First Step Advantage: Importance of Starting Right in Multi-Step Reasoning

Large Language Models (LLMs) can solve complex reasoning tasks by generating rationales for their predictions. Distilling these capabilities into a smaller, compact model can facilitate the creation of specialized, cost-effective models tailored for specific tasks. However, smaller models often face challenges in complex reasoning tasks and often deviate from the correct reasoning path. We show that LLMs can guide smaller models and bring them back to the correct reasoning path only if they intervene at the right time. We show that smaller models fail to reason primarily due to their difficulty in initiating the process, and that guiding them in the right direction can lead to a performance gain of over 100%. We explore different model sizes and evaluate the benefits of providing guidance to improve reasoning in smaller models

Face Cartoonisation For Various Poses Using StyleGAN

This paper presents an innovative approach to achieve face cartoonisation while preserving the original identity and accommodating various poses. Unlike previous methods in this field that relied on conditional-GANs, which posed challenges related to dataset requirements and pose training, our approach leverages the expressive latent space of StyleGAN. We achieve this by introducing an encoder that captures both pose and identity information from images and generates a corresponding embedding within the StyleGAN latent space. By subsequently passing this embedding through a pre-trained generator, we obtain the desired cartoonised output. While many other approaches based on StyleGAN necessitate a dedicated and fine-tuned StyleGAN model, our method stands out by utilizing an already-trained StyleGAN designed to produce realistic facial images. We show by extensive experimentation how our encoder adapts the StyleGAN output to better preserve identity when the objective is cartoonisation

Flavour Enhanced Food Recommendation

We propose a mechanism to use the features of flavour to enhance the quality of food recommendations. An empirical method to determine the flavour of food is incorporated into a recommendation engine based on major gustatory nerves. Such a system has advantages of suggesting food items that the user is more likely to enjoy based upon matching with their flavour profile through use of the taste biological domain knowledge. This preliminary intends to spark more robust mechanisms by which flavour of food is taken into consideration as a major feature set into food recommendation systems. Our long term vision is to integrate this with health factors to recommend healthy and tasty food to users to enhance quality of life.Comment: In Proceedings of 5th International Workshop on Multimedia Assisted Dietary Management, Nice, France, October 21, 2019, MADiMa 2019, 6 page

Effect of pretreatment and temperature on drying characteristics and quality of green banana peel

In banana cultivation, a considerable amount of the production is wasted every year because of various constraints present in the post-harvest management chain. Converting green banana pulp and peels into flour could help to reduce losses and enable the food sector to keep the product for an entire year or more. In order to use green banana fruit and peel flour in the food industry as a raw ingredient such as in bakery and confectionery items—namely biscuits, cookies, noodles, nutritious powder, etc.—it is essential to standardize the process for the production of the flour. As a result, the purpose of this study was to investigate the influence of pretreatment and temperature on the drying capabilities and quality of dried green banana peel. The green banana peel pieces were pretreated with 0.5 and 1.0% KMS (potassium metabisulfite), and untreated samples were taken as control, and dried at 40°, 50°, and 60 °C in a tray dryer. To reduce the initial moisture content of 90–91.58% (wb) to 6.25–9.73% (wb), a drying time of 510–360 min was required in all treatments. The moisture diffusivity (Deff) increased with temperature, i.e., Deff increased from 5.069–6.659 × 10−8, 6.013–7.653 × 10−8, and 4.969–6.510 × 10−8 m2/s for the control sample, 0.5% KMS, and 1.0% KMS, respectively. The Page model was determined to be the best suited for the drying data with the greatest R2 and the least χ2 and RSME values in comparison with the other two models. When 0.5% KMS-pretreated materials were dried at 60 °C, the water activity and drying time were minimal. Hue angle, chroma, and rehydration ratio were satisfactory and within the acceptable limits for 0.5% KMS-pretreated dried banana peel at 60 °C

Assessment of oral health among seafarers in Mundra Port, Kutch, Gujarat: a cross-sectional study

Background: Seafarer is a person who navigates waterborne vessels or assists as a crewmember in their operation and maintenance in all tough weather, but little research has been done to identify conditions that may lead to assess seafarer general health as well as oral health. Aim: To assess oral diseases including dental caries and periodontal conditions among seafarer’s population arrived in Mundra Port, Kutch, Gujarat, India. Materials and methods: A descriptive cross-sectional survey was conducted to assess oral health condition of seafarer community of Mundra Taluka of Kutch District, Gujarat, India, from July 2014 to September 2014. Results: Total of 385 subjects participated in the survey. Adverse habits show the overall 72.3% prevalence among the study population. Occurrence rate of caries, periodontal disease and prosthetic status were 88%, 75.1% and 6.5%, respectively. The best predictors for Decayed Missing Filled Teeth (DMFT), Community Periodontal Index (CPI) and prosthetic status were oral hygiene practices, adverse habit and educational status. Conclusions: Findings of the present study suggest that oral health condition of seafarer community was relatively poor, with high caries prevalence and poor periodontal health. This epidemiological survey has provided baseline information to underpin the implementation of oral health programmes

Fleet-Level Environmental Assessments for Feasibility of Aviation Emission Reduction Goals

13-C-AJFE-PU-013This is an open access paper under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Please cite this article as: Ogunsina, K.E., Chao, H., Kolencherry, N.J., Jain, S., Moolchandani, K.A., DeLaurentis, D., & Crossley, W.A. (2022). Fleet-Level Environmental Assessments for Feasibility of Aviation Emission Reduction Goals. ArXiv, https://doi.org/10.48550/arXiv.2210.11302The International Air Transport Association (IATA) is one of several organizations that have presented goals for future CO2 emissions from commercial aviation with the intent of alleviating the associated environmental impacts. These goals include attaining carbon-neutral growth in the year 2020 and total aviation CO2 emissions in 2050 equal to 50% of 2005 aviation CO2 emissions. This paper presents the use of a simulation-based approach to predict future CO2 emissions from commercial aviation based upon a set of scenarios developed as part of the Aircraft Technology Modeling and Assessment project within ASCENT, the FAA Center of Excellence for Alternative Jet Fuels and the Environment. Results indicate that, in future scenarios with increasing demand for air travel, it is difficult to reduce CO2 emissions in 2050 to levels equal to or below 2005 levels, although neutral CO2 growth after 2020 may be possible. Presented at the Council of Engineering Systems Universities (CESUN) conference in 201

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study

Background Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)