Quark Loop Contributions to Neutron, Deuteron, and Mercury EDMs from Supersymmetry without R parity

We present a detailed analysis of the neutron, deuteron and mercury electric dipole moment from supersymmetry without R parity, focusing on the quark-scalar loop contributions. Being proportional to top Yukawa and top mass, such contributions are often large. Analytical expressions illustrating the explicit role of the R-parity violating parameters are given following perturbative diagonalization of mass-squared matrices for the scalars. Dominant contributions come from the combinations $B_i \lambda^{\prime}_{ij1}$ for which we obtain robust bounds. It turns out that neutron and deuteron EDMs receive much stronger contributions than mercury EDM and any null result at the future deuteron EDM experiment or Los Alamos neutron EDM experiment can lead to extra-ordinary constraints on RPV parameter space. Even if R-parity violating couplings are real, CKM phase does induce RPV contribution and for some cases such a contribution is as strong as contribution from phases in the R-parity violating couplings.Hence, we have bounds directly on $|B_i \lambda^{\prime}_{ij1}|$ even if the RPV parameters are all real. Interestingly, even if slepton mass and/or $\mu_0$ is as high as 1 TeV, it still leads to neutron EDM that is an order of magnitude larger than the sensitivity at Los Alamos experiment. Since the results are not much sensitive to $\tan \beta$, our constraints will survive even if other observables tighten the constraints on $\tan \beta$.Comment: 16 pages, 10 figures, accepted for publication in Physical Review

Variability and uncertainty in empirical ground-motion prediction for probabilistic hazard and risk analyses

© The Author(s) 2015.The terms aleatory variability and epistemic uncertainty mean different things to people who routinely use them within the fields of seismic hazard and risk analysis. This state is not helped by the repetition of loosely framed generic definitions that actually inaccurate. The present paper takes a closer look at the components of total uncertainty that contribute to ground-motion modelling in hazard and risk applications. The sources and nature of uncertainty are discussed and it is shown that the common approach to deciding what should be included within hazard and risk integrals and what should be pushed into logic tree formulations warrants reconsideration. In addition, it is shown that current approaches to the generation of random fields of ground motions for spatial risk analyses are incorrect and a more appropriate framework is presented

Unsupervised Domain Adaptation with Semantic Consistency across Heterogeneous Modalities for MRI Prostate Lesion Segmentation

Any novel medical imaging modality that differs from previous protocols e.g. in the number of imaging channels, introduces a new domain that is heterogeneous from previous ones. This common medical imaging scenario is rarely considered in the domain adaptation literature, which handles shifts across domains of the same dimensionality. In our work we rely on stochastic generative modeling to translate across two heterogeneous domains at pixel space and introduce two new loss functions that promote semantic consistency. Firstly, we introduce a semantic cycle-consistency loss in the source domain to ensure that the translation preserves the semantics. Secondly, we introduce a pseudo-labelling loss, where we translate target data to source, label them by a source-domain network, and use the generated pseudo-labels to supervise the target-domain network. Our results show that this allows us to extract systematically better representations for the target domain. In particular, we address the challenge of enhancing performance on VERDICT-MRI, an advanced diffusion-weighted imaging technique, by exploiting labeled mp-MRI data. When compared to several unsupervised domain adaptation approaches, our approach yields substantial improvements, that consistently carry over to the semi-supervised and supervised learning settings

Harnessing uncertainty in domain adaptation for mri prostate lesion segmentation

The need for training data can impede the adoption of novel imaging modalities for learning-based medical image analysis. Domain adaptation methods partially mitigate this problem by translating training data from a related source domain to a novel target domain, but typically assume that a one-to-one translation is possible. Our work addresses the challenge of adapting to a more informative target domain where multiple target samples can emerge from a single source sample. In particular we consider translating from mp-MRI to VERDICT, a richer MRI modality involving an optimized acquisition protocol for cancer characterization. We explicitly account for the inherent uncertainty of this mapping and exploit it to generate multiple outputs conditioned on a single input. Our results show that this allows us to extract systematically better image representations for the target domain, when used in tandem with both simple, CycleGAN-based baselines, as well as more powerful approaches that integrate discriminative segmentation losses and/or residual adapters. When compared to its deterministic counterparts, our approach yields substantial improvements across a broad range of dataset sizes, increasingly strong baselines, and evaluation measures

Increased Hematopoietic Extracellular RNAs and Vesicles in the Lung during Allergic Airway Responses.

Extracellular RNAs (exRNAs) can be released by numerous cell types in vitro, are often protected within vesicles, and can modify recipient cell function. To determine how the composition and cellular sources of exRNAs and the extracellular vesicles (EVs) that carry them change in vivo during tissue inflammation, we analyzed bronchoalveolar lavage fluid (BALF) from mice before and after lung allergen challenge. In the lung, extracellular microRNAs (ex-miRNAs) had a composition that was highly correlated with airway-lining epithelium. Using cell type-specific membrane tagging and single vesicle flow, we also found that 80% of detected vesicles were of epithelial origin. After the induction of allergic airway inflammation, miRNAs selectively expressed by immune cells, including miR-223 and miR-142a, increased and hematopoietic-cell-derived EVs also increased >2-fold. These data demonstrate that infiltrating immune cells release ex-miRNAs and EVs in inflamed tissues to alter the local extracellular environment

Decoherent Scattering of Light Particles in a D-Brane Background

We discuss the scattering of two light particles in a D-brane background. It is known that, if one light particle strikes the D brane at small impact parameter, quantum recoil effects induce entanglement entropy in both the excited D brane and the scattered particle. In this paper we compute the asymptotic `out' state of a second light particle scattering off the D brane at large impact parameter, showing that it also becomes mixed as a consequence of quantum D-brane recoil effects. We interpret this as a non-factorizing contribution to the superscattering operator S-dollar for the two light particles in a Liouville D-brane background, that appears when quantum D-brane excitations are taken into account.Comment: 18 pages LATEX, one figure (incorporated

Synthesizing VERDICT maps from standard DWI data using GANs

VERDICT maps have shown promising results in clinical settings discriminating normal from malignant tissue and identifying specific Gleason grades non-invasively. However, the quantitative estimation of VERDICT maps requires a specific diffusion-weighed imaging (DWI) acquisition. In this study we investigate the feasibility of synthesizing VERDICT maps from standard DWI data from multi-parametric (mp)-MRI by employing conditional generative adversarial networks (GANs). We use data from 67 patients who underwent both standard DWI-MRI and VERDICT MRI and rely on correlation analysis and mean squared error to quantitatively evaluate the quality of the synthetic VERDICT maps. Quantitative results show that the mean values of tumour areas in the synthetic and the real VERDICT maps were strongly correlated while qualitative results indicate that our method can generate realistic VERDICT maps that could supplement mp-MRI assessment for better diagnosis

Quantum Mechanical Search and Harmonic Perturbation

Perturbation theory in quantum mechanics studies how quantum systems interact with their environmental perturbations. Harmonic perturbation is a rare special case of time-dependent perturbations in which exact analysis exists. Some important technology advances, such as masers, lasers, nuclear magnetic resonance, etc., originated from it. Here we add quantum computation to this list with a theoretical demonstration. Based on harmonic perturbation, a quantum mechanical algorithm is devised to search the ground state of a given Hamiltonian. The intrinsic complexity of the algorithm is continuous and parametric in both time T and energy E. More precisely, the probability of locating a search target of a Hamiltonian in N-dimensional vector space is shown to be 1/(1+ c N E^{-2} T^{-2}) for some constant c. This result is optimal. As harmonic perturbation provides a different computation mechanism, the algorithm may suggest new directions in realizing quantum computers.Comment: 6 pages, 4 figures, revtex

Characterization of wild and captive baboon gut microbiota and their antibiotic resistomes

Antibiotic exposure results in acute and persistent shifts in the composition and function of microbial communities associated with vertebrate hosts. However, little is known about the state of these communities in the era before the widespread introduction of antibiotics into clinical and agricultural practice. We characterized the fecal microbiota and antibiotic resistomes of wild and captive baboon populations to understand the effect of human exposure and to understand how the primate microbiota may have been altered during the antibiotic era. We used culture-independent and bioinformatics methods to identify functional resistance genes in the guts of wild and captive baboons and show that exposure to humans is associated with changes in microbiota composition and resistome expansion compared to wild baboon groups. Our results suggest that captivity and lifestyle changes associated with human contact can lead to marked changes in the ecology of primate gut communities.Environmental microbes have harbored the capacity for antibiotic production for millions of years, spanning the evolution of humans and other vertebrates. However, the industrial-scale use of antibiotics in clinical and agricultural practice over the past century has led to a substantial increase in exposure of these agents to human and environmental microbiota. This perturbation is predicted to alter the ecology of microbial communities and to promote the evolution and transfer of antibiotic resistance (AR) genes. We studied wild and captive baboon populations to understand the effects of exposure to humans and human activities (e.g., antibiotic therapy) on the composition of the primate fecal microbiota and the antibiotic-resistant genes that it collectively harbors (the “resistome”). Using a culture-independent metagenomic approach, we identified functional antibiotic resistance genes in the gut microbiota of wild and captive baboon groups and saw marked variation in microbiota architecture and resistomes across habitats and lifeways. Our results support the view that antibiotic resistance is an ancient feature of gut microbial communities and that sharing habitats with humans may have important effects on the structure and function of the primate microbiota