed occurs through the regular tRNA uptake pathway. Upon validation of the strategy with a reporter sequence, we developed a chimeric catalytic RNA composed of a specially designed trans-cleaving hammerhead ribozyme and a tRNA mimic. Organelle import of the chimeric ribozyme and specific target cleavage within mitochondria were demonstrated in transgenic tobacco cell cultures and Arabidopsis thaliana plants, providing the first directed knockdown of a mitochondrial RNA in a multicellular eukaryote. Further observations point to mitochondrial messenger RNA control mechanisms related to the plant developmental stage and culture conditions. Transformation of mitochondria is only accessible in yeast and in the unicellular alga Chlamydomonas. Based on the widespread tRNA import pathway, our data thus make a breakthrough for direct investigation and manipulation of mitochondrial genetics