r"""
Functions for compositional data analysis.
It was copied from the :mod:`skbio.stats.composition` module and edited slightly to
enable compatibility with the JAX package.
Many 'omics datasets are inherently compositional - meaning that they
are best interpreted as proportions or percentages rather than
absolute counts.
Formally, :math:`x` is a composition if :math:`\sum_{i=0}^D x_{i} = c`
and :math:`x_{i} > 0`, :math:`1 \leq i \leq D` and :math:`c` is a real
valued constant and there are :math:`D` components for each
composition. In this module :math:`c=1`. Compositional data can be
analyzed using Aitchison geometry. [1]_
However, in this framework, standard real Euclidean operations such as
addition and multiplication no longer apply. Only operations such as
perturbation and power can be used to manipulate this data.
This module allows two styles of manipulation of compositional data.
Compositional data can be analyzed using perturbation and power
operations, which can be useful for simulation studies. The
alternative strategy is to transform compositional data into the real
space. Right now, the centre log ratio transform (clr) and
the isometric log ratio transform (ilr) [2]_ can be used to accomplish
this. This transform can be useful for performing standard statistical
tools such as parametric hypothesis testing, regressions and more.
The major caveat of using this framework is dealing with zeros. In
the Aitchison geometry, only compositions with nonzero components can
be considered. The multiplicative replacement technique [3]_ can be
used to substitute these zeros with small pseudocounts without
introducing major distortions to the data.
References
----------
.. [1] V. Pawlowsky-Glahn, "Lecture Notes on Compositional Data Analysis"
(2007)
.. [2] J. J. Egozcue., "Isometric Logratio Transformations for
Compositional Data Analysis" Mathematical Geology, 35.3 (2003)
.. [3] J. A. Martin-Fernandez, "Dealing With Zeros and Missing Values in
Compositional Data Sets Using Nonparametric Imputation",
Mathematical Geology, 35.3 (2003)
Examples
--------
>>> import numpy as np
>>> import mushi.composition as cmp
Consider a very simple environment with only 3 species. The species
in the environment are equally distributed and their proportions are
equivalent:
>>> otus = np.array([1./3, 1./3., 1./3])
Suppose that an antibiotic kills off half of the population for the
first two species, but doesn't harm the third species. Then the
perturbation vector would be as follows
>>> antibiotic = np.array([1./2, 1./2, 1])
And the resulting perturbation would be
>>> cmp.perturb(otus, antibiotic)
DeviceArray([0.25, 0.25, 0.5 ], dtype=float64)
"""
# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# ----------------------------------------------------------------------------
from __future__ import absolute_import, division, print_function
import jax.numpy as np
import numpy as onp
import pandas as pd
import scipy.stats
[docs]def closure(mat):
"""
Performs closure to ensure that all elements add up to 1.
Parameters
----------
mat : array_like
a matrix of proportions where
rows = compositions
columns = components
Returns
-------
array_like, np.float64
A matrix of proportions where all of the values
are nonzero and each composition (row) adds up to 1
Raises
------
ValueError
Raises an error if any values are negative.
ValueError
Raises an error if the matrix has more than 2 dimension.
ValueError
Raises an error if there is a row that has all zeros.
Examples
--------
>>> import numpy as np
>>> import mushi.composition as cmp
>>> X = np.array([[2, 2, 6], [4, 4, 2]])
>>> cmp.closure(X)
DeviceArray([[0.2, 0.2, 0.6],
[0.4, 0.4, 0.2]], dtype=float64)
"""
mat = np.atleast_2d(mat)
# if np.any(mat < 0):
# raise ValueError("Cannot have negative proportions")
# if mat.ndim > 2:
# raise ValueError("Input matrix can only have two dimensions or less")
# if np.all(mat == 0, axis=1).sum() > 0:
# raise ValueError("Input matrix cannot have rows with all zeros")
mat = mat / mat.sum(axis=1, keepdims=True)
return mat.squeeze()
[docs]def multiplicative_replacement(mat, delta=None):
r"""Replace all zeros with small non-zero values
It uses the multiplicative replacement strategy [1]_ ,
replacing zeros with a small positive :math:`\delta`
and ensuring that the compositions still add up to 1.
Parameters
----------
mat: array_like
a matrix of proportions where
rows = compositions and
columns = components
delta: float, optional
a small number to be used to replace zeros
If delta is not specified, then the default delta is
:math:`\delta = \frac{1}{N^2}` where :math:`N`
is the number of components
Returns
-------
numpy.ndarray, np.float64
A matrix of proportions where all of the values
are nonzero and each composition (row) adds up to 1
Raises
------
ValueError
Raises an error if negative proportions are created due to a large
`delta`.
Notes
-----
This method will result in negative proportions if a large delta is chosen.
References
----------
.. [1] J. A. Martin-Fernandez. "Dealing With Zeros and Missing Values in
Compositional Data Sets Using Nonparametric Imputation"
Examples
--------
>>> import numpy as np
>>> import mushi.composition as cmp
>>> X = np.array([[.2,.4,.4, 0],[0,.5,.5,0]])
>>> cmp.multiplicative_replacement(X)
DeviceArray([[0.1875, 0.375 , 0.375 , 0.0625],
[0.0625, 0.4375, 0.4375, 0.0625]], dtype=float64)
"""
mat = closure(mat)
z_mat = (mat == 0)
num_feats = mat.shape[-1]
tot = z_mat.sum(axis=-1, keepdims=True)
if delta is None:
delta = (1. / num_feats)**2
zcnts = 1 - tot * delta
if np.any(zcnts) < 0:
raise ValueError('The multiplicative replacment created negative '
'proportions. Consider using a smaller `delta`.')
mat = np.where(z_mat, delta, zcnts * mat)
return mat.squeeze()
[docs]def perturb(x, y):
r"""
Performs the perturbation operation.
This operation is defined as
.. math::
x \oplus y = C[x_1 y_1, \ldots, x_D y_D]
:math:`C[x]` is the closure operation defined as
.. math::
C[x] = \left[\frac{x_1}{\sum_{i=1}^{D} x_i},\ldots,
\frac{x_D}{\sum_{i=1}^{D} x_i} \right]
for some :math:`D` dimensional real vector :math:`x` and
:math:`D` is the number of components for every composition.
Parameters
----------
x : array_like, float
a matrix of proportions where
rows = compositions and
columns = components
y : array_like, float
a matrix of proportions where
rows = compositions and
columns = components
Returns
-------
numpy.ndarray, np.float64
A matrix of proportions where all of the values
are nonzero and each composition (row) adds up to 1
Examples
--------
>>> import numpy as np
>>> import mushi.composition as cmp
>>> x = np.array([.1,.3,.4, .2])
>>> y = np.array([1./6,1./6,1./3,1./3])
>>> cmp.perturb(x,y)
DeviceArray([0.0625, 0.1875, 0.5 , 0.25 ], dtype=float64)
"""
x, y = closure(x), closure(y)
return closure(x * y)
[docs]def perturb_inv(x, y):
r"""
Performs the inverse perturbation operation.
This operation is defined as
.. math::
x \ominus y = C[x_1 y_1^{-1}, \ldots, x_D y_D^{-1}]
:math:`C[x]` is the closure operation defined as
.. math::
C[x] = \left[\frac{x_1}{\sum_{i=1}^{D} x_i},\ldots,
\frac{x_D}{\sum_{i=1}^{D} x_i} \right]
for some :math:`D` dimensional real vector :math:`x` and
:math:`D` is the number of components for every composition.
Parameters
----------
x : array_like
a matrix of proportions where
rows = compositions and
columns = components
y : array_like
a matrix of proportions where
rows = compositions and
columns = components
Returns
-------
numpy.ndarray, np.float64
A matrix of proportions where all of the values
are nonzero and each composition (row) adds up to 1
Examples
--------
>>> import numpy as np
>>> import mushi.composition as cmp
>>> x = np.array([.1,.3,.4, .2])
>>> y = np.array([1./6,1./6,1./3,1./3])
>>> cmp.perturb_inv(x,y)
DeviceArray([0.14285714, 0.42857143, 0.28571429, 0.14285714], dtype=float64)
"""
x, y = closure(x), closure(y)
return closure(x / y)
[docs]def power(x, a):
r"""
Performs the power operation.
This operation is defined as follows
.. math::
`x \odot a = C[x_1^a, \ldots, x_D^a]
:math:`C[x]` is the closure operation defined as
.. math::
C[x] = \left[\frac{x_1}{\sum_{i=1}^{D} x_i},\ldots,
\frac{x_D}{\sum_{i=1}^{D} x_i} \right]
for some :math:`D` dimensional real vector :math:`x` and
:math:`D` is the number of components for every composition.
Parameters
----------
x : array_like, float
a matrix of proportions where
rows = compositions and
columns = components
a : float
a scalar float
Returns
-------
numpy.ndarray, np.float64
A matrix of proportions where all of the values
are nonzero and each composition (row) adds up to 1
Examples
--------
>>> import numpy as np
>>> import mushi.composition as cmp
>>> x = np.array([.1,.3,.4, .2])
>>> cmp.power(x, .1)
DeviceArray([0.23059566, 0.25737316, 0.26488486, 0.24714631], dtype=float64)
"""
x = closure(x)
return closure(x**a).squeeze()
[docs]def inner(x, y):
r"""
Calculates the Aitchson inner product.
This inner product is defined as follows
.. math::
\langle x, y \rangle_a =
\frac{1}{2D} \sum\limits_{i=1}^{D} \sum\limits_{j=1}^{D}
\ln\left(\frac{x_i}{x_j}\right) \ln\left(\frac{y_i}{y_j}\right)
Parameters
----------
x : array_like
a matrix of proportions where
rows = compositions and
columns = components
y : array_like
a matrix of proportions where
rows = compositions and
columns = components
Returns
-------
numpy.ndarray
inner product result
Examples
--------
>>> import numpy as np
>>> import mushi.composition as cmp
>>> x = np.array([.1, .3, .4, .2])
>>> y = np.array([.2, .4, .2, .2])
>>> cmp.inner(x, y) # doctest: +ELLIPSIS
DeviceArray(0.21078525, dtype=float64)
"""
x = closure(x)
y = closure(y)
a, b = clr(x), clr(y)
return a.dot(b.T)
[docs]def clr(mat):
r"""
Performs centre log ratio transformation.
This function transforms compositions from Aitchison geometry to
the real space. The :math:`clr` transform is both an isometry and an
isomorphism defined on the following spaces
:math:`clr: S^D \rightarrow U`
where :math:`U=
\{x :\sum\limits_{i=1}^D x = 0 \; \forall x \in \mathbb{R}^D\}`
It is defined for a composition :math:`x` as follows:
.. math::
clr(x) = \ln\left[\frac{x_1}{g_m(x)}, \ldots, \frac{x_D}{g_m(x)}\right]
where :math:`g_m(x) = (\prod\limits_{i=1}^{D} x_i)^{1/D}` is the geometric
mean of :math:`x`.
Parameters
----------
mat : array_like, float
a matrix of proportions where
rows = compositions and
columns = components
Returns
-------
numpy.ndarray
clr transformed matrix
Examples
--------
>>> import numpy as np
>>> import mushi.composition as cmp
>>> x = np.array([.1, .3, .4, .2])
>>> cmp.clr(x)
DeviceArray([-0.79451346, 0.30409883, 0.5917809 , -0.10136628], dtype=float64)
"""
mat = closure(mat)
lmat = np.log(mat)
gm = lmat.mean(axis=-1, keepdims=True)
return (lmat - gm).squeeze()
[docs]def clr_inv(mat):
r"""
Performs inverse centre log ratio transformation.
This function transforms compositions from the real space to
Aitchison geometry. The :math:`clr^{-1}` transform is both an isometry,
and an isomorphism defined on the following spaces
:math:`clr^{-1}: U \rightarrow S^D`
where :math:`U=
\{x :\sum\limits_{i=1}^D x = 0 \; \forall x \in \mathbb{R}^D\}`
This transformation is defined as follows
.. math::
clr^{-1}(x) = C[\exp( x_1, \ldots, x_D)]
Parameters
----------
mat : array_like, float
a matrix of real values where
rows = transformed compositions and
columns = components
Returns
-------
numpy.ndarray
inverse clr transformed matrix
Examples
--------
>>> import numpy as np
>>> import mushi.composition as cmp
>>> x = np.array([.1, .3, .4, .2])
>>> cmp.clr_inv(x)
DeviceArray([0.21383822, 0.26118259, 0.28865141, 0.23632778], dtype=float64)
"""
return closure(np.exp(mat))
[docs]def ilr(mat, basis=None, check=True):
r"""
Performs isometric log ratio transformation.
This function transforms compositions from Aitchison simplex to
the real space. The :math: ilr` transform is both an isometry,
and an isomorphism defined on the following spaces
:math:`ilr: S^D \rightarrow \mathbb{R}^{D-1}`
The ilr transformation is defined as follows
.. math::
ilr(x) =
[\langle x, e_1 \rangle_a, \ldots, \langle x, e_{D-1} \rangle_a]
where :math:`[e_1,\ldots,e_{D-1}]` is an orthonormal basis in the simplex.
If an orthornormal basis isn't specified, the J. J. Egozcue orthonormal
basis derived from Gram-Schmidt orthogonalization will be used by
default.
Parameters
----------
mat: numpy.ndarray
a matrix of proportions where
rows = compositions and
columns = components
basis: numpy.ndarray, float, optional
orthonormal basis for Aitchison simplex
defaults to J.J.Egozcue orthonormal basis
Examples
--------
>>> import numpy as np
>>> import mushi.composition as cmp
>>> x = np.array([.1, .3, .4, .2])
>>> cmp.ilr(x)
DeviceArray([-0.7768362 , -0.68339802, 0.11704769], dtype=float64)
"""
mat = closure(mat)
if basis is None:
basis = _gram_schmidt_basis(mat.shape[-1])
elif check:
_check_orthogonality(clr_inv(basis))
return inner(mat, clr_inv(basis))
[docs]def ilr_inv(mat, basis):
r"""
Performs inverse isometric log ratio transform.
This function transforms compositions from the real space to
Aitchison geometry. The :math:`ilr^{-1}` transform is both an isometry,
and an isomorphism defined on the following spaces
:math:`ilr^{-1}: \mathbb{R}^{D-1} \rightarrow S^D`
The inverse ilr transformation is defined as follows
.. math::
ilr^{-1}(x) = \bigoplus\limits_{i=1}^{D-1} x \odot e_i
where :math:`[e_1,\ldots, e_{D-1}]` is an orthonormal basis in the simplex.
If an orthornormal basis isn't specified, the J. J. Egozcue orthonormal
basis derived from Gram-Schmidt orthogonalization will be used by
default.
Parameters
----------
mat: numpy.ndarray, float
a matrix of transformed proportions where
rows = compositions and
columns = components
basis: numpy.ndarray, float, optional
orthonormal basis for Aitchison simplex
defaults to J.J.Egozcue orthonormal basis
Examples
--------
>>> import numpy as np
>>> import mushi.composition as cmp
>>> x = np.array([.1, .3, .6,])
>>> basis = cmp._gram_schmidt_basis(4)
>>> cmp.ilr_inv(x, basis)
DeviceArray([0.34180297, 0.29672718, 0.22054469, 0.14092516], dtype=float64)
"""
return clr_inv(np.dot(mat, basis))
[docs]def centralize(mat):
r"""Center data around its geometric average.
Parameters
----------
mat : array_like, float
a matrix of proportions where
rows = compositions and
columns = components
Returns
-------
numpy.ndarray
centered composition matrix
Examples
--------
>>> import numpy as np
>>> import mushi.composition as cmp
>>> X = np.array([[.1,.3,.4, .2],[.2,.2,.2,.4]])
>>> cmp.centralize(X)
DeviceArray([[0.17445763, 0.30216948, 0.34891526, 0.17445763],
[0.32495488, 0.18761279, 0.16247744, 0.32495488]], dtype=float64)
"""
mat = closure(mat)
cen = scipy.stats.gmean(mat, axis=0)
return perturb_inv(mat, cen)
[docs]def ancom(table, grouping,
alpha=0.05,
tau=0.02,
theta=0.1,
multiple_comparisons_correction=None,
significance_test=None):
r""" Performs a differential abundance test using ANCOM.
This is done by calculating pairwise log ratios between all features
and performing a significance test to determine if there is a significant
difference in feature ratios with respect to the variable of interest.
In an experiment with only two treatments, this test tests the following
hypothesis for feature :math:`i`
.. math::
H_{0i}: \mathbb{E}[\ln(u_i^{(1)})] = \mathbb{E}[\ln(u_i^{(2)})]
where :math:`u_i^{(1)}` is the mean abundance for feature :math:`i` in the
first group and :math:`u_i^{(2)}` is the mean abundance for feature
:math:`i` in the second group.
Parameters
----------
table : pd.DataFrame
A 2D matrix of strictly positive values (i.e. counts or proportions)
where the rows correspond to samples and the columns correspond to
features.
grouping : pd.Series
Vector indicating the assignment of samples to groups. For example,
these could be strings or integers denoting which group a sample
belongs to. It must be the same length as the samples in `table`.
The index must be the same on `table` and `grouping` but need not be
in the same order.
alpha : float, optional
Significance level for each of the statistical tests.
This can can be anywhere between 0 and 1 exclusive.
tau : float, optional
A constant used to determine an appropriate cutoff.
A value close to zero indicates a conservative cutoff.
This can can be anywhere between 0 and 1 exclusive.
theta : float, optional
Lower bound for the proportion for the W-statistic.
If all W-statistics are lower than theta, then no features
will be detected to be differentially significant.
This can can be anywhere between 0 and 1 exclusive.
multiple_comparisons_correction : {None, 'holm-bonferroni'}, optional
The multiple comparison correction procedure to run. If None,
then no multiple comparison correction procedure will be run.
If 'holm-boniferroni' is specified, then the Holm-Boniferroni
procedure [1]_ will be run.
significance_test : function, optional
A statistical significance function to test for significance between
classes. This function must be able to accept at least two 1D
array_like arguments of floats and returns a test statistic and a
p-value. By default ``scipy.stats.f_oneway`` is used.
Returns
-------
pd.DataFrame
A table of features, their W-statistics and whether the null hypothesis
is rejected.
`"W"` is the W-statistic, or number of features that a single feature
is tested to be significantly different against.
`"reject"` indicates if feature is significantly different or not.
See Also
--------
multiplicative_replacement
scipy.stats.ttest_ind
scipy.stats.f_oneway
scipy.stats.wilcoxon
scipy.stats.kruskal
Notes
-----
The developers of this method recommend the following significance tests
([2]_, Supplementary File 1, top of page 11): the standard parametric
t-test (``scipy.stats.ttest_ind``) or one-way ANOVA
(``scipy.stats.f_oneway``) if the number of groups is greater
than 2, or non-parametric variants such as Wilcoxon rank sum
(``scipy.stats.wilcoxon``) or Kruskal-Wallis (``scipy.stats.kruskal``)
if the number of groups is greater than 2. Because one-way ANOVA is
equivalent to the standard t-test when the number of groups is two,
we default to ``scipy.stats.f_oneway`` here, which can be used when
there are two or more groups. Users should refer to the documentation
of these tests in SciPy to understand the assumptions made by each test.
This method cannot handle any zero counts as input, since the logarithm
of zero cannot be computed. While this is an unsolved problem, many
studies have shown promising results by replacing the zeros with pseudo
counts. This can be also be done via the ``multiplicative_replacement``
method.
References
----------
.. [1] Holm, S. "A simple sequentially rejective multiple test procedure".
Scandinavian Journal of Statistics (1979), 6.
.. [2] Mandal et al. "Analysis of composition of microbiomes: a novel
method for studying microbial composition", Microbial Ecology in Health
& Disease, (2015), 26.
Examples
--------
First import all of the necessary modules:
>>> import mushi.composition as cmp
>>> import pandas as pd
Now let's load in a pd.DataFrame with 6 samples and 7 unknown bacteria:
>>> table = pd.DataFrame([[12, 11, 10, 10, 10, 10, 10],
... [9, 11, 12, 10, 10, 10, 10],
... [1, 11, 10, 11, 10, 5, 9],
... [22, 21, 9, 10, 10, 10, 10],
... [20, 22, 10, 10, 13, 10, 10],
... [23, 21, 14, 10, 10, 10, 10]],
... index=['s1','s2','s3','s4','s5','s6'],
... columns=['b1','b2','b3','b4','b5','b6','b7'])
Then create a grouping vector. In this scenario, there
are only two classes, and suppose these classes correspond to the
treatment due to a drug and a control. The first three samples
are controls and the last three samples are treatments.
>>> grouping = pd.Series([0, 0, 0, 1, 1, 1],
... index=['s1','s2','s3','s4','s5','s6'])
Now run ``ancom`` and see if there are any features that have any
significant differences between the treatment and the control.
>>> results = cmp.ancom(table, grouping) # doctest: +SKIP
>>> results['W'] # doctest: +SKIP
b1 0
b2 4
b3 1
b4 1
b5 1
b6 0
b7 1
Name: W, dtype: np.int64
The W-statistic is the number of features that a single feature is tested
to be significantly different against. In this scenario, `b2` was detected
to have significantly different abundances compared to four of the other
species. To summarize the results from the W-statistic, let's take a look
at the results from the hypothesis test:
>>> results['reject'] # doctest: +SKIP
b1 False
b2 True
b3 False
b4 False
b5 False
b6 False
b7 False
Name: reject, dtype: bool
From this we can conclude that only `b2` was significantly
different between the treatment and the control.
"""
if not isinstance(table, pd.DataFrame):
raise TypeError('`table` must be a `pd.DataFrame`, '
'not %r.' % type(table).__name__)
if not isinstance(grouping, pd.Series):
raise TypeError('`grouping` must be a `pd.Series`,'
' not %r.' % type(grouping).__name__)
if np.any(table <= 0):
raise ValueError('Cannot handle zeros or negative values in `table`. '
'Use pseudo counts or ``multiplicative_replacement``.'
)
if not 0 < alpha < 1:
raise ValueError('`alpha`=%f is not within 0 and 1.' % alpha)
if not 0 < tau < 1:
raise ValueError('`tau`=%f is not within 0 and 1.' % tau)
if not 0 < theta < 1:
raise ValueError('`theta`=%f is not within 0 and 1.' % theta)
if multiple_comparisons_correction is not None:
if multiple_comparisons_correction != 'holm-bonferroni':
raise ValueError('%r is not an available option for '
'`multiple_comparisons_correction`.'
% multiple_comparisons_correction)
if (grouping.isnull()).any():
raise ValueError('Cannot handle missing values in `grouping`.')
if (table.isnull()).any().any():
raise ValueError('Cannot handle missing values in `table`.')
groups, _grouping = onp.unique(grouping, return_inverse=True)
grouping = pd.Series(_grouping, index=grouping.index)
num_groups = len(groups)
if num_groups == len(grouping):
raise ValueError(
"All values in `grouping` are unique. This method cannot "
"operate on a grouping vector with only unique values (e.g., "
"there are no 'within' variance because each group of samples "
"contains only a single sample).")
if num_groups == 1:
raise ValueError(
"All values the `grouping` are the same. This method cannot "
"operate on a grouping vector with only a single group of samples"
"(e.g., there are no 'between' variance because there is only a "
"single group).")
if significance_test is None:
significance_test = scipy.stats.f_oneway
table_index_len = len(table.index)
grouping_index_len = len(grouping.index)
mat, cats = table.align(grouping, axis=0, join='inner')
if (len(mat) != table_index_len or len(cats) != grouping_index_len):
raise ValueError('`table` index and `grouping` '
'index must be consistent.')
n_feat = mat.shape[1]
_logratio_mat = _log_compare(mat.values, cats.values, significance_test)
logratio_mat = _logratio_mat + _logratio_mat.T
# Multiple comparisons
if multiple_comparisons_correction == 'holm-bonferroni':
logratio_mat = np.apply_along_axis(_holm_bonferroni,
1, logratio_mat)
np.fill_diagonal(logratio_mat, 1)
W = (logratio_mat < alpha).sum(axis=1)
c_start = W.max() / n_feat
if c_start < theta:
reject = np.zeros_like(W, dtype=bool)
else:
# Select appropriate cutoff
cutoff = c_start - np.linspace(0.05, 0.25, 5)
prop_cut = np.array([(W > n_feat*cut).mean() for cut in cutoff])
dels = np.abs(prop_cut - np.roll(prop_cut, -1))
dels[-1] = 0
if (dels[0] < tau) and (dels[1] < tau) and (dels[2] < tau):
nu = cutoff[1]
elif (dels[0] >= tau) and (dels[1] < tau) and (dels[2] < tau):
nu = cutoff[2]
elif (dels[1] >= tau) and (dels[2] < tau) and (dels[3] < tau):
nu = cutoff[3]
else:
nu = cutoff[4]
reject = (W >= nu*n_feat)
labs = mat.columns
return pd.DataFrame({'W': pd.Series(W, index=labs),
'reject': pd.Series(reject, index=labs)})
def _holm_bonferroni(p):
""" Performs Holm-Bonferroni correction for pvalues
to account for multiple comparisons
Parameters
---------
p: numpy.array
array of pvalues
Returns
-------
numpy.array
corrected pvalues
"""
K = len(p)
sort_index = -np.ones(K, dtype=np.int64)
sorted_p = np.sort(p)
sorted_p_adj = sorted_p*(K-np.arange(K))
for j in range(K):
idx = (p == sorted_p[j]) & (sort_index < 0)
num_ties = len(sort_index[idx])
sort_index[idx] = np.arange(j, (j+num_ties), dtype=np.int64)
sorted_holm_p = [min([max(sorted_p_adj[:k]), 1])
for k in range(1, K+1)]
holm_p = [sorted_holm_p[sort_index[k]] for k in range(K)]
return holm_p
def _log_compare(mat, cats,
significance_test=scipy.stats.ttest_ind):
""" Calculates pairwise log ratios between all features and performs a
significiance test (i.e. t-test) to determine if there is a significant
difference in feature ratios with respect to the variable of interest.
Parameters
----------
mat: np.array
rows correspond to samples and columns correspond to
features (i.e. OTUs)
cats: np.array, float
Vector of categories
significance_test: function
statistical test to run
Returns:
--------
log_ratio : np.array
log ratio pvalue matrix
"""
r, c = mat.shape
log_ratio = np.zeros((c, c))
log_mat = np.log(mat)
cs = np.unique(cats)
def func(x):
return significance_test(*[x[cats == k] for k in cs])
for i in range(c-1):
ratio = (log_mat[:, i].T - log_mat[:, i+1:].T).T
m, p = np.apply_along_axis(func,
axis=0,
arr=ratio)
log_ratio[i, i+1:] = np.squeeze(np.array(p.T))
return log_ratio
def _gram_schmidt_basis(n):
"""
Builds clr transformed basis derived from
gram schmidt orthogonalization
Parameters
----------
n : int
Dimension of the Aitchison simplex
"""
basis = onp.zeros((n, n-1))
for j in range(n-1):
i = j + 1
e = onp.array([(1/i)]*i + [-1] +
[0]*(n-i-1))*np.sqrt(i/(i+1))
basis[:, j] = e
return basis.T
def _check_orthogonality(basis):
"""
Checks to see if basis is truly orthonormal in the
Aitchison simplex
Parameters
----------
basis: numpy.ndarray
basis in the Aitchison simplex
"""
if not np.allclose(inner(basis, basis), np.identity(len(basis)),
rtol=1e-4, atol=1e-6):
raise ValueError("Aitchison basis is not orthonormal")


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{% load tagsfilter %}
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{% csrf_token %}
{% if products.catalogid == 11 %}
{%else%}
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{{products.name}}
Customer Information:
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|
hwlib
C++ hardware library for V1IPAS-TI
|
#include <stdint.h>
Go to the source code of this file.
Macros | |
| #define | HWLIB_NORETURN __attribute__((noreturn)) |
| mark a function declaration as never returning More... | |
| #define | HWLIB_WEAK __attribute__((weak)) |
| mark a function definition as weak More... | |
| #define | HWLIB_INLINE __attribute__((always_inline)) |
| mark a function definition as to be (always) inlined More... | |
| #define | HWLIB_NOINLINE __attribute__((never_inline)) |
| mark a function definition as to be never inlined More... | |
| #define | HWLIB_HERE HWLIB_HERE2( __FILE__, __LINE__ ) |
| [file-name:line-number] macro More... | |
| #define | HWLIB_TRACE ( ::hwlib::cout << "\n" << HWLIB_HERE ) |
| trace macro More... | |
| #define | HWLIB_PANIC_WITH_LOCATION ::hwlib::panic( __FILE__, __LINE__ ) |
| panic-with-location macro More... | |
Typedefs | |
| typedef uint8_t | byte |
| byte (8 bit unsigned, 1 byte) type More... | |
| typedef uint_fast8_t | fast_byte |
| fast byte (8 bit unsigned) type More... | |
| #define HWLIB_HERE HWLIB_HERE2( __FILE__, __LINE__ ) |
[file-name:line-number] macro
The macro HWLIB_HERE tranlsates to a newline, the file-name, ":", and the line-number of the place where the HWLIB_HERE macro appears. This can be usefull for debug logging.
| #define HWLIB_INLINE __attribute__((always_inline)) |
mark a function definition as to be (always) inlined
Sometimes a function should always be inlined, for instance because it is called only once, or because inlining it will produce better code.
Use this attribute with care, because it can lead to much larger code.
This must be done by a macro because Doxygen can't handle attribute.
| #define HWLIB_NOINLINE __attribute__((never_inline)) |
mark a function definition as to be never inlined
Sometimes a function should not be inlined, for instance because inlining would affect it timing.
Use this attribute with care, in most cases the decision to inline or not should be left to the compiler.
This must be done by a macro because Doxygen can't handle attribute.
| #define HWLIB_NORETURN __attribute__((noreturn)) |
mark a function declaration as never returning
This is useful when other code relies a function not to return. It can also enable the compiler to generate slightly better code.
This must be done by a macro because Doxygen can't handle attribute.
| #define HWLIB_PANIC_WITH_LOCATION ::hwlib::panic( __FILE__, __LINE__ ) |
panic-with-location macro
This macro calls panic( FILE, LINE ).
| #define HWLIB_TRACE ( ::hwlib::cout << "\n" << HWLIB_HERE ) |
trace macro
The TRACE macro can be used like hwlib::cout to print to, but what is printed will be prefixed with a newfile and the HWLIB_HERE string.
| #define HWLIB_WEAK __attribute__((weak)) |
mark a function definition as weak
This allowed the function definition to be overruled by an (often application-defined) replacement.
This attribute also makes it possible to put a function definition in a header, because all but one of the definitions will be eliminated by the linker.
This must be done by a macro because Doxygen can't handle attribute.
| typedef uint8_t byte |
byte (8 bit unsigned, 1 byte) type
This type is used when the range 0..255 is sufficient, and size is important too, for instance when passing a large array to i2c or spi library functions.
For single (non-array) variables and parameters fast_byte will often be a better choice.
| typedef uint_fast8_t fast_byte |
fast byte (8 bit unsigned) type
This type is used when the range 0..255 is sufficient, but speed is important too, for instance as parameter (single or small array) to i2c or spi library functions.
On 8 bit targets, the byte type will (likely) be unsigned char. On 16 bit targets, it will (likely) be a 16-bit unsigned integer. On 32 bit targets, it will (likely) be a 32-bit unsigned integer. A consequence is that on 16 and 32 bit targets, latent bugs can exist that (using values > 255) that will only surface when an 8-bit target is used.


With this systems is possible manage vehicle maintenance and maintain historic about each.
The system has a alert system component to advice about tasks that need to be done in a vehicle.
| Name | Photo | Brand | Acquisition date | |
|---|---|---|---|---|
Print all data from all vehicles


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Howl's Moving Castle
Paul Blart: Mall Cop
Submit
Ticket cost:


public final class LogCategory { private final String name; public static final LogCategory OPENEJB = new LogCategory( "OpenEJB"); public static final LogCategory OPENEJB_ADMIN = OPENEJB.createChild("admin"); public static final LogCategory OPENEJB_STARTUP = OPENEJB.createChild("startup"); public static final LogCategory OPENEJB_STARTUP_CONFIG = OPENEJB_STARTUP.createChild("config"); public static final LogCategory OPENEJB_STARTUP_VALIDATION = OPENEJB_STARTUP.createChild("validation"); // other categories removed for code brevity private LogCategory(String name){ this.name = name; } public String getName() { return name; } /** * Creates a child category of this category. <B>Use this method sparingly</B>. This method is to be used in only those circumstances where the name of the * category is not known upfront and is a derived name. If you know the name of the category, it is highly recommended to add a static final field * of type LogCategory in this class * @param child * @return - LogCategory */ public LogCategory createChild(String child){ return new LogCategory(this.name+"."+child); } }
Logging
Logging for Users
All logging in OpenEJB is done using the openejb.base/conf/logging.properties file. When you download and extract OpenEJB, you will not find this file under the openejb.base/conf directory. However, when you start the server, this file magically appears. So what does this give you as a user of OpenEJB? Here are some of the benefits:
-
You do not have to author a logging.properties from scratch. You get one with sensible defaults.
-
If you did modify the default file, and you wanted to revert back to the default file generated by OpenEJB, all you have to do is
-
= Delete or rename the file e.g. rename it to logging.properties.BAK .
-
= Restart the server
OpenEJB will find that the logging.properties file is missing from the openejb.base/conf directory and it will automatically create a new one with the default configuration. The good thing is that if you modify the logging.properties file, OpenEJB will NOT over-write it.
The default logging configuration created by OpenEJB uses RollingFileAppender’s. The log files are located under the openejb.base/logs directory. By default it writes to just two files , openejb.log and transaction.log {info:title=Embedded Testing} When running tests using embedded OpenEJB, the logging.properties will be ignored. You need to use a file named embedded.logging.properties instead. Place this file under src/test/resources {info}
Logging for contributors/committers
The org.apache.openejb.util.Logger class is the one which is used for logging. This class is a wrapper around log4.
LogCategory
Each Logger instance belongs to a category represented by a org.apache.openejb.util.LogCategory instance. Here is what the LogCategory class looks like
Notice carefully how each LogCategory instance is created. The objective here is that each LogCategory should be a child of the OPENEJB category. If you need to add a new category and you know the name of the category upfront, simply open the LogCategory class and add another category. For example, if you needed to add a category named SHUTDOWN, here is the recommended way to add it
public static final LogCategory OPENEJB_SHUTDOWN = LogCategory.OPENEJB.createChild("shutdown");
Sometimes you may want to create a category whose name is "generated" at runtime. In that case you can use the "createChild" method of the LogCategory class to create this new category. For example, if you wanted to create a category of Logger for every deployed module, then assuming you have the moduleId information you could do something as follows to create a category:
String moduleId = "mymodule"; LogCategory generatedCategory = LogCategory.OPENEJB.createChild(moduleId);
Logger
The preference is to externalize all logging messages in properties file. Typically each package should have a file called Messages.properties. This file should have all the keys and the corresponding messages. Here are the steps you would follow to log a message:
-
For each message you need to log, you would open the Messages.properties file in the corresponding package and add a key-value pair for the message. For example, if you were authoring a class called org.apache.openejb.util.Connect, then you would add a key-value pair to the Messages.properties file located in the org.apache.openejb.util package.
-
Obtain a Logger instance in one of the following ways:
Using the package name
Logger logger = Logger.getInstance (LogCategory.OPENEJB, "org.apache.openejb.util");
Using the Class
Logger logger = Logger.getInsance(LogCategory.OPENEJB, Connect.class);
Get a child Logger for an existing Logger instance
Logger logger = Logger.getInsance(LogCategory.OPENEJB, Connect.class); Logger child = logger.getChildLogger("shutdown");
Get a child Logger of an existing Logger using a LogCategory
LogCategory childCategory = LogCategory.OPENEJB.createChild("shutdown"); Logger logger = Logger.getInstance(childCategory,Connect.class);
-
Call one of the following methods passing in the key as an argument debug error fatal info ** warning
Logger logger = Logger.getInstance(LogCategory.OPENEJB,Connect.class); logger.info("error.file");
The Messages.properties file under org.apache.openejb.util must have the "error.file" key in it with the corresponding message.
Logger Inheritance
Another interesting feature of the Logging framework is inheritance. Here is how it works:
Say you have the following Messages.properties files in the classpath.
-
org/apache/openejb/Messages.properties
-
org/apache/openejb/core/Messages.properties
-
org/apache/openejb/core/stateless/Messages.properties
Then you have a class such as org.apache.openejb.core.stateless.StatelessContainer (note the package) If that class referenced a message key "classNotFound" for example, the Logger would look for the message first in Messages.properties 3, then 2, then 1 and so on until it found the required message. This would allow better reuse of messages, more flexibility in where we put the Message.properties files, as well as the added bonus in that we no longer need to pass in the location of where our Message.properties file is
Logging for integrators
If you want to embed OpenEJB in your application and need to control the logging configuration of OpenEJB, simple set the openejb.logger.external system property to true. Now, its your applications' responsibility to configure logging, OpenEJB will simply use your configuration.
Origination of the Logging Idea
There has been a long discussion for this logging idea. Its going to be worth it to read the discussion at i18n and logging
Here is a extract from an email from David Blevins which talks about the existing logging framework. The current framework is more or less the same as this one, just some added features and a rewrite of the API
Each module has a file called default.logging.conf. This file contains the definition of all Loggers, their appenders and warning levels. However, we do not use default.logging.conf first. The basic idea is that first we look for say conf/logging.conf in the openejb.base directory. If we don’t find it there, we look for default.logging.conf in the classpath. If we did find default.logging.conf (which we should) and there is an openejb.base/conf/ directory then expand the default.logging.conf to openejb.base/conf/logging.conf where we expected to find the file in the first place. If there was no openejb.base/conf/ directory, then it’s safe to assume we’re running embedded (in a test case perhaps) and just use the default.logging.conf and do no extra work.
We have default.logging.conf which we use this way as well as default.openejb.conf and now more recently users.properties and groups.properties. We search on disk for the resource in openejb.base/conf/ if we don’t find them we unpack the default one we stuffed in openejb-core jar and extract it to disk in the openejb.base/conf directory if there is one — if there isn’t one we just use the default file.
The basic ideas behind the pattern are that: 1. If you’ve messed up your configuration, just delete or rename the respective files in your conf/ directory and new (working) ones will magically appear. 2. When upgrading its nice that our zip file won’t overwrite any existing files in conf/ 3. If you’re running embedded you don’t have to setup any directories or have any config files, we can run on defaults.
The ConfUtils.getConfResource utility to do that pattern generically , but so far we’re only using it for the users.properties and groups.properties files. We should be using it everywhere. Having the code in multiple places has lead to some inconsistencies such as we expand the default.openejb.conf file to conf/openejb.xml (not even the same file extension). We really don’t need the "default" part in our file names and the lingering usage of the "conf" file extension is something that needs to go bye-bye — we should use properties for properties files and xml for xml files, etc.


Nexus's mission is to achieve student body unification, improve the happiness of the students and strengthen the identity of the school, its people and its student council.
Just click on the blue-highlighted text to view the meetings.


US suggests Morales should step aside from Bolivia vote
by AFPThe United States on Thursday suggested that Bolivia's former president Evo Morales should stay out in upcoming elections, which Washington said should be "free, fair and transparent."
Secretary of State Mike Pompeo promised US support for the transitional government, which took office after veteran leftist Morales quit amid an uproar over the conduct of October 20 elections.
"We admire the Bolivian people for standing up for their constitution, their democracy, and for free, fair and transparent elections," Pompeo said in a statement.
"Those who participated in the egregious irregularities and manipulation of the vote in the flawed October 20 election must, for the good of Bolivia, step aside and let Bolivians rebuild their institutions," he said.
Morales -- Bolivia's first indigenous president, who had turbulent relations with the United States -- was seeking a fourth mandate despite a two-term limitation and faced accusations of vote-rigging.
In an interview published Thursday with German magazine Der Spiegel, Morales, who went into exile in Mexico, said: "I have the right to stand -- but if that harms pacifying the nation then I stand aside."
Pompeo called on all sides to refrain from violence after at least 32 people were killed in post-election unrest.
"Security services must respect the rights of peaceful protesters, and the Bolivian authorities must ensure accountability for any violations of the right of citizens," he said.


1.0.5
Return a copy of the current Ranges.
Range
:
This function return ACS format for each range.
(any)
(object)
options object for toAcs function.
string
:
This function return an Array of peaks.
Array
:
This function return the peak list as a object with x and y arrays
object
:
{x: Array, y: Array}
Joins coupling constants
Makes a array of object with multiplicity, delta and integral
(object
= {}
)
options for joinCouplings function.
Array<object>
:
This function normalize or scale the integral data
(object
= {}
)
object with the options
| Name | Description |
|---|---|
options.sum boolean?
|
anything factor to normalize the integrals, Similar to the number of proton in the molecule for a nmr spectrum |
options.factor number?
|
Factor that multiply the intensities, if [ options.sum ] is defined it is override |
Ranges
:
Update the multiplicity of the signals from J couplings.
This function return a Range instance from predictions.
(object)
predictions from a spin system.
Ranges
:
This function return Ranges instance from a SD instance
(SD)
SD instance
(object
= {}
)
options object to extractPeaks function
Ranges
:
This function converts an array of peaks [{x, y, width}] in a vector equally x,y vector from a given window TODO: This function is very general and should be placed somewhere else
(Array)
List of the peaks
(object
= {}
)
it has some options to
| Name | Description |
|---|---|
options.from number?
|
one limit of given window |
options.to number?
|
one limit of given window |
options.fnName string?
|
function name to generate the signals form |
options.nWidth number?
|
width factor of signal form |
options.nbPoints number?
|
number of points that the vector will have |
{x: Array, y: Array}
:


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import helper._ @import uk.gov.hmrc.cbcrfrontend.views.html._ @import tags._ @import uk.gov.hmrc.cbcrfrontend.config.FrontendAppConfig @this( mainTemplate: main_template ) @(form:Form[_])(implicit request: Request[_], messages: Messages, config:FrontendAppConfig) @error = @{form.error("utr").isDefined} @mainTemplate(if(form.hasErrors){"Error: " + Messages("utrCheck.title")}else{Messages("utrCheck.title")}) {


NO! At the moment, the editor is at a prototype stage. This feature will be enabled soon.
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The Admin feature has been specifically designed and authorized by the creators for maintenance of the website and for keeping tabs on the database and hence can be accessed by them only.


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- In other media, a Musical Gag is a joke within the item's music/soundtrack.
The following tropes are Musical Gags of the second type:
- The Day the Music Lied
- Letting the Air Out of the Band
- Musicalis Interruptus
- Record Needle Scratch (in most cases)
- Left the Background Music On
- Xylophone Gag (to an extent, anyway...)
On occasion, the Leitmotif for a Joke Character will itself be this.
To avoid excessive repetition, examples on this page should be of Musical Gags that don't already fit into the specific tropes above.
Examples:
open/close all folders
Type 1 — a joke from somewhere other than the lyrics:
Comic Strips
- In Peanuts, Schroeder whistles Beethoven's F minor sonata to call Snoopy in; it is this because that would require whistling two notes at once. Later strips have Woodstock whistling similarly complex melodies.
Literature
- Gödel, Escher, Bach: An Eternal Golden Braid has many of these within the dialogues:
- In "Canon by Intervallic Agumentation," a song based on the theme (famously used in Johann Sebastian Bach's Art of the Fugue) whose notes spell out B-A-C-H is played on a different record player, which distends the intervals so that the notes instead spell C-A-G-E, as in John Cage. When a third record player plays the song, the intervals are stretched even further so that the notes of the theme spell B-C-A-H.
- In "...Ant Fugue," the four characters enter one by one like the voices of a fugue, each delivering the same theme at a different philosophical pitch.
- In "Six-Part Ricercar," the Crab adds a mordent to the theme from Johann Sebastian Bach's The Musical Offering, making its notes spell "Babbage, C(harles)" backwards.
Music
- Tom Lehrer, a satirist most famous in the 1950's and 1960's, had a distinctive style involving writing and performing songs poking fun at various (then-)current events. Most of the jokes are in the lyrics, so they would not be Musical Gags — but there was the occasional melodic joke. For example, at the end of "New Math", Lehrer quips: "Come back tomorrow night, we're gonna do... fractions." This is followed up by a quick melody on the piano which seems to translate as either "Oh, brother!" or "Just kidding!".
- Peter Schickele, a composer who moonlights as a musical satirist, has created a number of compact discs purporting to follow the work of "P.D.Q. Bach", a fictional classical composer. Every one of PDQ Bach's works is actually filled with as many Musical Gags as Schickele could fit in, be it including the melody of "Pop Goes the Weasel" or an Overly Long Gag consisting of going up and down the 5-note scale and adding in increasingly nonsensical instruments. Much to his dismay, these musical parodies have become so popular that they have eclipsed his serious work.
- "Faerie's Aire And Death Waltz" by John Stump; aside from the nonsensical stage directions like "adagio cantabile with a rock tempo feel", "tune the uke", "cresc. or not", and "play ball!", even a layperson looking at the score should be easily able to tell that it's intentionally unplayable.
- Filk Songs, in addition to the silly lyrics, will often include gags.
- Mozart himself wrote a piece entitled A Musical Joke. There are a few wrong notes, but most of the joke is how the piece fails to develop (as if it were written by someone who was out of ideas).
- Camille Saint-Saens included a number of musical gags in The Carnival of the Animals. For example, movement twelve, "Fossils", includes quotes from Twinkle Twinkle Little Star and the French nursery rhyme Au Clair de la Lune, among other musical "fossils".
- The Led Zeppelin song "The Crunge" consists entirely of this trope. It is written to sound like James Brown style funk, which is written to be danced to, but there are so many changes in the time signature that any attempt to actually dance to it would result in a faceplant.
- Cledus T. Judd, being a Country Music parodist, does this frequently:
- He coughs during the last chorus of "If Shania Was Mine" (Shania Twain's "Any Man of Mine") and asks of the backing vocals, "Now was that Mutt Lange or Dwight Yoakam?" In the video, David Ball butts in with "At least he's not pickin' on me this time."
- "Jackson (Alan That Is)" interpolates Johnny Cash's "Jackson", the source of the parody, with Alan Jackson's "Chattahoochee".
- He likes to make the musical instruments miss notes on purpose, such as the piano riffs in "Every Light in the House Is Blown" ("Every Light in the House" by Trace Adkins) and "Coronary Life" ("Ordinary Life" by Chad Brock), and the deliberately off-key guitar solo in "Breath" ("Breathe" by Faith Hill).
- The piano/fiddle riff on "Just Another Day in Parodies" ("Just Another Day in Paradise" by Phil Vassar) plays a descending scale at the end instead of the right notes.
- "What the *$@# Did You Say" (Martina McBride's "Whatever You Say") uses a tuba in the second verse, an instrument blatantly not present in the original song.
- The guitar riff at the beginning of "My Cellmate Thinks I'm Sexy" (Kenny Chesney's "She Thinks My Tractor's Sexy") interpolates the "William Tell Overture".
- "I Was Country When Country Wasn't Pop" (Barbara Mandrell and George Jones' "I Was Country When Country Wasn't Cool") randomly throws in pop and rap elements.
- After the Truck Driver's Gear Change on "Bake Me a Country Ham" ("Paint Me a Birmingham" by Tracy Lawrence), Cledus raises the pitch on his voice to make him sound even more like Tracy Lawrence.
- "Weird Al" Yankovic, in "Smells Like Nirvana" ("Smells Like Teen Spirit" by Nirvana), gargles water to the beginning of the guitar solo, then has a tuba play the rest of it.
- In Johann Sebastian Bach's Battle of the Bands cantata "The Contest Between Phoebus and Pan" (BWV 201), the violins during Midas's aria proclaiming Pan as master do donkey imitations, foreshadowing Midas's Karmic Transformation.
Radio
- Hello Cheeky would have one or two humorous songs in each episode. One of them, I've Fallen For A Girl Called Agnes, is entirely about the fact that nothing rhymes with Agnes, and the rhythm of the song keeps changing in reference to the fact that the singer never finishes a verse.
Theater
- Chee-Chee, a famous musical flop by Rodgers and Hart based on the novel The Son of the Grand Eunuch, has a final scene where the main character is being taken away to have the relevant operation. The orchestra plays a few bars from The Nutcracker Suite.
- In one of Richard Rodgers' later works, the ballet "Slaughter on Tenth Avenue," three cops on a raid enter to the tune of "Three Blind Mice."
- In Of Thee I Sing, the French soldiers enter to the first few bars of "An American in Paris" (which, like the musical, was composed by George Gershwin).
- In The Mikado, when the Mikado's song mentions "Bach, interwoven/With Spohr and Beethoven" as a punishment for the music hall singer, woodwinds play the theme from Johann Sebastian Bach's Fugue in G minor (BWV 542).
- In Show Boat, the second verse of "Why Do I Love You?" has Ravenal singing "I'll come home as early as I can" to the portion of "Can't Help Lovin' Dat Man" whose original lyrics were "He can come home as late as can be."
- In A Little Night Music, the song "Perpetual Anticipation" begins as a three-part round.
Type 2 — a joke within the item's music/soundtrack:
Anime & Manga
- In Haruhi Suzumiya, the Five-Man Band calls itself the "SOS Brigade". One of the image songs' beat is the Morse Code for "SOS".
Films — Live-Action
- In Spaceballs, the pan across the Spaceballs' ship is made even funnier by the orchestra repeatedly trying to segue into the next part of the music only to find out that there's more of the ship to get past first.
- In the movie Down with Love, a character claims that something doesn't ring a bell. The soundtrack contradicts him.
- Looney Tunes: Back in Action: When Brendan Fraser's character opens the garage to reveal his AMC Gremlin, the score plays a snippet of the Gremlins theme.
Live-Action TV
- Much of the Source Music chosen for Lost was recorded by artists who died in plane crashes.
Pinballs
- The main soundtrack for The Twilight Zone is an instrumental version of the 1982 song "Twilight Zone", by Golden Earring.
- Similarly, Mary Shelley's Frankenstein features Edgar Winter's rock instrumental, "Frankenstein", as a selectable song.
- In Monster Bash, the mode "Ball & Chain" is accompanied by a rock version of Wagner's "Bridal Chorus".
Radio
- One that requires some explanation: Hamish and Dougal was a show spun off from I'm Sorry I Haven't a Clue, featuring two of Clue's regulars. In one episode, the rest of Clue's cast join Hamish And Dougal as guest stars. After they've all been introduced, we hear a brief burst of Clue's theme tune, The Shickel Shamble.
- Also, since Clue has a local pianist in the form of Colin Sell, he sometimes joins in on the joke-cracking. As one round of Just A Minim collapsed into shambles, he could be heard playing Enter The Gladiators (circus music), and you can sometimes hear him pausing as he's thrown off by one of the panellists or has to adjust his tempo.
- When Neil Innes was the pianist, Humph listed his acomplishments (The Rutles, Bonzo Dog Doo Dah Band, etc), concluding "All of which has brought him to where he is today. Standing in for Colin Sell." Neil instantly played the funeral march.
Video Games
- In Digital Devil Saga, there's an optional boss fight with the Demi-Fiend, the protagonist of Shin Megami Tensei III: Nocturne. This is the hardest fight in the game, requiring a very specific strategy, lots of grinding, and a generous helping of luck to win. The music that plays during the fight? Nocturne's regular battle theme. To him, you're just another random encounter.
- In a mission late in Mafia IIIs storyline - infiltrating the Frisco Fields party to hunt down Olivia Marcano - Lincoln is instructed by Donavan, while posing as a waiter, to dose the wine being served with "military-grade LSD". By the time he returns to the party after ducking back into the kitchens for a smoke break, the entire group of assembled guests are hallucinating on the floor or dancing. With the speech that had punctuated the party while they were all sober now gone, the scene is instead accompanied by a piano. One which is playing White Rabbit by Jefferson Airplane.
- In Metal Gear Solid, the first howl you'll hear in the ambient track of wolf sounds that plays in the dog-focused passages of the game 'howls' the first two notes of the game's operatic Leitmotif, "The Best Is Yet To Come".
Web Original
- SF Debris often uses musical bits of various lengths and styles to emphasize ridiculous elements in the works he reviews, to deliver subtle (or less-than-subtle) puns, and so forth. He also often interjects with short comedic songs (with or without music), usually to make a point absolutely clear.
Web Video
- Dragon Ball Z Abridged: When Krillin is deciding whether or not to kill 18, the theme for Lapis Lazuli from Steven Universe plays. As the episode breakdown confirms, this was an oblique joke about how Akira Toriyama stated 17 and 18's real names were Lapis and Lazuli.
Western Animation
- In the Pinky and the Brain episode "It's Only A Paper World," the title characters attempt to Take Over the World by building a life-sized replica of the planet Earth out of paper-mâché and luring the population onto it so they can rule the real Earth without interference. All the music in the episode is based on themes from Dvorak's New World symphony.
- Steven Universe associates most major characters with specific instruments. Peridot's is a triangle wave, and she has triangular hair. Ruby's is a square wave, as she has a cubic afro.
Other Examples:
Video Games
- On the original Skies of Arcadia Dreamcast discs, if you inserted one in a CD player, instead of the standard "Insert Dreamcast discs into your Dreamcast not your CD player" message, one of three randomly selected custom messages to this effect issued by the voice actors for Vyse, Aika, and Fina would play.
- In Castlevania: Symphony of the Night, Alucard would inform you that this disc only has game data. It would then play some music before going into the game data and being annoying static.
"Track one contains computer data, so please don't play it. But you probably won't listen to me anyway, will you?" * cue music with Evil Laughs*
Web Comics


Grumpy Emoji: Demo
Embed the CSS in your page and set the font-family
to
include “Grumpy”:
<link rel="stylesheet" href="grumpy.css"> <style>body { font-family: Grumpy, sans-serif; }</style>
Make sure that the font files are in the same directory as grumpy.css. Then, use emoji or dingbats from Unicode, for example, codepoint U+263A WHITE SMILING FACE:
☺
(Or place the character directly in the text.) The glyph used to
render this dingbat is the grumpy cat:
☺
Project website: codepoints.github.io/grumpy


A passionate Software Architect, which over the years has specialized in web solutions development, with a special focus on new technologies. Researcher and deeply involved in the Development Community.
Georgina Cunha Esteves comes from Viana do Castelo, Portugal and completed the Integrated Master in Informatics and Computing Engineering from Faculty of Engineering in University of Porto. She is currently a Test Automation Trainee at Farfetch.


We're sorry, we do not have any clinicians that are eligible to serve you at this time.
This is most likely because there are no providers with a medical license where you live.
However, we are continually adding clinicians and hope to cover your location very soon.
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it is possible that we were unable to match your account with your pre-registration information.
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Installation Problem
-
Shylo
Can anyone help? I had orignially download Synergy 3.0 and installed my user id and registration code. That was 2 weeks ago. Everything worked well until today. Today, I keep getting a message that the beta version has expired and after opening /volumes/preferencepanes and entering my account info, I still cannot get the program up and running. My original install was not the beta version 3.1b2. Any help would be appreciate...
-
Greg Hurrell
The 3.1 beta expired today. The final (non-beta) release is available for download:
https://wincent.com/download.php?item=Synergy.dmg
Change log:
https://wincent.com/a/products/synergy-classic/history/#3.1
Release announcement:
https://wincent.com/a/news/archives/2006/12/synergy_31_now.php
Reply
This topic is now closed.


{% extends "base.html" %} {% block title %}What else is there?{% endblock %} {% block body %}
Some links if you are interested...
| Link | Description | |
|---|---|---|
| METKA | https://metkaweb.fi/ | Info about the student union and activities planned by them |
| Helpdesk | https://hd.metropolia.fi/ | If you need to reset your password or want to report a fault |
| Personal drive Z: | https://webdisk.metropolia.fi/ | Alternatively you can access it through shell.metropolia.fi by SSH or SFTP |
| WiFi | https://tietohallinto.metropolia.fi/display/itservices/Wireless+network | Info on how to connect to Eduroam, the school WiFi infrastructure |
| Software | http://mcp.metropolia.fi/ | Get "free" software for use |
| Moodle | http://moodle.metropolia.fi/ | School e-learning environment |
| Lukkarit | http://lukkarit.metropolia.fi/ | School course schedule. Alternatively you can use Tuudo app |
| https://mail.metropolia.fi/ | School mail | |
| Health | https://oma.metropolia.fi/opiskelijoille/terveydenhuolto | Student healthcare information |


We are now in providence,RI, My address
Turn on the music!
Use the arrow keys to move the cursor and begin your scents safari!
Move the green cursor circle to signals and click it to explore!


1. Start with WooCommerce in 5 Steps.
2. You also need to be running WordPress on a self-hosted site. Click here for more information.
3. For instructions on how to attain your API keys click Here.


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Railways' freight business grows; Modi urges auditors to check frauds
Indian Railways' freight business grew 7 times in nearly 50 years; SBI admits to making poor business decisions in last decade; PM Modi encouraged auditors to develop technical tools to check frauds and improve efficiency in government departments and more news on the News Blast.


Speech
Ofsted's Chief Inspector, Sir Michael Wilshaw, spoke at the London Councils education summit in central London.
Hello everyone and thank you for inviting me to speak at your conference this morning.
As an ex-London teacher and headteacher of many years standing, it is great to be here. It is also entirely fitting that my first speech of this new academic year is at an event focusing on outcomes for disadvantaged pupils.
As most of you will know, this autumn term marks the end of my 5-year term of office as Her Majesty’s Chief Inspector. Throughout my tenure, I have been motivated by a commitment to improve the prospects of children from poor backgrounds and to reduce the long tail of underachievement – especially in our secondary schools.
It has been my driving mission and the issue that has exercised me more than any other. Not only is it a moral and economic imperative that as a nation we do better for our poorest youngsters, it is also key to raising school standards overall. Experience tells us that improvement in schools starts from the bottom. If you raise the performance of the poorest and most challenging pupils, you almost always raise performance across the board in the process.
You will know that I have been very critical of those parts of the country where poor children continue to be let down and where pupil premium money is being squandered.
I have also been determined to ensure that Ofsted inspectors are taking a tough line with those schools where poor children – especially the most able poor – consistently underperform.
That is why I am so pleased to have the opportunity to address you here today. As we all know, London is leading the way in narrowing the attainment gap between rich and poor.
It has maintained its status as the top performing region across all key stages when it comes to the performance of children eligible for free school meals.
As someone who has spent their entire professional career in this city, I never cease to be amazed by the transformation that London schools have undergone since the miserable decades of the 1970s, 80s and 90s when they were truly bottom of the national pile.
London Challenge was, of course, the catalyst that radically improved standards in our capital city. It took ownership of underperformance and confronted failure in a way that had never been done before. The fact that you have been able to sustain and build on the success of London Challenge is something you should be rightly proud of.
The soaring success of London schools – especially in inner-London – surely makes a mockery of the claim that opening up many more grammar schools is the key to unlocking the potential of disadvantaged children and to boosting social mobility.
The question I would put to those clamouring for a return to selection by ability at the age of 11 is this:
If grammar schools are the great answer, why aren’t there more of them in London?
If they are such a good thing for poor children, then why are poor children here in the capital doing so much better than their counterparts in those parts of the country that operate selection?
I appreciate that many grammar schools do a fine job in equipping their students with an excellent education. But we all know that their record of admitting children from non-middle-class backgrounds is pretty woeful.
We certainly see that in areas like Kent and Buckinghamshire. But we also see it in the small number of London boroughs that have grammar schools. The proportion of disadvantaged children in the 4 grammar schools in Bexley, for example, is around 9% compared to 28% in its non-selective schools. Similarly in Sutton, where 5 out of the 14 schools are grammar schools, the proportions are about 7% compared to 25%.
The notion that the poor stand to benefit from the return of grammar schools strikes me as quite palpable tosh and nonsense – and is very clearly refuted by the London experience.
Take my old stomping ground, Hackney, for example, which has more than its fair share of social problems and economic deprivation – and with not a grammar school to be found for miles around. Every single secondary school in the borough is currently judged by Ofsted to be good or outstanding. Nearly 8 out of 10 of Hackney’s most able pupils last year obtained at least a B grade at GCSE maths and English. SIxty two per cent of its youngsters go on to university compared to 48% in the country as a whole.
It is hard to believe that bringing back selection to this part of London would improve the prospects of children, especially from more disadvantaged backgrounds. In Hackney, the gap in attainment at key stage 4 between free school meal and non-free school meal pupils is 14.6%. In Kent, which operates selection, the gap is nearly 34%.
And let’s not delude ourselves. ‘A grammar school in every town’, as some are calling for, would also mean 3 secondary moderns in every town – a consequence rarely mentioned.
That is why I am in no doubt that a return to selection would be a profoundly retrograde step that would actually lead to overall standards sliding back, not improving.
Only now are we really starting to see the long-term impact of the education reforms that were embarked on under Tony Blair’s premiership and carried on by Michael Gove and his successors.
It’s still early days but it was heartening – for example - to see that the proportion of Team GB medallists who attended a comprehensive school climbed to 60% in Rio from 56% in London 4 years earlier. Similarly, the proportion of comprehensive-school-educated MPs went up from 43% in 2010 to 49% at last year’s general election. And last week, we heard that Oxford University’s intake of new students this autumn will include the highest proportion of state school pupils for at least 40 years.
Why jeopardise these nascent but welcome signs of progress by turning back the clock?
Instead, we should allow more time for the school-led improvement revolution of recent years to take proper effect. We should be doing what we can to replicate what has been happening in Hackney and other London boroughs in those parts of the country still languishing in the educational doldrums.
However, I fear the clarion call of the ‘bring back grammar schools’ lobby will grow ever louder and more persuasive unless more of our secondary schools start delivering for all of their children. Because let’s face it, in many ways, London is the striking exception to the rule.
You will know that in my last 2 Annual Reports, I have highlighted serious concerns about the performance of secondary schools in other parts of the country, especially in the North and the Midlands and those serving the poorest and most isolated communities. Too many schools are still letting down their most able pupils and failing to narrow the attainment gap between rich and poor. They are still not sending nearly enough youngsters to our top universities.
My final report later this year will no doubt say something similar. It is a situation that those of us committed to the principle of comprehensive education simply cannot allow to continue.
Opinion is often divided on the exact reasons why London continues to forge ahead of the rest of the country. There is undoubtedly some truth in the argument that as the nation’s all-powerful and dynamic capital city, it can attract the best teaching talent. The high proportion of children from aspirational immigrant families must also be a factor.
However, what has really made the difference can be summed up in one simple word: leadership.
London could never have succeeded in improving outcomes for its poorest children if it didn’t have the right calibre of people in charge of running its schools.
Leaders with an unwavering ambition and commitment to the cause of raising standards and narrowing the attainment gap. Leaders who know how to handle and exploit the freedoms that have been given to them. Leaders who make sure they can count on the full support of a dedicated teaching staff prepared to go the extra mile for the young people in their charge.
These leaders can be found in schools across the capital. In places like Mossbourne in Hackney, Woodside High School in Haringey, King Solomon Academy in Westminster and Ark Academy in Brent.
I mention these schools because they are ones I happen to know very well. But there are dozens of other examples that I could cite.
All of these leaders come with their own particular style and brand of headship. But the common characteristic they all share is an absolute understanding that if you succeed in improving the lot of the most disadvantaged children in your school, then you move everybody else up with them.
While there is much to celebrate here in London, we should guard against any sense of complacency or an assumption that excellence is spread evenly across the capital.
When it comes to educational performance, there is a tendency on the part of commentators to regard the capital as a single entity. Like the Emerald City in ‘The Wizard of Oz’, from a distance it all looks pretty perfect. But when you look more closely, the cracks are evident.
You will know as well as I do that there are pockets of London that are not doing as well – especially in some of the outer-London boroughs. In places like Richmond, Sutton, Bromley and Kingston-upon-Thames, the attainment gap between FSM and non- FSM pupils is as wide as in areas like Reading and West Berkshire, which have been criticised by Ofsted for failing their poorer children. It is surely no coincidence that the proportion of children eligible for free school meals is smaller in these more affluent boroughs than in other parts of the capital. Our research consistently shows that in the South East in particular, when poor children are in a significant minority at their school, they are more likely to do badly.
Across London as a whole, outcomes for certain ethnic groups remain stubbornly low. The performance of poor White British pupils, in particular, is still not good enough. Last year, only 33% of this group achieved the benchmark 5 good GCSEs including English and mathematics – above the national figure of 28% but still a cause for concern. In 2 London local authorities, poor White British children actually did worse than in the country as a whole.
The situation is no better for free school meal children, especially boys, from a Caribbean background. In 10 London boroughs, less than a third of these children attained 5 good GCSEs including English and mathematics in 2015.
As I have already highlighted, there are many schools in London that are doing a fantastic job in raising the attainment of poorer children, including White British and Caribbean youngsters.
If they can do it, why can’t others?
Well we certainly need more great leaders in those parts of the capital that are not performing as well.
And if you believe, as I do, that system improvement has to be led by the profession itself, we also need more leaders who know how to run a number of schools as executive headteachers.
This is one of the biggest challenges we face. We know that not every great headteacher is cut out to successfully run more than one school. That is why I have been so vocal about putting more emphasis on national leadership programmes.
As things currently stand, we lack the right infrastructure for identifying enough people with the potential to step up to headship as well as executive headship.
While great leadership is essential, political will and vision is another vital ingredient in transforming standards.
That is where you come in. You have a crucial role to play in championing and challenging your schools and refusing to accept mediocrity.
Back in the 1990s, school leaders like myself had the backing of recognisable local champions like Jules Pipe in Hackney and Robin Wales in Newham.
They were determined to take responsibility for the performance of all their local schools – not just those under the council’s direct control.
They expected results and did not tolerate excuses. Their priority was, and remains, the better education of children. They did not allow varying school structures to deflect them from that objective.
We need more local politicians like them across the country. Whether elected mayors, council leaders or cabinet members, we need people who are prepared to stand up and be counted, to shoulder responsibility for their local schools, to challenge and support them regardless of whether they are academies or not.
In saying all this today, I’m conscious I could be accused of preaching to the converted. Because if you didn’t already think this was part of your job then you probably wouldn’t be here in this room today.
Nevertheless, it is worth driving home the point that as education cabinet members, it is right that you take an interest in who is being appointed to run the academies and free schools in your area.
Are you meeting regularly with these leaders or are you content to leave it to the regional school commissioner?
Do you and your DCS take an active interest in these schools? Do you look at the data?
If you are worried about an underperforming school or multi-academy trust in your patch, do you write or pick up the phone to the RSC to voice your concern?
Don’t think that you are out of the loop. The best local authority leaders are the ones who are proactive in supporting and challenging their local academy schools.
As I prepare to step down from my role as Chief Inspector, I’d like to end today by assuring you that I remain an optimist. As a young teacher and later headteacher in this city, I never dreamt that London schools would be able to transform themselves in the way they have. We need to somehow bottle what has happened here and use it to raise standards across the whole of the country.
We need national policies to tackle the urgent challenges of teacher shortages and the quality of leadership. We need to ensure that the best multi-academy trusts, the best leaders and the best teachers are incentivised to work in the areas where they are needed most.
I would never underestimate how difficult it is to educate children who are poor and who lack all the advantages that a more affluent background confers. However, the experience of London shows that with enough collective commitment and ambition, we can break the pattern of underachievement and create a fairer and more equal society.
If London can succeed, then why not Manchester, Birmingham and Sheffield?
As leaders of education in our capital city, you are playing a vital role in supporting many excellent and inspiring schools that are demonstrating exactly what needs to be done.
I remain confident that if others can learn from these schools, we will have an education system that the country expects and our children deserve.
Thank you for listening.


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for food, at your party? I came up
with a list that might help,
and I can bring some of them. Stuffed crust pizza, Doritos,
Gushers, Dunkaroos, Fruit Roll Ups, Fruit By The Foot, Kool Aid,
Hawaiian Punch, Ring Pops, Tacos, Bagel Bites, Potato Skins, Fish
Sticks, Sloppy Joes, Chicken Pot Pie, Gatorade, Pepsi, Sunny D,
Lava cakes, Cookies, Hawaiian pizza, Corn Dogs, Fajitas, Capri
Sun.
Me: That sounds good!


SQL Server Security Countermeasures
Patch, Patch, Patch!!!
Set strong passwords for all accounts, especially “sa”
Configure firewall to block access to ports 1433, 2433, & 1434
Remove unnecessary log files that may contain “sa” password
Install net-lib support drivers on the server “and” client side
Encrypt communications via SSL
Encrypt SQL Server Communications (Enable SSL – 2000 and higher)
Install MCS on a server somewhere (myserver)
Log onto SQL box as administrator
Navigate http://myserver/certsrv
Click “Request a certificate”
Click “Advanced Request”
Click “Submit a certificate request to this CA using a form”
Fill in all fields (use DNS name for name and spell out state name) (see next page)
Click ‘Use local machine Store’ and submit
Click ‘Install this certificate’
Load Server Network Utility and select “Force Protocol Encryption”
Encrypt SQL Server Communications (Enable SSL)
If the client does not trust the CA then the client will not be able to connect
Have the client navigate to http://myserver/certsrv and click ‘Retrieve the CA certificate or certificate revocation list’
Click ‘Download CA certificate’
Open the certificate, click ‘Install Certificate’ and install it in the ‘Trusted Root Certificate Store’ (Do not trust the certificate wizard to automatically place it in the right place – manually place the certificate)


About Me
My name is Kyle Seiverd and I am a high school science teacher at Toms River High School North.
Passion: Wildlife Conservation
Hero: Dr.Jane Goodall
Favorite Animal: Elephant
Bucket List Item: Visit every continent (5 down, 2 more to go!)


The charts below explore the relationship between Petrocaribe oil financing in the Caribbean and voting alignment with the United States in the UN. The scatterplot at left shows the relationship between a Petrocaribe financing recipient's debt and the annual financing received from Venezuela relative to that countries GDP. The chart at right shows the relationship between each financing recipients' overall proximity to the U.S. voting position in the UN during the post Cold War period and the 2015 Department of State and USAID foreign assistance request base as a percent of GDP.


Tieto Education - Attendance System
Attendance
Today's Statistics
Monthly Statistics
Annual Attendance Statistics
Face Recognition
Notifications
Marcel
Present
Anara
Present
Geraldine
Present
Shabna
Present
Madhubala
Present


This is your life. Do what you love, and do it often. If you don’t like something, change it. If you don’t like your job, quit.
If you are looking for the love of your life, stop; They will be waiting for you when you start doing things you love.
If you don’t have enough time, stop watching TV.
Stop over analyzing, life is simple. All emotions are beautiful. When you eat, appreciate every last bite.
Open your mind, arms, and heart to new things and people, we are united in our differences.
Travel often; getting lost will help you find yourself.
Some opportunities only come once, seize them.
Life is about the people you meet, and the things you create with them so go out and start creating.
Life is short. Live your dream and share your passion.
Ask the next person you see what their passion is and share your inspiring dream with them.


TensorFlow Scala
type-safe linear algebra, tensors, and neural networks
This library is a Scala API for https://www.tensorflow.org. It attempts to provide most of the functionality provided by the official Python API, while at the same type being strongly-typed and adding some new features. It is a work in progress and a project I started working on for my personal research purposes. Much of the API should be relatively stable by now, but things are still likely to change. That is why there is no official release of this library yet.
Please refer to the main website for documentation and tutorials. Here are a few useful links:
Main Features
-
Easy manipulation of tensors and computations involving tensors (similar to NumPy in Python):
val t1 = Tensor( 1.2, 4.5) val t2 = Tensor(-0.2, 1.1) t1 + t2 == Tensor(1.0, 5.6)
-
High-level API for creating, training, and using neural networks. For example, the following code shows how simple it is to train a multi-layer perceptron for MNIST using TensorFlow for Scala. Here we omit a lot of very powerful features such as summary and checkpoint savers, for simplicity, but these are also very simple to use.
import org.platanios.tensorflow.api._ import org.platanios.tensorflow.api.tf.learn._ import org.platanios.tensorflow.data.loaders.MNISTLoader // Load and batch data using pre-fetching. val dataSet = MNISTLoader.load(Paths.get("/tmp")) val trainImages = DatasetFromSlices(dataSet.trainImages) val trainLabels = DatasetFromSlices(dataSet.trainLabels) val trainData = trainImages.zip(trainLabels) .repeat() .shuffle(10000) .batch(256) .prefetch(10) // Create the MLP model. val input = Input(UINT8, Shape(-1, 28, 28)) val trainInput = Input(UINT8, Shape(-1)) val layer = Flatten() >> Cast(FLOAT32) >> Linear(128, name = "Layer_0") >> ReLU(0.1f) >> Linear(64, name = "Layer_1") >> ReLU(0.1f) >> Linear(32, name = "Layer_2") >> ReLU(0.1f) >> Linear(10, name = "OutputLayer") val trainingInputLayer = Cast(INT64) val loss = SparseSoftmaxCrossEntropy() >> Mean() val optimizer = GradientDescent(1e-6) val model = Model(input, layer, trainInput, trainingInputLayer, loss, optimizer) // Create an estimator and train the model. val estimator = Estimator(model) estimator.train(trainData, StopCriteria(maxSteps = Some(1000000)))
And by changing a few lines to the following code, you can get checkpoint capability, summaries, and seamless integration with TensorBoard:
loss = loss >> tf.learn.ScalarSummary("Loss") // Collect loss summaries for plotting val summariesDir = Paths.get("/tmp/summaries") // Directory in which to save summaries and checkpoints val estimator = Estimator(model, Configuration(Some(summariesDir))) estimator.train( trainData, StopCriteria(maxSteps = Some(1000000)), Seq( SummarySaverHook(summariesDir, StepHookTrigger(100)), // Save summaries every 1000 steps CheckpointSaverHook(summariesDir, StepHookTrigger(1000))), // Save checkpoint every 1000 steps tensorBoardConfig = TensorBoardConfig(summariesDir)) // Launch TensorBoard server in the background
If you now browse to
https://<IP_ADDRESS>:6006
while training, you can see the training progress: -
Low-level graph construction API, similar to that of the Python API, but strongly typed wherever possible:
import org.platanios.tensorflow.api._ val inputs = tf.placeholder(FLOAT32, Shape(-1, 10)) val outputs = tf.placeholder(FLOAT32, Shape(-1, 10)) val predictions = tf.createWith(nameScope = "Linear") { val weights = tf.variable("weights", FLOAT32, Shape(10, 1), tf.zerosInitializer) tf.matmul(inputs, weights) } val loss = tf.sum(tf.square(predictions - outputs)) val optimizer = tf.train.AdaGrad(1.0) val trainOp = optimizer.minimize(loss)
-
Numpy-like indexing/slicing for tensors. For example:
tensor(2 :: 5, ---, 1) // is equivalent to numpy's 'tensor[2:5, ..., 1]'
-
Efficient interaction with the native library that avoids unnecessary copying of data. All tensors are created and managed by the native TensorFlow library. When they are passed to the Scala API (e.g., fetched from a TensorFlow session), we use a combination of weak references and a disposing thread running in the background. Please refer to
Disposer.scala
, for the implementation.
Funding
Funding for the development of this library has been generously provided by the following sponsors:
| CMU Presidential Fellowship awarded to Emmanouil Antonios Platanios |
National Science Foundation Grant #: IIS1250956 |
Air Force Office of Scientific Research Grant #: FA95501710218 |


Properties
avatar_url
avatar_url: string
comments_count
comments_count: number
country_code
country_code: number | null
created_at
created_at: string
description
description: string
first_name
first_name: string
followers_count
followers_count: number
followings_count
followings_count: number
full_name
full_name: string
groups_count
groups_count: number
last_modified
last_modified: string
last_name
last_name: string
likes_count
likes_count: number
permalink
permalink: string
permalink_url
permalink_url: string
playlist_count
playlist_count: number
playlist_likes_count
playlist_likes_count: number
reposts_count
reposts_count: number | null
track_count
track_count: number
username
username: string
verified
verified: boolean
visuals
visuals: object
Type declaration
-
enabled: boolean
-
tracking: null
-
urn: string
-


14th International Workshop on Software Clones (IWSC 2020)
February 18th, 2020, London, Ontario, Canada
Software clones are often a result of copying and pasting as an act of
ad-hoc reuse by programmers, and can occur at many levels, from simple
statement sequences to blocks, methods, classes, source files,
subsystems, models, architectures and entire designs, and in all
software artifacts (code, models, requirements or architecture
documentation, etc.). Software clone research is of high relevance for
software engineering research and practice today.
IWSC series of events has provided a common forum for this important
research area as it continues to grow in application breadth and
technical depth. IWSC aims to bring researchers and practitioners to
evaluate the current state of research, discuss common problems and
emerging directions. Also, we are interested in discussing and spreading
practical applications of clone technologies.
Further details about IWSC 2020
are here on this website (https://iwsc2020.github.io).
TOPICS OF INTEREST:
Topics of interest include but not restricted to the following aspects of software clone research:
- Techniques and studies of code clone search, detection, analysis, and management
- Industrial experience with clones and clone management
- Use cases for clones and clone management in the software lifecycle
- Similarity measures of source code and other development artifacts
- Clone detection across multiple programming languages
- Clone detection applied to non-source code artifacts
- Source code clone detection supported by other development artifacts
- Clone typologies: kinds, cause and effects, management strategies, etc.
- Visualization of clones
- Clone evolution and approaches to managing variation
- Clone analysis in families of similar systems and software product lines
- Economic and trade-off models for clone management
- Effects of clones on system complexity and quality
- Software licensing and plagiarism issues
- Evaluation and benchmarking of clone detection methods
- Clone refactoring techniques and studies
- System architecture and clones
- Higher-level clones in models and designs
- Clone-aware software design and development
- Security implications of software code cloning
- Other applications of clone detection and analysis techniques
Papers
The following types of papers are sought:
- Full papers (7 pages maximum) are expected to present novel research ideas
and open issues, significant empirical studies, or important viewpoints of
the field.
- Position papers (2 pages maximum) should raise new ideas and issues or
describe early research achievements, emphasizing originality and potential
to stimulate active discussion at the workshop.
- Tool demonstration papers (4 pages maximum) should describe clone-related
tools and their applications.
All types of papers will be formally reviewed by at least three members of
the program committee. Submissions should be relevant to the goals of the
workshop and hold potential for lively discussion and debate.
*** Selected papers will be promoted for a special issue with JSS (the Journal of Systems and Software)
How to Submit:
If the paper is accepted, at least one author must attend the workshop and present the paper. Accepted papers will appear in the IEEE Digital Library along with SANER proceedings.
IMPORTANT DATES:
- Abstract submission deadline:
December 15, 2019 AoE December 20, 2019 AoE
- Paper submission deadline: December 20, 2019 AoE
- Notifications: January 10, 2020 AoE
- Camera Ready deadline: January 14, 2020 AoE
- Workshop: February 18, 2020


Handles transition abort and transition redirect. Also adds any returned resolvables
to the pathContext for the current pathElement. If the transition is rejected, then a rejected
promise is returned here, otherwise undefined is returned.
Handles transition abort and transition redirect. Also adds any returned resolvables
to the pathContext for the current pathElement. If the transition is rejected, then a rejected
promise is returned here, otherwise undefined is returned.
Handles transition abort and transition redirect. Also adds any returned resolvables to the pathContext for the current pathElement. If the transition is rejected, then a rejected promise is returned here, otherwise undefined is returned.


--- layout: page title: Curve3D date: 2010-06-14 02:36:48.000000000 -04:00 comments: true categories: [] published: true ---
Curve3D is an open-source cross-browser 3D engine written in pure javascript. It works in all major current browsers (Firefox 3.6+, IE8+, Chrome 5+, Opera 10.5+). Curve3D includes a high performance math library, significantly faster than other javascript matrix math libraries currently available.
Curve3D is uniquely powerful for a pure javascript 3D engine. It is the only 3D engine that works natively in Internet Explorer (I could not find any others as of June 2010). It includes multiple render methods, including a pure CSS3 renderer, a pure canvas tag renderer, and a hybrid canvas/CSS3 renderer. It also supports switching the renderer on the fly.
The code is released under the New BSD license, and the source is available at the github project page.


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{{TrainerName}}


--- title: Hello World description: Congratulations you have successfuly setup Acetate! ---
{{description}}


 In 1964, the race started in Sainte Marie du
Mont and finished at Utah beach. In subsequent years, the race started at
Utah beach and finished in Sainte Marie du Mont.
Race Time Bias
(sec/km
There is insufficient data to calculate
the race time bias.
Competitive Level (1990-date)
There is insufficient data to calculate the competitive level.
Last Updated on 18 Aug 2016
Contributors: Yves Seigneuric (50), Thierry Lefeuvre (5)


Nyaa Pantsu Latest Anime © 2017- Nyaa Pantsu
License
Copyright (c) 2017 Nyaa Pantsu
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.


Register your application with gitlab and get a client_id and client_secret.
Record the client_id and client_secret in the "Client Id" and "Client Secret" fields of this form and press submit to save the configuration


Receive more information about our shops and stores. If you’d like to book a room for Cielito Lindo, click here.
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Yongsan police confirmed to have booked BTS's Jungkook for causing car accident
by beansssAccording to a representative from Seoul's Yongsan Police Station on November 8, BTS member Jungkook has been booked in documentation as the offender of a car accident, and will likely face a civil case.
As previously reported, Jungkook recently caused a car accident back on October 31 at approximately 4 AM KST, mistakenly running into a taxi. Afterward, the idol admitted that he violated traffic laws, giving his testimony to police officers on-site and also coming to a settlement with the victim.
The Yongsan Police representative curtly revealed that they cannot reveal much more details as the investigation is still ongoing and that the idol may be summoned for questioning soon.