Calibration-Free Driver Drowsiness Classification based on Manifold-Level Augmentation

Drowsiness reduces concentration and increases response time, which causes fatal road accidents. Monitoring drivers' drowsiness levels by electroencephalogram (EEG) and taking action may prevent road accidents. EEG signals effectively monitor the driver's mental state as they can monitor brain dynamics. However, calibration is required in advance because EEG signals vary between and within subjects. Because of the inconvenience, calibration has reduced the accessibility of the brain-computer interface (BCI). Developing a generalized classification model is similar to domain generalization, which overcomes the domain shift problem. Especially data augmentation is frequently used. This paper proposes a calibration-free framework for driver drowsiness state classification using manifold-level augmentation. This framework increases the diversity of source domains by utilizing features. We experimented with various augmentation methods to improve the generalization performance. Based on the results of the experiments, we found that deeper models with smaller kernel sizes improved generalizability. In addition, applying an augmentation at the manifold-level resulted in an outstanding improvement. The framework demonstrated the capability for calibration-free BCI.Comment: Submitted to 2023 11th IEEE International Winter Conference on Brain-Computer Interfac

Creating Value with Acquisition Based Dynamic Capabilities (ABDC): A Study of Mergers and Acquisitions in the Regulated Energy Industry

M&A research has consistently shown that value is destroyed for a majority of acquirers. Despite initial small positive gains at deal announcement, within a year of closing the transaction a majority of acquirers experience overall negative returns. Nevertheless, the constant pressures to grow leave company leaders few other viable options than pursuing M&A. This ever present cycle of value destruction is of interest to both scholars and practitioners. Of interest is what can be done differently by the acquirer to prevent the inevitable value erosion from occurring. To investigate this question, the author develops an adapted version of the Acquisition Based Dynamic Capabilities (ABDC) framework, a theoretical extension of Dynamic Capability theory. The framework is helpful in identifying what corporate M&A capabilities contribute to value creation through a transaction lifecycle. The adapted ABDC framework provides a means to quantify the differing impacts to value creation among the M&A capabilities of “Selecting and Identifying”, “Transacting and Executing” and “Reconfiguring and Integrating”. The empirical study utilizes 337 regulated energy, public company transactions, closed between 1995 and 2014. This industry is appropriate to study the application of this theory as it benefits from long dated deal timelines and specific milestone events (deal announcement, regulatory approval, financial closing, etc.) providing clear points of delineation for measurement purposes. Performance is measured using weak and semi-strong specifications of shareholder returns with a “golden set” of measures identified. Additionally, the impacts on the ABDC measures from shock waves, bandwagon effects, management traits, financial factors, deal complexity and other relevant factors are all evaluated to test for their impacts on the analyzed transactions. The results suggest that despite many acquirers receiving some positive value accretion from announcement and short-term post-closing returns, larger one year post-close reductions in value eclipse previous gains for most acquirers. The results validate the importance of the Reconfiguring and Integrating (R&I) phase of an acquisition. Comparisons to Top and Poor Performers provide a clear set of recommendations for future energy industry acquirers

Pioneering EEG Motor Imagery Classification Through Counterfactual Analysis

The application of counterfactual explanation (CE) techniques in the realm of electroencephalography (EEG) classification has been relatively infrequent in contemporary research. In this study, we attempt to introduce and explore a novel non-generative approach to CE, specifically tailored for the analysis of EEG signals. This innovative approach assesses the model's decision-making process by strategically swapping patches derived from time-frequency analyses. By meticulously examining the variations and nuances introduced in the classification outcomes through this method, we aim to derive insights that can enhance interpretability. The empirical results obtained from our experimental investigations serve not only to validate the efficacy of our proposed approach but also to reinforce human confidence in the model's predictive capabilities. Consequently, these findings underscore the significance and potential value of conducting further, more extensive research in this promising direction

Optofluidic ring resonator laser with an edible liquid laser gain medium

We demonstrate a biocompatible optofluidic laser with an edible liquid laser gain medium, made of riboflavin dissolved in water. The proposed laser platform is based on a pulled-glass-capillary optofluidic ring resonator (OFRR) with a high Q-factor, resulting in a lasing threshold comparable to that of conventional organic dye lasers that are mostly harmful, despite the relatively low quantum yield of the riboflavin. The proposed biocompatible laser can be realized by not only a capillary OFRR, but also by an optical-fiber-based OFRR that offers improved mechanical stability, and is promising technology for application to in vivo bio-sensing

Deep Chandra Monitoring Observations of NGC 4649: II. Wide-Field Hubble Space Telescope Imaging of the Globular Clusters

We present g and z photometry and size estimates for globular clusters (GCs) in the massive Virgo elliptical NGC 4649 (M60) using a five-pointing Hubble Space Telescope/Advanced Camera for Surveys mosaic. The metal-poor GCs show a monotonic negative metallicity gradient of (-0.43 +/- 0.10) dex per dex in radius over the full radial range of the data, out to ~ 24 kpc. There is evidence for substantial color substructure among the metal-rich GCs. The metal-poor GCs have typical sizes ~ 0.4 pc larger than the metal-rich GCs out to large galactocentric distances (~> 20 kpc), favoring an intrinsic explanation for the size difference rather than projection effects. There is no clear relation between half-light radius and galactocentric distance beyond ~ 15 kpc, suggesting that the sizes of GCs are not generically set by tidal limitation. Finally, we identify ~ 20 candidate ultra-compact dwarfs that extend down to surprisingly faint absolute magnitudes (M_z ~ -8.5), and may bridge the gap between this class and "extended clusters" in the Local Group. Three of the brighter candidates have published radial velocities and can be confirmed as bona fide ultra-compact dwarfs; follow-up spectroscopy will determine the nature of the remainder of the candidates.Comment: ApJ in press. For redacted long table 1, see: http://www.pa.msu.edu/~strader/4649/table.te

Heart rate variability as a preictal marker for determining the laterality of seizure onset zone in frontal lobe epilepsy

Determining the laterality of the seizure onset zone is challenging in frontal lobe epilepsy (FLE) due to the rapid propagation of epileptic discharges to the contralateral hemisphere. There is hemispheric lateralization of autonomic control, and heart rate is modulated by interactions between the sympathetic and parasympathetic nervous systems. Based on this notion, the laterality of seizure foci in FLE might be determined using heart rate variability (HRV) parameters. We explored preictal markers for differentiating the laterality of seizure foci in FLE using HRV parameters. Twelve patients with FLE (6 right FLE and 6 left FLE) were included in the analyzes. A total of 551 (460 left FLE and 91 right FLE) 1-min epoch electrocardiography data were used for HRV analysis. We found that most HRV parameters differed between the left and right FLE groups. Among the machine learning algorithms applied in this study, the light gradient boosting machine was the most accurate, with an AUC value of 0.983 and a classification accuracy of 0.961. Our findings suggest that HRV parameter-based laterality determination models can be convenient and effective tools in clinical settings. Considering that heart rate can be easily measured in real time with a wearable device, our proposed method can be applied to a closed-loop device as a real-time monitoring tool for determining the side of stimulation