Memory-Efficient Optical Flow via Radius-Distribution Orthogonal Cost Volume

The full 4D cost volume in Recurrent All-Pairs Field Transforms (RAFT) or global matching by Transformer achieves impressive performance for optical flow estimation. However, their memory consumption increases quadratically with input resolution, rendering them impractical for high-resolution images. In this paper, we present MeFlow, a novel memory-efficient method for high-resolution optical flow estimation. The key of MeFlow is a recurrent local orthogonal cost volume representation, which decomposes the 2D search space dynamically into two 1D orthogonal spaces, enabling our method to scale effectively to very high-resolution inputs. To preserve essential information in the orthogonal space, we utilize self attention to propagate feature information from the 2D space to the orthogonal space. We further propose a radius-distribution multi-scale lookup strategy to model the correspondences of large displacements at a negligible cost. We verify the efficiency and effectiveness of our method on the challenging Sintel and KITTI benchmarks, and real-world 4K ($2160\!\times\!3840$) images. Our method achieves competitive performance on both Sintel and KITTI benchmarks, while maintaining the highest memory efficiency on high-resolution inputs.Comment: 10 pages, 9 figure

Induction of PtoCDKB and PtoCYCB transcription by temperature during cambium reactivation in Populus tomentosa Carr.

Cell cycle progression requires interaction between cyclin-dependent kinase B (CDKB) and cyclin B (CYCB). The seasonal expression patterns of the CDKB and CYCB homologues from Populus tomentosa Carr. were investigated, and effects of temperature and exogenous indole-3-acetic acid (IAA) on their expression were further studied in water culture experiments. Based on the differential responses of dormant cambium cells to exogenous IAA, four stages of cambium dormancy were confirmed for P. tomentosa: quiescence 1 (Q1), rest, quiescence 2-1 (Q2-1), and quiescence 2-2 (Q2-2). PtoCDKB and PtoCYCB transcripts were strongly expressed in the active phases, weakly in Q1, and almost undetectable from rest until late Q2-2. Climatic data analysis showed a correlation between daily air temperature and PtoCDKB and PtoCYCB expression patterns. Water culture experiments with temperature treatment further showed that a low temperature (4 °C) kept PtoCDKB and PtoCYCB transcripts at undetectable levels, while a warm temperature (25 °C) induced their expression in the cambium region. Meanwhile, water culture experiments with exogenous IAA treatment showed that induction of PtoCDKB and PtoCYCB transcription was independent of exogenous IAA. The results suggest that, in deciduous hardwood P. tomentosa growing in a temperate zone, the temperature in early spring is a vital environmental factor for cambium reactivation. The increasing temperature in early spring may induce CDKB and CYCB homologue transcription in the cambium region, which is necessary for cambium cell division

Electrocardiogram Baseline Wander Suppression Based on the Combination of Morphological and Wavelet Transformation Based Filtering

One of the major noise components in electrocardiogram (ECG) is the baseline wander (BW). Effective methods for suppressing BW include the wavelet-based (WT) and the mathematical morphological filtering-based (MMF)algorithms. However, the T waveform distortions introduced by the WTand the rectangular/trapezoidal distortions introduced by MMF degrade the quality of the output signal. Hence, in this study, we introduce a method by combining the MMF and WTto overcome the shortcomings of both existing methods. To demonstrate the effectiveness of the proposed method, artificial ECG signals containing a clinicalBW are used for numerical simulation, and we also create a realistic model of baseline wander to compare the proposed method with other state-of-the-art methods commonly used in the literature. /e results show that the BW suppression effect of the proposed method is better than that of the others. Also, the new method is capable of preserving the outline of the BW and avoiding waveform distortions caused by the morphology filter, thereby obtaining an enhanced quality of ECG

Superradiant anomaly magnification in evolution of vector bosonic condensates bounded by a Kerr black hole with near-horizon reflection

Ultralight vector particles can form evolving condensates around a Kerr black hole (BH) due to superradiant instability. We study the effect of near-horizon reflection on the evolution of this system; by matching three pieces of asymptotic expansions of the Proca equation in Kerr metric and considering the leading order in the electric mode, we present explicit analytical expressions for the corrected energy level shifts and the superradiant instability rates. Particularly, in high-spin BH cases, we identify an anomalous situation where the superadiance rate is temporarily increased by the reflection parameter $\mathcal{R}$, which also occurs in the scalar scenario, but is largely magnified in vector condensates due to a faster growth rate in dominant mode; we constructed several featured quantities to illustrate this anomaly, and formalized the magnification with relevant correction factors, which may be of significance in future studies of gravitational waveforms of this monochromatic type. In addition, the duration of superradiance for the whole evolution is prolonged with a delay factor, which is calculated to be $(1+\mathcal{R})/({1-\mathcal{R}})$ approximately

Unsupervised Learning Method for the Wave Equation Based on Finite Difference Residual Constraints Loss

The wave equation is an important physical partial differential equation, and in recent years, deep learning has shown promise in accelerating or replacing traditional numerical methods for solving it. However, existing deep learning methods suffer from high data acquisition costs, low training efficiency, and insufficient generalization capability for boundary conditions. To address these issues, this paper proposes an unsupervised learning method for the wave equation based on finite difference residual constraints. We construct a novel finite difference residual constraint based on structured grids and finite difference methods, as well as an unsupervised training strategy, enabling convolutional neural networks to train without data and predict the forward propagation process of waves. Experimental results show that finite difference residual constraints have advantages over physics-informed neural networks (PINNs) type physical information constraints, such as easier fitting, lower computational costs, and stronger source term generalization capability, making our method more efficient in training and potent in application.Comment: in Chinese languag