Brick Walls and AdS/CFT

We discuss the relationship between the bulk-boundary correspondence in Rehren's algebraic holography (and in other 'fixed-background' approaches to holography) and in mainstream 'Maldacena AdS/CFT'. Especially, we contrast the understanding of black-hole entropy from the viewpoint of QFT in curved spacetime -- in the framework of 't Hooft's 'brick wall' model -- with the understanding based on Maldacena AdS/CFT. We show that the brick-wall modification of a Klein Gordon field in the Hartle-Hawking-Israel state on 1+2-Schwarzschild AdS (BTZ) has a well-defined boundary limit with the same temperature and entropy as the brick-wall-modified bulk theory. One of our main purposes is to point out a close connection, for general AdS/CFT situations, between the puzzle raised by Arnsdorf and Smolin regarding the relationship between Rehren's algebraic holography and mainstream AdS/CFT and the puzzle embodied in the 'correspondence principle' proposed by Mukohyama and Israel in their work on the brick-wall approach to black hole entropy. Working on the assumption that similar results will hold for bulk QFT other than the Klein Gordon field and for Schwarzschild AdS in other dimensions, and recalling the first author's proposed resolution to the Mukohyama-Israel puzzle based on his 'matter-gravity entanglement hypothesis', we argue that, in Maldacena AdS/CFT, the algebra of the boundary CFT is isomorphic only to a proper subalgebra of the bulk algebra, albeit (at non-zero temperature) the (GNS) Hilbert spaces of bulk and boundary theories are still the 'same' -- the total bulk state being pure, while the boundary state is mixed (thermal). We also argue from the finiteness of its boundary (and hence, on our assumptions, also bulk) entropy at finite temperature, that the Rehren dual of the Maldacena boundary CFT cannot itself be a QFT and must, instead, presumably be something like a string theory.Comment: 54 pages, 3 figures. Arguments strengthened in the light of B.S. Kay `Instability of Enclosed Horizons' arXiv:1310.739

The order of the quantum chromodynamics transition predicted by the standard model of particle physics

We determine the nature of the QCD transition using lattice calculations for physical quark masses. Susceptibilities are extrapolated to vanishing lattice spacing for three physical volumes, the smallest and largest of which differ by a factor of five. This ensures that a true transition should result in a dramatic increase of the susceptibilities.No such behaviour is observed: our finite-size scaling analysis shows that the finite-temperature QCD transition in the hot early Universe was not a real phase transition, but an analytic crossover (involving a rapid change, as opposed to a jump, as the temperature varied). As such, it will be difficult to find experimental evidence of this transition from astronomical observations.Comment: 7 pages, 4 figure

Orbifold equivalence for finite density QCD and effective field theory

In the large N_c limit, some apparently different gauge theories turn out to be equivalent due to large N_c orbifold equivalence. We use effective field theory techniques to explore orbifold equivalence, focusing on the specific case of a recently discovered relation between an SO(2N_c) gauge theory and QCD. The equivalence to QCD has been argued to hold at finite baryon chemical potential, \mu_B, so long as one deforms the SO(2N_c) theory by certain "double-trace" terms. The deformed SO(2N_c) theory can be studied without a sign problem in the chiral limit, in contrast to SU(N_c) QCD at finite \mu_B. The purpose of the double-trace deformation in the SO(2N_c) theory is to prevent baryon number symmetry from breaking spontaneously at finite density, which is necessary for the equivalence to large N_c QCD to be valid. The effective field theory analysis presented here clarifies the physical significance of double-trace deformations, and strongly supports the proposed equivalence between the deformed SO(2N_c) theory and large N_c QCD at finite density.Comment: 39 pages, 5 figures, 2 tables. v2: Minor typo fixes and clarification

Interactive Visual Labelling versus Active Learning: An Experimental Comparison

Methods from supervised machine learning allow the classification of new data automatically and are tremendously helpful for data analysis. The quality of supervised maching learning depends not only on the type of algorithm used, but also on the quality of the labelled dataset used to train the classifier. Labelling instances in a training dataset is often done manually relying on selections and annotations by expert analysts, and is often a tedious and time-consuming process. Active learning algorithms can automatically determine a subset of data instances for which labels would provide useful input to the learning process. Interactive visual labelling techniques are a promising alternative, providing effective visual overviews from which an analyst can simultaneously explore data records and select items to a label. By putting the analyst in the loop, higher accuracy can be achieved in the resulting classifier. While initial results of interactive visual labelling techniques are promising in the sense that user labelling can improve supervised learning, many aspects of these techniques are still largely unexplored. This paper presents a study conducted using the mVis tool to compare three interactive visualisations, similarity map, scatterplot matrix (SPLOM), and parallel coordinates, with each other and with active learning for the purpose of labelling a multivariate dataset. The results show that all three interactive visual labelling techniques surpass active learning algorithms in terms of classifier accuracy, and that users subjectively prefer the similarity map over SPLOM and parallel coordinates for labelling. Users also employ different labelling strategies depending on the visualisation used

Tenecteplase for ST-elevation myocardial infarction in a patient treated with drotrecogin alfa (activated) for severe sepsis: a case report

<p>Abstract</p> <p>Introduction</p> <p>Drotrecogin alfa (activated) (DrotAA), an activated protein C, promotes fibrinolysis in patients with severe sepsis. There are no reported cases or studies that address the diagnosis and treatment of myocardial infarction in septic patients treated with DrotAA.</p> <p>Case presentation</p> <p>A 59-year-old Caucasian man with septic shock secondary to community-acquired pneumonia treated with DrotAA, subsequently developed an ST-elevation myocardial infarction 12 hours after starting DrotAA. DrotAA was stopped and the patient was given tenecteplase thrombolysis resulting in complete resolution of ST-elevation and no adverse bleeding events. DrotAA was restarted to complete the 96-hour course. The sepsis resolved and the patient was discharged from hospital.</p> <p>Conclusion</p> <p>In patients with severe sepsis or septic shock complicated by myocardial infarction, it is difficult to determine if the myocardial infarction is an isolated event or caused by the sepsis process. The efficacy and safety of tenecteplase thrombolysis in septic patients treated with DrotAA need further study.</p

Studying Fake News via Network Analysis: Detection and Mitigation

Social media for news consumption is becoming increasingly popular due to its easy access, fast dissemination, and low cost. However, social media also enable the wide propagation of "fake news", i.e., news with intentionally false information. Fake news on social media poses significant negative societal effects, and also presents unique challenges. To tackle the challenges, many existing works exploit various features, from a network perspective, to detect and mitigate fake news. In essence, news dissemination ecosystem involves three dimensions on social media, i.e., a content dimension, a social dimension, and a temporal dimension. In this chapter, we will review network properties for studying fake news, introduce popular network types and how these networks can be used to detect and mitigation fake news on social media.Comment: Submitted as a invited book chapter in Lecture Notes in Social Networks, Springer Pres