Contextual Biasing of Named-Entities with Large Language Models

This paper studies contextual biasing with Large Language Models (LLMs), where during second-pass rescoring additional contextual information is provided to a LLM to boost Automatic Speech Recognition (ASR) performance. We propose to leverage prompts for a LLM without fine tuning during rescoring which incorporate a biasing list and few-shot examples to serve as additional information when calculating the score for the hypothesis. In addition to few-shot prompt learning, we propose multi-task training of the LLM to predict both the entity class and the next token. To improve the efficiency for contextual biasing and to avoid exceeding LLMs' maximum sequence lengths, we propose dynamic prompting, where we select the most likely class using the class tag prediction, and only use entities in this class as contexts for next token prediction. Word Error Rate (WER) evaluation is performed on i) an internal calling, messaging, and dictation dataset, and ii) the SLUE-Voxpopuli dataset. Results indicate that biasing lists and few-shot examples can achieve 17.8% and 9.6% relative improvement compared to first pass ASR, and that multi-task training and dynamic prompting can achieve 20.0% and 11.3% relative WER improvement, respectively.Comment: 5 pages, 4 figures. Conference: ICASSP 202

Determining the Frequency for Load-Independent Output Current in Three-Coil Wireless Power Transfer System

Conditions for load-independent output voltage or current in two-coil wireless power transfer (WPT) systems have been studied. However, analysis of load-independent output current in three-coil WPT system is still lacking in previous studies. This paper investigates the output current characteristics of a three-coil WPT system against load variations, and determines the operating frequency to achieve a constant output current. First, a three-coil WPT system is modeled by circuit theory, and the analytical expression of the root-mean-square of the output current is derived. By substituting the coupling coefficients, the quality factor, and the resonant frequency of each coil, we propose a method of calculating the frequency for load-independent output current in a three-coil WPT system, which indicates that there are two frequencies that can achieve load-independent output current. Experiments are conducted to validate these analytical results

JDGC: An integrated decentralized distributed computing platform for Java program

10.1108/17427370710847318International Journal of Pervasive Computing and Communications32190-20

An Integrated Decision-Making Model for Transformer Condition Assessment Using Game Theory and Modified Evidence Combination Extended by D Numbers

The power transformer is one of the most critical and expensive components for the stable operation of the power system. Hence, how to obtain the health condition of transformer is of great importance for power utilities. Multi-attribute decision-making (MADM), due to its ability of solving multi-source information problems, has become a quite effective tool to evaluate the health condition of transformers. Currently, the analytic hierarchy process (AHP) and Dempster–Shafer theory are two popular methods to solve MADM problems; however, these techniques rarely consider one-sidedness of the single weighting method and the exclusiveness hypothesis of the Dempster–Shafer theory. To overcome these limitations, this paper introduces a novel decision-making model, which integrates the merits of fuzzy set theory, game theory and modified evidence combination extended by D numbers, to evaluate the health condition of transformers. A four-level framework, which includes three factors and seventeen sub-factors, is put forward to facilitate the evaluation model. The model points out the following: First, the fuzzy set theory is employed to obtain the original basic probability assignments for all indices. Second, the subjective and objective weights of indices, which are calculated by fuzzy AHP and entropy weight, respectively, are integrated to generate the comprehensive weights based on game theory. Finally, based on the above two steps, the modified evidence combination extended by D numbers, which avoids the limitation of the exclusiveness hypothesis in the application of Dempster–Shafer theory, is proposed to obtain the final assessment results of transformers. Case studies are given to demonstrate the proposed modeling process. The results show the effectiveness and engineering practicability of the model in transformer condition assessment

Motion response and energy conversion performance of a heaving point absorber wave energy converter

The heaving wave energy converter (WEC) is one typical type of point absorber WECs with high energy conversion efficiency but significantly affected by the viscous effect. It is widely known that the bottom shape of such WECs plays an important role in influencing the viscosity, so a detailed qualitative investigation is essential. Here a numerical study is performed for the influence of bottom shape on motion response and energy conversion performance of a heaving WEC. The numerical model is developed based on the potential flow theory with a viscous correction in the frequency domain. Cylindrical WECs with flat, cone, and hemispherical bottoms and the same displacement are considered. WECs with larger diameter-to-draft ratios (DDRs) are found to experience a relatively smaller viscous effect and achieve effective energy conversion in a broader frequency range. With the same DDR, the flat bottom has the most considerable viscous effect, following by the cone bottom with conical angles 90° and the hemispherical bottom. When the DDR is relatively small, the hemispherical bottom had the best energy conversion performance. Similarly, when the DDR is relatively large, the energy conversion performance of the floater with a hemispherical bottom and a cone bottom with 90° is better, while that with the flat bottom is the worst

The Patterns and Mechanisms of Land Price Divergence in Multiple Industries from the Perspective of Element Flows: The Case of the Yangtze River Delta, China

An in-depth exploration of the inner mechanisms of the spatio-temporal evolution of land prices in multiple industries (residential, commercial service and industrial) is of great significance for improving the overall economic development efficiency and resource allocation capacity of the region. Based on urban land price transaction data provided by the China Land Market Network, 307 county-level units in the Yangtze River Delta were used as the study area and spatial auto-correlation and spatio-temporal hotspot analysis were used to explore the spatial variation and temporal changes of land prices in multiple industries in the Yangtze River Delta from 2008 to 2018. The three-dimensional driving theory of land “demand + supply + market” was used as the basis to construct the index system of influential factors and the Spatial Durbin Model was used to explore the mechanism of the spatio-temporal variation of land prices in multiple industries. The results show that the land prices of multiple industries in the Yangtze River Delta are generally high in the east and low in the west and high in the south and low in the north, which is spatially consistent with the level of regional economic development. Due to the disparity in economic development between the regions, factors such as population, capital, technology and information are redistributed and fed into each other’s cycles between cities. The resulting spatial differences in land market supply and demand are intrinsic to the spatial differentiation of urban land prices. It is further proposed that land prices are a monetized expression of the abundance of resources in a city and that land prices are determined by the combined ability of regional resource factors to be allocated. Thus, land price differentiation reflects differences in the level of comprehensive regional development. Finally, the dynamic interaction of various factors on land values is used to promote the division of urban functions and regional economic development, which is an effective way to promote high-quality integrated regional development

Optimal Decomposition for the Monthly Contracted Electricity of Cascade Hydropower Plants Considering the Bidding Space in the Day-Ahead Spot Market

With the gradual opening of China’s electricity market, it is effective for cascade hydropower plants to simultaneously participate in both the monthly contract market and the day-ahead spot market to obtain higher power generation benefits. Hence, this paper studies the optimal decomposition model for the monthly contracted electricity of cascade hydropower plants considering the bidding space in the day-ahead spot market. The close hydraulic and electric connection between cascade hydropower plants, the implementation requirements of contracted electricity, and the uncertainty of the day-ahead market clearing price are all well considered. Several linearization techniques are proposed to address the nonlinear factors, including the objective function and the power generation function. A successive approximation (SA) approach, along with a mixed-integer linear programming (MILP) approach, is then developed to solve the proposed model. The presented model is verified by taking the decomposition of the monthly contracted electricity of cascade hydropower plants in China as an example. The results indicate that the developed model has high computational efficiency and can increase the power generation benefits compared with the conventional deterministic model. The effect of the penalty coefficient for imbalanced monthly contracted electricity is also evaluated, which provides a practical reference for market managers

Gefitinib-Integrated Regimen versus Chemotherapy Alone in Heavily Pretreated Patients with Epidermal Growth Factor Receptor–Mutated Lung Adenocarcinoma: A Case-Control Study

BACKGROUND: The study aimed to compare the tolerability and efficacy of gefitinib combined with chemotherapy agents versus chemotherapy alone for the treatment of epidermal growth factor receptor (EGFR)–mutated lung adenocarcinoma in heavily pretreated patients. METHODS: The study was designed as a matched-pair case-control investigation to minimize intergroup heterogeneity. Patients were stratified into gefitinib plus chemotherapy and chemotherapy alone groups with matching for sex, age, ECOG performance status, progress-free survival (PFS) from previous EGFR tyrosine kinase inhibitor treatment, EGFR mutation types, and tumor metastasis status. RESULTS: Sixty-six patients were selected from our database using the matched-pair method. The median age was 61 years (95% confidence interval, 57-65 years). During a follow-up period of 14.5 months on average, the overall response rates of the gefitinib-integrated and chemotherapy alone groups were 9.1% and 6.5%, respectively (P > .05), whereas the corresponding disease-control rates were 39.4% and 30.3%, respectively (P > .05). No statistically significant differences in PFS (median, 4.2 vs 3.3 months; P = .06) and overall survival (median, 10.4 vs 7.9 months; P = .44) were observed between two groups. The 6-month survival rates of the gefitinib-integrated and chemotherapy alone groups were 21.2% and 12.1%, respectively (P < .05). Side effects were mild, and all treatments were well tolerated. CONCLUSIONS: Our results indicated that gefitinib-integrated therapy offered a trend to better PFS and an improved 6-month survival rate in heavily pretreated patients with metastatic EGFR-mutated lung adenocarcinoma. All treatments were well tolerated. Future prospective studies are warranted to confirm our findings