Evidence for a population of beamed radio intermediate quasars

Whether radio intermediate quasars possess relativistic jets as radio-loud quasars is an important issue in the understanding of the origin of radio emission in quasars. In this letter, using the two-epoch radio data obtained during Faint Image of Radio Sky at Twenty centimeter sky (FIRST) and NOAO VLA Sky Survey (NVSS), we identify 89 radio variable sources in the Sloan Digital Sky Survey. Among them, more than half are radio intermediate quasars (RL=f2cm/f2500A<250). For all objects with available multiple band radio observations, the radio spectra are either flat or inverted. The brightness temperature inferred from the variability is larger than the synchrotron-self Compton limit for a stationary source in 87 objects, indicating of relativistic beaming. Considering the sample selection and viewing angle effect, we conclude that relativistic jets probably exist in a substantianl fraction of radio intermediate quasars.Comment: 15 pages, 4 figures, 1 table, Accepted to the Astrophysical Journa

An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method.

Efficiently recognizing emotions is a critical pursuit in brain–computer interface (BCI), as it has many applications for intelligent healthcare services. In this work, an innovative approach inspired by the genetic code in bioinformatics, which utilizes brain rhythm code features consisting of δ, θ, α, β, or γ, is proposed for electroencephalography (EEG)-based emotion recognition. These features are first extracted from the sequencing technique. After evaluating them using four conventional machine learning classifiers, an optimal channel-specific feature that produces the highest accuracy in each emotional case is identified, so emotion recognition through minimal data is realized. By doing so, the complexity of emotion recognition can be significantly reduced, making it more achievable for practical hardware setups. The best classification accuracies achieved for the DEAP and MAHNOB datasets range from 83–92%, and for the SEED dataset, it is 78%. The experimental results are impressive, considering the minimal data employed. Further investigation of the optimal features shows that their representative channels are primarily on the frontal region, and associated rhythmic characteristics are typical of multiple kinds. Additionally, individual differences are found, as the optimal feature varies with subjects. Compared to previous studies, this work provides insights into designing portable devices, as only one electrode is appropriate to generate satisfactory performances. Consequently, it would advance the understanding of brain rhythms, which offers an innovative solution for classifying EEG signals in diverse BCI applications, including emotion recognition