Impact of Movements on Facial Expression Recognition

The ability to recognize human emotions can be a useful skill for robots. Emotion recognition can help robots understand our responses to robot movements and actions. Human emotions can be recognized through facial expressions. Facial Expression Recognition (FER) is a well-established research area, how- ever, the majority of prior research is based on static datasets of images. With robots often the subject is moving, the robot is moving, or both. The purpose of this research is to determine the impact of movement on facial expression recognition. We apply a pre-existing model for FER, which performs around 70.86% on a given collection of images. We experiment with three different conditions: No motion by subject or robot, motion by one of the human or robot, and finally both human and robot in motion. We then measure the impact on FER accuracy introduced by these movements. This research relates to Computer Vision, Machine Learning, and Human-Robot Interaction

Ultrafine particles, particle components and lung function at age 16 years:The PIAMA birth cohort study

Background: Particulate matter (PM) air pollution exposure has been linked to lung function in adolescents, but little is known about the relevance of specific PM components and ultrafine particles (UFP). Objectives: To investigate the associations of long-term exposure to PM elemental composition and UFP with lung function at age 16 years. Methods: For 706 participants of a prospective Dutch birth cohort, we assessed associations of forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) at age 16 with average exposure to eight elemental components (copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc) in PM2.5 and PM10, as well as UFP during the preceding years (age 13–16 years) estimated by land-use regression models. After assessing associations for each pollutant individually using linear regression models with adjustment for potential confounders, independence of associations with different pollutants was assessed in two-pollutant models with PM mass and NO2, for which associations with lung function have been reported previously. Results: We observed that for most PM elemental components higher exposure was associated with lower FEV1, especially PM10 sulfur [e.g. adjusted difference −2.23% (95% confidence interval (CI) −3.70 to −0.74%) per interquartile range (IQR) increase in PM10 sulfur]. The association with PM10 sulfur remained after adjusting for PM10 mass. Negative associations of exposure to UFP with both FEV1 and FVC were observed [-1.06% (95% CI: −2.08 to −0.03%) and −0.65% (95% CI: −1.53 to 0.23%), respectively per IQR increase in UFP], but did not persist in two-pollutant models with NO2 or PM2.5. Conclusions: Long-term exposure to sulfur in PM10 may result in lower FEV1 at age 16. There is no evidence for an independent effect of UFP exposure

IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions

Retrieval-Augmented Generation (RAG), by incorporating external knowledge with parametric memory of language models, has become the state-of-the-art architecture for open-domain QA tasks. However, common knowledge bases are inherently constrained by limited coverage and noisy information, making retrieval-based approaches inadequate to answer implicit reasoning questions. In this paper, we propose an Induction-Augmented Generation (IAG) framework that utilizes inductive knowledge along with the retrieved documents for implicit reasoning. We leverage large language models (LLMs) for deriving such knowledge via a novel prompting method based on inductive reasoning patterns. On top of this, we implement two versions of IAG named IAG-GPT and IAG-Student, respectively. IAG-GPT directly utilizes the knowledge generated by GPT-3 for answer prediction, while IAG-Student gets rid of dependencies on GPT service at inference time by incorporating a student inductor model. The inductor is firstly trained via knowledge distillation and further optimized by back-propagating the generator feedback via differentiable beam scores. Experimental results show that IAG outperforms RAG baselines as well as ChatGPT on two Open-Domain QA tasks. Notably, our best models have won the first place in the official leaderboards of CSQA2.0 (since Nov 1, 2022) and StrategyQA (since Jan 8, 2023)

Diverse investor reactions to the COVID-19 Pandemic : insights from an emerging market

We examine the reaction of different investor categories to the COVID-19 pandemic in the Indian market throughout 2020. Using quarterly ownership data, we find significant differences across various investor categories during the crisis and post-crisis periods. We find that domestic institutional investors (DIIs) exhibit 'flight-to-quality' behavior, foreign institutional investors (FIIs) exhibit 'fire-sale' behavior, and retail investors (RIs) act as informed investors who provide liquidity during the crisis period. We observe conservative behavior from DIIs and FIIs throughout 2020, during which RIs initially increase their holdings in high-risk stocks but move to high-quality stocks in the final quarter of 2020. FIIs contribute the most to lower stock returns and higher volatility during the crisis period. Using daily FII trade-level data, we find that long-term FIIs start buying high-quality stocks before other categories in the post-crisis period, with short-term FIIs driving returns and volatility during the crisis period

Ambient ultrafine particles and asthma onset until age 20: The PIAMA birth cohort

Rationale: Evidence regarding the role of long-term exposure to ultrafine particles (<0.1 μm, UFP) in asthma onset is scarce. Objectives: We examined the association between exposure to UFP and asthma development in the Dutch PIAMA (Prevention and Incidence of Asthma and Mite Allergy) birth cohort and assessed whether there is an association with UFP, independent of other air pollutants. Methods: Data from birth up to age 20 years from 3687 participants were included. Annual average exposure to UFP at the residential addresses was estimated with a land-use regression model. Overall and age-specific associations of exposure at the birth address and current address at the time of follow-up with asthma incidence were assessed using discrete-time hazard models adjusting for potential confounders. We investigated both single- and two-pollutant models accounting for co-exposure to other air pollutants (PM2.5 and PM10 mass concentrations, nitrogen dioxide, and PM2.5 absorbance). Measurements and main results: A total of 812 incident asthma cases were identified. Overall, we found that higher UFP exposure was associated with higher asthma incidence (adjusted odds ratio (95% confidence interval) 1.08 (1.02,1.14) and 1.06 (1.00, 1.12) per interquartile range increase in exposure at the birth address and current address at the time of follow-up, respectively). Age-specific associations were not consistent. The association was no longer significant after adjustment for other traffic-related pollutants (nitrogen dioxide and PM2.5 absorbance). Conclusions: Our findings support the importance of traffic-related air pollutants for asthma development through childhood and adolescence, but provide little support for an independent effect of UFP

Air pollution: metabolites and respiratory health across the life-course

Previous studies have explored the relationships of air pollution and metabolic profiles with lung function. However, the metabolites linking air pollution and lung function and the associated mechanisms have not been reviewed from a life-course perspective. Here, we provide a narrative review summarising recent evidence on the associations of metabolic profiles with air pollution exposure and lung function in children and adults. Twenty-six studies identified through a systematic PubMed search were included with 10 studies analysing air pollution-related metabolic profiles and 16 studies analysing lung function-related metabolic profiles. A wide range of metabolites were associated with short- and long-term exposure, partly overlapping with those linked to lung function in the general population and with respiratory diseases such as asthma and COPD. The existing studies show that metabolomics offers the potential to identify biomarkers linked to both environmental exposures and respiratory outcomes, but many studies suffer from small sample sizes, cross-sectional designs, a preponderance on adult lung function, heterogeneity in exposure assessment, lack of confounding control and omics integration. The ongoing EXposome Powered tools for healthy living in urbAN Settings (EXPANSE) project aims to address some of these shortcomings by combining biospecimens from large European cohorts and harmonised air pollution exposure and exposome data