AGN counts at 15um. XMM observations of the ELAIS-S1-5 sample

Context: The counts of galaxies and AGN in the mid infra-red (MIR) bands are important instruments for studying their cosmological evolution. However, the classic spectral line ratios techniques can become misleading when trying to properly separate AGN from starbursts or even from apparently normal galaxies. Aims: We use X-ray band observations to discriminate AGN activity in previously classified MIR-selected starburst galaxies and to derive updated AGN1 and (Compton thin) AGN2 counts at 15 um. Methods: XMM observations of the ELAIS-S1 15um sample down to flux limits ~2x10^-15 erg cm^-2 s^-1 (2-10 keV band) were used. We classified as AGN all those MIR sources with a unabsorbed 2-10 keV X-ray luminosity higher that ~10^42 erg/s. Results: We find that at least about 13(+/-6) per cent of the previously classified starburst galaxies harbor an AGN. According to these figures, we provide an updated estimate of the counts of AGN1 and (Compton thin) AGN2 at 15 um. It turns out that at least 24% of the extragalactic sources brighter than 0.6 my at 15 um are AGN (~13% contribution to the extragalactic background produced at fluxes brighter than 0.6 mJy).Comment: Accepted for publication on A&

A biologically plausible learning rule for deep learning in the brain

Researchers have proposed that deep learning, which is providing important progress in a wide range of high complexity tasks, might inspire new insights into learning in the brain. However, the methods used for deep learning by artificial neural networks are biologically unrealistic and would need to be replaced by biologically realistic counterparts. Previous biologically plausible reinforcement learning rules, like AGREL and AuGMEnT, showed promising results but focused on shallow networks with three layers. Will these learning rules also generalize to networks with more layers and can they handle tasks of higher complexity? Here, we demonstrate that these learning schemes indeed generalize to deep networks, if we include an attention network that propagates information about the selected action to lower network levels. The resulting learning rule, called Q-AGREL, is equivalent to a particular form of error-backpropagation that trains one output unit at any one time. To demonstrate the utility of the learning scheme for larger problems, we trained networks with two hidden layers on the MNIST dataset, a standard and interesting Machine Learning task. Our results demonstrate that the capability of Q-AGREL is comparable to that of error backpropagation, although the learning rate is 1.5-2 times slower because the network has to learn by trial-and-error and updates the action value of only one output unit at a time. Our results provide new insights into how deep learning can be implemented in the brain

Approximate logic synthesis: a survey

Approximate computing is an emerging paradigm that, by relaxing the requirement for full accuracy, offers benefits in terms of design area and power consumption. This paradigm is particularly attractive in applications where the underlying computation has inherent resilience to small errors. Such applications are abundant in many domains, including machine learning, computer vision, and signal processing. In circuit design, a major challenge is the capability to synthesize the approximate circuits automatically without manually relying on the expertise of designers. In this work, we review methods devised to synthesize approximate circuits, given their exact functionality and an approximability threshold. We summarize strategies for evaluating the error that circuit simplification can induce on the output, which guides synthesis techniques in choosing the circuit transformations that lead to the largest benefit for a given amount of induced error. We then review circuit simplification methods that operate at the gate or Boolean level, including those that leverage classical Boolean synthesis techniques to realize the approximations. We also summarize strategies that take high-level descriptions, such as C or behavioral Verilog, and synthesize approximate circuits from these descriptions

UVB radiation induced effects on cells studied by FTIR spectroscopy

We have made a preliminary analysis of the results about the eVects on tumoral cell line (lymphoid T cell line Jurkat) induced by UVB radiation (dose of 310 mJ/cm^2) with and without a vegetable mixture. In the present study, we have used two techniques: Fourier transform infrared spectroscopy (FTIR) and flow cytometry. FTIR spectroscopy has the potential to provide the identiWcation of the vibrational modes of some of the major compounds (lipid, proteins and nucleic acids) without being invasive in the biomaterials. The second technique has allowed us to perform measurements of cytotoxicity and to assess the percentage of apoptosis. We already studied the induction of apoptotic process in the same cell line by UVB radiation; in particular, we looked for correspondences and correlations between FTIR spetroscopy and flow cytometry data finding three highly probable spectroscopic markers of apoptosis (Pozzi et al. in Radiat Res 168:698-705, 2007). In the present work, the results have shown significant changes in the absorbance and spectral pattern in the wavenumber protein and nucleic acids regions after the treatments

Herschel Far-IR counterparts of SDSS galaxies: Analysis of commonly used Star Formation Rate estimates

We study a hundred of galaxies from the spectroscopic Sloan Digital Sky Survey with individual detections in the Far-Infrared Herschel PACS bands (100 or 160 $\mu$m) and in the GALEX Far-UltraViolet band up to z$\sim$0.4 in the COSMOS and Lockman Hole fields. The galaxies are divided into 4 spectral and 4 morphological types. For the star forming and unclassifiable galaxies we calculate dust extinctions from the UV slope, the H$\alpha$/H$\beta$ ratio and the $L_{\rm IR}/L_{\rm UV}$ ratio. There is a tight correlation between the dust extinction and both $L_{\rm IR}$ and metallicity. We calculate SFR$_{total}$ and compare it with other SFR estimates (H$\alpha$, UV, SDSS) finding a very good agreement between them with smaller dispersions than typical SFR uncertainties. We study the effect of mass and metallicity, finding that it is only significant at high masses for SFR$_{H\alpha}$. For the AGN and composite galaxies we find a tight correlation between SFR and L$_{IR}$ ($\sigma\sim$0.29), while the dispersion in the SFR - L$_{UV}$ relation is larger ($\sigma\sim$0.57). The galaxies follow the prescriptions of the Fundamental Plane in the M-Z-SFR space.Comment: 24 pages, 23 figures, accepted for publication in MNRA