Higgs-differential cross section at NNLO in dimensional regularisation

We present an analytic computation of the Higgs production cross section in the gluon fusion channel, which is differential in the components of the Higgs momentum and inclusive in the associated partonic radiation through NNLO in perturbative QCD. Our computation includes the necessary higher order terms in the dimensional regulator beyond the finite part that are required for renormalisation and collinear factorisation at N$^3$LO. We outline in detail the computational methods which we employ. We present numerical predictions for realistic final state observables, specifically distributions for the decay products of the Higgs boson in the $\gamma\gamma$ decay channel.Comment: 27 pages, 6 awesome figure

Robust T-Loss for Medical Image Segmentation

This paper presents a new robust loss function, the T-Loss, for medical image segmentation. The proposed loss is based on the negative log-likelihood of the Student-t distribution and can effectively handle outliers in the data by controlling its sensitivity with a single parameter. This parameter is updated during the backpropagation process, eliminating the need for additional computation or prior information about the level and spread of noisy labels. Our experiments show that the T-Loss outperforms traditional loss functions in terms of dice scores on two public medical datasets for skin lesion and lung segmentation. We also demonstrate the ability of T-Loss to handle different types of simulated label noise, resembling human error. Our results provide strong evidence that the T-Loss is a promising alternative for medical image segmentation where high levels of noise or outliers in the dataset are a typical phenomenon in practice. The project website can be found at https://robust-tloss.github.ioComment: Early accepted to MICCAI 202

SelfClean: A Self-Supervised Data Cleaning Strategy

Most benchmark datasets for computer vision contain irrelevant images, near duplicates, and label errors. Consequently, model performance on these benchmarks may not be an accurate estimate of generalization capabilities. This is a particularly acute concern in computer vision for medicine where datasets are typically small, stakes are high, and annotation processes are expensive and error-prone. In this paper we propose SelfClean, a general procedure to clean up image datasets exploiting a latent space learned with self-supervision. By relying on self-supervised learning, our approach focuses on intrinsic properties of the data and avoids annotation biases. We formulate dataset cleaning as either a set of ranking problems, which significantly reduce human annotation effort, or a set of scoring problems, which enable fully automated decisions based on score distributions. We demonstrate that SelfClean achieves state-of-the-art performance in detecting irrelevant images, near duplicates, and label errors within popular computer vision benchmarks, retrieving both injected synthetic noise and natural contamination. In addition, we apply our method to multiple image datasets and confirm an improvement in evaluation reliability

Towards Reliable Dermatology Evaluation Benchmarks

Benchmark datasets for digital dermatology unwittingly contain inaccuracies that reduce trust in model performance estimates. We propose a resource-efficient data cleaning protocol to identify issues that escaped previous curation. The protocol leverages an existing algorithmic cleaning strategy and is followed by a confirmation process terminated by an intuitive stopping criterion. Based on confirmation by multiple dermatologists, we remove irrelevant samples and near duplicates and estimate the percentage of label errors in six dermatology image datasets for model evaluation promoted by the International Skin Imaging Collaboration. Along with this paper, we publish revised file lists for each dataset which should be used for model evaluation. Our work paves the way for more trustworthy performance assessment in digital dermatology.Comment: Link to the revised file lists: https://github.com/Digital-Dermatology/SelfClean-Revised-Benchmark

Impact of acute changes of left ventricular contractility on the transvalvular impedance: validation study by pressure-volume loop analysis in healthy pigs

BACKGROUND: The real-time and continuous assessment of left ventricular (LV) myocardial contractility through an implanted device is a clinically relevant goal. Transvalvular impedance (TVI) is an impedentiometric signal detected in the right cardiac chambers that changes during stroke volume fluctuations in patients. However, the relationship between TVI signals and LV contractility has not been proven. We investigated whether TVI signals predict changes of LV inotropic state during clinically relevant loading and inotropic conditions in swine normal heart. METHODS: The assessment of RVTVI signals was performed in anesthetized adult healthy anesthetized pigs (n = 6) instrumented for measurement of aortic and LV pressure, dP/dtmax and LV volumes. Myocardial contractility was assessed with the slope (Ees) of the LV end systolic pressure-volume relationship. Effective arterial elastance (Ea) and stroke work (SW) were determined from the LV pressure-volume loops. Pigs were studied at rest (baseline), after transient mechanical preload reduction and afterload increase, after 10-min of low dose dobutamine infusion (LDDS, 10 ug/kg/min, i.v), and esmolol administration (ESMO, bolus of 500 µg and continuous infusion of 100 µg·kg-1·min-1). RESULTS: We detected a significant relationship between ESTVI and dP/dtmax during LDDS and ESMO administration. In addition, the fluctuations of ESTVI were significantly related to changes of the Ees during afterload increase, LDDS and ESMO infusion. CONCLUSIONS: ESTVI signal detected in right cardiac chamber is significantly affected by acute changes in cardiac mechanical activity and is able to predict acute changes of LV inotropic state in normal heart

Precision NNLO determination of alpha_s(M_Z) using an unbiased global parton set

We determine the strong coupling alpha_s at NNLO in perturbative QCD using the global dataset input to the NNPDF2.1 NNLO parton fit: data from neutral and charged current deep-inelastic scattering, Drell-Yan, vector boson production and inclusive jets. We find alpha_s(M_Z)=0.1173+- 0.0007 (stat), where the statistical uncertainty comes from the underlying data and uncertainties due to the analysis procedure are negligible. We show that the distribution of alpha_s values preferred by different experiments in the global fit is statistically consistent, without need for rescaling uncertainties by a "tolerance" factor. We show that if deep-inelastic data only are used, the best-fit value of alpha_s is somewhat lower, but consistent within one sigma with the global determination. We estimate the dominant theoretical uncertainty, from higher orders corrections, to be Delta alpha_s (pert) ~ 0.0009.Comment: 11 pages, 6 figures. Various small corrections and improvements: Chi2 values for PDF fits provided, discussion of pulls clarified. Final version, to be published in Phys.Lett.