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Sudden stratospheric warmings (SSWs) are the most dramatic events in the wintertime stratosphere. Such extreme events are characterized by substantial disruption to the stratospheric polar vortex, which can be categorized into displacement and splitting types depending on the morphology of the disrupted vortex. Moreover, SSWs are usually followed by anomalous tropospheric circulation regimes that are important for subseasonal-to-seasonal prediction. Thus, monitoring the genesis and evolution of SSWs is crucial and deserves further advancement. Despite several analysis methods that have been used to study the evolution of SSWs, the ability of deep learning methods has not yet been explored, mainly due to the relative scarcity of observed events. To overcome the limited observational sample size, we use data from historical simulations of the Whole Atmosphere Community Climate Model version 6 to identify thousands of simulated SSWs, and use their spatial patterns to train the deep learning model. We utilize a convolutional neural network combined with a variational auto-encoder (VAE)—a generative deep learning model—to construct a phase diagram that characterizes the SSW evolution. This approach not only allows us to create a latent space that encapsulates the essential features of the vortex structure during SSWs, but also offers new insights into its spatiotemporal evolution mapping onto the phase diagram. The constructed phase diagram depicts a continuous transition of the vortex pattern during SSWs. Notably, it provides a new perspective for discussing the evolutionary paths of SSWs: the VAE gives a better-reconstructed vortex morphology and more clearly organized vortex regimes for both displacement-type and split-type events than those obtained from principal component analysis. Our results provide an innovative phase diagram to portray the evolution of SSWs, in which particularly the splitting SSWs are better characterized. Our findings support the future use of deep learning techniques to study the underlying dynamics of extreme stratospheric vortex phenomena, and to establish a benchmark to evaluate model performance in simulating SSWs.

 

Data-free knowledge distillation (KD) helps transfer knowledge from a pre-trained model (known as the teacher model) to a smaller model (known as the student model) without access to the original training data used for training the teacher model. However, the security of the synthetic or out-of-distribution (OOD) data required in data-free KD is largely unknown and under-explored. In this work, we make the first effort to uncover the security risk of data-free KD w.r.t. untrusted pre-trained models. We then propose Anti-Backdoor Data-Free KD (ABD), the first plug-in defensive method for data-free KD methods to mitigate the chance of potential backdoors being transferred. We empirically evaluate the effectiveness of our proposed ABD in diminishing transferred backdoor knowledge while maintaining compatible downstream performances as the vanilla KD. We envision this work as a milestone for alarming and mitigating the potential backdoors in data-free KD. Codes are released at https://github.com/illidanlab/ABD . 

Polyatomic molecules have been identified as sensitive probes of charge-parity violating and parity violating physics beyond the Standard Model (BSM). For example, many linear triatomic molecules are both laser-coolable and have parity doublets in the ground electronic$\tilde{X}{}^2{\mathrm{\Sigma }}^{+}(010)$state arising from the bending vibration, both features that can greatly aid BSM searches. Understanding the$\tilde{X}{}^2{\mathrm{\Sigma }}^{+}(010)$state is a crucial prerequisite to precision measurements with linear polyatomic molecules. Here, we characterize the fundamental bending vibration of${}^{174}$YbOH using high-resolution optical spectroscopy on the nominally forbidden$\tilde{X}{}^2{\mathrm{\Sigma }}^{+}(010)$$\to \tilde{A}{}^2{\mathrm{\Pi }}_{1/2}(000)$transition at 588 nm. We assign 39 transitions originating from the lowest rotational levels of the$\tilde{X}{}^2{\mathrm{\Sigma }}^{+}(010)$state, and accurately model the state’s structure with an effective Hamiltonian using best-fit parameters. Additionally, we perform Stark and Zeeman spectroscopy on the$\tilde{X}{}^2{\mathrm{\Sigma }}^{+}(010)$state and fit the molecule-frame dipole moment to$D_{\mathrm{m}\mathrm{o}\mathrm{l}}=2.16(1)$Dand the effective electrong-factor to$g_S=2.07(2)$. Further, we use an empirical model to explain observed anomalous line intensities in terms of interference from spin–orbit and vibronic perturbations in the excited$\tilde{A}{}^2{\mathrm{\Pi }}_{1/2}(000)$state. Our work is an essential step toward searches for BSM physics in YbOH and other linear polyatomic molecules.

 

Antibody drug conjugates (ADC) are an emerging class of pharmaceuticals consisting of cytotoxic agents covalently attached to an antibody designed to target a specific cancer cell surface molecule followed by internalization and intracellular release of payload to exhibit its anticancer activity. Targeted delivery of cytotoxic payload to a variety of specific cells has been demonstrated to have significant enhancement in clinical efficacy and dramatic reduction in off‐target toxicity. Site‐specific conjugation of payload to the antibody is highly desirable for development of ADC with well‐defined antibody‐to‐drug ratio, enhanced internalization, reduced toxicity, improved stability, desired pharmacological profile and optimal therapeutic index. Here, we reported a site‐specific conjugation strategy for evaluation of antibody internalization and efficacy of ADC designed to target SSEA4 on solid tumors. This strategy stems from the azido‐fucose tag of a homogeneous antibody Fc‐glycan generated viain vitroglycoengineering approach for site‐specific conjugation and optimization of antibody‐drug ratio to exhibit optimal efficacy. The ADC consisting of a chimeric anti‐SSEA4 antibody chMC813‐70, conjugated to the antineoplastic agent monomethyl auristatin E via both cleavable and non‐cleavable linkers showed excellent cytotoxicity profile towards SSEA4‐bearing cancer cells. A clear distinction in cytotoxicity was observed among cancer cells with different SSEA4 expression levels.