We show that neural network classifiers can be helpful to discriminate Higgs production from background at LHC in the Higgs mass range MH ∼ 200GeV. We employ a common feed-forward neural network trained by the backpropagation algorithm for off-line analysis and the neural chip Totem, trained by the Reactive Tabu Search algorithm, which could be used for on-line analysis.

In an online experiment we examined the role of self-control in recognizing social cues in the context of disinhibited online behavior (e.g., flaming and trolling). We temporarily lowered participants' self-control capacity with an ego depletion paradigm (i.e., color Stroop task). Next, we measured participants' sensitivity to social cues with an emotional Stroop task containing neutral, negati...

We show that neural network classifiers can be helpful in discriminating Higgs production events from the huge background at LHC, assuming the case of a mass value MH ∼ 200 GeV. We use the high performance neurochip TOTEM, trained by the Reactive Tabu Search algorithm (RTS), which could be used for on-line purposes. Two different sets of input variables are compared.

We describe the discovery of colorless $C$-odd gluonic compound, odderon, by D0 and TOTEM Collaborations comparing elastic differential cross sections measured in $pp$ $p \bar{p}$ interactions at high energies.