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The growing demand for wireless communications and the emergence of 5G and beyond-5G networks have made dynamic spectrum sharing and efficient radio frequency (RF) spectrum management critical challenges for both researchers and regulators. This paper presents the COSMOS Zone Management System (ZMS), a cloud-native, web-based spectrum coordination platform designed to enable dynamic electromagnetic spectrum sharing in dense urban environments. Developed as part of a multi-university NSF SII-NRDZ collaboration, the ZMS operates within the COSMOS PAWR testbed in West Harlem, New York City, inside an FCC-designated Innovation Zone. The system leverages Spectrum Consumption Models (SCMs) standardized under IEEE 1900.5.2 to formally characterize RF device spectrum use and execute Compatibility Test (CT) computations for interference analysis and spectrum use authorization. Built on a Kubernetes-based microservices architecture, the ZMS integrates real-time spectrum monitoring, automated compatibility testing, interference alarm management, and external tool integrations — including NG-Scope for LTE network telemetry and Geo2SigMap for 3D radio propagation modeling. Two representative use cases demonstrate the system’s planning and coordination capabilities. The results illustrate the ZMS’s ability to serve as both an operational spectrum management tool and a research platform for studying spectrum coexistence in complex urban RF environments. 

The growing demand for wireless communications and the emergence of 5G and beyond-5G networks have made dynamic spectrum sharing and efficient radio frequency (RF) spectrum management critical challenges for both researchers and regulators. This paper presents the COSMOS Zone Management System (ZMS), a cloud-native, web-based spectrum coordination platform designed to enable dynamic electromagnetic spectrum sharing in dense urban environments. Developed as part of a multi-university NSF SII-NRDZ collaboration, the ZMS operates within the COSMOS PAWR testbed in West Harlem, New York City, inside an FCC-designated Innovation Zone. The system leverages Spectrum Consumption Models (SCMs) standardized under IEEE 1900.5.2 to formally characterize RF device spectrum use and execute Compatibility Test (CT) computations for interference analysis and spectrum use authorization. Built on a Kubernetes-based microservices architecture, the ZMS integrates real-time spectrum monitoring, automated compatibility testing, interference alarm management, and external tool integrations — including NG-Scope for LTE network telemetry and Geo2SigMap for 3D radio propagation modeling. Two representative use cases demonstrate the system’s planning and coordination capabilities. The results illustrate the ZMS’s ability to serve as both an operational spectrum management tool and a research platform for studying spectrum coexistence in complex urban RF environments. 

Modality fusion is a cornerstone of multimodal learning, enabling information integration from diverse data sources. However, vanilla fusion methods are limited by (1) inability to account for heterogeneous interactions between modalities and (2) lack of interpretability in uncovering the multimodal interactions inherent in the data. To this end, we propose I2MoE (Interpretable Multimodal Interaction-aware Mixture of Experts), an end-to-end MoE framework designed to enhance modality fusion by explicitly modeling diverse multimodal interactions, as well as providing interpretation on a local and global level. First, I2MoE utilizes different interaction experts with weakly supervised interaction losses to learn multimodal interactions in a data-driven way. Second, I2MoE deploys a reweighting model that assigns importance scores for the output of each interaction expert, which offers sample-level and dataset-level interpretation. Extensive evaluation of medical and general multimodal datasets shows that I2MoE is flexible enough to be combined with different fusion techniques, consistently improves task performance, and provides interpretation across various real-world scenarios. 

ABSTRACT We present a photometric and spectroscopic analysis of the fast-declining Type II SN 2020aze, observed in optical bands from 2.2 to 137.4 d post-explosion. The V-band light curve reaches a peak absolute magnitude of $$-16.97\ \pm$$ 0.20 mag by 15 d, followed by a recombination phase with a decline rate of $$2.04\pm 0.13$$ mag (100 d)$$^{-1}$$, lasting $$\sim$$120 d. Early spectra (<6.0 d) exhibits a transient weak narrow emission line at 4687 Å and a bump or ledge feature spanning 4400–4800 Å, attributed to narrow and broad blue-shifted He ii  $$\lambda$$4686, indicating interaction between the rapidly expanding ejecta and dense circumstellar material (CSM). Spectral comparison with literature models suggests a red supergiant progenitor with a weak wind and a mass-loss rate of $${\sim} 10^{-3}$$ M$$_\odot$$ yr$$^{-1}$$. Semi-analytical light curve modelling yields an initial radius of 1100 R$$_{\odot }$$, an ejecta mass of 12 M$$_\odot$$, a total explosion energy of 1.5$$\times 10^{51}$$ erg, and a progenitor mass of approximately 14 M$$_\odot$$. The combination of a steep luminosity decline, early interaction signatures, and an unusually extended photospheric phase highlights the complex interplay between pre-SN mass-loss, CSM interaction, and progenitor properties, positioning SN 2020aze as an important case for understanding the diversity of Type II SNe.