Generation of true quantum random numbers with on-demand probability distributions via single-photon quantum walks.

Random numbers are at the heart of diverse fields, ranging from simulations of stochastic processes to classical and quantum cryptography. The requirement for true randomness in these applications has motivated various proposals for generating random numbers based on the inherent randomness of quantum systems. The generation of true random numbers with arbitrarily defined probability distributions is highly desirable for applications, but it is very challenging. Here we show that single-photon quantum walks can generate multi-bit random numbers with on-demand probability distributions, when the required "coin" parameters are found with the gradient descent (GD) algorithm. Our theoretical and experimental results exhibit high fidelity for various selected distributions. This GD-enhanced single-photon system provides a convenient way for building flexible and reliable quantum random number generators. Multi-bit random numbers are a necessary resource for high-dimensional quantum key distribution.

The Rashba effect in two-dimensional hybrid perovskites: the impacts of halogens and surface ligands.

Two-dimensional (2D) hybrid organic-inorganic perovskites (HOIPs) have gained much research interest nowadays due to their outstanding optoelectronic properties; however, the properties of the Rashba effect in 2D HOIPs have not been fully interpreted. In this work, a detailed thickness dependent structural distortion along with the Rashba splitting energy were investigated. Three types of HOIPs, 2D MAPbCl3, 2D MAPbBr3 and 2D MAPbI3, were adopted to compare the effect of halogens; and three surface ligands, BA, tert-BA and PEA, were adopted to explore the effect of ligands. It turns out that the structural distortion degree decreases with oscillations as the thickness increases, the Rashba splitting magnitude follows the same tendency, and 2D MAPbI3 is less sensitive to the thickness change compared to 2D MAPbBr3 or 2D MAPbCl3. Furthermore, different ligands and their orientations could have dramatically different impacts on the Rashba splitting. The PEA ligands enhance the Rashba splitting magnitude while the BA ligands have the reverse effect, and the impact of tert-BA ligands is insensitive to the increasing thickness. The partial charge density analysis shows that the band edges could be contributed by a charge density at a specific layer in the structure; thus, the Rashba effect is layer dependent in 2D HOIPs. These results provide some new perspectives on the Rashba effect in 2D HOIPs.

The Stokes Shift and Exciton Fine Structure in Strongly Confined CsPbBr3 Perovskite Nanoplatelets.

The Stokes shift is negligible in bulk perovskites but large in two-dimensional (2D) CsPbBr3 perovskites. The issue has attracted a lot of discussion, but it remains controversial. Here, we report the temperature-dependent absorption and photoluminescence (PL) spectra of CsPbBr3 perovskite nanoplatelets (NPLs). We observe a temperature-dependent Stokes shift changing from 26 to 41 meV. This phenomenon was attributed to the exciton fine structure according to the great difference in peak width. The triple bright exciton levels all participate in the absorption process and result in a wide absorption peak, while only the lowest exciton level contributes to photon emission and exhibits a relatively narrow PL peak. The PL decay curves also present the characterization of bright and dark exciton couplings at low temperatures. The splitting of triple bright excitons is induced by the morphology anisotropy of the 2D structure, so the large Stokes shift is proposed to be an intrinsic property of 2D perovskites.

[Progress in quality analysis of honey by infrared spectroscopy].

The detection of the quality of honey and the differentiation of adulteration are very important for quality and safety assurance. Traditionally used chemical methods were expensive and complicated, therefore they are not suitable for the requirement of wide-scale detection. In the past decade, the detection technology of honey developed with a trend of fast and high throughput detection. Spectroscopy has the fast and non-contact characteristic, and was widely used in petrifaction. This technology also has the potential for application in honey analysis. In the present study, the progress in quantitative and qualitative analysis of honey by near infrared spectroscopy (NIR) and mid infrared spectroscopy (MIR) is reviewed. The application of this two spectroscopy methods to honey detection refers to several aspects, such as quality control analysis, determination of botanical origin, determination of geographical origin and detection of adulteration. The detailed information of the detection of honey by NIR and MIR spectroscopy was analyzed, containing detection principle, technology path, accuracy, influence factors, and the development trend.

[Possibilities of near-infrared spectroscopy for the assessment of principle components in honey].

The potential of near infrared spectroscopy (NIR) as a nondestructive method for determining the principle components of honeys was studied for 153 unifloral honeys and multifloral honey samples. Fourier transform near-infrared spectroscopy (FT-NIR), CCD near-infrared spectroscopy and PDA near-infrared spectroscopy were evaluated to quantitatively determine water content, fructose content and glucose content in honey. On the basis of partial-least square (PLS) regression, the models of honey were compared. The best calibration model gives the correlation coefficients of 0.978 5, 0.931 1 and 0.90 7 for water, fructose and glucose, respectively, with the root mean square error of prediction (RMSEP) of 0.410 8(%), 1.914 48(%) and 2.531 9(%) respectively. The results demonstrated that near-infrared spectrometry is a valuable, rapid and nondestructive tool for the quantitative analysis of the principle components in honey.

[Analysis of NIR characteristic wavelengths for apple flesh firmness based on GA and iPLS].

In the present study, the fruit flesh firmness of apple was analyzed by near infrared (NIR) spectroscopy using an FT-NIR spectrometer. The sensitive spectral regions that provide the lowest prediction error were analyzed by different well-known variable selection methods, including dynamic backward interval partial least-squares (dynamic biPLS), sequential application of backward interval partial least-squares and genetic algorithm(dynamic biPLS & GA-PLS), and iterative genetic algorithm partial least-squares (iterative GA-PLS). Iterative GA-PLS, dynamic biPLS & GA-PLS led to a distinct reduction in the number of spectral data points with better predictive quality. Furthermore, the majority of selected wavelengths were content with the characteristic of the sorption bands of fruit flesh firmness. Pectin constituents, complex non-starch polysaccharides, which are related to texture change in apple, play an important role in their harvest maturity, ripening and storage. Comparing NIR characteristic wavelengths of apple flesh firmness and typical absorption bands for pectin, it was found that characteristic wavelengths of apple flesh firmness were consistent with the pectins relevant spectral regions. Therefore, the NIR characteristic wavelengths of apple firmness based on GA and iPLS reflected the chemical component of apple and the results were reasonable.

Study of wavelet denoising in apple's charge-coupled device near-infrared spectroscopy.

Discrete wavelet transform was used to eliminate the noise in the charge-coupled device near-infrared (CCD-NIR) spectra of apple. The influence of three parameters (wavelet function, decomposition level, and threshold) on the predictive ability of the calibration model was investigated. The result showed that the db, sym, and bior wavelet families performed well, while the coif, dmey, and haar wavelets were not able to denoise effectively. The best decomposition level was 2. The threshold selection rules of the default, Birge-Massart, and Penalty had good denoising results, while SURE, Sqtwolog, Heuristic SURE, and Minimax set all detailed coefficients to zero due to their high threshold values. The best denoising result was obtained with the combination of the bior3.3 wavelet function, two levels of decomposition, default threshold selection rule, and the soft thresholding method. The optimal model of soluble solids content was constructed. The relative standard deviation of prediction decreased from 7.79 to 5.82% after wavelet denoising.

Evaluation of a laying-hen tracking algorithm based on a hybrid support vector machine.

BACKGROUND: Behavior is an important indicator reflecting the welfare of animals. Manual analysis of video is the most commonly used method to study animal behavior. However, this approach is tedious and depends on a subjective judgment of the analysts. There is an urgent need for automatic identification of individual animals and automatic tracking is a fundamental part of the solution to this problem. RESULTS: In this study, an algorithm based on a Hybrid Support Vector Machine (HSVM) was developed for the automated tracking of individual laying hens in a layer group. More than 500 h of video was conducted with laying hens raised under a floor system by using an experimental platform. The experimental results demonstrated that the HSVM tracker outperformed the Frag (fragment-based tracking method), the TLD (Tracking-Learning-Detection), the PLS (object tracking via partial least squares analysis), the MeanShift Algorithm, and the Particle Filter Algorithm based on their overlap rate and the average overlap rate. CONCLUSIONS: The experimental results indicate that the HSVM tracker achieved better robustness and state-of-the-art performance in its ability to track individual laying hens than the other algorithms tested. It has potential for use in monitoring animal behavior under practical rearing conditions.

The performance of nu-support vector regression on determination of soluble solids content of apple by acousto-optic tunable filter near-infrared spectroscopy.

The nu-support vector regression (nu-SVR) was used to construct the calibration model between soluble solids content (SSC) of apples and acousto-optic tunable filter near-infrared (AOTF-NIR) spectra. The performance of nu-SVR was compared with the partial least square regression (PLSR) and the back-propagation artificial neural networks (BP-ANN). The influence of SVR parameters on the predictive ability of model was investigated. The results indicated that the parameter nu had a rather wide optimal area (between 0.35 and 1 for the apple data). Therefore, we could determine the value of nu beforehand and focus on the selection of other SVR parameters. For analyzing SSC of apple, nu-SVR was superior to PLSR and BP-ANN, especially in the case of fewer samples and treating the noise polluted spectra. Proper spectra pretreatment methods, such as scaling, mean center, standard normal variate (SNV) and the wavelength selection methods (stepwise multiple linear regression and genetic algorithm with PLS as its objective function), could improve the quality of nu-SVR model greatly.