Automatic grammar enhancement for virtual assistant

Grammar rule generation for virtual assistant applications is difficult to scale due to the need for manual labeling. This disclosure describes a scalable solution to automatically generate enhanced grammar rules in predefined verticals. The techniques enable incremental improvements to interpretations of voice commands by a virtual assistant application. Queries that have not already been processed for a target vertical are identified and extracted from a corpus of user queries, e.g., a time-limited corpus in a particular language. Queries are analyzed to discover arguments and patterns that are specific to the vertical. Grammar rules are generated based on the arguments and patterns

Primer set 2.0 for highly parallel qPCR array targeting antibiotic resistance genes and mobile genetic elements

The high-throughput antibiotic resistance gene (ARG) qPCR array, initially published in 2012, is increasingly used to quantify resistance and mobile determinants in environmental matrices. Continued utility of the array; however, necessitates improvements such as removing or redesigning questionable primer sets, updating targeted genes and coverage of available sequences. Towards this goal, a new primer design tool (EcoFunPrimer) was used to aid in identification of conserved regions of diverse genes. The total number of assays used for diverse genes was reduced from 91 old primer sets to 52 new primer sets, with only a 10% loss in sequence coverage. While the old and new array both contain 384 primer sets, a reduction in old primer sets permitted 147 additional ARGs and mobile genetic elements to be targeted. Results of validating the updated array with a mock community of strains resulted in over 98% of tested instances incurring true positive/negative calls. Common queries related to sensitivity, quantification and conventional data analysis (e.g. Ct cutoff value, and estimated genomic copies without standard curves) were also explored. A combined list of new and previously used primer sets is provided with a recommended set based on redesign of primer sets and results of validation

A Dual-Objective Substation Energy Consumption Optimization Problem in Subway Systems

Maximizing regenerative energy utilization is an important way to reduce substation energy consumption in subway systems. Timetable optimization and energy storage systems are two main ways to improve improve regenerative energy utilization, but they were studied separately in the past. To further improve energy conservation while maintaining a low cost, this paper presents a strategy to improve regenerative energy utilization by an integration of them, which determines the capacity of each Wayside Energy Storage System (WESS) and correspondingly optimizes the timetable at the same time. We first propose a dual-objective optimization problem to simultaneously minimize substation energy consumption and the total cost of WESS. Then, a mathematical model is formulated with the decision variables as the configuration of WESS and timetable. Afterwards, we design an ϵ -constraint method to transform the dual-objective optimization problem into several single-objective optimization problems, and accordingly design an improved artificial bee colony algorithm to solve them sequentially. Finally, numerical examples based on the actual data from a subway system in China are conducted to show the effectiveness of the proposed method. Experimental results indicate that substation energy consumption is effectively reduced by using WESS together with a correspondingly optimized timetable. Note that substation energy consumption becomes lower when the total size of WESS is larger, and timetable optimization further reduces it. A set of Pareto optimal solutions is obtained for the experimental subway line—based on which, decision makers can make a sensible trade-off between energy conservation and WESS investment accordingly to their preferences

Optimized Control of Virtual Coupling at Junctions: A Cooperative Game-Based Approach

Recently, virtual coupling has aroused increasing interest in regard to achieving flexible and on-demand train operations. However, one of the main challenges in increasing the throughput of a train network is to couple trains quickly at junctions. Pre-programmed train operation strategies cause trains to decelerate or stop at junctions. Such strategies can reduce the coupling efficiency or even cause trains to fail to reach coupled status. To fill this critical gap, this paper proposes a cooperative game model to represent train coupling at junctions and adopts the Shapley theorem to solve the formulated game. Due to the discrete and high-dimensional characteristics of the model, the optimal solution method is non-convex and is difficult to solve in a reasonable amount of time. To find optimal operation strategies for large-scale models in a reasonable amount of time, we propose an improved particle swarm optimization algorithm by introducing self-adaptive parameters and a mutation method. This paper compares the strategy for train coupling at junctions generated by the proposed method with two naive strategies and unimproved particle swarm optimization. The results show that the operation time was reduced by using the proposed cooperative game-based optimization approach

Previous School Bullying-Associated Depression in Chinese College Students: The Mediation of Personality

Previous school bullying was associated with increased risk of depression in students. However, little was known about the role of the Big Five personality traits in this association. The purpose of this study was to investigate the possible mediation by the Big Five personality traits in this association in a large group of Chinese college students, and to provide help for educators to prevent students from serious psychological and mental diseases caused by school bullying. Random stratified cluster sampling was used to survey 2152 college students ranging from freshmen to seniors at three universities in Qiqihar city, Heilongjiang Province, China. The risk factors for previous school bullying included gender, living expenses per month, caregivers, parents often quarreling, and divorced parents. Males were more likely to be bullied at school than females. The influencing factors of depression include gender, caregivers, living expenses per month, frequent parents quarreling, and parental divorce. Females were more prone to depression than males. Depression was significantly correlated with all dimensions of school bullying and the Big Five personality traits (p < 0.05). The Big Five personality traits were found to play a significant mediating role between depression and school bullying in up to 45% of cases involving depression. Our major findings highlighted the promising role of personality-based intervention measures in reducing the risk of depression associated with school bullying in Chinese students

CI-STHPAN: Pre-trained Attention Network for Stock Selection with Channel-Independent Spatio-Temporal Hypergraph

Quantitative stock selection is one of the most challenging FinTech tasks due to the non-stationary dynamics and complex market dependencies. Existing studies rely on channel mixing methods, exacerbating the issue of distribution shift in financial time series. Additionally, complex model structures they build make it difficult to handle very long sequences. Furthermore, most of them are based on predefined stock relationships thus making it difficult to capture the dynamic and highly volatile stock markets. To address the above issues, in this paper, we propose Channel-Independent based Spatio-Temporal Hypergraph Pre-trained Attention Networks (CI-STHPAN), a two-stage framework for stock selection, involving Transformer and HGAT based stock time series self-supervised pre-training and stock-ranking based downstream task fine-tuning. We calculate the similarity of stock time series of different channel in dynamic intervals based on Dynamic Time Warping (DTW), and further construct channel-independent stock dynamic hypergraph based on the similarity. Experiments with NASDAQ and NYSE markets data over five years show that our framework outperforms SOTA approaches in terms of investment return ratio (IRR) and Sharpe ratio (SR). Additionally, we find that even without introducing graph information, self-supervised learning based on the vanilla Transformer Encoder also surpasses SOTA results. Notable improvements are gained on the NYSE market. It is mainly attributed to the improvement of fine-tuning approach on Information Coefficient (IC) and Information Ratio based IC (ICIR), indicating that the fine-tuning method enhances the accuracy and stability of the model prediction

Epidemiology and first aid measures in pediatric burn patients in northern China during 2016–2020: A single‐center retrospective study

Abstract Background and Aims Burn and scald injuries are the fourth most common type of trauma. Pediatric burns account for a high proportion of the total number of burn patients and impose a high burden on public health. Understanding the epidemiology of pediatric burns can help improve science education and reduce the incidence of burn injuries. Methods This study is a single‐center retrospective study. One thousand five hundred and twenty‐seven pediatric burn patients admitted to our burn center from January 2016 to December 2020 were included. Demographic and epidemiological data of included patients were extracted and analyzed. The correlations of categorical data were tested by the Chi‐square tests, and differences of continuous data were tested by the Kruskal–Wallis tests. A p‐value of less than 0.05 was considered to be statistically significant. Results The results showed that children under 3 years of age were most susceptible to burn and scald injuries. Burn injuries were most likely to occur in the season of winter and at the place of home. 56.6% of included patients did receive first aid measures, while 1.8% received gold‐standard first aid. Clinical variables related to the severity of injuries were statistically different between patients with and without cooling measures in first aid. Linear regression models showed that emergency treatment of burns in children and adolescents was associated with outcome indicators, including number of operations, total operation duration per total burn surface area (TBSA), cost per TBSA, and length of stay per TBSA. Conclusions This study summarized the epidemiology and outcomes of pediatric burn patients admitted to a burn center in northern China. Adopting cooling measures in first aid can reduce the severity of injuries and reduce the burden on the medical system. Education on burn prevention and first aid measures to caregivers of children, especially preschool children, should be strengthened