'Indeed', 'Really', 'In Fact', 'Actually'

Interjections, such as those in the title, together with a few similar devices, when qualifying clauses expressing truth-conditions, or that such conditions have been satisfied, are entitled ‘force-amplifiers’. Disputes between deflationary and inflationary truth-theories sometimes are assumed to turn on the supposed pivotal role that these devices are construed as playing in the interpretation of the clauses they qualify. I argue that they are not dispensable add-ons. Moreover, even in their absence the relevant clauses giving truth-conditions permit interpretations that aren’t deflationary-friendly. I maintain that this is a significant fact about the use to which writers put them. I then defend, a thesis about force-amplifiers that makes them indispensable to the interpretation of the relevant clauses, and that renders certain moves unavailable to popular deflationist treatments

A protocol paper on the preservation of identity: understanding the technology adoption patterns of older adults with age-related vision loss (ARVL)

There are a growing number of older adults with age-related vision loss (ARVL) for whom technology holds promise in supporting their engagement in daily activities. Despite the growing presence of technologies intended to support older adults with ARVL, there remains high rates of abandonment. This phenomenon of technology abandonment may be partly explained by the concept of self-image, meaning that older adults with ARVL avoid the use of particular technologies due to an underlying fear that use of such technologies may mark them as objects of pity, ridicule, and/or stigmatization. In response to this, the proposed study aims to understand how the decision-making processes of older adults with ARVL, as it relates to technology adoption, is influenced by the negotiation of identity. The study protocol will justify the need for this critical ethnographic study, unpack the theoretical underpinnings of this work, detail the sampling/recruitment strategy, describe the methods which included a home tour, go-along, and semi-structured in-depth interview, as well as the collective approach taken to analyze the data. The protocol concludes by examining the ethical tensions associated with this study, including a focus on the methods adopted as well as the ethical challenges inherent when working with an older adult population experiencing vision loss

Phenex: Ontological Annotation of Phenotypic Diversity

Phenex is a platform-independent desktop application designed to facilitate efficient and consistent annotation of phenotypic variation using Entity-Quality syntax, drawing on terms from community ontologies for anatomical entities, phenotypic qualities, and taxonomic names. Despite the centrality of the phenotype to so much of biology, traditions for communicating information about phenotypes are idiosyncratic to different disciplines. Phenotypes seem to elude standardized descriptions due to the variety of traits that compose them and the difficulty of capturing the complex forms and subtle differences among organisms that we can readily observe. Consequently, phenotypes are refractory to attempts at data integration that would allow computational analyses across studies and study systems. Phenex addresses this problem by allowing scientists to employ standard ontologies and syntax to link computable phenotype annotations to evolutionary character matrices, as well as to link taxa and specimens to ontological identifiers. Ontologies have become a foundational technology for establishing shared semantics, and, more generally, for capturing and computing with biological knowledge

Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio