Technologies of Earthquake Early Warning Systems: Prediction and Prevention

Destructive shaking movements triggered by earthquakes can cause significant losses such as human sacrifice, house damage and property loss. Sichuan earthquake of 2008, happened in southwestern China, caused over 69,000 death and 374,176 injured[1]. It is not just the earthquake itself that is deadly, the subsequent debris flows and plagues deprived more lives. For instance, outbreaks of the Plague of Justinian occur months or even up to a year after high-magnitude earthquakes. Large earthquakes initiate chains of surface and underground processes that last much longer than the brief moments of strong shaking [2]. Earthquake induced geohazards, including landslides in mountainous regions, floods from temporary lakes, plague after major rainfalls, remain a significant threat[2]. Even extreme geohazard like volcanic eruption can be triggered by earthquakes. An apparent question is raised: ‘what if we can predict shaking with previous seconds of warning once an earthquake rupture begins?’. Earthquake early warning (EEW) system shows us the answer, using seismic knowledge and the technology of monitoring systems to alert devices when shaking waves generated by an earthquake appeared (USGS, 2012). Accurate prediction of earthquake can give people more time to prepare for shaking and the geohazard events it triggers. Governments should provide more resources to EEW to reduce earthquake damage to people and property, especially in poor countries where the education about disaster prevention is immature

Prediction Study on PCI Failure of Reactor Fuel Based on a Radial Basis Function Neural Network

Pellet-clad interaction (PCI) is one of the major issues in fuel rod design and reactor core operation in water cooled reactors. The prediction of fuel rod failure by PCI is studied in this paper by the method of radial basis function neural network (RBFNN). The neural network is built through the analysis of the existing experimental data. It is concluded that it is a suitable way to reduce the calculation complexity. A self-organized RBFNN is used in our study, which can vary its structure dynamically in order to maintain the prediction accuracy. For the purpose of the appropriate network complexity and overall computational efficiency, the hidden neurons in the RBFNN can be changed online based on the neuron activity and mutual information. The presented method is tested by the experimental data from the reference, and the results demonstrate its effectiveness

Design Considerations for Traveling-Wave Modulator-Based CMOS Photonic Transmitters

Systematic design and simulation methodology for hybrid optical transmitters that combine CMOS circuits in a 130 nm process, and a traveling-wave Mach-Zehnder modulator (TWMZM) in 130 nm SOI CMOS process, is presented. A compact Verilog-A model for the TWMZM is adopted for the electrooptical simulation. A bond wire model using a high-frequency solver is included for accurate package simulation. Transmitter post-layout simulation result exhibits 5.48 dB extinction ratio, 9.6 ps peak-to-peak jitter, and the best power efficiency of 5.81 pJ/bit when operating up to 12.5 Gb/s non-return-to-zero data. A pulse amplitude modulation 4-level transmitter with detailed linearity design procedure is presented which has horizontal and vertical eye opening of 49 ps and 203 μW when operating at 25 Gb/s, and the power efficiency is 5.09 pJ/bit

Error-Robust Retrieval for Chinese Spelling Check

Chinese Spelling Check (CSC) aims to detect and correct error tokens in Chinese contexts, which has a wide range of applications. However, it is confronted with the challenges of insufficient annotated data and the issue that previous methods may actually not fully leverage the existing datasets. In this paper, we introduce our plug-and-play retrieval method with error-robust information for Chinese Spelling Check (RERIC), which can be directly applied to existing CSC models. The datastore for retrieval is built completely based on the training data, with elaborate designs according to the characteristics of CSC. Specifically, we employ multimodal representations that fuse phonetic, morphologic, and contextual information in the calculation of query and key during retrieval to enhance robustness against potential errors. Furthermore, in order to better judge the retrieved candidates, the n-gram surrounding the token to be checked is regarded as the value and utilized for specific reranking. The experiment results on the SIGHAN benchmarks demonstrate that our proposed method achieves substantial improvements over existing work.Comment: 11 pages, 3 figure

LLM-based NLG Evaluation: Current Status and Challenges

Evaluating natural language generation (NLG) is a vital but challenging problem in artificial intelligence. Traditional evaluation metrics mainly capturing content (e.g. n-gram) overlap between system outputs and references are far from satisfactory, and large language models (LLMs) such as ChatGPT have demonstrated great potential in NLG evaluation in recent years. Various automatic evaluation methods based on LLMs have been proposed, including metrics derived from LLMs, prompting LLMs, and fine-tuning LLMs with labeled evaluation data. In this survey, we first give a taxonomy of LLM-based NLG evaluation methods, and discuss their pros and cons, respectively. We also discuss human-LLM collaboration for NLG evaluation. Lastly, we discuss several open problems in this area and point out future research directions