We have examined a topology of 21 public transport networks in Poland. Our data exhibit several universal features in considered systems when they are analyzed from the point of view of evolving networks. Depending on the assumed definition of a network topology the degree distribution can follow a power law p(k) ~ k^(-\gamma) or can be described by an exponential function p(k) \~exp(-\alpha k). In the first case one observes that mean distances between two nodes are a linear function of logarithms of their degrees product.

Julian Sienkiewicz Janusz A. Holyst

We study the conductance through Aharonov-Bohm finite ladder rings with strongly interacting electrons, modelled by the prototypical t-J model. For a wide range of parameters we observe characteristic dips in the conductance as a function of magnetic flux, predicted so far only in chains which are a signature of spin and charge separation. These results open the possibility of observing this peculiar many-body phenomenon in anisotropic ladder systems and in real nanoscopic devices.

Julian Rincon K. Hallberg A. A. Aligia

We present measurements of the oscillation frequencies $\Delta m_d$ and $\Delta m_s$ of $B$ meson mixing as well as a measurement of the time-dependent CP-asymmetry in decays of $B^0\to J/\psi K_{\text{S}}^0$ based on $1.0\,\text{fb}^{-1}$ of data collected by the LHCb experiment in 2011. These measurements give valuable input to tests of the unitarity of the Cabibbo-Kobayashi-Maskawa matrix.

Julian Wishahi

We used the Ladder Network [Rasmus et al. (2015)] to perform Hyperspectral Image Classification in a semi-supervised setting. The Ladder Network distinguishes itself from other semi-supervised methods by jointly optimizing a supervised and unsupervised cost. In many settings this has proven to be more successful than other semi-supervised techniques, such as pretraining using unlabeled data. We furthermore show that the convolutional Ladder Network outperforms most of the current techniques used in hyperspectral image classification and achieves new state-of-the-art performance on the Pavia University dataset given only 5 labeled data points per class.

Julian Büchel Okan Ersoy

We study the conductance through finite Aharonov-Bohm rings of interacting electrons weakly coupled to non-interacting leads at two arbitrary sites. This model can describe an array of quantum dots with a large charging energy compared to the interdot overlap. As a consequence of the spin-charge separation, which occurs in these highly correlated systems, the transmittance is shown to present pronounced dips for particular values of the magnetic flux piercing the ring. We analyze this effect by numerical and analytical means and show that the zero-temperature equilibrium conductance in fact presents these striking features which could be observed experimentally.

Julián Rincón A. A. Aligia K. Hallberg

The Tutorial reports recent experimental advances in studies of the dynamics as well as the number and phase correlations of a Bose-Einstein condensed photon gas confined in a high-finesse dye-filled microcavity. Repeated absorption-emission-processes of photons on dye molecules here establish a thermal coupling of the photonic quantum gas to both a heat bath and a particle reservoir comprised of dye molecules. In this way, for the first time Bose-Einstein condensation under grand-canonical statistical ensemble conditions becomes experimentally accessible.

Julian Schmitt

Glitching pulsars fall broadly into two statistical classes: those with Poisson-like waiting times and power-law sizes, and those with unimodal waiting times and sizes. Previous glitch modeling based on a state-dependent Poisson process readily generates Poisson-like behaviour but struggles to produce unimodal waiting times or sizes. Here it is shown that, when some of the inputs to the model are modified, both classes of statistical behaviour can be reproduced by varying a single control parameter related to the spin-down rate. The implications for past and future glitch observations and the underlying microphysical mechanism are explored briefly.

Julian B. Carlin Andrew Melatos

Requirements often specify the expected system behavior by using causal relations (e.g., If A, then B). Automatically extracting these relations supports, among others, two prominent RE use cases: automatic test case derivation and dependency detection between requirements. However, existing tools fail to extract causality from natural language with reasonable performance. In this paper, we present our tool CiRA (Causality detection in Requirements Artifacts), which represents a first step towards automatic causality extraction from requirements. We evaluate CiRA on a publicly available data set of 61 acceptance criteria (causal: 32; non-causal: 29) describing the functionality of the German Corona-Warn-App. We achieve a macro F_1 score of 83%, which corroborates the feasibility of our approach.

Jannik Fischbach Julian Frattini Andreas Vogelsang

Motivated by the similarity to QCD, specifically the property of asymptotic freedom, we simulate the dynamics of the SU(2) $\times$ SU(2) model in two dimensions using the Hybrid Monte Carlo algorithm. By introducing Fourier Acceleration, we show that critical slowing down is largely avoided and increases the simulation efficiency by up to a factor of 300. This yields numerical predictions at a precision exceeding that of existing studies and allows us to verify the onset of asymptotic scaling.

Roger Horsley Brian Pendleton Julian Wack

The effects of heavy new particles beyond the Standard Model can be conveniently captured through higher-dimensional effective operators. As noted long ago by Weinberg, the amount of baryon and lepton number an operator can carry is intricately connected to its mass dimension. We derive an improved inequality for this connection and compare it to explicit operator constructions up to mass dimension 25. For the effective field theory of Standard Model plus right-handed neutrinos, our relationship is even an equality up to high mass dimension.

Julian Heeck Diana Sokhashvili