Abstract
We present detailed ultraviolet, optical, and near-infrared light curves of the Type Ia supernova (SN) 2012fr, which exploded in the Fornax cluster member NGC 1365. These precise ...high-cadence light curves provide a dense coverage of the flux evolution from −12 to +140 days with respect to the epoch of
B
-band maximum (
t
B
max
). Supplementary imaging at the earliest epochs reveals an initial slow and nearly linear rise in luminosity with a duration of ∼2.5 days, followed by a faster rising phase that is well reproduced by an explosion model with a moderate amount of
56
Ni mixing in the ejecta. From our analysis of the light curves, we conclude that: (i) the explosion occurred <22 hr before the first detection of the supernova, (ii) the rise time to peak bolometric (
λ
> 1800 Å) luminosity was 16.5 ± 0.6 days, (iii) the supernova suffered little or no host-galaxy dust reddening, (iv) the peak luminosity in both the optical and near-infrared was consistent with the bright end of normal Type Ia diversity, and (v) 0.60 ± 0.15
M
⊙
of
56
Ni was synthesized in the explosion. Despite its normal luminosity, SN 2012fr displayed unusually prevalent high-velocity Ca
ii
and Si
ii
absorption features, and a nearly constant photospheric velocity of the Si
ii
λ
6355 line at ∼12,000
km
s
−
1
that began ∼5 days before
t
B
max
. We also highlight some of the other peculiarities in the early phase photometry and the spectral evolution. SN 2012fr also adds to a growing number of Type Ia supernovae that are hosted by galaxies with direct Cepheid distance measurements.
Dysphagia is a very important issue in modern society, and it is common in stroke patients and the elderly. Many studies have shown that tongue strength can be used as an evaluation criterion for ...swallowing function. This work has implemented a methodology to assess and improvise the swallowing function of dysphagia patients. To execute the same, a tongue pressure instrument was used as a tool to assess tongue strength, and a surface electromyography instrument was used to collect electrical data on larynx muscles. In addition to this, an assessment task has been carried out that is combined with interesting games to increase users' willingness. After completing the task, the system collects tongue pressure and muscle electrical data. We use the Scoring Function calculation to quantify the user's swallowing performance quality. In addition to calculating the quality score of the motion, we also extract features from the collected data, build a variety of machine learning models to compare each model's classification effectiveness and select the best model to correctly predict the level of the user's swallowing function. Through this evaluation system, we hope to provide fast and accurate evaluation results, so that medical personnel can have more convenient and effective tools for dysphagia diagnosis and rehabilitation training.
Methamphetamine abuse is getting worse amongst the younger population. While there is methadone or buprenorphine harm-reduction treatment for heroin addicts, there is no drug treatment for addicts ...with methamphetamine use disorder (MUD). Recently, non-medication treatment, such as the cue-elicited craving method integrated with biofeedback, has been widely used. Further, virtual reality (VR) is proposed to simulate an immersive virtual environment for cue-elicited craving in therapy. In this study, we developed a VR system equipped with flavor simulation for the purpose of inducing cravings for MUD patients in therapy. The VR system was integrated with multi-model sensors, such as an electrocardiogram (ECG), galvanic skin response (GSR) and eye tracking to measure various physiological responses from MUD patients in the virtual environment. The goal of the study was to validate the effectiveness of the proposed VR system in inducing the craving of MUD patients via the physiological data. Clinical trials were performed with 20 MUD patients and 11 healthy subjects. VR stimulation was applied to each subject and the physiological data was measured at the time of pre-VR stimulation and post-VR stimulation. A variety of features were extracted from the raw data of heart rate variability (HRV), GSR and eye tracking. The results of statistical analysis found that quite a few features of HRV, GSR and eye tracking had significant differences between pre-VR stimulation and post-VR stimulation in MUD patients but not in healthy subjects. Also, the data of post-VR stimulation showed a significant difference between MUD patients and healthy subjects. Correlation analysis was made and several features between HRV and GSR were found to be correlated. Further, several machine learning methods were applied and showed that the classification accuracy between MUD and healthy subjects at post-VR stimulation attained to 89.8%. In conclusion, the proposed VR system was validated to effectively induce the drug craving in MUD patients.
Abstract
We present optical and near-infrared (NIR) (
ugriYJH
) photometry of host galaxies of Type Ia supernovae (SN Ia) observed by the
Carnegie Supernova Project-I
. We determine host galaxy ...stellar masses and, for the first time, study their correlation with SN Ia standardized luminosity across optical and NIR (
uBgVriYJH
) bands. In the individual bands, we find that SNe Ia are more luminous in more massive hosts with luminosity offsets ranging between −0.07 ± 0.03 and −0.15 ± 0.04 mag after light-curve standardization. The slope of the SN Ia Hubble residual-host mass relation is negative across all
uBgVriYJH
bands with values ranging between −0.029 ± 0.029 and −0.093 ± 0.031 mag dex
−1
—implying that SNe Ia in more massive galaxies are brighter than expected. The near-constant observed correlations across optical and NIR bands indicate that dust may not play a significant role in the observed luminosity offset–host mass correlation. We measure projected separations between SNe Ia and their host centers, and find that SNe Ia that explode beyond a projected 10 kpc have a 50%– 60% reduction of the dispersion in Hubble residuals across all bands—making them a more uniform subset of SNe Ia. Dust in host galaxies, peculiar velocities of nearby SN Ia, or a combination of both may drive this result as the color excesses of SNe Ia beyond 10 kpc are found to be generally lower than those interior, but there is also a diminishing trend of the dispersion as we exclude nearby events. We do not find that SN Ia average luminosity varies significantly when they are grouped in various host morphological types. Host galaxy data from this work will be useful, in conjunction with future high-redshift samples, in constraining cosmological parameters.
Invasive pulmonary aspergillosis (IPA) is classically considered an illness of severely immunocompromised patients with limited host defenses. However, IPA has been reported in immunocompetent but ...critically ill patients. This report describes two fatal cases of pathologically confirmed IPA in patients with influenza in the intensive care unit. One patient had influenza B infection, whereas the other had influenza A H1N1. Both patients died despite broad-spectrum antimicrobials, mechanical ventilation, and vasopressor support. Microscopic and histologic postmortem examination confirmed IPA. Review of the English language and foreign literature indicates that galactomannan antigen testing and classic radiologic findings for IPA may not be reliable in immunocompetent patients. Respiratory cultures which grow Aspergillus species in critically ill patients, particularly those with underlying influenza infection, should not necessarily be disregarded as contaminants or colonizers. Further research is needed to better understand the immunological relationship between influenza and IPA for improved prevention and treatment of influenza and Aspergillus co-infections.
•Influenza and invasive aspergillosis can occur in immunocompetent patients.•Aspergillus in patients with influenza should not be disregarded as contaminants.•Influenza may increase risk for invasive aspergillosis due to immunologic effects.
Early diagnosis and treatment can reduce the symptoms of Attention Deficit/Hyperactivity Disorder (ADHD) in children, but medical diagnosis is usually delayed. Hence, it is important to increase the ...efficiency of early diagnosis. Previous studies used behavioral and neuronal data during GO/NOGO task to help detect ADHD and the accuracy differed considerably from 53% to 92%, depending on the employed methods and the number of electroencephalogram (EEG) channels. It remains unclear whether data from a few EEG channels can still lead to a good accuracy of detecting ADHD. Here, we hypothesize that introducing distractions into a VR-based GO/NOGO task can augment the detection of ADHD using 6-channel EEG because children with ADHD are easily distracted. Forty-nine ADHD children and 32 typically developing children were recruited. We use a clinically applicable system with EEG to record data. Statistical analysis and machine learning methods were employed to analyze the data. The behavioral results revealed significant differences in task performance when there are distractions. The presence of distractions leads to EEG changes in both groups, indicating immaturity in inhibitory control. Importantly, the distractions additionally enhanced the between-group differences in NOGO α and γ power, reflecting insufficient inhibition in different neural networks for distraction suppression in the ADHD group. Machine learning methods further confirmed that distractions enhance the detection of ADHD with an accuracy of 85.45%. In conclusion, this system can assist in fast screenings for ADHD and the findings of neuronal correlates of distractions can help design therapeutic strategies.
E-Learning has become more and more popular in recent years with the advance of new technologies. Using their mobile devices, people can expand their knowledge anytime and anywhere. E-Learning also ...makes it possible for people to manage their learning progression freely and follow their own learning style. However, studies show that E-Learning can cause the user to experience feelings of isolation and detachment due to the lack of human-like interactions in most E-Learning platforms. These feelings could reduce the user's motivation to learn. In this paper, we explore and evaluate how well current chatbot technologies assist users' learning on E-Learning platforms and how these technologies could possibly reduce problems such as feelings of isolation and detachment. For evaluation, we specifically designed a chatbot to be an E-Learning assistant. The NLP core of our chatbot is based on two different models: a retrieval-based model and a QANet model. We designed this two-model hybrid chatbot to be used alongside an E-Learning platform. The core response context of our chatbot is not only designed with course materials in mind but also everyday conversation and chitchat, which make it feel more like a human companion. Experiment and questionnaire evaluation results show that chatbots could be helpful in learning and could potentially reduce E-Learning users' feelings of isolation and detachment. Our chatbot also performed better than the teacher counselling service in the E-Learning platform on which the chatbot is based.
Abstract
We present the largest and most homogeneous collection of near-infrared (NIR) spectra of Type Ia supernovae (SNe Ia): 339 spectra of 98 individual SNe obtained as part of the Carnegie ...Supernova Project-II. These spectra, obtained with the FIRE spectrograph on the 6.5 m Magellan Baade telescope, have a spectral range of 0.8–2.5
μ
m. Using this sample, we explore the NIR spectral diversity of SNe Ia and construct a template of spectral time series as a function of the light-curve-shape parameter, color stretch
s
BV
. Principal component analysis is applied to characterize the diversity of the spectral features and reduce data dimensionality to a smaller subspace. Gaussian process regression is then used to model the subspace dependence on phase and light-curve shape and the associated uncertainty. Our template is able to predict spectral variations that are correlated with
s
BV
, such as the hallmark NIR features: Mg
ii
at early times and the
H
-band break after peak. Using this template reduces the systematic uncertainties in
K
-corrections by ∼90% compared to those from the Hsiao template. These uncertainties, defined as the mean
K
-correction differences computed with the color-matched template and observed spectra, are on the level of 4 × 10
−4
mag on average. This template can serve as the baseline spectral energy distribution for light-curve fitters and can identify peculiar spectral features that might point to compelling physics. The results presented here will substantially improve future SN Ia cosmological experiments, for both nearby and distant samples.
Indoor positioning systems based on wireless local area networks are growing rapidly in importance and gaining commercial interest. Pedestrian dead reckoning (PDR) systems, which rely on inertial ...sensors, such as accelerometers, gyroscopes, or even magnetometers to estimate users' movement, have also been widely adopted for real-time indoor pedestrian location tracking. Since both kinds of systems have their own advantages and disadvantages, a maximum likelihood-based fusion algorithm that integrates a typical Wi-Fi indoor positioning system with a PDR system is proposed in this paper. The strength of the PDR system should eliminate the weakness of the Wi-Fi positioning system and vice versa. The intelligent fusion algorithm can retrieve the initial user location and moving direction information without requiring any user intervention. Experimental results show that the proposed positioning system has better positioning accuracy than the PDR system or Wi-Fi positioning system alone.
Abstract We present a method of extrapolating the spectroscopic behavior of Type Ia supernovae (SNe Ia) in the near-infrared (NIR) wavelength regime up to 2.30 μ m using optical spectroscopy. Such a ...process is useful for accurately estimating K-corrections and other photometric quantities of SNe Ia in the NIR. A principal component analysis is performed on data consisting of Carnegie Supernova Project I & II optical and NIR FIRE spectra to produce models capable of making these extrapolations. This method differs from previous spectral template methods by not parameterizing models strictly by photometric light-curve properties of SNe Ia, allowing for more flexibility of the resulting extrapolated NIR flux. A difference of around −3.1% to −2.7% in the total integrated NIR flux between these extrapolations and the observations is seen here for most test cases including Branch core-normal and shallow-silicon subtypes. However, larger deviations from the observation are found for other tests, likely due to the limited high-velocity and broad-line SNe Ia in the training sample. Maximum-light principal components are shown to allow for spectroscopic predictions of the color-stretch light-curve parameter, s BV , within approximately ±0.1 units of the value measured with photometry. We also show these results compare well with NIR templates, although in most cases the templates are marginally more fitting to observations, illustrating a need for more concurrent optical+NIR spectroscopic observations to truly understand the diversity of SNe Ia in the NIR.
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