Solar Jet on 2014 April 16 Modeled by Kelvin--Helmholtz Instability

We study here the arising of Kelvin--Helmholtz Instability (KHI) in one fast jet of 2014 April 16 observed by the Atmospheric Imaging Assembly (AIA) on board Solar Dynamics Observatory (SDO) in different UV and EUV wavelengths. The evolution of jet indicates the blob like structure at its boundary which could be the observational evidence of the KHI. We model the jet as a moving cylindrical magnetic flux tube of radius $a$ embedded in a magnetic field B_i and surrounded by rest magnetized plasma with magnetic field B_e. We explore the propagation of the kink MHD mode along the jet that can become unstable against the KHI if its speed exceeds a critical value. Concerning magnetic fields topology we consider three different configurations, notably of (i) spatially homogeneous magnetic fields (untwisted magnetic flux tube), (ii) internal (label `i') twisted magnetic field and external homogeneous one (label `e') (single-twisted flux tube), and (iii) both internal and external twisted magnetic fields (double-twisted magnetic flux tube). Plasma densities in the two media rho_i and rho_e are assumed to be homogeneous. The density contrast is defined in two ways: first as rho_e/rho_i and second as rho_e/(rho_i + rho_e). Computations show that the KHI can occur at accessible flow velocities in all the cases of untwisted and single-twisted flux tubes. It turns out, however, that in the case of a double-twisted flux tube the KHI can merge at an accessible jet speed only when the density contrast is calculated from the ratio rho_e/(rho_i} + rho_e). Evaluated KHI developing times and kink mode wave phase velocities at wavelength of 4 Mm lie in the ranges of 1--6.2 min and 202--271 km/s, respectively---all being reasonable for the modeled jet.Comment: 35 pages, 11 figure

Alexithymia and apathy in Parkinson's disease: neurocognitive correlates

Non-motor symptoms such as neuropsychiatric and cognitive dysfunction have been found to be common in Parkinson’s disease (PD) but the relation between such symptoms is poorly understood. We focused on alexithymia, an impairment of affective and cognitive emotional processing, as there is evidence for its interaction with cognition in other disorders. Twenty-two non-demented PD patients and 22 matched normal control adults (NC) were administered rating scales assessing neuropsychiatric status, including alexithymia, apathy, and depression, and a series of neuropsychological tests. As expected, PD patients showed more alexithymia than NC, and there was a significant association between alexithymia and disease stage. Alexithymia was associated with performance on non-verbally mediated measures of executive and visuospatial function, but not on verbally mediated tasks. By contrast, there was no correlation between cognition and ratings of either depression or apathy. Our findings demonstrate a distinct association of alexithymia with non-verbal cognition in PD, implicating right hemisphere processes, and differentiate between alexithymia and other neuropsychiatric symptoms in regard to PD cognition.Published versio

Generating Synthetic Data for Neural Keyword-to-Question Models

Search typically relies on keyword queries, but these are often semantically ambiguous. We propose to overcome this by offering users natural language questions, based on their keyword queries, to disambiguate their intent. This keyword-to-question task may be addressed using neural machine translation techniques. Neural translation models, however, require massive amounts of training data (keyword-question pairs), which is unavailable for this task. The main idea of this paper is to generate large amounts of synthetic training data from a small seed set of hand-labeled keyword-question pairs. Since natural language questions are available in large quantities, we develop models to automatically generate the corresponding keyword queries. Further, we introduce various filtering mechanisms to ensure that synthetic training data is of high quality. We demonstrate the feasibility of our approach using both automatic and manual evaluation. This is an extended version of the article published with the same title in the Proceedings of ICTIR'18.Comment: Extended version of ICTIR'18 full paper, 11 page

The impact of sleep quality on cognitive functioning in Parkinson's disease

In healthy individuals and those with insomnia, poor sleep quality is associated with decrements in performance on tests of cognition, especially executive function. Sleep disturbances and cognitive deficits are both prevalent in Parkinson's disease (PD). Sleep problems occur in over 75% of patients, with sleep fragmentation and decreased sleep efficiency being the most common sleep complaints, but their relation to cognition is unknown. We examined the association between sleep quality and cognition in PD. In 35 non-demented individuals with PD and 18 normal control adults (NC), sleep was measured using 24-hr wrist actigraphy over 7 days. Cognitive domains tested included attention and executive function, memory and psychomotor function. In both groups, poor sleep was associated with worse performance on tests of attention/executive function but not memory or psychomotor function. In the PD group, attention/executive function was predicted by sleep efficiency, whereas memory and psychomotor function were not predicted by sleep quality. Psychomotor and memory function were predicted by motor symptom severity. This study is the first to demonstrate that sleep quality in PD is significantly correlated with cognition and that it differentially impacts attention and executive function, thereby furthering our understanding of the link between sleep and cognition.Published versio