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Imaging systems are widely applied in harsh environments where the performance of shallow‐designed systems may deviate from expectation. As a representative scenario, environmental temperature variation may degrade image quality due to thermal defocus and sensor response, resulting in blur and noise. However, extensive athermalization in optics usually requires a complex design process and is limited by materials. Herein, a multibranch computational imaging scheme is developed, using emerging generative adversarial networks as the postprocessing to compensate for degradation of all kinds caused by thermal defocus and noise. In addition, a temperature controllable data acquisition, division, and mixture scheme is described to facilitate effective datasets for model robustness. Experiments on a vehicle lens and a mobile phone lens reveal that the proposed multibranch learned strategy notably increases image quality in the temperature range of 0–80 °C, and outperforms conventional athermalization in most instances, which is beneficial to lowering the design and manufacturing costs of imaging systems.

In this paper, we propose a new fluorescence emission difference microscopy (FED) technique based on polarization modulation. An electro-optical modulator (EOM) is used to switch the excitation beam between the horizontal and vertical polarization states at a high frequency, which leads to solid- and donut-shaped beams after spatial light modulation. Experiment on the fluorescent nanoparticles demonstrates that the proposed method can achieve [Formula: see text] spatial resolution. Using the proposed system, the dynamic imaging of subcellular structures in living cells over time is achieved.

Human decision-making processes are complex. It is thus challenging to mine human strategies from real games in social networks. To model human strategies in social dilemmas, we conducted a series of human subject experiments in which the temporal two-player non-cooperative games among 1092 players were intensively investigated. Our goal is to model the individuals’ moves in the next round based on the information observed in each round. Therefore, the developed model is a strategy model based on short-term memory. Due to the diversity of user strategies, we first cluster players’ behaviors to aggregate them with similar strategies for the following modeling. Through behavior clustering, our observations show that the performance of the tested binary strategy models can be highly promoted in the largest behavior groups. Our results also suggest that no matter whether in the classical mode or the dissipative mode, the influence of individual accumulated payoffs on individual behavior is more significant than the gaming result of the last round. This result challenges a previous consensus that individual moves largely depend on the gaming result of the last round. Therefore, our model provides a novel perspective for understanding the evolution of human altruistic behavior.

The isoselective ring-opening polymerization of rac-LA is a challenging goal. In this work, a series of potassium amidate complexes (K1–K10) were easily prepared and characterized using the 1H/13C NMR spectrum. The molecular structures of potassium complexes K2 and K10 were determined by X-ray diffraction, which showed that both were two-dimensional coordination polymers due to the adjacent π interactions of the aryl. In the presence of benzyl alcohol (BnOH), all of the potassium complexes exhibited a high catalytic activity toward the ring-opening polymerization of L-lactide and rac-LA, yielding linear polylactides capped with BnO or CH3O end groups. A significant solvent effect on the ROP of the L-LA was observed, with a superior efficiency in toluene than in THF and CH2Cl2. These complexes are iso-selective and act as active catalysts for the controlled ring-opening polymerization of rac-lactide, with a Pm from 0.54 to 0.76. This is a rare example of simple alkali metal complexes for the isoselective ROP of rac-lactide. The substituent greatly affected the monomer conversion and isoselectivities.

Abstract In this work, we develop conformal bootstrap for Galilean conformal field theory (GCFT). In a GCFT, the Hilbert space could be decomposed into quasiprimary states and its global descendants. Different from the usual conformal field theory, the quasiprimary states in a GCFT constitute multiplets, which are block-diagonized under the Galilean boost operator. More importantly the multiplets include the states of negative norms, indicating the theory is not unitary. We compute global blocks of the multiplets, and discuss the expansion of four-point functions in terms of the global blocks of the multiplets. Furthermore we do the harmonic analysis for the Galilean conformal symmetry and obtain an inversion formula. As the first step to apply the Galilean conformal bootstrap, we construct generalized Galilean free theory (GGFT) explicitly. We read the data of GGFT by using Taylor series expansion of four-point function and the inversion formula independently, and find exact agreement. We discuss some novel features in the Galilean conformal bootstrap, due to the non-semisimpleness of the Galilean conformal algebra and the non-unitarity of the GCFTs.

Abstract Many applications requiring both spectral and spatial information at high resolution benefit from spectral imaging. Although different technical methods have been developed and commercially available, computational spectral cameras represent a compact, lightweight, and inexpensive solution. However, the tradeoff between spatial and spectral resolutions, dominated by the limited data volume and environmental noise, limits the potential of these cameras. In this study, we developed a deeply learned broadband encoding stochastic hyperspectral camera. In particular, using advanced artificial intelligence in filter design and spectrum reconstruction, we achieved 7000–11,000 times faster signal processing and ~10 times improvement regarding noise tolerance. These improvements enabled us to precisely and dynamically reconstruct the spectra of the entire field of view, previously unreachable with compact computational spectral cameras.

Abstract SHP2 is a ubiquitous tyrosine phosphatase involved in regulating both tumor and immune cell signaling. In this study, we discovered a novel immune modulatory function of SHP2. Targeting this protein with allosteric SHP2 inhibitors promoted anti-tumor immunity, including enhancing T cell cytotoxic function and immune-mediated tumor regression. Knockout of SHP2 using CRISPR/Cas9 gene editing showed that targeting SHP2 in cancer cells contributes to this immune response. Inhibition of SHP2 activity augmented tumor intrinsic IFNγ signaling resulting in enhanced chemoattractant cytokine release and cytotoxic T cell recruitment, as well as increased expression of MHC Class I and PD-L1 on the cancer cell surface. Furthermore, SHP2 inhibition diminished the differentiation and inhibitory function of immune suppressive myeloid cells in the tumor microenvironment. SHP2 inhibition enhanced responses to anti-PD-1 blockade in syngeneic mouse models. Overall, our study reveals novel functions of SHP2 in tumor immunity and proposes that targeting SHP2 is a promising strategy for cancer immunotherapy.