Location, time, and preference-aware multi-user scheduling

This disclosure describes techniques to determine an optimal point-of-interest for multiple users to visit together. The optimality criteria include various user preferences, schedules, and routines, that are obtained and used with user permission. A point of interest can include a location of entertainment, business or leisure, e.g., a restaurant, a movie, an event, or other outing

The impact of empowerment on job satisfaction: A case study in a hotel

This study explores the impact of empowerment on job satisfaction based on a case study in a hotel. The purpose of this study was to determine the perceptions of employees in the subject hotel concerning empowerment and job satisfaction. Specially, the study addressed the following research questions: (1) To what extent do employees perceive that they are empowered? (2) To what extent do employees perceive they feel satisfied about their jobs? (3) To what extent does empowerment have an impact on job satisfaction? Additional analyses revealed different age, gender, education, ethnic background, length of service in the hotel industry, department and position of employees have an effect on perceptions about empowerment and job satisfaction. Recommendations for future research are discussed

Race Gaps in School Trust: Where They Come from and How to Resolve Them

Trust in American institutions varies widely among adults from different racial-ethnic backgrounds. This brief, from PRC faculty research associate David Yeager and colleagues, demonstrates that unjust treatment causes minority adolescents to lose trust in school, but a respectful "wise feedback" intervention can repair distrust.Population Research Cente

Prediction of Biocrude Yield in Hydrothermal Co-liquefaction of Different Biomass Feedstocks

Please click Additional Files below to see the full abstrac

Disentangled Variational Autoencoder for Emotion Recognition in Conversations

In Emotion Recognition in Conversations (ERC), the emotions of target utterances are closely dependent on their context. Therefore, existing works train the model to generate the response of the target utterance, which aims to recognise emotions leveraging contextual information. However, adjacent response generation ignores long-range dependencies and provides limited affective information in many cases. In addition, most ERC models learn a unified distributed representation for each utterance, which lacks interpretability and robustness. To address these issues, we propose a VAD-disentangled Variational AutoEncoder (VAD-VAE), which first introduces a target utterance reconstruction task based on Variational Autoencoder, then disentangles three affect representations Valence-Arousal-Dominance (VAD) from the latent space. We also enhance the disentangled representations by introducing VAD supervision signals from a sentiment lexicon and minimising the mutual information between VAD distributions. Experiments show that VAD-VAE outperforms the state-of-the-art model on two datasets. Further analysis proves the effectiveness of each proposed module and the quality of disentangled VAD representations. The code is available at https://github.com/SteveKGYang/VAD-VAE.Comment: Accepted by IEEE Transactions on Affective Computin

The Cleft Palate And Lip: Embryology, Genetics, Environmental Influences, And Approaches To Surgical Repair

The cleft palate and lip is one of the most common birth defects that may or may not be syndromic. Clefting may manifest unilaterally or bilaterally with varying degrees of severity. In embryo, the upper and lower jaws were formed from the first brachial arches that descend from both sides and fuse. Many genetic loci and cell-signaling pathways have been identified with the fusion event, in which polar neural crest cells undergo the epithelial-to-mesenchymal transition. Genetic mutations, environmental teratogens, and nutrition have been associated with the cleft palate and lip. The extracellular matrix has been extensively studied to understand cell-cell communication and is crucial in tissue engineering. The gold standard today for palatal reconstruction remains to be an autogenous graft from the anterior iliac crest. Autogenous bone grafts have many disadvantages such as donor site morbidity. New approaches in tissue engineering involving stems cells, growth factors, and biomaterial scaffolding have been identified to avoid autogenous bone grafts. Mesenchymal cells may be harvested from dental tissue and adipocytes. Three-dimensional printing and computer-aided design are becoming widely used in oral surgery. More research are underway to overcome the challenges in soft tissue reconstruction of the soft palate

MentalLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models

With the development of web technology, social media texts are becoming a rich source for automatic mental health analysis. As traditional discriminative methods bear the problem of low interpretability, the recent large language models have been explored for interpretable mental health analysis on social media, which aims to provide detailed explanations along with predictions. The results show that ChatGPT can generate approaching-human explanations for its correct classifications. However, LLMs still achieve unsatisfactory classification performance in a zero-shot/few-shot manner. Domain-specific finetuning is an effective solution, but faces 2 challenges: 1) lack of high-quality training data. 2) no open-source LLMs for interpretable mental health analysis were released to lower the finetuning cost. To alleviate these problems, we build the first multi-task and multi-source interpretable mental health instruction (IMHI) dataset on social media, with 105K data samples. The raw social media data are collected from 10 existing sources covering 8 mental health analysis tasks. We use expert-written few-shot prompts and collected labels to prompt ChatGPT and obtain explanations from its responses. To ensure the reliability of the explanations, we perform strict automatic and human evaluations on the correctness, consistency, and quality of generated data. Based on the IMHI dataset and LLaMA2 foundation models, we train MentalLLaMA, the first open-source LLM series for interpretable mental health analysis with instruction-following capability. We also evaluate the performance of MentalLLaMA on the IMHI evaluation benchmark with 10 test sets, where their correctness for making predictions and the quality of explanations are examined. The results show that MentalLLaMA approaches state-of-the-art discriminative methods in correctness and generates high-quality explanations.Comment: Work in progres

A Comparative Study on the Performance of Fiber-Based Biosorbents in the Purification of Biodiesel Derived from Camelina sativa

Biodiesel has received great interest as a promising substitute for petrodiesel. Biodiesel purification which follows the transesterification process is typically carried out using a wet washing process that generates large amounts of wastewater. Consequently, alternative methods are emerging as sustainable options for biodiesel purification. One of such methods is a dry washing process. In this paper, the performance of three dry washing media (commercially available BD-Zorb, sawdust and wood shavings) were evaluated as potentially suitable options for the purification of biodiesel derived from Camelina sativa. The results indicate that for the crude camelina biodiesel with an initial soap content of 9007 ppm, BD-Zorb exhibited the best purification performance. The soap removal capacity of BD-Zorb, sawdust, and wood shavings was 51.1 mL/g, 24.4 mL/g, and 9.4 mL/g respectively. The primary mechanism of soap removal using sawdust and wood shavings media was physical filtration and adsorption. While for adsorbent BD-Zorb, soap removal mechanism included adsorption and ion exchange due to the existence of a small amount of resins. The ion exchange led to a high acid number (1 mg KOH/g) of the purified biodiesel, and failed to meet the ASTM D6751 specifications (<0.5 mg KOH/g)

HETest: A Homomorphic Encryption Testing Framework

In this work, we present a generic open-source software framework that can evaluate the correctness and performance of homomorphic encryption software. Our framework, called HEtest, automates the entire process of a test: generation of data for testing (such as circuits and inputs), execution of a test, comparison of performance to an insecure baseline, statistical analysis of the test results, and production of a LaTeX report. To illustrate the capability of our framework, we present a case study of our analysis of the open-source HElib homomorphic encryption software. We stress though that HEtest is written in a modular fashion, so it can easily be adapted to test any homomorphic encryption software