"Microscopic" approach to the Ricci dark energy

A derivation of the Ricci dark energy from quantum field theory of fluctuating "matter" fields in a classical gravitational background is presented. The coupling to the dark energy, the parameter 'a', is estimated in the framework of our formalism, and qualitatively it appears to be within observational expectations.Comment: 7 page

ARCHModels.jl: Estimating ARCH Models in Julia

This paper introduces ARCHModels.jl, a package for the Julia programming language that implements a number of univariate and multivariate autoregressive conditional heteroskedasticity models. This model class is the workhorse tool for modeling the conditional volatility of financial assets. The distinguishing feature of these models is that they model the latent volatility as a (deterministic) function of past returns and volatilities. This recursive structure results in loop-heavy code which, due to its just-in-time compiler, Julia is well-equipped to handle. As such, the entire package is written in Julia, without any binary dependencies. We benchmark the performance of ARCHModels.jl against popular implementations in MATLAB, R, and Python, and illustrate its use in a detailed case study

On the State Complexity of Partial Derivative Automata For Regular Expressions with Intersection

Extended regular expressions (with complement and intersection) are used in many applications due to their succinctness. In particular, regular expressions extended with intersection only (also called semi-extended) can already be exponentially smaller than standard regular expressions or equivalent nondeterministic finite automata (NFA). For practical purposes it is important to study the average behaviour of conversions between these models. In this paper, we focus on the conversion of regular expressions with intersection to nondeterministic finite automata, using partial derivatives and the notion of support. First, we give a tight upper bound of 2O(n) for the worst-case number of states of the resulting partial derivative automaton, where n is the size of the expression. Using the framework of analytic combinatorics, we then establish an upper bound of (1.056 + o(1))n for its asymptotic average-state complexity, which is significantly smaller than the one for the worst case. (c) IFIP International Federation for Information Processing 2016

SCORE performance in Central and Eastern Europe and former Soviet Union: MONICA and HAPIEE results

Aims: The Systematic COronary Risk Evaluation (SCORE) scale assesses 10 year risk of fatal atherosclerotic cardiovascular disease (CVD), based on conventional risk factors. The high-risk SCORE version is recommended for Central and Eastern Europe and former Soviet Union (CEE/FSU), but its performance has never been systematically assessed in the region. We evaluated SCORE performance in two sets of population-based CEE/FSU cohorts. Methods and results: The cohorts based on the World Health Organization MONitoring of trends and determinants in CArdiovascular disease (MONICA) surveys in the Czech Republic, Poland (Warsaw and Tarnobrzeg), Lithuania (Kaunas), and Russia (Novosibirsk) were followed from the mid-1980s. The Health, Alcohol, and Psychosocial factors in Eastern Europe (HAPIEE) study follows Czech, Polish (Krakow), and Russian (Novosibirsk) cohorts from 2002–05. In Cox regression analyses, the high-risk SCORE ≥5% at baseline significantly predicted CVD mortality in both MONICA [n = 15 027; hazard ratios (HR), 1.7–6.3] and HAPIEE (n = 20 517; HR, 2.6–10.5) samples. While SCORE calibration was good in most MONICA samples (predicted and observed mortality were close), the risk was underestimated in Russia. In HAPIEE, the high-risk SCORE overpredicted the estimated 10 year mortality for Czech and Polish samples and adequately predicted it for Russia. SCORE discrimination was satisfactory in both MONICA and HAPIEE. Conclusion: The high-risk SCORE underestimated the fatal CVD risk in Russian MONICA but performed well in most MONICA samples and Russian HAPIEE. This SCORE version might overestimate the risk in contemporary Czech and Polish populations

Onderzoeksrapportage impact Dutch Grand Prix Zandvoort 2023

Gemeente Zandvoort en de organisatie van de Dutch Grand Prix (DGP) hebben Breda University of Applied Sciences (BUas) gevraagd om de economische, sociale en maatschappelijke impact van het evenement Dutch Grand Prix 2023 en haar side events (onder de noemer Zandvoort Racefestival) te onderzoeken.Het onderzoek is uitgevoerd middels online en/of face-to-face afgenomen gestructureerde vragenlijsten onder 726 bezoekers van het circuit, 286 bezoekers aan het dorp Zandvoort, 108 ondernemers van Zandvoort en 3418 bewoners van Zandvoort (736), Bloemendaal (37), Haarlem (2322), Haarlemmermeer (162), Heemstede (59) en Noordwijk (102). Daarnaast is aanvullende informatie opgevraagd bij de organisatie van de Dutch Grand Prix, Stichting Zandvoort Beyond, Zandvoort Marketing en Gemeente Zandvoort.Voor het berekenen van de economische impact is gebruik gemaakt van de richtlijnen zoals deze opgesteld zijn door de Werkgroep Evaluatie Sportevenementen (WESP). Er is inzicht verkregen in de additionele bestedingen van DGP-bezoekers en de DGP-organisatie.In de berekening van economische impact is niet gecorrigeerd voor verdringingseffecten. Sponsoractivaties (zoals afhuur van gelegenheden in Zandvoort, inhuur personeel, verzorgen eten en drinken voor genodigden) zijn niet in kaart gebracht. Bestedingen van bezoekers die niet het circuit maar wel het dorp hebben bezocht tijdens het raceweekend zijn eveneens niet meegenomen in de berekening van de economische impact. Het winstcijfer van de DGP-organisatie wordt niet gedeeld en is ook niet meegenomen in de berekening van de economische impact

Onderzoeksrapportage impact Dutch Grand Prix Zandvoort 2023

Gemeente Zandvoort en de organisatie van de Dutch Grand Prix (DGP) hebben Breda University of Applied Sciences (BUas) gevraagd om de economische, sociale en maatschappelijke impact van het evenement Dutch Grand Prix 2023 en haar side events (onder de noemer Zandvoort Racefestival) te onderzoeken.Het onderzoek is uitgevoerd middels online en/of face-to-face afgenomen gestructureerde vragenlijsten onder 726 bezoekers van het circuit, 286 bezoekers aan het dorp Zandvoort, 108 ondernemers van Zandvoort en 3418 bewoners van Zandvoort (736), Bloemendaal (37), Haarlem (2322), Haarlemmermeer (162), Heemstede (59) en Noordwijk (102). Daarnaast is aanvullende informatie opgevraagd bij de organisatie van de Dutch Grand Prix, Stichting Zandvoort Beyond, Zandvoort Marketing en Gemeente Zandvoort.Voor het berekenen van de economische impact is gebruik gemaakt van de richtlijnen zoals deze opgesteld zijn door de Werkgroep Evaluatie Sportevenementen (WESP). Er is inzicht verkregen in de additionele bestedingen van DGP-bezoekers en de DGP-organisatie.In de berekening van economische impact is niet gecorrigeerd voor verdringingseffecten. Sponsoractivaties (zoals afhuur van gelegenheden in Zandvoort, inhuur personeel, verzorgen eten en drinken voor genodigden) zijn niet in kaart gebracht. Bestedingen van bezoekers die niet het circuit maar wel het dorp hebben bezocht tijdens het raceweekend zijn eveneens niet meegenomen in de berekening van de economische impact. Het winstcijfer van de DGP-organisatie wordt niet gedeeld en is ook niet meegenomen in de berekening van de economische impact

Cross-shell excitation in two-proton knockout: Structure of 52^{52}Ca

The two-proton knockout reaction $^9$Be($^{54}$Ti,$^{52}$Ca$+ \gamma$) has been studied at 72 MeV/nucleon. Besides the strong feeding of the $^{52}$Ca ground state, the only other sizeable cross section proceeds to a 3$^-$ level at 3.9 MeV. There is no measurable direct yield to the first excited 2$^+$ state at 2.6 MeV. The results illustrate the potential of such direct reactions for exploring cross-shell proton excitations in neutron-rich nuclei and confirms the doubly-magic nature of $^{52}$Ca

Connectionist modal logic: Representing modalities in neural networks

AbstractModal logics are amongst the most successful applied logical systems. Neural networks were proved to be effective learning systems. In this paper, we propose to combine the strengths of modal logics and neural networks by introducing Connectionist Modal Logics (CML). CML belongs to the domain of neural-symbolic integration, which concerns the application of problem-specific symbolic knowledge within the neurocomputing paradigm. In CML, one may represent, reason or learn modal logics using a neural network. This is achieved by a Modalities Algorithm that translates modal logic programs into neural network ensembles. We show that the translation is sound, i.e. the network ensemble computes a fixed-point meaning of the original modal program, acting as a distributed computational model for modal logic. We also show that the fixed-point computation terminates whenever the modal program is well-behaved. Finally, we validate CML as a computational model for integrated knowledge representation and learning by applying it to a well-known testbed for distributed knowledge representation. This paves the way for a range of applications on integrated knowledge representation and learning, from practical reasoning to evolving multi-agent systems