Schema Graph-Guided Prompt for Multi-Domain Dialogue State Tracking

Tracking dialogue states is an essential topic in task-oriented dialogue systems, which involve filling in the necessary information in pre-defined slots corresponding to a schema. While general pre-trained language models have been shown effective in slot-filling, their performance is limited when applied to specific domains. We propose a graph-based framework that learns domain-specific prompts by incorporating the dialogue schema. Specifically, we embed domain-specific schema encoded by a graph neural network into the pre-trained language model, which allows for relations in the schema to guide the model for better adaptation to the specific domain. Our experiments demonstrate that the proposed graph-based method outperforms other multi-domain DST approaches while using similar or fewer trainable parameters. We also conduct a comprehensive study of schema graph architectures, parameter usage, and module ablation that demonstrate the effectiveness of our model on multi-domain dialogue state tracking

Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State Tracking

As an essential component in task-oriented dialogue systems, dialogue state tracking (DST) aims to track human-machine interactions and generate state representations for managing the dialogue. Representations of dialogue states are dependent on the domain ontology and the user's goals. In several task-oriented dialogues with a limited scope of objectives, dialogue states can be represented as a set of slot-value pairs. As the capabilities of dialogue systems expand to support increasing naturalness in communication, incorporating dialogue act processing into dialogue model design becomes essential. The lack of such consideration limits the scalability of dialogue state tracking models for dialogues having specific objectives and ontology. To address this issue, we formulate and incorporate dialogue acts, and leverage recent advances in machine reading comprehension to predict both categorical and non-categorical types of slots for multi-domain dialogue state tracking. Experimental results show that our models can improve the overall accuracy of dialogue state tracking on the MultiWOZ 2.1 dataset, and demonstrate that incorporating dialogue acts can guide dialogue state design for future task-oriented dialogue systems.Comment: Published in Spoken Dialogue Systems I, Interspeech 2021. Code is now publicly available on Github: https://github.com/youlandasu/ACT-AWARE-DS

Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference

Large language models (LLMs) based on transformers have made significant strides in recent years, the success of which is driven by scaling up their model size. Despite their high algorithmic performance, the computational and memory requirements of LLMs present unprecedented challenges. To tackle the high compute requirements of LLMs, the Mixture-of-Experts (MoE) architecture was introduced which is able to scale its model size without proportionally scaling up its computational requirements. Unfortunately, MoE's high memory demands and dynamic activation of sparse experts restrict its applicability to real-world problems. Previous solutions that offload MoE's memory-hungry expert parameters to CPU memory fall short because the latency to migrate activated experts from CPU to GPU incurs high performance overhead. Our proposed Pre-gated MoE system effectively tackles the compute and memory challenges of conventional MoE architectures using our algorithm-system co-design. Pre-gated MoE employs our novel pre-gating function which alleviates the dynamic nature of sparse expert activation, allowing our proposed system to address the large memory footprint of MoEs while also achieving high performance. We demonstrate that Pre-gated MoE is able to improve performance, reduce GPU memory consumption, while also maintaining the same level of model quality. These features allow our Pre-gated MoE system to cost-effectively deploy large-scale LLMs using just a single GPU with high performance

Application of Head-up Tilt Table Testing in Children

Background/PurposeWe investigated the application of head-up tilt table testing (HUT) and management of neurocardiogenic syncope (NCS) in children, as pediatric studies are limited.MethodsSeventy-nine patients (ages 6-18 years) underwent HUT for evaluation of syncope. Patient triggers and premonitory symptoms allowed the clinical diagnosis of NCS or non-NCS. Results were divided into four hemodynamic types (1, 2A, 2B, and 3) according to patient response to HUT.ResultsNCS occurred in 65 patients and non-NCS in 14 patients. Isoproterenol infusion significantly increased the sensitivity of the test (from 28% to 45%) and was associated with a slight decrease in the specificity (from 93% to 86%). Subjects in the type 1 group accounted for the majority of responses to the test (69%). There were no complications associated with the test. At follow-up (16.6 ± 9.3 months), the overall recurrence rate was 30.8% but NCS was less severe in most patients. The recurrence rate was similar for patients with a positive or negative HUT and for both pharmacologically and non-pharmacologically treated patients.ConclusionHUT can be safely performed with a high specificity in children, with the sensitivity of HUT improved by isoproterenol. Therefore, a positive response to treatment is reassuring to the physician and family. NCS is generally a self-limited condition despite a high recurrence rate

Association of multi-criteria derived air toxics hazard score with lung cancer incidence in a major metropolitan area

BackgroundLung cancer remains a major health problem world-wide. Environmental exposure to lung cancer carcinogens can affect lung cancer incidence. We investigated the association between lung cancer incidence and an air toxics hazard score of environmental carcinogen exposures derived previously under the exposome concept.MethodsLung cancer cases diagnosed in Philadelphia and the surrounding counties between 2008 and 2017 were identified from the Pennsylvania Cancer Registry. Age-adjusted incidence rates at the ZIP code level were calculated based on the residential address at diagnosis. The air toxics hazard score, an aggregate measure for lung cancer carcinogen exposures, was derived using the criteria of toxicity, persistence, and occurrence. Areas with high incidence or hazard score were identified. Spatial autoregressive models were fitted to evaluate the association, with and without adjusting for confounders. Stratified analysis by smoking prevalence was performed to examine potential interactions.ResultsWe observed significantly higher age-adjusted incidence rates in ZIP codes that had higher air toxics hazard score values after controlling for demographic variables, smoking prevalence, and proximity to major highways. Analyzes stratified by smoking prevalence suggested that exposure to environmental lung carcinogens had a larger effect on cancer incidence in locations with higher smoking prevalence.ConclusionThe positive association between the multi-criteria derived air toxics hazard score and lung cancer incidence provides the initial evidence to validate the hazard score as an aggregate measure of carcinogenic exposures in the environment. The hazard score can be used to supplement the existing risk factors in identifying high risk individuals. Communities with higher incidence/hazard score may benefit from greater awareness of lung cancer risk factors and targeted screening programs

Understanding the predictive value of continuous markers for censored survival data using a likelihood ratio approach

Abstract Background The likelihood ratio function (LR), the ratio of conditional probabilities of obtaining a specific marker value among those with the event of interest over those without, provides an easily interpretable way to quantify the update of the risk prediction due to the knowledge of the marker value. The LR has been explored for both binary and continuous markers for binary events (e.g., diseased or not), however the use of the LR in censored data has not been fully explored. Methods We extend the concept of LR to a time-dependent LR (TD-LR) for survival outcomes that are subject to censoring. Estimation for the TD-LR is done using Kaplan-Meier estimation and a univariate Cox proportional hazards (PH) model. A “scale invariant” approach based on marker quantiles is provided to allow comparison of predictive values between markers with different scales. Relationships to time-dependent receiver-operator characteristic (ROC) curves, area under the curve (AUC), and optimal cut-off values are considered. Results The proposed methods were applied to data from a bladder cancer clinical trial to determine whether the neutrophil-to-lymphocyte ratio (NLR) is a valuable biomarker for predicting overall survival following surgery or combined chemotherapy and surgery. The TD-LR method yielded results consistent with the original findings while providing an easily interpretable three-dimensional surface display of how NLR related to the likelihood of event in the trial data. Conclusions The TD-LR provides a more nuanced understanding of the relationship between continuous markers and the likelihood of events in censored survival data. This method also allows more straightforward communication with a clinical audience through graphical presentation.https://deepblue.lib.umich.edu/bitstream/2027.42/149185/1/12874_2019_Article_721.pd

Influence of Socioeconomic Factors, Gender and Indigenous Status on Smoking in Taiwan.

The indigenous Austronesian minority of Taiwan is heavily affected by health disparities which may include suffering from a greater burden of the tobacco epidemic. While a lack of representative data has historically precluded an investigation of the differences in smoking between Taiwanese ethnicities, these data have recently become available through an annual population-based telephone survey conducted by the Health Promotion Administration, Ministry of Health and Welfare (previously known as the Bureau of Health Promotion (BHP), Department of Health). We used the BHP monitoring data to observe the prevalence of smoking and environmental tobacco smoke exposure among indigenous and non-indigenous Taiwanese surrounding a tobacco welfare tax increase in 2006, investigate ethnic differences in smoking prevalence and environmental tobacco smoke exposure each year between 2005 and 2008, and perform multiple logistic regression to estimate measures of association between potential risk factors and smoking status. Despite significant ethnic and gender differences in smoking prevalence, smoking status was not found to be significantly associated with ethnicity after controlling for socioeconomic and demographic factors