acm HDOJ 1051WoodenSticks
Description:
There is a pile of n wooden sticks. The length and weight of each stick are known in advance. The sticks are to be processed by a woodworking machine in one by one fashion. It needs some time, called setup time, for the machine to prepare processing a stick. The setup times are associated with cleaning operations and changing tools and shapes in the machine. The setup times of the woodworking machine are given as follows:
(a) The setup time for the first wooden stick is 1 minute.
(b) Right after processing a stick of length l and weight w , the machine will need no setup time for a stick of length l and weight w if l<=l and w<=w . Otherwise, it will need 1 minute for setup.
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
This paper presents an interactive technique that
produces static hairstyles by generating individual hair strands
of the desired shape and color, subject to the presence of gravity
and collisions. A variety of hairstyles can be generated by
adjusting the wisp parameters, while the deformation is solved
efficiently, accounting for the effects of gravity and collisions.
Wisps are generated employing statistical approaches. As for
hair deformation, we propose a method which is based on
physical simulation concepts but is simplified to efficiently
solve the static shape of hair. On top of the statistical wisp
model and the deformation solver, a constraint-based styler
is proposed to model artificial features that oppose the natural
flow of hair under gravity and hair elasticity, such as a hairpin.
Our technique spans a wider range of human hairstyles than
previously proposed methods, and the styles generated by this
technique are fairly realistic.
This guide provides an in-depth look at the construction and underlying theory of a fully functional virtual machine and an entire suite of related development tools.
ApMl provides users with the ability to crawl the web and download pages to their computer in a directory structure suitable for a Machine Learning system to both train itself and classify new documents. Classification Algorithms include Naive Bayes, KNN
與MS-DOS兼容的DOS操作系統(tǒng)， FreeDOS like。
aldera OpenDOS Machine Readable Source Kit (M.R.S) 7.01
BUILDING THE MRS KIT
The Caldera OpenDOS MRS kit contains the following components: IBMBIO, IBMDOS and COMMAND
THIS book covers the Java™ Native Interface (JNI). It will be useful to you if
you are interested in any of the following:
• integrating a Java application with legacy code written in languages such as C
or C++
• incorporating a Java virtual machine implementation into an existing application
written in languages such as C or C++
• implementing a Java virtual machine
• understanding the technical issues in language interoperability, in particular
how to handle features such as garbage collection and multithreading
A salient-boundary extraction software package based on the paper: S. Wang, T. Kubota, J. M. Siskind, J. Wang. Salient Closed Boundary Extraction with Ratio Contour, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(4):546-561, 2005
