473 research outputs found
Assessing business proposals: genre conventions and audience response in document design
We carried out two studies in which several genre conventions were tested on professional readers to verify the usefulness of applying genre conventions to business proposals. In the first study, 39 male business clients of the company IBM Netherlands compared an authentic busi ness proposal with a modified version that conformed to genre con ventions of document structure. Readers' preferences and reading behavior were noted and observed. In the second study, the same group of IBM business clients compared fragments of proposals that differed in stylistic genre conventions. Readers' preferences were noted and verified. Results of the first study indicated that applying genre conventions to document structure improved the readers' selection of information. Results of the second study revealed that readers disap proved of persuasive style shifts, while opinions differed with respect to shifts from impersonal to personal style
A Framework for Directional and Higher-Order Reconstruction in Photoacoustic Tomography
Photoacoustic tomography is a hybrid imaging technique that combines high
optical tissue contrast with high ultrasound resolution. Direct reconstruction
methods such as filtered backprojection, time reversal and least squares suffer
from curved line artefacts and blurring, especially in case of limited angles
or strong noise. In recent years, there has been great interest in regularised
iterative methods. These methods employ prior knowledge on the image to provide
higher quality reconstructions. However, easy comparisons between regularisers
and their properties are limited, since many tomography implementations heavily
rely on the specific regulariser chosen. To overcome this bottleneck, we
present a modular reconstruction framework for photoacoustic tomography. It
enables easy comparisons between regularisers with different properties, e.g.
nonlinear, higher-order or directional. We solve the underlying minimisation
problem with an efficient first-order primal-dual algorithm. Convergence rates
are optimised by choosing an operator dependent preconditioning strategy. Our
reconstruction methods are tested on challenging 2D synthetic and experimental
data sets. They outperform direct reconstruction approaches for strong noise
levels and limited angle measurements, offering immediate benefits in terms of
acquisition time and quality. This work provides a basic platform for the
investigation of future advanced regularisation methods in photoacoustic
tomography.Comment: submitted to "Physics in Medicine and Biology". Changes from v1 to
v2: regularisation with directional wavelet has been added; new experimental
tests have been include
Adviseren met perspectief: rapporten en presentaties maken / Louise Cornelis. - Bussum : Coutinho, 2002. - ISBN 9062833020
Automatic and efficient tomographic reconstruction algorithms
In this thesis we present several methods to automate tomographic reconstruction algorithms and several novel tomographic reconstruction algorithms with the focus on being easily applicable and efficient to use.</table
Causal connectives have presuppositions. Effects on coherence and discourse structure
Readers may feel uncomfortable reading the sentence: Although Greta Garbo was considered to be the yardstick of beauty, she never married. “Why”, is their question, “does the writer want us to believe that normally, beautiful women marry?” The writer’s opinion they refer to, is not expressed in the sentence explicitly. However, all readers will infer this opinion, in order to make a sensible interpretation of the sentence: we expect beautiful women to marry, and the fact that Greta Garbo did not is an exception to this rule. The writer’s opinion is in fact a presupposition, triggered by the use of although.
This book is a detailed study of the discourse semantic properties of
causal connectives and their presuppositions. The interpretation process
of connectives like although and because, and their Dutch counterparts, will be followed from the recognition of subtle meaning differences of a connective used in different contexts, an explanation for these
differences in terms of presuppositions, an analysis of the way these presuppositions manipulate lexical knowledge to infer causal coherence relations and the effect of these coherence relations on antecedents of
propositional anaphors in discourse structure.
Everyone having an interest in semantics, pragmatics, discourse representation, argumentation, computational linguistics or the linguistic analysis of conjunction may want to read this book
Verbal redundancy in a procedural animation: On-screen labels improve retention but not behavioral performance
Multimedia learning research has shown that presenting the same words as spoken text and as written text to accompany graphical information hinders learning (i.e., redundancy effect). However, recent work showed that a “condensed” form of written text (i.e., on-screen labels) that overlaps with the spoken text, and thus is only partially redundant, can actually foster learning. This study extends this line of research by focusing on the usefulness of on-screen labels in an animation explaining a procedural task (i.e., first-aid procedure). The experiment had a 2 × 2 × 2 between-subject design (N = 129) with the factors spoken text (yes vs. no), written text (yes vs. no), and on-screen labels (yes vs. no). Learning outcomes were measured as retention accuracy and behavioral performance accuracy. Results showed that on-screen labels improved retention accuracy (but not behavioral performance accuracy) of the procedure, especially when presented together with spoken text. So, on-screen labels appear to be promising for learning from procedural animations
Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D Computed Tomography
At X-ray beamlines of synchrotron light sources, the achievable
time-resolution for 3D tomographic imaging of the interior of an object has
been reduced to a fraction of a second, enabling rapidly changing structures to
be examined. The associated data acquisition rates require sizable
computational resources for reconstruction. Therefore, full 3D reconstruction
of the object is usually performed after the scan has completed. Quasi-3D
reconstruction -- where several interactive 2D slices are computed instead of a
3D volume -- has been shown to be significantly more efficient, and can enable
the real-time reconstruction and visualization of the interior. However,
quasi-3D reconstruction relies on filtered backprojection type algorithms,
which are typically sensitive to measurement noise. To overcome this issue, we
propose Noise2Filter, a learned filter method that can be trained using only
the measured data, and does not require any additional training data. This
method combines quasi-3D reconstruction, learned filters, and self-supervised
learning to derive a tomographic reconstruction method that can be trained in
under a minute and evaluated in real-time. We show limited loss of accuracy
compared to training with additional training data, and improved accuracy
compared to standard filter-based methods
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