First CMB Constraints on the Inflationary Reheating Temperature

We present the first Bayesian constraints on the single field inflationary reheating era obtained from Cosmic Microwave Background (CMB) data. After demonstrating that this epoch can be fully characterized by the so-called reheating parameter, we show that it is constrained by the seven years Wilkinson Microwave Anisotropies Probe (WMAP7) data for all large and small field models. An interesting feature of our approach is that it yields lower bounds on the reheating temperature which can be combined with the upper bounds associated with gravitinos production. For large field models, we find the energy scale of reheating to be higher than those probed at the Large Hadron Collider, Ereh > 17.3 TeV at 95% of confidence. For small field models, we obtain the two-sigma lower limits Ereh > 890 TeV for a mean equation of state during reheating = -0.3 and Ereh > 390 GeV for = -0.2. The physical origin of these constraints is pedagogically explained by means of the slow-roll approximation. Finally, when marginalizing over all possible reheating history, the WMAP7 data push massive inflation under pressure (p < 2.2 at 95% of confidence where p is the power index of the large field potentials) while they slightly favor super-Planckian field expectation values in the small field models.Comment: 18 pages, 15 figures, uses RevTeX. References added, matches published versio

Continuous Estimation of Emotions in Speech by Dynamic Cooperative Speaker Models

Automatic emotion recognition from speech has been recently focused on the prediction of time-continuous dimensions (e.g., arousal and valence) of spontaneous and realistic expressions of emotion, as found in real-life interactions. However, the automatic prediction of such emotions poses several challenges, such as the subjectivity found in the definition of a gold standard from a pool of raters and the issue of data scarcity in training models. In this work, we introduce a novel emotion recognition system, based on ensemble of single-speaker-regression-models (SSRMs). The estimation of emotion is provided by combining a subset of the initial pool of SSRMs selecting those that are most concordance among them. The proposed approach allows the addition or removal of speakers from the ensemble without the necessity to re-build the entire machine learning system. The simplicity of this aggregation strategy, coupled with the flexibility assured by the modular architecture, and the promising results obtained on the RECOLA database highlight the potential implications of the proposed method in a real-life scenario and in particular in WEB-based applications

I hear you eat and speak: automatic recognition of eating condition and food type, use-cases, and impact on ASR performance

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient

Hunting Down the Best Model of Inflation with Bayesian Evidence

We present the first calculation of the Bayesian evidence for different prototypical single field inflationary scenarios, including representative classes of small field and large field models. This approach allows us to compare inflationary models in a well-defined statistical way and to determine the current "best model of inflation". The calculation is performed numerically by interfacing the inflationary code FieldInf with MultiNest. We find that small field models are currently preferred, while large field models having a self-interacting potential of power p>4 are strongly disfavoured. The class of small field models as a whole has posterior odds of approximately 3:1 when compared with the large field class. The methodology and results presented in this article are an additional step toward the construction of a full numerical pipeline to constrain the physics of the early Universe with astrophysical observations. More accurate data (such as the Planck data) and the techniques introduced here should allow us to identify conclusively the best inflationary model.Comment: 12 pages, 2 figures, uses RevTeX. Misprint corrected, references added. Matches published versio

Dark energy from primordial inflationary quantum fluctuations

We show that current cosmic acceleration can be explained by an almost massless scalar field experiencing quantum fluctuations during primordial inflation. Provided its mass does not exceed the Hubble parameter today, this field has been frozen during the cosmological ages to start dominating the universe only recently. By using supernovae data, completed with baryonic acoustic oscillations from galaxy surveys and cosmic microwave background anisotropies, we infer the energy scale of primordial inflation to be around a few TeV, which implies a negligible tensor-to-scalar ratio of the primordial fluctuations. Moreover, our model suggests that inflation lasted for an extremely long period. Dark energy could therefore be a natural consequence of cosmic inflation close to the electroweak energy scale.Comment: 5 pages, 2 figures, uses RevTeX. Physical discussion extended, misprints corrected, references added. Matches published versio

The ICL-TUM-PASSAU approach for the MediaEval 2015 "affective impact of movies" task

In this paper we describe the Imperial College London, Technische Universitat München and University of Passau (ICL+TUM+PASSAU) team approach to the MediaEval's "Affective Impact of Movies" challenge, which consists in the automatic detection of affective (arousal and valence) and violent content in movie excerpts. In addition to the baseline features, we computed spectral and energy related acoustic features, and the probability of various objects being present in the video. Random Forests, AdaBoost and Support Vector Machines were used as classification methods. Best results show that the dataset is highly challenging for both affect and violence detection tasks, mainly because of issues in inter-rater agreement and data scarcity

Wavelet-Bayesian inference of cosmic strings embedded in the cosmic microwave background

Cosmic strings are a well-motivated extension to the standard cosmological model and could induce a subdominant component in the anisotropies of the cosmic microwave background (CMB), in addition to the standard inflationary component. The detection of strings, while observationally challenging, would provide a direct probe of physics at very high energy scales. We develop a new framework for cosmic string inference, constructing a Bayesian analysis in wavelet space where the string-induced CMB component has distinct statistical properties to the standard inflationary component. Our wavelet-Bayesian framework provides a principled approach to compute the posterior distribution of the string tension $G\mu$ and the Bayesian evidence ratio comparing the string model to the standard inflationary model. Furthermore, we present a technique to recover an estimate of any string-induced CMB map embedded in observational data. Using Planck-like simulations we demonstrate the application of our framework and evaluate its performance. The method is sensitive to $G\mu \sim 5 \times 10^{-7}$ for Nambu-Goto string simulations that include an integrated Sachs-Wolfe (ISW) contribution only and do not include any recombination effects, before any parameters of the analysis are optimised. The sensitivity of the method compares favourably with other techniques applied to the same simulations.Comment: 18 pages, 14 figures, minor changes to match version accepted by MNRA

Methane emissions from floodplains in the Amazon Basin: challenges in developing a process-based model for global applications

Tropical wetlands are estimated to represent about 50% of the natural wetland methane (CH₄) emissions and explain a large fraction of the observed CH₄ variability on timescales ranging from glacial–interglacial cycles to the currently observed year-to-year variability. Despite their importance, however, tropical wetlands are poorly represented in global models aiming to predict global CH₄ emissions. This publication documents a first step in the development of a process-based model of CH₄ emissions from tropical floodplains for global applications. For this purpose, the LPX-Bern Dynamic Global Vegetation Model (LPX hereafter) was slightly modified to represent floodplain hydrology, vegetation and associated CH₄ emissions. The extent of tropical floodplains was prescribed using output from the spatially explicit hydrology model PCR-GLOBWB. We introduced new plant functional types (PFTs) that explicitly represent floodplain vegetation. The PFT parameterizations were evaluated against available remote-sensing data sets (GLC2000 land cover and MODIS Net Primary Productivity). Simulated CH₄ flux densities were evaluated against field observations and regional flux inventories. Simulated CH₄ emissions at Amazon Basin scale were compared to model simulations performed in the WETCHIMP intercomparison project. We found that LPX reproduces the average magnitude of observed net CH₄ flux densities for the Amazon Basin. However, the model does not reproduce the variability between sites or between years within a site. Unfortunately, site information is too limited to attest or disprove some model features. At the Amazon Basin scale, our results underline the large uncertainty in the magnitude of wetland CH₄ emissions. Sensitivity analyses gave insights into the main drivers of floodplain CH₄ emission and their associated uncertainties. In particular, uncertainties in floodplain extent (i.e., difference between GLC2000 and PCR-GLOBWB output) modulate the simulated emissions by a factor of about 2. Our best estimates, using PCR-GLOBWB in combination with GLC2000, lead to simulated Amazon-integrated emissions of 44.4 ± 4.8 Tg yr⁻¹. Additionally, the LPX emissions are highly sensitive to vegetation distribution. Two simulations with the same mean PFT cover, but different spatial distributions of grasslands within the basin, modulated emissions by about 20%. Correcting the LPX-simulated NPP using MODIS reduces the Amazon emissions by 11.3%. Finally, due to an intrinsic limitation of LPX to account for seasonality in floodplain extent, the model failed to reproduce the full dynamics in CH₄ emissions but we proposed solutions to this issue. The interannual variability (IAV) of the emissions increases by 90% if the IAV in floodplain extent is accounted for, but still remains lower than in most of the WETCHIMP models. While our model includes more mechanisms specific to tropical floodplains, we were unable to reduce the uncertainty in the magnitude of wetland CH₄ emissions of the Amazon Basin. Our results helped identify and prioritize directions towards more accurate estimates of tropical CH₄ emissions, and they stress the need for more research to constrain floodplain CH₄ emissions and their temporal variability, even before including other fundamental mechanisms such as floating macrophytes or lateral water fluxes

From heaviness to lightness during inflation

We study the quantum fluctuations of scalar fields with a variable effective mass during an inflationary phase. We consider the situation where the effective mass depends on a background scalar field, which evolves during inflation from being frozen into a damped oscillatory phase when the Hubble parameter decreases below its mass. We find power spectra with suppressed amplitude on large scales, similar to the standard massless spectrum on small scales, and affected by modulations on intermediate scales. We stress the analogies and differences with the parametric resonance in the preheating scenario. We also discuss some potentially observable consequences when the scalar field behaves like a curvaton.Comment: 23 pages; 8 figures; published versio

Localisation of massive fermions on the brane

We construct an explicit model to describe fermions confined on a four dimensional brane embedded in a five dimensional anti-de Sitter spacetime. We extend previous works to accommodate massive bound states on the brane and exhibit the transverse structure of the fermionic fields. We estimate analytically and calculate numerically the fermion mass spectrum on the brane, which we show to be discrete. The confinement life-time of the bound states is evaluated, and it is shown that existing constraints can be made compatible with the existence of massive fermions trapped on the brane for durations much longer than the age of the Universe.Comment: 20 pages, LaTeX-RevTex, 15 figures, submitted to PR