Do Ads Add Up? The Impact of Parties' Advertisements on the Stability of Vote Choice at the Swiss National Elections 2011

Our contribution analyses the influence of campaign advertisements on vote choice in the 2011 elections to the Swiss National Council. Concretely, we ask whether and to what extent the relative exposure to party ads of a preferred party exerts a reinforcing effect on an individual's party choice. We make use of the two-wave panel structure contained in the RCS survey data of the Selects 2011 and combine it with data on advertisements in 20 important national and regional newspapers. We find that increasing exposure to the campaign of one's preferred party may reinforce individuals with strong party attachment in their initial vote choice. Yet this effect only materializes with substantial campaign duration and exposure. Additional and exploratory analyses revealed that particularly the two recently emerged parties, the GLP and BDP, might have made a slight difference by potentially persuading defecting voters with the help of their campaign.

Melting of metasomatized peridotite at 4–6 GPa and up to 1200 °C: an experimental approach

The phase assemblages and compositions in a K-bearing lherzolite + H2O system are determined between 4 and 6 GPa and 850–1200 °C, and the melting reactions occurring at subarc depth in subduction zones are constrained. Experiments were performed on a rocking multi-anvil apparatus. The experiments had around 16 wt% water content, and hydrous melt or aqueous fluid was segregated and trapped in a diamond aggregate layer. The compositions of the aqueous fluid and hydrous melt phases were measured using the cryogenic LA-ICP-MS technique. The residual lherzolite consists of olivine, orthopyroxene, clinopyroxene, and garnet, while diamond (C) is assumed to be inert. Hydrous and alkali-rich minerals were absent from the run products due to preferred dissolution of K2O (and Na2O) to the aqueous fluid/hydrous melt phases. The role of phlogopite in melting relations is, thus, controlled by the water content in the system: at the water content of around 16 wt% used here, phlogopite is unstable and thus does not participate in melting reactions. The water-saturated solidus, i.e., the first appearance of hydrous melt in the K–lherzolite composition, is located between 900 and 1000 °C at 4 GPa and between 1000 and 1100 °C at 5 and 6 GPa. Compositional jumps between hydrous melt and aqueous fluid at the solidus include a significant increase in the total dissolved solids load. All melts/fluids are peralkaline and calcium-rich. The melting reactions at the solidus are peritectic, as olivine, clinopyroxene, garnet, and H2O are consumed to generate hydrous melt plus orthopyroxene. Our fluid/melt compositional data demonstrate that the water-saturated hybrid peridotite solidus lies above 1000 °C at depths greater than 150 km and that the second critical endpoint is not reached at 6 GPa for a K2O–Na2O–CaO–FeO–MgO–Al2O3–SiO2–H2O–Cr2O3(–TiO2) peridotite composition.

Electron and photon energy calibration with the ATLAS detector using LHC Run 1 data

This paper presents the electron and photon energy calibration achieved with the ATLAS detector using about 25 fb−1 of LHC proton–proton collision data taken at centre-of-mass energies of √s = 7 and 8 TeV. The reconstruction of electron and photon energies is optimised using multivariate algorithms. The response of the calorimeter layers is equalised in data and simulation, and the longitudinal profile of the electromagnetic showers is exploited to estimate the passive material in front of the calorimeter and reoptimise the detector simulation. After all corrections, the Z resonance is used to set the absolute energy scale. For electrons from Z decays, the achieved calibration is typically accurate to 0.05% in most of the detector acceptance, rising to 0.2% in regions with large amounts of passive material. The remaining inaccuracy is less than 0.2–1% for electrons with a transverse energy of 10 GeV, and is on average 0.3% for photons. The detector resolution is determined with a relative inaccuracy of less than 10% for electrons and photons up to 60 GeV transverse energy, rising to 40% for transverse energies above 500 GeV.

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon’s implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike’s preceding ISI. As we show, the EIF’s exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron’s ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing computational theories about UP states during slow wave sleep and present possible extensions of the model in the context of spike-frequency adaptation.