NeuMa - the absolute Neuromarketing dataset en route to an holistic understanding of consumer behaviour

Neuromarketing is a continuously evolving field that utilises neuroimaging technologies to explore consumers’ behavioural responses to specific marketing-related stimulation, and furthermore introduces novel marketing tools that could complement the traditional ones like questionnaires. In this context, the present paper introduces a multimodal Neuromarketing dataset that encompasses the data from 42 individuals who participated in an advertising brochure-browsing scenario. In more detail, participants were exposed to a series of supermarket brochures (containing various products) and instructed to select the products they intended to buy. The data collected for each individual executing this protocol included: (i) encephalographic (EEG) recordings, (ii) eye tracking (ET) recordings, (iii) questionnaire responses (demographic, profiling and product related questions), and (iv) computer mouse data. NeuMa dataset has both dynamic and multimodal nature and, due to the narrow availability of open relevant datasets, provides new and unique opportunities for researchers in the field to attempt a more holistic approach to neuromarketing

Minimal separators in graph classes defined by small forbidden induced subgraphs

Minimal separators in graphs are an important concept in algorithmic graph theory. In particular, many problems that are NP-hard for general graphs are known to become polynomial-time solvable for classes of graphs with a polynomially bounded number of minimal separators. Several well-known graph classes have this property, including chordal graphs, permutation graphs, circular-arc graphs, and circle graphs. We perform a systematic study of the question which classes of graphs defined by small forbidden induced subgraphs have a polynomially bounded number of minimal separators. We focus on sets of forbidden induced subgraphs with at most four vertices and obtain an almost complete dichotomy, leaving open only two cases

Jet energy measurement with the ATLAS detector in proton-proton collisions at sqrt(s)=7 TeV

The jet energy scale (JES) and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of √s = 7 TeV corresponding to an integrated luminosity of 38 pb−1. Jets are reconstructed with the anti-kt algorithm with distance parameters R = 0.4 or R = 0.6. Jet energy and angle corrections are determined from Monte Carlo simulations to calibrate jets with transverse momenta pT ≥ 20 GeV and pseudorapidities |h| < 4.5. The JES systematic uncertainty is estimated using the single isolated hadron response measured in situ and in test-beams, exploiting the transverse momentum balance between central and forward jets in events with dijet topologies and studying systematic variations in Monte Carlo simulations. The JES uncertainty is less than 2.5% in the central calorimeter region (|h| < 0.8) for jets with 60 ≤ pT < 800 GeV, and is maximally 14% for pT < 30 GeV in the most forward region 3.2 ≤ |h| < 4.5. The uncertainty for additional energy from multiple proton-proton collisions in the same bunch crossing is less than 1.5% per additional collision for jets with pT > 50 GeV after a dedicated correction for this effect. The JES is validated for jet transverse momenta up to 1 TeV to the level of a few percent using several in situ techniques by comparing a well-known reference such as the recoiling photon pT, the sum of the transverse momenta of tracks associated to the jet, or a system of low-pT jets recoiling against a high-pT jet. More sophisticated jet calibration schemes are presented based on calorimeter cell energy density weighting or hadronic properties of jets, providing an improved jet energy resolution and a reduced flavour dependence of the jet response. The JES systematic uncertainty determined from a combination of in situ techniques are consistent with the one derived from single hadron response measurements over a wide kinematic range. The nominal corrections and uncertainties are derived for isolated jets in an inclusive sample of high-pT jets. Special cases such as event topologies with close-by jets, or selections of samples with an enhanced content of jets originating from light quarks, heavy quarks or gluons are also discussed and the corresponding uncertainties are determined