Cell Detection with Star-convex Polygons

Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications. Recent successful learning-based approaches include per-pixel cell segmentation with subsequent pixel grouping, or localization of bounding boxes with subsequent shape refinement. In situations of crowded cells, these can be prone to segmentation errors, such as falsely merging bordering cells or suppressing valid cell instances due to the poor approximation with bounding boxes. To overcome these issues, we propose to localize cell nuclei via star-convex polygons, which are a much better shape representation as compared to bounding boxes and thus do not need shape refinement. To that end, we train a convolutional neural network that predicts for every pixel a polygon for the cell instance at that position. We demonstrate the merits of our approach on two synthetic datasets and one challenging dataset of diverse fluorescence microscopy images.Comment: Conference paper at MICCAI 201

Slave to the Algorithm? Why a \u27Right to an Explanation\u27 Is Probably Not the Remedy You Are Looking For

Algorithms, particularly machine learning (ML) algorithms, are increasingly important to individuals’ lives, but have caused a range of concerns revolving mainly around unfairness, discrimination and opacity. Transparency in the form of a “right to an explanation” has emerged as a compellingly attractive remedy since it intuitively promises to open the algorithmic “black box” to promote challenge, redress, and hopefully heightened accountability. Amidst the general furore over algorithmic bias we describe, any remedy in a storm has looked attractive. However, we argue that a right to an explanation in the EU General Data Protection Regulation (GDPR) is unlikely to present a complete remedy to algorithmic harms, particularly in some of the core “algorithmic war stories” that have shaped recent attitudes in this domain. Firstly, the law is restrictive, unclear, or even paradoxical concerning when any explanation-related right can be triggered. Secondly, even navigating this, the legal conception of explanations as “meaningful information about the logic of processing” may not be provided by the kind of ML “explanations” computer scientists have developed, partially in response. ML explanations are restricted both by the type of explanation sought, the dimensionality of the domain and the type of user seeking an explanation. However, “subject-centric explanations (SCEs) focussing on particular regions of a model around a query show promise for interactive exploration, as do explanation systems based on learning a model from outside rather than taking it apart (pedagogical versus decompositional explanations) in dodging developers\u27 worries of intellectual property or trade secrets disclosure. Based on our analysis, we fear that the search for a “right to an explanation” in the GDPR may be at best distracting, and at worst nurture a new kind of “transparency fallacy.” But all is not lost. We argue that other parts of the GDPR related (i) to the right to erasure ( right to be forgotten ) and the right to data portability; and (ii) to privacy by design, Data Protection Impact Assessments and certification and privacy seals, may have the seeds we can use to make algorithms more responsible, explicable, and human-centered

Applying the Free-Energy Principle to Complex Adaptive Systems

The free energy principle is a mathematical theory of the behaviour of self-organising systems that originally gained prominence as a unified model of the brain. Since then, the theory has been applied to a plethora of biological phenomena, extending from single-celled and multicellular organisms through to niche construction and human culture, and even the emergence of life itself. The free energy principle tells us that perception and action operate synergistically to minimize an organism’s exposure to surprising biological states, which are more likely to lead to decay. A key corollary of this hypothesis is active inference—the idea that all behavior involves the selective sampling of sensory data so that we experience what we expect to (in order to avoid surprises). Simply put, we act upon the world to fulfill our expectations. It is now widely recognized that the implications of the free energy principle for our understanding of the human mind and behavior are far-reaching and profound. To date, however, its capacity to extend beyond our brain—to more generally explain living and other complex adaptive systems—has only just begun to be explored. The aim of this collection is to showcase the breadth of the free energy principle as a unified theory of complex adaptive systems—conscious, social, living, or not

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Automatically Detecting the Resonance of Terrorist Movement Frames on the Web

The ever-increasing use of the internet by terrorist groups as a platform for the dissemination of radical, violent ideologies is well documented. The internet has, in this way, become a breeding ground for potential lone-wolf terrorists; that is, individuals who commit acts of terror inspired by the ideological rhetoric emitted by terrorist organizations. These individuals are characterized by their lack of formal affiliation with terror organizations, making them difficult to intercept with traditional intelligence techniques. The radicalization of individuals on the internet poses a considerable threat to law enforcement and national security officials. This new medium of radicalization, however, also presents new opportunities for the interdiction of lone wolf terrorism. This dissertation is an account of the development and evaluation of an information technology (IT) framework for detecting potentially radicalized individuals on social media sites and Web fora. Unifying Collective Action Framing Theory (CAFT) and a radicalization model of lone wolf terrorism, this dissertation analyzes a corpus of propaganda documents produced by several, radically different, terror organizations. This analysis provides the building blocks to define a knowledge model of terrorist ideological framing that is implemented as a Semantic Web Ontology. Using several techniques for ontology guided information extraction, the resultant ontology can be accurately processed from textual data sources. This dissertation subsequently defines several techniques that leverage the populated ontological representation for automatically identifying individuals who are potentially radicalized to one or more terrorist ideologies based on their postings on social media and other Web fora. The dissertation also discusses how the ontology can be queried using intuitive structured query languages to infer triggering events in the news. The prototype system is evaluated in the context of classification and is shown to provide state of the art results. The main outputs of this research are (1) an ontological model of terrorist ideologies (2) an information extraction framework capable of identifying and extracting terrorist ideologies from text, (3) a classification methodology for classifying Web content as resonating the ideology of one or more terrorist groups and (4) a methodology for rapidly identifying news content of relevance to one or more terrorist groups

Proust\u27s Medusa: Ovid, Evolution, and Modernist Metamorphosis

Ovid\u27s Metamorphoses has served as an indispensable text for Modernism, not least for such foundational Modernists as T. S. Eliot, Ezra Pound and Wyndham Lewis. This dissertation examines how these writers characteristically employ Ovidian metamorphoses with a specifically evolutionary inflection, particularly in a post- Darwinian world informed by varying -often authoritarian- notions of biological adaptation, as well as an increasing emphasis on Mendalian genetics as the determining factor in what would become known as the Modern Synthesis in evolutionary theory. Using the theoretical platforms of both Queer Theory and Object Ontology, this dissertation proposes that a more pluralized, less authoritative appreciation of Darwinian change can be seen in the very different Ovidianism of Marcel Proust\u27s A la recherche du temps perdu, especially in the well-known English translation by C. K. Scott Moncrieff. Primarily concerned with the importance of Ovid\u27s idiosyncratic version of the Medusa- Perseus myth to Proust\u27s project, this study argues that Proust\u27s Albertine serves as a singularly Ovidian Medusa, yet one with specifically biological and evolutionary resonances that queer the more rigid and narrow Darwinism of The Men of 1914

Edible Subjectivities: Meat in Science Fiction

This dissertation argues for the critical urgency of both challenging the constitution of subjectivity itself and disputing the a priori exclusion of other animals from attaining some kind of ethico-political subject-status. Deploying a Baradian performative posthumanist analysis attentive to patterns of difference, I engage the theoretical tools of ecofeminism, critical animal studies (CAS), and material ecocriticism to interrogate subjectivity by attending closely and critically to twentieth and twenty-first century Euro- American Anglophone science fiction (SF) stories about meat. Meat animal narratives open the subject to alternative modes of knowing that anthropocentric epistemologies foreclose, intervening against the structural exclusions imposed by various material- discursive apparatuses of domination that define, authorize and enact subjectivity as always and only human, over and against the figure of the animal. SF, a genre of alterity that has long been at the vanguard of literary engagements with nonhuman subjectivities, likewise works to subvert hegemonic notions of the subject as always- already human and complicate overdetermined configurations of the subject as an ontologically predetermined entity. Engaging SF narratives about human cattle dystopias, alien encounters, in vitro meat and alimentary xeno-symbiogenesis, I approach subjectivity as an emergent phenomenon born of the intra-action of differentially materialized agential entanglements, andcruciallytheir constitutive exclusions. Rejecting subject-object dualism as an unliveable onto-epistemological paradigm that excludes anything edible from relations of respectful use, I argue for the necessity of enacting subjectivities in terms of concrete practices of restraint and humility, with humans firmly situated as embodied animal beings, enmeshed with and accountable to a much larger community of more than human, more than animal and more than animate actants on a finite planet