1,299 research outputs found
Learning scale-variant and scale-invariant features for deep image classification
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
‘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
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
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).
Properties and N-Terminal amino acid sequences of three Erythrina lectins from Costa Rica (Leguminosae)
What we mean when we talk about bisexuality : a critical discourse analysis of self-definitions by bi-identified people online
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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