Photophysics and Photochemistry Enabled by Ligand-to-Metal-Charge-Transfer: Unusual Carbon-Carbon Bond Formation at a Zirconium Center

Six Group 4 metallocene derivatives of group 4 incorporating a redox active benzene-1,2-thiolate ligand (bdt) were prepared. These complexes were characterized using one- and two-dimensional nuclear magnetic resonance (NMR) spectroscopy. Additionally, X-ray crystallographic analyses of these complexes, with the exception of Cp2Zrbdt (Cp = cyclopentadienyl), reveal C2v symmetric structure in the solid-state. All species display electronic absorption bands that are ligand-to-metal charge transfer (LMCT) in character well into the visible region. Spectroscopic data combine with time dependent-density functional theory calculations showed fluctuation in solution which promotes non-radiative energy loss and leads to the absence of emission in solution at room temperature. Cyclic voltammograms have one reversible reduction event that is metal-centered. Except for Cp*2Hfbdt (Cp* = permethylcyclopentadienyl), the remaining five complexes exhibit an irreversible oxidation event. (MePMPMe)2ZrBn2 (MePMPMe = 3,5-dimethyl-2-(2-pyridyl)pyrrolide; Bn = benzyl) was synthesized and characterized via X-ray crystallography and NMR spectroscopy. The complex has LMCT absorption in the blue light region and is photoactive upon irradiation with blue LED. Photolysis of (MePMPMe)2ZrBn2 in the presence of excess diphenylacetylene proceeds with the formation of (MePMPMe)2(η4-C4Ph4) – the first zirconium complex featuring a coordinated cyclobutadienyl ligand. Using 4-methylbenzyl bromide as a mild oxidant, (MePMPMe)2ZrBr2 was cleanly made. A mixture of bibenzyl byproducts suggests a radical mechanism. Testing disulfide substrates lead to the formation of two new multinuclear zirconium thiophenolate complexes identified via X-ray crystallography. Expanding the substrate scope of the photoreaction by less substituted alkyne such as 1-phenyl-1-propyne, phenylacetylene, and ethynylanisole derivatives resulted in catalytic trimerization, forming benzene derivatives that are generally selective toward one isomer. Precatalyst (MePMPMe)2ZrBn2 can also tolerate benzonitrile. Preliminary experiments with 1,6-heptadiene also catalytically produced a new organic product. CO addition produced a new zirconium species that is NMR silent. Overall, the photoreaction selectivity and fast reactivity are specific to the photochemical pathway compared to thermal condition or using KC8 reductant to reduce the Zr center. Using photoactive (MePMPMe)2ZrBn2 can form new zirconium complexes and catalytically generate organic molecules with complimentary selectivity to using late transition metals catalysts. With further exploration, this methodology can be a useful tool in inorganic and organic synthesis

Persistent Test-time Adaptation in Episodic Testing Scenarios

Current test-time adaptation (TTA) approaches aim to adapt to environments that change continuously. Yet, when the environments not only change but also recur in a correlated manner over time, such as in the case of day-night surveillance cameras, it is unclear whether the adaptability of these methods is sustained after a long run. This study aims to examine the error accumulation of TTA models when they are repeatedly exposed to previous testing environments, proposing a novel testing setting called episodic TTA. To study this phenomenon, we design a simulation of TTA process on a simple yet representative $\epsilon$-perturbed Gaussian Mixture Model Classifier and derive the theoretical findings revealing the dataset- and algorithm-dependent factors that contribute to the gradual degeneration of TTA methods through time. Our investigation has led us to propose a method, named persistent TTA (PeTTA). PeTTA senses the model divergence towards a collapsing and adjusts the adaptation strategy of TTA, striking a balance between two primary objectives: adaptation and preventing model collapse. The stability of PeTTA in the face of episodic TTA scenarios has been demonstrated through a set of comprehensive experiments on various benchmarks

MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation

Few-shot instance segmentation extends the few-shot learning paradigm to the instance segmentation task, which tries to segment instance objects from a query image with a few annotated examples of novel categories. Conventional approaches have attempted to address the task via prototype learning, known as point estimation. However, this mechanism depends on prototypes (\eg mean of $K-$shot) for prediction, leading to performance instability. To overcome the disadvantage of the point estimation mechanism, we propose a novel approach, dubbed MaskDiff, which models the underlying conditional distribution of a binary mask, which is conditioned on an object region and $K-$shot information. Inspired by augmentation approaches that perturb data with Gaussian noise for populating low data density regions, we model the mask distribution with a diffusion probabilistic model. We also propose to utilize classifier-free guided mask sampling to integrate category information into the binary mask generation process. Without bells and whistles, our proposed method consistently outperforms state-of-the-art methods on both base and novel classes of the COCO dataset while simultaneously being more stable than existing methods. The source code is available at: https://github.com/minhquanlecs/MaskDiff.Comment: Accepted at AAAI 2024 (oral presentation