state of art language modeling methods:
An Empirical Study of Smoothing Techniques for Language Modeling.pdf
BLEU, a Method for Automatic Evaluation of Machine Translation.pdf
Class-based n-gram models of natural language.pdf
Distributed Language Modeling for N-best List Re-ranking.pdf
Distributed Word Clustering for Large Scale Class-Based Language Modeling in.pdf
BugNET is an issue tracking and project issue management solution built using the ASP.NET web application framework. Email notifications, reporting and per project configuration of fields and values allows efficient management of bugs, feature requests, and other issues for projects of any Scale.
ecos RTOS 原理介紹和應用開發(fā)The design philosophy of eCos was to augment an open-source RTOS (which meant no
per-unit royalties) with source-level con?guration tools that would enable embedded developers
to Scale their RTOS from hundreds of bytes to hundreds of kilobytes without needing to manu-
ally change a line of source code.
This is a simple algorithm that downloads trading data from yahoo database. It is basically a large Scale application of sqq.m which was originally submitted by Michael Boldin, link at acknowledgements.
Some of the functionalities of the package:
- User defined ticker list.
- Function for downloading most recent SP500 composition in ticker list.
- Control for bad data (e.g. a certain percentage of prices missing).
- Choice of frequency of data (e.g. weekly prices).
- Choice of starting and ending data.
- Function for saving the whole data in a pre-formatted excel file together with a full reports on missing data.
The code performs a number (ITERS) of iterations of the
Bailey s 6-step FFT algorithm (following the ideas in the
CMU Task parallel suite).
1.- Generates an input signal vector (dgen) with size
n=n1xn2 stored in row major order
In this code the size of the input signal
is NN=NxN (n=NN, n1=n2=N)
2.- Transpose (tpose) A to have it stored in column
major order
3.- Perform independent FFTs on the rows (cffts)
4.- Scale each element of the resulting array by a
factor of w[n]**(p*q)
5.- Transpose (tpose) to prepair it for the next step
6.- Perform independent FFTs on the rows (cffts)
7.- Transpose the resulting matrix
The code requires nested Parallelism.
list of matlab m-files on matlab 7.0. learning , support vector machine and some utility routines : autocorrelation, linearly Scale randomize the row order of a matrix
ESRIMapObjectsLT 2 and MicrosoftVisual Basic6 to build an application that uses maps.
Display a map with multiple layers.
Control panning and zooming.
Create a toolbar control.
Base the display of map layers on Scale.
Perform spatial and logical queries.
Display features with thematic renderers.
Add vector data and images to a map programmatically.
The object detector described below has been initially proposed by
P.F. Felzenszwalb in [Felzenszwalb2010]. It is based on a
Dalal-Triggs detector that uses a single filter on histogram of
oriented gradients (HOG) features to represent an object category.
This detector uses a sliding window approach, where a filter is
applied at all positions and Scales of an image. The first
innovation is enriching the Dalal-Triggs model using a
star-structured part-based model defined by a “root” filter
(analogous to the Dalal-Triggs filter) plus a set of parts filters
and associated deformation models. The score of one of star models
at a particular position and Scale within an image is the score of
the root filter at the given location plus the sum over parts of the
maximum, over placements of that part, of the part filter score on
its location minus a deformation cost easuring the deviation of the
part from its ideal location relative to the root. Both root and
part filter scores are defined by the dot product between a filter
(a set of weights) and a subwindow of a feature pyramid computed
from the input image. Another improvement is a representation of the
class of models by a mixture of star models. The score of a mixture
model at a particular position and Scale is the maximum over
components, of the score of that component model at the given
location.
