This paper proposes a method of inputting a planar log-aesthetic curve with compound-rhythm using four control points. The log-aesthetic curve does not exhibit any undulations of curvature as its curvature increases or decreases monotonically and it is suitable for practical product design. We report an method to input the compound-rhythm log-aesthetic curve made up of two log-aesthetic curve s...

We give a maximal independent set (MIS) algorithm that runs in $O(\log \log \Delta)$ rounds in the congested clique model, where $\Delta$ is the maximum degree of the input graph. This improves upon the $O(\frac{\log(\Delta) \cdot \log \log \Delta}{\sqrt{\log n}} + \log \log \Delta )$ rounds algorithm of [Ghaffari, PODC '17], where $n$ is the number of vertices of the input graph. In the first ...

fracture system in carbonate reservoirs is very complex. understanding the features of fractures such as dip, strike and the other parameters can help to distinguish the behaviour of reservoir in any exploration and production plan. image log is the best way to investigate feature planes like fracture and bedding. image log in one of drilled well #10x393 of bangestan reservoir in gachsaran oilf...

We present an algorithm that computes the Lempel-Ziv decomposition in O(n(log σ+log log n)) time and n log σ+ǫn bits of space, where ǫ is a constant rational parameter, n is the length of the input string, and σ is the alphabet size. The n log σ bits in the space bound are for the input string itself which is treated as read-only.

We study the resilience of the classical pseudo-random generator (PRG) of Nisan [Nis92] against space-bounded machines that make multiple passes over the input. Our motivation comes from the derandomization of BPNC. Observe that if for every log-space machine that reads its input n times there is a PRG fooling this machine, then in particular we fool NC circuits. Nisan’s PRG is known to fool lo...

A classic PCFG is a generative parsing model, a model of the joint probability P (x, y) of input and output. In this lecture, I will first review the distinction between generative and discriminative models (§1) and introduce the widely used log-linear models (§2). I will then discuss three different ways in which these models are used in current parsing systems: local discriminative models (§3...