I am an interaction designer who explores creative ways to convey broader visions through functional features that deliver value and fulfill contextual goals by leveraging a human-centered approach.
My interests lie at the intersection of experiences and interactions, both digital and tangible. My curiosity about human behavior towards others and technology, and semiotics of communication drive my work.
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| 021:087 Khan | : |
And (remember) Dhan-Nun (Jonah), when he went off in anger, and imagined that We shall not punish him (i.e. the calamites which had befallen him)! But he cried through the darkness (saying): La ilaha illa Anta [none has the right to be worshipped but You (O Allah)], Glorified (and Exalted) are You [above all that (evil) they associate with You]. Truly, I have been of the wrong-doers." |
| 021:087 Maulana | : |
And Dhu-l-Nun, when he went away in wrath, and he thought that We would not straiten him, so he called out among afflictions: There is no God but Thou, glory be to Thee! Surely I am of the sufferers of loss. |
| 021:087 Pickthal | : |
And (mention) Dhu'n-Nun, when he went off in anger and deemed that We had no power over him, but he cried out in the darkness, saying: There is no Allah save Thee. Be Thou Glorified! Lo! I have been a wrong-doer. |
| 021:087 Rashad | : |
And Zan-Noon (Jonah, "the one with an 'N' in his name"), abandoned his mission in protest, thinking that we could not control him. He ended up imploring from the darkness (of the big fish's belly): "There is no god other than You. Be You glorified. I have committed a gross sin." |
| 021:087 Sarwar | : |
Dhun Nun went away in anger and thought that We would never have power over him, but in darkness he cried, "Lord, You are the Only God whom I glorify. I have certainly done wrong to myself (so forgive me)". |
| 021:087 Shakir | : |
And Yunus, when he went away in wrath, so he thought that We would not straiten him, so he called out among afflictions: There is no god but Thou, glory be to Thee; surely I am of those who make themselves to suffer loss. |
| 021:087 Sherali | : |
And remember Dhu'l-Nun, when he went away in anger, and he was sure in his mind that WE would not cause him distress and he cried out in the midst of his afflictions: 'There is no god but Thou, Holy art Thou. I have indeed been of the wrongdoers.' |
| 021:087 Yusufali | : |
And remember Zun-nun, when he departed in wrath: He imagined that We had no power over him! But he cried through the deptHs of darkness, "There is no god but thou: glory to thee: I was indeed wrong!" |
Concepts/Themes Covered: أركان الإسلام > الصلاة > الدعاء > المأثور من الدعاء Pillars of Islam> The Prayers>The Invocation> What is said Concerning Invocation
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


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THE LOST WORLD: JURASSIC PARK A film review by Cameron Shelley Copyright 1997 Cameron Shelley
(Universal)
Review by: Cameron Shelley -- May 25, 1997. Review URL: http://watarts.uwaterloo.ca/~cpshelle/Reviews/tlw.html Cast: Jeff Goldblum (Dr. Ian Malcolm), Julianne Moore (Dr. Sarah Harding), Richard Shiff (Eddie Carr), Vince Vaughn (Nick Van Owen), Arliss Howard (Peter Ludlow), Pete Postlethwaite (Roland Tembo), Vanessa Lee Chester (Kelly Malcolm), Richard Attenborough (John Hammond).
Screenplay: Michael Crichton and David Koepp
Director: Steven Spielberg
Producer: Bonnie Curtis, Kathleen Kennedy, Jerry Molen, Steven Spielberg, and Colin Wilson
The Lost World: Jurassic Park gives the distinct impression of being part sequel and part remake. I think it is largely disappointing in either respect, although it does noticeably worse as a sequel than as a remake. Let me explain.
There are, of course, no fixed criteria according to which a movie constitutes a good sequel to another one, but TLW does meet the minimal requirements. Many of the same characters (or their equivalents) from Jurassic Park return to face the results of that film. But aside from story continuity, one might expect a sequel to develop some of the themes raised in the original, or introduce some new ones. Any such development in TLW is purely incidental. In the original JP, the dinosaurs of Isla Nublar served both as a cautionary example of the results of greed and crass commercialism, and as reminders of how to lead a life in accord with the dictates of nature. The result was not really a convincing lesson so much as a warm, fuzzy feeling, but at least these themes were used aptly to tie the narrative together into a coherent whole. In TLW, Spielberg picks up these themes again, but in a purely repetitive way. The crass commercialism has been transfered from John Hammond to his shifty nephew Arliss Ludlow, while the exposure of nurturing talents is transferred from Dr.s Grant and Sattler to the dinosaurs themselves (most notably the T-rex) which have been running loose on Isla Sorna (Site B) the whole time. Dr. Malcolm makes a return visit, and some attempt is made to explore the nurturing side of his nature, but this element drops out abruptly around the middle of the movie, after his daughter drop kicks a Velociraptor (shades of Gymkata)! Of course, no one expects much themewise from a big budget, effects picture, but the attempts made in this picture are notably haphazard and half-hearted. Sequels don't have to be played from the same hand as their predecessors; something more original would have almost certainly been an improvement.
In other respects, TLW works best where it almost constitutes a remake of the original. The first act of the movie revisits much of the look and feel (and story - with some insertions from the original novel) of JP and seems to land Spielberg in his element, with the result that he recreates much of JP's suspense and excitement. The timing, direction, and special effects were generally very good and fastened all eyes on the screen. (The sound was a bit of a drawback - more on that later.) Spielberg clearly realized that he could not draw out the exposure of the audience to the dinosaurs as much as in JP, so he switched to presenting more dinosaurs doing more things - good move. Scenes were smoothly and deftly linked by simple but effective use of visual continuities, and the frequent facial closeups made the events more involving than they might otherwise have been (although this last technique started to look mechanical later in the film). This first part of the picture works well at the level of an amusement park ride (in fact, I believe the Universal Studios ride based on TLW is already up and running), with the humans going from chasing the dinosaurs to being chased by them. Lots of suspense, some action, no drama. Ok.
But with the exit from Isla Sorna, the movie completely changes tone and coasts home. Roland Tembo, the big game hunter who leads Ludlow's dinosaur salvage operation, bows out; a shame, as he was the most interesting and best rendered character in the film. Two live T-rex (a juvenile and its parent) are shipped to San Diego to be part of a glorified carnival exhibit. Suspense goes out the window in favor of a comic carnivorous romp through various neighborhoods of San Diego. The comedy works at a superficial level: The audience recognized and laughed at the car crashes, the fleeing humans, and the numerous little movie in-jokes. In the first half, people were marked for death due to their moral failures; now they die for a quick laugh. This aspect of the TLW is the biggest letdown compared with JP. In JP the comedy was subtler and fundamental to the development of the plot, whereas here it is manufactured and attached with duct tape. But even without considering JP, the second act of TLW succeeds perhaps too well as a burlesque of the first act, and inadvertently plants the suggestion that TLW is just like any generic B movie, but distinguished by a monster budget.
As a sideline, I should add that I saw TLW in a cineplex without the DTS sound system. Unfortunately, it appears that Spielberg is distributing the movie in only this sound format, with the result that the sound as I got it lacked some depth. In an effects picture, this is an important consideration. Moviegoers should either seek out cinemas with the DTS system, or take umbrage at this odd (grasping?) commercial tactic and avoid TLW altogether.
Anyway, although TLW features lots of technically excellent effects and direction, it also features a story that lacks basic coherence and eventually even undercuts itself. TLW does best where it recreates the sense of menace and spectacle from JP, but fails as its attempts at independent development simply turn into diversions.
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Biography
Unlike the Shelby siblings, Thomas is a very calm and observant man, showcasing his wit and ruthlessness to anyone who threatens him or his family. He is widely respected among his local community and is seen as a dangerous foe by his enemies. Under his leadership, the Peaky Blinders have risen from a local Birmingham street gang to a national enterprise that has gone toe-to-toe with the New York Mafia.
Background
Thomas Shelby and his siblings are Gypsies from both sides of their family. It is implied by their aunt, Polly, who is their father's sister and Thomas himself when he states in Series 1 that the Lee Family are kin to the Shelby family from their mother's side. It can be concluded that perhaps both of Thomas' parents are partially Romani Gypsies. Thomas was in love with a girl named Greta Jurossi before the First World War, as stated by Jessie Eden. Gretta grew sick, and Thomas stayed by her bedside for three months before she passed away and he went to war. It is implied that there was a drastic personality shift before and after his experiences in the First World War, during which he was a Tunneller or Sapper (a volunteer digging treacherous tunnels covertly, in order to place enormous amount of explosives beneath enemy lines, a tactic used several times in the war to devastating effect). After the War, he began experiencing symptoms of post-traumatic stress disorder. Thomas Shelby is shown to be a member of the Small Heath Rifles at the rank of sergeant major – a non-commissioned officer in charge of well-being, morale and training of the entire unit (all other sergeants and corporals answer to him as well as to the actual commanding officers). His flawless conduct in this role (made even more difficult on account of his youth at the time) earned him unwavering respect both from members of his unit and other veterans alike and cemented his reputation in the streets. As to his heritage, Thomas claims that he is related to the Lee family on his mother's side and Johnny Dogs claims that Thomas's grandfather was a "king", most likely a King of the Gypsies. He is the second of the Shelby siblings.
BORN: 1890
ORIGIN: Small Heath, Birmingham, England
ALLEGIANCE: Shelby Family, Peaky Blinders
World War One
1914-1918
Thomas Shelby is a former British soldier and his war record states that he fought bravely at the Battle of Verdun and the Battle of Somme. His actions at Mons reportedly saved thousands of allied lives, for which he received medals of honour after the War, including the Military Medal and the Distinguished Conduct Medal.
SERVICE: British Army
YEARS OF SERVICE: 1914-1918
UNIT: 179th Tunnelling Company
BATTLES: Mons, The Somme, Verdun
Shelby Company Limited
1919
Thomas is instrumental in the success of the Shelby family business, which became a Limited Company under Thomas's ownership, as well as the first Shelby family company to have a legal racetrack pitch. Shelby Company Limited is making over 100 pounds a day, on average. Thomas pushes the company further by expanding it south to London, as well as by sending crates of cars (with hidden bottles of single malt Scotch Whisky inside—these bottles are then sold in North America in order to bring in profits for the company). Thanks to the actions of Thomas and the family over the past few years, the company is even more successful than it had been, as it continues to expand and grow, as well as thanks to the cash provided by Winston Churchill.
Member of Parliament
1929
After the vendetta with the New York Mafia,and during the year of the Wall Street Crash, Thomas Shelby became a Member of Parliament as a socialist politician while lending the chair of the illegal business to his older brother, Arthur Shelby.


Note
Click here to download the full example code
Compute MNE inverse solution on evoked data in a mixed source space¶
Create a mixed source space and compute MNE inverse solution on evoked dataset.
# Author: Annalisa Pascarella<EMAIL_ADDRESS>#
# License: BSD (3-clause)
import os.path as op
import matplotlib.pyplot as plt
from nilearn import plotting
import mne
from mne.minimum_norm import make_inverse_operator, apply_inverse
# Set dir
data_path = mne.datasets.sample.data_path()
subject = 'sample'
data_dir = op.join(data_path, 'MEG', subject)
subjects_dir = op.join(data_path, 'subjects')
bem_dir = op.join(subjects_dir, subject, 'bem')
# Set file names
fname_mixed_src = op.join(bem_dir, '%s-oct-6-mixed-src.fif' % subject)
fname_aseg = op.join(subjects_dir, subject, 'mri', 'aseg.mgz')
fname_model = op.join(bem_dir, '%s-5120-bem.fif' % subject)
fname_bem = op.join(bem_dir, '%s-5120-bem-sol.fif' % subject)
fname_evoked = data_dir + '/sample_audvis-ave.fif'
fname_trans = data_dir + '/sample_audvis_raw-trans.fif'
fname_fwd = data_dir + '/sample_audvis-meg-oct-6-mixed-fwd.fif'
fname_cov = data_dir + '/sample_audvis-shrunk-cov.fif'
Set up our source space.
# List substructures we are interested in. We select only the
# sub structures we want to include in the source space
labels_vol = ['Left-Amygdala',
'Left-Thalamus-Proper',
'Left-Cerebellum-Cortex',
'Brain-Stem',
'Right-Amygdala',
'Right-Thalamus-Proper',
'Right-Cerebellum-Cortex']
# Get a surface-based source space, here with few source points for speed
# in this demonstration, in general you should use oct6 spacing!
src = mne.setup_source_space(subject, spacing='oct5',
add_dist=False, subjects_dir=subjects_dir)
# Now we create a mixed src space by adding the volume regions specified in the
# list labels_vol. First, read the aseg file and the source space bounds
# using the inner skull surface (here using 10mm spacing to save time,
# we recommend something smaller like 5.0 in actual analyses):
vol_src = mne.setup_volume_source_space(
subject, mri=fname_aseg, pos=10.0, bem=fname_model,
volume_label=labels_vol, subjects_dir=subjects_dir,
add_interpolator=False, # just for speed, usually this should be True
verbose=True)
# Generate the mixed source space
src += vol_src
# Visualize the source space.
src.plot(subjects_dir=subjects_dir)
n = sum(src[i]['nuse'] for i in range(len(src)))
print('the src space contains %d spaces and %d points' % (len(src), n))
Out:
Setting up the source space with the following parameters:
SUBJECTS_DIR = /home/circleci/mne_data/MNE-sample-data/subjects
Subject = sample
Surface = white
Octahedron subdivision grade 5
>>> 1. Creating the source space...
Doing the octahedral vertex picking...
Loading /home/circleci/mne_data/MNE-sample-data/subjects/sample/surf/lh.white...
Mapping lh sample -> oct (5) ...
Triangle neighbors and vertex normals...
Loading geometry from /home/circleci/mne_data/MNE-sample-data/subjects/sample/surf/lh.sphere...
Setting up the triangulation for the decimated surface...
loaded lh.white 1026/155407 selected to source space (oct = 5)
Loading /home/circleci/mne_data/MNE-sample-data/subjects/sample/surf/rh.white...
Mapping rh sample -> oct (5) ...
Triangle neighbors and vertex normals...
Loading geometry from /home/circleci/mne_data/MNE-sample-data/subjects/sample/surf/rh.sphere...
Setting up the triangulation for the decimated surface...
loaded rh.white 1026/156866 selected to source space (oct = 5)
You are now one step closer to computing the gain matrix
BEM : /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-5120-bem.fif
grid : 10.0 mm
mindist : 5.0 mm
MRI volume : /home/circleci/mne_data/MNE-sample-data/subjects/sample/mri/aseg.mgz
Loaded inner skull from /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-5120-bem.fif (2562 nodes)
Surface CM = ( 0.7 -10.0 44.3) mm
Surface fits inside a sphere with radius 91.8 mm
Surface extent:
x = -66.7 ... 68.8 mm
y = -88.0 ... 79.0 mm
z = -44.5 ... 105.8 mm
Grid extent:
x = -70.0 ... 70.0 mm
y = -90.0 ... 80.0 mm
z = -50.0 ... 110.0 mm
4590 sources before omitting any.
2961 sources after omitting infeasible sources.
Source spaces are in MRI coordinates.
Checking that the sources are inside the bounding surface and at least 5.0 mm away (will take a few...)
1372 source space points omitted because they are outside the inner skull surface.
283 source space points omitted because of the 5.0-mm distance limit.
Thank you for waiting.
1306 sources remaining after excluding the sources outside the surface and less than 5.0 mm inside.
Selecting voxels from Left-Amygdala
2 sources remaining after excluding sources too far from VOI voxels
Adjusting the neighborhood info...
Surface CM = ( 0.7 -10.0 44.3) mm
Surface fits inside a sphere with radius 91.8 mm
Surface extent:
x = -66.7 ... 68.8 mm
y = -88.0 ... 79.0 mm
z = -44.5 ... 105.8 mm
Grid extent:
x = -70.0 ... 70.0 mm
y = -90.0 ... 80.0 mm
z = -50.0 ... 110.0 mm
4590 sources before omitting any.
2961 sources after omitting infeasible sources.
Source spaces are in MRI coordinates.
Checking that the sources are inside the bounding surface and at least 5.0 mm away (will take a few...)
1372 source space points omitted because they are outside the inner skull surface.
283 source space points omitted because of the 5.0-mm distance limit.
Thank you for waiting.
1306 sources remaining after excluding the sources outside the surface and less than 5.0 mm inside.
Selecting voxels from Left-Thalamus-Proper
9 sources remaining after excluding sources too far from VOI voxels
Adjusting the neighborhood info...
Surface CM = ( 0.7 -10.0 44.3) mm
Surface fits inside a sphere with radius 91.8 mm
Surface extent:
x = -66.7 ... 68.8 mm
y = -88.0 ... 79.0 mm
z = -44.5 ... 105.8 mm
Grid extent:
x = -70.0 ... 70.0 mm
y = -90.0 ... 80.0 mm
z = -50.0 ... 110.0 mm
4590 sources before omitting any.
2961 sources after omitting infeasible sources.
Source spaces are in MRI coordinates.
Checking that the sources are inside the bounding surface and at least 5.0 mm away (will take a few...)
1372 source space points omitted because they are outside the inner skull surface.
283 source space points omitted because of the 5.0-mm distance limit.
Thank you for waiting.
1306 sources remaining after excluding the sources outside the surface and less than 5.0 mm inside.
Selecting voxels from Left-Cerebellum-Cortex
33 sources remaining after excluding sources too far from VOI voxels
Adjusting the neighborhood info...
Surface CM = ( 0.7 -10.0 44.3) mm
Surface fits inside a sphere with radius 91.8 mm
Surface extent:
x = -66.7 ... 68.8 mm
y = -88.0 ... 79.0 mm
z = -44.5 ... 105.8 mm
Grid extent:
x = -70.0 ... 70.0 mm
y = -90.0 ... 80.0 mm
z = -50.0 ... 110.0 mm
4590 sources before omitting any.
2961 sources after omitting infeasible sources.
Source spaces are in MRI coordinates.
Checking that the sources are inside the bounding surface and at least 5.0 mm away (will take a few...)
1372 source space points omitted because they are outside the inner skull surface.
283 source space points omitted because of the 5.0-mm distance limit.
Thank you for waiting.
1306 sources remaining after excluding the sources outside the surface and less than 5.0 mm inside.
Selecting voxels from Brain-Stem
21 sources remaining after excluding sources too far from VOI voxels
Adjusting the neighborhood info...
Surface CM = ( 0.7 -10.0 44.3) mm
Surface fits inside a sphere with radius 91.8 mm
Surface extent:
x = -66.7 ... 68.8 mm
y = -88.0 ... 79.0 mm
z = -44.5 ... 105.8 mm
Grid extent:
x = -70.0 ... 70.0 mm
y = -90.0 ... 80.0 mm
z = -50.0 ... 110.0 mm
4590 sources before omitting any.
2961 sources after omitting infeasible sources.
Source spaces are in MRI coordinates.
Checking that the sources are inside the bounding surface and at least 5.0 mm away (will take a few...)
1372 source space points omitted because they are outside the inner skull surface.
283 source space points omitted because of the 5.0-mm distance limit.
Thank you for waiting.
1306 sources remaining after excluding the sources outside the surface and less than 5.0 mm inside.
Selecting voxels from Right-Amygdala
1 sources remaining after excluding sources too far from VOI voxels
Adjusting the neighborhood info...
Surface CM = ( 0.7 -10.0 44.3) mm
Surface fits inside a sphere with radius 91.8 mm
Surface extent:
x = -66.7 ... 68.8 mm
y = -88.0 ... 79.0 mm
z = -44.5 ... 105.8 mm
Grid extent:
x = -70.0 ... 70.0 mm
y = -90.0 ... 80.0 mm
z = -50.0 ... 110.0 mm
4590 sources before omitting any.
2961 sources after omitting infeasible sources.
Source spaces are in MRI coordinates.
Checking that the sources are inside the bounding surface and at least 5.0 mm away (will take a few...)
1372 source space points omitted because they are outside the inner skull surface.
283 source space points omitted because of the 5.0-mm distance limit.
Thank you for waiting.
1306 sources remaining after excluding the sources outside the surface and less than 5.0 mm inside.
Selecting voxels from Right-Thalamus-Proper
7 sources remaining after excluding sources too far from VOI voxels
Adjusting the neighborhood info...
Surface CM = ( 0.7 -10.0 44.3) mm
Surface fits inside a sphere with radius 91.8 mm
Surface extent:
x = -66.7 ... 68.8 mm
y = -88.0 ... 79.0 mm
z = -44.5 ... 105.8 mm
Grid extent:
x = -70.0 ... 70.0 mm
y = -90.0 ... 80.0 mm
z = -50.0 ... 110.0 mm
4590 sources before omitting any.
2961 sources after omitting infeasible sources.
Source spaces are in MRI coordinates.
Checking that the sources are inside the bounding surface and at least 5.0 mm away (will take a few...)
1372 source space points omitted because they are outside the inner skull surface.
283 source space points omitted because of the 5.0-mm distance limit.
Thank you for waiting.
1306 sources remaining after excluding the sources outside the surface and less than 5.0 mm inside.
Selecting voxels from Right-Cerebellum-Cortex
44 sources remaining after excluding sources too far from VOI voxels
Adjusting the neighborhood info...
Reading /home/circleci/mne_data/MNE-sample-data/subjects/sample/mri/aseg.mgz...
Reading /home/circleci/mne_data/MNE-sample-data/subjects/sample/mri/aseg.mgz...
Reading /home/circleci/mne_data/MNE-sample-data/subjects/sample/mri/aseg.mgz...
Reading /home/circleci/mne_data/MNE-sample-data/subjects/sample/mri/aseg.mgz...
Reading /home/circleci/mne_data/MNE-sample-data/subjects/sample/mri/aseg.mgz...
Reading /home/circleci/mne_data/MNE-sample-data/subjects/sample/mri/aseg.mgz...
Reading /home/circleci/mne_data/MNE-sample-data/subjects/sample/mri/aseg.mgz...
the src space contains 9 spaces and 2169 points
We could write the mixed source space with:
>>> write_source_spaces(fname_mixed_src, src, overwrite=True)
We can also export source positions to nift file and visualize it again:
nii_fname = op.join(bem_dir, '%s-mixed-src.nii' % subject)
src.export_volume(nii_fname, mri_resolution=True)
plotting.plot_img(nii_fname, cmap='nipy_spectral')
# Compute the fwd matrix
fwd = mne.make_forward_solution(
fname_evoked, fname_trans, src, fname_bem,
mindist=5.0, # ignore sources<=5mm from innerskull
meg=True, eeg=False, n_jobs=1)
leadfield = fwd['sol']['data']
print("Leadfield size : %d sensors x %d dipoles" % leadfield.shape)
src_fwd = fwd['src']
n = sum(src_fwd[i]['nuse'] for i in range(len(src_fwd)))
print('the fwd src space contains %d spaces and %d points' % (len(src_fwd), n))
# Load data
condition = 'Left Auditory'
evoked = mne.read_evokeds(fname_evoked, condition=condition,
baseline=(None, 0))
noise_cov = mne.read_cov(fname_cov)
# Compute inverse solution and for each epoch
snr = 3.0 # use smaller SNR for raw data
inv_method = 'dSPM' # sLORETA, MNE, dSPM
parc = 'aparc' # the parcellation to use, e.g., 'aparc' 'aparc.a2009s'
lambda2 = 1.0 / snr ** 2
# Compute inverse operator
inverse_operator = make_inverse_operator(evoked.info, fwd, noise_cov,
depth=None, fixed=False)
stc = apply_inverse(evoked, inverse_operator, lambda2, inv_method,
pick_ori=None)
# Get labels for FreeSurfer 'aparc' cortical parcellation with 34 labels/hemi
labels_parc = mne.read_labels_from_annot(
subject, parc=parc, subjects_dir=subjects_dir)
Out:
Reading FreeSurfer lookup table
Source space : <SourceSpaces: [<surface (lh), n_vertices=155407, n_used=1026, coordinate_frame=MRI (surface RAS)>, <surface (rh), n_vertices=156866, n_used=1026, coordinate_frame=MRI (surface RAS)>, <volume (Left-Amygdala), n_used=2, coordinate_frame=MRI (surface RAS)>, <volume (Left-Thalamus-Proper), n_used=9, coordinate_frame=MRI (surface RAS)>, <volume (Left-Cerebellum-Cortex), n_used=33, coordinate_frame=MRI (surface RAS)>, <volume (Brain-Stem), n_used=21, coordinate_frame=MRI (surface RAS)>, <volume (Right-Amygdala), n_used=1, coordinate_frame=MRI (surface RAS)>, <volume (Right-Thalamus-Proper), n_used=7, coordinate_frame=MRI (surface RAS)>, <volume (Right-Cerebellum-Cortex), n_used=44, coordinate_frame=MRI (surface RAS)>]>
MRI -> head transform : /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw-trans.fif
Measurement data : sample_audvis-ave.fif
Conductor model : /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-5120-bem-sol.fif
Accurate field computations
Do computations in head coordinates
Free source orientations
Read 9 source spaces a total of 2169 active source locations
Coordinate transformation: MRI (surface RAS) -> head
0.999310 0.009985 -0.035787 -3.17 mm
0.012759 0.812405 0.582954 6.86 mm
0.034894 -0.583008 0.811716 28.88 mm
0.000000 0.000000 0.000000 1.00
Read 306 MEG channels from info
84 coil definitions read
Coordinate transformation: MEG device -> head
0.991420 -0.039936 -0.124467 -6.13 mm
0.060661 0.984012 0.167456 0.06 mm
0.115790 -0.173570 0.977991 64.74 mm
0.000000 0.000000 0.000000 1.00
MEG coil definitions created in head coordinates.
Source spaces are now in head coordinates.
Setting up the BEM model using /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-5120-bem-sol.fif...
Loading surfaces...
Homogeneous model surface loaded.
Loading the solution matrix...
Loaded linear_collocation BEM solution from /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-5120-bem-sol.fif
Employing the head->MRI coordinate transform with the BEM model.
BEM model sample-5120-bem-sol.fif is now set up
Source spaces are in head coordinates.
Checking that the sources are inside the bounding surface and at least 5.0 mm away (will take a few...)
84 source space point omitted because of the 5.0-mm distance limit.
84 source space point omitted because of the 5.0-mm distance limit.
Thank you for waiting.
Setting up compensation data...
No compensation set. Nothing more to do.
Composing the field computation matrix...
Computing MEG at 2001 source locations (free orientations)...
Finished.
Leadfield size : 306 sensors x 6003 dipoles
the fwd src space contains 9 spaces and 2001 points
Reading /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-ave.fif ...
Read a total of 4 projection items:
PCA-v1 (1 x 102) active
PCA-v2 (1 x 102) active
PCA-v3 (1 x 102) active
Average EEG reference (1 x 60) active
Found the data of interest:
t = -199.80 ... 499.49 ms (Left Auditory)
0 CTF compensation matrices available
nave = 55 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)
365 x 365 full covariance (kind = 1) found.
Read a total of 4 projection items:
PCA-v1 (1 x 102) active
PCA-v2 (1 x 102) active
PCA-v3 (1 x 102) active
Average EEG reference (1 x 59) active
info["bads"] and noise_cov["bads"] do not match, excluding bad channels from both
Computing inverse operator with 305 channels.
305 out of 306 channels remain after picking
Selected 305 channels
Whitening the forward solution.
Created an SSP operator (subspace dimension = 3)
Computing data rank from covariance with rank=None
Using tolerance 3.5e-13 (2.2e-16 eps * 305 dim * 5.2 max singular value)
Estimated rank (mag + grad): 302
MEG: rank 302 computed from 305 data channels with 3 projectors
Setting small MEG eigenvalues to zero (without PCA)
Creating the source covariance matrix
Adjusting source covariance matrix.
Computing SVD of whitened and weighted lead field matrix.
largest singular value = 4.45582
scaling factor to adjust the trace = 5.72916e+19
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 55
Created the regularized inverter
Created an SSP operator (subspace dimension = 3)
Created the whitener using a noise covariance matrix with rank 302 (3 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "Left Auditory"...
Picked 305 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained 59.0% variance
Combining the current components...
dSPM...
[done]
Reading labels from parcellation...
read 34 labels from /home/circleci/mne_data/MNE-sample-data/subjects/sample/label/lh.aparc.annot
read 34 labels from /home/circleci/mne_data/MNE-sample-data/subjects/sample/label/rh.aparc.annot
Average the source estimates within each label of the cortical parcellation and each sub structure contained in the src space
src = inverse_operator['src']
label_ts = mne.extract_label_time_course(
[stc], labels_parc, src, mode='mean', allow_empty=True)
# plot the times series of 2 labels
fig, axes = plt.subplots(1)
axes.plot(1e3 * stc.times, label_ts[0][0, :], 'k', label='bankssts-lh')
axes.plot(1e3 * stc.times, label_ts[0][71, :].T, 'r', label='Brain-stem')
axes.set(xlabel='Time (ms)', ylabel='MNE current (nAm)')
axes.legend()
mne.viz.tight_layout()
Out:
Extracting time courses for 75 labels (mode: mean)
Total running time of the script: ( 0 minutes 48.770 seconds)
Estimated memory usage: 1354 MB


Workouts
Table view |
Grid view |
Update Workouts |
Show:
| no. | Start time | Duration | Duration rank # |
Distance km |
Distance rank # |
Calories | Avg speed km/h |
Avg speed rank # |
Activity |
| {{workout.id}} | {{workout.readableStartTime}} | {{workout.readableDuration}} | {{workout.durationRank}} | {{workout.distance}} | {{workout.distanceRank}} | {{workout.calories}} | {{workout.speedAvg}} | {{workout.speedAvgRank}} | {{workout.sport}} |


Created an web app for common people as well as for travellers to find any street of the world and get to know important information, article about that street; Used Wikipedia API and Google-StreetView API
Angular JS web application to display the menu of a Restaurant. Included Filtering feature using Angular Filters and Angular form with form validation feature for submitting the feedback of the Restaurant.
Built data driven application which displays the detail of many Restaurants where user can perform CRUD operations. Used flask frameworks and SQLAlchemy, made an API Endpoints for Get Requests using Jsonify class.


{block name="appendCss"}{/block}
{include file="index@common/top" /} {include file="index@common/header" /} {include file="index@common/menu" /} {block name="container"}{/block} {include file="index@common/footer" /} {block name="appendJs"}{/block}


Picture¶
Class to dectect edges in a picture
-
class
picture_class.
Picture
(file_name, threshold=0.1)¶ Picture class.
This class offers a series of methods to load in a picture using matplotlib.image and then find the edges between pixels with different colors in it.
Note
Do not include the self parameter in the
Args
section.- Parameters
file_name (str) – The name of the image file to be loaded as a Picture object.
threshold (float) – Threshold value to determine whether or not an edge is present. This is passed as an attribute and then called in by the highlight_edges_grad method. Default value is 0.1 .
-
file_name
¶ file name of the loaded image
- Type
str
-
image
¶ [R G B] values of each pixel in the image
- Type
np.array
-
contours
¶ copy of the [R G B] values of each pixel in the image, will be used to draw the detected edge over the original image
- Type
np.array
-
height
¶ height of the image in px
- Type
int
-
width
¶ width of the image in px
- Type
int
-
edges
¶ array of zeros with same dimensions as the image. Whenever an edge is found, value of the corresponding pixel is switched to 1 (if the highlight_edges method is called), or the “color difference” value is stored in the corresponding pixel (if the highlight_edges_grad method is called).
- Type
np.array
-
alpha
¶ True if the loaded image has an alpha channel, False otherwise
- Type
bool
Written: Gullo, Mikulas, Paolo 2020
-
assess_difference
(pixel_a, pixel_b)¶ This function checks if two adjacent pixels have the exact same RGB value
- Parameters
pixel_a (float, list) – [r,g,b] values for pixel A
pixel_b (float, list) – [r,g,b] values for pixel B
- Returns
True if the two pixel have the same RGB values
- Return type
bool
-
assess_gradient
(pixel_a, pixel_b)¶ This function measures the “color difference” between two adjacent pixels
- Parameters
pixel_a (float, list) – [r,g,b] values for pixel A
pixel_b (float, list) – [r,g,b] values for pixel B
- Returns
“color difference” between two pixels, defined as the sum of the absolute difference over the R, G and B parameters
- Return type
float
-
find_edges
()¶ This method calls the vertical_scan and horizontal_scan methods to find edges within the image.
-
find_edges_grad
()¶ This method calls the vertical_scan_grad and horizontal_scan_grad methods to find edges within the image.
-
highlight_edges
()¶ This method cycles over the image rows and columns in self.edges. Whenever it finds a nonzero value, it changes the corresponding pixel value in self.contours to [1,1,1] or [1,1,1,1] (depending on wether or not self.alpha is true).
-
highlight_edges_grad
(threshold)¶ This method cycles over the image rows and columns in self.edges. Whenever it finds a value above the threshold, it changes the corresponding pixel value in self.contours to [1,1,1] or [1,1,1,1] (depending on wether or not self.alpha is true).
-
horizontal_scan
(row_index)¶ - This function performs a linear scan over a given row. It calls the assess_difference method on each couple of pixel in the row,
and every time this returns False it changes the corresponding pixel value to 1 in self.edges.
- Parameters
row_index (int) – index of row to scan in self.image
-
horizontal_scan_grad
(row_index)¶ This function performs a linear scan over a given row. It calls the assess_gradient method on each couple of pixel in the row. For each pixel, it stores in self.edges the maximum value between the current value in self.edges and the “color difference” returned by assess_gradient.
- Parameters
row_index (int) – index of row to scan in self.image
-
paint_contours
(grad=False, threshold=0.1)¶ This method runs the find_edges and highlight_edges methods over the image
- Parameters
grad (bool) – if True, it will call the find_edges_grad and highlight_edges_grad methods instead. Default value is False
threshold (float) – value over which a “color difference” detected by highlight_edges_grad is considered an edge
-
vertical_scan
(col_index)¶ - This function performs a linear scan over a given column. It calls the assess_difference method on each couple of pixel in the column,
and every time this returns False it changes the corresponding pixel value to 1 in self.edges.
- Parameters
col_index (int) – index of column to scan in self.image
-
vertical_scan_grad
(col_index)¶ This function performs a linear scan over a given column. It calls the assess_gradient method on each couple of pixel in the column. For each pixel, it stores in self.edges the maximum value between the current value in self.edges and the “color difference” returned by assess_gradient.
- Parameters
col_index (int) – index of column to scan in self.image


Compound filters: Use a white-transparent gradient as a filter input
To change image, drag-and-drop an image file over the canvas
Filter effect
Final filter opacity
Pattern gradient
Gradient easing
Blend filter
Displace filter scaling
Scale X:
Scale Y:
Filter dimensions
Width:
Height:
Filter offset
Offset X:
Offset Y:
Pattern dimensions
Width:
Height:
Pattern matrix
Shift X:
Skew X:
Stretch X:
Shift Y:
Skew Y:
Stretch Y:
Test purpose
Create a test Picture entity
Create a set of transparent-white gradients to be loaded into the example filters
Create the example filters:
Glass bars/rings - super-imposes a set of bars (or rings) over the image, using: 'process-image (linear gradient); blend (multiply)
Displace bars/rings - use the gradient to displace parst of the image, using: process-image (radial gradient); displace
Etching - create a black-white rough drawing effect, as seen in old books with illustrations, using: process-image (linear gradient); grayscale; gaussian-blur; matrix; invert-channels; threshold; blend (screen)
Green monitor - create a late-20th-century computer green-screen monitor effect, using: process-image (linear gradient); grayscale; modulate-channels; blend (luminosity)
Manipulate the gradient Cell size, matrix, and easing settings to change the appearance of the output.
Manipulate the filter settings to change the appearance of the output.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 6
A thick juicy steak is a real treat and so easy to cook for a dinner party, but here’s how to cook it without having to disappear to the kitchen at the last minute and fill the room with smoke!
Melt a knob of the butter in a small pan and cook the shallots over a low heat until softened. Remove from the heat, transfer to a bowl and leave to cool for 10 minutes.
Mash the Stilton with a fork in a bowl and beat in the remaining softened butter and chives. Add the melted butter and shallots and season with salt and black pepper. Mix well.
Place the butter on a sheet of cling film and form into a sausage shape. Roll up in the cling film, twisting the ends to secure. Chill for at least 30 minutes before using.
To cook the steaks, heat a large non-stick frying pan over a high heat. Brush each steak with a little oil and season with salt and freshly ground black pepper. Fry each steak for 1½-2 minutes (or according to thickness) on each side, or until golden-brown. You will need to cook the steaks two or three at a time. Transfer to a baking tray. Set aside to get cold and transfer to the fridge until needed.
To finish the steaks before serving, preheat the oven to 220C/425F/Gas 7. Bake the steaks for six minutes or until piping hot. Add two minutes for medium, and four minutes for medium to well done. Cut six discs from the shallot butter and sit one disc on top of each hot steak and set aside to rest for two minutes before serving.


{% extends "layout.html" %} {% from "govuk/components/button/macro.njk" import govukButton %} {% from "govuk/components/input/macro.njk" import govukInput %} {% from "govuk/components/date-input/macro.njk" import govukDateInput %} {% from "govuk/components/error-summary/macro.njk" import govukErrorSummary %} {% from "govuk/components/textarea/macro.njk" import govukTextarea %} {% from "govuk/components/file-upload/macro.njk" import govukFileUpload %} {% from "components/file-upload.html" import fileUpload %} {% block content %} {{ errorSummary(errors) }}
Appeals should be made within 14 days from the date the decision letter was sent. You may still be able to appeal. Please tell us why your appeal is late below, and provide supporting evidence if you have it.


protected fun setInterruptible(interruptible: Boolean): Unit
Sets whether or not this command can be interrupted.
interruptible - whether or not this command can be interrupted
interruptible


If you have a support agreement with Nimbits Inc. and you receive a notice that we have deployed a new version of Nimbits Server to your instance, there are a few steps you need to take.
To avoid upgrading your instance with a change that may be disruptive, we always deploy new code with a new version number. You have complete control over when that new version goes live and you can test your applications against this never version to ensure a smooth transition.
You also have the ability to use traffic splitting so only a small segment of network traffic will use the new code. Learn more about Traffic Splitting
To activate a new version, log into the App Engine Console, select Versions from the side bar, and decided what version to make the default. If you have any problems, you can roll back to previous versions safely.


{% extends "base.html" %} {% block body %}
Created by longoria
Objective-C library for interacting with the Kippt API
Some usage examples can be found at: https://github.com/longoria/LBCKipptKitSandbox
| Developer | longoria |
|---|---|
| Platform | Library |
| Added | April 5. 2013 |
| Get the App |


Group calls for implementation of Child Rights Act
...says 4 girls under age 15 get married every 60 seconds globally
by Damilola OJETUNDEAdvertisement
SAVE the Children, a leading child rights organisation has called for the actual implementation of the Child Rights Act (CRA) in Nigeria in commemoration of the 30th United Nations Convention on the Rights of the Child to mark the world children’s day.
At a digital literacy training for 30 girls from Government Secondary School, Gwagalada, Abuja, Save the Children stressed the need for the implementation of the CRA in Nigeria, noting that Nigerian children are currently witnessing the erosion of their fundamental human rights by both state and non-state actors.
Ammanuel Mamo, Director of Advocacy and Campaign, Save the Children Nigeria said in his speech that the CRA should not just stop at being domesticated only by states.
“In Nigeria for example, there are so many states that have already domesticated the Child Rights Act, which is a very good and important step forward,” Mamo said.
However, it does not end right there. It has to be implemented. “Implementation starts with budgeting for it, clarifying the plan of actions and clarifying accountability towards that,” he added.
Advertisement
Out of the 36 states and the FCT, 11 states are yet to domesticate the CRA. The states include Adamawa, Bauchi, Yobe, Sokoto, Borno, Zamfara, Gombe, Katsina, Kebbi, Jigawa, and Kano.
“We would like to take this opportunity to urge them to immediately domesticate the Act as a very important step forward for the realisation of the Child Rights Act, Mamo said about those states who are yet to domesticate the CRA.
Save the Children disclosed that there are about 1.7 billion children who are affected by violence every year. 5.9 million children are affected by preventable causes like pneumonia each year, while four girls under the age of 15 get married every minute in the world, amounting to 5,760 married under-aged girls in a day.
The organization, therefore, called on the government to define specific actions to take in ensuring that further progress is made towards realising the rights of every child with a focus on the most marginalised and that children should be included in a meaningful way of the decision-making process of the government.


This program and its accompanying materials are made available under the terms of the New BSD License which accompanies this distribution, and is available here.
The source code for this program is hosted on Google Code here.
When using this work or data derived from this work, please cite:
GLAMM: Genome-Linked Application for Metabolic Maps John T. Bates; Dylan Chivian; Adam P. Arkin Nucleic Acids Research 2011; doi: 10.1093/nar/gkr433


This sample demonstrates
a connection-less NDIS 6.0 protocol. The driver supports sending and receiving
raw Ethernet frames using ReadFile/WriteFile calls from user-mode. It only
receives frames with a specific EtherType field. As an NDIS protocol, it
illustrates how to establish and tear down bindings to Ethernet adapters, i.e.
those that export medium type NdisMedium802_3. It shows how to set a
packet filter, send and receive data, and handle plug-and-play events.
From the
Free or Checked Build environment, execute build -ceZ in the ndisprot
directory.
The driver is installed
using the INF file ndisprot.inf, which is provided in the driver directory. In
Network Connections UI, select an adapter and open Properties.
Click Install,
then Protocol, then Add, and then Have disk. Then point to
the location of the .inf and driver.
Select Sample NDIS
Protocol Driver and click OK. After installing the protocol, copy
over the test application prottest.exe to a convenient location. Please note
that the driver service has been set to manual start in the INF file. As a
result, it doesn't get loaded automatically when you install.
To start
the driver, type
Net start ndisprot
To stop
the driver, type
Net stop ndisprot
To test
the driver, run prottest. For help on usage, run prottest -?
usage: PROTTEST [options] <devicename>
options:
-e: Enumerate devices
-r: Read
-w: Write (default)
-l <length>: length of each packet (default: 100)
-n <count>: number of packets (defaults to infinity)
-m <MAC address> (defaults to local MAC)
Prottest exercises the IOCTLs supported
by NDISPROT, and sends and/or receives data on the selected device. In order to
use prottest, the user must have administrative privilege. Users should pass
down a big enough buffer in order to receive the entire received data. If the
length of the buffer passed down is smaller than the length of the received
data, NDISPROT will only copy part of the data and discard the rest when the
given buffer is full.
Use the –e option to enumerate all
devices to which NDISPROT is bound:
C:\prot>prottest -e
0. \DEVICE\{9273DA7D-5275-4B9A-AC56-68A49D121F1F}
- Intel-Based 10/100 Ethernet Card
The
following command sends and receives 2 packets on a device). Since these packets are sent to
the local MAC address (default), both packets are received. The device
name parameter to prottest is picked up from the output of prottest –e (see
above).
C:\prot>prottest -n 2 \DEVICE\{9273DA7D-5275-4B9A-AC56-68A49D121F1F}
DoWriteProc: finished sending 2 packets of 100 bytes each
DoReadProc finished: read 2 packets
For
security reasons, this driver does not allow packets with fake MAC addresses to
be sent from usermode applications.
With a checked version of
ndisprot.sys, you can control the volume of debug information generated by
changing the variable ndisprotDebugLevel. Refer to debug.h for more
information.
Directory: Test
File Description
prottest.c User-mode test application
Directory: Sys
File Description
debug.c Routines to aid debugging
debug.h Debug macro definitions
macros.h Spinlock, event, referencing macros
ndisbind.c NDIS protocol entry points to handle binding/unbinding from adapters
ndisprot.h Data structure definitions
ndisprot.inf INF file for installing NDISPROT
ntdisp.c NT Entry points and dispatch routines for NDISPROT
protuser.h IOCTL and associated structure definitions
recv.c NDIS protocol entry points for receiving data, and IRP_MJ_READ processing
send.c NDIS protocol routines for sending data, and IRP_MJ_WRITE processing
|
|
© Microsoft Corporation
2000


Add names to wheel
Spin!
×
We have a winner!
×
Note
50 randomly picked names were added to the wheel, instead of all names. The number is kept at 50 to make the names easy to read.


Bolivians queue for chicken and fuel as protests cripple highways
LA PAZ: Bolivians suffered long lines on the streets of La Paz on Sunday (Nov 17) to secure chicken, eggs and cooking fuel as supporters of ousted President Evo Morales continued to cripple the country's highways, isolating population centres from lowland farms.
Officials said a massive Hercules military plane had touched down in the highland capital of La Paz Saturday with a cargo full of meat products, bypassing the barricaded highways that drop down out of the city.
Presidency minister Jerjes Justiniano told reporters the government had established an "air bridge" to La Paz. He said officials hoped to do the same with other major Bolivian cities cut off from supplies.
The Andean nation erupted into crisis following the country's Oct 20 election. Then-President Morales, who won the election, resigned last Sunday after an Organization of American States audit revealed evidence of vote rigging. He fled to Mexico.
Morales' supporters took to the streets shortly after, sometimes armed with homemade bazookas, handguns and grenades, barricading roads and skirmishing with security forces.
As violence has escalated, many in the poorer regions of La Paz had taken to cooking over firewood as long lines for liquefied gas, canisters and scarce food complicated life.
"I hope things calm down," said Josue Pillco, a construction worker from a working-class La Paz neighbourhood. "We're not getting any food or gasoline."
Violent protests Friday around Cochabamba, a coca-growing region and stronghold of Morales' supporters, left at least nine dead, officials said.
The deaths have prompted allegations of human rights abuses by police forces.
UN envoy Jean Arnault said a team would hold meetings with politicians and social groups beginning Sunday to end the violence and push for "free and transparent elections."


[gecode-users] Gecode/J 1.0.0 released
Mikael Zayenz Lagerkvist
lagerkvist at gecode.org
Thu Sep 21 15:50:12 CEST 2006
We would like to announce the release of
Gecode/J 1.0.0
http://www.gecode.org/gecodej
Gecode/J is a Java interface to the Gecode C++ constraint progamming
library. Gecode/J is mainly aimed at education, presenting a slightly
simplified interface compared to Gecode. Using Gecode/J, modeling
constraint problems, programming custom propagators, branchings, and
search engines is possible directly in Java. There is also an
interactive graphical search tool, the Gecode/J Explorer, available.
Familiar langauge
Many students are already familiar with Java, making it easier for
them to use the library.
Free
Gecode/J is licensed under a BSD-style license, the same as
Gecode. All parts, including both source code and documentation, are
available for download and use.
Graphical search
The Gecode/J Exoplorer enables graphical search, both interactive
and using depth-first or branch-and-bound search. It is possible to
inspect nodes both before and after propagation as well as to write
custom inspection actions, for example to give a graphical view of
the problem state.
The Gecode/J Explorer is written completely in Java, using the
Gecode/J interface. It can be ported to other Space-based systems.
Extensible
It is possible to write custom propagators, branchings, and search
engines in Java, without needing to write a single line of C++ code.
For extensions to the base Gecode-library, such as new variable-
domains or constraints, these can be added to Gecode/J with reltive
ease.
Documentation
Gecode/J comes with extensive reference documentation.
Cheers,
The Gecode/J Team
More information about the gecode-users
mailing list


public interface Player
| 修飾子とタイプ | メソッドと説明 |
|---|---|
Agent |
attack()
Decide agent who to be attacked by wolves
|
void |
dayStart()
Called when the day started
|
Agent |
divine()
Decide agent to divine by Seer
|
void |
finish()
Finalize
|
java.lang.String |
getName()
get player name
|
Agent |
guard()
Decide agent to guard by BodyGuard
|
void |
initialize(GameInfo gameInfo)
Called when the game started
|
java.lang.String |
talk()
Each player can talk thier opinions throw this method
|
void |
update(GameInfo gameInfo)
Called when the game information updated
|
Agent |
vote()
ExecuteVote agent to execute
|
java.lang.String |
whisper()
Each wolves can talk thier opinions throw this method
|
java.lang.String getName()
void update(GameInfo gameInfo)
void initialize(GameInfo gameInfo)
void dayStart()
java.lang.String talk()
java.lang.String whisper()
Agent vote()
Agent attack()
Agent divine()
Agent guard()
void finish()


Herriman, Utah, October 1, 2016
Yellow Fork Technologies LLC has just released a major update to their app, Little Family Tree, making it FREE to download through the mobile app stores. Little Family Tree is an app that teaches children about their family history through interactive games and activities with information obtained from an online family tree.
Today's children intuitively understand how to use touch devices such as smartphones and tablets. Little Family Tree brings a child's personal family history to them through this learning medium and shares it in a way that is accessible to them. An adult logs into an online family tree account, such as FamilySearch, and the app synchronizes the data while children play. There is no need to manually upload or enter family history data into the app and any changes made or photos added to the online tree will synchronize with the app. The app is read-only and does not change the data on the online tree.
Playing Little Family Tree teaches children to: identify family members by name, relationship, and picture, understand who and where they come from, and recognize and navigate a family tree, all in a fun and engaging way. Most importantly, they will feel a connection to their ancestors as they learn about them.
Little Family Tree is available for iOS and Android smartphones and tablets and may be downloaded directly from the app stores. The FREE version includes five games; five additional games are available in the premium version for $3.99 in the U.S., priced accordingly in other regions. More information, videos, and tutorials can be found on the website at http://www.littlefamilytree.com.
Little Family Tree is the inaugural product of Yellow Fork Technologies, LLC, of Utah. Yellow Fork Technologies, LLC was founded in 2015 with the key mission to make family history more accessible and engaging through technology. http://www.yellowforktech.com


{% extends "base.html" %} {% block title %}
Medieval catalogues > UNIVERSITY AND COLLEGE LIBRARIES OF CAMBRIDGE > Donor documents > Will of William Lyndwood, 22 November 1443; University
{% endif %}2 identified entries found.
All data was derived from the List of Identifications by Professor Richard Sharpe.
{% if not printing %}
A key to codes used in the List is available (opens in new tab).{% endif %}


This keyboard is designed for use with various languages of Western and Central Africa, including:
| Fula | Bokyi | Bura-Pabir |
Most computers will already include fonts to display the diacritic marks on this keyboard correctly.
Type diacritics (on keys 1..9) after the base character.
Each diacritic mark is shown above an a character in the On Screen Keyboard as an example of how it will appear. When you type a diacritic mark, it will be placed above or below the previous letter — the a will not appear.
The "\" is considered a deadkey. Type "\" followed by any of the following characters to output the character below:


Written by: Adam Chase and Ira Ungerleider
Transcribed by guineapig
[Scene: Monica and Rachel's, everyone is looking at papers.]
Joey: How could someone get a hold of your credit card number?
Monica: I have no idea. But look how much they spent!
Rachel: Monica, would you calm down? The credit card people said that you only have to pay for the stuff that you bought.
Monica: I know. It's just such reckless spending.
Ross: I think when someone steals your credit card, they've kind of already thrown caution to the wind.
Chandler: Wow, what a geek. They spent $69.95 on a Wonder Mop.
Monica: That's me.
Phoebe: Oh! The yuk! Ross, he's doing it again! (Points to a lamp which is shaking behind the sofa)
Ross: Marcel, stop humping the lamp! Stop humping! Now Marcel, come back- (Marcel runs toward Rachel's room) come here, Marcel-
Rachel: Oh no, not in my room! I'll get him.
Monica: Ross, you've got to do something about the humping.
Ross: What? It's, it's just a phase.
Chandler: Well, that's what we said about Joey...
Ross: Would you all relax? It's not that big a deal.
Rachel: (Out of shot) Stop it! Marcel! Bad monkey!
Ross: What?
Rachel: Let's just say my Curious George doll is no longer curious.
Opening Credits
[Scene: Monica and Rachel's, late at night Monica is still examining her bill as Rachel emerges from her room.]
Rachel: Oh, Monica. You are not still going over that thing.
Monica: This woman's living my life.
Rachel: What?
Monica: She's living my life, and she's doing it better than me! Look at this, look. She buys tickets for plays that I wanna see. She, she buys clothes from stores that I'm intimidated by the sales people. She spent three hundred dollars on art supplies.
Rachel: You're not an artist.
Monica: Yeah, well I might be if I had the supplies! I mean, I could do all this stuff. Only I don't.
Rachel: Oh, Monica, c'mon, you do cool things.
Monica: Oh really? Okay, let's compare, shall we.
Rachel: (Yawning) Oh, it's so late for 'Shall we'...
Monica: Do I go horseback riding in the park? Do I take classes at the New School?
Rachel: (Yawning) Nooo...
Monica: This is so unfair! She's got everything I want, and she doesn't have my mother.
[Scene: Central Perk, Joey and Chandler are discussing stage names.]
Chandler: How about Joey... Pepponi?
Joey: No, still too ethnic. My agent thinks I should have a name that's more neutral.
Chandler: Joey... Switzerland?
(The waitress brings their coffee.)
Joey: Plus, y'know, I think it should be Joe. Y'know, Joey makes me sound like I'm, I dunno, this big. (Waitress looks at him funny) Which I'm not.
Chandler: Joe...Joe...Joe...Stalin?
Joey: Stalin...Stalin...do I know that name? It sounds familiar.
Chandler: Well, it does not ring a bell with me...
Joey: (Writes it down) Joe Stalin. Y'know, that's pretty good.
Chandler: Might wanna try Joseph.
(Joey visibly thinks 'Of course!' and writes it down.)
Joey: Joseph Stalin. I think you'd remember that!
Chandler: Oh yes!  Joseph Stalin is the Fiddler on the Roof.
[Scene: Monica and Rachel's, Monica is there as Phoebe and Rachel enter.]
Rachel: Hey.
Phoebe: Hey.
Monica: Hi. (On the phone) Hi, uh, yes, this is Monica Geller. Um, I believe I'm taking some classes with you and I was wondering what they were.
Phoebe: What are you doing?
Monica: (Hushes her) Alright, great. Thanks a lot. (Hangs up) I'm going to tap class.
Rachel: What, what, so that you can dance with the woman that stole your credit card?
Monica: This woman's got my life, I should get to see who she is.
Rachel: Go to the post office! I'm sure her picture's up! ...Okay, Monica, y'know what, honey, you're kinda losing it here! I mean, this is really becoming like a weird obsession thing.
Phoebe: This is madness. It's madness, I tell you, for the love of God, Monica, don't do it!! ...Thank you.
[Scene: A Tap Class, the girls are standing at the door.]
Monica: What d'you think?
Phoebe: Lotsa things.
(They go in and sit down.)
Rachel: Which one do you think she is?
(The teacher comes up to them.)
Teacher: May I help you?
Monica: Oh, no thanks, we're just here to observe.
Teacher: You don't observe a dance class. You dance
a dance class. Spare shoes are over there.Rachel: What does she mean?
Phoebe: I think she means (Imitates) 'You dance
a dance class'. Oh, c'mon, c'mon. (They put on some spare shoes)Monica: Okay, d'y'see anybody you think could be me?
Teacher: (To the class) People! Last time there were some empty yoghurt containers lying around after class. Let's not have that happen again!
Rachel: She could be you.
(Music starts)
Teacher: Let's get started. Five, six, a-five six seven eight...
(Everyone starts to dance in unison. Monica flounders)
Monica: Okay, I'm not getting this!
Phoebe: (Dancing in a swirly, Phoebe kind of way) I'm totally getting it!

(Rachel taps into view; she is in perfect sync with the rest of the class)
Rachel: What? You just click when they click.
Teacher: Alright people, now everyone grab a partner.
(The girls are unsure how to pair off. Phoebe settles it)
Phoebe: Okay.  I'm with Rachel.
Monica: Great. It's gym class all over again.
Phoebe and Rachel: Aww.
Teacher: Well that's all right, you can come up to the front and dance with me.
Monica: Why don't I just take off my clothes and have a nightmare.
(She starts to walk very slowly toward the front of the room. The teacher grabs her hand and pulls her. Suddenly a woman bursts in)
Woman: It's okay, it's okay, I'm here, I'm here. Sorry I'm late, okay, here I am. Who's the new tense girl?
Teacher: She's your partner.
Woman: Hi. I'm Monica.
Monica: Oh. Monica! ...Hi. I'm Mo- ...nana.
Woman: (Fake Monica) Monana?
Monica: Yeah. It's Dutch.
Fake Monica: You're kidding! I-I spent three years in Amsterdam. (Asks her something in Dutch)
Monica: Um, Pennsylvania Dutch.
Teacher: And we're dancing. A-five, six, seven, eight...
[Scene: Central Perk, Ross is entering.]
Ross: (Mortified) Hi.
Chandler and Joey: Hey.
Joey: Where've you been?
Ross: At the vet.

Ross: She says Marcel's humping thing's not a phase.
Joey: (To Chandler) Hey! He beat ya.
Ross: She says as time goes on, he's gonna start getting agressive and violent.
Chandler: So what does this mean?
Ross: I'm gonna have to give him up.
Commercial Break
[Scene: Central Perk, scene continued from earlier. They guys are sitting there like the Three Monkeys.]
Joey: I can't believe it, Ross. This sucks!
Chandler: I don't get it, I mean, you just got him. How can he be an adult already?
Ross: I know. I know.
Joey: Isn't there any way you can keep him?
Ross: No, no. The vet says unless he's in a place where he has regular access to some... monkey lovin,' he's just gonna get vicious. I've just gotta get him into a zoo.
Joey: How do you get a monkey into a zoo?
Chandler: I know that one! ...No, that's Popes into a Volkswagen.
Ross: Well, we're applying to a lot of them. Naturally our first choice would be one of the bigger state zoos, y'know, like, uh, San Diego... right? But that might just be a pipe dream, because, y'know, he's out of state. Uh, my vet, uh, knows someone at Miami, so that's a possibility.
Chandler: Yeah, but that's like two blocks away from the beach. I mean, it's a total party zoo.
(Phoebe, Monica, and Rachel enters.)
Phoebe: Hey. We found her, we found the girl.
Chandler: What?
Joey: Did you call the cops?
Rachel: Nope. We took her to lunch.
Chandler: Ah. Your own brand of vigilante justice.
Ross: What?! Are you insane? This woman stole from you. She stole. She's a stealer.
Monica: Y'know what? After you're with this woman for like ten minutes, you forget all that. I mean, she is this astounding person, with this, with this amazing spirit.
Ross: Yeah, which she probably stole from some cheerleader.
Chandler: ...Take off their hats!
Phoebe: Popes in a Volkswagen! ...I love that joke.
[Scene: Monica and Rachel's, Monica, Rachel and Fake Monica are there.]
Rachel: No way. No way did you do this.
Fake Monica: Monana was very brave.
Monica: It was so wild. We told them we were the Gunnersens in room six fifteen. Only to find out the Boston Celtics had taken over the entire sixth floor!
Fake Monica: So once they caught on to the fact that we're, y'know, short and have breasts...
Monica: ...They threw us out! I was thrown out of a hotel! Me!
Rachel: Go Monana! Well, you ladies are not the only ones living the dream. I get to go pour coffee for people I don't know. Don't wait up. (Exits)
Fake Monica: Oh, by the way, tomorrow we're auditioning for a Broadway show.
Monica: 'Scuse me?
Fake Monica: There's an open call for Cats. I'm thinking we go down there, sing Memories and make complete fools of ourselves. Whaddya say?
Monica: Nononononono. Think who you're dealing with here.
Fake Monica: Well, that's just probably 'cause of your Amish background.
Monica: What?
Fake Monica: Well, you're Pennsylvania Dutch, right?
Monica: Right. Till I bought a blow dryer, then I was shunned.
 And then one day I saw a movie that changed my life. Did you ever see Dead Poets' Society?
Monica: Uh-huh.
Fake Monica: I thought that movie was so incredibly... boring. I mean, that thing at the end where the kid kills himself because he can't be in the play? What was that?! It's like, kid, wait a year, leave home, do some community theatre. I walked out of there and I thought, 'Now, that's two hours of my life that I'm never getting back.'
Monica: Wow. Then I would definitely not recommend Mrs. Doubtfire.
[Scene: Monica and Rachel's, tiem lapse. Everyone but Joey and Monica are there.]
Ross: (Reading letters) Oh God. (To Marcel) We didn't get into Scranton. (To the others) That was like our safety zoo. They take like dogs and cows. See? I don't know who this is harder on, me or him.
Phoebe: I'd say that chair's taking the brunt.
Ross: Marcel! Marcel! Marcel, no! Good boy. See, how can nobody want him?
Rachel: Oh, somebody will.
Joey: (entering) You know there already is a Joseph Stalin?
Chandler: You're kidding.
Joey: Apparently he was this Russian dictator who slaughtered all these people. You'd think you would've known that!
Chandler: Y'know, you'd think I would've.
Joey: Phoebe. Whaddyou think a good stage name for me would be?
Phoebe: ...Flame Boy.
[Scene: Central Perk, Ross is talking to Dr. Baldhara, a zookeeper.]
Ross: Where exactly is your zoo?
Dr. Baldhara: Well, it's technically not a zoo per se, it's more of an interactive wildlife experience. Let me ask you some questions about, is it, uh, Marcel?
Ross: Yes.
Dr. Baldhara: Does he, uh, fight with other animals?
Ross: No-no, he's, he's very docile.
Dr. Baldhara: Even if he were... cornered?
Ross: Well I, I don't know. Why?
Dr. Baldhara: Uh, how is he at handling small objects?
Ross: He can hold a banana, if that's whatcha mean...
Dr. Baldhara: How about a hammer, or a small blade?
Ross: Why- why- why would he need a blade?
Dr. Baldhara: Well, if he's up against a jungle cat or an animal with horns, you've got to give the little guy something. Otherwise it's just cruel.
(Chandler and Joey burst in, with Marcel)
Chandler and Joey: He- he- he got in, he- he got in to San Diego.
Joey: We, we come back from our walk and the- the phone was ringing...
Chandler: ...He's in.
Ross: He's in! Oh, did you hear that, Marcel? San Diego. San Diego!
Dr. Baldhara: You're making a big mistake here. I mean, San Diego's all well and good, but if you give him to me, I'll start him off against a blind rabbit and give you twenty percent of the gains.
[Scene: Monica and Rachel's, Rachel is dusting. She comes to the table, lifts all the magazines and wipes under them, then just puts them down again. Monica bursts in, obviously drunk.]
Monica: Yo- hooo!
Rachel: Where the hell've you been?
Monica: Monica and I just crashed an embassy party.
Rachel: Are you drunk?!
Monica: Noooo! (Comes closer and whispers) I'm lying. I am so drunk.
Rachel: Oh God, oh. Great, Monica, y'know what, you could've called, I have been up here, I've been worried...
(Monica is drinking from the tap)
Rachel: Monica? Monica!
Monica: Water rules!
Rachel: Yes, yes, it does. Okay, look, the restaurant called, they wanna know if you're gonna be showing up for work?
Monica: Nope. Going to the Big Apple Circus today.
Rachel: Okay Monica, what are you doing?  This is not you!
Monica: No, it is me! Y'know, I'm not just the person who needs to fluff the pillows and pay the bills as soon as they come in! Y'know, when I'm with her, I am so much more than that. I'm- I'm Monana!
(The phone rings and Rachel answers)
Rachel: Hello? Yes, she is, hold on a second, please. Monana, it's for you, the credit card people.
Monica: Helloooo? Yeah. Oh my God. Thanks.
Rachel: What?
Monica: They've arrested Monica.
[Scene: New York City Department of Correction, Monica is visiting Fake Monica.]
Monica: Hi.
Fake Monica: Hey.
Monica: How are you?
Fake Monica: I'm not too bad. Fortunately, blue's my colour. How-how did you know I was here?
Monica: Because... I'm Monica Geller. It was my credit card you were using.
Fake Monica: That I was not expecting.
 Thanks.
Monica: No, thank you! You have given me so much! I mean, if it wasn't for you, I would never have gotten to sing Memories on the stage at the Wintergarden Theater!
Fake Monica: Well, actually, you only got to sing 'Memo-'.
Monica: I just can't believe you're in here. I mean, what am I gonna do without you? Who's gonna crash the embassy parties with me? Who's gonna take me to the Big Apple Circus?

Monica: Well, not... worried, just... wondering.
Fake Monica: There's nothing to wonder about, Monica.
Monica: Not necessarily...
Fake Monica: Yes necessarily! I mean, I dunno what it is, maybe it's the Amish thing.
Monica: Um, I'm not actually Amish.
Fake Monica: Really? Then why are you like that?
[Scene: Tap Class, Monica is standing by the door.]
Teacher: You by the door. In or out?
Monica: In. (She joins in the dancing.
Monica: Yeah, but at least I'm doing it!
[Scene: The Airport, everyone but Monica is there to see off Marcel.]
PA: This is the final boarding call for flight 67 to San Diego, boarding at gate 42A.
Phoebe: Okay. Good-bye, little monkey guy.  Okay, but don't eat it 'till you get on the plane.
Ross: Aww. Thank you, Aunt Phoebe.
Phoebe: Oh!
Chandler: Okay, bye, champ. Now, I know there's gonna be a lot of babes in San Diego, but remember, there's also a lot to learn.
Joey: I dunno what to say, Ross. Uh, it's a monkey.
Ross: Just, just say what you feel.
Joey: Marcel, I'm hungry.
Ross: That was good.
Rachel: (Brings Marcel a teddy bear) Marcel, this is for you. It's, uh, just, y'know, something to, um, do on the plane.
Ross: Uh, if you guys don't mind, I'd like to take a moment, just me and him.
All: Oh, sure. Sure, absolutely. (They just stand there, then realise what he means and go to the other end of the room)
Ross: Marcel, c'mere, c'mere. (He sits down and Marcel jumps down and sits beside him) Well buddy, this is it. There's just a coupla things I want to say.  You've been more than just a pet to me, you've been more like a be- (Marcel climbs down and starts humping his leg) Okay, Marcel, please, could you leave my leg alone? Could you just stop humping me for two seconds?! Marcel, would- okay, just take him away. Just take him.
(Marcel is put in a cage and taken away.)
Closing Credits
[Scene: A Theater, there is a casting session going on for a play.]

Casting Director No. 1: That's fine, thank you.
Casting Director No. 2: Next. (Joey walks onstage)
Joey: Hi, uh, I'll be reading for the role of Mercutio.
Casting Director No. 2: Name?
Joey: Holden McGroin.
End


CONTRIBUTIONS TO THE EXISTENCE AND STABILITY OF SOLUTIONS TO NONLINEAR VOLTERRA INTEGRAL EQUATIONS
by
James R. Ward
A thesis submitted in partiai fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Mathematics in The University of South Florida
March, 1975
Thesis supervisor: Associate Professor Athanassios G. Kartsatos


This vignette documents some “best practices” for using the function
anticlustering()
(and they are mostly applicable to
kplus_anticlustering()
as well). In many cases, the
suggestions pertain to overriding the default values of arguments, which
seems to be a difficult decision for users. However, I advise you: Do
not stick with the defaults; check out the results of different
anticlustering specifications; repeat the process; play around; read the
documentation (especially ?anticlustering
); change
arguments arbitrarily; compare the output. Nothing can break.1
This document uses somewhat imperative language; nuance and explanations are given in the package documentation, the other vignettes, and the papers by Papenberg and Klau (2021; https://doi.org/10.1037/met0000301) and Papenberg (2023; https://doi.org/10.1111/bmsp.12315). Note that deciding which anticlustering objective to use usually requires substantial content considerations and cannot be reduced to “which one is better”. However, some hints are given below.
method = "local-maximum"
instead of the default
method = "exchange"
. It is unambiguously
better.
repetitions
.standardize = TRUE
instead of the default
standardize = FALSE
.2
objective = "diversity"
when the
group sizes are not equal (preferably, use
objective = "kplus"
).objective = "variance"
.objective = "kplus"
over
objective = "variance"
(or even better: check out the
function kplus_anticlustering()
).objective = "kplus"
instead of the default
objective = "diversity"
.standardize = TRUE
.fast_anticlustering()
(which is
usually slower than anticlustering()
).method = "ilp"
): Install the SYMPHONY solver instead of the
GLPK solver.objective = "dispersion"
is the only objective
where you should first try out the optimal method instead of a
heuristic, i.e., use method = "ilp"
or the function
optimal_dispersion()
.3
objective = "diversity"
because computing the distance
matrix may overwhelm your computer. Instead use
objective = "variance"
or
objective = "kplus"
.Papenberg, M., & Klau, G. W. (2021). Using anticlustering to partition data sets into equivalent parts. Psychological Methods, 26(2), 161–174. https://doi.org/10.1037/met0000301.
Papenberg, M. (2023). K-plus Anticlustering: An Improved k-means Criterion for Maximizing Between-Group Similarity. British Journal of Mathematical and Statistical Psychology. Advance online publication. https://doi.org/10.1111/bmsp.12315
Well, actually your R session can break if you use an
optimal method (method = "ilp"
) with a data set that is too
large.↩
You might ask why standardize = TRUE
is not
the default. Actually, there are two reasons. First, the argument was
not always available in anticlust
and changing the default
behaviour of a function when releasing a new version is oftentimes
undesirable. Second, it seems like a big decision to me to just change
users’ data by default. In doubt, just compare the results of using
standardize = TRUE
and standardize = FALSE
and
decide for yourself which you like best. Standardization may not be the
best choice in all settings.↩
The maximum dispersion problem can be solved optimally for rather large data sets, especially for \(K = 2\). For \(K = 3\) and \(K = 4\), several 100 elements can usually be processed, especially when installing the SYMPHONY solver.↩
What is considered very large may depend on the specifications of your computer. I used to have problems computing a distance matrix for \(N > 20000\).↩


Mission Vision Statement
OUR VISION
Our vision at Florey Primary School is to empower students to acquire, demonstrate, articulate and value knowledge and skills that will support them, as life-long learners, to participate in and contribute to the global world and practise the core values of the school: respect, tolerance & inclusion, and excellence.
OUR MISSION
Is to enable all learners' access to learning through the provision of:
- differentiated, in-depth and cohesive learning programs aligned to year level content and achievement standards informed by the Australian curriculum
- highly effective teachers, focussed on improving student outcomes, through their commitment to ongoing professional development, quality teaching, evidence based practices, coaching and mentoring and collaboration
- a quality inclusive learning environment that is responsive to student voice
enriching, engaging resources
- opportunities for community and parents to participate in learning and decision making partnerships.
About the System
The Rene Cayetano Elementary School Learning Management System is a teacher-to-student-to-parents learning management system where the teachers uses the system as a learning application to send activities, quizzes, exams for the students to answer, and send the results to their parents.
What Does This Do:
We created a web application for the following reasons:
• Light-weight and user-friendly for the students, teachers, and parents.
• Parents can monitor and track the academic performance, and grade of their child.
• Portability for teachers able to send activities, handouts, textbooks online.
• Portability for students able to read handouts & textbooks, and answer activities online.


Overview
mcerp is a stochastic calculator for Monte Carlo methods that uses
latin-hypercube sampling to perform non-order specific
error propagation (or uncertainty analysis). With this package you can
easily and transparently track the effects of uncertainty
through mathematical calculations. Advanced mathematical functions, similar
to those in the standard math module can also be evaluated directly.
Due to the nature of random sampling techniques, calculation results will vary
from session to session (but be consistent within the session) since new
samples are only generated when variables are newly defined or re-defined.
By default, each variable uses 10,000 samples that are sufficiently random.
The number of samples can be changed by assigning an integer value to the
mcerp.npts object (typically, values between 1,000 and 1,000,000 are
sufficiently large to ensure small errors in the resulting statistics).
This should only be changed prior to performing calculations since
all subsequent calculations assume that each input has the same number of
sampled points (this could be changed through resampling, I suppose...).
In order to correctly use mcerp, knowledge of the distributions from
the scipy.stats sub-module is helpful, but not required since many
of the most common distributions can be created with convenient functions
(see table below).
The result of all calculations generates a mean, variance, and
standardized skewness and kurtosis coefficients (this means that a
Normal distribution has a kurtosis of 3, NOT 0).
Required Packages
The following packages should be installed automatically (if using pip
or easy_install), otherwise they will need to be installed manually:
These packages come standard in Python(x,y), Spyder, and other
scientific computing python bundles.
Installation
Make sure you have the SciPy and NumPy and Matplotlib packages installed!
This package won’t work without them.
You have several easy, convenient options to install the mcerp
package (administrative privileges may be required)
Simply copy the unzipped mcerp-XYZ directory to any other location that
python can find it and rename it mcerp.
From the command-line, do one of the following:
Manually download the package files below, unzip to any directory, and run:
$ [sudo] python setup.py install
If setuptools is installed, run:
$ [sudo] easy_install --upgrade mcerp
If pip is installed, run:
$ [sudo] pip install --upgrade mcerp
Python 3
To use this package with Python 3.x, you will need to run the 2to3
conversion tool at the command-line using the following syntax while in the
unzipped mcerp directory:
This should take care of the main changes required. Then, run:
$ python3 setup.py install
If bugs continue to pop up, please email the author.


Rotation 2
For our second rotation we worked on Autodesk Fusion 360 to create objects we could later have in real life using 3D printing.
The first challenge of this rotation functioned as an introduction to the Autodesk application. Fortunately for myself I've used the app before, and others like it, so I didn't have much trouble with the challenge. We simply had to recreate an object on the computer with only a picture for reference.
There was a second part to the first challenge in which we were given a catalog of about 50 different desings, and we had to replicate at least five of them in addition to the one given to us on paper. While working through these designs I did learn different ways to fillet and chamfer edges that will be useful in the future.
The final challenge of the rotation was to choose one of four tasks to construct something on Autodesk that we could actually print later on. I decided to make a laptop stand just because I have a laptop, but no stand. I watched an online tutorial to build it, and the guy explaining the process actually gave a lot of useful tips and trick of Autodesk that I didn't know before, so I'm glad I chose the stand. I've included an image of the stand that I made on Autodesk as well as a link to the file of the laptop stand.


{{ circularity.meta.meta_title }}
{% include component/header_w.html %}
{% include circularity/main_rich.html %}
{% include circularity/performance.html %}
{% include circularity/buble_rich.html %}
{% include circularity/goals_rich.html %}
{% include circularity/space_rich.html %}
{% include circularity/slider_rich.html %}
{% include circularity/article_img_right.html %}
{% include circularity/presentation.html %}
{% include circularity/next_rich.html %}
{% include circularity/better_rich.html %}
{% include component/footer.html %}
{% lazyScript %}
{% includeScript "../../src/js/circularity.js" %}


AFIP Case Preparation for DummiesLink to the AFIP course information. Look for a link to "case preparation." Read and print this document. 1.
Identify a case you want to take. This could be from your saved
worklist or from another resident's suggestion. Check out cases that
other residents have suggested here.
What they want is really good path imaging correlation, not necessarily
a wierdo case. They also have suggestions of what they are looking for
(by organ system) in the case preparation document. Get the path slides:3.
Before you do anything with the images, call the pathology department
(7-3720) to see if they have tissue and ask them politely to prepare
slides of the tissue. If you didn't see gross path on the timeline, ask
them about if they have gross path images (if they don't, start over at
Step #1). Get the DICOM images:5. Call/email Stentor support (2-HELP/pager 5265)) to tell them that you need DICOM images for AFIP and ask who you need to send the case information to .6. Email this person the MRN and Accession number(s) of the case you need. 7. Arrange to pick up, or sent through interoffice mail to the department. Get the gross path images:8.  Right click on the image and select 'Save/Image to file'. 10. Save as type "TIFF" 11. Save it to your jump drive 12. Repeat process for any other images. 13. Burn a CD-ROM with these images. 14. Fill out the case report information on the AFIP case preparation instructions (you will need to look through MARS to get the reports). 15. Print Path, Operative, and Radiology Reports from MARS. Make sure they are appropriately deidentified. Final Checklist:CASE MATERIAL |


Regex stands for "Regular Expressions", which is a means to match stuff. There are different flavors of regular expression, and this extension uses the JavaScript one. I recommend trying out your patterns at RegexPal.
The quick things to know about regex match patterns are:
.*
matches anything, so you usually want to start and end your pattern with ithttp:\/\/
)Effects are the way the favicon gets modified:
The color used by the applied effect (see above).
The first (in the list) pattern that matches gets applied. You can drag-and-drop to re-order the rows above to give them priority.


mobile home
see lista backpack
shopsurvival kit
see checkliston the road for 6 months, i never thought i would survive the harsh heat and seclusion of the australian outback. i have never been so nervous about not having enough water or an empty tank it is ...(read more) the scariest moment in my life. But I do not regret having to seize this opportunity to explore the wonders of Australia from a different prespective away from tourism videos.


{% extends "maintenance.html" %} {% block page_title %}Applying hotfixes...{% endblock page_title %} {% block maintenance-content %}
We need to deploy hotfixes on production systems due to important issues or urgent requests.
Please be patient, this operation takes a few minutes to complete.
{% endblock maintenance-content %}


Before Change
test_output)
results = {}
for i, metric in enumerate(BENCHMARK_METRICS):
results[metric] = float(matches[i])
return results
After Change
All units are in MB/s, as these are the units guaranteed to be output
by the test.
lines = [line.strip() for line in test_output.splitlines()]
host_to_device_results_start = _FindIndexOfLineThatStartsWith(
lines, "Host to Device Bandwidth")
device_to_host_results_start = _FindIndexOfLineThatStartsWith(
lines, "Device to Host Bandwidth")
device_to_device_results_start = _FindIndexOfLineThatStartsWith(
lines, "Device to Device Bandwidth")


The paper presents an extensive study of the effects of experience replay in Q-learning based methods.
It focuses explicitly on the replay capacity and replay ratio (ratio of learning updates to experience collected).
Replay capacity is defined as the total number of transitions stored in the replay buffer.
Age of a transition (stored in the replay buffer) is defined as the number of gradient steps taken by the agent since the transition was stored.
More is the replay capacity, more will be the age of the oldest transition (also referred to as the age of the oldest policy).
More is the replay capacity, more will be the degree of “off-policyness” of the transitions in the buffer (with everything else held constant).
Replay ratio is the number of gradient updates per environment transition. This ratio can be used as a proxy for how often the agent uses old data (vs. collecting new data) and is related to off-policyness.
In DQN paper, the replay ratio is set to be 0.25.
For experiments, a subset (of 14 games) is selected from Atari ALE (Arcade Learning Environment) with sticky actions.
Each experiment is repeated with three seeds.
Rainbow is used as the base algorithm.
Total number of gradient updates and batch size (per gradient update) are fixed for all the experiments.
Rainbow used replay capacity of 1M and oldest policy of age 250K.
In experiments, replay capacity varies from 0.1M to 10M ( 5 values), and the age of the oldest policy varies from 25K to 25M (4 values).
With the age of the oldest policy fixed, performance improves with higher replay capacity, probably due to increased state-action coverage.
With fixed replay capacity, reducing the oldest policy’s age improves performance, probably due to the reduced off-policyness of the data in the replay buffer.
However, in some specific instances (with sparse reward, hard exploration setup), performance can drop when reducing the oldest policy’s age.
Increasing replay capacity, while keeping the replay ratio fixed, provides varying improvements and depends on the particular values of replacy capacity and replay ratio.
The paper reports the effect of these choices for DQN as well.
Unlike Rainbow, DQN does not improve with larger replay capacity, irrespective of whether the replay ratio or age of the oldest policy is kept fixed.
Given that the Rainbow agent is a DQN agent with additional components, the paper explores which of these components leads to an improvement in Rainbow’s performance as replay capacity increases.
Four new DQN variants are created by adding each of Rainbow’s four components to the base DQN agent.
DQN with n-step returns is the only variant that benefits by increased replay capacity.
The usefulness of n-step returns is further validated by verifying that Rainbow agent without n-step returns does not benefit by increased replay capacity. While Rainbow agent without any other component benefits by the increased capacity.
Prioritized Experience Replay does not significantly affect the performance with increased replay capacity.
The observation that n-step returns are critical for taking advantage of larger replay sizes is surprising because the uncorrected n-step returns are theoretically not suitable for off-policy learning.
The paper tests the limits of increasing replay capacity (with n-step returns) by performing experiments in the offline-RL setup, the agent collects a dataset of about 200M frames. These frames are used to train another agent.
Even in this extreme setup, n-step returns improve the learning agent’s performance.
Hypothesis 1: n-step returns help to counter the increased off-policyness produced by a larger replay buffer.
Hypothesis 2: Increasing the replay buffer’s capacity may reduce the variance of the n-step returns.
This hypothesis is evaluated by training on environments with lesser variance or by turning off the sticky actions in the atari domain.
While the hypothesis does explain the gains by using n-step returns to some extent, n-step gains are observed even in environments with low variance.


Cryptowedding was created for the most romantic and the most modern people.
Do you have a BTC wallet? If you do, than you shoud know about blockchain and cryptocurrency. This service uses two BTC wallets to create the one wallet with two signatures that is, with two owners. Of course, these owners are the owners of the original wallets.
In addition to creation of the multisignature wallet, the CryptoWedding allows you to attach a text message. This can be used to miraculously make the proposal to your second half by sending a link to the transaction with your text. You can come up with myself how to use it.
If you don't have a BTC wallet or just want to know about blockchain tecnology you can visit this section.
Consider Alice and Bob both have their wallets. So Alice knows her private key and Bob knows his one. But they also knows both their public keys. Imagine that Alice and Bob decide to to use some part of their money for some common goals. Multisig wallet used in Bitcoin network is the solution of the problem.
To create the wallet with two owners we need just two public keys. So Alice and Bob create their multisig. After that it has its own adress and balance, but outgoing transactions to be valid require both private keys.
By this service you can create multisig wallet for two owners. Just enter the public keys in the form below. You can add a text if you want to save your message in the history of the Bitcoin Blockchain. After you did that, you see the target adress. Send BTC amount as much as you want. Your multisig wallet is created! Its starting amount equals amount you sent minus network fee.
Cryptowedding was created for the most romantic and the most modern people.
Cryptowedding was created for the most romantic and the most modern people.
One of the goals of this project is blockchane technology popularisation. If you have any questions or some ideas to improve the service please contact us by email crypto@crypto.crypto


NOTES - PROJECT 0
In this lesson I learned about basics of HTML & what are components required to
build HTML page. So below are my notes.
I will start with example, what parts are invloved when we access certain webpage.
It consist of
Server - Its a computer which host website. Website is made from HTML language. It contains images, notes, audio or video, etc.
PC - Its a computer from where client access website, by using application like IE, Firefox, Chrome.
HTML - Its protocol which talks between client & server, so that webpage could be viewed by client in IE, Firefox.
Internet - Its final and important part of all, it connects two machine like PC & Server. So that they could talk to each other.
BASICS OF HTML
Webpage is constructed using HTML - Hyper Text Markup Language. Contents of webpage such as docuement, image, audio or video
is accessed through HTML. Fundamental building blocks are as follow,
TAGS
Tags are used to format contents in HTML page. Format of specifying any tag is < NAME > Content < /NAME >
(dont' consider white spaces, during code writing) where Name is Tag name, Content is any normal content or it could other tag as well. First Name is called opening tag and other one is closing tag.
ELEMENTS
It is composed of opening tag, content & closing tag. There are two types of it Inline & Block.
Lets discuss some of tags
1] To make any content bold, we use b tag. For Example UDACITY NANODEGREE
2] To make italics, we use em tag. For Example UDACITY NANODEGREE
3] To give line break, we use br tag. This tag is called void tag because there is no closing tag means no contents.
4] To insert link for other webpage, we use anchor tag. For Example UDACITY NANODEGREE
In summary, webpages are constructed using HTML language. TAGs & ELEMENTs are basic building blocks of particular HTML page.


Color Enhancer
Enable?
Color adjustment
Setup:
Step 1: Select the row with the faintest stars:
⋆
Step 2: Adjust the slider until all of the stars are visible in the selected row


I love to watch programming related presentations online, it’s sad, but I love it. I typically put these on during my hour commute to and from work and just listen. I did however, run across a presentation from a google conference a few years ago that made me go home and actually watch the presentation. It was Douglas Crockford’s “JavaScript: The Good Parts” presentation. The reason I started over was I didn’t get very far into listening when I realized I had a problem.
Early into “The Good Parts” Doug said something to the extent of “JavaScript is a language that people jump in and think they know, but don’t”. Only a few minutes later into the presentation after some examples, I had an epiphany. I was one of those people. I had just jumped into the language and treated it like Java. It’s an utter miracle that my stuff every worked. I decided to start over and take the language and patterns very, very seriously.
Here is a compiled playlist of the important sections of his talks starting from the very origins of JavaScript all the way to advanced usages.
Take the time to watch this whole playlist. I promise you it will make you better in the long run. If at the very least at code reviews.


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The super-magnet using field electricity generator is a new design on the principle of Faraday, the inner rotatory pule revolves in a electrostatic cage which strenthen its magnetic field by 7.32 times of the traditional generators hence it'll be a better option against the present ones for the coming days.
Super Magnet Inspired Field Energy Generator
Dhirendra Pratap Singh Suryavanshi
India's largest network of 5-star pet sitters and dog walkers, founded in 2019, provides in-home dog boarding, pet-sitting, dog-walking, or day care. Petixa India connects pet parents with dog people who'll treat their pets like family. Generated 632 jobs till 8th of July 2020, and we are constantly growing day by day.
PETIXA India!
Parth Mishra
The dolphy automatic sanitizer is an ultra-sanitary sanitizer
dispenser that has a built-in motion detector, when it sense
your hands under the spot to dispense, it'll provide you the
perfect amount of liquid sanitizer automatically to get your
hands quickly clean
Smart Sensor Technology
No Touch = No Germs
Auto Hand Sanitizer
Harsh Jain