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Under complex well conditions, neutron-induced gamma spectra often suffer from drift and resolution degradation, leading to weakened characteristic peaks and increased peak overlap. These nonlinear degradations limit the accuracy of conventional linear spectral decomposition methods used in pulsed neutron logging. To address this issue, we propose a nonlinear quadratic spectral decomposition (NQSD) method. The method incorporates spectral drift and resolution degradation as parameters in a nonlinear model. These parameters are then solved iteratively using a two-step nonlinear weighted least squares approach. To improve computational efficiency in practical applications, a neural-network-based classifier is developed to identify whether input spectra exhibit sufficiently severe nonlinear degradation to require the NQSD method. The classifier achieves an overall accuracy of 90.24% on an independent field dataset. Monte Carlo simulations demonstrate that the proposed NQSD method reduces the overall mean absolute error (MAE) of elemental content by 86.8% compared with the conventional weighted least squares (WLS) method. Finally, the proposed method is validated on a coal-bearing interval in Shanxi. The results show that compared with WLS, NQSD reduces the overall MAE of elemental content by 59.8%, with the MAE of calcium, silicon, and total organic carbon (TOC) decreasing by 91.9%, 82.4%, and 27.3%, respectively. These results indicate that the proposed method provides reliable spectral analysis for pulsed neutron logging under complex well conditions, supporting more accurate formation evaluation.

The event-by-event initial state fluctuations constitute a critical physical quantity for understanding the dynamics of high-energy heavy-ion collisions. Due to the extremely limited available experimental data of such fluctuations, it is difficult to constrain or direct the characterization of the underlying initial state properties. This work constrains the correlation function between the initial eccentricity and the asymmetry by employing a parton distribution function that incorporates spin polarization induced by thermal vorticity and thermal shear. Based on the kinetic theory calculations, we found that the asymmetry fluctuations considering spin polarization have a significant splitting effect compared to those without spin polarization. These splitting exhibit pronounced system size and energy dependence. Moreover, the ratios of correlation function display similar system size and energy dependence--particularly in the low-temperature regime ($T \lesssim$ 0.12 GeV). These results suggest that spin polarization may serve as a useful constraint method for the event-by-event initial state fluctuations of heavy-ion collisions.

Neutron radiography (NR) has long been regarded as an important technique for detecting residual ceramic cores in aero-engine turbine blades because of its strong penetrating capability and high sensitivity to light elements. However, neutron radiographic images of turbine blades generally exhibit low contrast, and small residual ceramic core features are often indistinct and easily confused with background noise or structural textures. To improve the performance of automated defect detection, we developed an adaptive multi-scale image fusion enhancement algorithm and a defect data augmentation scheme for residual ceramic core detection in NR. First, building upon the tracer and doping approaches, a purely image-processing-based local contrast enhancement algorithm for residual ceramic core regions was further proposed to improve the visual perceptual quality of residual ceramic core images. Second, a residual ceramic core image data augumentation method based on stable diffusion inpainting was further proposed to address the scarcity of neutron radiographic images containing residual ceramic cores and the overwhelming proportion of images without residual ceramic cores using existing real residual ceramic core image data. Experimental results demonstrate that the proposed method effectively enhances the contrast of residual ceramic core images and improves the detection performance using YOLOv10 and RT-DETR as baseline models, achieving precision, recall, $\mathrm{mAP@0.5}$, and $\mathrm{mAP@0.5:0.95}$ of 91.44$\%$, 89.56$\%$, 87.60$\%$, and 63.73$\%$ for YOLOv10, and 89.26$\%$, 87.48$\%$, 88.13$\%$, and 65.57$\%$ for RT-DETR, respectively. These results demonstrate the potential of the proposed method for practical residual ceramic core detection in NR.

Chiral phonons carry angular momentum and couple lattice dynamics to magnetic and transport phenomena. In magnets, however, a systematic classification of momentum-space phonon angular momentum (PAM) textures and experimental signatures of the resulting thermal responses remain elusive. Here we classify the PAM textures for all 122 magnetic point groups (MPGs) using two equivalent approaches: directly solving the linear symmetry constraints on the multipole expansion coefficients and constructing the allowed textures from group-representation basis functions. Both methods yield the same fivefold classification according to the leading nonzero order: $s$-wave (monopole), $p$-wave (dipole), $d$-wave (quadrupole), $f$-wave (octupole), and $g$-wave (hexadecapole). We further show that a temperature gradient generates a nonequilibrium PAM density whose $n$th-order term in $\nabla T$ contains an energy-weighted PAM $2^n$-pole tensor and a kinematic remainder, thereby linking the response order and partial-wave channel through exact selection rules. Because the Berry curvature obeys the same symmetry constraints, this taxonomy is in one-to-one correspondence with Berry-curvature multipoles and their nonlinear Hall responses. We propose a cantilever torque measurement whose allowed angular harmonics are fixed by the MPG, with period $2π/n$ for a pure $n$th harmonic, providing a direct probe of the partial-wave content of PAM in both nonmagnetic and magnetic materials.

Recommendation models consume increasingly long, attribute-rich user histories, making small continuous updates and repeated large reads a central storage asymmetry. We present USDB, ByteDance's unified sequence storage practice for streaming and batch workloads. CascadeLSM time-based compaction stages materialize application-specific histories; differentiated replicas share persistent files; partial dumps reconstruct inference inputs from captured recent state and versioned history; and a hybrid columnar format accelerates projected reads. These designs reuse maintenance results across ingestion, inference, and training. Production migration records show 85.8% lower mean write latency, while normalized deployment accounting shows 42.1% lower infrastructure cost. Controlled comparisons with an Optimized RocksDB sequence baseline show faster long-history reads across projections and client loads. Aggregation pushdown sustains 8.5x the throughput of ordinary reads under a constrained receive budget. Production reconstruction and direct-file training results demonstrate how shared sequence maintenance supports fresh serving and repeated historical access.