@article{HanneBurchertDeBleseretal.2015, author = {Hanne, Sandra and Burchert, Frank and De Bleser, Ria and Vasishth, Shravan}, title = {Sentence comprehension and morphological cues in aphasia: What eye-tracking reveals about integration and prediction}, series = {Journal of neurolinguistics : an international journal for the study of brain function in language behavior and experience}, volume = {34}, journal = {Journal of neurolinguistics : an international journal for the study of brain function in language behavior and experience}, publisher = {Elsevier}, address = {Oxford}, issn = {0911-6044}, doi = {10.1016/j.jneuroling.2014.12.003}, pages = {83 -- 111}, year = {2015}, abstract = {Comprehension of non-canonical sentences can be difficult for individuals with aphasia (IWA). It is still unclear to which extent morphological cues like case marking or verb inflection may influence IWA's performance or even help to override deficits in sentence comprehension. Until now, studies have mainly used offline methods to draw inferences about syntactic deficits and, so far, only a few studies have looked at online syntactic processing in aphasia. We investigated sentence processing in German-speaking IWA by combining an offline (sentence-picture matching) and an online (eye-tracking in the visual-world paradigm) method. Our goal was to determine whether IWA are capable of using inflectional morphology (number-agreement markers on verbs and case markers in noun phrases) as a cue to sentence interpretation. We report results of two visual-world experiments using German reversible SVO and OVS sentences. In each study, there were eight IWA and 20 age-matched controls. Experiment 1 targeted the role of unambiguous case morphology, while Experiment 2 looked at processing of number-agreement cues at the verb in caseambiguous sentences. IWA showed deficits in using both types of morphological markers as a cue to non-canonical sentence interpretation and the results indicate that in aphasia, processing of case-marking cues is more vulnerable as compared to verbagreement morphology. We ascribe this finding to the higher cue reliability of agreement cues, which renders them more resistant against impairments in aphasia. However, the online data revealed that IWA are in principle capable of successfully computing morphological cues, but the integration of morphological information is delayed as compared to age-matched controls. Furthermore, we found striking differences between controls and IWA regarding subject-before-object parsing predictions. While in case-unambiguous sentences IWA showed evidence for early subjectbefore-object parsing commitments, they exhibited no straightforward subject-first prediction in case-ambiguous sentences, although controls did so for ambiguous structures. IWA delayed their parsing decisions in case-ambiguous sentences until unambiguous morphological information, such as a subject-verbnumber-agreement cue, was available. We attribute the results for IWA to deficits in predictive processes based on morphosyntactic cues during sentence comprehension. The results indicate that IWA adopt a wait-and-see strategy and initiate prediction of upcoming syntactic structure only when unambiguous case or agreement cues are available. (C) 2015 Elsevier Ltd. All rights reserved.}, language = {en} } @article{deAbreueLimaLeifelsNikoloski2018, author = {de Abreu e Lima, Francisco Anastacio and Leifels, Lydia and Nikoloski, Zoran}, title = {Regression-based modeling of complex plant traits based on metabolomics data}, series = {Plant Metabolomics}, volume = {1778}, journal = {Plant Metabolomics}, publisher = {Humana Press Inc.}, address = {New York}, isbn = {978-1-4939-7819-9}, issn = {1064-3745}, doi = {10.1007/978-1-4939-7819-9_23}, pages = {321 -- 327}, year = {2018}, abstract = {Bridging metabolomics with plant phenotypic responses is challenging. Multivariate analyses account for the existing dependencies among metabolites, and regression models in particular capture such dependencies in search for association with a given trait. However, special care should be undertaken with metabolomics data. Here we propose a modeling workflow that considers all caveats imposed by such large data sets.}, language = {en} } @article{WardVegaBaumann2012, author = {Ward, Nigel G. and Vega, Alejandro and Baumann, Timo}, title = {Prosodic and temporal features for language modeling for dialog}, series = {Speech communication}, volume = {54}, journal = {Speech communication}, number = {2}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0167-6393}, doi = {10.1016/j.specom.2011.07.009}, pages = {161 -- 174}, year = {2012}, abstract = {If we can model the cognitive and communicative processes underlying speech, we should be able to better predict what a speaker will do. With this idea as inspiration, we examine a number of prosodic and timing features as potential sources of information on what words the speaker is likely to say next. In spontaneous dialog we find that word probabilities do vary with such features. Using perplexity as the metric, the most informative of these included recent speaking rate, volume, and pitch, and time until end of utterance. Using simple combinations of such features to augment trigram language models gave up to a 8.4\% perplexity benefit on the Switchboard corpus, and up to a 1.0\% relative reduction in word error rate (0.3\% absolute) on the Verbmobil II corpus.}, language = {en} } @article{TakounaSachsMeinel2014, author = {Takouna, Ibrahim and Sachs, Kai and Meinel, Christoph}, title = {Multiperiod robust optimization for proactive resource provisioning in virtualized data centers}, series = {The journal of supercomputing : an internat. journal of supercomputer design, analysis and use}, volume = {70}, journal = {The journal of supercomputing : an internat. journal of supercomputer design, analysis and use}, number = {3}, publisher = {Springer}, address = {Dordrecht}, issn = {0920-8542}, doi = {10.1007/s11227-014-1246-2}, pages = {1514 -- 1536}, year = {2014}, language = {en} } @article{VockPreckelRolling2011, author = {Vock, Miriam and Preckel, Franzis and Rolling, Heinz}, title = {Mental abilities and school achievement a test of a mediation hypothesis}, series = {Intelligence}, volume = {39}, journal = {Intelligence}, number = {5}, publisher = {Elsevier}, address = {New York}, issn = {0160-2896}, doi = {10.1016/j.intell.2011.06.006}, pages = {357 -- 369}, year = {2011}, abstract = {This study analyzes the interplay of four cognitive abilities - reasoning, divergent thinking, mental speed, and short-term memory - and their impact on academic achievement in school in a sample of adolescents in grades seven to 10 (N = 1135). Based on information processing approaches to intelligence, we tested a mediation hypothesis, which states that the complex cognitive abilities of reasoning and divergent thinking mediate the influence of the basic cognitive abilities of mental speed and short-term memory on achievement. We administered a comprehensive test battery and analyzed the data through structural equation modeling while controlling for the cluster structure of the data. Our findings support the notion that mental speed and short-term memory, as ability factors reflecting basic cognitive processes, exert an indirect influence on academic achievement by affecting reasoning and divergent thinking (total indirect effects: beta=.22 and .24. respectively). Short-term memory also directly affects achievement (beta=.22).}, language = {en} } @article{MaierHartungdeWiljesetal.2020, author = {Maier, Corinna and Hartung, Niklas and de Wiljes, Jana and Kloft, Charlotte and Huisinga, Wilhelm}, title = {Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy}, series = {CPT: Pharmacometrics \& Systems Pharmacology}, volume = {XX}, journal = {CPT: Pharmacometrics \& Systems Pharmacology}, publisher = {Nature Publ. Group}, address = {London}, issn = {2163-8306}, doi = {10.1002/psp4.12492}, pages = {12}, year = {2020}, abstract = {An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model-based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP-based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP-based approaches and show how probabilistic statements about key markers related to chemotherapy-induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas.}, language = {en} }