arXiv subjects

Svetoslav Ivanov

Publications and source records attributed to Svetoslav Ivanov.

8 recordsLinked to original sources

arXiv2026

Routine Blood Tests Outperform CRP for Distinguishing Bacterial From Viral Infection in Children

Acute infectious diseases are among the leading causes of medical consultations and hospitalizations in children worldwide. These infections are predominantly caused by viruses or bacteria, yet differentiating between the two remains a common clinical challenge. As a result, pediatricians often default to the safer option of prescribing antibiotics contributing to the growing problem of antimicrobial resistance. The objective is to assess the additional predictive value of CBC towards determining the current infection. This retrospective study used data from 906 pediatric patients aged between 2 and 14 years who were tested positive either for viral or bacterial infection between 2022 and 2026. Inclusion criteria further required availability of CBC results and CRP level measurements. These laboratory parameters as well as age were used as input features for several supervised classification models. Model performance was evaluated using AUC, sensitivity and specificity. The best performing model is XGBoost, which included all features, achieving out of-sample performance of AUC of 81.7% and sensitivity of 70.8%, specificity of 79.2%. All trained models outperform a CRP-based only decision-rule model in terms of AUC. We suggest that the decision to prescribe antibiotics should be based on a number of factors, including but not limited to CBC, some of which are not currently incorporated into routine practice.

cs.LG↗

arXiv2025

Blind unblinding procedure for the PADME X17 data sample

The PADME experiment at the Frascati DA$Φ$NE LINAC has performed a search for the hypothetical X17 particle, with a mass of around 17 MeV, by scanning the energy of a positron beam striking a fixed target. The X17 should be produced from the resulting $e^+e^-$ annihilation. Since the expected mass of this particle is only roughly known, data sidebands cannot be clearly defined. Furthermore, the need to keep the analysis blind to potentially sizable signal contributions prevents a clear assessment even of the quality of the data sample in this search. In light of these challenges, this paper presents an innovative strategy adopted by the PADME Collaboration to perform data quality checks without disclosing the X17 sample. Moreover, the procedure designed to eventually unblind the data is described, together with the statistical approach adopted to extract the limits on the coupling between the X17 and the Standard Model.

hep-ex↗

arXiv2023

Status and Prospects of PADME

The Positron Annihilation to Dark Matter Experiment (PADME) was designed and constructed to search for dark photons ($A'$) in the process $e^+e^-\rightarrowγA'$, using the positron beam at the Beam Test Facility (BTF) at the National Laboratories of Frascati (LNF). Since the observation of an anomalous spectra in internal pair creation decays of nuclei seen by the collaboration at the ATOMKI institute, the PADME detector has been modified and a new data-taking run has been undertaken to probe the existance of the so-called ``X17" particle

hep-ex↗

arXiv2023

Dark sector studies with the PADME experiment

The Positron Annihilation to Dark Matter Experiment (PADME) uses the positron beam of the DA$Φ$NE Beam-Test Facility, at the Laboratori Nazionali di Frascati (LNF) to search for a Dark Photon $A'$. The search technique studies the missing mass spectrum of single-photon final states in $e^+e^-\rightarrow A'γ$ annihilation in a positron-on-thin-target experiment. This approach facilitates searches for new particles such as long lived Axion-Like-Particles, protophobic X bosons and Dark Higgs. This talk illustrated the scientific program of the experiment and its first physics results. In particular, the measurement of the cross-section of the SM process $e^+e^-\rightarrow γγ$ at $\sqrt{s}$=21 MeV was shown.

hep-ex↗

arXiv2021

Optimal Linear Combination of Classifiers

The question of whether to use one classifier or a combination of classifiers is a central topic in Machine Learning. We propose here a method for finding an optimal linear combination of classifiers derived from a bias-variance framework for the classification task.

cs.LG↗

arXiv2020

Arbitrary polarization control by magnetic field variation

We propose a universal scheme for the construction of a device for arbitrary to arbitrary polarization transformation, which consists of two quarter-wave plates and two Faraday rotators. Using this device, one can continuously change the retardance and the rotation angle simply by changing the magnetic fields in each Faraday rotator.

physics.optics↗

arXiv2019

Tutorial on Implied Posterior Probability for SVMs

Implied posterior probability of a given model (say, Support Vector Machines (SVM)) at a point $\bf{x}$ is an estimate of the class posterior probability pertaining to the class of functions of the model applied to a given dataset. It can be regarded as a score (or estimate) for the true posterior probability, which can then be calibrated/mapped onto expected (non-implied by the model) posterior probability implied by the underlying functions, which have generated the data. In this tutorial we discuss how to compute implied posterior probabilities of SVMs for the binary classification case as well as how to calibrate them via a standard method of isotonic regression.

cs.LG↗

arXiv2019

A Note on Posterior Probability Estimation for Classifiers

One of the central themes in the classification task is the estimation of class posterior probability at a new point $\bf{x}$. The vast majority of classifiers output a score for $\bf{x}$, which is monotonically related to the posterior probability via an unknown relationship. There are many attempts in the literature to estimate this latter relationship. Here, we provide a way to estimate the posterior probability without resorting to using classification scores. Instead, we vary the prior probabilities of classes in order to derive the ratio of pdf's at point $\bf{x}$, which is directly used to determine class posterior probabilities. We consider here the binary classification problem.

cs.LG↗