Some integration formulae which simplify the evaluation of certain integrals in common use by engineers

Integration formulas to simplify evaluation of certain commonly used integral

Preliminary analysis of the effects of pressure space correlations on the vibrations of Apollo flight structure

Vibration response of Apollo skin structure to convected boundary layer turbulenc

Mazurka De Crystal

https://digitalcommons.library.umaine.edu/mmb-ps/2240/thumbnail.jp

On the Proper Treatment of the N400 and P600 in Language Comprehension

Event-Related Potentials (ERPs)—stimulus-locked, scalp-recorded voltage fluctuations caused by post-synaptic neural activity—have proven invaluable to the study of language comprehension. Of interest in the ERP signal are systematic, reoccurring voltage fluctuations called components, which are taken to reflect the neural activity underlying specific computational operations carried out in given neuroanatomical networks (cf. Näätänen and Picton, 1987). For language processing, the N400 component and the P600 component are of particular salience (see Kutas et al., 2006, for a review). The typical approach to determining whether a target word in a sentence leads to differential modulation of these components, relative to a control word, is to look for effects on mean amplitude in predetermined time-windows on the respective ERP waveforms, e.g., 350–550 ms for the N400 component and 600–900 ms for the P600 component. The common mode of operation in psycholinguistics, then, is to tabulate the presence/absence of N400- and/or P600-effects across studies, and to use this categorical data to inform neurocognitive models that attribute specific functional roles to the N400 and P600 component (see Kuperberg, 2007; Bornkessel-Schlesewsky and Schlesewsky, 2008; Brouwer et al., 2012, for reviews). Here, we assert that this Waveform-based Component Structure (WCS) approach to ERPs leads to inconsistent data patterns, and hence, misinforms neurocognitive models of the electrophysiology of language processing. The reason for this is that the WCS approach ignores the latent component structure underlying ERP waveforms (cf. Luck, 2005), thereby leading to conclusions about component structure that do not factor in spatiotemporal component overlap of the N400 and the P600. This becomes particularly problematic when spatiotemporal component overlap interacts with differential P600 modulations due to task demands (cf. Kolk et al., 2003). While the problem of spatiotemporal component overlap is generally acknowledged, and occasionally invoked to account for within-study inconsistencies in the data, its implications are often overlooked in psycholinguistic theorizing that aims to integrate findings across studies. We believe WCS-centric theorizing to be the single largest reason for the lack of convergence regarding the processes underlying the N400 and the P600, thereby seriously hindering the advancement of neurocognitive theories and models of language processing

Evidence for a Tagging Model of Human Lexical Category Disambiguation.

We investigate the explanatory power of very simple statistical mechanisms within a modular model of the Human Sentence Processing Mechanism. In particular, we borrow the idea of a 'part-of-speech tagger' from the field of Naniral Language Processing, and use this to explain a number of existing experimental results in the area of lexical category disambiguation. Not only can each be explained without the need to posit extra mechanisms or constraints, but the exercise also suggests a novel account for some established data

An InceptionTime-Inspired Convolutional Neural Network to Detect Cardiac Abnormalities in Reduced-Lead ECG Data

Cardiovascular disease is the leading cause of death worldwide. The twelve-lead electrocardiogram (ECG) is a common tool for diagnosing cardiac abnormalities, but its interpretation requires a trained cardiologist. Thus there is growing interest in automated ECG diagnosis, especially using fewer leads. Hence the PhysioNet-CinC Challenge 2021: Will two (leads) do? The University of Bath team (UoB HBC) developed InceptionTime-inspired deep convolutional neural networks, using parallel 1D convolutions of varying length, for twelve-, six-, four-, three-, and two-lead models. The twelve-lead model achieved a Challenge metric score of 0.35 on the test set, placing the University of Bath team 23rd out of 39 entries. Though the twelve-lead model performed best, three-lead performance was lower by only 0.25 %, suggesting potential for reliable reduced-lead diagnoses. Furthermore, the three-lead model performed consistently better than the six-lead, highlighting the importance of selection of type of lead, not just their number

Interaction between Faraday rotation and Cotton-Mouton effects in polarimetry modeling for NSTX

The evolution of electromagnetic wave polarization is modeled for propagation in the major radial direction in the National Spherical Torus Experiment (NSTX) with retroreflection from the center stack of the vacuum vessel. This modeling illustrates that the Cotton-Mouton effect-elliptization due to the magnetic field perpendicular to the propagation direction-is shown to be strongly weighted to the high-field region of the plasma. An interaction between the Faraday rotation and Cotton-Mouton effects is also clearly identified. Elliptization occurs when the wave polarization direction is neither parallel nor perpendicular to the local transverse magnetic field. Since Faraday rotation modifies the polarization direction during propagation, it must also affect the resultant elliptization. The Cotton-Mouton effect also intrinsically results in rotation of the polarization direction, but this effect is less significant in the plasma conditions modeled. The interaction increases at longer wavelength, and complicates interpretation of polarimetry measurements.Comment: Contributed paper published as part of the Proceedings of the 18th Topical Conference on High-Temperature Plasma Diagnostics, Wildwood, New Jersey, May, 201

On the predictability of event boundaries in discourse : An ERP investigation

When reading a text describing an everyday activity, comprehenders build a model of the situation described that includes prior knowledge of the entities, locations, and sequences of actions that typically occur within the event. Previous work has demonstrated that such knowledge guides the processing of incoming information by making event boundaries more or less expected. In the present ERP study, we investigated whether comprehenders’ expectations about event boundaries are influenced by how elaborately common events are described in the context. Participants read short stories in which a common activity (e.g., washing the dishes) was described either in brief or in an elaborate manner. The final sentence contained a target word referring to a more predictable action marking a fine event boundary (e.g., drying) or a less predictable action, marking a coarse event boundary (e.g., jogging). The results revealed a larger N400 effect for coarse event boundaries compared to fine event boundaries, but no interaction with description length. Between 600 and 1000 ms, however, elaborate contexts elicited a larger frontal positivity compared to brief contexts. This effect was largely driven by less predictable targets, marking coarse event boundaries. We interpret the P600 effect as indexing the updating of the situation model at event boundaries, consistent with Event Segmentation Theory (EST). The updating process is more demanding with coarse event boundaries, which presumably require the construction of a new situation model