dwr.xml說明文檔。配置文件init部分聲明那些用于建立遠程bean和在方法調用中轉換bean的類.這部分是可選擇性配置的,多數情況下可以不必使用它,如果你想定義一個新的creator或者converter那么就必須在部分中聲明,but do double check on the ones that are currently available first.
Good morning, dear teachers. I am very glad to be here for your interview. my name is xx.I am 21 years old. I come from Dafang, a small town of Guizhou province. My undergraduate period will be accomplished in East China Jiaotong University. I major in electrical engineering and automation. I am interesting in computer, especially in program design. I am a hard study student, especially in the things which I interesting in. I am a person with great perseverance. During the days I preparing for the postgraduate examination, I insist on study for more than 10 hours every day. Just owing to this, I could pass the first examination finally. I am also a person with great ambition.
The literature of cryptography has a curious history. Secrecy, of course, has always played a central
role, but until the First World War, important developments appeared in print in a more or less
timely fashion and the field moved forward in much the same way as other specialized disciplines.
As late as 1918, one of the most influential cryptanalytic papers of the twentieth century, William F.
Friedman’s monograph The Index of Coincidence and Its Applications in Cryptography, appeared as
a research report of the private Riverbank Laboratories [577]. And this, despite the fact that the work
had been done as part of the war effort. In the same year Edward H. Hebern of Oakland, California
filed the first patent for a rotor machine [710], the device destined to be a mainstay of military
cryptography for nearly 50 years.
軟件簡介:HI-TECH PICC 是一款高效的C編譯器,支持Microchip PICmicro 10/12/14/16/17系列控制器。是一款強勁的標準C編譯器,完全遵守ISO/ANSI C,支持所有的數據類型包括24 and 32 bit IEEE 標準浮點類型。智能優化產生高質量的代碼。屬于第三方開發工具。能和MPLAB整合,內嵌開發環境(HI-TIDE)。
Hi-tech PICC Compiler v8.注冊碼
Serial: HCPIC-88888
First Name: ONE
Last Name: TWO
Company Name:ONE TWO
Registration: 任意填,但一定要填
Activation: NPCBACMJKLPCADKLOEDBFPIOCIBAEIDI
gcclib
This gcc 1.40 suits for Linux kernel 0.11 - 0.95
Installtion hints
-----------------
This suit contains include.taz, local.taz and this README file.
You must download the bootimage and rootimage and install them first.
The include.taz contains all the include files for using with gcc 1.40.
The local.taz contains all the gcc tools & libs stored in two sepearted
directories:
/usr/local/lib
/usr/local/bin
You should copy the linux/ asm/ sys/ subdirectories into the include
directory from the corresponding kernel source.
Installation
------------
Goto the /usr directory. Untar the include.taz to the directory /usr/include.
Untar the local.taz to the directory /usr/local. That s it!
Example:
--------
cd /usr
tar zxvf include.taz
tar zxvf local.taz
Batch version of the back-propagation algorithm.
% Given a set of corresponding input-output pairs and an initial network
% [W1,W2,critvec,iter]=batbp(NetDef,W1,W2,PHI,Y,trparms) trains the
% network with backpropagation.
%
% The activation functions must be either linear or tanh. The network
% architecture is defined by the matrix NetDef consisting of two
% rows. The first row specifies the hidden layer while the second
% specifies the output layer.
%
% Train a two layer neural network with the Levenberg-Marquardt
% method.
%
% If desired, it is possible to use regularization by
% weight decay. Also pruned (ie. not fully connected) networks can
% be trained.
%
% Given a set of corresponding input-output pairs and an initial
% network,
% [W1,W2,critvec,iteration,lambda]=marq(NetDef,W1,W2,PHI,Y,trparms)
% trains the network with the Levenberg-Marquardt method.
%
% The activation functions can be either linear or tanh. The
% network architecture is defined by the matrix NetDef which
% has two rows. The first row specifies the hidden layer and the
% second row specifies the output layer.
Train a two layer neural network with a recursive prediction error
% algorithm ("recursive Gauss-Newton"). Also pruned (i.e., not fully
% connected) networks can be trained.
%
% The activation functions can either be linear or tanh. The network
% architecture is defined by the matrix NetDef , which has of two
% rows. The first row specifies the hidden layer while the second
% specifies the output layer.