Machine learning for fiber nonlinearity mitigation in long-haul coherent optical transmission systems

Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmission capacity in current optical transmission systems. Digital nonlinearity compensation techniques such as digital backpropagation can perform well but require high computing resources. Machine learning can provide a low complexity capability especially for high-dimensional classification problems. Recently several supervised and unsupervised machine learning techniques have been investigated in the field of fiber nonlinearity mitigation. This paper offers a brief review of the principles, performance and complexity of these machine learning approaches in the application of nonlinearity mitigation

Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction

In this paper we develop a novel neural network model for predicting implied volatility surface. Prior financial domain knowledge is taken into account. A new activation function that incorporates volatility smile is proposed, which is used for the hidden nodes that process the underlying asset price. In addition, financial conditions, such as the absence of arbitrage, the boundaries and the asymptotic slope, are embedded into the loss function. This is one of the very first studies which discuss a methodological framework that incorporates prior financial domain knowledge into neural network architecture design and model training. The proposed model outperforms the benchmarked models with the option data on the S&P 500 index over 20 years. More importantly, the domain knowledge is satisfied empirically, showing the model is consistent with the existing financial theories and conditions related to implied volatility surface.Comment: 8 pages, SIGKDD 202

Smartphone data usage : downlink and uplink asymmetry

Mobile phone usage has changed significantly over the past few years and smartphone data usage is still not well understood on a statistically significant scale. This Letter analyses 2.1 million smartphone usage data values and explore the current wireless downlink–uplink demand asymmetry for different time periods and across different radio access networks. The current data demand over 2G networks remains largely symmetric with strong temporal variations, whereas the demand over 3G networks is asymmetric with surprisingly weak temporal variations is shown here

Mutually Guided Few-shot Learning for Relational Triple Extraction

Knowledge graphs (KGs), containing many entity-relation-entity triples, provide rich information for downstream applications. Although extracting triples from unstructured texts has been widely explored, most of them require a large number of labeled instances. The performance will drop dramatically when only few labeled data are available. To tackle this problem, we propose the Mutually Guided Few-shot learning framework for Relational Triple Extraction (MG-FTE). Specifically, our method consists of an entity-guided relation proto-decoder to classify the relations firstly and a relation-guided entity proto-decoder to extract entities based on the classified relations. To draw the connection between entity and relation, we design a proto-level fusion module to boost the performance of both entity extraction and relation classification. Moreover, a new cross-domain few-shot triple extraction task is introduced. Extensive experiments show that our method outperforms many state-of-the-art methods by 12.6 F1 score on FewRel 1.0 (single-domain) and 20.5 F1 score on FewRel 2.0 (cross-domain).Comment: Accepted by ICASSP 202

Seismic retrofit design and risk assessment of an irregular thermal power plant building

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154642/1/tal1719_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154642/2/tal1719.pd

Wetting and deposition characteristics of air-assisted spray droplet on large broad-leaved crop canopy

Precision and efficient pesticide spraying is an important part of precision agriculture, banana is a large broad-leaved plant, with pests and diseases, has a high demand for spraying and pest control. The purpose of this study was to clarify the wettability of different pesticides on the banana leaf surface, and the effects of nozzle type and working parameters on the deposition distribution performance under air-assisted spray conditions. The wettability test results of different pesticides on banana leaf surfaces showed that the wettability of the adaxial side was always stronger than that of the abaxial side, the smaller the surface tension of the droplets, the better the wettability on the surface. The spray experiment was carried out on the previously developed air-assisted sprayer with the latest developed intelligent variable spray control system. Three types of nozzles were used to spray with different combinations of working parameters. The deposition distribution performance on the banana leaf surface was obtained by image processing using a self-compiled program. The experimental results show that the nozzle type, wind speed, and spray pressure have significant effects on the deposition distribution performance. Through the study of the interaction and coupling effect of nozzle type and working parameters on the spray droplet deposition distribution on both sides of banana leaves, the results show that under the conditions of hollow cone nozzle, 0.5Mpa spray pressure and 3-5 m/s wind speed, the spray coverage and droplet density are in the optimal state. This is mainly due to the low spray pressure and/or wind speed is not enough to make the banana leaves vibrate and improve the performance of pesticide deposition. excessive spray pressure and/or wind speed will cause large deformation of banana leaves and make them airfoil stable, which reduces the surface deposition performance. It is of great significance for promoting sustainable and intelligent phytoprotection