Browsing by Subject "entropy"

Now showing 1 - 4 of 4
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    Counting Preimages
    (Cambridge, 2017) Misiurewicz, Michal; Rodrigues, Ana; Department of Mathematical Sciences, School of Science
    For non-invertible maps, subshifts that are mainly of finite type and piecewise monotone interval maps, we investigate what happens if we follow backward trajectories, which are random in the sense that, at each step, every preimage can be chosen with equal probability. In particular, we ask what happens if we try to compute the entropy this way. It turns out that, instead of the topological entropy, we get the metric entropy of a special measure, which we call the fair measure. In general, this entropy (the fair entropy) is smaller than the topological entropy. In such a way, for the systems that we consider, we get a new natural measure and a new invariant of topological conjugacy.
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    Information Theory in Computational Biology: Where We Stand Today
    (MDPI, 2020-06) Chanda, Pritam; Costa, Eduardo; Hu, Jie; Sukumar, Shravan; Van Hemert, John; Walia, Rasna; Computer and Information Science, School of Science
    "A Mathematical Theory of Communication" was published in 1948 by Claude Shannon to address the problems in the field of data compression and communication over (noisy) communication channels. Since then, the concepts and ideas developed in Shannon's work have formed the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. In this article we review the basic information theory based concepts and describe their key applications in multiple major areas of research in computational biology-gene expression and transcriptomics, alignment-free sequence comparison, sequencing and error correction, genome-wide disease-gene association mapping, metabolic networks and metabolomics, and protein sequence, structure and interaction analysis.
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    Investigating Real-Time Entropy Features of DDoS Attack Based on Categorized Partial-Flows
    (IEEE, 2020-01) Lotfalizadeh, Hamidreza; Kim, Dongso S.; Electrical and Computer Engineering, School of Engineering and Technology
    With the advent of IoT devices and exponential growth of nodes on the internet, computer networks are facing new challenges, with one of the more important ones being DDoS attacks. In this paper, new features to detect initiation and termination of DDoS attacks are investigated. The method to extract these features is devised with respect to some openflowbased switch capabilities. These features provide us with a higher resolution to view and process packet count entropies, thus improving DDoS attack detection capabilities. Although some of the technical assumptions are based on SDN technology and openflow protocol, the methodology can be applied in other networking paradigms as well.
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    A second-order maximum entropy model predicts correlated network states, but not their evolution over time
    (Springer (Biomed Central Ltd.), 2007-07-06) Tang, Aonan; Hobbs, Jon; Chen, Wei; Jackson, David; Smith, Jodi L; Patel, Hema; Beggs, John M; Department of Neurological Surgery, IU School of Medicine