Medium effects in the pion pole mechanism (photon photon --> pion-zero --> neutrino-R antineutrino-L (neutrino-L antineutrino-R)) of neutron star cooling

Nuclear medium effects in the neutrino cooling of neutron stars through the exotic reaction channel \gamma \gamma --> \pi^0--> \nu_R \bar{\nu_L} (\nu_L \bar{\nu_R}) are incorporated. Throughout the paper we discuss different possibilities of right-handed neutrinos, massive left-handed neutrinos and standard massless left-handed neutrinos (reaction is then allowed only with medium modified vertices). It is demonstrated that multi-particle effects suppress the rate of this reaction channel by 6-7 orders of magnitude that does not allow to decrease existing experimental upper limit on the corresponding \pi^0\nu\bar{\nu} coupling. Other possibilities of the manifestation of the given reaction channel in differente physical situations, e.g. in the quark color superconducting cores of some neutron stars, are also discussed. We demonstrate that in the color-flavor-locked superconducting phase for temperatures T < (0.1-10) MeV (depending on the effective pion mass and the decay width) the process is feasibly the most efficient neutrino cooling process, although the absolute value of the reaction is rather small.Comment: Replaced with revised version. New appendix, many clarifying comments, corrected figs 3 and

Fetal and early neonatal interleukin-6 response

In 1998, a systemic fetal cytokine response, defined as a plasma interleukin-6 (IL-6) value above 11 pg/mL, was reported to be a major independent risk factor for the subsequent development of neonatal morbid events even after adjustments for gestational age and other confounders. Since then, the body of literature investigating the use of blood concentrations of IL-6 as a hallmark of the fetal inflammatory response syndrome (FIRS), a diagnostic marker of early-onset neonatal sepsis (EONS) and a risk predictor of white matter injury (WMI), has grown rapidly. In this article, we critically review: IL-6 biological functions; current evidence on the association between IL-6, preterm birth, FIRS and EONS; IL-6 reference intervals and dynamics in the early neonatal period; IL-6 response during the immediate postnatal period and perinatal confounders; accuracy and completeness of IL-6 diagnostic studies for EONS (according to the Standards for Reporting of Diagnostic Accuracy statement); and recent breakthroughs in the association between fetal blood IL-6, EONS, and WMI

Light composite Higgs boson from the normalized Bethe-Salpeter equation

Scalar composite boson masses have been computed in QCD and Technicolor theories with the help of the homogeneous Bethe-Salpeter equation (BSE), resulting in a scalar mass that is twice the dynamically generated fermion or technifermion mass ($m_{dyn}$). We show that in the case of walking (or quasi-conformal) technicolor theories, where the $m_{dyn}$ behavior with the momenta may be quite different from the one predicted by the standard operator product expansion, this result is incomplete and we must consider the effect of the normalization condition of the BSE to determine the scalar masses. We compute the composite Higgs boson mass for several groups with technifermions in the fundamental and higher dimensional representations and comment about the experimental constraints on these theories, which indicate that models based on walking theories with fermions in the fundamental representation may, within the limitations of our approach, have masses quite near the actual direct exclusion limit.Comment: 9 pages, 4 figures, minor corrections, to appear in Physical Review

Automatic Synchronization of Multi-User Photo Galleries

In this paper we address the issue of photo galleries synchronization, where pictures related to the same event are collected by different users. Existing solutions to address the problem are usually based on unrealistic assumptions, like time consistency across photo galleries, and often heavily rely on heuristics, limiting therefore the applicability to real-world scenarios. We propose a solution that achieves better generalization performance for the synchronization task compared to the available literature. The method is characterized by three stages: at first, deep convolutional neural network features are used to assess the visual similarity among the photos; then, pairs of similar photos are detected across different galleries and used to construct a graph; eventually, a probabilistic graphical model is used to estimate the temporal offset of each pair of galleries, by traversing the minimum spanning tree extracted from this graph. The experimental evaluation is conducted on four publicly available datasets covering different types of events, demonstrating the strength of our proposed method. A thorough discussion of the obtained results is provided for a critical assessment of the quality in synchronization.Comment: ACCEPTED to IEEE Transactions on Multimedi

Theoretical fits of the \delta Cephei light, radius and radial velocity curves

We present a theoretical investigation of the light, radius and radial velocity variations of the prototype $\delta$ Cephei. We find that the best fit model accounts for luminosity and velocity amplitudes with an accuracy better than $0.8\sigma$, and for the radius amplitude with an accuracy of $1.7\sigma$. The chemical composition of this model suggests a decrease in both helium (0.26 vs 0.28) and metal (0.01 vs 0.02) content in the solar neighborhood. Moreover, distance determinations based on the fit of light curves agree at the $0.8\sigma$ level with the trigonometric parallax measured by the Hubble Space Telescope (HST). On the other hand, distance determinations based on angular diameter variations, that are independent of interstellar extinction and of the $p$-factor value, indicate an increase of the order of 5% in the HST parallax.Comment: accepted for publication on ApJ Letter

Controlled Tactile Exploration and Haptic Object Recognition

In this paper we propose a novel method for in-hand object recognition. The method is composed of a grasp stabilization controller and two exploratory behaviours to capture the shape and the softness of an object. Grasp stabilization plays an important role in recognizing objects. First, it prevents the object from slipping and facilitates the exploration of the object. Second, reaching a stable and repeatable position adds robustness to the learning algorithm and increases invariance with respect to the way in which the robot grasps the object. The stable poses are estimated using a Gaussian mixture model (GMM). We present experimental results showing that using our method the classifier can successfully distinguish 30 objects.We also compare our method with a benchmark experiment, in which the grasp stabilization is disabled. We show, with statistical significance, that our method outperforms the benchmark method

Partial Force Control of Constrained Floating-Base Robots

Pre-print of paper presented at Intelligent Robots and Systems (IROS 2014), IEEE International Conference on, Chicago, USA, 2014Legged robots are typically in rigid contact with the environment at multiple locations, which add a degree of complexity to their control. We present a method to control the motion and a subset of the contact forces of a floating-base robot. We derive a new formulation of the lexicographic optimization problem typically arising in multitask motion/force control frameworks. The structure of the constraints of the problem (i.e. the dynamics of the robot) allows us to find a sparse analytical solution. This leads to an equivalent optimization with reduced computational complexity, comparable to inverse-dynamics based approaches. At the same time, our method preserves the flexibility of optimization based control frameworks. Simulations were carried out to achieve different multi-contact behaviors on a 23-degree-offreedom humanoid robot, validating the presented approach. A comparison with another state-of-the-art control technique with similar computational complexity shows the benefits of our controller, which can eliminate force/torque discontinuities

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging simple Rules

Reinforcement Learning (RL) generally suffers from poor sample complexity, mostly due to the need to exhaustively explore the state-action space to find well-performing policies. On the other hand, we postulate that expert knowledge of the system often allows us to design simple rules we expect good policies to follow at all times. In this work, we hence propose a simple yet effective modification of continuous actor-critic frameworks to incorporate such rules and avoid regions of the state-action space that are known to be suboptimal, thereby significantly accelerating the convergence of RL agents. Concretely, we saturate the actions chosen by the agent if they do not comply with our intuition and, critically, modify the gradient update step of the policy to ensure the learning process is not affected by the saturation step. On a room temperature control case study, it allows agents to converge to well-performing policies up to 6-7x faster than classical agents without computational overhead and while retaining good final performance.Comment: Submitted to CDC 202