3,039 research outputs found

    Decompositions of Triangle-Dense Graphs

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    High triangle density -- the graph property stating that a constant fraction of two-hop paths belong to a triangle -- is a common signature of social networks. This paper studies triangle-dense graphs from a structural perspective. We prove constructively that significant portions of a triangle-dense graph are contained in a disjoint union of dense, radius 2 subgraphs. This result quantifies the extent to which triangle-dense graphs resemble unions of cliques. We also show that our algorithm recovers planted clusterings in approximation-stable k-median instances.Comment: 20 pages. Version 1->2: Minor edits. 2->3: Strengthened {\S}3.5, removed appendi

    Non-malleable codes for space-bounded tampering

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    Non-malleable codes—introduced by Dziembowski, Pietrzak and Wichs at ICS 2010—are key-less coding schemes in which mauling attempts to an encoding of a given message, w.r.t. some class of tampering adversaries, result in a decoded value that is either identical or unrelated to the original message. Such codes are very useful for protecting arbitrary cryptographic primitives against tampering attacks against the memory. Clearly, non-malleability is hopeless if the class of tampering adversaries includes the decoding and encoding algorithm. To circumvent this obstacle, the majority of past research focused on designing non-malleable codes for various tampering classes, albeit assuming that the adversary is unable to decode. Nonetheless, in many concrete settings, this assumption is not realistic

    Implementation of mean-timing and subsequent logic functions on an FPGA

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    This article describes the implementation of a mean-timer and coincidence logic on a Virtex-5 FPGA for trigger purposes in a particle physics experiment. The novel feature is that the mean-timing and the coincidence logic are not synchronized with a clock which allows for a higher resolution of approximately 400 ps, not limited by a clock frequency.Comment: 15 pages, 11 figure

    Experimental studies of the NaCs 12(0+) [7¹Σ+] state

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    We present results from experimental studies of the 11(0+) and 12(0+) electronic states of the NaCs molecule. An optical-optical double resonance method is used to obtain Doppler-free excitation spectra. Selected data from the 11(0+) and 12(0+) high-lying electronic states are used to obtain Rydberg-Klein-Rees and Inverse Perturbation Approach potential energy curves. Interactions between these two electronic states are evident in the patterns observed in the bound-bound and bound-free fluorescence spectra. A model, based on two separate interaction mechanisms, is presented to describe how the wavefunctions of the two states mix. The electronic parts of the wavefunctions interact via spin-orbit coupling, while the individual rotation-vibration levels interact via a second mechanism, which is likely to be non-adiabatic coupling. A modified version of the BCONT program was used to simulate resolved fluorescence from both upper states. Parameters of the model that describe the two interaction mechanisms were varied until simulations were able to adequately reproduce experimental spectra.National Science Foundation (U.S.) (grant no. PHY-0968898)National Science Foundation (U.S.) (grant no. PHY-1403060)National Science Foundation (U.S.) (grant no. CHE–1361865

    Automated Glaucoma Detection Using Hybrid Feature Extraction in Retinal Fundus Images

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    Glaucoma is one of the most common causes of blindness. Robust mass screening may help to extend the symptom-free life for affected patients. To realize mass screening requires a cost-effective glaucoma detection method which integrates well with digital medical and administrative processes. To address these requirements, we propose a novel low cost automated glaucoma diagnosis system based on hybrid feature extraction from digital fundus images. The paper discusses a system for the automated identification of normal and glaucoma classes using higher order spectra (HOS), trace transform (TT), and discrete wavelet transform (DWT) features. The extracted features are fed to a support vector machine (SVM) classifier with linear, polynomial order 1, 2, 3 and radial basis function (RBF) in order to select the best kernel for automated decision making. In this work, the SVM classifier, with a polynomial order 2 kernel function, was able to identify glaucoma and normal images with an accuracy of 91.67%, and sensitivity and specificity of 90% and 93.33%, respectively. Furthermore, we propose a novel integrated index called Glaucoma Risk Index (GRI) which is composed from HOS, TT, and DWT features, to diagnose the unknown class using a single feature. We hope that this GRI will aid clinicians to make a faster glaucoma diagnosis during the mass screening of normal/glaucoma images
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