1,075 research outputs found

    Query-dependent metric learning for adaptive, content-based image browsing and retrieval

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    Patentable Discovery?

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    Discoveries are property that people did not create or produce, but that which they obtained through the unearthing of an object or an idea. Finding an object, or being the first to obtain certain knowledge, may allow you to file a patent and gain exclusive rights in your discovery. This Comment addresses the issue of whether such discoveries are patentable. The author argues that despite some decisions and dicta to the contrary, the courts and the Patent Office should explicitly recognize the patentability of discoveries that are not inventions

    Classifying textile designs using region graphs

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    How we say what we do and why it is important: An idiosyncratic analysis of mental health nursing identity on social media

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    This paper is the culmination of a qualitative research project into mental health nursing (MHN) identity via exploration of a social media campaign organized in 2018 by the UK Mental Health Nurses Association. Through engagement with this campaign and a multimethod approach, this paper proposes a new and novel heuristic framework for exploring MHN identity holistically, through what is termed the 6Ps of MHN identity. The 6Ps – encompassing the professional, personal, practical, proximal, philosophical, and political aspects of identity – were previously shared with members of the MHN research community at both the 2019 and 2020 proceedings of the International Mental Health Nursing Research Conference. To examine the identity expressed in the social media campaign, all contributions by nurses were amalgamated into one ‘text’ for analysis. When this text was examined, the focus was the particular language used by MHNs. This granular analysis concentrated on word choice, form, and frequency as the constituent aspects of meaning. Even when it was necessary to examine larger grammatical units, the key nouns – grammatical objects and subjects – were the primary focus of analysis. Following this, the author – a mental health nurse themselves – applied their personal understanding of the field of practice to the text to arrive at an understanding of its contents. This approach is the first in the field of MHN identity research to examine the profession’s identity as expressed by members on social media, as well as the linguistic form of that expression

    Cam-softmax for discriminative deep feature learning

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    Unsupervised Mapping and Semantic User Localisation from First-Person Monocular Video

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    We propose an unsupervised probabilistic framework for learning a human-centred representation of a person’s environment from first-person video. Specifically, non-geometric maps modelled as hierarchies of probabilistic place graphs and view graphs are learned. Place graphs model a user’s patterns of transition between physical locations whereas view graphs capture an aspect of user behaviour within those locations. Furthermore, we describe an implementation in which the notion of place is divided into stations and the routes that interconnect them. Stations typically correspond to rooms or areas where a user spends time. Visits to stations are temporally segmented based on qualitative visual motion. We describe how to learn maps online in an unsupervised manner, and how to localise the user within these maps. We report experiments on two datasets, including comparison of performance with and without view graphs, and demonstrate better online mapping than when using offline clustering.<br/
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