An unsymmetrical ligand, 2‐(2‐pyridinyl)‐7‐(pyrazol‐1‐yl)‐1,8‐naphthyridine (L5) was prepared for the construction of a series of dinuclear complexes. Treatment of L5 with [Ru2(µ‐OAc)4Cl] followed by anion metathesis afforded [(L5)(µ‐OAc)3Ru2](PF6) (3). Reaction of L5 with 2 equiv. of Ni(OAc)2 provided [Ni4(L5)2(µ‐OH)4(CF3COO)2](CF3COO)2 (5). Reaction of [Re2(CO)8(CH3CN)2] with L5 in a refluxing chlorobenzene solution gave a mixture of dirhenium (6) and monorhenium (7) complexes. The monocobalt complex 8 was obtained from complexation of L5 with CoCl2. These new complexes were characterized by elemental analysis and spectroscopic techniques. The structures of complexes 3, 5 and 8 were further confirmed by X‐ray crystallography. Nickel complex 5 was evaluated as a catalyst for reduction reactions involving the conversion of ester functionalities into their corresponding alcohols.
Understanding and comprehending video content is crucial for many real-world applications such as search and recommendation systems. While recent progress of deep learning has boosted performance on various tasks using visual cues, deep cognition to reason intentions, motivation, or causality remains challenging. Existing datasets that aim to examine video reasoning capability focus on visual signals such as actions, objects, relations, or could be answered utilizing text bias. Observing this, we propose a novel task, along with a new dataset: Trope Understanding in Movies and Animations (TrUMAn) with 2423 videos associated with 132 tropes, intending to evaluate and develop learning systems beyond visual signals. Tropes are frequently used storytelling devices for creative works. By coping with the trope understanding task and enabling the deep cognition skills of machines, data mining applications and algorithms could be taken to the next level. To tackle the challenging TrUMAn dataset, we present a Trope Understanding and Storytelling (TrUSt) with a new Conceptual Storyteller module, which guides the video encoder by performing video storytelling on a latent space. Experimental results demonstrate that state-of-theart learning systems on existing tasks reach only 12.01% of accuracy with raw input signals. Also, even in the oracle case with humanannotated descriptions, BERT contextual embedding achieves at most 28% of accuracy. Our proposed TrUSt boosts the model performance and reaches 13.94% accuracy. We also provide detailed analysis to pave the way for future research. TrUMAn is publicly available at: https://www.cmlab.csie.ntu.edu.tw/project/trope
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