Optical Information Transmission and Multimode Fluorescence Anticounterfeiting of Ca2-xMgxGe7O16:Mn2.

Multimode emission of Mn2+ for multimode fluorescence anticounterfeiting is achieved by cation site and interstitial occupancy in Ca2-xMgxGe7O16. The rings in Ca2-xMgxGe7O16 have a significant distortion for Mn2+ ions to enter the ring interstitials with a luminescence center at 665 nm, which is supported by XRD refinement results and first-principles calculations. The interstitial Mn2+ ion has good thermal stability with an activation energy of 0.36 eV. Surprisingly, these two luminescence centers, the cation site Mn and the interstitial Mn, have an obvious afterglow, and the disappearing afterglow will reappear by heating or irradiating with the 980 nm laser. The afterglow is significantly enhanced, as MnO2 is used as the manganese source, which is explained in detail by the thermal luminescence spectrum. Finally, Ca2-xMgxGe7O16:Mn2+ fully demonstrates its excellent prospects in fluorescent anticounterfeiting, information encryption, and optical information storage.

Gradient Harmonization in Unsupervised Domain Adaptation.

Unsupervised domain adaptation (UDA) intends to transfer knowledge from a labeled source domain to an unlabeled target domain. Many current methods focus on learning feature representations that are both discriminative for classification and invariant across domains by simultaneously optimizing domain alignment and classification tasks. However, these methods often overlook a crucial challenge: the inherent conflict between these two tasks during gradient-based optimization. In this paper, we delve into this issue and introduce two effective solutions known as Gradient Harmonization, including GH and GH++, to mitigate the conflict between domain alignment and classification tasks. GH operates by altering the gradient angle between different tasks from an obtuse angle to an acute angle, thus resolving the conflict and trade-offing the two tasks in a coordinated manner. Yet, this would cause both tasks to deviate from their original optimization directions. We thus further propose an improved version, GH++, which adjusts the gradient angle between tasks from an obtuse angle to a vertical angle. This not only eliminates the conflict but also minimizes deviation from the original gradient directions. Finally, for optimization convenience and efficiency, we evolve the gradient harmonization strategies into a dynamically weighted loss function using an integral operator on the harmonized gradient. Notably, GH/GH++ are orthogonal to UDA and can be seamlessly integrated into most existing UDA models. Theoretical insights and experimental analyses demonstrate that the proposed approaches not only enhance popular UDA baselines but also improve recent state-of-the-art models.