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In the last two decades the study of random instances of constraint satisfaction problems (CSPs) has flourished across several disciplines, including computer science, mathematics and physics. The diversity of the developed methods, on the rigorous and non-rigorous side, has led to major advances regarding both the theoretical as well as the applied viewpoints. Based on a ceteris paribus approach in terms of the density evolution equations known from statistical physics, we focus on a specific prominent class of regular CSPs, the so-called occupation problems, and in particular on $r$-in-$k$ occupation problems. By now, out of these CSPs only the satisfiability threshold – the largest degree for which the problem admits asymptotically a solution – for the $1$-in-$k$ occupation problem has been rigorously established. Here we determine the satisfiability threshold of the $2$-in-$k$ occupation problem for all $k$. In the proof we exploit the connection of an associated optimization problem regarding the overlap of satisfying assignments to a fixed point problem inspired by belief propagation, a message passing algorithm developed for solving such CSPs.
Chapter 33 introduces the strong data-processing inequalities (SDPIs), which are quantitative strengthening of the DPIs in Part I. As applications we show how to apply SDPI to deduce lower bounds for various estimation problems on graphs or in distributed settings. The purpose of this chapter is two-fold. First, we want to introduce general properties of the SDPI coefficients. Second, we want to show how SDPIs help prove sharp lower (impossibility) bounds on statistical estimation questions. The flavor of the statistical problems in this chapter is different from the rest of the book in that here the information about the unknown parameter θ is either more “thinly spread” across a high-dimensional vector of observations than in classical X = θ + Z type of models (see spiked Wigner and tree-coloring examples), or distributed across different terminals (as in correlation and mean estimation examples).
For irrigation management and maintenance, it is necessary to determine the volume of water that is applied to the field and the rate at which water is applied. Measurement devices are therefore included in irrigation systems. This chapter discusses different methods and devices that are commonly employed for determining flow rates and volume in agricultural irrigation.
Borda's mouthpiece consists of a long straight tube projecting into a large vessel, where fluid enters the tube in a free surface flow that tends to become uniform far downstream in the tube. A two-dimensional approximation to this flow under gravity in the upper part of the tube leads to an evaluation of the contraction coefficient, the ratio of the constant depth of the uniform flow to the width of the tube. The analysis also applies to flow under gravity past a sluice gate, if the semi-infinite wall above the channel is rotated to the vertical. The contraction coefficient depends upon the Froude number F, and is generally less than the zero gravity value of 1/2 that is approached as F → ∞.
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