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Shape-Memory Capability of Copolyetheresterurethane Microparticles Prepared via Electrospraying
(2015)
Multifunctional thermo-responsive and degradable microparticles exhibiting a shapememory effect (SME) have attracted widespread interest in biomedicine as switchable delivery vehicles or microactuators. In this work almost spherical solid microparticles with an average diameter of 3.9 +/- 0.9 mm are prepared via electrospraying of a copolyetheresterurethane named PDC, which is composed of crystallizable oligo(p-dioxanone) (OPDO) hard and oligo(e-caprolactone) (OCL) switching segments. The PDC microparticles are programmed via compression at different pressures and their shapememory capability is explored by off-line and online heating experiments. When a low programming pressure of 0.2 MPa is applied a pronounced thermally-induced shape-memory effect is achieved with a shape recovery ratio about 80%, while a high programming pressure of 100 MPa resulted in a weak shape-memory performance. Finally, it is demonstrated that an array of PDC microparticles deposited on a polypropylene (PP) substrate can be successfully programmed into a smart temporary film, which disintegrates upon heating to 60 degrees C.
Previous research with younger adults has revealed differences between native (L1) and non-native late-bilingual (L2) speakers with respect to how morphologically complex words are processed. This study examines whether these L1/L2 differences persist into old age. We tested masked-priming effects for derived and inflected word forms in older L1 and L2 speakers of German and compared them to results from younger L1 and L2 speakers on the same experiment (mean ages: 62 vs. 24). We found longer overall response times paired with better accuracy scores for older (L1 and L2) participants than for younger participants. The priming patterns, however, were not affected by chronological age. While both L1 and L2 speakers showed derivational priming, only the L1 speakers demonstrated inflectional priming. We argue that general performance in both L1 and L2 is affected by aging, but that the more profound differences between native and non-native processing persist into old age.
Idioms in the World
(2019)
German orthography systematically marks all nouns (even other nominalized word classes) by capitalizing their first letter. It is often claimed that readers benefit from the uppercase-letter syntactic and semantic information, which makes the processing of sentences easier (e.g., Bock et al., 1985, 1989). In order to test this hypothesis, we asked 54 German readers to read single sentences systematically manipulated by a target word (N). In the experimental condition (EXP), we used semantic priming (in the following example: sick -> cold) in order to build up a strong expectation of a noun, which was actually an attribute for the following noun (N+1) (translated to English e.g., "The sick writer had a cold (N) nose (N+1) ..."). The sentences in the control condition were built analogously, but word N was purposefully altered (keeping word length and frequency constant) to make its interpretation as a noun extremely unlikely (e.g., "The sick writer had a blue (N) nose (N+1) ..."). In both conditions, the sentences were presented either following German standard orthography (Cap) or in lowercase spelling (NoCap). The capitalized nouns in the EXP/Cap condition should then prevent garden-path parsing, as capital letters can be recognized parafoveally. However, in the EXP/NoCap condition, we expected a garden-path effect on word N+1 affecting first-pass fixations and the number of regressions, as the reader realizes that word N is instead an adjective. As the control condition does not include a garden-path, we expected to find (small) effects of the violation of the orthographic rule in the CON/NoCap condition, but no garden-path effect. As a global result, it can be stated that reading sentences in which nouns are not marked by a majuscule slows a native German reader down significantly, but from an absolute point of view, the effect is small. Compared with other manipulations (e.g., transpositions or substitutions), a lowercase letter still represents the correct allograph in the correct position without affecting phonology. Furthermore, most German readers do have experience with other alphabetic writing systems that lack consistent noun capitalization, and in (private) digital communication lowercase nouns are quite common. Although our garden-path sentences did not show the desired effect, we found an indication of grammatical pre-processing enabled by the majuscule in the regularly spelled sentences: In the case of high noun frequency, we post hoc located parafovea-on-fovea effects, i.e., longer fixation durations, on the attributive adjective (word N). These benefits of capitalization could only be detected under specific circumstances. In other cases, we conclude that longer reading durations are mainly the result of disturbance in readers' habituation when the expected capitalization is missing.
Reflecting in written form on one's teaching enactments has been considered a facilitator for teachers' professional growth in university-based preservice teacher education. Writing a structured reflection can be facilitated through external feedback. However, researchers noted that feedback in preservice teacher education often relies on holistic, rather than more content-based, analytic feedback because educators oftentimes lack resources (e.g., time) to provide more analytic feedback. To overcome this impediment to feedback for written reflection, advances in computer technology can be of use. Hence, this study sought to utilize techniques of natural language processing and machine learning to train a computer-based classifier that classifies preservice physics teachers' written reflections on their teaching enactments in a German university teacher education program. To do so, a reflection model was adapted to physics education. It was then tested to what extent the computer-based classifier could accurately classify the elements of the reflection model in segments of preservice physics teachers' written reflections. Multinomial logistic regression using word count as a predictor was found to yield acceptable average human-computer agreement (F1-score on held-out test dataset of 0.56) so that it might fuel further development towards an automated feedback tool that supplements existing holistic feedback for written reflections with data-based, analytic feedback.
Previous research has shown that heritage speakers struggle with inflectional morphology. 'Limitations of online resources' for processing a non-dominant language has been claimed as one possible reason for these difficulties. To date, however, there is very little experimental evidence on real-time language processing in heritage speakers. Here we report results from a masked priming experiment with 97 bilingual (Turkish/German) heritage speakers and a control group of 40 non-heritage speakers of Turkish examining regular and irregular forms of the Turkish aorist. We found that, for the regular aorist, heritage speakers use the same morphological decomposition mechanism ('affix stripping') as control speakers, whereas for processing irregularly inflected forms they exhibited more variability (i.e., less homogeneous performance) than the control group. Heritage speakers also demonstrated semantic priming effects. At a more general level, these results indicate that heritage speakers draw on multiple sources of information for recognizing morphologically complex words.
'Complex systems are information processors' is a statement that is frequently made. Here we argue for the distinction between information processing-in the sense of encoding and transmitting a symbolic representation-and the formation of correlations (pattern formation/self-organisation). The study of both uses tools from information theory, but the purpose is very different in each case: explaining the mechanisms and understanding the purpose or function in the first case, versus data analysis and correlation extraction in the latter. We give examples of both and discuss some open questions. The distinction helps focus research efforts on the relevant questions in each case.
Substantial research has examined cognition in aging bilinguals. However, less work has investigated the effects of aging on language itself in bilingualism. In this article I comprehensively review prior research on this topic, and interpret the evidence in light of current theories of aging and theories of bilingualism. First, aging indeed appears to affect bilinguals' language performance, though there is considerable variability in the trajectory across adulthood (declines, age-invariance, and improvements) and in the extent to which these trajectories resemble those found in monolinguals. I argue that these age effects are likely explained by the key opposing forces of increasing experience and cognitive declines in aging. Second, consistent with some theoretical work on bilingual language processing, the grammatical processing mechanisms do not seem to change between younger and older bilingual adults, even after decades of immersion. I conclude by discussing how future research can further advance the field.
Intrinsic decomposition refers to the problem of estimating scene characteristics, such as albedo and shading, when one view or multiple views of a scene are provided. The inverse problem setting, where multiple unknowns are solved given a single known pixel-value, is highly under-constrained. When provided with correlating image and depth data, intrinsic scene decomposition can be facilitated using depth-based priors, which nowadays is easy to acquire with high-end smartphones by utilizing their depth sensors. In this work, we present a system for intrinsic decomposition of RGB-D images on smartphones and the algorithmic as well as design choices therein. Unlike state-of-the-art methods that assume only diffuse reflectance, we consider both diffuse and specular pixels. For this purpose, we present a novel specularity extraction algorithm based on a multi-scale intensity decomposition and chroma inpainting. At this, the diffuse component is further decomposed into albedo and shading components. We use an inertial proximal algorithm for non-convex optimization (iPiano) to ensure albedo sparsity. Our GPU-based visual processing is implemented on iOS via the Metal API and enables interactive performance on an iPhone 11 Pro. Further, a qualitative evaluation shows that we are able to obtain high-quality outputs. Furthermore, our proposed approach for specularity removal outperforms state-of-the-art approaches for real-world images, while our albedo and shading layer decomposition is faster than the prior work at a comparable output quality. Manifold applications such as recoloring, retexturing, relighting, appearance editing, and stylization are shown, each using the intrinsic layers obtained with our method and/or the corresponding depth data.
Language processing requires memory retrieval to integrate current input with previous context and making predictions about upcoming input. We propose that prediction and retrieval are two sides of the same coin, i.e. functionally the same, as they both activate memory representations. Under this assumption, memory retrieval and prediction should interact: Retrieval interference can only occur at a word that triggers retrieval and a fully predicted word would not do that. The present study investigated the proposed interaction with event-related potentials (ERPs) during the processing of sentence pairs in German. Predictability was measured via cloze probability. Memory retrieval was manipulated via the position of a distractor inducing proactive or retroactive similarity-based interference. Linear mixed model analyses provided evidence for the hypothesised interaction in a broadly distributed negativity, which we discuss in relation to the interference ERP literature. Our finding supports the proposal that memory retrieval and prediction are functionally the same.