On Quadratic Interpolation of Image Cross-Correlation for Subpixel Motion Extraction

International audienceDigital image correlation techniques are well known for motion extraction from video images. Following a two-stage approach, the pixel-level displacement is first estimated by maximizing the cross-correlation between two images, then the estimation is refined in the vicinity of the cross-correlation peak. Among existing subpixel refinement methods, quadratic surface fitting (QSF) provides good performances in terms of accuracy and computational burden. It estimates subpixel displacement by interpolating cross-correlation values with a quadratic surface. The purpose of this paper is to analytically investigate the QSF method. By means of counterexamples, it is first shown in this paper that, contrary to a widespread intuition, the quadratic surface fitted to the pixel-level cross-correlation values in the neighborhood of the cross-correlation peak does not always have a maximum. The main contribution of this paper then consists in establishing the mathematical conditions ensuring the existence of a maximum of this fitted quadratic surface, based on a rigorous analysis. Algorithm modifications for handling the failure cases of the QSF method are also proposed in this paper, in order to consolidate it for subpixel motion extraction. Experimental results based on two typical types of images are also reported

Analysis of Quadratic Surface Fitting for Subpixel Motion Extraction from Video Images

International audienceDigital image correlation is a popular method for estimating object displacement in successive images. At the pixel level, displacement is estimated by maximizing the crosscorrelation between two images. To achieve subpixel accuracy, displacement estimation can be refined in the vicinity of the crosscorrelation peak. Among existing refinement methods, quadratic surface fitting provides a good trade-off between accuracy and computational burden. The purpose of this paper is to analyze the quadratic surface fitting method. It is shown that the quadratic surface fitted to the cross-correlation values in the vicinity of the cross-correlation peak does not always have a maximum. Then the conditions ensuring the existence of a maximum are analyzed. The reported results consolidate the theoretic basis of the quadratic surface fitting method for subpixel motion extraction

Fairness-guided Few-shot Prompting for Large Language Models

Large language models have demonstrated surprising ability to perform in-context learning, i.e., these models can be directly applied to solve numerous downstream tasks by conditioning on a prompt constructed by a few input-output examples. However, prior research has shown that in-context learning can suffer from high instability due to variations in training examples, example order, and prompt formats. Therefore, the construction of an appropriate prompt is essential for improving the performance of in-context learning. In this paper, we revisit this problem from the view of predictive bias. Specifically, we introduce a metric to evaluate the predictive bias of a fixed prompt against labels or a given attributes. Then we empirically show that prompts with higher bias always lead to unsatisfactory predictive quality. Based on this observation, we propose a novel search strategy based on the greedy search to identify the near-optimal prompt for improving the performance of in-context learning. We perform comprehensive experiments with state-of-the-art mainstream models such as GPT-3 on various downstream tasks. Our results indicate that our method can enhance the model's in-context learning performance in an effective and interpretable manner

Experimental investigation of structural modal identification using pixels intensity and motion signals from video-based imaging devices: performance, comparison and analysis

International audienceThis paper aims to experimentally evaluate a simplified vision-based method for structural health monitoring (SHM). Contrary to conventional solutions that rely on extracting motions through image processing, this paper proposes to conduct the SHM analysis by the direct processing of pixel brightness without extracting the motion signals beforehand. After some pre-processing steps, it is shown that the brightness data reveal essential information about the dynamic characteristics of the monitored vibrating structure. Furthermore, the low-level information of the pixel is compensated by an efficient selection of the so called "active pixels" throughout the image time flow. Finally, a subspace system identification-based method is applied to the brightness data, so that the modal parameters with uncertainty bounds are estimated within a large range of model orders displayed in a stabilization diagram. The experiment database consists of image time flows of a cantilever beam excited by a shake table driven by a band limited random noise. Modal vibrations range from 1 to 173 Hz

Reweighted Mixup for Subpopulation Shift

Subpopulation shift exists widely in many real-world applications, which refers to the training and test distributions that contain the same subpopulation groups but with different subpopulation proportions. Ignoring subpopulation shifts may lead to significant performance degradation and fairness concerns. Importance reweighting is a classical and effective way to handle the subpopulation shift. However, recent studies have recognized that most of these approaches fail to improve the performance especially when applied to over-parameterized neural networks which are capable of fitting any training samples. In this work, we propose a simple yet practical framework, called reweighted mixup (RMIX), to mitigate the overfitting issue in over-parameterized models by conducting importance weighting on the ''mixed'' samples. Benefiting from leveraging reweighting in mixup, RMIX allows the model to explore the vicinal space of minority samples more, thereby obtaining more robust model against subpopulation shift. When the subpopulation memberships are unknown, the training-trajectories-based uncertainty estimation is equipped in the proposed RMIX to flexibly characterize the subpopulation distribution. We also provide insightful theoretical analysis to verify that RMIX achieves better generalization bounds over prior works. Further, we conduct extensive empirical studies across a wide range of tasks to validate the effectiveness of the proposed method.Comment: Journal version of arXiv:2209.0892

Systematic biases in determining dust attenuation curves through galaxy SED fitting

While the slope of the dust attenuation curve ($\delta$) is found to correlate with effective dust attenuation ($A_V$) as obtained through spectral energy distribution (SED) fitting, it remains unknown how the fitting degeneracies shape this relation. We examine the degeneracy effects by fitting SEDs of a sample of local star-forming galaxies (SFGs) selected from the Galaxy And Mass Assembly survey, in conjunction with mock galaxy SEDs of known attenuation parameters. A well-designed declining starburst star formation history is adopted to generate model SED templates with intrinsic UV slope ($\beta_0$) spanning over a reasonably wide range. The best-fitting $\beta_0$ for our sample SFGs shows a wide coverage, dramatically differing from the limited range of $\beta_0<-2.2$ for a starburst of constant star formation. Our results show that strong degeneracies between $\beta_0$, $\delta$, and $A_V$ in the SED fitting induce systematic biases leading to a false $A_V$--$\delta$ correlation. Our simulation tests reveal that this relationship can be well reproduced even when a flat $A_V$--$\delta$ relation is taken to build the input model galaxy SEDs. The variations in best-fitting $\delta$ are dominated by the fitting errors. We show that assuming a starburst with constant star formation in SED fitting will result in a steeper attenuation curve, smaller degeneracy errors, and a stronger $A_V$--$\delta$ relation. Our findings confirm that the $A_V$--$\delta$ relation obtained through SED fitting is likely driven by the systematic biases induced by the fitting degeneracies between $\beta_0$, $\delta$, and $A_V$.Comment: 21 pages, 13 figures, accepted for publication in the MNRAS, Comments welcome

Constitutively decreased TGFBR1 allelic expression is a common finding in colorectal cancer and is associated with three TGFBR1 SNPs

Purpose: Constitutively decreased TGFBR1 allelic expression is emerging as a potent modifier of colorectal cancer risk in mice and humans. This phenotype was first observed in mice, then in lymphoblastoid cell lines from patients with microsatellite stable colorectal tumors. Patients and Methods: We assessed the frequency of constitutively decreased TGFBR1 allelic expression and association with SNPs covering the TGFBR1 locus using RNA and DNA extracted from the peripheral blood lymphocytes of 118 consecutive patients with biopsy-proven adenocarcinoma of the colon or the rectum. Results: We found that 11(9.3%) of 118 patients exhibited decreased TGFBR1 allelic expression (TGFBR1 ASE). TGFBR1 ASE was strongly associated with three SNPs in linkage disequilibrium with each other: rs7034462 (p = 7.2 × 10-4), TGFBR1*6A (p = 1.6 × 10-4) and rs11568785 (p = 1.4 × 10-4). Conclusion: These results confirm the high prevalence of constitutively decreased TGFBR1 allelic expression among patients with colorectal cancer. The association of this phenotype with TGFBR1*6A, rs7034462 and rs1156875 suggests an association between TGFBR1 SNPs and colorectal cancer, which warrants additional studies

Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison

Abstract This study quantifies the state-of-the-art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multi-model dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–2020 for predictions of Pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on June 1, July 1, August 1, and September 1. This diverse set of statistical and dynamical models can individually predict linearly detrended Pan-Arctic SIE anomalies with skill, and a multi-model median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to Pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and Central Arctic sectors. The skill of dynamical and statistical models is generally comparable for Pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least three months in advance.</jats:p