MLSS-VO: A Multi-Level Scale Stabilizer with Self-Supervised Features for Monocular Visual Odometry in Target Tracking

In this study, a multi-level scale stabilizer intended for visual odometry (MLSS-VO) combined with a self-supervised feature matching method is proposed to address the scale uncertainty and scale drift encountered in the field of monocular visual odometry. Firstly, the architecture of an instance-level recognition model is adopted to propose a feature matching model based on a Siamese neural network. Combined with the traditional approach to feature point extraction, the feature baselines on different levels are extracted, and then treated as a reference for estimating the motion scale of the camera. On this basis, the size of the target in the tracking task is taken as the top-level feature baseline, while the motion matrix parameters as obtained by the original visual odometry of the feature point method are used to solve the real motion scale of the current frame. The multi-level feature baselines are solved to update the motion scale while reducing the scale drift. Finally, the spatial target localization algorithm and the MLSS-VO are applied to propose a framework intended for the tracking of target on the mobile platform. According to the experimental results, the root mean square error (RMSE) of localization is less than 3.87 cm, and the RMSE of target tracking is less than 4.97 cm, which demonstrates that the MLSS-VO method based on the target tracking scene is effective in resolving scale uncertainty and restricting scale drift, so as to ensure the spatial positioning and tracking of the target.

Probing the spin correlations of $$t{\bar{t}} $$ production at NLO QCD+EW

In this work we investigate the NLO QCD+EW corrections to the top quark pair production and their effects on the spin correlation coefficients and asymmetries at fixed-order top quark pair production and LO decay in the dilepton channel, within the narrow-width approximation. The spin correlations are implicitly measured through the lepton kinematics. Moreover we study the EW effects to the leptonic differential distributions. We find that the EW corrections to the $$t {\bar{t}}$$ t t ¯ production are within the NLO QCD theoretical uncertainties for the spin correlation coefficients and the leptonic asymmetries. On the other hand, for the differential distributions we find that the EW corrections exceed the NLO QCD scale uncertainty band in the high rapidity regimes and are of the order of the NLO QCD scale uncertainty in the case of invariant mass and transverse momentum distributions.

The role of the threshold variable in soft-gluon resummation of the $$ t\overline{t}h $$ production process

We study the role of the scale of the threshold variable in soft-gluon threshold resummation. We focus on the computation of the resummed total cross section, the final-state invariant-mass distribution, and transverse-momentum distribution of the Higgs boson when produced in association with a top-anti-top quark pair for the Large Hadron Collider operating at 13 TeV. We show that different choices for the scale of the threshold variable result in differences at next-to-leading power, i.e. contributions that are down by one power of the threshold variable. These contributions are noticeable numerically, although their effect on the resummed observables lies within the scale uncertainty of those observables. The average central results, obtained after combining several central- scale choices, agree remarkably well for different choices of the threshold variable. However, different threshold choices do affect the resulting scale uncertainty. To compute our results, we introduce a novel numerical method that we call the deformation method, which aids the stabilization of the inverse Mellin transform in cases where the analytical Mellin transform of the partonic cross section is unknown. We show that this method leads to a factor of 10 less function evaluations, while gaining a factor of 4 − 5 in numerical precision when compared to the standard method.

Local-scale uncertainty of seasonal mean and extreme values of in-situ snow depth and snow fall measurements

Measurements of snow depth and snowfall on the daily scale can vary strongly over short distances. However, it is not clear if there is a seasonal dependence in these variations and how they impact common snow climate indicators based on mean values, as well as estimated return levels of extreme events based on maximum values. To analyse the impacts of local-scale variations we compiled a unique set of parallel snow measurements from the Swiss Alps consisting of 30 station pairs with up to 77 years of parallel data. Station pairs are mostly located in the same villages (or within 3 km horizontal and 150 m vertical distances). Investigated snow climate indicators include average snow depth, maximum snow depth, sum of new snow, days with snow on the ground, days with snowfall as well as snow onset and disappearance dates, which are calculated for various seasons (December to February (DFJ), November to April (NDJFMA), and March to April (MA)). We computed relative and absolute error metrics for all these indicators at each station pair to demonstrate the potential uncertainty. We found the largest relative inter-pair differences for all indicators in spring (MA) and the smallest in DJF. Furthermore, there is hardly any difference between DJF and NDJFMA which show median uncertainties of less than 5 % for all indicators. Local-scale uncertainty ranges between less than 24 % (DJF) and less than 43 % (MA) for all indicators and 75 % of all station pairs. Highest (lowest) percentage of station pairs with uncertainty less than 15 % is observed for days with snow on the ground with 90 % (average snow depth, 30 %). Median differences of snow disappearance dates are rather small (three days) and similar to the ones found for snow onset dates (two days). An analysis of potential sunshine duration could not explain the higher uncertainties in spring. To analyse the impact of local-scale variations on the estimation of extreme events, 50-year return levels were quantified for maximum snow depth and maximum 3-day new snow sum, which are often used for prevention measures. The found return levels are within each other’s 95 % confidence intervals for all (but two) station pairs, revealing no striking differences. The findings serve as an important basis for our understanding of uncertainties of commonly used snow indicators and extremal indices. Knowledge about such uncertainties in combination with break-detection methods is the groundwork in view of any homogenization efforts regarding snow time series.

On the impact of non-factorisable corrections in VBF single and double Higgs production

We study the non-factorisable QCD corrections, computed in the eikonal approximation, to Vector-Boson Fusion single and double Higgs production and show the combined factorisable and non-factorisable corrections for both processes at $$ \mathcal{O}\left({\alpha}_s^2\right) $$ O α s 2 . We investigate the validity of the eikonal approximation with and without selection cuts, and carry out an in-depth study of the relative size of the non-factorisable next-to-next-to-leading order corrections compared to the factorisable ones. In the case of single Higgs production, after selection cuts are applied, the non-factorisable corrections are found to be mostly contained within the factorisable scale uncertainty bands. When no cuts are applied, instead, the non-factorisable corrections are slightly outside the scale uncertainty band. Interestingly, for double Higgs production, we find that both before and after applying cuts, non-factorisable corrections are enhanced compared to the single Higgs case. We trace this enhancement to the existence of delicate cancellations between the various leading-order Feynman diagrams, which are partly spoiled by radiative corrections. All contributions studied here have been implemented in proVBFH v1.2.0 and proVBFHH v1.1.0.