Communique, 26 January 2004

Main story: "Netware? Beware!". Second story: "Web cache-'n'-carry shuts up shop". Item: "Basement jacks for self-service musicians...". Item: "No-strings-attached networking". Feature: "ISS-Windows98 relationship: the 5-year ditch?". Contact information: "Contacting Information Systems Services"

Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning

Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases. In this paper, we propose a method for retinal layer segmentation and quantification of uncertainty based on Bayesian deep learning. Our method not only performs end-to-end segmentation of retinal layers, but also gives the pixel wise uncertainty measure of the segmentation output. The generated uncertainty map can be used to identify erroneously segmented image regions which is useful in downstream analysis. We have validated our method on a dataset of 1487 images obtained from 15 subjects (OCT volumes) and compared it against the state-of-the-art segmentation algorithms that does not take uncertainty into account. The proposed uncertainty based segmentation method results in comparable or improved performance, and most importantly is more robust against noise

Variant supercurrent multiplets

In N = 1 rigid supersymmetric theories, there exist three standard realizations of the supercurrent multiplet corresponding to the (i) old minimal, (ii) new minimal and (iii) non-minimal off-shell formulations for N = 1 supergravity. Recently, Komargodski and Seiberg in arXiv:1002.2228 put forward a new supercurrent and proved its consistency, although in the past it was believed not to exist. In this paper, three new variant supercurrent multiplets are proposed. Implications for supergravity-matter systems are discussed.Comment: 11 pages; V2: minor changes in sect. 3; V3: published version; V4: typos in eq. (2.3) corrected; V5: comments and references adde

Role of surgery in pancreatic neuroendocrine tumor

published_or_final_versio

Delayed maximum northern European summer temperatures during the Last Interglacial as a result of Greenland Ice Sheet melt

This is the author accepted manuscript. The final version is available from the Geological Society of America via the DOI in this record.Here we report a new quantitative mean July temperature reconstruction using non-biting midges (chironomids) from the Danish Last Interglacial (LIG) site Hollerup (spanning 127–116 ka). We find that peak mean July temperatures of 17.5 °C, similar to those of the present day (1961–1990 CE), were reached shortly before the onset of the regional Carpinus pollen zone. Through comparison to terrestrial and marine sequences we demonstrate that peak summer warmth took place some three millennia after the onset of LIG warming in Europe, a marked delay in line with records from the North Atlantic. Crucially, the warmest northern European summer temperatures appear to follow maximum Greenland Ice Sheet mass loss, implying that meltwater substantially reduced Atlantic Meridional Overturning Circulation and depressed European temperatures during the early part of the interglacial.Turney and Fogwill thank the Australian Research Council (grants FL100100195, FT120100004, LP120200724). Thanks to Bjørn Buchardt for providing the C:N data, Angela Self for help with statistical analysis, David Campbell and Alan Bedford for laboratory work, and three reviewers for their constructive comments

Comments on Supercurrent Multiplets, Supersymmetric Field Theories and Supergravity

We analyze various supersymmetry multiplets containing the supercurrent and the energy-momentum tensor. The most widely known such multiplet, the Ferrara-Zumino (FZ) multiplet, is not always well-defined. This can happen once Fayet-Iliopoulos (FI) terms are present or when the Kahler form of the target space is not exact. We present a new multiplet S which always exists. This understanding of the supersymmetry current allows us to obtain new results about the possible IR behavior of supersymmetric theories. Next, we discuss the coupling of rigid supersymmetric theories to supergravity. When the theory has an FZ-multiplet or it has a global R-symmetry the standard formalism can be used. But when this is not the case such simple gauging is impossible. Then, we must gauge the current S. The resulting theory has, in addition to the graviton and the gravitino, another massless chiral superfield Phi which is essential for the consistency of the theory. Some of the moduli of various string models play the role of Phi. Our general considerations, which are based on the consistency of supergravity, show that such moduli cannot be easily lifted thus leading to constraints on gravity/string models.Comment: 27 pages. v2: references added and minor changes. v3: minor changes. v4: minor clarification

Bio-inspired Attentive Segmentation of Retinal OCT Imaging

Albeit optical coherence imaging (OCT) is widely used to assess ophthalmic pathologies, localization of intra-retinal boundaries suffers from erroneous segmentations due to image artifacts or topological abnormalities. Although deep learning-based methods have been effectively applied in OCT imaging, accurate automated layer segmentation remains a challenging task, with the flexibility and precision of most methods being highly constrained. In this paper, we propose a novel method to segment all retinal layers, tailored to the bio-topological OCT geometry. In addition to traditional learning of shift-invariant features, our method learns in selected pixels horizontally and vertically, exploiting the orientation of the extracted features. In this way, the most discriminative retinal features are generated in a robust manner, while long-range pixel dependencies across spatial locations are efficiently captured. To validate the effectiveness and generalisation of our method, we implement three sets of networks based on different backbone models. Results on three independent studies show that our methodology consistently produces more accurate segmentations than state-of-the-art networks, and shows better precision and agreement with ground truth. Thus, our method not only improves segmentation, but also enhances the statistical power of clinical trials with layer thickness change outcomes