WJt - NxJ array of MODWT wavelet coefficents where N = number of time points J = number of levels. wtfname - (optional) string containing name of a WMTSA-supported MODWT wavelet filter. Valid Values: see modwt_filter
OUTPUT
DJt - NxJ array of reconstituted details of data series for J0 scales. att - structure containing IMODWT transform attributes.
SIDE EFFECTS
1. wavelet is a WMTSA-supported MODWT wavelet filter; otherwise error.
DESCRIPTION
The output parameter att is a structure with the following fields: name - name of transform (= 'MODWT') wtfname - name of MODWT wavelet filter npts - number of observations (= length(X)) J0 - number of levels boundary - boundary conditions
EXAMPLE
[DJt, att] = imodwt_details(WJt, VJ0, 'la8');
NOTES
ALGORITHM
See pages 177-179 of WMTSA for description of Pyramid Algorithm for the inverse MODWT multi-resolution analysis.
REFERENCES
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis. Cambridge: Cambridge University Press.
SEE ALSO
imodwtj, imodwt, imodwt_smooth, modwt_filter, modwt
AUTHOR
Charlie Cornish
CREATION DATE
2003-05-01
COPYRIGHT
REVISION
$Revision: 612 $
[top]
NAME
imodwt -- Calculate the inverse (partial) maximal overlap discrete wavelet transform (IMODWT).
USAGE
X = imodwt(WJt, VJt, att)
INPUTS
* WJt -- MODWT wavelet coefficents (N x J x NChan array). * VJt -- MODWT scaling coefficients (N x {1,J} x NChan vector). * att -- MODWT transform attributes (struct).
OUTPUT
* X -- reconstituted set of observations (vector).
DESCRIPTION
imodwt computes the reconstituted time series from the MODWT wavelet and scaling coefficients.
EXAMPLE
X = imodwt(WJt, VJt, att);
ERRORS
WMTSA:InvalidNumArguments = 'Invalid number of arguments specified in function call' WMTSA:InvalidWavelet = 'Invalid wavelet filter specified'
NOTES
1. Tests indicate othat riginal and reconstituted time series agree within a precision of 10^-11, which is larger than the 10^15 numeric precision. 2. If the MODWT was calculated using 'reflection' boundary conditions, which extends the time series, and the computed coefficients, have been truncated to the length of the original series. The inverse MODWT will yield a reconstituted time series that differs from the original. If using 'reflection' boundary conditions, compute the MODWT with the opt.TruncateCoefs = 0 option (the default) to compute IMODWT which yields and exact replica of the original.
ALGORITHM
See pages 177-179 of WMTSA for description of Pyramid Algorithm for the inverse MODWT.
REFERENCES
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis. Cambridge: Cambridge University Press.
SEE ALSO
modwt, imodwtj TOOLBOX wmtsa/dwt CATEGORY modwt
AUTHOR
Charlie Cornish
CREATION DATE
2003-05-01
COPYRIGHT
(c) 2003, 2004, 2005 Charles R. Cornish
REVISION
$Revision: 632 $
[top]
NAME
fuzzy_diff -- Find differences in two arrays exceeding fuzzy_tolerance.
USAGE
result = fuzzy_diff(a, b, [fuzzy_tol], [mode])
INPUTS
a = first array or vector of values b = second array or vector of values fuzzy_tol = (optional) minimum tolerance or threshold Valid values: >= 0 Default: 0 mode = (optional) format of returned result Valid Values: 'details', 'summary' Default: 'details'
OUTPUT
result = array or number containing the result of comparison.
DESCRIPTION
fuzzy_diff compares two arrays and allows for approximate equality when strict equality may not exist due to minor differences due to rounding errors. fuzzy_diff subtracts two arrays and identifies those elements whose differences exceed the fuzzy tolerance threshold. The function has 2 modes of operation: details = return an array of size(a) with elements = 0, for differences between a and b < fuzzy_tolerance = a-b, for differences between a and b >= fuzzy_tolerance summary = return a number whose values = 0, for no differences within fuzzy_tolerance > 0, number of elements >= fuzzy_tolerance Note: fuzzy_diff compares to arrays on an absolute threshold (fuzzy_tol). Use nsigdig_diff to compare arrays that differ by a significant number of digits.
AUTHOR
Charlie Cornish
CREATION DATE
2003-05-01
COPYRIGHT
REVISION
$Revision: 612 $
[top]
NAME
flipvec_case -- munit test case for flipvec.
USAGE
run_tcase('flipvector_tcase')
INPUTS
(none)
OUTPUTS
* tc -- tcase case struct (tcase_s)
AUTHOR
Charlie Cornish
CREATION DATE
2004-Apr-25
COPYRIGHT
(c) Charles R. Cornish 2004, 2005
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
flipvec -- Flip a vector. SYNOPSIS [y] = flipvec(x)
INPUTS
* x -- vector of values.
OUTPUTS
* y -- vector of values in reversed order.
DESCRIPTION
Function checks whether item is a vector (row or column), i.e. has ndim = 2 and a singleton dimension, and then flips the vector (i.e. reverse order).
ERRORS
WMTSA:InvalidNumArguments WMTSA:NotAVector
SEE ALSO
isvector TOOLBOX wmtsa/utils CATEGORY
AUTHOR
Charlie Cornish
CREATION DATE
2004-Apr-27
COPYRIGHT
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
filter_center_of_energy -- Calculate filter center of energy. SYNOPSIS [coe] = filter_center_of_energy(a)
INPUTS
a = filter coefficients for filter a.
OUTPUTS
coe = center of energy for filter a.
DESCRIPTION
EXAMPLE
WARNINGS
ERRORS
NOTES
BUGS
TODO
ALGORITHM
REFERENCES
SEE ALSO
TOOLBOX CATEGORY
AUTHOR
Charlie Cornish
CREATION DATE
COPYRIGHT
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
filter_autocorrelation_width - Calculate autocorrelation width o scaling filter. SYNOPSIS [width_a] = filter_autocorrelation_width(wtfname, j, [method])
INPUTS
wtfname = string containing name of a WMTSA-supported MODWT wavelet filter, case-insensitve j = jth level (index) of scale or a range of j levels of scales (integer or vector of integers). method = (optional) name of method to use for calculations.
OUTPUTS
width_a = filter autocorrelation width for specified levels (integer or Jx1 vector of integers).
SIDE EFFECTS
1. j > 0, otherwise error.
DESCRIPTION
The function calculates the filter autocorrelation width of scaling filters for the specified wavelet. The user may optionally specify one of two methods of calculation: 'quick' - width_a = 2^j (default) 'long' - Equation 103 of WMTSA (see ALGORTHIM section.
EXAMPLE
[width_a] = filter_autocorrelation_width('haar', 5,) [width_a] = filter_autocorrelation_width('la8', 1:10, 'quick') [width_a] = filter_autocorrelation_width('d4', [2,3,5,7], 'long')
ALGORITHM
width_a {g_j,l} = (sum(g_j,l))^2 / sum(g_j,l^2) See equation 103 of WMTSA.
REFERENCES
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis. Cambridge: Cambridge University Press.
SEE ALSO
dwt_equivalent_filter, dwt_filter TOOLBOX WMTSA CATEGORY
AUTHOR
Charlie Cornish
CREATION DATE
2004-12-24
COPYRIGHT
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
figure_footer -- Print the string in the footer text area of a figure. SYNOPSIS [htext] = figure_footer(str, hfigure)
INPUTS
str = string to print in footer. hfigure = (optional) handle to figure to print footer. Default: current figure
OUTPUTS
htext = handle to text object of figure footer.
SIDE EFFECTS
DESCRIPTION
EXAMPLE
WARNINGS
ERRORS
NOTES
BUGS
TODO
ALGORITHM
REFERENCES
SEE ALSO
AUTHOR
Charlie Cornish
CREATION DATE
2003/06/04
COPYRIGHT
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
figure_datestamp -- Print a datestamp in footer area of a figure. SYNOPSIS [htext] = figure_datestamp(filename, hfigure)
INPUTS
filename = (optional) string containing filename. hfigure = (optional) handle to figure to print footer. Default: current figure
OUTPUTS
htext = handle to text object of figure footer.
SIDE EFFECTS
DESCRIPTION
EXAMPLE
WARNINGS
ERRORS
NOTES
BUGS
TODO
ALGORITHM
REFERENCES
SEE ALSO
AUTHOR
Charlie Cornish
CREATION DATE
2003/06/04
COPYRIGHT
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
equivalent_filter_width -- Calculate width of the equivalent wavelet or scaling filter. SYNOPSIS [Lj] = equivalent_filter_width(L, j)
INPUTS
* L -- width of wavelet or scaling filter (unit scale). * j -- jth level (index) of scale or a range of j levels of scales. (integer or vector of integers).
OUTPUTS
* Lj -- equivalent width of wavelet or scaling filter for specified levels (integer or Jx1 vector of integers). SIDEEFFECTS 1. L > 0, otherwise error. 2. j > 0, otherwise error.
DESCRIPTION
Given the length of a wavelet or scaling filter, the function calculates the width of the equivalent filter a level or range of levels j for the specified base filter width L.
ALGORITHM
Lj = (2^j - 1) * (L - 1) + 1 (equation 96a of WMTSA)
REFERENCES
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis. Cambridge: Cambridge University Press.
AUTHOR
Charlie Cornish
CREATION DATE
2003-05-24
COPYRIGHT
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
encode_errmsg -- Encode error message for specified err_id.
USAGE
[errmsg] = encode_errmsg(err_id, err_table_ps, [varargin])
INPUTS
* err_id -- error message id (character string). * err_table_ps -- error lookup table (character string or struct). * varargin -- (optional) supplemental values to encode in errmsg.
OUTPUTS
* errmsg -- encoded error message (character string).
SIDE EFFECTS
DESCRIPTION
encode_errmsg encodes an error message (errmsg) for a specified error message id (err_id). The function loads the error message table (err_table) from the specified path, searches for the matching (err_table.err_id) entry and returns the error message template (err_table.errmsg). Based on the number of message arguments (err_table.nargs), the function encodes the errmsg using the variable number of arguments (varargin) passed on the function call. The err_table is a structure array with the following fields: * err_id -- error message id (character string). * err_msg -- error message template (character string). * nargs -- number of supplement arguments to use for encoding errmsg. The input argument 'err_table_ps' may be either a character string or a struct. If a character string, err_table_ps is full path to a function or script containing the error table struct to run and load. If a struct, then the struct passed as the value in err_table_ps argument is used.
EXAMPLE
% Error table load via function wmtsa_err_table. % Name of required argument is 'transform'. errmsg = encode_errmsg('WMTSA:missingRequiredArgument', ... wmtsa_err_table, 'transform'));
WARNINGS
ERRORS
NOTES
SEE ALSO
TOOLBOX wmtsa/utils CATEGORY WMTSA Utilities
AUTHOR
Charlie Cornish
CREATION DATE
2004-06-25
COPYRIGHT
(c) 2004, 2005 Charles R. Cornish
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
dwtjm -- Calculate jth level DWT coefficients (MATLAB implementation).
USAGE
[Wout, Vout] = dwtjm(Vin, h, g, j)
INPUTS
* Vin -- Input series for j-1 level (i.e. DWT scaling coefficients) * h -- DWT wavelet filter coefficients. * g -- DWT scaling filter coefficients. * j -- level (index) of scale.
OUTPUTS
* Wout -- DWT wavelet coefficients for jth scale. * Vout -- DWT scaling coefficients for jth scale.
SIDE EFFECTS
DESCRIPTION
dwtjm is an implementation in MATLAB code of the DWT transform for the jth level, and is included in the toolkit for illustrative purposes to demonstrate the pyramid algothrim. For speed considerations, the dwt function uses the C implementation of the DWT transform, modwtj, which linked in as a MEX function.
EXAMPLE
X = wmtsa_data('ecg'); wtf = dwt_filter('haar'); % Compute the j = 1 level coefficients for ECG time series. j = 1; [Wout, Vout] = dwtjm(X, h, g, j);
NOTES
BUGS
TODO
ALGORITHM
See page 100-101 of WMTSA for DWT pyramid algorithm.
REFERENCES
SEE ALSO
dwtj, dwt, dwt_filter TOOLBOX wmtsa CATEGORY dwt
AUTHOR
Charlie Cornish
CREATION DATE
2005-Sep-03
COPYRIGHT
(c) 2005 Charles R. Cornish MATLAB VERSION 7.0
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
dwt_wavelet_transfer_function -- Calculate transfer function for frequencies f for specified DWT wavelet filter and (optionally) jth level. SYNOPSIS H_j = dwt_wavelet_transfer_function(f, wavelet, [j])
INPUTS
f - vector of sinsuoidal frequency. wtfname - name of a WMSTA-supported MODWT scaling filter. j - (optional) level (index) of scale.
OUTPUTS
H - vector of the transfer function values for DWT wavelet filter h at frequencies f. H_j - vector of the transfer function values for DWT wavelet filter h at frequencies f at jth level.
SIDE EFFECTS
DESCRIPTION
EXAMPLE
NOTES
ALGORITHM
See page 69 of WMTSA for H. See page 96 of WMTSA for H_j.
REFERENCES
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis. Cambridge: Cambridge University Press.
SEE ALSO
AUTHOR
Charlie Cornish
CREATION DATE
2003-10-08
COPYRIGHT
REVISION
$Revision: 612 $
[top]
NAME
dwt_wavelet_sgf -- Calculate squared gain function for specified DWT scaling filter at frequencies f and at (optionally) jth level. SYNOPSIS Hs_j = dwt_wavelet_sgf(f, wtfname, [j])
INPUTS
f = vector of sinsuoidal frequency. wtfname = name of a WMSTA-supported MODWT scaling filter. j = (optional) level (index) of scale.
OUTPUTS
Hs = vector of squared gain function values for DWT wavelet filter h at frequencies f. Hs_j = vector of squared gain function values for DWT wavelet filter h at frequencies f at jth level.
SIDE EFFECTS
DESCRIPTION
EXAMPLE
NOTES
ALGORITHM
See page 69 of WMTSA for Hs. See page 154 of WMTSA for Hs_j.
REFERENCES
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis. Cambridge: Cambridge University Press.
SEE ALSO
dwt_wavelet_transfer_function
AUTHOR
Charlie Cornish
CREATION DATE
2003-10-08
COPYRIGHT
REVISION
$Revision: 612 $
[top]
NAME
dwt_verification_tcase -- munit test case to verify results of dwt transform.
USAGE
run_tcase('dwt_verification_tcase')
INPUTS
OUTPUTS
tc = tcase structure for dwt testcase.
DESCRIPTION
SEE ALSO
dwt_wvar
AUTHOR
Charlie Cornish
CREATION DATE
2004-Apr-25
COPYRIGHT
CREDITS
REVISION
$Revision: 612 $
NAME
w_att_s -- wavelet transform attributes struct.
DESCRIPTION
w_att_s struct has fields: * Transform -- name of transform ('MODWT') (string). * WTF -- name of wavelet transform filter or a wtf_s struct (string or struct). * NX -- number of observations in original series (= length(X)) (integer). * NW -- number of wavelet coefficients (integer). * J0 -- number of levels of partial decompsition (integer). * NChan -- number of channels in a multivariate dataset (integer). * Boundary -- boundary conditions applied (string). * Aligned -- Boolean flag indicating whether coefficients are aligned with original series (1 = true) or not (0 = false) (boolean). * RetrainVJ -- Boolean flag indicating whether VJ scaling coefficients at all levels have been retained (1= true) or not (0 = false) (boolean). Possible values for transform are: DWT, MODWT.
SEE ALSO
NAME
wtf_s -- wavelet transform filter struct.
DESCRIPTION
wtf_s struct has fields: * g -- scaling (low-pass) filter coefficients (vector). * h -- wavelet (high-pass) filter coefficients (vector). * L -- filter length (= number of coefficients) (integer). * Name -- name of wavelet filter (character string). * Class -- class of wavelet filters (character string). * Transform -- name of transform (character string). Possible values for transform are: DWT, MODWT.
SEE ALSO
[top]
NAME
WMTSA DWT Structures -- structs used in WMTSA DWT toolbox.
DESCRIPTION
MATLAB structures used in WMTSA DWT toolbox. TOOLBOX wmtsa/dwt CATEGORY
[top]
NAME
dwt_scaling_transfer_function -- Calculate transfer function for frequencies f for specified DWT scaling filter and (optionally) jth level. SYNOPSIS G_j = dwt_scaling_transfer_function(f, wtfname, [j])
INPUTS
f - vector of sinsuoidal frequency. wtfname - name of a WMSTA-supported DWT scaling filter. j - (optional) level (index) of scale.
OUTPUTS
G - vector of the transfer function values for DWT scaling filter h at frequencies f. G_j - vector of the transfer function values for DWT scaling filter h at frequencies f at jth level.
SIDE EFFECTS
DESCRIPTION
EXAMPLE
NOTES
ALGORITHM
See page 76 of WMTSA for G. See page 97 of WMTSA for G_j.
REFERENCES
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis. Cambridge: Cambridge University Press.
SEE ALSO
AUTHOR
Charlie Cornish
CREATION DATE
2003-10-08
COPYRIGHT
REVISION
$Revision: 612 $
[top]
NAME
modwt_scaling_sgf -- Calculate squared gain function for frequencies f for specified DWT scaling filter and (optionally) jth level. SYNOPSIS Gs_j = dwt_scaling_sgf(f, wtfname, [j])
INPUTS
f = vector of sinsuoidal frequency. wtfname = name of a WMSTA-supported DWT scaling filter. j = (optional) level (index) of scale.
OUTPUTS
Gs = vector of squared gain function values for DWT scaling filter h at frequencies f. Gs_j = vector of squared gain function values for DWT scaling filter h at frequencies f at jth level.
SIDE EFFECTS
DESCRIPTION
EXAMPLE
NOTES
ALGORITHM
See page 76 of WMTSA for Gs. See page 154 of WMTSA for Gs_j.
REFERENCES
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis. Cambridge: Cambridge University Press.
SEE ALSO
dwt_scaling_transfer_function
AUTHOR
Charlie Cornish
CREATION DATE
2003-10-08
COPYRIGHT
REVISION
$Revision: 612 $
[top]
NAME
dwt_functionality_tcase -- munit test case to test dwt functionality.
USAGE
run_tcase('dwt_functionality_tcase')
INPUTS
OUTPUTS
tc = tcase structure for dwt testcase.
DESCRIPTION
AUTHOR
Charlie Cornish
CREATION DATE
2004-Apr-25
COPYRIGHT
(c) 2004, 2005 Charles R. Cornish
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
dwt_filter_tcase -- munit test case to test dwt_filter.
USAGE
run_tcase('dwt_filter_tcase')
INPUTS
OUTPUTS
tc = tcase structure for dwt_filter testcase.
SIDE EFFECTS
DESCRIPTION
SEE ALSO
dwt_filter
AUTHOR
Charlie Cornish
CREATION DATE
2005-03-01
COPYRIGHT
(c) 2004, 2005 Charles R. Cornish
CREDITS
REVISION
$Revision: 615 $
[top]
NAME
dwt_filter -- Define the DWT filter coefficients. SYNOPSIS [wtf] = dwt_filter(wtfname)
INPUTS
* wtfname -- name of wavelet transform filter (string, case-insenstive).
OUTPUTS
* wtf -- wavelet tranform filter struct (wtf_s).
SIDE EFFECTS
wtfname is a valid wavelet filter name; otherwise error.
DESCRIPTION
dwt_filter returns a wtf_s struct containing the DWT wavelet (high-pass) and scaling (low-pass) filter coefficients.
NOTES
dwt_filter is deprecated by the wtfilter function. dwt_filter is a pass thru to wtfilter and maintained for backward compatiablity and convenice. The wtf_s struct has fields: * g -- scaling (low-pass) filter coefficients (vector). * h -- wavelet (high-pass) filter coefficients (vector). * L -- filter length (= number of coefficients) (integer). * name -- name of wavelet filter (string). * wtfclass -- class of wavelet filters (string). * transform -- name of transform (string). Typing dwt_filter('list') displays a list of supported filters. Typing dwt_filter('all') returns a struct array of wtf_s of all supported filters.
ERRORS
WMTSA:InvalidNumArguments
EXAMPLE
[h, g, L, name] = dwt_filter('la8');
NOTES
dwt_filter is a wrapper function around the wtfilter function.
REFERENCES
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis. Cambridge: Cambridge University Press.
SEE ALSO
wtfilter, modwt_filter TOOLBOX wmtsa/dwt CATEGORY Filters: Filters
AUTHOR
Charlie Cornish Brandon Whitcher
CREATION DATE
2003-09-18
COPYRIGHT
(c) Charles R. Cornish 2005
CREDITS
Based on the original function (myfilter.m) by Brandon Whitcher.
REVISION
$Revision: 612 $
[top]
NAME
dwt_equivalent_filter -- Calculate DWT equivalent filter for J levels. SYNOPSIS [hJ, gJ, LJ] = dwt_equivalent_filter(wtfname, J)
INPUTS
wtfname = string containing name of a WMTSA-supported wavelet filter, case-insensitve. J = number of levels (integer > 0)
OUTPUTS
hJ = 1xJ numeric cell array containing equivalent wavelet filter coefficients. gJ = 1xJ numeric cell array containing equivalent scaling filter coefficients. LJ = 1xJ numeric cell array containing widths of the equivalent filters (L_j).
SIDE EFFECTS
DESCRIPTION
EXAMPLE
WARNINGS
ERRORS
NOTES
BUGS
TODO
ALGORITHM
REFERENCES
SEE ALSO
AUTHOR
Charlie Cornish
CREATION DATE
2004-01-27
COPYRIGHT
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
dwt2vector -- Convert DWT coefficient cell array to vector representation. SYNOPSIS [W] = dwt2vector(WJ, VJ, att)
INPUTS
* WJ -- DWT wavelet coefficents (cell array of length J0 of NJ(:,1) x NChan matrices). * VJ -- DWT scaling coefficents (cell array of length J0 of NJ(:,2) x NChan matrices). * att -- structure containing DWT transform attributes.
OUTPUTS
* W -- vector of DWT coefficients. (vector or cell array of vectors).
SIDE EFFECTS
DESCRIPTION
USAGE
WARNINGS
ERRORS
EXAMPLE
NOTES
BUGS
TODO
ALGORITHM
REFERENCES
SEE ALSO
TOOLBOX CATEGORY
AUTHOR
Charlie Cornish
CREATION DATE
COPYRIGHT
(c) 2005 Charles R. Cornish MATLAB VERSION 7.0
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
dwt -- Compute the (partial) discrete wavelet transform (DWT).
USAGE
[W, V, att, NJ] = dwt(X, [wtf], [nlevels], [boundary], [{opts}])
INPUTS
* X -- set of observations (vector of length N or matrix of size N x Nchan) * wtf -- (optional) wavelet transform filter name or struct (string, case-insensitve or wtf struct). Default: 'la8' * nlevels -- (optional) maximum level J0 (integer) or method of calculating J0 (character string). Valid values: integer>0 or a valid method name Default: 'conservative' * boundary -- (optional) boundary conditions to use (character string) Valid values: 'circular' or 'reflection' Default: 'reflection' * opts -- (optional) Additional function options.
OUTPUTS
* WJ -- DWT wavelet coefficents (cell array of length J0 of NJ(:,1) x NChan matrices). * VJ -- DWT scaling coefficents (cell array of length J0 of NJ(:,2) x NChan matrices). * att -- structure containing DWT transform attributes. * NJ -- Jx2 matrix containing number of DWT wavelet (WJ) and scaling (VJ) coefficients at each level j.
SIDE EFFECTS
1. wavelet is a WMTSA-supported DWT wavelet filter; otherwise error.
DESCRIPTION
EXAMPLE
WARNINGS
ERRORS
NOTES
BUGS
TODO
ALGORITHM
REFERENCES
SEE ALSO
AUTHOR
Charlie Cornish
CREATION DATE
COPYRIGHT
CREDITS
REVISION
$Revision: 630 $
[top]
NAME
dhm -- Generate simulation of stationary process using Davies-Harte method. SYNOPSIS Y_t = wmtsa_gen_stationary_process(s_X, Z)
INPUTS
* s_X -- ACVS of SDF of process (M lags, D deltas). * Z -- N sets M IID Gaussian RVs.
OUTPUTS
* Y_t -- simulated time series.
SIDE EFFECTS
DESCRIPTION
USAGE
WARNINGS
ERRORS
EXAMPLE
NOTES
BUGS
TODO
ALGORITHM
REFERENCES
SEE ALSO
TOOLBOX CATEGORY
AUTHOR
Charlie Cornish
CREATION DATE
COPYRIGHT
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
data_tsuite -- munit test suite to test data.
USAGE
INPUTS
OUTPUTS
ts = tsuite structure for data testsuite.
SIDE EFFECTS
DESCRIPTION
AUTHOR
Charlie Cornish
CREATION DATE
2005-08-01
COPYRIGHT
(c) 2005 Charles R. Cornish
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
compare_struct_fieldvalues -- Compare two structs for match of fieldvalues. SYNOPSIS [tf, msg] = compare_struct_fieldvalues(s1, s2) [tf, msg] = compare_struct_fieldvalues(s1, s2, 'exact') [tf, msg] = compare_struct_fieldvalues(s1, s2, 'sorted')
INPUTS
* s1 -- first struct to compare. * s2 -- second struct to compare. * match -- type of match. Possible values: 'exact', 'sorted'
OUTPUTS
* tf -- flag indicating whether structs' fieldvalues match (Boolean). * mismatches -- list of fieldnames with mismatched values.
SIDE EFFECTS
Function call requires a minimum of 2 input arguments; otherwise error.
DESCRIPTION
compare_struct_filenames compares the fieldvalues in two structs.
USAGE
WARNINGS
ERRORS
EXAMPLE
NOTES
BUGS
TODO
ALGORITHM
REFERENCES
SEE ALSO
TOOLBOX CATEGORY
AUTHOR
Charlie Cornish
CREATION DATE
COPYRIGHT
(c) 2005 Charles R. Cornish MATLAB VERSION 7.0
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
compare_struct_fieldnames -- Compare two structs for match of fieldnames. SYNOPSIS [tf, msg] = compare_struct_fieldnames(s1, s2) [tf, msg] = compare_struct_fieldnames(s1, s2, 'exact') [tf, msg] = compare_struct_fieldnames(s1, s2, 'sorted')
INPUTS
* s1 -- first struct to compare. * s2 -- second struct to compare. * match -- type of match. Possible values: 'exact', 'sorted'
OUTPUTS
* tf -- flag indicating whether structs' fieldnames match (Boolean). * mismatches -- list of pairs of non-matching fieldnames (Nx2 cell array of strings).
SIDE EFFECTS
Function call requires a minimum of 2 input arguments; otherwise error.
DESCRIPTION
compare_struct_filenames compares the fieldnames in two structs.
USAGE
WARNINGS
ERRORS
EXAMPLE
NOTES
BUGS
TODO
ALGORITHM
REFERENCES
SEE ALSO
TOOLBOX CATEGORY
AUTHOR
Charlie Cornish
CREATION DATE
COPYRIGHT
(c) 2005 Charles R. Cornish MATLAB VERSION 7.0
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
columnize -- Convert array in to a column vector. SYNOPSIS y = columnize(X)
INPUTS
* X -- an array.
OUTPUTS
* y -- column vector
DESCRIPTION
columnize converts an array into a column vector. TOOLBOX utils CATEGORY
AUTHOR
Charlie Cornish
CREATION DATE
2005-Sep-11
COPYRIGHT
(c) 2005 Charles R. Cornish MATLAB VERSION 7.0
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
colorspecname2rgb -- Look up RGB value by ColorSpec name.
USAGE
[rgb] = colorspecname2rgb(colorspecname)
INPUTS
colorspecname - name of ColorSpec in specified format, in short or long format.
OUTPUTS
rbg - three-element row vector whose elements specify the intensities of the red, green, and blue components of the color; the intensities must be in the range [0 1].
SIDE EFFECTS
rgb must be RGB value for one of eight primary colors; otherwise error.
DESCRIPTION
Funciton converts a ColorSpec name, in either short (single character) or long (single word) format into RGB 3-element vector to specifying its RGB value. Possible ColorSpec names: yellow magenta cyan red green blue white black
SEE ALSO
ColorSpec TOOLBOX wmtsa CATEGORY utils
AUTHOR
Charlie Cornish
CREATION DATE
2004-Apr-05
COPYRIGHT
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
argterr_tcase -- munit test case to test argterr.
USAGE
run_tcase(@argterr_tcase)
INPUTS
OUTPUTS
tc = tcase structure for argterr testcase.
SIDE EFFECTS
DESCRIPTION
AUTHOR
Charlie Cornish
CREATION DATE
2005-07-18
COPYRIGHT
(c) 2005 Charles R. Cornish
CREDITS
REVISION
$Revision: 612 $
[top]
NAME
argterr - Check the data type and size of a function argument. SYNOPSIS [errmsg] = argterr(func, arg, type, [arg_size], [mode]) [errmsg] = argterr(func, arg, type, [arg_size], [mode], [msg], 'string') [errstruct] = argterr(func, arg, type, [arg_size], [mode], [msg], 'struct')
INPUTS
* func -- checked function (string or function handle). * arg -- the argument to check (object) * datatypes -- expected data type(s) of the arg. See verify_datatypes function for possibles datatypes to check. (string or string cell array). * arg_size -- (optional) expected size of array (integer vector). * mode -- (optional) output display mode (charcter string) * msg -- (optional) message string to be displayed (string). * return_type -- (optional) type of output argument (string). 'string' = return error message string (default). 'struct' = return error message struct.
OUTPUTS
* errmsg -- error message (string). * errstruct -- error struct with fields: message, identifier.
SIDE EFFECTS
Function call requires a minimum of three input arguments; otherwise error.
DESCRIPTION
argterr checks the data type(s) and optionally the size of an argument to a function call. If arg does not have the specified data type(s), an error message or error struct is returned. arg may be a single Possible values for 'datatypes' to check include: * 'posint' -- All are positive integers --> integer value(s) > 0. * 'int0' -- All are positive integers plus zero --> integer value(s) >= 0. * 'int','integer' -- All are integers --> any integer value(s). * 'num','numeric' -- All are numeric. * 'struct','structure' -- Is a structure. * 'char','character','string' - Is a character string. * 'scalar -- Is a point (size of all dimensions = 1). * 'vec','vector' -- Is a vector (i.e. MxN, with M and/or N = 1). * 'nonsingleton','truevector' -- Is a vector (i.e. MxN with M *or* N = 1). * 'row','rowvector' -- Is a row vector (i.e. M x 1). * 'col','columnvector' -- Is a column vector (i.e. 1 x N). * 'finite' -- All are finite. * 'nonsparse' -- Is a non-sparse matrix. The input argument 'mode' controls the display of diagnostic information to the consolue and has possible values: * 0 -- silent * 1 -- verbose The default value is 1 (silent).
EXAMPLE
arg = [0 2]; % arg is an integer. err = argterr('myfunction', arg, 'posint') % Result: err = 1 err = argterr('myfunction', arg, 'int', [1 2], '') % Result: err = 0 [err, errmsg] = argterr('myfunction', arg, {'int', 'vector'}) % Result: err = 0
NOTES
argterr is modelled after errargt, which is part of MATLAB wavelet toolkit.
SEE ALSO
verify_datatype
AUTHOR
Charlie Cornish
CREATION DATE
2003-06-22
COPYRIGHT
(c) 2003, 2004, 2005 Charles R. Cornish
CREDITS
argterr is inspired by the function errargt by M. Misiti, Y. Misiti, G. Oppenheim, J.M. Poggi which is part of the MATLAB wavelet toolkit. MATLAB VERSION 7.0
REVISION
$Revision: 612 $
[top]
NAME
advance_wavelet_filter -- Calculate the advance of the wavelet filter at jth level for a given wavelet. SYNOPSIS nuHj = advance_wavelet_filter(wtfname, j)
INPUTS
wtfname = string containing name of WMTSA-supported wavelet filter. j = jth level (index) of scale or a range of j levels of scales (integer or Jx1 vector of integers).
OUTPUTS
nuHj = advance of wavelet filter at jth level (integer or vector of integers).
SIDE EFFECTS
wavelet is a WMTSA-supported wavelet filter; otherwise error.
DESCRIPTION
EXAMPLE
ALGORITHM
nuHj = - (2^(j-1) * (L-1) + nu); For details, see equation 114b of WMTSA.
REFERENCES
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis. Cambridge: Cambridge University Press.
SEE ALSO
advance_time_series_filter, dwt_filter
AUTHOR
Charlie Cornish
CREATION DATE
2003-05-08
COPYRIGHT
REVISION
$Revision: 612 $
[top]
NAME
advance_time_series_filter -- Calculate the advance of the time series or filter for a given wavelet. SYNOPSIS nu = advance_time_series_filter(wtfname)
INPUTS
wtfname - string containing name of WMTSA-supported wavelet filter.
OUTPUTS
nu - advance of time series for specified wavelet filter.
SIDE EFFECTS
wavelet is a WMTSA-supported wavelet filter; otherwise error.
DESCRIPTION
EXAMPLE
ALGORITHM
For Least Asymmetric filters, equation 112e of WMTSA: nu = -L/2 + 1, for L/2 is even; = -L/2, for L = 10 or 18; = -L/2 + 2, for L = 14. For Best Localized filter, page 119 of WMTSA. nu = -5, for L = 14; = -11, for L = 18; = -9, for L = 20. For Coiflet filters, page 124 and equation 124 of WMTSA: nu = -2*L/3 + 1
REFERENCES
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis. Cambridge: Cambridge University Press.
SEE ALSO
dwt_filter
AUTHOR
Charlie Cornish
CREATION DATE
2003-05-08
COPYRIGHT
REVISION
$Revision: 612 $
[top]
NAME
advance_scaling_filter -- Calculate the value to advance scaling filter at jth level for a given wavelet. SYNOPSIS nuGj = advance_scaling_filter(wtfname, level)
INPUTS
wtfname = string containing name of WMTSA-supported wavelet filter. j = jth level (index) of scale or a range of j levels of scales (integer or vector of integers).
OUTPUTS
nuGj = advance of scaling filter at specified levels.
SIDE EFFECTS
wavelet is a WMTSA-supported scaling filter; otherwise error.
DESCRIPTION
EXAMPLE
ALGORITHM
nuGj = (2^j - 1) * nu For details, see equation 114a of WMTSA.
REFERENCES
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis. Cambridge: Cambridge University Press.
SEE ALSO
advance_time_series_filter, dwt_filter
AUTHOR
Charlie Cornish
CREATION DATE
2003-05-08
COPYRIGHT
REVISION
$Revision: 612 $


A view defines the current set of data visible
and accessible from an open file as an ordered set of etypes.
Each process has its own view of the file,
defined by three quantities:
a displacement, an etype, and a filetype.
The pattern described by a filetype is repeated,
beginning at the displacement, to define the view.
The pattern of repetition is defined to be the same pattern
that MPI_TYPE_CONTIGUOUS would produce if it were passed
the filetype and an arbitrarily large count.
Figure 25
shows how the tiling works; note
that the filetype in this example must have explicit
lower and upper bounds set in order for the initial and final holes to be
repeated in the view.
Views can be changed by the user during program execution.
The default view is a linear byte stream
(displacement is zero, etype and filetype equal to MPI_BYTE).
Figure 25: Etypes and filetypes
A group of processes can use complementary views to
achieve a global data distribution such as a scatter/gather pattern
(see Figure 26
).
Figure 26: Partitioning a file among parallel processes


--- layout: post title: Mission to Brighton date: 2005-01-31 00:00:00 categories: hilarity personal ---
"We don't have the 250 million pounds to send our own probe to Titan. So we're going to send three virtual probes over somewhere a little easier to get to - Brighton!": Mission to Brighton


My name is Rene Montoya-Menjivar from South Gate, California which is a city in South Los Angeles. The goals I have at the moment is creating programs or systems to improve on exisiting mathematical methods of teaching and/or assessment. My whole passion has been on sharing math with others and breaking this stigma of it being so hard. Especially amongst people of color! :) Intersting facts about me are that I have a deep love for the Japanese language, and took 3 years of courses in college. I also am the first in my family to receive a college degree.
About Me
Education
- BA in Mathematics - Azusa Pacific University
- MA in Math Education - DePaul University
Work Experience
- Math Tutoring
- Math Teaching
- College Advising
- Math Solution Writer
Top 10 Favorite RPDR Drag Queens
- Raja
- Aquaria
- Naomi Smalls
- Alaska
- Jujubee
- Adore Delano
- Violet Chachki
- Divina D'Campo
- Shea Coulee
- Crystal


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Charles Barkley highlights congratulatory wishes for Evansville
by Myron MedcalfAs Evansville's K.J. Riley and his teammates rode the bus back to campus following Tuesday night's 67-64 upset of No. 1 Kentucky in Lexington, coach Walter McCarty told his energized team to quiet down because Charles Barkley wanted to congratulate it.
He put the TNT analyst and Hall of Famer on speakerphone.
"I didn't even know he knew Charles Barkley," said Riley, who led all scorers with 18 points. "He congratulated us and he told us that the hard work is paying off."
Overall, McCarty said he had 400 congratulatory text messages waiting for him after Tuesday's win, which was tied for the third-largest upset in college basketball over the last 15 seasons (Evansville was a 25-point underdog), per ESPN Stats & Information.
Rick Pitino, McCarty's coach when Kentucky won the national title in 1996, reached out. Georgetown's Patrick Ewing, McCarty's teammate when he played for the New York Knicks during the 1996-97 season, texted him to praise his team, too. Oklahoma City Thunder coach Billy Donovan, an assistant when McCarty was at Kentucky, also contacted him after the victory.
Sam Cunliffe's two free throws with 6.8 seconds to play sealed the win for Evansville as Kentucky's Tyrese Maxey missed a deep 3-pointer at the buzzer to tie the game. Kentucky made just 37 percent of its field goal attempts and committed 11 turnovers in the first half.
Kentucky had previously won 52 consecutive home games against unranked opponents. Evansville, picked to finish eighth in the Missouri Valley Conference preseason poll, finished 11-21 overall and 5-13 during the 2018-19 campaign, McCarty's first as the team's head coach.
| Year | Team | Spread | Opp. |
|---|---|---|---|
| '07 | Gardner Webb | +26 | at UK |
| '15 | W. Illinois | +25.5 | at Wis. |
| '17 | Wofford | +25 | at UNC |
| '19 | Evansville | +25 | at UK |
It was the program's first victory over a No. 1 team.
ESPN's BPI gave Evansville a 4 percent chance to win.
McCarty said his team's experience fueled the win. He said he told his team that it could win at Kentucky, where he won a multitude of games as a player, if it stood strong in the opening minutes and refused to allow the crowd to become a factor.
"They were not going to get rattled," he said. "They weren't afraid. Nothing against Kentucky, but they're young. It feels good that people are paying attention to us."
When they returned to campus, the Purple Aces were greeted by hundreds of students. Riley said he didn't get to bed until 2 a.m. He was also inundated with texts. One in particular surprised him.
"It was from a Kentucky fan," Riley said. "He said, 'I'm a Kentucky fan but you're my favorite player now.'"


India (Bhārat Gaṇarājya),[e] is a country in South Asia. It is the seventh-largest country by area, the second-most populous country (with over 1.3 billion people),[13][14][f][g] and the most populous democracy in the world. It is bounded by the Indian Ocean on the south, the Arabian Sea on the southwest, and the Bay of Bengal on the southeast.
Australia,[12] is a sovereign country comprising the mainland of the Australian continent, the island of Tasmania and numerous smaller islands. It is the largest country in Oceania and the world's sixth-largest country by total area. The neighbouring countries are Papua New Guinea, Indonesia and East Timor to the north; the Solomon.
©Copyright content.


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static GaussianFactorGraph | buildLinearOrientationGraph (const NonlinearFactorGraph &g) |
| |
|
static Values | normalizeRelaxedRotations (const VectorValues &relaxedRot3) |
| |
|
static Values | computeOrientationsChordal (const NonlinearFactorGraph &pose3Graph) |
| | Return the orientations of a graph including only BetweenFactors<Pose3>
|
| |
|
static Values | computeOrientationsGradient (const NonlinearFactorGraph &pose3Graph, const Values &givenGuess, size_t maxIter=10000, const bool setRefFrame=true) |
| | Return the orientations of a graph including only BetweenFactors<Pose3>
|
| |
|
static void | createSymbolicGraph (const NonlinearFactorGraph &pose3Graph, KeyVectorMap *adjEdgesMap, KeyRotMap *factorId2RotMap) |
| |
|
static Vector3 | gradientTron (const Rot3 &R1, const Rot3 &R2, const double a, const double b) |
| |
|
static NonlinearFactorGraph | buildPose3graph (const NonlinearFactorGraph &graph) |
| | Select the subgraph of betweenFactors and transforms priors into between wrt a fictitious node.
|
| |
| static Values | computePoses (const Values &initialRot, NonlinearFactorGraph *poseGraph, bool singleIter=true) |
| | Use Gauss-Newton optimizer to optimize for poses given rotation estimates. More...
|
| |
|
static Values | initializeOrientations (const NonlinearFactorGraph &graph) |
| | "extract" the Pose3 subgraph of the original graph, get orientations from relative orientation measurements using chordal method.
|
| |
|
static Values | initialize (const NonlinearFactorGraph &graph, const Values &givenGuess, bool useGradient=false) |
| | "extract" the Pose3 subgraph of the original graph, get orientations from relative orientation measurements (using either gradient or chordal method), and finish up with 1 GN iteration on full poses.
|
| |
|
static Values | initialize (const NonlinearFactorGraph &graph) |
| | Calls initialize above using Chordal method.
|
| |


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Nothing to see here 🙈 because you are not the organization owner. Please contact {{ organization.owner.email }} for assistance.
{% endif %}


Book Reviews
You cannot open a book without learning something.
Confucius
-
Codacy is based on
git. Developers use git everyday. I use git everyday. Why not to learn a little more about this amazing distributed version control system? Free version available on
git website. Kindle formart, pdf file, epub, or html version, you can choose from all these free formats.
-
Code Complete
(2015)
After already had a look at this book back on 2012/2013, I decided to have a closer look at it once again. The main focus of this book are guidelines for better code style, so you can reduce the number and severity of future bugs. It also provides some definitions for several code metrics. I believe this book was kind of revolutionary, but those concepts are hugely addressed in ordinary computer science courses.
-
Programming Language Pragmatics
(2015)
A very complete book, no doubt about that. Perhaps too low level for my personal taste. Nevertheless, a book capable of explaining how programming languages are implemented in a fairly comprehensible text
-
A great book for getting an overlook in several languages and programming paradigms that will certainly make you want to learn something new.
-
Play for Scala
(2014)
Another book I decided to give a look in order to better understand the Scala world, this time focusing on the Play framework. Once again, a book that unfortunately got a bit deprecated by the time I decided to read it. Many functionalities and API were already deprecated, and some more recent where not approached yet.
-
Programming in Scala
(2014)
A book I decided to read to better understand Scala. Although the fact that's a good book it's a pity that it still uses Scala 2.8, while Scala 2.10 was the latest version by the time of reading (November 2014). Between several differences, in this book
Akka was not the default actor system in Scala yet.


import pptree
from mcts import parameters as p
from mcts.smiles import SMILES
[docs]class Node:
"""
Representation of a node in the MCTS tree
Each node own its SMILES, number of visits and score
"""
def __init__(self, smiles=SMILES(), parent=None):
"""
Initialisation of a new node
:param smiles: the state of the SMILES on the node
:type smiles: SMILES
:param parent: parent of the node
:type parent: Node
"""
self.smiles = smiles
self.parent = parent
self.children = []
self.visits = 0
self.score = 0.0
[docs] def new_child(self, child_smile):
"""
Add a new child to the current node
:param child_smile: the SMILES used to create the new node
:type child_smile: SMILES
:return: None
"""
self.children.append(Node(child_smile, self))
[docs] def update(self, reward):
"""
Recursive function to update a Node
:param reward: reward to update the score of the Node
:type reward: float
:return: None
"""
self.score += reward
self.visits += 1
if self.parent:
self.parent.update(reward)
[docs] def fully_expanded(self):
"""
Return True if the number of children is equal to the size of the vocabulary (minus 2 for \\n and &)
or if the SMILES of the node is greater than 80 (maximum for the RNN)
:return: True if the node is terminal in the tree else False
"""
return (len(self.children) == (len(p.tokens) - 2)) or (len(self.smiles.element) > 80)
[docs] def out_pptree(self, parent=None):
"""
Recursive function
Return instance of pptree module to print the tree
:param parent: parent of the tree (for the recursive part of the function)
:type parent: Node
:return: instance of pptree.Node
"""
name = repr(self)
if parent:
current = pptree.Node(name, parent)
else:
current = pptree.Node(name)
for c in self.children:
c.out_pptree(current)
return current
[docs] def get_height(self):
"""
Return the height of the node
:return: the height of the node
"""
if not self.children:
return 1
else:
return 1 + max([n.get_height() for n in self.children])
[docs] def get_size(self):
"""
Return the size of the Node
:return: the size of the Node
"""
if not self.children:
return 1
else:
return 1 + sum([n.get_size() for n in self.children])
[docs] def echo(self):
"""
Print the current tree
:return: None
"""
pptree.print_tree(self.out_pptree())
print("Size of the tree : %d" % self.get_size())
print("Height of the tree : %d" % self.get_height())
def __eq__(self, other):
"""
Equality between two nodes
:param other: an other node
:type other: Node
:return: True if other is the same node
"""
return str(self.smiles) == str(other.smiles)
def __repr__(self):
"""
Representation of a Node
:return: string representing the node
"""
return str(self.smiles) + " " + str(int(self.visits)) + " " + str(round(self.score, 2))


R = Frames.from_nxz(n_x, n_z);
It is assumed that the two input vectors n_x and n_z are resolved in frame 1 and are directed along the x and z axis of frame 2 (i.e., n_x and n_z are orthogonal to each other). The function returns the orientation object R to rotate from frame 1 to frame 2.
The function is robust in the sense that it always returns an
orientation object R, even if n_z is not orthogonal to n_x.
This is performed in the following way:
If n_x and n_z are not orthogonal to each other, first a unit
vector e_z is determined that is orthogonal to n_x and is lying in
the plane spanned by n_x and n_z. If n_x and n_z are parallel or
nearly parallel to each other, a vector e_z is selected arbitrarily
such that n_x and e_z are orthogonal to each other.
TransformationMatrices.from_nxz.
function from_nxz extends Modelica.Icons.Function; input Real n_x[3](each final unit = "1") "Vector in direction of x-axis of frame 2, resolved in frame 1"; input Real n_z[3](each final unit = "1") "Vector in direction of z-axis of frame 2, resolved in frame 1"; output Orientation R "Orientation object to rotate frame 1 into frame 2"; end from_nxz;


We all know that real estate plays an integral role in the U.S. economy. Residential real estate helps provide housing for families, and it happens to be the most significant source of wealth and savings for many people. Also, the home is the most extensive and most expensive purchase a person makes in his or her lifetime. Real estate trends have changed over time, and many factors weigh-in like recession, pandemic, market boom, etc. Therefore, understanding the real estate market is compelling to get more empowered as a buyer or seller.
Objective
- To create visualizations using Tableau to understand the various trends in the data and uncover some meaningful insights.
- To build a price forecast model and determine the direction of future trends for informed decision-making.
We got the objective. What Next??
Data understanding and preparation
Zillow calculates a Zestimate, an estimate of value for each home. Zestimate is based on a suite of sophisticated “automated
valuation models” (AVM). The models are re-trained three
times a week based on the latest data, and each home’s
Zestimate is updated daily
The dataset has the ZHVI (Zillow Home Value Index) and it is defined as the median of all Zestimates in a region or price tier.
Zillow Observed Rent Index (ZORI) - A smoothed measure of the typical observed market rate rent across a given region. ZORI is a repeat-rent index that is weighted to the rental housing stock to ensure representativeness across the entire market, not just those homes currently listed for-rent. The index is dollar-denominated by computing the mean of listed rents that fall into the 40th to 60th percentile range for all homes and apartments in a given region, which is once again weighted to reflect the rental housing stock.
The price forecasting model will use the past data to forecast the future trend and values. Hence, the dataset requires price data over time to build the price forecast model.
The resources collected using Kaggle are state-wise historical data from Zillow with many other features like median rental prices, median prices over different categories of homes, ZHVI, ZORI which is used in the analysis.


Clues are in alphabetical order of their solutions. Each group of solutions with the same initial letter contains at least one thematic solution, not further defined in its clue. Solve the clues and place the solutions in the grid, jigsaw-wise, as they will fit. (Five of the thematic solutions occupy two or three spaces in the grid.)
Clues: A. A lump in one's throat (5,5) A. Attack, losing head in drinking bout (6) A. Location for Ballykissangel party (7) B. Hot, having turned over books above (5,6) B. Man of note scarcely heard down under (7) B. Shades, not drawn first around one (6) C. Overturned output of substantial UAE factories (4,2,4) C. Converse with nature out East on a loch with the whole group (8) D. Distributed mail in the German car (7) D. Warning to avoid going round the bend (5) E. Lectern's Roman standard (5) E. Drink and drive? On the contrary (3,2,5) E. Registering game opponents, incapably drunk (9) E. Rugby player dismissed clubs and exclusive circles - not half! (9) E. Muse was, for Caesar, love (5) E. Before round half nine - here's the fastest deliveryman (5) E. Fascinating sight of French engineer, we're told (6) F. Ship's boat returns right away in splendid surroundings (9) F. Film director's way (5) G. Coarse during short walk (9) G. Area of an estate having weed the Spanish removed (7) H. German chap chewing Dane's kebab (5,5,5) H. Treasure from wandering tribe is spoken of (5) I. Some magic ingredients making money (5,5) I. Very worried, endlessly unfit and cold (2,1,5) I. Individuals entering one country or another (9) J. European agreement with baron in the old country (9) J. Dancing - including very popular dance (6) K. Ship's member on German waterway, reportedly (4) K. Car firm's odd parts of tour car discarded (3,3) L. In favour of being omitted from golden parachute, sadly (6,2,4) L. Sat uncomfortably in heather, deep-rooted (7) L. Cattle and one tailless creature in waterfall (8) M. West, the actress back in dreamland (3) M. Former president from island meeting endless hold-up (7) M. County by North Sea, in turmoil (10) N. Style poor Jane's accepted (7) N. Feature of cockney speech in drama (3) O. Appeal to boss to stock new shrub (8) O. Upset the whole school during radio broadcast (4,7) P. Left gateway bearing suitcase (4) P. Open vessel heading north around Baltic, regularly (6) Q. Almost gag, about to bring up a little apple (6) Q. After start of questioning, lashed soundly and jibed (7) R. Small island seen during the monsoon season (7) R. Copy from traveller around country - article's missing (9) R. Regularly plays harp back at resort (4) R. Jilts after worries (10) S. Oxygen provided during close call (5) S. Jack and Mary? (10) T. Pop group ringing sound starts (7) T. ie, very true (4,2,2) T. Impress, admitting award is excessive (3,4) U. No odd letters from our gillie (4) U. Naturalist's flower (4) V. Found in microwave, always (4) V. Examine bits of crack, hash and grass (5) W. Funeral party starts with audiences keening erratically (4) W. Hideouts for hostilities? (7) W. Chintz lines were re-ordered (6,9) X. Composer having skin broken with axe (7) X. Abnormal dryness round top of toe ignored (5) Y. Police HQ's indication of approval for benchmark (9) Y. Greyhounds rip kid apart (9,7) Z. Lazily, jogger spurns statue centrally at temple tower (8) Z. Unpleasant spot on island (4)


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About
This website is part of the course websoft Course home page.
This course will progress through the stages of web development from basics html,css forward through js, .net(with xampp) and some bootstrapping.
How about a link to the course repo! websoft repo
Heres a little meme for ya


Messenger:
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(Only Leaders of level 4 or above Clans can do this.)


William Dickerson
New York, NY, USA
Computer Science, Engineering
& Other Adventures
About
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EcoCAR
Award-winning SUV, 118 mpg
Under the hood: Control software developed in Simulink, running in real-time on dSPACE and MotoTron hardware
Yardstick
Measure distances on the map
Under the hood: Leaflet.js
Balance
How long can you balance the globe on the platform?
Under the hood: Three.js for the 3D animations, plus the HTML5 "deviceorientation" event
Compare Two Cities
A side-by-side visual comparison of two cities
Under the hood: d3.js and the SVG viewbox
Line Art
Make colorful mosaics, quickly
Under the hood: Simple canvas commands plus an 18th-century boundary tracing algorithm. Cool!
Fuzzy Words
Free your mind to write!
Under the hood: React, Bootstrap.
Remind
An SMS reminder bot
Under the hood: Java, Watson Assistant, Twilio, Db2
State to the Show
Track Mississippi State baseball alumni in the MLB!
Under the hood: Golang and the MLB stats api
Dickerdoodle
A multiplayer drawing game
Under the hood: React, DynamoDB, AWS API Gateway, AWS Lambda
littleapp.io
Some useful little apps
Under the hood: CloudFormation, CloudFront, CodePipeline, React
City Access
Where can a Subway ride take you in NYC, DC, and Boston?
Under the hood: ES6, Leaflet v1.0.0, and D3v4


Preparation time
overnight
Cooking time
less than 10 mins
Serves
Makes 1 large jar
Dietary
Put the onions in a large bowl with the salt and mix together. Cover and leave overnight.
Rinse well in cold water and allow to dry as much as possible.
Pack the onions into sterilised jars.
Put the vinegar, chilli flakes and sugar into a pan and slowly bring to the boil to dissolve the sugar.
Pour the vinegar over the onions and seal the jars.
Store in a cool, dark cupboard for two weeks before eating.


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About Us:
The Gamer is a website for selling latest and greatest gaming consoles and
their exclusive games online. Different types of consoles currently on sale
are from these three major manufacturers Playstations, Xbox's and
Nintendo.Mission:
Our mission at Gamer Website is to sell latest gaming consoles and its supported accessories. We also provide our customer with the best service by providing 100% genuine and quality product from their original brands. Our main mission is to gain 100% customer satisfaction by providing them with the latest and best quality consoles. We also have tie up with top-most courier companies in the country to provide our customer with reliable services by providing them with free home deliveries and we also offer 1-day and 2-day deliveries as per our customer requirements. This makes us one the very few website to provide such a fast delivery.
Vision:
Our vision at Gamer Website is to extend the limits of the sales team and imbue them within a sense of what they can accomplish, both together and as individuals. When the leader of a small business sales team develops a vision statement, that leader expresses the vision of the team's future. This describes the goals the sales team must reach at our website, but seldom includes any specific methods used to arrive at those goals.
Console Brands offered by us:
PlayStation
Sony Playstation range includes Ps4,Ps3 and Ps vita.We provide genuine playstation consoles with 1-year manufacturer warranty and all original accesories..
Xbox
Xbox by Microsoft includes best selling Xbox one.We provide genuine Xbox console with 1-year manufacturer warranty and all original accesories.
Nintendo
Nintendo Wii is the first motion gaming console.We provide genuine Nintendo console with 1-year manufacturer warranty and all original accesories.
PlayStation
Sony Playstation range includes Ps4,Ps3 and Ps vita.We provide genuine playstation consoles with 1-year manufacturer warranty and all original accesories..
Xbox
Xbox by Microsoft includes best selling Xbox one.We provide genuine Xbox console with 1-year manufacturer warranty and all original accesories.
Nintendo
Nintendo Wii is the first motion gaming console.We provide genuine Nintendo console with 1-year manufacturer warranty and all original accesories.


{% comment %} * * image_url composition was changed to allow the use of url location from another * site as flickr.com, for example * {% endcomment %}


A fresh challenge
Getting back into the software industry
Opportunity to work on multiple projects
Travel
Applied Psychology and Computing degree focused on Experience Design
I have been working as a Web Developer in various roles for almost 20 years
Mentoring
Enthusiasm
Delegation
Collaboration
I believe these core skills and tenets, along with solid organisation and communication skills mark me out as a strong candidate for the positon
A disconnect between product and delivery teams
Perceived limitations of the platform restrict innovation
Bloated, legacy codebase leads to technical inefficiencies
Not all employees recognise importance of design and can be dismissive
Development roadmap will improve many areas e.g. DX API and FnX
Closer engagement between product and delivery teams. Cross-team workshops
Brown bag sessions/role shadowing to improve understanding of discreet roles
Implementation of design metrics when evaluating client success
Ensure XD representation on all projects
I believe Pega are fairly mature in this area
Journey Centric Delivery model ensures focus on customer journeys regardless of physical/digital bounds
The momentum of Design Thinking
Pega is already making progress in this area
Design presence growing in leadership roles
Move to an As-a-Service company
Focus on initiatives to reward client success above utilization (design metrics)
Can grow organically through investment in other areas
With the move to an As-a-Service company, continuous iteration will be more valuable than ever
Large license deals replaced by client centered relationships and smaller incremental releases based on quantitative and qualitative data from user testing
Constantly improving a product is more efficient than wholesale "big bang" approach. Less risk, quicker ROI, leading to stronger client relationships
Design needs to be owned by the whole team
Working on a long, large project has seen other members of the team learn about the importance of design. Knowledge that can be taken forward to other projects
There have been improvements e.g. cross training BAs in Design Thinking


PNP calls for strengthening of anti-corruption agencies
As the world observes International Anti-Corruption Day, the People's National Party (PNP) says it remains committed to promoting integrity and trust in politics.
Further, the PNP says it will lead a government that repudiates corruption and fosters transparency for the benefit of all law-abiding Jamaicans.
“It is our belief that without a strong and efficient anti-corruption agency and a commitment from government to engender a climate of integrity, trust, and justice, the future of the nation remains at high risk,” a spokesperson said in a statement.
The PNP says it y will continue to push for further strengthening and the independence of the nation’s anti-corruption mechanisms.
Additionally, the party says it will insist that public officials and others who are found to be culpable of breaching the law must face the court and justice must be served.
“The PNP believes that the hard-earned public revenues must be protected from those who seek to plunder the public purse for their own selfish purposes and therefore, as a government in waiting, the party's internal Integrity Commission has ramped up its efforts to ensure that only persons who are of the highest integrity go forward to represent the PNP as candidates to face the electorate, whether in a by-election, local government election or the general polls,” the spokesperson said.


John Conway's
Game of
LIFE
Click on cells to make them alive.
Cells will obey rules to reproduce or die.
Set the number of rounds to simulate.
Press "Run Simulation"!
Controls
Rounds to simulate:


A back-end framework for the cloud.
The server is running. To get started run:
curl /v1/setup
After you run setup and get your API keys, all subsequent requests must be signed using those API keys. The signature algoritm is AWS version 4 and is well documented here.


Enable experimental features.
Warnings:
- No official support. Beta features are provided on a strictly best-effort basis.
- Unstable code. Beta features may be added, changed, or removed between releases, without warning.
- Unstable configuration. Beta feature configuration may be changed between releases, without warning.
- Manual configuration updates required. Unlike regular features, you must update the configuration of beta features manually when their config schema changes.
Recommendations:
- Test on a non-production Jenkins. This will allow you see whether the beta feature will work in your environment.
- Read the plugin release notes. Always read the release notes before upgrading the plugin, to check for beta feature changes.
- Use Jenkins Configuration As Code (CasC). CasC will reject configuration that contains outdated or deleted properties, and warn you about it.


As a former college athlete and coach I have the drive and the passion to be the ultimate team player. Following my college coaching experience I have decided to take my drive and passion to the tech world.
I am currently expanding my degree in communications, experience with scoring programs and statistics into being a full stack developer. As a result of my experience in college athletics I know what it means to set goals, work to achieve those goals, and complete them. In addition, I have gathered the tools necessary to help encourage and motivate others to reach those goals so that the whole team can soar to new heights. I look forward to using my skills, passion and drive to strive for greatness into the tech world.
Blair Betts
26
Cary North Carolina
Showcasing some of my best work
Choose your Star Wars character as you duel it out to see who is the best Jedi or Sith.
Start by selecting your character. Select who your are going to battle. Then let the battle comense! You can enhance this epic battle with some epic battle music. Do you have what it takes to be the last one standing? For behind the scenes on how this game works click the web design button below.
12 May, 2019
HTML5, SCSS, JS, jQuery
Welcome to this sports edition of Hangman
How well do you know your sports? Test your skills with this sports edition of hangman. The categories range accross baseball, basketball, football, hockey, and even golf. Can you figure out the word before all your guesses are up? Have any good words or wants to see how this game works click the web design button below.
12 May, 2019
HTML5, SCSS, JS
Hi boys and girls welcome to world of Disney. How well do you know your Disney Trivia?
Start this Disney trivia and see how many questions you can get right. Take this multiple choice quiz and see how well you know the parks as well as the movies. See how this game was made by hitting the web desgin button below.
12 May, 2019
HTML5, SCSS, JS
Are you looking for your next place to live?
Enter in what city you want to search and get information about the housing market, economic status, fun activities to do as well as what the weather is like in your new Perfect Fit home. Not sure which one to pick click the compare button to see multiple cities or compare a city you're currently living in to one that you want to live in. Want to see the magic behind finding your Perfect Fit? Click the web desgin button below.
12 May, 2019
HTML5, SCSS, JS
Ever want to create you're own train schedule?
Enter in what train name is, where it goes, what time is the first stop, as well as how often does it come. See your train appear and let you know when the next stop is. Want to see how this whole schedule works? Click the web desgin button below.
12 May, 2019
HTML5, SCSS, JS
Want to see funny videos of your favorite topics?
Enter in your favorite video game or any topic you wish to search and watch the giphys appear. Click on the giphy to let it play. If you want it stop just click the giphy again and it will stop. Don't see a topic you want to search type it in and add a button so that you can search for it. Want to see how this works click the web design button below.
12 May, 2019
HTML5, SCSS, JS
In this app you will need to have node.js to be able to see what happens. You will need to type in node then liri.js first into the terminal Then after you type this you will have one of four commands you can type in. Spotify-this-song concert-this movie-this do-what-it-says For spotify-this-song you can type any of you favorite songs in and see what album it was on, who wrote the song as well as a preview of the song. video :[Spotify-this-song] ("https://www.youtube.com/watch?v=CM_YjNoXhVY") For concert-this type in any of your favorite musical artist and see when and where they are playing next in concert. video :[concert-this] ("https://www.youtube.com/watch?v=JNwPazXY7io") For movie-this type in your favorite movie and see all kinds of information about it. video : [movie-this] ("https://www.youtube.com/watch?v=xwDFQaIJREc") For do-what-it-says it will read the file random.txt and display what ever the file tells it to do. If you want to add a file please look at the previous example and copy the format. video : [do-what-it-says] ("https://www.youtube.com/watch?v=APbKeKL5gFU")
12 May, 2019
HTML5, SCSS, JS
For this node.js app to start you will need to type into the terminal node and the either bamazonCustomer.js or bamazonManager.js. Once you have done that all you need to do to is follow the commands that appear in the terminal to search around bamazon to find all of the products they have to offer.
12 May, 2019
HTML5, SCSS, JS, Node.js, SQL
In this app you get to find out which of our lucky members of our Frined Finder app will become your next best friend. Follow the instructions and take the survery and find out who your next best friend will be.
12 May, 2019
HTML5, SCSS, JS, Node.js
Have you ever wanted to make your own custom burger and the devour it right on the spot..... virtually. Well in this app you can. Simply type in what ever burger you desire in the text box and the see it appear just waiting to devour it. Create as many or as few burgers as you want.
12 May, 2019
HTML5, SCSS, JS, Node.js, SQL
Welcome to B2C bridging the gap between buisness and customers. There are two sides to this app. From the customer side you can login and check in to your favorite restaurants and see where you enjoy eating the most. From the business side you can see which customers have checked in the most as well as which one has been most recently. This is the next step in getting repeat service to all restaurants every where. (This app is still a work in progress more technoligies to come)
12 May, 2019
HTML5, SCSS, JS, Node.js, SQL, Vue.JS
Dish-It the app that you search to see what you want to eat. Scroll through every option to see what users have said about different dishes from different restaurants. Gone out recently? Make a post yourself! Let others know what you though about what you just ate. (More technoligies to come)
17, July 2019
HTML5, SCSS, JS, Node.js, React, Mongo
How good is your memory? Can you click on all of the images without clicking one twice? See how high of a score you can get!
26, June 2019
HTML5, SCSS, JS, Node.js, React
Feel free to contact me anytime


I've always been curious about functional programming, and seeing how this week's blog topic considers it, I thought it good to take the plunge and finally give it a little more research! The following is a basic comparison between functional programming and object-oriented programming.
First of all, there are three basic programming paradigms, four if you include object-oriented programming:
- Imperative Programming
- Logic Programming
- Functional Programming
Imperative programming follows the imperative mood in the same way it is utilized in natural language:
- "You, feed the cat."
- "Make my day."
It is often called procedural programming, and utilizes modifications, assignments, and control structure such as if-else statements. Can you see how those aspects of programming mirror the imperative sentences above?
As you might have noticed when playing around with the core language, Ruby supports imperative programming. Much of Ruby's object-oriented structure relies on imperative principles. This is something that makes object-oriented programming tick. If you're working with objects and methods, you need to rely on changing them, having them interact with other objects, and logging results in the form of even more objects!
Functional programming (and logic programming) are more on the declarative side of things. Why? Think about mathematics. In mathematics, theories exist without mutations. In other words, you can't change something while keeping its identity the same.
1 + 1 = 2
You often probably take it for granted, but the number one is an immutable concept. It is and only can ever represent a numerical one. Same for the number two. Thus, you can form an unalterable theory about how one and one interact. What if you could change the essence of one but keep its id the number one, the same?
It's useful to apply to this structure programming. Not everything should be imperative, and certain fields benefit from greater breadth. Transfer this over to programming languages, and think about strings. They exist as String objects. Many languages make these immutable. Ruby does not.
As you can see, the contents of the string object changes, but the id does not. Think about how this can create chaos, mathematically or even just in the code itself. Sometimes it's good to know an object's contents are rigid and can't be mutated. You can't build a theory around something you can't control!
The main principles of functional programming focus on dealing with immutables. There are no loops, assignments, mutable variables, or other imperative control structures in functional languages.
It also places an emphasis on the functions. This is another reason why object-oriented programming isn't considered to be a basic paradigm by some. You can actually implement some functional language principles in an OOP when you emphasize functions more in your code. JavaScript, for example, can mimic a functional programming language because its functions are objects. According to The Ruby Programming Language, "Ruby's methods are not objects in the way that strings, numbers, and arrays are. It is possible, however, to obtain a Method object that represents a given method, and we can invoke methods indirectly through Method objects."
In other words, you can get Ruby methods to show as an object via some work-around, but they're not fundamentally workable objects. A functional programming language places significant emphasis on the versatility of functions.
On the flip side, Ruby blocks work much in the way that functions do in functional languages. So, Ruby can act as a functional language. You just have to utilize its versatility.
The benefits of a functional language are simpler reasoning principles. If things can more appropriately mimic theory, than the code can more strictly accomplish goals. Functional programming is thus a smart way to expand on operations because functional code incorporates tighter, theory-derived computations. We get more brevity and concurrency. With mutations occurring in object-oriented programming, you can't execute much simultaneously. You wouldn't be able to be sure what is what at any given time.
But object-oriented programming is good for expansion of data. When you need to create more things to work with you utilize classes and methods. There is more emphasis on the importance of the objects you work with over the functions you use to manipulate them.
In simple terms, the differences are most apparent in their naming. Functional programming emphasizes the functions. Object-oriented programming emphasizes the objects.


Well, the way they make shows is, they make one show. That show's called a pilot. Then they show that show to the people who make shows, and on the strength of that one show they decide if they're going to make more shows.
Cuidado! Aqui vai um alerta mas eu não sei o que escrever
Well, the way they make shows is, they make one show. That show's called a pilot. Then they show that show to the people who make shows, and on the strength of that one show they decide if they're going to make more shows.


New sensor
The name given to the energy sensor. Used to identify the sensor in configuration and sensor readings.
{{sensor.data.id}}
This defines the type of power used (i.e.: AC or DC) and the type of the reading (i.e.: current or voltage). This is shown on the sensor readings page to add context to the reading.


Tracker external event test
On this page you can test how to send custom information via event stream.
Send us love!


I am currently working as a Researcher at Huawei Singapore Research Center. I obtained my
Ph.D. from Nanyang
Technological University (NTU) in 2022, where I was a founding member of the NTU-NLP Group and was supervised
by Professor Shafiq Joty.
Notably, my doctoral dissertation was met with significant acclaim, leading to the conferral
of the distinguished Outstanding Ph.D. Thesis Award. I worked as a research intern at Meta AI hosted by Professor Philipp Koehn.
My research interest falls broadly in the
application of Machine Learning, especially Natural Language Processing. I am also
interested in Multimodal Representation Learning. I invest considerable time and effort in
delving into the intricacies of large-scale models, aiming to unravel their nuanced
functionalities and underlying mechanisms.
Tasnim Mohiuddin , Philipp Koehn, Vishrav Chaudhary, James Cross, Shruti Bhosale,
and Shafiq Joty, "Data Selection Curriculum for Neural Machine Translation".
In Findings of
2022 Conference on Empirical Methods in Natural Language Processing
(EMNLP 2022),
Abu Dhabi, UAE.
[PDF]
[Code]
M Saiful Bari, Tasnim Mohiuddin (Equal Contributions), and Shafiq Joty,
"UXLA: : A Robust Unsupervised Data Augmentation Framework for Cross-Lingual
NLP".
In Proceedings of
The Joint Conference of the 59th Annual Meeting of the Association for Computational
Linguistics and the 11th International Joint Conference on Natural Language
Processing
(ACL-IJCNLP 2021).
[PDF]
[Code]
Tasnim Mohiuddin , M Saiful Bari, and Shafiq Joty,
"AugVic: Exploiting BiText Vicinity for Low-Resource NMT".
In Findings of
The Joint Conference of the 59th Annual Meeting of the Association for Computational
Linguistics and the 11th International Joint Conference on Natural Language
Processing
(ACL-IJCNLP 2021).
[PDF]
[Code]
Tasnim Mohiuddin , Prathyusha Jwalapuram, Xiang Lin, and Shafiq Joty,
"Rethinking Coherence Modeling: Synthetic vs. Downstream Tasks".
In Proceedings of the
16th conference of the European Chapter of the Association for Computational
Linguistics
(EACL 2021), Kyiv, Ukraine.
[PDF]
[Resource]
Tasnim Mohiuddin , M Saiful Bari, and Shafiq Joty,
"LNMap: Departures from Isomorphic Assumption in Bilingual Lexicon Induction Through
Non-Linear Mapping in Latent Space".
In Proceedings of the
Conference on Empirical Methods in Natural Language Processing
(EMNLP 2020).
[PDF]
[Presentation]
[Code]
Han-Cheol Moon,
Tasnim Mohiuddin (Equal Contributions), Shafiq Joty, and Chi Xu,
"A Unified Neural Coherence Model". In Proceedings of the
Conference on Empirical Methods in Natural Language Processing and International
Joint Conference on Natural Language Processing
(EMNLP-IJCNLP 2019), Hong Kong, China.
[PDF]
[Presentation]
[Code]
Tasnim Mohiuddin and Shafiq Joty,
"Revisiting Adversarial Autoencoder for Unsupervised Word Translation with Cycle
Consistency
and Improved Training". In Proceedings of the
North American Chapter of the Association for Computational Linguistics: Human
Language Technologies
(NAACL-HLT 2019), Minneapolis, USA.
[PDF]
[Presentation]
[Poster]
[Code]
Tasnim Mohiuddin , Thanh-Tung Nguyen, and Shafiq Joty,
"Adaptation of Hierarchical Structured Models for Speech Act Recognition in
Asynchronous
Conversation"
. In Proceedings of the
North American Chapter of the Association for Computational Linguistics: Human
Language Technologies
(NAACL-HLT 2019), Minneapolis, USA.
[PDF]
[Presentation]
[Poster]
[Code]
Tasnim Mohiuddin , Shafiq Joty, and Dat Nguyen,
"Coherence Modeling of Asynchronous Conversations: A Neural Entity Grid
Approach". In
Proceedings of the
56th Annual Meeting of the Association
for Computational Linguistics
(ACL 2018), Melbourne, Australia.
[PDF]
[Poster]
[Code]
Tasnim Mohiuddin and
Shafiq Joty,
"Unsupervised Word Translation with Adversarial Autoencoder".
Computational Linguistics
(Special Issue of Computational Linguistics on Multilingual and Interlingual
Semantic Representations for Natural Language Processing) : pages XXX - XXX (2020),
MIT press (June 2020).
[PDF]
[Presentation]
Shafiq Joty and
Tasnim Mohiuddin,
"Modeling Speech Acts in Asynchronous Conversations: A Neural-CRF Approach".
Computational Linguistics
(Special Issue on Language in Social Media)
44:4 , pages 859 - 894 (2018),
MIT press (2018).
[PDF]
[Code]
Neural Machine Translation (NMT) models are typically trained on heterogeneous
data that are concatenated and randomly shuffled. However, not all of the
training data are equally useful to the model. Curriculum training aims to
present the data to the NMT models in a meaningful order.
In this project, we introduce a two-stage training framework for NMT where we
fine-tune a base NMT model on subsets of data, selected by both deterministic
scoring using pre-trained methods and online scoring that considers prediction
scores of the emerging NMT model.
Through comprehensive experiments on six language pairs comprising low- and
high-resource languages, we have shown that our curriculum strategies
consistently demonstrate better quality and faster convergence.
Publication: EMNLP-2022
The ability to edit images in a realistic and visually appealing manner is a
fundamental requirement in various computer vision applications. In this paper,
we present ImEW, a unified framework designed for solving image editing
tasks.
ImEW utilizes off-the-shelf foundation models to address four essential
editing tasks: object removal, object translation, object replacement, and
generative fill beyond the image frame. These tasks are accomplished by
leveraging the capabilities of state-of-the-art foundation models, namely the
Segment Anything Model, Grounding DINO, LaMa, and Stable Diffusion. These models
have undergone extensive training on large-scale datasets and have exhibited
exceptional performance in understanding image context, object manipulation, and
texture synthesis.
Through extensive experimentation, we demonstrate the effectiveness and
versatility of ImEW in accomplishing image editing tasks across a wide
range of real-world scenarios. The proposed framework opens up new possibilities
for realistic and visually appealing image editing and enables diverse
applications requiring sophisticated image modifications. Additionally, we
discuss the limitations and outline potential directions for future research in
the field of image editing using off-the-shelf foundation models, enabling
continued advancements in this domain.
The success of Neural Machine Translation (NMT) largely depends on the
availability of large bitext training corpora. Due to the lack of such large
corpora in low-resource language pairs, NMT systems often exhibit poor
performance. Extra relevant monolingual data often helps, but acquiring it could
be quite expensive, especially for low-resource languages. Moreover, domain
mismatch between bitext (train/test) and monolingual data might degrade the
performance. To alleviate such issues, we propose AugVic, a novel data
augmentation framework for low-resource NMT which exploits the vicinal samples
of the given bitext without using any extra monolingual data explicitly.
It can diversify the in-domain bitext data with finer-level control. Through
extensive experiments on four low-resource language pairs comprising data from
different domains, we have shown that our method is comparable to the
traditional back-translation that uses extra in-domain monolingual data. When we
combine the synthetic parallel data generated from AugVic with the ones
from the extra monolingual data, we achieve further improvements. We show that
AugVic helps to attenuate the discrepancies between relevant and
distant-domain monolingual data in traditional back-translation.
Publication: ACL-2021
Transfer learning has yielded state-of-the-art (SoTA) results in many supervised
natural language processing tasks. However, annotated data for every target task
in every target language is rare, especially for low-resource languages. We
target to solve cross-lingual adaptation problems from a source language
distribution to an unknown target language distribution, assuming no training
labels are available for the target language task.
In this project, we propose UXLA, a generic data augmentation
framework for
self-supervised learning in zero-resource transfer learning scenarios. At its
core, UXLA performs simultaneous self-training with data augmentation and
unsupervised sample selection. We augment data from the unlabeled
training examples in the target language as well as from the virtual input
samples (eg sentences) generated from the vicinity distribution of the source
and target language sentences. With the augmented data, UXLA performs
simultaneous self-learning with an effective distillation strategy to learn a
strongly adapted cross-lingual model from noisy (pseudo) labels for the target
language task. We propose novel ways to generate virtual input samples using
XLMR - a multilingual masked language model, and get reliable task labels by
simultaneous multilingual co-training.
To show our proposed methods' effectiveness, we conduct
extensive experiments on zero-resource cross-lingual transfer tasks for Named
Entity Recognition (XNER) and Natural Language Inference (XNLI).
UXLA achieves SoTA results in both tasks, outperforming the baselines by a
good margin. With an in-depth model dissection, we demonstrate the cumulative
contributions of different components to UXLA's success.
Publication: ACL-2021
Most of the successful and predominant methods for Bilingual Lexicon Induction
(BLI) are mapping-based, where a linear mapping function is learned with the
assumption that the word embedding spaces of different languages exhibit similar
geometric structures i.e. approximately isomorphic}). However, several
recent studies have criticized this simplified assumption showing that it does
not hold in general even for closely related languages. In this work, we propose
a novel semi-supervised method to learn cross-lingual word embeddings for
BLI. Our model is independent of the isomorphic assumption and uses
non-linear mapping in the latent space of two independently pre-trained}
autoencoders. Through extensive experiments on fifteen (15) different language
pairs (in both directions) comprising resource-rich and low-resource languages
from two different datasets, we demonstrate that our method outperforms existing
models by a good margin. Ablation studies show the importance of different model
components and the necessity of non-linear mapping.
Publication: EMNLP-2020
Suppose we are given
monolingual word embeddings for source and target languages. We do not
have any
initial dictionary or external cross-lingual signal. Our
goal is to learn word translation (a.k.a. bilingual lexicon induction)
i.e. for
a given source word, we want its translation in the target domain.
Cross-lingual word embeddings learned from monolingual embeddings have a
crucial
role in many downstream tasks, ranging from machine translation to transfer
learning.
Adversarial training has shown impressive success in learning
cross-lingual embeddings
and the associated word translation task without any parallel data by mapping
monolingual
embeddings to a shared space. In this project, we investigate
adversarial autoencoder for unsupervised word translation and propose
two novel extensions to it that yield more stable training and improved
results.
Our method includes
regularization terms to enforce
cycle consistency and
input reconstruction, and puts the target encoders as an adversary
against the
corresponding discriminator.
We use two types of
refinement procedures sequentially after obtaining the trained encoders
and mappings
from the adversarial training, namely,
refinement with Procrustes solution and
refinement with symmetric re-weighting.
Extensive experimentations with European, non-European and low-resource
languages
show that our method achieves better performance than existing adversarial and
non-adversarial
approaches and is also competitive with the supervised system. Along with
performing
comprehensive ablation studies to understand the contribution of different
components
of our adversarial model, we also conduct a thorough analysis of the refinement
procedures
to understand their effects.
Publication: NAACL-HLT 2019 , CL Journal 2020
Sentences in a text or a conversation do not occur independently; rather they
are connected to form a coherent discourse
that is easy to comprehend.
Coherence models are computational models that can distinguish a coherent
discourse
from incoherent ones. It has ranges of applications in text generation,
summarization,
and coherence scoring.
In this project, we conduct our research in
two steps.
First, we propose improvements to the recently proposed
neural entity grid model by
lexicalizing its entity transitions. We propose methods based on word
embeddings
to achieve better generalization with the lexicalized model.
Second, we extend the model to asynchronous conversations by
incorporating the
underlying conversational structure in the entity grid representation and
feature
computation. For this, we propose a novel grid representation for asynchronous
conversations
and adapt the convolution layer of the neural model accordingly.
Our model achieves state of the art results on standard coherence
assessment tasks
in monologue and conversations outperforming existing models. We also
demonstrate
its effectiveness in reconstructing thread structures.
Publication: ACL 2018
In this project, we work on the limitations of existing models which
underperform on tasks that require the model to be sensitive
to local contexts such as candidate ranking in conversational dialogue and in
machine
translation.
We propose a
unified coherence model that incorporates
sentence grammar,
inter-sentence coherence relations, and
global coherence patterns in a single framework. We use an
LSTM sentence encoder with explicit language model loss to capture the
syntax.
Inter-sentence discourse relations are modeled with a
bilinear layer, and a
lightweight convolution-pooling is used to capture the attention and
topic structures
(global coherence patterns ).
We evaluate our models on both local and global discrimination tasks on the
benchmark
dataset. Our results show that our approach outperforms existing methods by a
wide
margin in both tasks.
Publication: EMNLP-IJCNLP 2019
With the advent of Internet technologies, communication media like emails and
discussion forums have become common-place
for discussing work, issues, events, and experiences. Participants in these
media
interact with each other
asynchronously by writing at different times. Participants in an
asynchronous
conversation interact with each other in complex ways, performing certain
communicative
acts like asking questions, requesting information or suggesting something.
These
are called
speech acts.
Unlike synchronous conversations (e.g. meeting, phone), modeling
conversational
dependencies between sentences in an asynchronous conversation are challenging.
The
conversational flow often lacks sequential dependencies in its
temporal/chronological
order.
For example, if we arrange the sentences as they arrive in the
conversation,
it becomes hard to capture any dependency between the acts types because the two
components of the adjacency pairs can be far apart in the sequence. This leaves
us
with
one open research question:
how do we model the dependencies between sentences in a single comment and
between
sentences across different comments? Another major problem is
insufficient training data in the asynchronous domains.
In this project, we propose methods to effectively leverage abundant
unlabeled conversational
data and the available labeled data from synchronous domains. We carry out our
research
in
three main steps.
First, we introduce an end-to-end neural architecture based on a
hierarchical
LSTM encoder with a Softmax or conditional random fields (CRF) output layer, and
show that our method outperforms existing methods when trained on in-domain data
only.
Second, we improve our initial SAR models by semi-supervised learning in
the
form of pretrained word embeddings learned from a large unlabeled conversational
corpus.
Finally, we adapt our hierarchical LSTM encoder using domain adversarial
training
to leverage the labeled data from synchronous domains by explicitly modeling the
shift in the two domains.
We evaluate our models on three different asynchronous datasets containing
forum
and email conversations, and on the MRDA meeting corpus. Our main findings are:
Publications: CL Journal 2018, NAACL-HLT 2019


Gustaf Nilsson Kotte / @gustaf_nk
Design an API for a kanban board.
The API will have multiple clients.
GET/POST items/
{
id: 123,
name: "...",
description: "...",
status: "backlog|working|verify|done"
}
Don't trust clients
Implement the state machine on the right. The primary action for each state is the "downward" arrow, except for the state "done" where the primary action is to move the item back to the "working" state.
function nextStates(item) {
if (item.state == 'backlog')
return ['working'];
else if (item.state == 'working')
return ['backlog', 'verify'];
...
}
function getPrimary(state) {
if (item.state == 'backlog')
return 'working';
...
}
function nextStates(item) {
dict = {
'backlog' => ['working'],
'working' => ['backlog, verify'],
...
return dict[item.state];
}
function getPrimary(state) {
if (item.state == 'backlog')
return 'working';
...
}
function nextStates(item) {
dict = {
'backlog' => ['working'],
'working' => ['backlog, verify'],
...
return dict[item.state];
}
function isPrimary(state, nextState) {
return nextState == nextStates(state).last();
}
function nextStates(item) {
var stateMachine = new StateMachine(conf.json);
return stateMachine.getTransitions(item.state);
}
function isPrimary(state, nextState) {
return nextState == nextStates(state).last();
}
Many possibilities!
http://martinfowler.com/articles/richardsonMaturityModel.html
You do stuff by reading pages
and then either
follow links or submit forms.
— Jon Moore
Collections: "board", backlog, working, verify, done
Single: details, edit
Write: being able to change the state of an item
according to the rules
| Name | Summary | Read/write | Comment |
|---|---|---|---|
| HAL | Resources and relations | R | Write support with addon |
| Collection+JSON | Collections over JSON | RW | |
| Siren | ~ JSON port of HTML | RW | |
| JSON-LD | ~ RDF over JSON | R |

<%= item.name %>

<%= item.status %>

<%= item.description %>

...

...

<form data-rel="move backlog" action="/items/backlog" method="POST">
<input name="id" type="hidden" value="4">
<input title="submit" type="submit" value="Move to backlog">
</form>
<form data-rel="move verify next" action="/items/verify" method="POST">
<input name="id" type="hidden" value="4">
<input title="submit" type="submit" value="Move to verify">
</form>

Not harder than writing a "regular" client, just different...
...which means it's harder, since new to many developers :(
Gustaf Nilsson Kotte / @gustaf_nk
Code: https://github.com/gustafnk/kanban-awd-api
Demo: http://kanban-awd.herokuapp.com/ and http://kanban-api.herokuapp.com
Slides: http://www.slideshare.net/GustafKotte/design-hypermedia-apis


Spend more time performing and less time looking for gigs. Sign up to be a part of SoundTrip's Pool of Musicians. It's totally free!
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$> init hterm.css
_ _ _____ ______________ ___
| | | |_ _| ___| ___ \ \/ |
| |_| | | | | |__ | |_/ / . . | ___ ___ ___
| _ | | | | __|| /| |\/| | / __/ __/ __|
| | | | | | | |___| |\ \| | | || (__\__ \__ \
\_| |_/ \_/ \____/\_| \_\_| |_(_)___|___/___/
A terminal-like stylesheet for HTML
Version: 0.1-20190823
----------------------------------------------
Source: http://bit.ly/hterm-css-source
Demo: http://bit.ly/hterm-css-demo
Github: http://bit.ly/hterm-css-github
[2019-08-23 18:16] This is a demonstration of the "tc-blue" class from hterm.css
[2019-08-23 18:17] This is a demonstration of the "tc-green" class from hterm.css
[2019-08-23 18:18] This is a demonstration of the "tc-grey" class from hterm.css
[2019-08-23 18:19] This is a demonstration of the "tc-red" class from hterm.css
[2019-08-23 18:20] This is a demonstration of the "tc-blue-bg" class from hterm.css
[2019-08-23 18:21] This is a demonstration of the "tc-green-bg" class from hterm.css
[2019-08-23 18:22] This is a demonstration of the "tc-grey-bg" class from hterm.css
[2019-08-23 18:23] This is a demonstration of the "tc-red-bg" class from hterm.css
[2019-08-23 18:24] This is a demonstration of the "tc-blue-bg-w" class from hterm.css
[2019-08-23 18:25] This is a demonstration of the "tc-green-bg-w" class from hterm.css
[2019-08-23 18:26] This is a demonstration of the "tc-grey-bg-w" class from hterm.css
[2019-08-23 18:27] This is a demonstration of the "tc-red-bg-w" class from hterm.css
[2019-08-23 18:28] Printing repository link ...
[2019-08-23 18:29] Printing demo page link ...
[2019-08-23 18:30] Printing link to live hterm stylesheet ...
[2019-08-23 18:31] GITHUB REPOSITORY LINK: https://github.com/willinspire/hterm.css
[2019-08-23 18:32] DEMO PAGE LINK: https://willinspire.github.io/hterm.css/
[2019-08-23 18:33] STYLESHEET LINK: https://willinspire.github.io/hterm.css/hterm.css
[2019-08-23 18:34] Done!
$>


Ozone Recommendation for AMIP II
An intercomparison of existing climatologies
Recommended data set
Return to AMIP home page
Last update October 18, 1996. For further information, contact the AMIP Project Office (
<EMAIL_ADDRESS>)
LLNL Disclaimers
UCRL-MI-125581


Created by Nickolay Tsvetinov (Meddle) / @ntzvetinov
Data is everything, it has its flow...
React to changes!
React to errors!
You know of it!
M$ Excel
The 'Reactive Sum'.
a = 4;
b = 5;
c = a + b;
print c; // 9
a = 42;
print c; // 9 normal
print c; // 47 reactive
Message driven
Resilent
Elastic
Responsive
Bindings
Data driven
Fast
Optimized
RX.js
Bacon.js
Kefir.js
ProAct.js
var data = {
a: 4,
b: 5,
c: function () {
return this.a + this.b;
}
};
var cool = ProAct.prob(data);
console.log(cool.c); // 9
cool.a = 42;
console.log(cool.c); // 47


---
layout: post
title: ! 'OMG: I can see the fnords! (Or least one...)'
published: true
date: 2009-01-05
categories: []
posterous_url: http://verdammelt.posterous.com/omg-i-can-see-the-fnords-or-le
posterous_slug: omg-i-can-see-the-fnords-or-le
---
Getting off the Red Line in Harvard Sq. after work today i saw a small
sticker pasted over the writing that is just to the side of the doors.
The small sticker said simply "fnord".
Of course these stickers have existed for decades now i am sure. But
somehow, coincidentally, improbably, i have never seen one until just
now - a mere week(-ish) or so after i read The Illumanatus! Trilogy.
 I wish i had seen it earlier so i could have at least gotten a cellphone
picture of it. But of course what would it have looked like? A digital
photograph of a fnord?


replicator
`Prev `__
R
`Next `__
--------------
**replicator**: n.
Any construct that acts to produce copies of itself; this could be a
living organism, an idea (see `*meme* <../M/meme.html>`__), a program
(see `*quine* <../Q/quine.html>`__, `*worm* <../W/worm.html>`__,
`*wabbit* <../W/wabbit.html>`__, `*fork bomb* <../F/fork-bomb.html>`__,
and `*virus* <../V/virus.html>`__), a pattern in a cellular automaton
(see `*life* <../L/life.html>`__, sense 1), or (speculatively) a robot
or `*nanobot* <../N/nanobot.html>`__. It is even claimed by some that
`*Unix* <../U/Unix.html>`__ and `*C* <../C/C.html>`__ are the symbiotic
halves of an extremely successful replicator; see `*Unix
conspiracy* <../U/Unix-conspiracy.html>`__.
--------------
+-------------------------------------+----------------------------+--------------------------+
| `Prev `__ | `Up <../R.html>`__ | `Next `__ |
+-------------------------------------+----------------------------+--------------------------+
| religious issues | `Home <../index.html>`__ | reply |
+-------------------------------------+----------------------------+--------------------------+