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A new series of five three-dimensional Ln(III) metal-organic frameworks (MOFs) formulated as [Ln(μ-L)(μ-HCOO)(μ-OH)(μ-O)(DMF)(HO)] {Ln = Tb (1), Eu (2), Gd (3), Dy (4), and Er (5)} was successfully obtained via a solvothermal reaction between the corresponding lanthanide(III) nitrates and 2-(6-carboxypyridin-3-yl)terephthalic acid (HL). All of the obtained compounds were fully characterized, and their structures were established by single-crystal X-ray diffraction. All products are isostructural and possess porous 3D networks of the fluorite topological type, which are driven by the cubane-like [Ln(μ-OH)(μ-O)(μ-HCOO)] blocks and μ-L spacers. Luminescent and sensing properties of 1-5 were investigated in detail, revealing a unique capability of Tb-MOF (1) for sensing acetone and metal(III) cations (Fe or Ce) with high efficiency and selectivity. Apart from a facile recyclability after sensing experiments, the obtained Tb-MOF material features a remarkable stability in a diversity of environments such as common solvents, aqueous solutions of metal ions, and solutions with a broad pH range from 4 to 11. In addition, compound 1 represents a very rare example of the versatile Ln-MOF probe capable of sensing Ce or Fe cations or acetone molecules.

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We present TrackFormer, an end-to-end multi-object tracking and segmentation model based on an encoderdecoder Transformer architecture. Our approach introduces track query embeddings which follow objects through a video sequence in an autoregressive fashion. New track queries are spawned by the DETR object detector and embed the position of their corresponding object over time. The Transformer decoder adjusts track query embeddings from frame to frame, thereby following the changing object positions. TrackFormer achieves a seamless data association between frames in a new tracking-by-attention paradigm by self-and encoder-decoder attention mechanisms which simultaneously reason about location, occlusion, and object identity. TrackFormer yields state-ofthe-art performance on the tasks of multi-object tracking (MOT17) and segmentation (MOTS20). We hope our unified way of performing detection and tracking will foster future research in multi-object tracking and video understanding. Code will be made publicly available.