Keck/MOSFIRE Spectroscopy of z=7-8 Galaxies: Lyα\alpha Emission from a Galaxy at z=7.66

We report the results from some of the deepest Keck/Multi-Object Spectrometer For Infra-Red Exploration data yet obtained for candidate $z \gtrsim 7$ galaxies. Our data show one significant line detection with 6.5$\sigma$ significance in our combined 10 hr of integration which is independently detected on more than one night, thus ruling out the possibility that the detection is spurious. The asymmetric line profile and non-detection in the optical bands strongly imply that the detected line is Ly$\alpha$ emission from a galaxy at $z$(Ly$\alpha)=7.6637 \pm 0.0011$, making it the fourth spectroscopically confirmed galaxy via Ly$\alpha$ at $z>7.5$. This galaxy is bright in the rest-frame ultraviolet (UV; $M_{\rm UV} \sim -21.2$) with a moderately blue UV slope ($\beta=-2.2^{+0.3}_{-0.2}$), and exhibits a rest-frame Ly$\alpha$ equivalent width of EW(Ly$\alpha$) $\sim 15.6^{+5.9}_{-3.6}$ \AA. The non-detection of the 11 other $z \sim$ 7-8 galaxies in our long 10 hr integration, reaching a median 5$\sigma$ sensitivity of 28 \AA\ in the rest-frame EW(Ly$\alpha$), implies a 1.3$\sigma$ deviation from the null hypothesis of a non-evolving distribution in the rest-frame EW(Ly$\alpha$) between $3<z<6$ and $z=$ 7-8. Our results are consistent with previous studies finding a decline in Ly$\alpha$ emission at $z>6.5$, which may signal the evolving neutral fraction in the intergalactic medium at the end of the reionization epoch, although our weak evidence suggests the need for a larger statistical sample to allow for a more robust conclusion.Comment: 10 pages, 4 figures, ApJ, in pres

Efficient and Direct Inference of Heart Rate Variability using Both Signal Processing and Machine Learning

Heart Rate Variability (HRV) measures the variation of the time between consecutive heartbeats and is a major indicator of physical and mental health. Recent research has demonstrated that photoplethysmography (PPG) sensors can be used to infer HRV. However, many prior studies had high errors because they only employed signal processing or machine learning (ML), or because they indirectly inferred HRV, or because there lacks large training datasets. Many prior studies may also require large ML models. The low accuracy and large model sizes limit their applications to small embedded devices and potential future use in healthcare. To address the above issues, we first collected a large dataset of PPG signals and HRV ground truth. With this dataset, we developed HRV models that combine signal processing and ML to directly infer HRV. Evaluation results show that our method had errors between 3.5% to 25.7% and outperformed signal-processing-only and ML-only methods. We also explored different ML models, which showed that Decision Trees and Multi-level Perceptrons have 13.0% and 9.1% errors on average with models at most hundreds of KB and inference time less than 1ms. Hence, they are more suitable for small embedded devices and potentially enable the future use of PPG-based HRV monitoring in healthcare

Effects of informative and confirmatory feedback on brain activation during negative feedback processing

The current study compared the effects of informative and confirmatory feedback on brain activation during negative feedback processing. For confirmatory feedback trials, participants were informed that they had failed the task, whereas informative feedback trials presented task relevant information along with the notification of their failure. Fourteen male undergraduates performed a series of spatial-perceptual tasks and received feedback while their brain activity was recorded. During confirmatory feedback trials, greater activations in the amygdala, dorsal anterior cingulate cortex, and the thalamus (including the habenular) were observed in response to incorrect responses. These results suggest that confirmatory feedback induces negative emotional reactions to failure. In contrast, informative feedback trials elicited greater activity in the dorsolateral prefrontal cortex (DLPFC) when participants experienced failure. Further psychophysiological interaction (PPI) analysis revealed a negative coupling between the DLPFC and the amygdala during informative feedback relative to confirmatory feedback trials. These findings suggest that providing task-relevant information could facilitate implicit down-regulation of negative emotions following failure

A Bi-Level Weibull Model with Applications to Two Ordered Events

In this paper, we propose and study a new bivariate Weibull model, called Bi-levelWeibullModel, which arises when one failure occurs after the other. Under some specific regularity conditions, the reliability function of the second event can be above the reliability function of the first event, and is always above the reliability function of the transformed first event, which is a univariate Weibull random variable. This model is motivated by a common physical feature that arises fromseveral real applications. The two marginal distributions are a Weibull distribution and a generalized three-parameter Weibull mixture distribution. Some useful properties of the model are derived, and we also present the maximum likelihood estimation method. A real example is provided to illustrate the application of the model