1,299 research outputs found

    Learning scale-variant and scale-invariant features for deep image classification

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    Convolutional Neural Networks (CNNs) require large image corpora to be trained on classification tasks. The variation in image resolutions, sizes of objects and patterns depicted, and image scales, hampers CNN training and performance, because the task-relevant information varies over spatial scales. Previous work attempting to deal with such scale variations focused on encouraging scale-invariant CNN representations. However, scale-invariant representations are incomplete representations of images, because images contain scale-variant information as well. This paper addresses the combined development of scale-invariant and scale-variant representations. We propose a multi- scale CNN method to encourage the recognition of both types of features and evaluate it on a challenging image classification task involving task-relevant characteristics at multiple scales. The results show that our multi-scale CNN outperforms single-scale CNN. This leads to the conclusion that encouraging the combined development of a scale-invariant and scale-variant representation in CNNs is beneficial to image recognition performance

    Decline and decadence in Iraq and Syria after the age of Avicenna? : ʿAbd al-Laṭīf al-Baghdādī (1162–1231) between myth and history

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    ‘Abd al-Laṭīf al-Baghdādī’s (d. 1231) work Book of the Two Pieces of Advice (Kitāb al Nasīḥatayn) challenges the idea that Islamic medicine declined after the twelfth century AD. Moreover, it offers some interesting insights into the social history of medicine. ‘Abd al-Laṭīf advocated using the framework of Greek medical epistemology to criticize the rationalist physicians of his day; he argued that female and itinerant practitioners, relying on experience, were superior to some rationalists. He lambasted contemporaneous medical education because it put too much faith in a restricted number of textbooks such as the Canon by Ibn Sīnā (Avicenna, d. 1037) or imperfect abridgments

    Many Task Learning with Task Routing

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    Typical multi-task learning (MTL) methods rely on architectural adjustments and a large trainable parameter set to jointly optimize over several tasks. However, when the number of tasks increases so do the complexity of the architectural adjustments and resource requirements. In this paper, we introduce a method which applies a conditional feature-wise transformation over the convolutional activations that enables a model to successfully perform a large number of tasks. To distinguish from regular MTL, we introduce Many Task Learning (MaTL) as a special case of MTL where more than 20 tasks are performed by a single model. Our method dubbed Task Routing (TR) is encapsulated in a layer we call the Task Routing Layer (TRL), which applied in an MaTL scenario successfully fits hundreds of classification tasks in one model. We evaluate our method on 5 datasets against strong baselines and state-of-the-art approaches.Comment: 8 Pages, 5 Figures, 2 Table

    Exposure to socially responsible investing of mutual funds in the Euronext stock markets

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    This paper analyses fund management and exposure on the Euronext stock exchanges. Especially, we investigate to what extent mutual funds are engaged in socially responsible investing (SRI). In order to accomplish this goal, we use regression analysis to measure the exposure of mutual funds to stock market indices based on a selection of companies that satisfy criteria of SRI. We measure the exposure in Belgium, France, and the Netherlands for almost 800 investment funds during the 1990s. We conclude that most funds have a significant exposure to the SRI index. Furthermore, we find a home bias in SRI as the exposure to the SRI index for Europe is much higher than that for America (JEL G11, G24, Z13).

    The role of antithrombin in venous and arterial thrombosis

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    The role of antithrombin in venous and arterial thrombosis

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    What we mean when we talk about bisexuality : a critical discourse analysis of self-definitions by bi-identified people online

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    A Critical Discourse Analysis on the language used by bisexual people when defining bisexuality. The aim of the thesis is to define current discourses about bisexuality within the modern frameworks of sexuality and gender and explain why bisexuality lacks the stability and visibility of other minority sexualities. The material is gathered from profiles submitted to a website with the purpose to educate about and bring visibility to bisexuality. Research is conducted according to Fairclough’s methodology and Halliday’s Systemic Functional Linguistics within the framework of postmodern critical theory. Despite the frequent use of relational intensive process verbs, clauses defining bisexuality are marked as subjective with the choice of Subject and circumstantial information. Bisexuality is defined by what it is not by rejection of stereotypes. The lack of established cultural imagery shows that bisexuality cannot be performed as an identity. Additionally, it cannot be categorized as a sexual orientation, because it is not supported by the gender frameworks that define sexual attraction. For bisexuality to exist as a solid sexual identity, sexual and gender frameworks need to be redefined
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