A Benchmark for Understanding and Generating Dialogue between Characters in Stories

Many classical fairy tales, fiction, and screenplays leverage dialogue to advance story plots and establish characters. We present the first study to explore whether machines can understand and generate dialogue in stories, which requires capturing traits of different characters and the relationships between them. To this end, we propose two new tasks including Masked Dialogue Generation and Dialogue Speaker Recognition, i.e., generating missing dialogue turns and predicting speakers for specified dialogue turns, respectively. We build a new dataset DialStory, which consists of 105k Chinese stories with a large amount of dialogue weaved into the plots to support the evaluation. We show the difficulty of the proposed tasks by testing existing models with automatic and manual evaluation on DialStory. Furthermore, we propose to learn explicit character representations to improve performance on these tasks. Extensive experiments and case studies show that our approach can generate more coherent and informative dialogue, and achieve higher speaker recognition accuracy than strong baselines

Vanadium Contamination and Associated Health Risk of Farmland Soil near Smelters throughout China

Whereas there is broad consensus that smelting causes serious soil contamination during vanadium production, little is known about the vanadium content of soil near smelters and the associated health risk at continental scale. This study is the first to map the distribution of vanadium in farmland soil surrounding smelters throughout mainland China, and assess the associated health risk. Analysis of 76 samples indicated that the average vanadium content in such soil was 115.5 mg/kg - far higher than the 82 mg/kg background content in China (p < 0.05). Southwest China (198.0 mg/kg) and North China (158.3 mg/kg) possessed highest vanadium contents. Vanadium content was strongly related to longitude, altitude, and atmospheric temperature. The reducible fraction accounted for the largest percentages in vanadium speciation. The average Pollution Load Index for all samples was 1.51, denoting significant metal enrichment. The Children's hazard index was higher than unity, indicating elevated health risk. The relative contribution of vanadium to the total health risk ranged from 6.02% to 34.5%, while nickel and chromium were the two main contributors in most regions. This work may serve as a model providing an overview of continental vanadium contamination around smelters, and draw attention to their possible health risks

Unveiling Excitonic Dynamics in High-Efficiency Nonfullerene Organic Solar Cells to Direct Morphological Optimization for Suppressing Charge Recombination

Nonfullerene acceptors (NFAs)-based organic solar cells (OSCs) have recently drawn considerable research interests; however, their excitonic dynamics seems quite different than that of fullerene acceptors-based devices and remains to be largely explored. A random terpolymer of PBBF11 to pair with a paradigm NFA of 3,9-bis(2-methylene-(3-(1,1-dicyanomethylene)-indanone)-5,5,11,11-tetrakis(4-hexylphenyl)-dithieno[2,3-d:2′,3′-d′]-s-indaceno[1,2-b:5,6-b′]dithiophene (ITIC) such that both complementary optical absorption and very small offsets of both highest occupied molecular orbital and lowest unoccupied molecular orbital energy levels are acquired is designed and synthesized. Despite the small energy offsets, efficient electron/hole transfer between PBBF11 and ITIC is both clearly observed from steady-state photoluminescence and transient absorption spectra and also supported by the measured low exciton binding energy in ITIC. Consequently, the PBBF11:ITIC-based OSCs afford an encouraging power conversion efficiency (PCE) of 10.02%. Although the good miscibility of PBBF11 and ITIC induces a homogenous blend film morphology, it causes severe charge recombination. The fullerene acceptor of PC 71 BM with varying loading ratios is therefore added to modulate film morphology to effectively reduce the charge recombination. As a result, the optimal OSCs based on PBBF11:ITIC:PC 71 BM yield a better PCE of 11.4% without any additive or annealing treatment. </p

A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models

Synthetic health data have the potential to mitigate privacy concerns when sharing data to support biomedical research and the development of innovative healthcare applications. Modern approaches for data generation based on machine learning, generative adversarial networks (GAN) methods in particular, continue to evolve and demonstrate remarkable potential. Yet there is a lack of a systematic assessment framework to benchmark methods as they emerge and determine which methods are most appropriate for which use cases. In this work, we introduce a generalizable benchmarking framework to appraise key characteristics of synthetic health data with respect to utility and privacy metrics. We apply the framework to evaluate synthetic data generation methods for electronic health records (EHRs) data from two large academic medical centers with respect to several use cases. The results illustrate that there is a utility-privacy tradeoff for sharing synthetic EHR data. The results further indicate that no method is unequivocally the best on all criteria in each use case, which makes it evident why synthetic data generation methods need to be assessed in context

Talk2Care: Facilitating Asynchronous Patient-Provider Communication with Large-Language-Model

Despite the plethora of telehealth applications to assist home-based older adults and healthcare providers, basic messaging and phone calls are still the most common communication methods, which suffer from limited availability, information loss, and process inefficiencies. One promising solution to facilitate patient-provider communication is to leverage large language models (LLMs) with their powerful natural conversation and summarization capability. However, there is a limited understanding of LLMs' role during the communication. We first conducted two interview studies with both older adults (N=10) and healthcare providers (N=9) to understand their needs and opportunities for LLMs in patient-provider asynchronous communication. Based on the insights, we built an LLM-powered communication system, Talk2Care, and designed interactive components for both groups: (1) For older adults, we leveraged the convenience and accessibility of voice assistants (VAs) and built an LLM-powered VA interface for effective information collection. (2) For health providers, we built an LLM-based dashboard to summarize and present important health information based on older adults' conversations with the VA. We further conducted two user studies with older adults and providers to evaluate the usability of the system. The results showed that Talk2Care could facilitate the communication process, enrich the health information collected from older adults, and considerably save providers' efforts and time. We envision our work as an initial exploration of LLMs' capability in the intersection of healthcare and interpersonal communication.Comment: Under submission to CHI202

The Effect of the Antimicrobial Peptide Plectasin on the Growth Performance, Intestinal Health, and Immune Function of Yellow-Feathered Chickens

The goal of the study was to test the effects of an antibiotic substitute, plectasin, on the growth performance, immune function, intestinal morphology and structure, intestinal microflora, ileal mucosal layer construction and tight junctions, ileal immune-related cytokines, and blood biochemical indices of yellow-feathered chickens. A total of 1,500 one-day-old yellow-feathered chicks were randomly divided into four dietary treatment groups with five replicates in each group and 75 yellow-feathered chicks in each replication, as follows: basal diet (group A); basal diet supplemented with 10 mg enramycin/kg of diet (group B), basal diet supplemented with 100 mg plectasin/kg of diet (group C), and basal diet supplemented with 200 mg plectasin/kg of diet (group D). It was found that the dietary antimicrobial peptide plectasin could improve the ADG and had better F/G for the overall period of 1–63 days. Dietary plectasin can enhance H9N2 avian influenza virus (AIV) and Newcastle disease virus (NDV) antibody levels of yellow-feathered chickens at 21, and 35 days of age. Dietary plectasin can enhance the intestine structure, inhibit Escherichia coli and proinflammatory cytokines in the ileum, and ameliorate the blood biochemical indices of yellow-feathered chickens at 21 days of age. This study indicates that the antimicrobial peptide plectasin has beneficial effects on the growth performance, intestinal health and immune function of yellow-feathered chickens

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent years, we have witnessed significant progress in developing more advanced AI models on cloud servers that surpass traditional deep learning models owing to model innovations (e.g., Transformers, Pretrained families), explosion of training data and soaring computing capabilities. However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed. In this survey, we conduct a systematic review for both cloud and edge AI. Specifically, we are the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism. We also discuss potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning. Finally, we discuss the promising directions and challenges in this field.Comment: 20 pages, Transactions on Knowledge and Data Engineerin