Identification of Conversation Partners from Egocentric Video

Communicating in noisy, multi-talker environments is challenging, especially for people with hearing impairments. Egocentric video data can potentially be used to identify a user's conversation partners, which could be used to inform selective acoustic amplification of relevant speakers. Recent introduction of datasets and tasks in computer vision enable progress towards analyzing social interactions from an egocentric perspective. Building on this, we focus on the task of identifying conversation partners from egocentric video and describe a suitable dataset. Our dataset comprises 69 hours of egocentric video of diverse multi-conversation scenarios where each individual was assigned one or more conversation partners, providing the labels for our computer vision task. This dataset enables the development and assessment of algorithms for identifying conversation partners and evaluating related approaches. Here, we describe the dataset alongside initial baseline results of this ongoing work, aiming to contribute to the exciting advancements in egocentric video analysis for social settings.Comment: First Joint Egocentric Vision (EgoVis) Workshop at CVPR 202

A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding

The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on linear mappings between features of sound streams and EEG responses (forward model), or vice versa (backward model). It has been shown that when the envelope of attended speech and EEG responses are used to derive such mapping functions, the model estimates can be used to discriminate between attended and unattended talkers. However, the predictive/reconstructive performance of the models is dependent on how the model parameters are estimated. There exist a number of model estimation methods that have been published, along with a variety of datasets. It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different estimation methods to classify attended speakers from multi-channel EEG data. The performance of the model estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams. We find that when forward models predict the EEG from the attended audio, regularized models do not improve regression or classification accuracies. When backward models decode the attended speech from the EEG, regularization provides higher regression and classification accuracies

A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding

The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on linear mappings between features of sound streams and EEG responses (forward model), or vice versa (backward model). It has been shown that when the envelope of attended speech and EEG responses are used to derive such mapping functions, the model estimates can be used to discriminate between attended and unattended talkers. However, the predictive/reconstructive performance of the models is dependent on how the model parameters are estimated. There exist a number of model estimation methods that have been published, along with a variety of datasets. It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different estimation methods to classify attended speakers from multi-channel EEG data. The performance of the model estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams. We find that when forward models predict the EEG from the attended audio, regularized models do not improve regression or classification accuracies. When backward models decode the attended speech from the EEG, regularization provides higher regression and classification accuracies

Infant Ustekinumab Clearance, Risk of Infection, and Development After Exposure During Pregnancy

Background:Evidence on ustekinumab safety in pregnancy is gradually expanding, but its clearance in the postnatal period is unknown. The aim of this study was to investigate ustekinumab concentrations in umbilical cord blood and rates of clearance after birth, as well as how these correlate with maternal drug concentrations, risk of infection, and developmental milestones during the first year of life. Methods: Pregnant women with inflammatory bowel disease were prospectively recruited from 19 hospitals in Denmark and the Netherlands between 2018 and 2022. Infant infections leading to hospitalization/antibiotics and developmental milestones were assessed. Serum ustekinumab concentrations were measured at delivery and specific time points. Nonlinear regression analysis was applied to estimate clearance. Results:In 78 live-born infants from 76 pregnancies, we observed a low risk of adverse pregnancy outcomes and normal developmental milestones. At birth, the median infant-mother ustekinumab ratio was 2.18 (95% confidence interval, 1.69–2.81). Mean time to infant clearance was 6.7 months (95% confidence interval, 6.1–7.3 months). One in 4 infants at 6 months had an extremely low median concentration of 0.015 μg/mL (range 0.005–0.12 μg/mL). No variation in median ustekinumab concentration was noted between infants with (2.8 [range 0.4–6.9] μg/mL) and without (3.1 [range 0.7–11.0] μg/mL) infections during the first year of life (P = .41). Conclusions: No adverse signals after intrauterine exposure to ustekinumab were observed with respect to pregnancy outcome, infections, or developmental milestones during the first year of life. Infant ustekinumab concentration was not associated with risk of infections. With the ustekinumab clearance profile, live attenuated vaccination from 6 months of age seems of low risk.</p