scholarly journals Sensitivity to losses and defects of the symmetry-induced transmission enhancement through diffusive slabs

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Élie Chéron ◽  
Simon Félix ◽  
Vincent Pagneux
Keyword(s):  
Probabilistic Model ◽  
Arbitrary Number ◽  
Absorption Length ◽  
Transmission Enhancement ◽  
Disordered Medium

Abstract We inspect the robustness to absorption and to symmetry defects of the symmetry-induced broadband enhancement through opaque barriers in disordered slabs. The sensitivity of this phenomenon to symmetry defects is found to be strongly related to the distance from to barrier to the nearest defect, and, following, we propose a probabilistic model to estimate the conductance of a medium with an arbitrary number of randomly distributed defects. Also, the conductance enhancement is shown to be robust to absorption in the disordered medium, though being of course weakened. For sufficiently opaque barriers, the conditions of an optimal enhancement are mainly driven by the absorption length of the medium.

scholarly journals Modelling Medium Access Control in IEEE 802.15.4 Nonbeacon-Enabled Networks with Probabilistic Timed Automata

2013 ◽  
Vol 9 (2) ◽  
pp. 157-188 ◽  
Author(s):  
Tatjana Kapus
Keyword(s):  
Access Control ◽  
Medium Access Control ◽  
Probabilistic Model ◽  
Arbitrary Number ◽  
Ieee 802.15.4 ◽  
Timed Automata ◽  
Probabilistic Model Checking ◽  
Personal Area Networks ◽  
Medium Access ◽  
Probabilistic Timed Automata

This paper concerns the formal modelling of medium access control in nonbeacon-enabled IEEE 802.15.4 wireless personal area networks with probabilistic timed automata supported by the PRISM probabilistic model checker. In these networks, the devices contend for the medium by executing an unslotted carrier sense multiple access with collision avoidance algorithm. In the literature, a model of a network which consists of two stations sending data to two different destination stations is introduced. We have improved this model and, based on it, we propose two ways of modelling a network with an arbitrary number of sending stations, each having its own destination. We show that the same models are valid representations of a star-shaped network with an arbitrary number of stations which send data to the same destination station. We also propose how to model such a network if some of the sending stations are not within radio range of the others, i.e. if they are hidden. We present some results obtained for these models by probabilistic model checking using PRISM.

Kinematic analysis and deformation of a planar lattice with an arbitrary number of panels

2020 ◽  
pp. 15-19
Author(s):  
M.N. Kirsanov
Keyword(s):  
Exact Solution ◽  
Kinematic Analysis ◽  
Arbitrary Number ◽  
Design Parameters ◽  
Lattice Deformation ◽  
Planar Lattice ◽  
Force Loading

Formulae are obtained for calculating the deformations of a statically determinate lattice under the action of two types of loads in its plane, depending on the number of panels located along one side of the lattice. Two options for fixing the lattice are analyzed. Cases of kinematic variability of the structure are found. The distribution of forces in the rods of the lattice is shown. The dependences of the force loading of some rods on the design parameters are obtained. Keywords: truss, lattice, deformation, exact solution, deflection, induction, Maple system. [email protected]

Assessment and Analysis of Credit Risk with Using of Logical and Probabilistic Model

2002 ◽  
Author(s):  
Vassilij Karassev ◽  
Andrey Roukine ◽  
E.D. Dmitrievich Solojentsev
Keyword(s):  
Credit Risk ◽  
Probabilistic Model

scholarly journals Joint Semantic Synthesis and Morphological Analysis of the Derived Word

2018 ◽  
Vol 6 ◽  
pp. 33-48 ◽  
Author(s):  
Ryan Cotterell ◽  
Hinrich Schütze
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Probabilistic Model ◽  
Morphological Analysis ◽  
Semantic Representation ◽  
English Word ◽  
Word Formation ◽  
Additive Models ◽  
Segmentation Accuracy ◽  
Morphological Productivity

Much like sentences are composed of words, words themselves are composed of smaller units. For example, the English word questionably can be analyzed as question+ able+ ly. However, this structural decomposition of the word does not directly give us a semantic representation of the word’s meaning. Since morphology obeys the principle of compositionality, the semantics of the word can be systematically derived from the meaning of its parts. In this work, we propose a novel probabilistic model of word formation that captures both the analysis of a word w into its constituent segments and the synthesis of the meaning of w from the meanings of those segments. Our model jointly learns to segment words into morphemes and compose distributional semantic vectors of those morphemes. We experiment with the model on English CELEX data and German DErivBase (Zeller et al., 2013) data. We show that jointly modeling semantics increases both segmentation accuracy and morpheme F1 by between 3% and 5%. Additionally, we investigate different models of vector composition, showing that recurrent neural networks yield an improvement over simple additive models. Finally, we study the degree to which the representations correspond to a linguist’s notion of morphological productivity.

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