NA61/SHINE online noise filtering using machine learning methods

The NA61/SHINE is a high-energy physics experiment operating at the SPS accelerator at CERN. The physics program of the experiment was recently extended, requiring a significant upgrade of the detector setup. The main goal of the upgrade is to increase the event flow rate from 80Hz to 1kHz by exchanging the read-out electronics of the NA61/SHINE main tracking detectors (Time-Projection-Chambers - TPCs). As the amount of collected data will increase significantly, a tool for online noise filtering is needed. The standard method is based on the reconstruction of tracks and removal of clusters which do not belong to any particle trajectory. However, this method takes a substantial amount of time and resources. A novel approach based on machine learning methods is presented in this proceedings

Comparison of the effects of TENS stimulation and water immersion on relieving labour pain suffered byprimiparas

Objectives: The aim of this study was to compare pain suffered by primiparas when delivering a child in a traditional way with deliveries where either TENS stimulation or water immersion was used.Material and methods: Primiparas were divided into 3 groups. In group 1 there were 45 women for whom TENS stimulation was applied during delivery. Group 2 consisted of 38 women who remained in the water during the actual birth of the baby. Group 3 served as the control group and was composed of 32 women. The intensity of pain during delivery was assessed by means of a numerical scale. During the first delivery period, pain was assessed three times at cervical dilation of 2, 3 and 4 fingers.Results: The analysis of pain suffered by primiparas at 2-finger widening showed no statistically significant differences between the groups. However, the analysis of pain experienced at 3-finger opening showed significant differences between the group of women using TENS stimulation in comparison with the control group. When comparing pain at 4-finger opening, statistically significant differences were found between the group of women who delivered in water in comparison to both the control group and the group using TENS stimulation.Conclusions: TENS stimulation and water immersion are good methods to relieve labour pain; particularly helpful in the first period of labour. They are also safe, alternative, non-pharmacological methods of reducing labour pain

Universe from vacuum in loop-string cosmology

In this paper we study the description of the Universe based on the low energy superstring theory modified by the Loop Quantum Gravity effects.This approach was proposed by De Risi et al. in the Phys. Rev. D {\bf 76} (2007) 103531. We show that in the contrast with the string motivated pre-Big Bang scenario, the cosmological realisation of the $t$-duality transformation is not necessary to avoid an initial singularity. In the model considered the universe starts its evolution in the vacuum phase at time $t\to - \infty$. In this phase the scale factor $a\to 0$, energy density $\rho \to 0$ and coupling of the interactions $g^2_s \to 0$. After this stage the universe evolves to the non-singular hot Big Bang phase $\rho \to \rho_{\text{max}} < \infty$. Then the standard classical universe emerges. During the whole evolution the scale factor increases monotonically. We solve this model analytically. We also propose and solve numerically the model with an additional dilaton potential in which the universe starts the evolution from the asymptotically free vacuum phase $g^2_s \to 0$ and then evolves non-singularly to the emerging dark energy dominated phase with the saturated coupling constant $g^2_s \to \text{const}$.Comment: JHEP3 LaTeX class, 19 pages, 9 figures, v2: added some comments and references, v3: new numerical result added, new figure

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset