@article{CunningsPattersonFelser2015, author = {Cunnings, Ian and Patterson, Clare and Felser, Claudia}, title = {Structural constraints on pronoun binding and coreference: evidence from eye movements during reading}, series = {Frontiers in psychology}, volume = {6}, journal = {Frontiers in psychology}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-1078}, doi = {10.3389/fpsyg.2015.00840}, pages = {18}, year = {2015}, abstract = {A number of recent studies have investigated how syntactic and non-syntactic constraints combine to cue memory retrieval during anaphora resolution. In this paper we investigate how syntactic constraints and gender congruence interact to guide memory retrieval during the resolution of subject pronouns. Subject pronouns are always technically ambiguous, and the application of syntactic constraints on their interpretation depends on properties of the antecedent that is to be retrieved. While pronouns can freely corefer with non-quantified referential antecedents, linking a pronoun to a quantified antecedent is only possible in certain syntactic configurations via variable binding. We report the results from a judgment task and three online reading comprehension experiments investigating pronoun resolution with quantified and non-quantified antecedents. Results from both the judgment task and participants' eye movements during reading indicate that comprehenders freely allow pronouns to corefer with non-quantified antecedents, but that retrieval of quantified antecedents is restricted to specific syntactic environments. We interpret our findings as indicating that syntactic constraints constitute highly weighted cues to memory retrieval during anaphora resolution.}, language = {en} } @article{FelserPattersonCunnings2015, author = {Felser, Claudia and Patterson, Clare and Cunnings, Ian}, title = {Structural constraints on pronoun binding and coreference: Evidence from eye movements during reading}, series = {Frontiers in psychology}, volume = {6}, journal = {Frontiers in psychology}, number = {840}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-1078}, doi = {10.3389/fpsyg.2015.00840}, year = {2015}, abstract = {A number of recent studies have investigated how syntactic and non-syntactic constraints combine to cue memory retrieval during anaphora resolution. In this paper we investigate how syntactic constraints and gender congruence interact to guide memory retrieval during the resolution of subject pronouns. Subject pronouns are always technically ambiguous, and the application of syntactic constraints on their interpretation depends on properties of the antecedent that is to be retrieved. While pronouns can freely corefer with non-quantified referential antecedents, linking a pronoun to a quantified antecedent is only possible in certain syntactic configurations via variable binding. We report the results from a judgment task and three online reading comprehension experiments investigating pronoun resolution with quantified and non-quantified antecedents. Results from both the judgment task and participants' eye movements during reading indicate that comprehenders freely allow pronouns to corefer with non-quantified antecedents, but that retrieval of quantified antecedents is restricted to specific syntactic environments. We interpret our findings as indicating that syntactic constraints constitute highly weighted cues to memory retrieval during anaphora resolution.}, language = {en} } @article{PathirajaLeeuwen2022, author = {Pathiraja, Sahani Darschika and Leeuwen, Peter Jan van}, title = {Multiplicative Non-Gaussian model error estimation in data assimilation}, series = {Journal of advances in modeling earth systems : JAMES}, volume = {14}, journal = {Journal of advances in modeling earth systems : JAMES}, number = {4}, publisher = {American Geophysical Union}, address = {Washington}, issn = {1942-2466}, doi = {10.1029/2021MS002564}, pages = {23}, year = {2022}, abstract = {Model uncertainty quantification is an essential component of effective data assimilation. Model errors associated with sub-grid scale processes are often represented through stochastic parameterizations of the unresolved process. Many existing Stochastic Parameterization schemes are only applicable when knowledge of the true sub-grid scale process or full observations of the coarse scale process are available, which is typically not the case in real applications. We present a methodology for estimating the statistics of sub-grid scale processes for the more realistic case that only partial observations of the coarse scale process are available. Model error realizations are estimated over a training period by minimizing their conditional sum of squared deviations given some informative covariates (e.g., state of the system), constrained by available observations and assuming that the observation errors are smaller than the model errors. From these realizations a conditional probability distribution of additive model errors given these covariates is obtained, allowing for complex non-Gaussian error structures. Random draws from this density are then used in actual ensemble data assimilation experiments. We demonstrate the efficacy of the approach through numerical experiments with the multi-scale Lorenz 96 system using both small and large time scale separations between slow (coarse scale) and fast (fine scale) variables. The resulting error estimates and forecasts obtained with this new method are superior to those from two existing methods.}, language = {en} } @article{HiortSchlaffnerSteenetal.2022, author = {Hiort, Pauline and Schlaffner, Christoph N. and Steen, Judith A. and Renard, Bernhard Y. and Steen, Hanno}, title = {multiFLEX-LF: a computational approach to quantify the modification stoichiometries in label-free proteomics data sets}, series = {Journal of proteome research}, volume = {21}, journal = {Journal of proteome research}, number = {4}, publisher = {American Chemical Society}, address = {Washington}, issn = {1535-3893}, doi = {10.1021/acs.jproteome.1c00669}, pages = {899 -- 909}, year = {2022}, abstract = {In liquid-chromatography-tandem-mass-spectrometry-based proteomics, information about the presence and stoichiometry ofprotein modifications is not readily available. To overcome this problem,we developed multiFLEX-LF, a computational tool that builds uponFLEXIQuant, which detects modified peptide precursors and quantifiestheir modification extent by monitoring the differences between observedand expected intensities of the unmodified precursors. multiFLEX-LFrelies on robust linear regression to calculate the modification extent of agiven precursor relative to a within-study reference. multiFLEX-LF cananalyze entire label-free discovery proteomics data sets in a precursor-centric manner without preselecting a protein of interest. To analyzemodification dynamics and coregulated modifications, we hierarchicallyclustered the precursors of all proteins based on their computed relativemodification scores. We applied multiFLEX-LF to a data-independent-acquisition-based data set acquired using the anaphase-promoting complex/cyclosome (APC/C) isolated at various time pointsduring mitosis. The clustering of the precursors allows for identifying varying modification dynamics and ordering the modificationevents. Overall, multiFLEX-LF enables the fast identification of potentially differentially modified peptide precursors and thequantification of their differential modification extent in large data sets using a personal computer. Additionally, multiFLEX-LF candrive the large-scale investigation of the modification dynamics of peptide precursors in time-series and case-control studies.multiFLEX-LF is available athttps://gitlab.com/SteenOmicsLab/multiflex-lf.}, language = {en} } @article{FranckeHeistermannKoehlietal.2022, author = {Francke, Till and Heistermann, Maik and K{\"o}hli, Markus and Budach, Christian and Schr{\"o}n, Martin and Oswald, Sascha}, title = {Assessing the feasibility of a directional cosmic-ray neutron sensing sensor for estimating soil moisture}, series = {Geoscientific Instrumentation, Methods and Data Systems}, volume = {11}, journal = {Geoscientific Instrumentation, Methods and Data Systems}, publisher = {Copernicus Publ.}, address = {G{\"o}ttingen}, issn = {2193-0864}, doi = {10.5194/gi-11-75-2022}, pages = {75 -- 92}, year = {2022}, abstract = {Cosmic-ray neutron sensing (CRNS) is a non-invasive tool for measuring hydrogen pools such as soil moisture, snow or vegetation. The intrinsic integration over a radial hectare-scale footprint is a clear advantage for averaging out small-scale heterogeneity, but on the other hand the data may become hard to interpret in complex terrain with patchy land use. This study presents a directional shielding approach to prevent neutrons from certain angles from being counted while counting neutrons entering the detector from other angles and explores its potential to gain a sharper horizontal view on the surrounding soil moisture distribution. Using the Monte Carlo code URANOS (Ultra Rapid Neutron-Only Simulation), we modelled the effect of additional polyethylene shields on the horizontal field of view and assessed its impact on the epithermal count rate, propagated uncertainties and aggregation time. The results demonstrate that directional CRNS measurements are strongly dominated by isotropic neutron transport, which dilutes the signal of the targeted direction especially from the far field. For typical count rates of customary CRNS stations, directional shielding of half-spaces could not lead to acceptable precision at a daily time resolution. However, the mere statistical distinction of two rates should be feasible.}, language = {en} } @article{ZappaSchlafferBroccaetal.2022, author = {Zappa, Luca and Schlaffer, Stefan and Brocca, Luca and Vreugdenhil, Mariette and Nendel, Claas and Dorigo, Wouter}, title = {How accurately can we retrieve irrigation timing and water amounts from (satellite) soil moisture?}, series = {International journal of applied earth observation and geoinformation}, volume = {113}, journal = {International journal of applied earth observation and geoinformation}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1569-8432}, doi = {10.1016/j.jag.2022.102979}, pages = {12}, year = {2022}, abstract = {While ensuring food security worldwide, irrigation is altering the water cycle and generating numerous environmental side effects. As detailed knowledge about the timing and the amounts of water used for irrigation over large areas is still lacking, remotely sensed soil moisture has proved potential to fill this gap. However, the spatial resolution and revisit time of current satellite products represent a major limitation to accurately estimating irrigation. This work aims to systematically quantify their impact on the retrieved irrigation information, hence assessing the value of satellite soil moisture for estimating irrigation timing and water amounts. In a real-world experiment, we modeled soil moisture using actual irrigation and meteorological data, obtained from farmers and weather stations, respectively. Modeled soil moisture was compared against various remotely sensed products differing in terms of spatio-temporal resolution to test the hypothesis that high-resolution observations can disclose the irrigation signal from individual fields while coarse-scale satellite products cannot. Then, in a synthetic experiment, we systematically investigated the effect of soil moisture spatial and temporal resolution on the accuracy of irrigation estimates. The analysis was further elaborated by considering different irrigation scenarios and by adding realistic amounts of random errors in the soil moisture time series. We show that coarse-scale remotely sensed soil moisture products achieve higher correlations with rainfed simulations, while high-resolution satellite observations agree significantly better with irrigated simulations, suggesting that high-resolution satellite soil moisture can inform on field-scale (similar to 40 ha) irrigation. A thorough analysis of the synthetic dataset showed that satisfactory results, both in terms of detection (F-score > 0.8) and quantification (Pearson's correlation > 0.8), are found for noise-free soil moisture observations either with a temporal sampling up to 3 days or if at least one-third of the pixel covers the irrigated field(s). However, irrigation water amounts are systematically underestimated for temporal samplings of more than one day, and decrease proportionally to the spatial resolution, i.e., coarsening the pixel size leads to larger irrigation underestimations. Although lower spatial and temporal resolutions decrease the detection and quantification accuracies (e.g., R between 0.6 and 1 depending on the irrigation rate and spatio-temporal resolution), random errors in the soil moisture time series have a stronger negative impact (Pearson R always smaller than 0.85). As expected, better performances are found for higher irrigation rates, i.e. when more water is supplied during an irrigation event. Despite the potentially large underestimations, our results suggest that high-resolution satellite soil moisture has the potential to track and quantify irrigation, especially over regions where large volumes of irrigation water are applied to the fields, and given that low errors affect the soil moisture observations.}, language = {en} }