让每一篇论文都经得起检验，
让有意义的研究被看见。

Single-pixel cameras have recently emerged as promising alternatives to multi-pixel sensors due to reduced costs and superior durability, which are particularly attractive for mid-infrared (MIR) imaging pertinent to applications including industry inspection and biomedical diagnosis. To date, MIR single-pixel photon-sparse imaging has yet been realized, which urgently calls for high-sensitivity optical detectors and high-fidelity spatial modulators. Here, we demonstrate a MIR single-photon computational imaging with a single-element silicon detector. The underlying methodology relies on nonlinear structured detection, where the encoded time-varying pump patterns are optically imprinted onto the MIR object image through sum-frequency generation. Simultaneously, the MIR radiation is spectrally translated into the visible region, thus permitting infrared single-photon upconversion detection. Then, the use of advanced algorithms of compress sensing and deep learning allows us to reconstruct MIR images under sub-Nyquist sampling and photon-starving illumination. The presented paradigm of single-pixel upconversion imaging is featured with single-pixel simplicity, single-photon sensitivity, and room-temperature operation, which would establish a new path for sensitive imaging at longer infrared wavelengths or terahertz frequencies, where high-sensitivity photon counters and high-fidelity spatial modulators are typically hard to access.

Near-Infrared (NIR) light emitting metal halides are emerging as a new generation of optical materials owing to their appealing features, which include low-cost synthesis, solution processability and adjustable optical properties. NIR emitting perovskite-based light-emitting diodes (LEDs) have reached an external quantum efficiency (EQE) over 20% and a device stability of over 10,000 h. Such results have sparked an interest in exploring new NIR metal halide emitters. In this review, we summarize several different types of NIR-emitting metal halides, including lead/tin bromide/iodide perovskites, lanthanide ions doped/based metal halides, double perovskites, low dimensional hybrid and Bi3+/Sb3+/Cr3+ doped metal halides, and assess their recent advancements. The characteristics and mechanisms of narrow-band or broadband NIR luminescence in all these materials are discussed in detail. We also highlight the various applications of NIR-emitting metal halides and provide an outlook for the field.

Light-emitting diodes (LEDs) based on halide perovskite nanocrystals have attracted extensive attention due to their considerable luminescence efficiency, wide color gamut, high color purity, and facile material synthesis. Since the first demonstration of LEDs based on MAPbBr3 nanocrystals were reported in 2014, the community has witnessed a rapid development in their performances. In this review, we provide a historical perspective of the development of LEDs based on halide perovskite nanocrystals and then present a comprehensive survey of current strategies to high-efficiency lead-based perovskite nanocrystals LEDs, including synthesis optimization, ion doping/alloying and shell coating. We then review the basic characteristics and emission mechanisms of lead-free perovskite and perovskite-related nanocrystals emitters in environmentally friendly LEDs, from the standpoint of different emission colors. Finally, we cover the progress in LED applications and provide an outlook of the opportunities and challenges for future developments in this field.

We present improved cosmological constraints from a re-analysis of the Dark Energy Survey (DES) 5-year sample of Type Ia supernovae (DES-SN5YR). This re-analysis includes an improved photometric cross-calibration, recent white dwarf observations to cross-calibrate between DES and low redshift surveys, retraining the SALT3 light curve model and fixing a numerical approximation in the host galaxy colour law. Our fully recalibrated sample, which we call DES-Dovekie, comprises $\sim$1600 likely Type Ia SNe from DES and $\sim$200 low-redshift SNe from other surveys. With DES-Dovekie, we obtain ${\mathrm{\Omega }}_{\mathrm{m}}=0.330\pm 0.015$ in Flat $\mathrm{\Lambda }$CDM which changes ${\mathrm{\Omega }}_{\mathrm{m}}$ by $-0.022$ compared to DES-SN5YR. Combining DES-Dovekie with CMB data from Planck, ACT and SPT and the DESI DR2 measurements in a Flat $w_0{w}_a$CDM cosmology, we find $w_0=-0.803\pm 0.054$, $w_a=-0.72\pm 0.21$. Our results hold a significance of $3.2\sigma$, reduced from $4.2\sigma$ for DES-SN5YR, to reject the null hypothesis that the data are compatible with the cosmological constant. This significance is equivalent to a Bayesian model preference odds of approximately 5:1 in favour of the Flat $w_0{w}_a$CDM model. Using generally accepted thresholds for model preference, our updated data exhibits only a weak preference for evolving dark energy.

Integrated photonics provides a powerful approach for developing compact, stable and scalable architectures for the generation, manipulation and detection of quantum states of light. To this end, several material platforms are being developed in parallel, each providing its specific assets, and hybridization techniques to combine their strengths are now possible. This review focuses on AlGaAs, a III-V semiconductor platform combining a mature fabrication technology, direct band-gap compliant with electrical injection, low-loss operation, large electro-optic effect, and compatibility with superconducting detectors for on-chip detection. We detail recent implementations of room-temperature sources of quantum light based on the high second- and third-order optical nonlinearities of the material, as well as photonic circuits embedding various functionalities ranging from polarizing beamsplitters to Mach-Zehnder interferometers, modulators and tunable filters. We then present several realizations of quantum state engineering enabled by these recent advances and discuss open perspectives and remaining challenges in the field of integrated quantum photonics with AlGaAs.

We propose and experimentally demonstrate a method to strongly increase the sensitivity of spin measurements on nitrogen-vacancy (NV) centers in diamond, which can be readily implemented in existing quantum sensing experiments. While charge state transitions of this defect are generally considered a parasitic effect to be avoided, we show here that these can be used to significantly increase the NV center’s spin contrast, a key quantity for high sensitivity magnetometry and high fidelity state readout. The protocol consists of a two-step procedure, in which the charge state of the defect is first purified by a strong laser pulse, followed by weak illumination to obtain high spin polarization. We observe a relative improvement of the readout contrast by 17 %, and infer a reduction of the initialization error of more than 50 %. The contrast enhancement is accompanied by a beneficial increase of the readout signal. For long sequence durations, typically encountered in high-resolution magnetometry, a measurement speedup by a factor of >1.5 is extracted, and we find that the technique is beneficial for sequences of any duration. Additionally, our findings give detailed insight into the charge and spin polarization dynamics of the NV center, and provide actionable insights for direct optical, spin-to-charge, and electrical readout of solid-state spin centres.

In the last few decades, Markov chain Monte Carlo (MCMC) methods have been widely applied to Bayesian updating of structural dynamic models in the field of structural health monitoring. Recently, several MCMC algorithms have been developed that incorporate neural networks to enhance their performance for specific Bayesian model updating problems. However, a common challenge with these approaches lies in the fact that the embedded neural networks often necessitate retraining when faced with new tasks, a process that is time-consuming and significantly undermines the competitiveness of these methods. This paper introduces a newly developed adaptive meta-learning stochastic gradient Hamiltonian Monte Carlo (AM-SGHMC) algorithm. The idea behind AM-SGHMC is to optimize the sampling strategy by training adaptive neural networks, and due to the adaptive design of the network inputs and outputs, the trained sampler can be directly applied to various Bayesian updating problems of the same type of structure without further training, thereby achieving meta-learning. Additionally, practical issues for the feasibility of the AM-SGHMC algorithm for structural dynamic model updating are addressed, and two examples involving Bayesian updating of multi-story building models with different model fidelity are used to demonstrate the effectiveness and generalization ability of the proposed method.

The best known methods for estimating hazard rate functions in survival analysis models are either purely parametric or purely nonparametric. The parametric ones are sometimes too biased while the nonparametric ones are sometimes too variable. In the present paper a certain semiparametric approach to hazard rate estimation, proposed in Hjort (1991), is developed further, aiming to combine parametric and nonparametric features. It uses a dynamic local likelihood approach to fit the locally most suitable member in a given parametric class of hazard rates, and amounts to a version of nonparametric parameter smoothing within the parametric class. Thus the parametric hazard rate estimate at time $s$ inserts a parameter estimate that also depends on $s$. We study bias and variance properties of the resulting estimator and methods for choosing the local smoothing parameter. It is shown that dynamic likelihood estimation often leads to better performance than the purely nonparametric methods, while also having capacity for not losing much to the parametric methods in cases where the model being smoothed is adequate.