The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG

An ongoing interest towards identification based on biosignals, such as electroencephalogram (EEG), magnetic resonance imaging (MRI), is growing in the past decades. Previous studies indicated that the inherent information about brain activity may be used to identify individual during resting-state of eyes open (REO) and eyes closed (REC). Electroencephalographic (EEG) records the data from the scalp, and it is believed that the noisy EEG signals can influence the accuracies of one experiment causing unreliable results. Therefore, the stability and time-robustness of inter-individual features can be investigated for the purpose of individual identification. In this work, we conducted three experiments with the time interval of at least 2 weeks, and used different types of measures (Power Spectral Density, Cross Spectrum, Channel Coherence and Phase Lags) to extract the individual features. The Pearson Correlation Coefficient (PCC) is calculated to measure the level of linear correlation for intra-individual, and Support Vector Machine (SVM) is used to obtain the related classification accuracy. Results show that the classification accuracies of four features were 85–100% for intra-experiment dataset, and were 80–100% for fusion experiments dataset. For inter-experiments classification of REO features, the optimized frequency range is 13–40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. For inter-experiments classification of REC, the optimized frequency range is 8–40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. The classification results of Phase Lags are much lower than the other three features. These results show the time-robustness of EEG, which can further use for individual identification system.

Performance of Tank Mix Partners with Isoxaflutole Across the Cotton Belt

BASF Corporation has developed P-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitor-resistant cotton and soybean that will allow growers to use isoxaflutole in future weed management programs. In 2019 and 2020, a multi-state research project was conducted non-crop to examine weed control following isoxaflutole applied preemergence alone and with a number of tank mix partners at high and low labeled rates. At 28 DAT, Palmer amaranth was controlled ≥95% at 6 of 7 locations with isoxaflutole plus the high rate of diuron or fluridone. These same combinations provided the greatest control 42 DAT at 4 of 7 locations. Where large crabgrass was present, isoxaflutole plus the high rate of diuron, fluridone, pendimethalin, or S-metolachlor or isoxaflutole plus the low rate of fluometuron controlled large crabgrass ≥95% in 2 of 3 locations 28 DAT. In 2 of 3 locations, isoxaflutole plus the high rate of pendimethalin or S-metolachlor improved large crabgrass control 42 DAT when compared to isoxaflutole alone. At 21 DAT, morningglory was controlled ≥95% at all locations with isoxaflutole plus the high rate of diuron and at 3 of 4 locations with isoxaflutole plus the high rate of fluometuron. At 42 DAT at all locations, isoxaflutole plus diuron or fluridone and isoxaflutole plus the high rate of fluometuron improved morningglory control compared to isoxaflutole alone. These results suggest that isoxaflutole applied preemergence alone or in tank mixture is efficacious on a number of cross-spectrum annual weeds in cotton and extended weed control may be achieved when isoxaflutole is tank mixed with a number of soil residual herbicides.

A 3-CS Distributed Federated Transfer Learning Framework for Intelligent Edge Optical Networks

With the rapid development of optical network and edge computing, the operation efficiency of the edge optical network has become more and more important, requiring an intelligent approach to enhance the network performance. To enhance the intelligence of the edge optical network, this article firstly provides the demand for the development of edge optical networks. Then, a cross-scene, cross-spectrum, and cross-service (3-CS) architecture for edge optical networks is presented. Finally, a federated transfer learning (FTL) framework, realizing a distributed intelligence edge optical network, is proposed. The usability of the proposed framework is verified by simulation.

A fragile cross-spectrum Israeli government may emerge

Significance Lapid on March 5 received the mandate to form a new government, after incumbent Prime Minister Binyamin Netanyahu tried and failed in the wake of indecisive March 23 polls. He now has 28 days to finalise a coalition of at least seven parties. Impacts If Lapid fails, Israel will probably hold its fifth election since April 2019, extending the period of political paralysis. Non-ideological economic issues would be top of the government’s agenda, to avoid internal friction and address post-pandemic problems. The departure of Netanyahu could open up more options in dialogue with the administration of US President Joe Biden.