Fuzzy Bigraphs: An Exercise in Fuzzy Communicating Agents

Bigraphs and their algebra is a model of concurrency. Fuzzy bigraphs are a generalization of birgraphs intended to be a model of concurrency that incorporates vagueness. More specifically, this model assumes that agents are similar, communication is not perfect, and, in general, everything is or happens to some degree.Comment: 11 pages, 3 figure

Natural linewidth analysis of d-band photoemission from Ag(110)

We report a high-resolution angle-resolved study of photoemission linewidths observed for Ag(110). A careful data analysis yields k$-resolved upper limits for the inverse inelastic lifetimes of$d$-holes at the X-point of the bulk band structure. At the upper$d$-band edge the hole-lifetime is$\tau_h \geq 22 $fs, i.e. more than one order of magnitude larger than predicted for a free-electron gas. Following calculations for$d$-hole dynamics in Cu (I.\ Campillo et al., Phys. Rev. Lett., in press) we interpret the lifetime enhancement by a small scattering cross-section of $d$- and $sp$-states below the Fermi level. With increasing distance to $E_F$ the $d$-hole lifetimes get shorter because of the rapidly increasing density of d-states and contributions of intra-$d$-band scattering processes, but remain clearly above free-electron-model predictions.Comment: 14 pages, 7 figure

Neutrino-Nucleus Cross Section Measurements using Stopped Pions and Low Energy Beta Beams

Two new facilities have recently been proposed to measure low energy neutrino-nucleus cross sections, the nu-SNS (Spallation Neutron Source) and low energy beta beams. The former produces neutrinos by pion decay at rest, while the latter produces neutrinos from the beta decays of accelerated ions. One of the uses of neutrino-nucleus cross section measurements is for supernova studies, where typical neutrino energies are 10s of MeV. In this energy range there are many different components to the nuclear response and this makes the theoretical interpretation of the results of such an experiment complex. Although even one measurement on a heavy nucleus such as lead is much anticipated, more than one data set would be still better. We suggest that this can be done by breaking the electron spectrum down into the parts produced in coincidence with one or two neutrons, running a beta beam at more than one energy, comparing the spectra produced with pions and a beta beam or any combination of these.Comment: 6 pages, 6 figure

Prediction of Stock Market Volatility Utilizing Sentiment from News and Social Media Texts : A study on the practical implementation of sentiment analysis and deep learning models for predicting day-ahead volatility

This thesis studies the impact of sentiment on the prediction of volatility for 100 of the largest stocks in the S&P500 index. The purpose is to find out if sentiment can improve the forecast of day-ahead volatility wherein volatility is measured as the realized volatility of intraday returns. The textual data has been gathered from three different sources: Eikon, Twitter, and Reddit. The data consists of respectively 397 564 headlines from Eikon, 35 811 098 tweets, and 4 109 008 comments from Reddit. These numbers represent the uncleaned data before filtration. The data has been collected for the period between 01.08.2021 and 31.08.2022. Sentiment is calculated by the FinBERT model, an NLP model created by further pre-training of the BERT model on financial text. To predict volatility with the sentiment from FinBERT, three different deep learning models have been applied: A feed forward neural network, a recurrent neural network, and a long short-term memory model. They are used to solve both regression and classification problems. The inference analysis shows significant effects from the computed sentiment variables, and it implies that there exists a correlation between the number of text items and volatility. This is in line with previous literature on sentiment and volatility. The results from the deep learning models show that sentiment has an impact on the prediction of volatility. Both in terms of lower MSE and MAE for the regression problem and higher accuracy for the classification problem. Moreover, this thesis looks at potential weaknesses that could influence the validity of the results. Potential weaknesses include how sentiment is represented, noise in the data, and the Absftarcatc tthat the FinBERT model is not trained on financial oriented text from social media.nhhma

Water justice and Europe’s Right2Water movement

In 2013 the European Citizens’ Initiative (ECI) ‘Right2Water’ collected 1.9 million signatures across Europe against water privatization. It became the first ever successful ECI and has built a Europe-wide movement. Right2Water sought for Europe’s legal enforcement of the Human Right to Water and Sanitation (HRWS) as a strategic political tool to challenge European Union market policies. The paper examines the ECI from a social movement perspective. Although the European Commission subscribed that ‘water is a public good, not a commodity’, its implementation is subject to continuing politics and socio-political struggle, with growing urgency in times of the Covid-19 pandemic crisis