Excited neutrino search potential of the FCC-based electron-hadron colliders

The production potential of the excited neutrinos at the FCC-based electron-hadron colliders, namely the ERL60$\otimes$FCC with $\sqrt{s}=3.46$ TeV, the ILC$\otimes$FCC with $\sqrt{s}=10$ TeV, and the PWFA-LC$\otimes$FCC with $\sqrt{s}=31.6$ TeV, has been analyzed. The branching ratios of the excited neutrinos have been calculated for the different decay channels and shown that the dominant channel is $\nu^{\star}\rightarrow eW^{+}$. We have calculated the production cross sections with the process of $ep\rightarrow\nu^{\star}q\rightarrow eW^{+}q$ and the decay widths of the excited neutrinos with the process of $\nu^{\star}\rightarrow eW^{+}$. The signals and corresponding backgrounds are studied in detail to obtain accessible mass limits. It is shown that the discovery limits obtained on the mass of the excited neutrino are $2452$ GeV for $L_{int}=100$ $fb^{-1}$, $5635$ GeV for $L_{int}=10$ $fb^{-1}$ ($6460$ GeV for $L_{int}=100$ $fb^{-1}$), and $10200$ GeV for $L_{int}=1$ $fb^{-1}$ ($13960$ GeV for $L_{int}=10$ $fb^{-1}$), for the center-of-mass energies of $3.46$, $10$, and $31.6$ TeV, respectively.Comment: 16 pages, 9 figures, 5 table

Excited muon searches at the FCC based muon-hadron colliders

We study the excited muon production at the FCC based muon-hadron colliders. We give the excited muon decay widths and production cross section. We deal with the $\mu p\rightarrow\mu^{\star}q\rightarrow\mu\gamma q$ process and we plot the transverse momentum, rapidity and invariant mass distributions of final state particles to get the discovery cuts. By using discovery cuts, we get the mass limits for excited muons. It is shown that the discovery limits on the mass of $\mu^{\star}$ are 2.2 TeV, 5.9 TeV and 7.5 TeV for $\mu63$-FCC, $\mu750$-FCC and $\mu1500$-FCC, respectively.Comment: 13 pages, 10 figures, 3 tables, version of published in Adv. High Energy Physic

The archipelago of press restriction in Turkey

Turkey’s independent media died a slow and painful death, a result of years of co-option, censorship and repression. But critical journalism faded with a whimper and not a bang even before Erdogan and the Justice and Development Party (AKP) came to power

Right for the Right Reason: Training Agnostic Networks

We consider the problem of a neural network being requested to classify images (or other inputs) without making implicit use of a "protected concept", that is a concept that should not play any role in the decision of the network. Typically these concepts include information such as gender or race, or other contextual information such as image backgrounds that might be implicitly reflected in unknown correlations with other variables, making it insufficient to simply remove them from the input features. In other words, making accurate predictions is not good enough if those predictions rely on information that should not be used: predictive performance is not the only important metric for learning systems. We apply a method developed in the context of domain adaptation to address this problem of "being right for the right reason", where we request a classifier to make a decision in a way that is entirely 'agnostic' to a given protected concept (e.g. gender, race, background etc.), even if this could be implicitly reflected in other attributes via unknown correlations. After defining the concept of an 'agnostic model', we demonstrate how the Domain-Adversarial Neural Network can remove unwanted information from a model using a gradient reversal layer.Comment: Author's original versio

Co doping induced structural and optical properties of sol-gel prepared ZnO thin films

Cataloged from PDF version of article.The preparation conditions for Co doping process into the ZnO structure were studied by the ultrasonic spray pyrolysis technique. Structural and optical properties of the Co:ZnO thin films as a function of Co concentrations were examined. It was observed that hexagonal wurtzite structure of ZnO is dominant up to the critical value, and after the value, the cubic structural phase of the cobalt oxide appears in the X-ray diffraction patterns. Every band-edge of Co:ZnO films shifts to the lower energies and all are confirmed with the PL measurements. Co substitution in ZnO lattice has been proved by the optical transmittance measurement which is observed as the loss of transmission appearing in specific region due to Co2+ characteristic transitions. © 2014 Elsevier B.V. All rights reserved

Mitigating Gender Bias in Machine Learning Data Sets

Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society. Gender bias has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine learning and performing more rigorous analysis of training data. Mitigating bias when algorithms are trained on textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data for machine learning.The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact.Comment: 10 pages, 5 figures, 5 Tables, Presented as Bias2020 workshop (as part of the ECIR Conference) - http://bias.disim.univaq.i