Jožef Stefan Institute

About: Jožef Stefan Institute is a facility organization based out in Ljubljana, Slovenia. It is known for research contribution in the topics: Liquid crystal & Dielectric. The organization has 3828 authors who have published 12614 publications receiving 291025 citations.

Physics of leptoquarks in precision experiments and at particle colliders

Abstract: We present a comprehensive review of physics effects generated by leptoquarks (LQs), i.e., hypothetical particles that can turn quarks into leptons and vice versa, of either scalar or vector nature. These considerations include discussion of possible completions of the Standard Model that contain LQ fields. The main focus of the review is on those LQ scenarios that are not problematic with regard to proton stability. We accordingly concentrate on the phenomenology of light leptoquarks that is relevant for precision experiments and particle colliders. Important constraints on LQ interactions with matter are derived from precision low-energy observables such as electric dipole moments, (g-2) of charged leptons, atomic parity violation, neutral meson mixing, Kaon, B, and D meson decays, etc. We provide a general analysis of indirect constraints on the strength of LQ interactions with the quarks and leptons to make statements that are as model independent as possible. We address complementary constraints that originate from electroweak precision measurements, top, and Higgs physics. The Higgs physics analysis we present covers not only the most recent but also expected results from the Large Hadron Collider (LHC). We finally discuss direct LQ searches. Current experimental situation is summarized and self-consistency of assumptions that go into existing accelerator-based searches is discussed. A progress in making next-to-leading order predictions for both pair and single LQ productions at colliders is also outlined.

Information-Based Evaluation Criterion for Classifier's Performance

Abstract: In the past few years many systems for learning decision rules from examples were developed. As different systems allow different types of answers when classifying new instances, it is difficult to appropriately evaluate the systems' classification power in comparison with other classification systems or in comparison with human experts. Classification accuracy is usually used as a measure of classification performance. This measure is, however, known to have several defects. A fair evaluation criterion should exclude the influence of the class probabilities which may enable a completely uninformed classifier to trivially achieve high classification accuracy. In this paper a method for evaluating the information score of a classifier's answers is proposed. It excludes the influence of prior probabilities, deals with various types of imperfect or probabilistic answers and can be used also for comparing the performance in different domains.