Can we identify non-stationary dynamics of trial-to-trial variability?"

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings

Efficient bounds on quantum communication rates via their reduced variants

We investigate one-way communication scenarios where Bob manipulating on his parts can transfer some sub-system to the environment. We define reduced versions of quantum communication rates and further, prove new upper bounds on one-way quantum secret key, distillable entanglement and quantum channel capacity by means of their reduced versions. It is shown that in some cases they drastically improve their estimation.Comment: 6 pages, RevTe

A multi-wavelength view on the dusty Wolf-Rayet star WR 48a

We present results from the first attempts to derive various physical characteristics of the dusty Wolf-Rayet star WR 48a based on a multi-wavelength view of its observational properties. This is done on the basis of new optical and near-infrared spectral observations and on data from various archives in the optical, radio and X-rays. The optical spectrum of WR 48a is acceptably well represented by a sum of two spectra: of a WR star of the WC8 type and of a WR star of the WN8h type. The strength of the interstellar absorption features in the optical spectra of WR 48a and the near-by stars D2-3 and D2-7 (both members of the open cluster Danks 2) indicates that WR 48a is located at a distance of ~4 kpc from us. WR 48a is very likely a thermal radio source and for such a case and smooth (no clumps) wind its radio emission suggests a relatively high mass-loss rate of this dusty WR star (dM/dt = a few x 10^(-4) solar masses per year). Long timescale (years) variability of WR 48a is established in the optical, radio and X-rays. Colliding stellar winds likely play a very important role in the physics of this object. However, some LBV-like (luminous blue variable) activity could not be excluded as well.Comment: Accepted for publication in MNRAS; 16 pages, 16 figures, 6 table