Faddeev calculation of a K−ppK^- p p quasi-bound state

We report on the first genuinely three-body ${\bar K}NN - \pi \Sigma N$ coupled-channel Faddeev calculation in search for quasi-bound states in the $K^- p p$ system. The main absorptivity in the $K^- p$ subsystem is accounted for by fitting to $K^- p$ data near threshold. Our calculation yields one such quasi-bound state, with $I=1/2$, $J^{\pi}=0^-$, bound in the range $B \sim 55-70$ MeV, with a width of $\Gamma \sim 95-110$ MeV. These results differ substantially from previous estimates, and are at odds with the $K^- p p \to \Lambda p$ signal observed by the FINUDA collaboration.Comment: Minor editorial revision; version accepted for publication in Phys. Rev. Let

ΛN\Lambda N space-exchange correlation effects in Λ5_\Lambda ^5He hypernucleus

A complete realistic study of $_\Lambda ^5$He hypernucleus is presented using a realistic Hamiltonian and wave function. This study takes into account all relevant dynamical correlations along with $\Lambda N$ space-exchange correlation (SEC). We also compute $\Lambda NN$ force and the corelation induced by this force. The SEC affects the central repulsive $\lambda N$ correlation significantly at $r \le 2.0$ fm, specially at its peak and in its vicinity. SEC significantly affects energy breakdown of the hypernucleus, $\Lambda$-seperation energy, nuclear core (NC) polarization and density profiles. A lage NC polarization is found with and without SEC, respectively. The SEC effect is relatively large in two-pion exchange component of $\Lambda NN$ force. Therefore, any attempt to pin down the strength of this force with no SEC would be flawed.Comment: 4 pages, two figure

Conditional BRUNO: A neural process for exchangeable labelled data

We present a neural process that models exchangeable sequences of high-dimensional complex observations conditionally on a set of labels or tags. Our model combines the expressiveness of deep neural networks with the data-efficiency of Gaussian processes, resulting in a probabilistic model for which the posterior distribution is easy to evaluate and sample from, and the computational complexity scales linearly with the number of observations. The advantages of the proposed architecture are demonstrated on a challenging few-shot view reconstruction task which requires generalisation from short sequences of viewpoints

On the Surface Structure of Strange Superheavy Nuclei

Bound, strange, neutral superheavy nuclei, stable against strong decay, may exist. A model effective field theory calculation of the surface energy and density of such systems is carried out assuming vector meson couplings to conserved currents and scalar couplings fit to data where it exists. The non-linear relativistic mean field equations are solved assuming local baryon sources. The approach is calibrated through a successful calculation of the known nuclear surface tension.Comment: 12 pages, 9 figure

Deep deterministic uncertainty: a new simple baseline

Reliable uncertainty from deterministic single-forward pass models is sought after because conventional methods of uncertainty quantification are computationally expensive. We take two complex single-forward-pass uncertainty approaches, DUQ and SNGP, and examine whether they mainly rely on a well-regularized feature space. Crucially, without using their more complex methods for estimating uncertainty, we find that a single softmax neural net with such a regularized feature-space, achieved via residual connections and spectral normalization, outperforms DUQ and SNGP's epistemic uncertainty predictions using simple Gaussian Discriminant Analysis post-training as a separate feature-space density estimator-without fine-tuning on OoD data, feature ensembling, or input pre-procressing. Our conceptually simple Deep Deterministic Uncertainty (DDU) baseline can also be used to disentangle aleatoric and epistemic uncertainty and performs as well as Deep Ensembles, the state-of-the art for uncertainty prediction, on several OoD bench-marks (CIFAR-10/100 vs SVHN/Tiny-ImageNet, ImageNet vs ImageNet-O), active learning settings across different model architectures, as well as in large scale vision tasks like semantic segmentation, while being computationally cheaper

Interaction of Silicon Dangling Bonds with Insulating Surfaces

We use first principles density functional theory calculations to study the interaction of a model dangling bond silicon tip with the surfaces of CaF2, Al2O3, TiO2, and MgO. In each case the strongest interaction is with the highest anions in the surface. We show that this is due to the onset of chemical bonding with the surface anions, which can be controlled by an electric field across the system. Combining our results and previous studies on semiconductor surfaces suggests that using dangling bond Si tips can provide immediate identification of surface species in atomically resolved noncontact atomic force microscopy and facilitate selective measurements of short-range interactions with surface sites.Peer reviewe

The bumpy light curve of supernova iPTF13z

A Type IIn supernova (SN) is dominated by the interaction of SN ejecta with the circumstellar medium (CSM). Some SNe IIn (e.g., SN 2006jd) have episodes of re-brightening ("bumps") in their light curves. We present iPTF13z, a SN IIn discovered by the intermediate Palomar Transient Factory (iPTF) and characterised by several bumps in its light curve. We analyse this peculiar behaviour trying to infer the properties of the CSM and of the SN explosion, as well as the nature of its progenitor star. We obtained multi-band optical photometry for over 1000 days after discovery with the P48 and P60 telescopes at Palomar Observatory. We obtained low-resolution optical spectra in the same period. We did an archival search for progenitor outbursts. We analyse our photometry and spectra, and compare iPTF13z to other SNe IIn. A simple analytical model is used to estimate properties of the CSM. iPTF13z was a SN IIn showing a light curve with five bumps during its decline phase. The bumps had amplitudes between 0.4 and 0.9 mag and durations between 20 and 120 days. The most prominent bumps appeared in all our different optical bands. The spectra showed typical SN IIn characteristics, with emission lines of H$\alpha$ (with broad component FWHM ~$10^{3}-10^{4} ~{\rm ~km ~s^{-1}}$ and narrow component FWHM ~$10^2 \rm ~km ~s^{-1}$) and He I, but also with Fe II, Ca II, Na I D and H$\beta$ P-Cygni profiles (with velocities of ~$10^{3}$ ${\rm ~km ~s^{-1}}$). A pre-explosion outburst was identified lasting $\gtrsim 50$ days, with $M_r \approx -15$ mag around 210 days before discovery. Large, variable progenitor mass-loss rates (~> 0.01 $M_{\odot} \rm ~yr^{-1}$) and CSM densities (~> 10$^{-16}$ g cm$^{-3}$) are derived. We suggest that the light curve bumps of iPTF13z arose from SN ejecta interacting with denser regions in the CSM, possibly produced by the eruptions of a luminous blue variable star.Comment: Version 2: Update to match published paper. 21 pages, 14 figures, abstract abridged to comply with arXiv length limit. In version 1 of the paper on arXiv, Table 3 had some erroneous entries. Table 3 is now corrected and available via VizieR. Version 1 comment: Accepted for publication in Astronomy & Astrophysics (24 pages, 14 figures, abstract abridged by 20 % not to exceed the arXiv length limit

Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives. In this paper we make the observation that the performance of such systems is strongly dependent on the relative weighting between each task's loss. Tuning these weights by hand is a difficult and expensive process, making multi-task learning prohibitive in practice. We propose a principled approach to multi-task deep learning which weighs multiple loss functions by considering the homoscedastic uncertainty of each task. This allows us to simultaneously learn various quantities with different units or scales in both classification and regression settings. We demonstrate our model learning per-pixel depth regression, semantic and instance segmentation from a monocular input image. Perhaps surprisingly, we show our model can learn multi-task weightings and outperform separate models trained individually on each task

A critical analysis of the tools to evaluate neuropsychiatric lupus.

Neuropsychiatric symptoms occur commonly in patients with systemic lupus erythematosus, but they are not always due to active disease. It is crucial to identify cases that are due to active systemic lupus erythematosus so that appropriate treatment can be instituted. There is no single serological or imaging test that distinguishes active neuropsychiatric systemic lupus erythematosus from neuropsychiatric manifestations caused by other factors such as infection. Most patients with neuropsychiatric systemic lupus erythematosus have generalised features of disease activity. Raised anti-dsDNA and low C3 complement levels are often seen, but are not an invariable guide. The presence of antiphospholipid antibodies is more suggestive of thrombotic than inflammatory causation. A number of other autoantibody tests have been proposed as biomarkers for neuropsychiatric systemic lupus erythematosus, but results in clinical studies have been inconsistent and none has so far entered routine clinical practice. Cerebrospinal fluid features and magnetic resonance imaging appearances are non-specific in neuropsychiatric systemic lupus erythematosus, but are useful in excluding other causes of neuropsychiatric symptoms. Newer magnetic resonance imaging sequences show promise for distinguishing new neuropsychiatric systemic lupus erythematosus activity from previous damage and recent research suggests these may correlate with changes in cognitive function in patients with systemic lupus erythematosus. However, formal cognitive testing is seldom carried out in the acute setting