5,528 research outputs found

    Stylized Adversarial Defense

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    Deep Convolution Neural Networks (CNNs) can easily be fooled by subtle, imperceptible changes to the input images. To address this vulnerability, adversarial training creates perturbation patterns and includes them in the training set to robustify the model. In contrast to existing adversarial training methods that only use class-boundary information (e.g., using a cross entropy loss), we propose to exploit additional information from the feature space to craft stronger adversaries that are in turn used to learn a robust model. Specifically, we use the style and content information of the target sample from another class, alongside its class boundary information to create adversarial perturbations. We apply our proposed multi-task objective in a deeply supervised manner, extracting multi-scale feature knowledge to create maximally separating adversaries. Subsequently, we propose a max-margin adversarial training approach that minimizes the distance between source image and its adversary and maximizes the distance between the adversary and the target image. Our adversarial training approach demonstrates strong robustness compared to state of the art defenses, generalizes well to naturally occurring corruptions and data distributional shifts, and retains the model accuracy on clean examples.Comment: Code is available at this http https://github.com/Muzammal-Naseer/SA

    Climate Change Policy, and Policy Change in China

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    Solving the climate change problem by limiting global greenhouse gas (GHG) emissions will necessitate action by the world’s two largest emitters, the United States and China. Neither has so far committed to quantitative emissions limits. Some argue that China cannot be engaged on the basis of its national interest in climate policy, on the ground that China’s national net benefits of limiting greenhouse gas emissions would be negative, as a result of significant GHG abatement costs and potential net gains to China from a warmer world. This premise has led some observers to advocate other approaches to engaging China, such as appeal to moral obligation. This Article argues that appeal to national net benefits is still the best approach to engage China. First, appealing to China’s asserted moral obligation to limit its GHG emissions may be ineffective or even counterproductive. Even if climate change is a moral issue for American leaders, framing the issue that way may not be persuasive to Chinese leaders. Second, the concern that China’s national net benefits of climate policy are negative is based on older forecasts of costs and benefits. More recent climate science, of which the Chinese leadership is aware, indicates higher damages to China from climate change and thus greater net benefits to China from climate policy. Third, the public health co-benefits of reducing other air pollutants along with GHGs may make GHG emissions limits look more attractive to China. Fourth, the distribution of climate impacts within China may be as important as the net aggregate: climate change may exacerbate political and social stresses within China, which the leadership may seek to avoid in order to maintain political stability. Fifth, the costs of abatement may decline as innovation in China accelerates. Sixth, as China becomes a great power in world politics, and as climate change affects China’s allies, leadership on climate policy may look more favorable to China’s elites. Seventh, the design of the international climate treaty regime itself can offer positive incentives to China. Taken together, these factors point to a potential and even ongoing shift in Chinese climate policy. They illustrate how the international law and politics of climate change depend on domestic politics and institutions. And they suggest that the United States, if it too takes effective action, can make the case for enlightened pragmatism as a basis to engage China in a cooperative global climate policy regime

    Resilience and development: Mobilizing for transformation

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    In 2014, the Third International Conference on the resilience of social-ecological systems chose the theme “resilience and development: mobilizing for transformation.” The conference aimed specifically at fostering an encounter between the experiences and thinking focused on the issue of resilience through a social and ecological system perspective, and the experiences focused on the issue of resilience through a development perspective. In this perspectives piece, we reflect on the outcomes of the meeting and document the differences and similarities between the two perspectives as discussed during the conference, and identify bridging questions designed to guide future interactions. After the conference, we read the documents (abstracts, PowerPoints) that were prepared and left in the conference database by the participants (about 600 contributions), and searched the web for associated items, such as videos, blogs, and tweets from the conference participants. All of these documents were assessed through one lens: what do they say about resilience and development? Once the perspectives were established, we examined different themes that were significantly addressed during the conference. Our analysis paves the way for new collective developments on a set of issues: (1) Who declares/assign/cares for the resilience of what, of whom? (2) What are the models of transformations and how do they combine the respective role of agency and structure? (3) What are the combinations of measurement and assessment processes? (4) At what scale should resilience be studied? Social transformations and scientific approaches are coconstructed. For the last decades, development has been conceived as a modernization process supported by scientific rationality and technical expertise. The definition of a new perspective on development goes with a negotiation on a new scientific approach. Resilience is presently at the center of this negotiation on a new science for development. (Résumé d'auteur

    Resilient Urban Futures

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    This open access book addresses the way in which urban and urbanizing regions profoundly impact and are impacted by climate change. The editors and authors show why cities must wage simultaneous battles to curb global climate change trends while adapting and transforming to address local climate impacts. This book addresses how cities develop anticipatory and long-range planning capacities for more resilient futures, earnest collaboration across disciplines, and radical reconfigurations of the power regimes that have institutionalized the disenfranchisement of minority groups. Although planning processes consider visions for the future, the editors highlight a more ambitious long-term positive visioning approach that accounts for unpredictability, system dynamics and equity in decision-making. This volume brings the science of urban transformation together with practices of professionals who govern and manage our social, ecological and technological systems to design processes by which cities may achieve resilient urban futures in the face of climate change

    Resilient Urban Futures

    Get PDF
    This open access book addresses the way in which urban and urbanizing regions profoundly impact and are impacted by climate change. The editors and authors show why cities must wage simultaneous battles to curb global climate change trends while adapting and transforming to address local climate impacts. This book addresses how cities develop anticipatory and long-range planning capacities for more resilient futures, earnest collaboration across disciplines, and radical reconfigurations of the power regimes that have institutionalized the disenfranchisement of minority groups. Although planning processes consider visions for the future, the editors highlight a more ambitious long-term positive visioning approach that accounts for unpredictability, system dynamics and equity in decision-making. This volume brings the science of urban transformation together with practices of professionals who govern and manage our social, ecological and technological systems to design processes by which cities may achieve resilient urban futures in the face of climate change

    Deep generative modelling of the imaged human brain

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    Human-machine symbiosis is a very promising opportunity for the field of neurology given that the interpretation of the imaged human brain is a trivial feat for neither entity. However, before machine learning systems can be used in real world clinical situations, many issues with automated analysis must first be solved. In this thesis I aim to address what I consider the three biggest hurdles to the adoption of automated machine learning interpretative systems. For each issue, I will first elucidate the reader on its importance given the overarching narratives of both neurology and machine learning, and then showcase my proposed solutions to these issues through the use of deep generative models of the imaged human brain. First, I start by addressing what is an uncontroversial and universal sign of intelligence: the ability to extrapolate knowledge to unseen cases. Human neuroradiologists have studied the anatomy of the healthy brain and can therefore, with some success, identify most pathologies present on an imaged brain, even without having ever been previously exposed to them. Current discriminative machine learning systems require vast amounts of labelled data in order to accurately identify diseases. In this first part I provide a generative framework that permits machine learning models to more efficiently leverage unlabelled data for better diagnoses with either none or small amounts of labels. Secondly, I address a major ethical concern in medicine: equitable evaluation of all patients, regardless of demographics or other identifying characteristics. This is, unfortunately, something that even human practitioners fail at, making the matter ever more pressing: unaddressed biases in data will become biases in the models. To address this concern I suggest a framework through which a generative model synthesises demographically counterfactual brain imaging to successfully reduce the proliferation of demographic biases in discriminative models. Finally, I tackle the challenge of spatial anatomical inference, a task at the centre of the field of lesion-deficit mapping, which given brain lesions and associated cognitive deficits attempts to discover the true functional anatomy of the brain. I provide a new Bayesian generative framework and implementation that allows for greatly improved results on this challenge, hopefully, paving part of the road towards a greater and more complete understanding of the human brain

    Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models

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    We describe CounterSynth, a conditional generative model of diffeomorphic deformations that induce label-driven, biologically plausible changes in volumetric brain images. The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations. Focusing on demographic attributes, we evaluate the quality of synthesised counterfactuals with voxel-based morphometry, classification and regression of the conditioning attributes, and the Fréchet inception distance. Examining downstream discriminative performance in the context of engineered demographic imbalance and confounding, we use UK Biobank and OASIS magnetic resonance imaging data to benchmark CounterSynth augmentation against current solutions to these problems. We achieve state-of-the-art improvements, both in overall fidelity and equity. The source code for CounterSynth is available at https://github.com/guilherme-pombo/CounterSynth
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