Text Simplification and Generation Y: An Eye Tracking Study

While important information is often communicated via text, people read only a small fraction of textual content. Ignoring text is particularly prevalent among Generation Y, who prefer image-based communication and exhibit impatient viewing behavior. One way to improve the effectiveness of text-based communication for younger users is to construct textual information in a way that it can be understood with short glances, a hallmark of Generation Y’s impatient viewing behavior. To test this assertion, we used a set of plain language standards (PLS) to simplify a text passage from an actual website. The results of our eye tracking study showed that PLS were successful in improving textual communication for Generation Y users. The simplified text passage was processed with shorter glances, facilitated a more effective visual search behavior, and improved task performance significantly

Raising Course Efficacy to Improve Management Student Learning: Three Field Experiments

“Means efficacy” complements self-efficacy. It refers to one’s belief in the usefulness of external resources or tools that may be useful for performance. Research has confirmed the hypothesis that enhancing means efficacy boosts performance. Course efficacy is students’ belief in the usefulness of a course. Two pilot studies and three field experiments tested the means efficacy-performance hypothesis casting university courses as the means. The manipulation check validated the experimental treatment in only one pilot and there was no evidence that the treatment contributed to performance. Explanations of these results and ideas for future research are suggested

Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management

Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model’s architecture. However, contributions regarding improvement of different aspects in deep learning, such as custom loss function for prognostic and health management are scarce. There is therefore an opportunity to improve upon the effectiveness of deep learning for the system’s prognostics and diagnostics without modifying the models’ architecture. To address this gap, the use of two different dynamically weighted loss functions, a newly proposed weighting mechanism and a focal loss function for prognostics and diagnostics task are investigated. A dynamically weighted loss function is expected to modify the learning process by augmenting the loss function with a weight value corresponding to the learning error of each data instance. The objective is to force deep learning models to focus on those instances where larger learning errors occur in order to improve their performance. The two loss functions used are evaluated using four popular deep learning architectures, namely, deep feedforward neural network, one-dimensional convolutional neural network, bidirectional gated recurrent unit and bidirectional long short-term memory on the commercial modular aero-propulsion system simulation data from NASA and air pressure system failure data for Scania trucks. Experimental results show that dynamically-weighted loss functions helps us achieve significant improvement for remaining useful life prediction and fault detection rate over non-weighted loss function predictions

Altered placental development in undernourished rats: role of maternal glucocorticoids

Maternal undernutrition (MUN) during pregnancy may lead to fetal intrauterine growth restriction (IUGR), which itself predisposes to adult risk of obesity, hypertension, and diabetes. IUGR may stem from insufficient maternal nutrient supply or reduced placental nutrient transfer. In addition, a critical role for maternal stress-induced glucocorticoids (GCs) has been suggested to contribute to both IUGR and the ensuing risk of adult metabolic syndrome. While GC-induced fetal organ defects have been examined, there have been few studies on placental responses to MUN-induced maternal stress. Therefore, we hypothesize that 50% MUN associates with increased maternal GC levels and decreased placental HSD11B. This in turn leads to decreased placental and fetal growth, hence the need to investigate nutrient transporters. We measured maternal serum levels of corticosterone, and the placental basal and labyrinth zone expression of glucocorticoid receptor (NR3C1), 11-hydroxysteroid dehydrogenase B 1 (HSD11B-1) predominantly activates cortisone to cortisol and 11-dehydrocorticosterone (11-DHC) to corticosterone, although can sometimes drive the opposing (inactivating reaction), and HSD11B-2 (only inactivates and converts corticosterone to 11-DHC in rodents) in control and MUN rats at embryonic day 20 (E20). Moreover, we evaluated the expression of nutrient transporters for glucose (SLC2A1, SLC2A3) and amino acids (SLC38A1, 2, and 4). Our results show that MUN dams displayed significantly increased plasma corticosterone levels compared to control dams. Further, a reduction in fetal and placental weights was observed in both the mid-horn and proximal-horn positions. Notably, the placental labyrinth zone, the site of feto-maternal exchange, showed decreased expression of HSD11B1-2 in both horns, and increased HSD11B-1 in proximal-horn placentas, but no change in NR3C1. The reduced placental GCs catabolic capacity was accompanied by downregulation of SLC2A3, SLC38A1, and SLC38A2 expression, and by increased SLC38A4 expression, in labyrinth zones from the mid- and proximal-horns. In marked contrast to the labyrinth zone, the basal zone, which is the site of hormone production, did not show significant changes in any of these enzymes or transporters. These results suggest that dysregulation of the labyrinth zone GC "barrier", and more importantly decreased nutrient supply resulting from downregulation of some of the amino acid system A transporters, may contribute to suboptimal fetal growth under MUN

Generation of counterpropagating and spectrally uncorrelated photon-pair states by spontaneous four-wave mixing in photonic crystal waveguides

In this work, we propose and theoretically analyze a new scheme for generation of counterpropagating photon pairs in photonic crystal waveguides through the process of spontaneous four-wave mixing. Using the fundamental properties of periodic Bloch modes in a standard photonic crystal waveguide, we demonstrate how modal phase-matching can be reached between forward-propagating pump modes and counterpropagating signal and idler modes, for generation of degenerate and non-degenerate photon pairs. We then show how this scheme can be used for generation of photon pairs that are nearly uncorrelated in the spectral degree of freedom. Such a source will be highly interesting as a heralded source of single photons, especially as the spectrally separable signal and idler photons are also spatially separated directly at the source. We conduct our investigation based on a design in silicon, yet our design concept is general and can in principle be applied to any nanostructured material platform

Mode-Multiplexed Transmission over Conventional Graded-Index Multimode Fibers

We present experimental results for combined mode-multiplexed and wavelength multiplexed transmission over conventional graded-index multimode fibers. We use mode-selective photonic lanterns as mode couplers to precisely excite a subset of the modes of the multimode fiber and additionally to compensate for the differential group delay between the excited modes. Spatial mode filters are added to suppress undesired higher order modes. We transmit 30-Gbaud QPSK signals over 60 WDM channels, 3 spatial modes, and 2 polarizations, reaching a distance of 310 km based on a 44.3 km long span. We also report about transmission experiments over 6 spatial modes for a 17-km single-span experiment. The results indicate that multimode fibers support scalable mode-division multiplexing approaches, where modes can be added over time if desired. Also the results indicate that mode-multiplexed transmission distance over 300 km are possible in conventional multimode fibers

Learning to Abstain From Uninformative Data

Learning and decision-making in domains with naturally high noise-to-signal ratio, such as Finance or Healthcare, is often challenging, while the stakes are very high. In this paper, we study the problem of learning and acting under a general noisy generative process. In this problem, the data distribution has a significant proportion of uninformative samples with high noise in the label, while part of the data contains useful information represented by low label noise. This dichotomy is present during both training and inference, which requires the proper handling of uninformative data during both training and testing. We propose a novel approach to learning under these conditions via a loss inspired by the selective learning theory. By minimizing this loss, the model is guaranteed to make a near-optimal decision by distinguishing informative data from uninformative data and making predictions. We build upon the strength of our theoretical guarantees by describing an iterative algorithm, which jointly optimizes both a predictor and a selector, and evaluates its empirical performance in a variety of settings