Spin-wave coupling to electromagnetic cavity fields in dysposium ferrite

Coupling of spin-waves with electromagnetic cavity field is demonstrated in an antiferromagnet, dysprosium ferrite (DyFeO3). By measuring transmission at 0.2-0.35 THz and sweeping sample temperature, magnon-photon coupling signatures were found at crossings of spin-wave resonances with Fabry-Perot cavity modes formed in samples. The obtained spectra are explained in terms of classical electrodynamics and a microscopic model.Comment: 3 pages, 2 figure

The thermodynamics of prediction

A system responding to a stochastic driving signal can be interpreted as computing, by means of its dynamics, an implicit model of the environmental variables. The system's state retains information about past environmental fluctuations, and a fraction of this information is predictive of future ones. The remaining nonpredictive information reflects model complexity that does not improve predictive power, and thus represents the ineffectiveness of the model. We expose the fundamental equivalence between this model inefficiency and thermodynamic inefficiency, measured by dissipation. Our results hold arbitrarily far from thermodynamic equilibrium and are applicable to a wide range of systems, including biomolecular machines. They highlight a profound connection between the effective use of information and efficient thermodynamic operation: any system constructed to keep memory about its environment and to operate with maximal energetic efficiency has to be predictive.Comment: 5 pages, 1 figur

Thermodynamics of natural images

The scale invariance of natural images suggests an analogy to the statistical mechanics of physical systems at a critical point. Here we examine the distribution of pixels in small image patches and show how to construct the corresponding thermodynamics. We find evidence for criticality in a diverging specific heat, which corresponds to large fluctuations in how "surprising" we find individual images, and in the quantitative form of the entropy vs. energy. The energy landscape derived from our thermodynamic framework identifies special image configurations that have intrinsic error correcting properties, and neurons which could detect these features have a strong resemblance to the cells found in primary visual cortex

Field Theoretical Analysis of On-line Learning of Probability Distributions

On-line learning of probability distributions is analyzed from the field theoretical point of view. We can obtain an optimal on-line learning algorithm, since renormalization group enables us to control the number of degrees of freedom of a system according to the number of examples. We do not learn parameters of a model, but probability distributions themselves. Therefore, the algorithm requires no a priori knowledge of a model.Comment: 4 pages, 1 figure, RevTe

The role of input noise in transcriptional regulation

Even under constant external conditions, the expression levels of genes fluctuate. Much emphasis has been placed on the components of this noise that are due to randomness in transcription and translation; here we analyze the role of noise associated with the inputs to transcriptional regulation, the random arrival and binding of transcription factors to their target sites along the genome. This noise sets a fundamental physical limit to the reliability of genetic control, and has clear signatures, but we show that these are easily obscured by experimental limitations and even by conventional methods for plotting the variance vs. mean expression level. We argue that simple, global models of noise dominated by transcription and translation are inconsistent with the embedding of gene expression in a network of regulatory interactions. Analysis of recent experiments on transcriptional control in the early Drosophila embryo shows that these results are quantitatively consistent with the predicted signatures of input noise, and we discuss the experiments needed to test the importance of input noise more generally.Comment: 11 pages, 5 figures minor correction

On the criticality of inferred models

Advanced inference techniques allow one to reconstruct the pattern of interaction from high dimensional data sets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to a phase transition. On one side, we show that the reparameterization invariant metrics in the space of probability distributions of these models (the Fisher Information) is directly related to the model's susceptibility. As a result, distinguishable models tend to accumulate close to critical points, where the susceptibility diverges in infinite systems. On the other, this region is the one where the estimate of inferred parameters is most stable. In order to illustrate these points, we discuss inference of interacting point processes with application to financial data and show that sensible choices of observation time-scales naturally yield models which are close to criticality.Comment: 6 pages, 2 figures, version to appear in JSTA

Hygroscopic and chemical characterisation of Po Valley aerosol

Continental summer-time aerosol in the Italian Po Valley was characterised in terms of hygroscopic properties and the influence of chemical composition therein. Additionally, the ethanol affinity of particles was analysed. The campaign-average minima in hygroscopic growth factors (HGFs, at 90% relative humidity) occurred just before and during sunrise from 03:00 to 06:00 LT (all data are reported in the local time), but, more generally, the hygroscopicity during the whole night is very low, particularly in the smaller particle sizes. The average HGFs recorded during the low HGF period were in a range from 1.18 (for the smallest, 35nm particles) to 1.38 (for the largest, 165 nm particles). During the day, the HGF gradually increased to achieve maximum values in the early afternoon hours 12:00–15:00, reaching 1.32 for 35 nm particles and 1.46 for 165 nm particles. Two contrasting case scenarios were encountered during the measurement period: Case 1 was associated with westerly air flow moving at a moderate pace and Case 2 was associated with more stagnant, slower moving air from the north-easterly sector. Case 1 exhibited weak diurnal temporal patterns, with no distinct maximum or minimum in HGF or chemical composition, and was associated with moderate non-refractory aerosol mass concentrations (for 50% size cut at 1 μ) of the order of 4.5 μg m⁻³. For Case 1, organics contributed typically 50% of the mass. Case 2 was characterised by >9.5 μg m⁻³ total non-refractory mass (<1 μ) in the early morning hours (04:00), decreasing to ~3 μg m⁻³ by late morning (10:00) and exhibited strong diurnal changes in chemical composition, particularly in nitrate mass but also in total organic mass concentrations. Specifically, the concentrations of nitrate peaked at night-time, along with the concentrations of hydrocarbon-like organic aerosol (HOA) and of semi-volatile oxygenated organic aerosol (SV-OOA). In general, organic growth factors (OGFs) followed a trend which was opposed to HGF and also to the total organic mass as measured by the aerosol mass spectrometer. The analysis of the HGF probability distribution function (PDF) reveals an existence of a predominant "more hygroscopic" (MH) mode with HGF of 1.5 around noon, and two additional modes: one with a "less hygroscopic" (LH) HGF of 1.26, and another with a "barely hygroscopic" (BH) mode of 1.05. Particles sized 165 nm exhibited moderate diurnal variability in HGF, ranging from 80% at night to 95% of "more hygroscopic" growth factors (i.e. HGFs 1.35–1.9) around noon. The diurnal changes in HGF progressively became enhanced with decreasing particle size, decreasing from 95% "more hygroscopic" growth factor fraction at noon to 10% fraction at midnight, while the "less hygroscopic" growth factor fraction (1.13–1.34) increased from 5% at noon to > 60% and the "barely hygroscopic" growth factor fraction (1.1–1.2) increased from less than 2% at noon to 30% at midnight. Surprisingly, the lowest HGFs occurred for the period when nitrate mass reached peak concentrations (Case 2). We hypothesised that the low HGFs of nitrate-containing particles can be explained by a) an organic coating suppressing the water-uptake, and/or by b) the existence of nitrates in a less hygroscopic state, e.g. as organic nitrates. The latter hypothesis allows us to explain also the reduced OGFs observed during the early morning hours (before dawn) when nitrate concentrations peaked, based on the evidence that organic nitrates have significant lower ethanol affinity than other SV-OOA compounds

The Effect of Nonstationarity on Models Inferred from Neural Data

Neurons subject to a common non-stationary input may exhibit a correlated firing behavior. Correlations in the statistics of neural spike trains also arise as the effect of interaction between neurons. Here we show that these two situations can be distinguished, with machine learning techniques, provided the data are rich enough. In order to do this, we study the problem of inferring a kinetic Ising model, stationary or nonstationary, from the available data. We apply the inference procedure to two data sets: one from salamander retinal ganglion cells and the other from a realistic computational cortical network model. We show that many aspects of the concerted activity of the salamander retinal neurons can be traced simply to the external input. A model of non-interacting neurons subject to a non-stationary external field outperforms a model with stationary input with couplings between neurons, even accounting for the differences in the number of model parameters. When couplings are added to the non-stationary model, for the retinal data, little is gained: the inferred couplings are generally not significant. Likewise, the distribution of the sizes of sets of neurons that spike simultaneously and the frequency of spike patterns as function of their rank (Zipf plots) are well-explained by an independent-neuron model with time-dependent external input, and adding connections to such a model does not offer significant improvement. For the cortical model data, robust couplings, well correlated with the real connections, can be inferred using the non-stationary model. Adding connections to this model slightly improves the agreement with the data for the probability of synchronous spikes but hardly affects the Zipf plot.Comment: version in press in J Stat Mec

Network information and connected correlations

Entropy and information provide natural measures of correlation among elements in a network. We construct here the information theoretic analog of connected correlation functions: irreducible $N$--point correlation is measured by a decrease in entropy for the joint distribution of $N$ variables relative to the maximum entropy allowed by all the observed $N-1$ variable distributions. We calculate the ``connected information'' terms for several examples, and show that it also enables the decomposition of the information that is carried by a population of elements about an outside source.Comment: 4 pages, 3 figure

Intrinsic limitations of inverse inference in the pairwise Ising spin glass

We analyze the limits inherent to the inverse reconstruction of a pairwise Ising spin glass based on susceptibility propagation. We establish the conditions under which the susceptibility propagation algorithm is able to reconstruct the characteristics of the network given first- and second-order local observables, evaluate eventual errors due to various types of noise in the originally observed data, and discuss the scaling of the problem with the number of degrees of freedom