mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data

We present the R-package mgm for the estimation of k-order Mixed Graphical Models (MGMs) and mixed Vector Autoregressive (mVAR) models in high-dimensional data. These are a useful extensions of graphical models for only one variable type, since data sets consisting of mixed types of variables (continuous, count, categorical) are ubiquitous. In addition, we allow to relax the stationarity assumption of both models by introducing time-varying versions MGMs and mVAR models based on a kernel weighting approach. Time-varying models offer a rich description of temporally evolving systems and allow to identify external influences on the model structure such as the impact of interventions. We provide the background of all implemented methods and provide fully reproducible examples that illustrate how to use the package

Shot noise and photon-induced correlations in 500 GHz SIS detectors

Photon-induced current correlations in SIS detectors can result in an output noise that is greater or less than shot noise. Evidence of these correlations had been observed for 100 GHz rf by accurate noise measurements as reported in our previous work. We now present a detailed analysis of these current correlations for frequencies between 100 and 500 GHz. We also report new measurements of photon-induced noise in a 490 GHz SIS mixer, and discuss the Gaussian beam techniques used to eliminate the thermal background radiation. For small 490 GHz rf power, the output noise is equal to shot noise. The results of the 100 and 490 GHz photon noise measurement are summarized in context to shot noise and the effect of the current correlations predicted by the theoretical model

Dicke-type phase transition in a multimode optomechanical system

We consider the "membrane in the middle" optomechanical model consisting of a laser pumped cavity which is divided in two by a flexible membrane that is partially transmissive to light and subject to radiation pressure. Steady state solutions at the mean-field level reveal that there is a critical strength of the light-membrane coupling above which there is a symmetry breaking bifurcation where the membrane spontaneously acquires a displacement either to the left or the right. This bifurcation bears many of the signatures of a second order phase transition and we compare and contrast it with that found in the Dicke model. In particular, by studying limiting cases and deriving dynamical critical exponents using the fidelity susceptibility method, we argue that the two models share very similar critical behaviour. For example, the obtained critical exponents indicate that they fall within the same universality class. Away from the critical regime we identify, however, some discrepancies between the two models. Our results are discussed in terms of experimentally relevant parameters and we evaluate the prospects for realizing Dicke-type physics in these systems.Comment: 14 pages, 6 figure

Impurity in a bosonic Josephson junction: swallowtail loops, chaos, self-trapping and the poor man's Dicke model

We study a model describing $N$ identical bosonic atoms trapped in a double-well potential together with a single impurity atom, comparing and contrasting it throughout with the Dicke model. As the boson-impurity coupling strength is varied, there is a symmetry-breaking pitchfork bifurcation which is analogous to the quantum phase transition occurring in the Dicke model. Through stability analysis around the bifurcation point, we show that the critical value of the coupling strength has the same dependence on the parameters as the critical coupling value in the Dicke model. We also show that, like the Dicke model, the mean-field dynamics go from being regular to chaotic above the bifurcation and macroscopic excitations of the bosons are observed. Overall, the boson-impurity system behaves like a poor man's version of the Dicke model.Comment: 17 pages, 16 figure

When users control the algorithms: Values expressed in practices on the twitter platform

Recent interest in ethical AI has brought a slew of values, including fairness, into conversations about technology design. Research in the area of algorithmic fairness tends to be rooted in questions of distribution that can be subject to precise formalism and technical implementation. We seek to expand this conversation to include the experiences of people subject to algorithmic classification and decision-making. By examining tweets about the “Twitter algorithm” we consider the wide range of concerns and desires Twitter users express. We find a concern with fairness (narrowly construed) is present, particularly in the ways users complain that the platform enacts a political bias against conservatives. However, we find another important category of concern, evident in attempts to exert control over the algorithm. Twitter users who seek control do so for a variety of reasons, many well justified. We argue for the need for better and clearer definitions of what constitutes legitimate and illegitimate control over algorithmic processes and to consider support for users who wish to enact their own collective choices

Stock market and investment good prices: implications of macroeconomics

Stock market prices are procyclical, while investment good prices are countercyclical. A real business cycle model calibrated to these observations implies that 75% of the cyclical variation in aggregate output is due to an investment-specific technology shock, while the rest is due to an aggregate productivity shock. To test this conclusion, we investigate the model's implications for asset prices and business cycles. The model does not do significantly worse than existing models on these dimensions, and on two dimensions it does notably better. It is consistent with the facts: (i) employment and investment across different sectors comove over the business cycle: and (ii) high interest rates lead low aggregate output. Fact (ii) is often interpreted as reflecting the business cycle effects of monetary policy shocks. Our result suggest that (ii) may, at least to some extent, also reflect the effects of real shocks.Stock - Prices ; Investments

Optical Flow in Mostly Rigid Scenes

The optical flow of natural scenes is a combination of the motion of the observer and the independent motion of objects. Existing algorithms typically focus on either recovering motion and structure under the assumption of a purely static world or optical flow for general unconstrained scenes. We combine these approaches in an optical flow algorithm that estimates an explicit segmentation of moving objects from appearance and physical constraints. In static regions we take advantage of strong constraints to jointly estimate the camera motion and the 3D structure of the scene over multiple frames. This allows us to also regularize the structure instead of the motion. Our formulation uses a Plane+Parallax framework, which works even under small baselines, and reduces the motion estimation to a one-dimensional search problem, resulting in more accurate estimation. In moving regions the flow is treated as unconstrained, and computed with an existing optical flow method. The resulting Mostly-Rigid Flow (MR-Flow) method achieves state-of-the-art results on both the MPI-Sintel and KITTI-2015 benchmarks.Comment: 15 pages, 10 figures; accepted for publication at CVPR 201