The Axigluon, a Four-Site Model and the Top Quark Forward-Backward Asymmetry at the Tevatron

Very recently, it has been shown by other authors that the CDF measurement of the top quark forward-backward asymmetry can be explained by means of a heavy and broad axigluon. In order to work, this mechanism needs that the axigluon coupling to the top quark must be different than the coupling to light quarks and both must be stronger than the one predicted in classical axigluon models. In this paper, we argue that this kind of axigluon can be accommodated in an extended chiral color model we proposed previously. Additionally, we show that the desired features can be derived from a simple four-site model with delocalized fermions.Comment: Typos corrected and refrerences adde

Coupling and higher-order effects in the 12C(d,p)13C and 13C(p,d)12C reactions

Coupled channels calculations are performed for the 12C(d,p)13C and 13C(p,d)12C reactions between 7 and 60 MeV to study the effect of inelastic couplings in transfer reactions. The effect of treating transfer beyond Born approximation is also addressed. The coupling to the 12C 2+ state is found to change the peak cross-section by up to 15 %. Effects beyond Born approximation lead to a significant renormalization of the cross-sections, between 5 and 10 % for deuteron energies above 10 MeV, and larger than 10 % for lower energies. We also performed calculations including the remnant term in the transfer operator, which has a small impact on the 12C(d,p)13C(g.s.) and 13C(p,d)12C(g.s.) reactions. Above 30 MeV deuteron energy, the effect of the remnant term is larger than 10 % for the 12C(d,p)13C(3.09 MeV) reaction and is found to increase with decreasing neutron separation energy for the 3.09 MeV state of 13C. This is of importance for transfer reactions with weakly bound nuclei.Comment: 7 pages, 7 figures, submitted to Phys. Rev.

An unsupervised learning approach to ultrasound strain elastography with spatio-temporal consistency

Quasi-static ultrasound elastography (USE) is an imaging modality that measures deformation (i.e. strain) of tissue in response to an applied mechanical force. In USE, the strain modulus is traditionally obtained by deriving the displacement field estimated between a pair of radio-frequency data. In this work we propose a recurrent network architecture with convolutional long-short-term memory decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. The network is trained in an unsupervised way, by optimising a similarity metric between the reference and compressed image. Our training loss is also composed of a regularisation term that preserves displacement continuity by directly optimising the strain smoothness, and a temporal continuity term that enforces consistency between successive strain predictions. In addition, we propose an open-access in vivo database for quasi-static USE, which consists of radio-frequency data sequences captured on the arm of a human volunteer. Our results from numerical simulation and in vivo data suggest that our recurrent neural network can account for larger deformations, as compared with two other feed-forward neural networks. In all experiments, our recurrent network outperformed the state-of-the-art for both learning-based and optimisationbased methods, in terms of elastographic signal-to-noise ratio, strain consistency, and image similarity. Finally, our open-source code provides a 3D-slicer visualisation module that can be used to process ultrasound RF frames in real-time, at a rate of up to 20 frames per second, using a standard GPU

An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation

Quasi-static ultrasound elastography (USE) is an imaging modality that consists of determining a measure of deformation (i.e. strain) of soft tissue in response to an applied mechanical force. The strain is generally determined by estimating the displacement between successive ultrasound frames acquired before and after applying manual compression. The computational efficiency and accuracy of the displacement prediction, also known as time-delay estimation, are key challenges for real-time USE applications. In this paper, we present a novel deep-learning method for efficient time-delay estimation between ultrasound radio-frequency (RF) data. The proposed method consists of a convolutional neural network (CNN) that predicts a displacement field between a pair of pre- and post-compression ultrasound RF frames. The network is trained in an unsupervised way, by optimizing a similarity metric between the reference and compressed image. We also introduce a new regularization term that preserves displacement continuity by directly optimizing the strain smoothness. We validated the performance of our method by using both ultrasound simulation and in vivo data on healthy volunteers. We also compared the performance of our method with a state-of-the-art method called OVERWIND [17]. Average contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of our method in 30 simulation and 3 in vivo image pairs are 7.70 and 6.95, 7 and 0.31, respectively. Our results suggest that our approach can effectively predict accurate strain images. The unsupervised aspect of our approach represents a great potential for the use of deep learning application for the analysis of clinical ultrasound data

An unsupervised learning-based shear wave tracking method for ultrasound elastography

Shear wave elastography involves applying a non-invasive acoustic radiation force to the tissue and imaging the induced deformation to infer its mechanical properties. This work investigates the use of convolutional neural networks to improve displacement estimation accuracy in shear wave imaging. Our training approach is completely unsupervised, which allows to learn the estimation of the induced micro-scale deformations without ground truth labels. We also present an ultrasound simulation dataset where the shear wave propagation has been simulated via finite element method. Our dataset is made publicly available along with this paper, and consists in 150 shear wave propagation simulations in both homogenous and hetegeneous media, which represents a total of 20,000 ultrasound images. We assessed the ability of our learning-based approach to characterise tissue elastic properties (i.e., Young's modulus) on our dataset and compared our results with a classical normalised cross-correlation approach

(De)Bonding with embryos: The emotional choreographies of Portuguese IVF patients

In this article we develop the new concept of emotional choreography to describe how patients bond, debond and/or rebond with their embryos created in vitro using assisted reproductive technologies (ART). Using this concept, we explore how the patients' management of their own emotions intertwines with political, scientific, and religious factors. Our analysis relies on and further advances Thompson's concepts of ethical and ontological “choreography”. It is through these forms of choreography that complex contemporary biomedical issues with high political, ethical, and scientific stakes are negotiated, and through which different actors, entities, practices, roles, and norms undergo mutual constitution, reinforcement and (re)definition. Our article draws on the analysis of 69 in-depth interviews and the results of an online survey with 85 respondents.info:eu-repo/semantics/publishedVersio

Random parking, Euclidean functionals, and rubber elasticity

We study subadditive functions of the random parking model previously analyzed by the second author. In particular, we consider local functions $S$ of subsets of $\mathbb{R}^d$ and of point sets that are (almost) subadditive in their first variable. Denoting by $\xi$ the random parking measure in $\mathbb{R}^d$, and by $\xi^R$ the random parking measure in the cube $Q_R=(-R,R)^d$, we show, under some natural assumptions on $S$, that there exists a constant $\bar{S}\in \mathbb{R}$ such that % $$\lim_{R\to +\infty} \frac{S(Q_R,\xi)}{|Q_R|}\,=\,\lim_{R\to +\infty}\frac{S(Q_R,\xi^R)}{|Q_R|}\,=\,\bar{S}$$ % almost surely. If $\zeta \mapsto S(Q_R,\zeta)$ is the counting measure of $\zeta$ in $Q_R$, then we retrieve the result by the second author on the existence of the jamming limit. The present work generalizes this result to a wide class of (almost) subadditive functions. In particular, classical Euclidean optimization problems as well as the discrete model for rubber previously studied by Alicandro, Cicalese, and the first author enter this class of functions. In the case of rubber elasticity, this yields an approximation result for the continuous energy density associated with the discrete model at the thermodynamic limit, as well as a generalization to stochastic networks generated on bounded sets.Comment: 28 page

Generation of Vorticity and Velocity Dispersion by Orbit Crossing

We study the generation of vorticity and velocity dispersion by orbit crossing using cosmological numerical simulations, and calculate the backreaction of these effects on the evolution of large-scale density and velocity divergence power spectra. We use Delaunay tessellations to define the velocity field, showing that the power spectra of velocity divergence and vorticity measured in this way are unbiased and have better noise properties than for standard interpolation methods that deal with mass weighted velocities. We show that high resolution simulations are required to recover the correct large-scale vorticity power spectrum, while poor resolution can spuriously amplify its amplitude by more than one order of magnitude. We measure the scalar and vector modes of the stress tensor induced by orbit crossing using an adaptive technique, showing that its vector modes lead, when input into the vorticity evolution equation, to the same vorticity power spectrum obtained from the Delaunay method. We incorporate orbit crossing corrections to the evolution of large scale density and velocity fields in perturbation theory by using the measured stress tensor modes. We find that at large scales (k~0.1 h/Mpc) vector modes have very little effect in the density power spectrum, while scalar modes (velocity dispersion) can induce percent level corrections at z=0, particularly in the velocity divergence power spectrum. In addition, we show that the velocity power spectrum is smaller than predicted by linear theory until well into the nonlinear regime, with little contribution from virial velocities.Comment: 27 pages, 14 figures. v2: reorganization of the material, new appendix. Accepted by PR

Emerging Concepts in Innate Lymphoid Cells, Memory, and Reproduction

Members of the innate immune system, innate lymphoid cells (ILCs), encompass five major populations (Natural Killer (NK) cells, ILC1s, ILC2s, ILC3s, and lymphoid tissue inducer cells) whose functions include defense against pathogens, surveillance of tumorigenesis, and regulation of tissue homeostasis and remodeling. ILCs are present in the uterine environment of humans and mice and are dynamically regulated during the reproductive cycle and pregnancy. These cells have been repurposed to support pregnancy promoting maternal immune tolerance and placental development. To accomplish their tasks, immune cells employ several cellular and molecular mechanisms. They have the capacity to remember a previously encountered antigen and mount a more effective response to succeeding events. Memory responses are not an exclusive feature of the adaptive immune system, but also occur in innate immune cells. Innate immune memory has already been demonstrated in monocytes/macrophages, neutrophils, dendritic cells, and ILCs. A population of decidual NK cells characterized by elevated expression of NKG2C and LILRB1 as well as a distinctive transcriptional and epigenetic profile was found to expand during subsequent pregnancies in humans. These cells secrete high amounts of interferon-γ and vascular endothelial growth factor likely favoring placentation. Similarly, uterine ILC1s in mice upregulate CXCR6 and expand in second pregnancies. These data provide evidence on the development of immunological memory of pregnancy. In this article, the characteristics, functions, and localization of ILCs are reviewed, emphasizing available data on the uterine environment. Following, the concept of innate immune memory and its mechanisms, which include epigenetic changes and metabolic rewiring, are presented. Finally, the emerging role of innate immune memory on reproduction is discussed. Advances in the comprehension of ILC functions and innate immune memory may contribute to uncovering the immunological mechanisms underlying female fertility/infertility, placental development, and distinct outcomes in second pregnancies related to higher birth weight and lower incidence of complications

Cosmological Consequences of Nearly Conformal Dynamics at the TeV scale

Nearly conformal dynamics at the TeV scale as motivated by the hierarchy problem can be characterized by a stage of significant supercooling at the electroweak epoch. This has important cosmological consequences. In particular, a common assumption about the history of the universe is that the reheating temperature is high, at least high enough to assume that TeV-mass particles were once in thermal equilibrium. However, as we discuss in this paper, this assumption is not well justified in some models of strong dynamics at the TeV scale. We then need to reexamine how to achieve baryogenesis in these theories as well as reconsider how the dark matter abundance is inherited. We argue that baryonic and dark matter abundances can be explained naturally in these setups where reheating takes place by bubble collisions at the end of the strongly first-order phase transition characterizing conformal symmetry breaking, even if the reheating temperature is below the electroweak scale $\sim 100$ GeV. We also discuss inflation as well as gravity wave smoking gun signatures of this class of models.Comment: 22 pages, 7 figure