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

A Guided Ant Colony Optimization Algorithm for Conflict-free Routing Scheduling of AGVs Considering Waiting Time

Efficient conflict-free routing scheduling of automated guided vehicles (AGVs) in automated logistic systems can improve delivery time, prevent delays, and decrease handling cost. Once potential conflicts present themselves on their road ahead, AGVs may wait for a while until the potential conflicts disappear besides altering their routes. Therefore, AGV conflict-free routing scheduling involves making routing and waiting time decisions simultaneously. This work constructs a conflict-free routing scheduling model for AGVs with consideration of waiting time. The process of the model is based on calculation of the travel time and conflict analysis at the links and nodes. A guided ant colony optimization (GACO) algorithm, in which ants are guided to avoid conflicts by adding a guidance factor to the state transition rule, is developed to solve the model. Simulations are conducted to validate the effectiveness of the model and the solution method

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

Native mass spectrometry and structural studies reveal modulation of MsbA–nucleotide interactions by lipids

The ATP-binding cassette (ABC) transporter, MsbA, plays a pivotal role in lipopolysaccharide (LPS) biogenesis by facilitating the transport of the LPS precursor lipooligosaccharide (LOS) from the cytoplasmic to the periplasmic leaflet of the inner membrane. Despite multiple studies shedding light on MsbA, the role of lipids in modulating MsbA-nucleotide interactions remains poorly understood. Here we use native mass spectrometry (MS) to investigate and resolve nucleotide and lipid binding to MsbA, demonstrating that the transporter has a higher affinity for adenosine 5’-diphosphate (ADP). Moreover, native MS shows the LPS-precursor 3-deoxy-D-manno-oct-2-ulosonic acid (Kdo)2-lipid A (KDL) can tune the selectivity of MsbA for adenosine 5’-triphosphate (ATP) over ADP. Guided by these studies, four open, inward-facing structures of MsbA are determined that vary in their openness. We also report a 2.7 Å-resolution structure of MsbA in an open, outward-facing conformation that is not only bound to KDL at the exterior site, but with the nucleotide binding domains (NBDs) adopting a distinct nucleotide-free structure. The results obtained from this study offer valuable insight and snapshots of MsbA during the transport cycle

Interference avoidance strategy for ultra dense network with pilot reuse

In the ultra dense network (UDN),the pilot reuse scheme would produce significant interference,which will affect the accuracy of channel estimation.To solve this problem,an interference avoidance strategy for UDN with pilot reuse was proposed.An interference model of subcarriers for UDN was provided and the interference probability of subcarriers was derived.Then,based on the model,a pilot position selection model was proposed and an interference avoidance strategy for UDN with pilot reuse was provided.The simulation results show that compared with the traditional channel estimation algorithm,the channel estimation with proposed interference avoidance strategy can effectively avoid the interference and ensure the accuracy of channel estimation in UDN with pilot reuse