Postcranial disparity of galeaspids and the evolution of swimming speeds in stem-gnathostomes

Galeaspids are extinct jawless relatives of living jawed vertebrates whose contribution to understanding the evolutionary assembly of the gnathostome bodyplan has been limited by absence of postcranial remains. Here, we describe Foxaspis novemura gen. et sp. nov., based on complete articulated remains from a newly discovered Konservat-Lagerst¨atte in the Early Devonian (Pragian,∼410 Ma) of Guangxi, South China. F. novemura had a broad, circular dorso-ventrally compressed headshield, slender trunk and strongly asymmetrical hypochordal tail fin comprised of nine ray-like scale-covered digitations.This tail morphology contrasts with the symmetrical hypochordal tail fin of Tujiaaspis vividus, evidencing disparity in galeaspid postcranial anatomy. Analysis of swimming speed reveals galeaspids as moderately fast swimmers, capable of achieving greater cruising swimming speeds than their more derived jawless and jawed relatives. Our analyses reject the hypothesis of a driven trend towards increasingly active food acquisition which has been invoked to characterize early vertebrate evolution

Arbuscular Mycorrhizal Fungi Diversity and Contribution to Crop Growth in Agricultural Fields

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Effect of low-Raman window position on correlated photon-pair generation in a chalcogenide Ge11.5As24Se64.5 nanowire

We investigated correlated photon-pair generation via spontaneous four-wave mixing in an integrated chalcogenideGe11.5As24Se64.5photonicnanowire. The coincidence to accidental ratio, a key measurement for the quality of correlated photon-pair sources, was measured to be only 0.4 when the photon pairs were generated at 1.9 THz detuning from the pump frequency due to high spontaneous Raman noise in this regime. However, the existence of a characteristic low-Raman window at around 5.1 THz in this material's Raman spectrum and dispersion engineering of the nanowire allowed us to generate photon pairs with a coincidence to accidental ratio of 4.5, more than 10 times higher than the 1.9 THz case. Through comparing the results with those achieved in chalcogenide As2S3waveguides which also exhibit a low Raman-window but at a larger detuning of 7.4 THz, we find that the position of the characteristic low-Raman window plays an important role on reducing spontaneous Raman noise because the phonon population is higher at smaller detuning. Therefore the ultimate solution for Raman noise reduction in Ge11.5As24Se64.5 is to generate photon pairs outside the Raman gain band at more than 10 THz detuning

MM DialogueGAT- A Fusion Graph Attention Network for Emotion Recognition using Multi-model System

Emotion recognition is an important part of human-computer interaction and human communication information is multi-model. Despite advancements in emotion recognition models, certain challenges persist. The first problem pertains to the predominant focus in existing research on mining the interaction information between modes and the context information in the dialogue process but neglects to mine the role information between multi-model states and context information in the dialogue process. The second problem is in the context information of the dialogue where the information is not completely transmitted in a temporal structure. Aiming at these two problems, we propose a multi-model fusion dialogue graph attention network (MM DialogueGAT). To solve the problem 1, the bidirectional GRU mechanism is used to extract the information from each model. In the multi-model information fusion problem, different model configurations and different combinations use the cross-model multi-head attention mechanism to establish a multi-head attention layer. Text, video and audio information are used as the main and auxiliary modes for information fusion. To solve the problem 2, in the temporal context information extraction problem, the GAT graph structure is used to capture the context information in the mode. The results show that our model achieves good results using the IMEOCAP datasets