Perturbative quantum gauge fields on the noncommutative torus

Using standard field theoretical techniques, we survey pure Yang-Mills theory on the noncommutative torus, including Feynman rules and BRS symmetry. Although in general free of any infrared singularity, the theory is ultraviolet divergent. Because of an invariant regularization scheme, this theory turns out to be renormalizable and the detailed computation of the one loop counterterms is given, leading to an asymptoticaly free theory. Besides, it turns out that non planar diagrams are overall convergent when $\theta$ is irrational.Comment: Latex 2e, 19 pages 5 eps figures, typos corrected and 1 reference adde

Exact solitons on noncommutative tori

We construct exact solitons on noncommutative tori for the type of actions arising from open string field theory. Given any projector that describes an extremum of the tachyon potential, we interpret the remaining gauge degrees of freedom as a gauge theory on the projective module determined by the tachyon. Whenever this module admits a constant curvature connection, it solves exactly the equations of motion of the effective string field theory. We describe in detail such a construction on the noncommutative tori. Whereas our exact solution relies on the coupling to a gauge theory, we comment on the construction of approximate solutions in the absence of gauge fields.Comment: 22 pages, JHEP style, typos corrected and references improve

Parametric Representation of Rank d Tensorial Group Field Theory: Abelian Models with Kinetic Term ∑s∣ps∣+μ\sum_{s}|p_s| + \mu

We consider the parametric representation of the amplitudes of Abelian models in the so-called framework of rank $d$ Tensorial Group Field Theory. These models are called Abelian because their fields live on $U(1)^D$. We concentrate on the case when these models are endowed with particular kinetic terms involving a linear power in momenta. New dimensional regularization and renormalization schemes are introduced for particular models in this class: a rank 3 tensor model, an infinite tower of matrix models $\phi^{2n}$ over $U(1)$, and a matrix model over $U(1)^2$. For all divergent amplitudes, we identify a domain of meromorphicity in a strip determined by the real part of the group dimension $D$. From this point, the ordinary subtraction program is applied and leads to convergent and analytic renormalized integrals. Furthermore, we identify and study in depth the Symanzik polynomials provided by the parametric amplitudes of generic rank $d$ Abelian models. We find that these polynomials do not satisfy the ordinary Tutte's rules (contraction/deletion). By scrutinizing the "face"-structure of these polynomials, we find a generalized polynomial which turns out to be stable only under contraction.Comment: 69 pages, 35 figure

The form factors existing in the b->s g^* decay and the possible CP violating effects in the noncommutative standard model

We study the form factors appearing in the inclusive decay b -> s g^*, in the framework of the noncommutative standard model. Here g^* denotes the virtual gluon. We get additional structures and the corresponding form factors in the noncommutative geometry. We analyse the dependencies of the form factors to the parameter p\Theta k where p (k) are the four momenta of incoming (outgoing) b quark (virtual gluon g^*, \Theta is a parameter which measures the noncommutativity of the geometry. We see that the form factors are weaklyComment: 8 pages, 7 figure

Revitalization of selected brownfields in urban space of Skierniewice with particular emphasis on environmentally friendly elements.

Transformation of urban space is a result of constant changes, which can also lead to a deterioration of the state of such space. City revitalization programs allow for the release of brownfields from crisis situations and adapting them to the needs of local communities, with particular emphasis on environmentally friendly elements. The aim of the article was to present the concept of revitalization activities, proposed for introduction in the brownfield site, in the city of Skierniewice, currently used by Stal-Car. These activities include treatments that can result in the creation of urban public spaces with environmentally friendly elements.Przekształcenie przestrzeni miejskiej to efekt ciągłych przemian, które mogą prowadzić również do pogorszenia jej stanu. Programy rewitalizacji miast pozwalają na wyprowadzenie ze stanu kryzysowego m.in. terenów poprzemysłowych i dostosowanie ich do wymogów lokalnych społeczności, ze szczególnym uwzględnieniem elementów przyjaznych środowisku. W artykule za cel przyjęto stworzenie koncepcji działań rewitalizacyjnych proponowanych do wprowadzenia na terenie poprzemysłowym w Skierniewicach, obecnie użytkowanym przez firmę Stal-Car. Działania te obejmują zabiegi, których następstwem może być powstanie miejskiej przestrzeni publicznej z elementami przyjaznymi środowisku

Quantum creep and quantum creep transitions in 1D sine-Gordan chains

Discrete sine-Gordon (SG) chains are studied with path-integral molecular dynamics. Chains commensurate with the substrate show the transition from collective quantum creep to pinning at bead masses slightly larger than those predicted from the continuous SG model. Within the creep regime, a field-driven transition from creep to complete depinning is identified. The effects of disorder in the external potential on the chain's dynamics depend on the potential's roughness exponent $H$, i.e., quantum and classical fluctuations affect the current self-correlation functions differently for $H = 1/2$.Comment: 4 pages, 3 figure

Driver Drowsiness Immediately before Crashes – A Comparative Investigation of EEG Pattern Recognition

Periodogram and other spectral power estimation methods are established in quantitative EEG analysis. Their outcome in case of drowsy subjects fulfilling a sustained attention task is difficult to interpret. Two novel kind of EEG analysis based on pattern recognition were proposed recently, namely the microsleep (MS) and the alpha burst (AB) pattern recognition. We compare both methods by applying them to the same experimental data and relating their output variables to two independent variables of driver drowsiness. The latter was an objective lane tracking performance variable and the first was a subjective variable of self-experienced sleepiness. Results offer remarkable differences between both EEG analysis methodologies. The expected increase with time since sleep as well as with time on task, which also exhibited in both independent variables, was not identified after applying AB recognition. The EEG immediately before fatigue related crashes contained both patterns. MS patterns were remarkably more frequent before crashes; almost every crash (98.5 %) was preceded by MS patterns, whereas less than 64 % of all crashes had AB patterns within a 10 sec pre-crash interval

Extensible Structure-Informed Prediction of Formation Energy with Improved Accuracy and Usability employing Neural Networks

In the present paper, we introduce a new neural network-based tool for the prediction of formation energies of atomic structures based on elemental and structural features of Voronoi-tessellated materials. We provide a concise overview of the connection between the machine learning and the true material-property relationship, how to improve the generalization accuracy by reducing overfitting, and how new data can be incorporated into the model to tune it to a specific material system. The present work resulted in three final models optimized for (1) highest test accuracy on the Open Quantum Materials Database (OQMD), (2) performance in the discovery of new materials, and (3) performance at a low computational cost. On a test set of 21,800 compounds randomly selected from OQMD, they achieve a mean average error (MAE) of 28, 40, and 42 meV/atom, respectively. The second model provides better predictions on materials far from ones reported in OQMD, while the third reduces the computational cost by a factor of 8. We collect our results in a new open-source tool called SIPFENN (Structure-Informed Prediction of Formation Energy using Neural Networks). SIPFENN not only improves the accuracy beyond existing models but also ships in a ready-to-use form with pre-trained neural networks and a GUI interface. By virtue of this, it can be included in DFT calculations routines at nearly no cost