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

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