api.samples package¶
Submodules¶
api.samples.box_2d module¶
-
class
api.samples.box_2d.
Box2d
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.sample.Sample
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
api.samples.box_3d module¶
-
class
api.samples.box_3d.
Box3d
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.sample.Sample
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
attributes
()¶
-
compute_iou
(box, return_max=False, map2yaw=None)¶ Compute the iou score between all the elements of self and of box.
Return a matrix len(self), len(box) when row,col are indexed in the same order as self, box.
If return_max=True: return only a single number for each element of self (the max value).
Important note: By default the computation is performed in the sbg ref where only one angle (yaw) is not zero, unless map2yaw is provided (a callable) which brings all the boxes in a referential where only one rotation (yaw).
-
confidences
()¶
-
label_names
()¶ mapping from label encoding to name of each annotation
-
mapto
(referential_or_ds: str = None, ignore_orientation: bool = False, reference_ts: int = -1, dtype=<class 'numpy.float64'>)¶ Will map each box in another referential.
-
num_pts_in
(pt_cloud, margin=0)¶ Will return for each box, the mask of those points from pt_cloud that are inside the box. Args: pt_cloud - (M,3) margin (optional) - positive float- increases the size of box
Out: mask - boolean (n_boxe,M)
-
set_angle_to_domain
(domain=[0, 6.283185307179586])¶ Will set the angles to a given domain
-
api.samples.echo module¶
-
class
api.samples.echo.
Echo
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.sample.Sample
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
amplitude_img
(options='max_amplitude', dtype=<class 'numpy.float32'>, extrema=None)¶
-
property
amplitudes
¶
-
cache
()¶
-
channel_index_to_image_coord
(index)¶
-
clip_to_fov_mask
(pts: numpy.ndarray) → numpy.ndarray¶
-
property
data
¶
-
distance_img
(options='min_distance', dtype=<class 'numpy.float32'>, extrema=None)¶
-
property
distances
¶
-
property
flags
¶
-
get_cloud
(referential: str = None, ignore_orientation: bool = False, undistort: bool = False, reference_ts: int = -1, dtype: numpy.dtype = <class 'numpy.float64'>)¶
-
property
h
¶
-
image_coord_to_channel_index
(row, col)¶
-
property
indices
¶
-
property
mask
¶
-
property
masked
¶
-
other_field_img
(field, dtype=<class 'numpy.float32'>)¶
-
point_cloud
(referential: str = None, ignore_orientation: bool = False, undistort: bool = False, reference_ts: int = -1, dtype: numpy.dtype = <class 'numpy.float64'>)¶ Compute a 3D point cloud from raw data
- Parameters
referential – The target sensor referential or full datasource name
ignore_orientation – Ignore the source sensor orientation (default: {False})
undistort – Apply motion compensation to 3d points.
reference_ts – (only used if referential == ‘world’ and/or undistort == True), refer to compute_transform()’s documentation
dtype – the output numpy data type
-
quad_cloud
(referential=None, ignore_orientation=False, undistort=False, reference_ts=-1, dtype=<class 'numpy.float64'>)¶ Compute a 3d surface cloud from raw data (quads made of 2 triangles)
- Parameters
referential – The target sensor referential or full datasource name
ignore_orientation – Ignore the source sensor orientation (default: {False})
undistort – Apply motion compensation to 3d points.
reference_ts – (only used if referential == ‘world’ and/or undistort == True), refer to compute_transform()’s documentation
dtype – the output numpy data type
-
property
raw
¶
-
property
specs
¶
-
property
timestamps
¶
-
property
v
¶
-
api.samples.echo_xyzit module¶
-
class
api.samples.echo_xyzit.
EchoXYZIT
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.xyzit.XYZIT
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
point_cloud
(referential=None, ignore_orientation=False, undistort=False, reference_ts=-1, dtype=<class 'numpy.float64'>)¶ Compute a 3D point cloud from raw data
- Parameters
referential – The target sensor referential or full datasource name
ignore_orientation – Ignore the source sensor orientation (default: {False})
undistort – Apply motion compensation to 3d points.
reference_ts – (only used if referential == ‘world’ and/or undistort == True), refer to compute_transform()’s documentation
dtype – the output numpy data type
-
api.samples.fast_trace module¶
-
class
api.samples.fast_trace.
FastTrace
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.trace.Trace
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
property
max_range
¶
-
processed
(trace_processing: Callable)¶
-
property
raw
¶
-
property
raw_array
¶
-
api.samples.image module¶
-
class
api.samples.image.
Image
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.sample.Sample
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
property
camera_matrix
¶
-
property
distortion_coeffs
¶
-
project_pts
(pts, mask_fov=False, output_mask=False, undistorted=False, margin=0)¶ projects 3D points from camera referential to the image plane.
- Parameters
- (undistorted) –
- –
- –
- –
margin (optionnal) – margin (in pixels) outside the image unaffected by the fov mask
-
projection_mask
(pts, projection, margin=0)¶
-
raw_image
()¶
-
property
und_camera_matrix
¶ the undistorted new camera matrix
-
undistort_image
()¶
-
api.samples.image_cylinder module¶
-
class
api.samples.image_cylinder.
ImageCylinder
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.image.Image
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
project_pts
(pts, mask_fov=False, output_mask=False, undistorted=False, margin=0)¶ project 3D in the 2D cylindrical referiencial
- Parameters
pts – 3D point in the center camera referential (3xN)
mask_fov (optionnal) – removes points outside the fov
output_mask (optionnal) – if True, returns the mask applied to the points
undistorted (=False) – Does nothing in the case of ImageCylinder, as it is always undistorted
margin (optionnal) – margin (in pixels) outside the image unaffected by the fov mask
- Returns
2D points in cylindrical image referential mask (optionnal): returned if output_mask is True
- Return type
2xN
-
undistort_image
()¶
-
api.samples.image_fisheye module¶
-
class
api.samples.image_fisheye.
ImageFisheye
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.image.Image
A derivation of a standard Flir Image, whenever the lens used is of type fisheye (from immervision)
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
project_pts
(pts, mask_fov=False, output_mask=False, undistorted=False, margin=0)¶ projects 3D points from camera referential to the image plane.
- Parameters
- (undistorted) –
- –
- –
- –
margin (optionnal) – margin (in pixels) outside the image unaffected by the fov mask
-
undistort_image
()¶
-
api.samples.lane module¶
-
class
api.samples.lane.
Lane
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.sample.Sample
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
api.samples.poly_2d module¶
-
class
api.samples.poly_2d.
Poly2d
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.sample.Sample
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
areas
()¶
-
colored_image
(resolution: tuple = None)¶
-
mask_category
(category: str, resolution: tuple = None, mode: str = 'binary')¶
-
static
resize_mask
(mask, resolution)¶
-
api.samples.rpm module¶
-
class
api.samples.rpm.
RPM
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.sample.Sample
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
meters_per_second
()¶
-
api.samples.sample module¶
-
class
api.samples.sample.
Sample
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
object
-
LUT
= {}¶
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
compute_transform
(referential_or_ds: str = None, ignore_orientation: bool = False, reference_ts: int = -1, dtype=<class 'numpy.float64'>) → numpy.ndarray¶ Compute the transform from this Sample sensor to another sensor referential.
By default the transform between the two sensors reference frames include the contribution from the orientation member. If ones wishes to ignore this contribution it can set the ignore_orientation parameter to True.
- Parameters
referential_or_ds – The other sensor name or full datasource name, e.g. flir_tfc or flir_tfc_img (default: {None})
ignore_orientation – Ignore the source sensor orientation (default: {False})
reference_ts – (only used if referential_or_ds == ‘world’), the timestamp at which we want to ‘jump’ from EgomotionProvider’s referential (e.g. the IMU) to the to ‘world’ referential. Also note that the ‘world’ referential is actually EgomotionProdiver’s referential at some reference time ‘t’ (refer to your actual EgomotionProvider’s configuration, by default ‘t’ is EgomotionProvider’s initial timestamp). ‘world’ referential is thus commonly refered to as the ‘EgoZero’ referential
dtype – the output numpy data type
- Returns
The [4, 4] transformation matrix
-
property
extrinsics
¶
-
property
intrinsics
¶
-
property
label
¶
-
property
orientation
¶
-
pretty_print
()¶
-
property
raw
¶
-
property
sensor_type
¶
-
property
time_of_issues
¶
-
property
timestamp
¶
-
transform
(pts: numpy.ndarray, referential_or_ds: str, ignore_orientation: bool = False, reference_ts: int = -1, reverse: bool = False, dtype=<class 'numpy.float64'>) → numpy.ndarray¶ Transform 3D points from this Sample sensor to another sensor referential.
- Parameters
pts – The [N, 3] points to be transformed
referential_or_ds – The target sensor referential or full datasource name
ignore_orientation – Ignore the source sensor orientation (default: {False})
reference_ts – refer to compute_transform()’s doc (only used if referential_or_ds == ‘world’)
reverse – apply the reverse transformation
dtype – the output numpy data type
- Returns
The transformed points
-
transform_image
(image)¶
-
static
transform_pts
(matrix4x4, ptsNx3)¶
-
undistort_points
(pts_Local_batches: list, timestamps: numpy.ndarray, reference_ts: int = -1, to_world: bool = False, dtype=<class 'numpy.float64'>)¶ Transform 3D points that have not been sampled simultaneously to their ‘correct’ place referential.
- Parameters
pts_Local_batches – a list of arrays of [N, 3] points to be transformed
timestamps – the N timestamps (common for all point batches)
to_world – If ‘True’, leave undistorted points in ‘world’ referential, otherwise project them back to local referential
reference_ts – only used if to_world == False, let the use chose at what time undistorted points are projected back to the local referential (useful to compare points from different sensors in a common local referential)
dtype – the output numpy data type
- Returns
The transformed points
-
-
api.samples.sample.
warn_if_less_than_64bit
(dtype: numpy.dtype)¶
api.samples.seg_2d module¶
-
class
api.samples.seg_2d.
Seg2d
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.sample.Sample
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
poly2d
(confidence_threshold=0.5)¶
-
api.samples.seg_2d_image module¶
-
class
api.samples.seg_2d_image.
Seg2dImage
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.sample.Sample
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
poly2d
(confidence_threshold=0.5)¶
-
seg2d
()¶
-
api.samples.seg_3d module¶
-
class
api.samples.seg_3d.
Seg3d
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.sample.Sample
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
colors
(mode=None)¶
-
mask_category
(category)¶
-
api.samples.trace module¶
-
class
api.samples.trace.
Trace
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.sample.Sample
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
property
max_range
¶
-
processed
(trace_processing: Callable)¶
-
property
raw
¶
-
property
raw_array
¶
-
static
saturation_flags
(traces)¶
-
property
specs
¶
-
property
timestamps
¶
-
api.samples.xyvit module¶
-
class
api.samples.xyvit.
XYVIT
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.xyzit.XYZIT
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
point_cloud
(referential=None, ignore_orientation=False, undistort=False, reference_ts=-1, dtype=<class 'numpy.float64'>)¶ Compute a 2D BEV point cloud from raw data
-
api.samples.xyzit module¶
-
class
api.samples.xyzit.
XYZIT
(index, datasource, virtual_raw=None, virtual_ts=None)¶ Bases:
pioneer.das.api.samples.sample.Sample
-
__init__
(index, datasource, virtual_raw=None, virtual_ts=None)¶ virtual_raw and virtual_ts are meant for samples which dont exist in a physical datasource. For example interpolated samples, or samples derived from other samples.
-
property
amplitudes
¶
-
property
distances
¶
-
get_cloud
(referential=None, ignore_orientation=False, undistort=False, reference_ts=-1, dtype=<class 'numpy.float64'>)¶
-
point_cloud
(referential=None, ignore_orientation=False, undistort=False, reference_ts=-1, dtype=<class 'numpy.float64'>)¶ Compute a 3D point cloud from raw data
- Parameters
referential – The target sensor referential or full datasource name
ignore_orientation – Ignore the source sensor orientation (default: {False})
undistort – Apply motion compensation to 3d points.
reference_ts – (only used if referential == ‘world’ and/or undistort == True), refer to compute_transform()’s documentation
dtype – the output numpy data type
-
property
timestamps
¶
-


Heave to lanyard gunwalls mizzenmast long boat salmagundi sheet ahoy pillage yawl. Overhaul booty bilge reef sails boom scourge of the seven seas boatswain piracy clipper rum. Holystone bounty square-rigged sheet Barbary Coast furl execution dock sloop scallywag pirate.
 Draught yo-ho-ho sloop come about landlubber or just lubber red ensign broadside tackle gunwalls lateen sail. List nipper aye Barbary Coast rope's end Letter of Marque red ensign Arr topsail Chain Shot.


The Explorer window is a HexEdit Pro modeless dialog that resembles a Windows Explorer window but has several special features for use within HexEdit Pro, namely:
Double-click a file in the file view to open it in HexEdit Pro.
Right-click a file to display the Explorer context menu with 2 additional items allowing you to open the file or open it read-only.
Enter file type filter(s) to see a subset of files.
You can choose filters from your own set of custom filters, or from the history list of previously entered filters.
Type a path into the address box and press Enter to display the contents, or select from the history list of previously entered ones.
Easily toggle the display of hidden files.
Toggle between having folder and files views arranged vertically or horizontally to suit you screen layout and dimensions.
Display columns specific to HexEdit Pro such as time last opened, comments etc.
Show or hide columns b clicking any header and choosing from the list
Back Button
Takes you back to the previous folder visited.
Forward Button
Takes you forward to the next folder if you have use the above Back button..
Parent Button
Takes you to the parent folder - ie, up one level in the directory tree.
Filter
Restricts the files are shown in the File View window. Only files that match the filter pattern are displayed. Type in a filter or select one from the predefined filters list using the button to the right.
Predefined Filters Button.
HexEdit Pro allows you to set up your own list of custom filters using the File Type Filters page of the Options dialog. Click the right-arrow button to select from the list of custom filters or use the bottom entry to edit the list
Views Button
Shows a drop-down menu with options on how to view the files. The set of options depends on the version of Windows you are using but includes icon and details views. Details view provides the most information including several resizable columns that display such information as file size, last modification time, attributes etc.
Also shows options to turn on or off the display of hidden files (files with the hidden attribute set).
Orientation button
This button toggles between having the folders/files panes positioned vertically or horizontally.
Address
This displays and allows you to enter the full path of the folder being displayed. A drop-down history list is available using the down-arrow button.
Refresh Button
Click this button to refresh the folder and file views if you think something may have changed (file added, deleted, modified etc).
Folders View
The left ot top pane shows all the folders in the local file system in a hierarchical (tree) view. Click on any folder to show its contents in the adjacent file view.
File View
The right or bottom pane shows the files and folders in the current folder.
You can click a column header to sort by that column. Drag a column to change the order of the columns. Drag the separator between columns to resize them.
Right-click any column header to see a list of columns you can display or hide.


I grew up in the suburbs of Chicago and have always been in love with this beautiful city and all of the adventures that it has to offer.
I attended the University of Wisconsin, Madison where I studied Business and graduated in 2015 with a Bachelor's of Business Administration degree in Finance. I also had the opportunity to spend the second semester of my junior year studying abroad in Sevilla, Spain and traveling (as cheaply as possible) around Europe.
After graduation I spent two years working in the Financial Services industry as an analyst. This was an excellent opportunity for me to gain experience and establish myself as a professional. However, the path I was on left me feeling unfulfilled and I decided to make a career change.
I recently received my certificate in Web Development from Northwestern University's School of Professional Studies. I am currently seeking a full-time position in the tech industry in Chicago.
I enjoy learning and experimenting with new programming languages. I am currently familiar with HTML5, CSS, Bootstrap, JavaScript, jQuery, Node.js, Express.js, React.js and more. In the near future I plan to dabble with Ruby on Rails, Php, and Python.
I love working with people! I have worked in various roles where I have ranged from being a team leader to being a member. I found that all successful teams require open lines of communication and respect for other people's opinions.
Aspiring Web Developer


BEAN 1997 A film review by Timothy Voon Copyright 1997 Timothy Voon
Cast : Rowan Atkinson - Mr. Bean, Larry Drake, Johnny Galecki, Richard Gant, Andy Lawrence, Peter MacNicol, Sandra Oh, Pamela Reed, Tricia Vessey, Harris Yulin. Directed by Mel Smith. Written by Richard Curtis (I), Robin Driscol.
Bean fans, he's here! Billed as the 'Ultimate Disaster Movie', when in truth this is the 'Pen-Ultimate Disaster Movie.' Move over Twister, Volcano and up-coming Titanic, make-way for Bean - the man who can generate laughs with the cataclysmic propensity of Hiroshima.
Yes, it was funny. Watch out U.S.A. here comes the infestation known as Bean.  Beware this lean, mean, fighting machine known as Bean, coming your way October.
Watch the Bean reduce a 50 million painting of ‘The Whistler's Mother' into a caricature of 'Hagar', and hear his insightful thoughts on the painting after. He makes Tom Cruise look like an amateur, when it comes to a 'Mission Impossible' break-in, with only the use of a skate board, laxatives, eggs and underwear. Witness the life-saving techniques of Bean as he removes bullets from downed cops, and the 'Bean-Defibrillator Method' of arousing coma victims; and at the end of the day watch him warm the hearts of his audience, as he brings a broken family together again.
Comment: Bean is his name. Disaster is his game. Hear him roar!
Timothy Voon e-mail<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


14 June 2001
Disease Outbreak Reported
WHO has received reports of a total of 6266 cases including 311 deaths (case-fatality rate of 5.0%) as of 31 May 2001 in ten out of the eleven regions in the country. Since the previous report, more accurate figures have become available with the review of the database of reported cases and deaths. Three regions, Amhara, Oromiya and Southern Nations Nationalities Region, still report newly affected districts. The current trend of the epidemic remains uncertain.
In collaboration with the Ethiopian Ministry of Health, work in controlling the epidemic has been carried out by partners in the country, including the International Federation of Red Cross and Red Crescent Societies (IFRC), the Ethiopian Red Cross Society and Médecins sans Frontières, France, Belgium, Holland and Switzerland.


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_Book_of_Shadows:_Blair_Witch_2_ (R) no stars (out of ****)
Much like how _The_Exorcist_ begat _Exorcist_II:_The_Heretic_, last year's shock success _The_Blair_Witch_Project_ has spawned a baffling and altogether atrocious follow-up, _Book_of_Shadows:_Blair_Witch_2_. Less a sequel than an extremely loose spinoff--which has just about nothing to do with either part of its title, I might add--this film won't win over the original's many detractors, and it is certain to incense the first's cult of ardent supporters.
Director Joe Berlinger has described _Book_of_Shadows_ as an "anti-sequel," and the premise he and writing collaborator Dick Beebe (_BWP_ writers-directors Daniel Myrick and Eduardo Sanchez only take an executive producing credit here) does hold some potential for an interesting deconstruction of not merely the first _Blair_Witch_, but the pop culture phenomenon it created. _BW2_ doesn't take place in the same "universe" inhabited by the original's characters of Heather, Mike, and Josh; it takes place in the world of the here and now--that is, a world where _The_Blair_Witch_Project_ is a blockbuster movie that has sent tourism soaring in the filming location of Burkittsville, Maryland. The film opens with a witty faux-documentary segment that succinctly covers the media frenzy around the film and fans' obsession with all things Blair.
Once the nominal plot kicks in after the credit roll, _BW2_ begins is big downhill plunge. Among the many _BWP_ fans who have flocked to those familiar woods is the "Blair Witch Hunt" tour group: Jeff (Jeffrey Donovan), the tour organizer and former mental patient; Erica (Erica Leerhsen), a practicing Wiccan who wants debunk the negative perception of her religion; Stephen (Stephen Barker Turner), a supernatural skeptic who is writing a book on the Blair Witch myth with his pregnant believer girlfriend Tristen (Tristen Skyler); and Kim (Kim Director), token Goth chick. They set up camp at the ruins of the house featured in _BWP_'s finale, and after a night of hard drinking, everyone wakes up the next morning to find all their video equipment destroyed--and their memory similarly damaged; no one has any recollection of all that went on that night, let alone falling asleep. Perhaps holding the answers are their mysteriously untouched videotapes, and the five retreat to Jeff's abandoned warehouse home to study them.
Given that _The_Blair_Witch_Project_'s ambiguous, debatable ending was a major reason for the film's cult status, it was perhaps a wise move on the part of Berlinger and Beebe to not offer any explanation about what exactly happened. I don't think anyone goes into _Book_of_Shadows_ expecting to find any specific answers about the first film's doomed trio, anyway; however, I'm sure the common expectation is for some additional background on the Blair Witch. Alas, what the audience gets is a repetitive and incredibly tedious chain of events where the five have strange hallucinations when not watching playback of their videotapes. A few oblique references are made to the Blair Witch legend, but they will be largely incomprehensible to anyone who hasn't done extensive pre-viewing research at the Blair Witch website. Unlike the original film, where the expanded backstory works as an enhancement to a stand-alone film, it's practically required reading if one wants to make sense of whatever place _BW2_ has in the larger mythology--though even Blair Witch fanatics may have difficulty sorting it all out as well.
But ties to the bigger Blair Witch picture are secondary to the casual viewer, who simply wants to be scared. Not only is there anything remotely spooky to be found in _Book_of_Shadows_, no sense of suspense is ever generated due to a self-defeating storytelling choice: flash forwards. In haphazardly jumping back and forth in time, Berlinger indiscreetly gives away who exactly will survive and who will die, not to mention the exact fate of these survivors; throughout the film he even drops not-so-subtle hints--visual ones, no less--of what went down during those missing hours. With all its secrets and would-be shocks more than telegraphed far in advance, there is a dull inevitability attached to what should have been the film's "climactic" moments.
So, for most of _Book_of_Shadows_, one wonders what Berlinger's intentions were, he clearly didn't set out to make the simple scary movie everyone wants and expects. The answer finally comes in the film's final moments, which indeed reveals an aim "above" standard genre satisfaction: to address the zeitgeist question of popular entertainment's connection to real-life crime. It's an admirable goal within the context of a horror film, but there's a fine line between "ambitious" and "pretentious." By not offering anything fresh nor particularly insightful on the subject, and without going about it in an entertaining nor convincing way (the abysmal acting--or, rather, yelling--sends any foundation in reality out the window), Berlinger and his empty _Book_of_Shadows_ end up on the wrong side of that line.
©2000 Michael Dequina
Michael Dequina<EMAIL_ADDRESS>|<EMAIL_ADDRESS>|<EMAIL_ADDRESS>Mr. Brown's Movie Site: http://www.mrbrownmovies.com CinemaReview Magazine: http://www.CinemaReview.com on ICQ: #25289934 | on AOL Instant Messenger: MrBrown23
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Click here for more from the Tacotron team.
| "neutral prosody": baseline model that is not trained with style tokens. |
| "token id": force the style attention head to only attend to the specified style token. Note: this method can lead to unintelligible speech since the training process learns to rely on a mixture of tokens, but it is a useful technique for getting a quick idea of the prosodic style each token corresponds to. |
| "mix w/ token id(s)": broadcast-add the embedding vector of a token to the full style embedding matrix to bias the overall style. Multiple styles can be mixed by consecutively applying the operation. It is also possible to do more sophisticated mixing and style recreation (e.g. time-varying), which is not shown. |
| neutral prosody | |
|---|---|
| token 0 | |
| token 1 | |
| token 2 | |
| token 3 | |
| token 4 | |
| token 5 | |
| token 6 | |
| token 7 | |
| token 8 | |
| token 9 | |
| mix w/ token 1 (sloppy) |
|
| mix w/ token 4 (high pitched) |
|
| mix w/ token 7 (prominence) |
|
| mix w/ token 1+4 (sloppy & high pitched) |
| neutral prosody | |
|---|---|
| token 0 | |
| token 1 | |
| token 2 | |
| token 3 | |
| token 4 | |
| token 5 | |
| token 6 | |
| token 7 | |
| token 8 | |
| token 9 | |
| mix w/ token 1 (sloppy) |
|
| mix w/ token 4 (high pitched) |
|
| mix w/ token 7 (prominence) |
|
| mix w/ token 1+4 (sloppy & high pitched) |


API Documentation
Språkbanken Text is working actively to develop a language technology infrastructure. At present, this encompasses corpus infrastructure, lexical infrastructure, and metadata.
On this portal you can browse the documentations of Språkbanken Text's APIs. Most of our software is available under the MIT license.
All documentation found on this portal is based on Open API Specification. OAS files can among other things be used for generating API client libraries, using tools like openapi-generator.
List of Språkbanken Text's APIs
-
{{api-list}}


Plugin Information |
|---|
View DocLinks on the plugin site for more information. |
What does this do?
This plugin allows you to publish your documents that are created in the build steps as links on the project page.
Configuration
Project configuration
FreeStyle project
M2 project
If your project is M2 style, then you have to configure each modules. You can see the links on the module pages, not project page.
History
Version 0.6.1 (Mar 5, 2016)
- FIXED: problems when disabling / uninstalling maven-plugin (JENKINS-30766)
Version 0.6 (Sep 28, 2013)
- added a feature to publish artifacts as documents (JENKINS-19456)
Version 0.5 (Oct 19, 2011)
- Added German I18N
Version 0.4 (Sat 15, 2011)
- Added "archive recursively" option. (JENKINS-8507)
Version 0.3 (Feb 15, 2010)
- Update code for more recent Hudson.
Version 0.2.2 (Jul 25, 2009)
- Check if user has Job.CONFIGURE, not ADMINISTER when configuring project.
Version 0.2.1 (Jul 20, 2009)
- Replaced deprecated classes.
- Fixed a bit.
Version 0.2 (Jul 19, 2009)
- Supported M2 project.
Version 0.1 (Jul 11, 2009)
- First release.


Machine and even better Deep Learning is an old field with roots based on work decades ago, and is being used to drive the development of such things such as self-driving technology.
Python is an all-around general purpose programming language, which is easy to learn and use, and is used in hundreds of projects such as sci-kit learn, numpy, and much, much more.
Computer Vision is a cool subdivision in computing, the idea that a computer can see, decipher what things are, and have a computer make decisions based on it is pretty exciting


--- title: Jekyll Serif - Jekyll Small Business Theme layout: default bodyClass: page-home ---
How do influencers get their BitClout out of the platform? What is the value of something you can't sell?
SwapShop is an off-ramp for the BitClout economy that allows users to trade BitClout for coins with real market value.


knitr::opts_chunk$set(comment=NA)
#devtools::install_github("cboettig/gpmanagement@b3b765cbceb51c9b0b8cb2724e395353ec365df9")
library("MDPtoolbox")
library("gpmanagement")
library("tidyr")
library("dplyr")
library("ggplot2")
library("lazyeval")
True model
p <- c(2, 100, 50)
f <- function (x, h){
sapply(x, function(x) {
x <- pmax(0, x - h)
x * exp(p[1] * (1 - x/p[2]) * (x - p[3])/p[2])
})
}
sigma_g <- 0.1
pdfn <- function(x, mu, sigma = sigma_g){
dlnorm(x, log(mu), sdlog = sigma)
}
z_g <- function() rlnorm(1, 0, sigma_g)
set.seed(0)
Tobs <- 40
W <- Tobs
x <- numeric(Tobs)
x[1] <- 60
for(t in 1:(Tobs-1))
x[t+1] = z_g() * f(x[t], h=0)
obs <- data.frame(x = c(rep(0, W),
pmax(rep(0,Tobs-1), x[1:(Tobs-1)])),
y = c(rep(0, W),
x[2:Tobs]))
xObs <- obs$x
yObs <- obs$y
xPred <- seq(0, max(xObs), length = 50)
qplot(seq_along(x), x) + geom_line()
xObs <- rep(0,5)
yObs <- rep(0,5)
Now the GP estimation from NIMBLE. Updated to compute the prior for the length scale using Daniel’s scaling argument.
priors_template <- nimbleCode({
rho ~ dunif(0, X)
sigGP ~ dunif(0, 1e5)
sigOE ~ dunif(0, 1e5)
})
nimbleCodeSub <- function(code, .values){
as.expression(sapply(code, lazyeval::interp, .values = .values)[-1])
}
priors <- nimbleCodeSub(priors_template,
.values = list(X = diff(range(xObs))/(2*sqrt(6))) )
(Note that at this time all nimble run calls must be made in one block since we cannot cache nimble pointers).
fit <- gp_setup(xObs, yObs, xPred, priors = priors)
NaNs were detected in model variable: logProb_rho.
## Fit the GP
system.time(fit$Cmcmc$run(100000))
user system elapsed
5.476 0.024 5.498
samples <- as.matrix(fit$Cmcmc$mvSamples)
## Perform prediction
system.time(fit$Cpred$run(samples))
user system elapsed
5.792 0.000 5.790
## Extract variables for future use
E <- fit$Cpred$getE()
C <- fit$Cpred$getC()
samples <- as.data.frame(samples)
Posteriors
df <- tidyr::gather(samples)
ggplot(df) +
geom_density(aes(value)) +
facet_wrap(~key, scale='free')
obs <- data.frame(x = xObs, y = yObs)
pred <- data.frame(x = xPred, y = E, ymin = E - sqrt(diag(C)), ymax = E + sqrt(diag(C)))
ggplot2::ggplot(pred) +
geom_ribbon(aes(x = x,y = y, ymin = ymin, ymax = ymax), fill = "grey80") +
geom_line(aes(x = x, y = y), size=1) +
geom_point(data = obs, aes(x,y)) +
geom_line(data = data.frame(x = xPred, true = f(xPred,0)), aes(x,true), col="red") +
coord_cartesian(xlim = range(c(xObs, xPred)), ylim = range(c(yObs,E))) +
theme_bw()
Decision theory
states <- xPred # Vector of all possible states
actions <- states # Vector of actions: harvest
Let’s consider a slight variation of the most trivial utility function: one which explicitly adds a cost to completely exhausting the stock (or reducing the stock by more than, say 95% in this case.) This should be somewhat similar to the impact of no discount rate.
# Utility function
discount = 0.99
#get_utility <- function(x,h) pmin(x,h)
#R <- outer(states, actions, get_utility)
R <- sapply(actions, function(h){
sapply(states, function(x){
if(h < 0.95 * x)
h
else
- 1 * max(states)
})
})
Implementing policy
z <- function() rlnorm(1, meanlog = 0, sdlog = sigma_g)
simulate_policy <- function(states, actions, policy, f, z, s0, steps = 50, utility = function(s,a) NA, discount = 1){
s <- numeric(steps)
a <- numeric(steps)
u <- numeric(steps)
s[1] <- s0
for(t in 1:(steps-1)){
a[t] <- actions[policy[which.min(abs(states - s[t]))]]
s[t+1] <- z() * f(s[t], a[t])
u[t] <- utility(s[t], a[t]) * discount ^ t
}
# Final action determined but not implemented
a[steps] <- actions[policy[which.min(abs(states - s[t]))]]
data.frame(time = 1:steps, state = s, action = a, utility = u)
}
GP model
gp_matrix <- function(states, actions, E, C){
transition <- array(0, dim = c(length(states), length(states), length(actions)))
K <- length(states)
sigmas <- sqrt(diag(C))
for (k in 1:length(states)) {
for (i in 1:length(actions)) {
nextpop <- E[k] - actions[i]
if(nextpop <= 0) {
transition[k, , i] <- c(1, rep(0, K - 1))
} else {
transition[k, , i] <- dnorm(states, nextpop, sigmas[i]) / sum(dnorm(states, nextpop, sigmas[i]))
}
}
}
transition
}
P_gp <- gp_matrix(states, actions, E, C)
mdp_check(P = P_gp, R = R)
[1] ""
gp <- mdp_value_iteration(P_gp, R, discount = discount, epsilon = 0.00001, max_iter = 5e3, V0 = numeric(length(states)))
[1] "MDP Toolbox: iterations stopped, epsilon-optimal policy found"
ggplot(data.frame(stock = states, escapement = states - actions[gp$policy])) +
geom_point(aes(stock, escapement)) + xlab("Population size") + ylab("Escapement")
data.frame(reps = 1:50) %>%
group_by(reps) %>%
do(simulate_policy(states,actions, gp$policy, f, z, s0 = 100, steps = 20, utility = pmin, discount = discount)[c("time", "state", "utility")]) ->
sims
mean(sims$utility)
[1] 4.751511
ggplot(sims) + geom_line(aes(time, state, group = reps), alpha = 0.3, col = "darkblue")
Simulate under the true model
data.frame(reps = 1:50) %>%
group_by(reps) %>%
do(simulate_policy(states, actions, gp$policy, f, z, s0 = 100, steps = 20, utility = pmin, discount = discount)[c("time", "state", "utility")]) ->
sims
mean(sims$utility)
[1] 4.751511
(Average utility is approximate here since it does not include penalty; since a function and not a matrix is requred by this function at this time.)
ggplot(sims) + geom_line(aes(time, state, group = reps), alpha = 0.3, col = "darkblue")
Comparison to true model
P <- transition_matrix(states, actions, f, pdfn)
#get_utility <- function(x,h) pmin(x,h)
#R <- outer(states, actions, get_utility)
mdp_check(P = P, R = R)
[1] ""
mdp <- mdp_value_iteration(P, R, discount = discount, epsilon = 0.001, max_iter = 5e3, V0 = numeric(length(states)))
[1] "MDP Toolbox: iterations stopped, epsilon-optimal policy found"
ggplot(data.frame(stock = states,
escapement = states - actions[gp$policy],
optimal_escapement = states - actions[mdp$policy])) +
geom_point(aes(stock, escapement)) +
geom_point(aes(stock, optimal_escapement), col="red", alpha=.4) +
xlab("Population size") + ylab("Escapement")
Note that the altered award structure has almost no effect on the optimal policy given the true model, other than to avoid harvesting directly to zero even when the stock cannot persist, due to the explicit penalty for doing so.
data.frame(reps = 1:50) %>%
group_by(reps) %>%
do(simulate_policy(states,actions, mdp$policy, f, z, s0 = 100, steps = 20, utility = pmin, discount = discount)[c("time", "state", "utility")]) ->
sims
mean(sims$utility)
[1] 10.19602
ggplot(sims) + geom_line(aes(time, state, group = reps), alpha = 0.3, col = "darkblue")


Preping for day trip to Corvallis. Will be dropping the dogs off for their Thanksgiving vacation with my folks.
11/22/2008, 9:38:31 AM
Favs: 0
Retweets: 0
link--- layout: tweet ---
Preping for day trip to Corvallis. Will be dropping the dogs off for their Thanksgiving vacation with my folks.
11/22/2008, 9:38:31 AM
Favs: 0
Retweets: 0
link


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{% extends "base.html" %} {% load i18n %} {% load bootstrap %} {% block content %}
{% trans "This website allows you to deal with shopping lists easily ! You can create recipes and plan your meals in advance so that your lists are created automatically." %}
{% trans "The website is under construction, sorry for any unexpected trouble." %}


{% extends "template.html" %} {% block main %}
In the most simple case, you simply want to serve the contents of a directory:
{{ source("ResourceHandling.java") | raw }}Or if you have bundled some static data into an uber jar, you'll want to serve it from the classpath:
ResourceHandlerBuilder.classpathHandler("/web")
Or perhaps you want to serve from a local path during development and from the classpath when running as an uber jar. In this case, the following builder can be used:
ResourceHandlerBuilder.fileOrClasspath("src/main/resources/web", "/web")
This means: "if there is a directory called src/main/resources/web
then use that, otherwise load from
/web
on the classpath".
If you wish to serve the files from another path, you can add a resource handler to a
{{ javadoc("io.muserver.ContextHandlerBuilder") | raw }}. The following would server all resources from
the /web
classpath path on the /static/
path:
Mu Server comes with some opinionated response headers. For example, Javascript and CSS files are served with
a short-lived cache value, while HTML pages have no cache, and most text files are served with utf-8
encoding.
You can browse the full list of mime types and headers.
To customize response headers on a request-by-request basis, a {{ javadoc("io.muserver.handlers.ResourceCustomizer") | raw }}
can be used. For example, if you have a convention that any javascript files that end with the extension .immutable.js
should never be downloaded twice, then you can set an aggressive cache control header for these files like so:
You can also customize headers based on file extensions. For example, perhaps you have .dan
files
that do not have any mime type or headers configured by default. To set custom headers for .dan
files,
you would use the following code:
You can try downloading a .dan file and viewing the response headers here: /dan-file.dan
Range requests are enabled for all resource downloads. This allows for resumable downloads and seeking while streaming audio and video files.
See the {{ javadoc("io.muserver.handlers.ResourceHandlerBuilder") | raw }} JavaDoc for more options.
{% endblock %}


Chance's Pizza Place
Pizza Size:
Small
Medium
Large
Extra Large
Pizza Sauce:
Marinara
Alfredo
Pesto
Olive Oil
Toppings:
Pepperoni
Sausage
Bacon
Mushrooms
Chicken
Ham
Pineapple
Extra Cheese
Black Olives
Onions
Submit Order
Reset Form
Total: $


The JavaBeans™ architecture is based on a component model which enables developers to create software units called components. Components are self-contained, reusable software units that can be visually assembled into composite components, applets, applications, and servlets using visual application builder tools. JavaBean components are known as beans.
A set of APIs describes a component model for a particular language. The
JavaBeans API
specification
describes the core detailed elaboration for the JavaBeans
component architecture.
Beans are dynamic in that they can be changed or customized. Through the design mode of a builder tool you can use the Properties window of the bean to customize the bean and then save (persist) your beans using visual manipulation. You can select a bean from the toolbox, drop it into a form, modify its appearance and behavior, define its interaction with other beans, and combine it and other beans into an applet, application, or a new bean.
The following list briefly describes key bean concepts.
Builder tools discover a bean's features (that is, its properties, methods, and events) by a process known as introspection. Beans support introspection in two ways:
By adhering to specific rules,
known as design patterns, when
naming bean features. The
Introspector
class examines beans for
these design patterns to discover bean features.
The Introspector
class relies on the
core reflection API. The trail
Getting Started is an excellent place to learn about reflection.
By explicitly providing property, method, and event information with
a related bean
information class. A bean information
class implements the BeanInfo
interface. A BeanInfo
class
explicitly lists those bean features that
are to be exposed to application builder tools.
Properties are the appearance and behavior characteristics of a bean that can be changed at design time. Builder tools introspect on a bean to discover its properties and expose those properties for manipulation.
Beans expose properties so they can be customized at design time. Customization is supported in two ways: by using property editors, or by using more sophisticated bean customizers.
Beans use events to communicate with other beans. A bean that is to receive events (a listener bean) registers with the bean that fires the event (a source bean). Builder tools can examine a bean and determine which events that bean can fire (send) and which it can handle (receive).
Persistence enables beans to save and restore their state. After changing a bean's properties, you can save the state of the bean and restore that bean at a later time with the property changes intact. The JavaBeans architecture uses Java Object Serialization to support persistence.
A bean's methods are no different from Java methods, and can be called from other beans or a scripting environment. By default all public methods are exported.
Beans vary in functionality and purpose. You have probably met some of the following beans in your programming practice:


I majored in Communication Arts as an undergrad, graduating Magna Cum Laude with a 4.0 GPA with multiple appearances on the Dean's List.
After an internship and a few entry level positions, I finally began working as a Communications Coordinator.
Bitten by the entrepreneurial bug, I decided to start an eCommerce jewelry boutique. Wildly successful upon opening, I soon realized working for yourself, BY YOURSELF is not all its cracked up to be...
I've always had a love for coding, I started doing it just for fun when I was about 12 years old. I finally decided to put all my efforts into web development by attending General Assembly's Web Development Immersive. My life was changed. I look forward to utilizing the skills I've acquired (and the ones I've yet to learn) to help create new and amazing things that will change the world.


CAMPUS
LIFE
Life at UC San Diego is about activating your passions. There are hundreds of ways to have fun, get fit, gain skills and make friends.
Learn moreCOLLEGE IS MORE THAN STUDYING
It’s a time to challenge preconceived notions and pursue your passions. So jump in. Lead a student organization. Discover new lands through study abroad. Join an intercollegiate team. Volunteer in the community. Now is the time!

STUDENT SUCCESS
Success is a team effort. We are here to help you power up your academic ability, discover opportunities to excel and gain transferable skills. Let’s start now.
ORGANIZATIONS
What are you passionate about? Become a changemaker, get out of your comfort zone and establish connections with more than 570 student organizations.
STUDY ABROAD
Studying abroad is more than an adventure.  Where will your journey begin?
Read MoreDiscover your home away from home.
HOUSING
Living on campus means you are always connected to new social opportunities, cultural experiences, and academic enrichment. And, the beach is just across the street.
Learn moreDINING
Need fuel to boost your brainpower? We’ve got you covered with 37 restaurants, specialty eateries and markets across campus with selections to satisfy any palate.
Learn moreSAFETY
A safe and welcoming space to learn is integral to student success. Our programs ensure students have peace of mind and access to safety resources.
Learn moreNeed a diversion? We've got a few ideas.
GREEK LIFE
In addition to friendship and fun, Greek life offers a unique way to engage in service and develop leadership skills. Explore more than 40 fraternities and sororities available.
Learn MoreVOLUNTEER
Giving back is a way of life at UC San Diego. Join a volunteer activity or launch your own initiative and make a difference in the lives of others.
Get InfoSCENIC SAN DIEGO
From downtown nightlife to niche neighborhood eateries and unrivaled outdoor adventures in the surrounding ocean, mountains and desert, it is easy to fall in love with San Diego. Extending along 70 miles of coastline, California’s second largest city is a diverse blend of culture, nature and recreation.
Read MoreGET OUT AND MOVE
ATHLETICS
Whether you are a shining star on the field or a zealous fan in the stands, athletic events are a great outlet and way to build Triton pride.
RECREATION
Channel your inner acrobat, join an inner-tube water polo game, or venture out on a backpacking trip. Get fit with recreation classes and programs.
ARTS AND
CULTURE
From music and dance to theatre and film, there’s always a creative buzz on campus. Find your own way to create and exchange with emerging and well-known art makers.
UPCOMING EVENTS
December 2
De-stress with Biofeedback
December 2
International Friday Café
72° and sunny almost all year round.
You'll want to come visit.
Come for the tour, stay for the beach!
Can't make it out here?


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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface JobStatusProvider
This service provides the current job processing status.
| Field Summary | |
|---|---|
static java.lang.String |
PROPERTY_EVENT_ID
This is a unique identifer which can be used to cancel the job. |
| Method Summary | |
|---|---|
void |
cancelJob(java.lang.String jobId)
Cancel this job. |
void |
cancelJob(java.lang.String topic,
java.lang.String jobId)
Cancel this job. |
java.util.Collection<org.osgi.service.event.Event> |
getAllJobs(java.lang.String topic,
java.util.Map<java.lang.String,java.lang.Object>... filterProps)
Return all jobs either running or scheduled. |
java.util.Collection<org.osgi.service.event.Event> |
getCurrentJobs(java.lang.String topic)
Return the jobs which are currently in processing. |
java.util.Collection<org.osgi.service.event.Event> |
getCurrentJobs(java.lang.String topic,
java.util.Map<java.lang.String,java.lang.Object>... filterProps)
Return the jobs which are currently in processing. |
java.util.Collection<org.osgi.service.event.Event> |
getScheduledJobs(java.lang.String topic)
Return a list of currently schedulded jobs. |
java.util.Collection<org.osgi.service.event.Event> |
getScheduledJobs(java.lang.String topic,
java.util.Map<java.lang.String,java.lang.Object>... filterProps)
Return a list of currently schedulded jobs. |
java.util.Collection<org.osgi.service.event.Event> |
scheduledJobs(java.lang.String topic)
Deprecated. Use getScheduledJobs(String) instead. |
void |
wakeUpJobQueue(java.lang.String jobQueueName)
Wake up the named job queue. |
| Field Detail |
|---|
static final java.lang.String PROPERTY_EVENT_ID
| Method Detail |
|---|
@Deprecated java.util.Collection<org.osgi.service.event.Event> scheduledJobs(java.lang.String topic)
getScheduledJobs(String)
instead.
java.util.Collection<org.osgi.service.event.Event> getScheduledJobs(java.lang.String topic)
topic
- Topic can be used as a filter, if it is non-null, only jobs with this topic will be returned.
java.util.Collection<org.osgi.service.event.Event> getCurrentJobs(java.lang.String topic)
topic
- Topic can be used as a filter, if it is non-null, only jobs with this topic will be returned.
java.util.Collection<org.osgi.service.event.Event> getScheduledJobs(java.lang.String topic, java.util.Map<java.lang.String,java.lang.Object>... filterProps)
topic
- Topic can be used as a filter, if it is non-null, only jobs with this topic will be returned.filterProps
- A list of filter property maps. Each map acts like a template. The searched job
must match the template (AND query). By providing several maps, different filters
are possible (OR query).
java.util.Collection<org.osgi.service.event.Event> getCurrentJobs(java.lang.String topic, java.util.Map<java.lang.String,java.lang.Object>... filterProps)
topic
- Topic can be used as a filter, if it is non-null, only jobs with this topic will be returned.filterProps
- A list of filter property maps. Each map acts like a template. The searched job
must match the template (AND query). By providing several maps, different filters
are possible (OR query).
java.util.Collection<org.osgi.service.event.Event> getAllJobs(java.lang.String topic, java.util.Map<java.lang.String,java.lang.Object>... filterProps)
getScheduledJobs(String, Map...)
and getCurrentJobs(String, Map...)
topic
- Topic can be used as a filter, if it is non-null, only jobs with this topic will be returned.filterProps
- A list of filter property maps. Each map acts like a template. The searched job
must match the template (AND query). By providing several maps, different filters
are possible (OR query).
void cancelJob(java.lang.String jobId)
jobId
- The unique identifer as found in the property PROPERTY_EVENT_ID
.void cancelJob(java.lang.String topic, java.lang.String jobId)
topic
- The job topic as put into the property EventUtil.PROPERTY_JOB_TOPIC
.jobId
- The unique identifer as put into the property EventUtil.PROPERTY_JOB_ID
.void wakeUpJobQueue(java.lang.String jobQueueName)
jobQueueName
- The name of the queue.
|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


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执行动画
span one
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Installed Rio recently and am impressed how smooth and fast this terminal is. Already have set it up as my default go-to terminal! Looking forward to the continued development and what is to come. Keep up the awesome job Raphael!
Daria Vaskovich Edlund • Software Engineer at Viaplay
Rio has become my standard terminal emulator for some time and it works well in professional use. Raphael is doing a great work and is awesome at listening to feedback to the community for new and improved features.
Simon Nyberg • Principal at Viaplay
Rio is amazing! either still beta, could full fill all need working heavily with git/docker/spring/npm , I really recommend to get a try and Kudos to @raphamorims for fantastic job.
Matheus Monte
Ex Amazon and Samsung
It is a privilege to be invited to beta test Rio! It has brought me closer to the code of the project, and I have learned a lot about how to develop a terminal emulator.
Thiago Avelino
CTO/CPO at Buser


Data visualization
After downloading the dataset, it would be interesting to view the data first. In this case our dataset is in .csv format, how is he not so big, for a quick view we will use LibreOffice Calc, it is free and has open source.
Opening this file, we can quickly see the “face” of our data.
As we can see, for example, on line 13, the price of a Water (1.5 liter bottle) that varies according to the city, as well as Internet (60 Mbps or More, Unlimited Data, Cable / ADSL) on line 29. With that we may ask: which cities have the highest / lowest cost of living based on the price of the items listed?
Data manipulation
As previously mentioned we will invert the data in such a way that the lines represent the cities and the columns the items. We will do this using LibreOffice Calc.
- select all data
- copy selected data
- create new worksheet and click on A1 cell
- paste using the shortcut ctrl + shift + v
- click on checkbox option “Transpose” and press button “OK”
Now we have the data as we wanted it…


The model uses Content Based Recommendations to find similar movies. The idea behind the filtering is that "If someone likes a particular item, they will also prefer an item that is similar to it." The dataset is taken from Kaggle.com.


Nodes SHOULD use unique names. The tool will check the names for each node in your package against other packages already published to the catalog and highlight any duplicates.
A user cannot install two different nodes that use the same name.
https://nodered.org/docs/creating-nodes/packaging#packagejson


menu
Home
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AboutUs
Ruin Details
3D Models Of Ruins
How To Use
Map Of The Heritage Site
Site Map
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Type a command in the format X Y color to place a pixel. For example, 5 9 red will place a red pixel at (5, 9). The board is 0 by 0 pixels. The available colors are:
X Y color
5 9 red


Richard Phillips Feynman (/ˈfaɪnmən/; May 11, 1918 – February 15, 1988) was an American theoretical physicist known for his work in the path integral formulation of quantum mechanics, the theory of quantum electrodynamics, and the physics of the superfluidity of supercooled liquid helium, as well as in particle physics for which he proposed the parton model. For his contributions to the development of quantum electrodynamics, Feynman, jointly with Julian Schwinger and Sin'ichirō Tomonaga, received the Nobel Prize in Physics in 1965.
Time line:
Early life
- 1918 - Born.
- 1928 - Family moved to Far Rockaway, Queens
Education
- 1939 - eanrs Bachelor's Degree circa.
- 1942 - earns Ph.D circa.
Manhattan Project
- 1942 - joins the Manhattan Project as the leader of the theoretical division circa.
- 1942 - marries highschool sweetheart Arline Greenbaum circa.
Cornell
- 1945 - Arline Greenbaum dies.
- 1945 - Taught physics at Cornell circa.
- 1948 - presented his work "Renormalized non-relativistic quantum equation for the Lamb shift" to his peers at the Pocono Conference.
Caltech
- 1950 - moved to CalTech to teach and stayed there; started work in QED circa.
- 1952 - remarries Mary Louise Bell circa.
- 1956 - and Bell divorce circa.
- 1960 - remarries again to Gweneth Howarth circa.
- 1965 - wins Nobel Prize in physics and delivers speech.
- 1986 - becomes a member of the Rogers Commission and discovers the problem circa.
- 1988 - passes away at age 69.


bambleweeny API Reference
Request Content-Types: application/json
Response Content-Types: application/json
Authentication
oauth2
Authentication
Get Access Token
This endpoint is used to retrieve an access token to perform authenticated requests against the 'resources' endpoints. Login with username (email) and password. For admin, the username is simply 'admin'.
- username: string
- password: string
Request Example
{
"username": "joe",
"password": "secret"
}
Response Example (200 OK)
{
"access_token": "string",
"token_type": "string"
}
Response Example (403 Forbidden)
{
"message": "string"
}
Resources
Get Resource
Retrieve a resource identified by the UUID.
(no description)
Response Example (200 OK)
{
"content": "string",
"owner": "string",
"acl": "string"
}
Delete Resource
Delete a resource identified by a UUID.
(no description)
List Resources
List all resources of the user. Admin will see a list of resources for all users.
Response Example (200 OK)
{
"resources": [
{
"owner": "string",
"id": "string",
"acl": "string"
}
]
}
Create Resource
Create a resource from JSON data.
- content: string
Request Example
{
"content": "lorem ipsum"
}
Response Example (201 Created)
"object"
User Management
Get User
Get a user record identified by a numceric user id.
(no description)
Response Example (200 OK)
{
"email": "string",
"quota": "string"
}
Response Example (401 Unauthorized)
"object"
Set Quota
Set the quota for a user (number of resources a user can have).
- quota: string
(no description)
Request Example
{
"quota": "19"
}
Response Example (200 OK)
{
"message": "string"
}
Delete User
Delete a user record identified by a numceric user id.
(no description)
List Users
List all users in the system.
Response Example (200 OK)
{
"users": [
{
"id": "string",
"email": "string",
"quota": "string"
}
]
}
Create User
Create a user. The new user can then make authenticated requests with access tokens retrieved from the /auth/token endpoint.
- email: string
- password: string
Request Example
{
"email"<EMAIL_ADDRESS>"password": "changeme"
}
Response Example (201 Created)
{
"id": "string"
}
Configuration
Change Admin Password
Update the admin password - default password is 'changeme'.
- password: string
Request Example
{
"password": "c5XU3l901Jp"
}


- Standard for transferring tokens and
approving tokens to be spent by another
on-chain contract-development
Approve Attack vector
- If an account changes approval from 5 to 3, due to front running, the attacker can transfer 8 tokens
- Work around is to change approval from 5 to 0 first. If no attack, change to 2 later. Otherwise, leave approval at 0.
- The worse case in this approach is lose 5 tokens
- Openzeppelin uses decrease/increase as solution
approve
and transferFrom
approve
and transferFrom
- A contract that will accept WETH tokens and issue another tokens
- Demonstrate the use of ERC20 standard approve and transferFrom use case
Metamask
- Ethereum Wallet plugin for chrome
- A wallet allows you to manage your crypto assets and interact with smart contracts
Create/Import Account
- Create Account
- Import Account
- Private Key
- JSON wallet file
What is a JSON Wallet File?
Testnet
- Testnet is a blockchain network for testing purposes
- The following article has a good description of each testnet:
Faucets
- Faucets give testnet ether, to be used for testing
Blockchain Explorer
- Blockchain Explorer is used to lookup a blockchain for information about account, transaction, block, ens name, etc.
Verify Contract
- After a contract is verified on etherscan, the source code is available and you can read or write to the contract on etherscan
- Demo for verifying a contract
Details of token
What did we observe?
Class Activity : ERC20 Token Payment
- Use account 1 to deploy Token.sol contract
- Use account 1 to deploy WETH.sol contract
- Use account 1 to mint token
- Use account 2 to deploy the TokenSale contract using account 1 as the wallet.


Identifier
Url
Venue has no link
Venue has a link
Title
Start Date
End Date
Time
Venue
Venue Url
Venue has no link
Venue has a link
Image
Audience
Theme
Cost
Content


For positive integers $r$ and $s$, we derived the beta $(r, s)$ density
$$ f(x) = \frac{(r+s-1)!}{(r-1)!(s-1)!} x^{r-1}(1-x)^{s-1}, ~~~ 0 < x < 1 $$by studying order statistics of i.i.d. uniform $(0, 1)$ random variables. The beta family can be extended to include parameters $r$ and $s$ that are positive but not integers. This is possible because of facts that you have shown in exercises:
For fixed positive numbers $r$ and $s$, not necessarily integers, the beta $(r, s)$ density is defined by
$$ f(p) = \begin{cases} \frac{\Gamma(r+s)}{\Gamma(r)\Gamma(s)} x^{r-1}(1-x)^{s-1}, ~~~ 0 < x < 1 \\ 0 ~~~~~~~~~~~ \text{otherwise} \end{cases} $$We will not prove that this function integrates to 1, but it is true and should be believable because we have seen it to be true for integer values of the parameters.
Beta distributions are often used to model random proportions. In the previous section ou saw the beta $(1, 1)$ distribution, also known as the uniform, used in this way to model a randomly picked coin.
You also saw that given that we know the value of $p$ for the coin we are tossing, the tosses are independent, but when we don't know $p$ then the first two tosses are no longer independent. Knowledge of how the first toss came out tells us something about $p$, which in turn affects the probabilities of how the second toss might come out.
We will now extend this to more than two tosses and to a general beta prior.
Let $X$ have the beta $(r, s)$ distribution. We can think of this distribution as quantifying our uncertainty about $X$ before we have gathered data. We will call it the prior distribution of $X$.
Given $X = p$, let $I_1, I_2, \ldots $ be i.i.d. Bernoulli $(p)$. That is, given $X = p$, toss a $p$-coin repeatedly and record the results as $I_1, I_2, \ldots$.
Let $S_n = I_1 + I_2 + \cdots + I_n$ be the number of heads in the first $n$ tosses. Then the conditional distribution of $S_n$ given $X = p$ is binomial $(n, p)$. It can be used to calculate the likelihood of the observed number of heads given a value of $p$.
Before running the experiment, our prior opinion is that $X$ has the beta $(r, s)$ distribution. How should we update that opinion after we have tossed $n$ times and seen the number of heads?
To answer this, we will find $P(X \in dp \mid S_n = k)$ using Bayes' Rule. To reduce some writing, we will use $C(r, s)$ for the constant in the beta $(r, s)$ density.
For $0 < p < 1$,
\begin{align*} P(X \in dp \mid S_n = k) ~ &= ~ \frac{P(X \in dp, S_n = k)}{P(S_n = k)} \\ \\ &= ~ \frac{P(X \in dp)P(S_n = k \mid X = p)}{P(S_n = k)} ~~~~ \text{(Bayes' Rule)} \\ \\ &\sim ~ \frac{C(r, s) p^{r-1}(1-p)^{s-1}dp \binom{n}{k} p^k (1-p)^{n-k}}{P(S_n = k)} \\ \\ &= ~ C^*(r, s, n, k) p^{r+k-1}(1-p)^{s + (n-k) - 1} dp \end{align*}where $C^*(r, s, n, k)$ does not involve $p$.
You can see at once that this is the beta $(r+k, s+n-k)$ density:
$$ f_{X \mid S_n = k} (p) ~ = ~ C(r+k, s+n-k) p^{r+k-1}(1-p)^{s + n - k - 1}, ~~~ 0 < p < 1 $$This is called the posterior density of $X$ given $S_n = k$.
The prior distribution of the probability of heads is from the beta family. The posterior distribution of the probability of heads, given the number of heads, is another beta density. The beta prior and binomial likelihood combine to result in a beta posterior. The beta family is therefore called a family of conjugate priors for the binomial distribution: the posterior is another member of the same family as the prior.
The posterior mean of $X$ given $S_n = k$ is just the expectation of the beta posterior density:
$$ E(X \mid S_n = k) ~ = ~ \frac{r+k}{r+s+n} $$Let's examine these results in an example. Suppose the prior distribution of $X$ is beta $(5, 3)$, and thus the prior mean is $E(X) = 5/8 = 0.625$. Now suppose we are given that $S_{100} = 70$. Then the posterior distribution of $X$ given $S_{100} = 70$ is beta $(75, 33)$ with mean $75/108 = 0.694$.
The graph below shows the two densities along with the corresponding means. Run the cell again, keeping $r = 5$ and $s = 3$ but changing $n$ to 10 and $k$ to 7; then again changing $n$ to 1000 and $k$ to $700$. The observed proportion is 0.7 in all cases. Notice how increasing the sample size concentrates the prior around 0.7. We will soon see the reason for this.
Also try other values of the parameters as well as $n$ and $k$, including values where the observed proportion is quite different from the mean of the prior.
# Prior: beta (r, s)
# Given: S_n = k
r = 5
s = 3
n = 100
k = 70
plot_prior_and_posterior(r, s, n, k)
You can see how the data dominate the prior. The posterior distribution is concentrated around the posterior mean. The prior mean was 0.625, but given that we got 70 heads in 100 tosses, the posterior mean is 0.694 which is very close to the observerd proportion 0.7.
The formula for the posterior mean shows that for large $n$ it is likely to be close to the observed proportion of heads. Given $S_n = k$, the posterior mean is
$$ E(X \mid S_n = k) ~ = ~ \frac{r + k}{r + s + n} $$Therefore as a random variable, the posterior mean is $$ E(X \mid S_n) ~ = ~ \frac{r + S_n}{r + s + n} $$
As the number of tosses $n$ gets large, the number of heads $S_n$ is likely to get large too. So the value of $S_n$ is likely to dominate the numerator, and $n$ will dominate the denominator, because $r$ and $s$ are constants. Thus for large $n$, the posterior mean is likely to be close to $S_n/n$.
As we saw in the previous section, the chance that a random coin lands heads is the expected value of its random probability of heads. Apply this to our current setting to see that
$$ P(S_1 = 1) ~ = ~ P(\text{first toss is a head}) ~ = ~ E(X) ~ = ~ \frac{r}{r+s} $$Now suppose that we have the results of the first $n$ tosses, and that $k$ of those tosses were heads. Given that $S_n = k$, the possible values of $S_{n+1}$ are $k$ and $k+1$. We can now use our updated distribution of $X$ and the same reasoning as above to see that
$$ P(S_{n+1} = k+1 \mid S_n = k) ~ = ~ P(\text{toss } n+1 \text{ is a head} \mid S_n = k) ~ = ~ E(X \mid S_n = k) ~ = ~ \frac{r+k}{r + s + n} $$We can work out $P(S_{n+1} = k \mid S_n = k)$ by the complement rule. We now have a transition function. Given that $S_n = k$, the conditional distribution of $S_{n+1}$ is given by
$$ S_{n+1} = \begin{cases} k ~~~~~~~~ \text{ with probability } (s + n - k)/(r + s + n) \\ k+1 ~~ \text{ with probability } (r+k)/(r + s + n) \end{cases} $$In other words, given the results of the first $n$ tosses, the chance that Toss $n+1$ is a tail is proportional to $s$ plus the number of failures. The chance that Toss $n+1$ is a head is proportional to $r$ plus the number of successes.
You can think of the sequence $\{ S_n: n \ge 1 \}$ as a Markov chain, but keep in mind that the transition probabilities are not time-homogenous – the formulas involve $n$.


if you click many times on the same element, then no new clicked event is dispatched.
This also applies to the host nodes, so that the host nodes only respond when they are
clicked the first time. Ie. the event must propagate or remake itself for each new element clicked.


Carl Boettiger is an NSF post-doctoral researcher at UC Santa Cruz, working on prediction and management of ecological regime shifts. He is a founding member of rOpenSci and keeps an open lab notebook.


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SILENT TOUCH, THE (director: Krzysztof Zanussi; cast: Aleksander Bardini (Professor Jerzy Kern), Lothaire Bluteau (Stefan), Sofie Gråbøl (Annette Berg), Max von Sydow (Henrik Kesdi), Sarah Miles ( Henrik'sWife), Peter Hesse Overgaard (Joseph), 1992-Poland)
It is hard to imagine how a film about a musical genius, Henrik Kesdi, that has a renown veteran actor (von Sydow) in it, who is quite adept at playing the part called for, of such a curmudgeon and unsociable giant in the annals of musical composition, and the film being so meritoriously scored musically as to give it the proper classical musical atmosphere for the story to take off on, plus it is directed by an innovative and critically acclaimed Polish director, known for his intellectual films that are implicit studies in character (CAMOUFLAGE, CONTRACT, ILLUMINATION), should fall so short in its ability to be a creative film, and is only carried to the level of marginal respectability by the weight of the above named actor and this after a brilliant opening scene that portends to a film that will be interminably beguiling, that is based on an idea for a film that is both original and captivating, just baffles me, as I try to ponder what went wrong and how moribund and cliche-ridden the story came to be.
The film opens with the Polish musicologist student, Stefan (Lothaire Bluteau), tossing in his sleep from a recurring dream in which he hears a few bars of magical music. He goes in the middle of the night with this news to see his music professor Kern (Bardini), who says this sound might be linked with someone he went to school with, the composer Henrik Kesdi, who had a very promising future, but during WW11, his wife was a holocaust victim and as a protest to the cruelty of the world, he has chosen to remain silent ever since. Since Kesdi is living in Denmark, Stefan goes there to see the reclusive composer.
Henrik is seen in his splendid country house as a bitter, crotchety, infirmed 74-year-old, who has remarried a much younger woman, Helena (Miles), who is willing to put up with his manipulative behavior, as he claims that all he wants is to be left alone--- to enjoy my incontinence in private.
Stefan forces his way into the house of the composer, even though he is not welcomed at first, by being able to cure the composer of his backaches by using a divining rod and discovering a stream under his bedroom and telling the composer to switch rooms when he sleeps. Well, this cure might work on film, and who knows, this information might have some medical merit that I am not aware of, but as part of the plot, Stefan as the healer, ingratiating himself with the composer to break his musical silence and give it all for the world of music, started to give me a headache, as it reminded me of all the formula films of this nature that I have sat through in the past, thinking that this easily could have been the theme of a sports film or a multitude of other films, and that the splendid idea that opened the picture was about to be destroyed by contrivance and implausibility. O great muses, where are you when the filmmaker needed you most !
>From here on in, it is all downhill, as you can already guess the glorious ending to the film, of Henrik creating that great score, so what is left, is filling in the details leading up to this grand accomplishment, and for that we can use an attractive young Danish girl, and that will come in the form of a musical secretary, Annette (Gråbøl), who will tease the young musicologist but be passionately attracted to the cunningly considerate and intellectually gifted older maestro, who she will become pregnant with, as Helena will be very undertanding of her husband's needs and they will form a pretty odd menage-a-trois, as Stefan will depart for Poland, having brought goodness into the world in the form of musical genius, but feeling empty inside because his love for Annette is unrequited.
The moral of the story centers around whether you believe one's self-sacrifice for the good of the world is a genuine and necessary one, or that unfilled desires are the height of all deceits. These and other interesting questions could have been delved into, if the plot wasn't so ludicrous, and if the film wasn't so uninspired, as to make it almost unbearable to stomach. Tis a pity !
REVIEWED ON 5/6/99 GRADE: C-
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
© ALL RIGHTS RESERVED DENNIS SCHWARTZ
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


I am a PhD candidate at the RCSI in Dublin. Please check out my blog pages, my CV and my contact page.


For this project, we were challenged to build a product for peer economy, bringing two different user roles, with different goals, together in competition of a task. Our team chose to design for people who have a parking space they might want to share and people who could use the parking space. The challenge here is how to let the two peers are not afraid of using a non-traditional service and bring value to them. Park Helper is our solution. The service focuses on long-term parking and delivers monetary, reassurance, and help value to the two parties.
Date: Oct - Nov 2016, 3 weeks
Team: Xiaonan Chen, Justin Fanzo, Marc Estruch Tena, Angel Yu
My Role: Researcher, Designer
We conducted 12 interviews in Shadyside and Oakland of Pittsburgh, 5 with property owners and 7 with drivers who look for parking spots. For property owners, we asked their acceptance and concersn about lending their parking space to strangers; their opinions of getting extra income by lending personal space out. For drivers, we asked them their bad experience when looking for parking space; their acceptance and concerns about using non-traditional parking space; their preference when choosing a parking space.
12
Interviews
5
Property Owners
7
Drivers
I’d love to rent only if it does not interfere my parking time. Time is hard to control.
I will be more willing to lend out if I know the person.
There’s personal stuff in my parking lot. Strangers may get information of the house.
It’s smart to have someone in the driveway if people are in vacation. There’s value in having the illusion of somecone coming and going. It’s not about the money, it’s about security.
My top concerns about parking in general are cost, distance, and safety.
Once I parked in an illegal spot and got towed. Parking space is limited in big cities.
It could be a bit insecure to get into a stranger’s closed space, especially for female.
The service should be reliable and trustworthy, creating a network with people who will be more likely to use your spot.
We also conducted secondary research and cometitive analysis to see how other products are designed to ease users’ worry when using stranger’s service and to find the right market position for our design.
Identity Vetting
this is a paragraph.this is a paragraph.this is a paragraph.this is a paragraph.this is a paragraph.
Rating System
this is a paragraph.this is a paragraph.this is a paragraph.this is a paragraph.this is a paragraph.
Community Building
this is a paragraph.this is a paragraph.this is a paragraph.this is a paragraph.this is a paragraph.
Based on our findings through interview and secondary research, we gained 6 key insights:
Users need the other party’s information be checked to build up trust. Vetted personal information could ease users’ worries, which is also common seen in other peer economy service, like Airbnb and Uber.
Easy communication is needed in using peer-to-peer parking service. Users hope the communication cost is as little as possible.
Property owners are more open to lend parking space in long-term parking. Because long-term parking is less influenced by schedule changing and therefore causes less parking time confliction.
Driveways renting is prefereed by both drivers and property owners. They want to save the trouble of exchanging keys and ensure personal or property security and privacy.
Parking fee is a nice-to-have income source for property owners, but is a priority consideration for drivers. Drivers hope the parking cost could be as little as possible, while property owners are less sensitive towards ths issue.
Property owners need others to help check their house when they’re absent to get reassured. This creates sense of security for property owners.
Based on these insights, our team decided to focus only on weekly or long-term parking, which is 7 days or longer, and driveway parking. We hoped to build a service to enable drivers to provide extra help (like house check) to property owners in need in exchange for low-cost parking space. Also, our service needs to create feeling of trust and security to get both parties reassured.
Based on these insights, our team decided to focus our design on two aspects:
Combining given course materials and our interview findings, we created two personas and used storyboards to help us better empathize user needs.
After multiple rounds of discussion and brainstorming as well as 20 exploratory scenarios, our team further narrowed down our targeted users to:
(1) Drivers who are looking for cheap parking spots;
(2) Property owners who need others to help check their house or provide other assistance, like families on vacation, mobility difficulties elderly, sick person, etc.
Based on the needs of these two groups, we finally settled on the idea of designing a weekly-based parking service that requires drivers to complete daily simple tasks to exchange cheap parking spots. The following key features are included to ensure the process is seamless and effortless for both parties.
Intuitive Design. Easy onboard and usage for non-expert mobile users.
Customized parking log. Property owners can select from a list of simple tasks to ask the drivers to complete everyday. These tasks require little effort to do, such as checking mailbox, checking front door, etc.
Location-sensing reminder. The app senses whereabouts of the driver and automatically reminds him or her to log after the parking.
Request extra help and give discount. Property owners can add extra services that drivers can perform for extra discount. Such tasks are voluntary, and the amount of discount is set by the property owner.
Based on the interaction flow we had, we then created the navigation map to guide later design. Due to the time limitation, we were only required to have the MVP (Minimum Viable Product) for idea communication. Below is the hi-fi prototype of the chosen screens to convey the idea.


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Booth, William (SchoolTeacher)
| Birth Name | Booth, William (SchoolTeacher) |
| Gender | male |
| Age at Death | about 80 years, 1 month, 19 days |
Narrative
SMH - BOOTH - February 20, 1910, at his late residence, Earlshome, 26 Chandos St, Ashfield, WILLIAM BOOTH, in his 80th year.
FUNERAL - BOOTH - The Funeral of the late WILLIAM BOOTH, will leave his late residence, Earlshome, 26 Chandos St, Ashfield, To-morrow, Tuesday Morning, at 9.45am, for Strathfield Station, thence by train for internment at Sutton Forest CofE Cemetery.
I found a Marriage in B.D.&M's for William Booth to Frances Darrietta at Kogarah in 1903. But I also found a death for Frances Darrietta in Canterbury in 1925, so is it the same lady?? why is her death recorded in her maiden name??(or her 1st husbands name)??
Events
| Event | Date | Place | Description | Sources |
|---|---|---|---|---|
| Birth | about 1830 | County Leitrim Ireland | ||
|
|
||||
| Death | 1910-02-20 | Ashfield NSW | ||
|
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||||
| Burial | 1910-02-24 | CofE Cemetery Sutton Forest NSW | ||
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Parents
| Relation to main person | Name | Birth date | Death date | Relation within this family (if not by birth) |
|---|---|---|---|---|
| Mother | Booth, Mary | |||
| Booth, William (SchoolTeacher) | about 1830 | 1910-02-20 |
Families
Family of Booth, William (SchoolTeacher) and Sewell, Ruth |
||||||||||||||||||||
| Married | Wife | Sewell, Ruth ( * 1848-12-13 + 1874-08-28 ) | ||||||||||||||||||
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| Children |
| Name | Birth Date | Death Date |
|---|---|---|
| Booth, Mary Doctor | 1869 | 1956-11-28 |
| Booth, Eliza A. | 1871 | |
| Booth, Pheobe Elizabeth | 1873 | 1875 |
| Booth, Kate E. | about 1874 | 1875 |
| Booth, Thomas | about 1876 | 1935 |
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | 44FEFA65E5C4D5119853B7EB0FA9CA6DAC20 |
Family of Booth, William (SchoolTeacher) and Darrietta, Frances S.
| Event | Date | Place | Description | Sources |
|---|---|---|---|---|
| Marriage | 1903 | Kogarah NSW #3770 | ||
|
|
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | E3E677F350FADA119522CD49D8F7D4795155 |
Attributes
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | 4EFEFA65E5C4D5119853B7EB0FA9CA6DB6C0 |
Pedigree
-
Booth, Mary
- Booth, William (SchoolTeacher)


XACC Tutorials¶
First off, make sure you have successfully built XACC (see XACC Install).
Rigetti QVM Tutorial¶
Create a new directory called test-xacc-rigetti and cd into it. Let’s now create a test-xacc-rigetti.cpp file and get it started with the following boilerplate code:
#include "XACC.hpp"
int main(int argc, char** argv) {
// Initialize XACC - find all available
// compilers and accelerators, parse command line.
xacc::Initialize(argc, argv);
// ... Code to come ...
// Finalize the framework.
xacc::Finalize();
}
Building this code is straightforward with CMake. Create a CMakeLists.txt file in the same directory as the test-xacc-rigetti.cpp file, and add the following to it:
# Start a CMake project
project(test-xacc-rigetti CXX)
# Set the minimum version to 3.2
cmake_minimum_required(VERSION 3.2)
# Find XACC
find_package(XACC REQUIRED)
# Find Boost
find_package(Boost COMPONENTS system program_options filesystem chrono thread REQUIRED)
# Include all XACC Include Directories
include_directories(${XACC_INCLUDE_DIRS})
# Link to the XACC Library Directory,
link_directories(${XACC_LIBRARY_DIR})
# Create the executabe
add_executable(test-xacc-rigetti test-xacc-rigetti.cpp)
# Like the necessary libaries
target_link_libraries(test-xacc-rigetti ${XACC_LIBRARIES} dl pthread)
Now from within the test-xacc-rigetti directory, run the following:
$ mkdir build
$ cd build
$ cmake ..
$ make
This will build test-xacc-rigetti.cpp and provide you with a test-xacc-rigetti executable. Run that executable to ensure that your build worked (you should see the following output): .. code:
$ make
$ ./test-xacc-rigetti
[2017-06-20 16:14:07.076] [xacc-console] [info] [xacc] Initializing XACC Framework
[2017-06-20 16:14:07.091] [xacc-console] [info] [xacc::compiler] XACC has 1 Compilers available.
[2017-06-20 16:14:07.091] [xacc-console] [info] [xacc::accelerator] XACC has 1 Accelerators available.
[2017-06-20 16:14:07.091] [xacc-console] [info]
[xacc] XACC Finalizing
[xacc::compiler] Cleaning up Compiler Registry.
[xacc::accelerator] Cleaning up Accelerator Registry.
Now that we have our build and initial boilerplate code setup, let’s actually write some quantum code, specifically teleporting the state of one qubit to another. Following the XACC.hpp include statement at the top of the file, add the following quantum kernel declaration:
const std::string src("__qpu__ teleport (qbit qreg) {\n"
" cbit creg[3];\n"
" // Init qubit 0 to 1\n"
" X(qreg[0]);\n"
" // Now teleport...\n"
" H(qreg[1]);\n"
" CNOT(qreg[1],qreg[2]);\n"
" CNOT(qreg[0],qreg[1]);\n"
" H(qreg[0]);\n"
" creg[0] = MeasZ(qreg[0]);\n"
" creg[1] = MeasZ(qreg[1]);\n"
" if (creg[0] == 1) Z(qreg[2]);\n"
" if (creg[1] == 1) X(qreg[2]);\n"
" // Check that 3rd qubit is a 1\n"
" creg[2] = MeasZ(qreg[2]);\n"
"}\n");
Now we are ready to build and execute this kernel using the XACC Runtime API. After the call to xacc::Initialize, add the following:
// Create a reference to the Rigetti
// QPU at api.rigetti.com/qvm
auto qpu = xacc::getAccelerator("rigetti");
// Allocate a register of 3 qubits
auto qubitReg = qpu->createBuffer("qreg", 3);
// Create a Program
xacc::Program program(qpu, src);
// Request the quantum kernel representing
// the above source code
auto teleport = program.getKernel("teleport");
// Execute!
teleport(qubitReg);
The code above starts by getting a reference to the RigettiAccelerator. With that reference, we then allocate a register of qubits to operate the teleport kernel on. Next, we instantiate an XACC Program instance, which keeps track of the desired Accelerator and the source code to be compiled. The Program instance orchestrates the compilation of the quantum kernel to produce the XACC intermediate representation, and then handles the creation of an executable classical kernel function that offloads the compiled quantum code to the specified Accelerator. Finally, the user requests a reference to the executable kernel functor, and executes it on the provided register of qubits.
The total test-xacc-rigetti.cpp file should look like this: .. code:
#include "XACC.hpp"
// Quantum Kernel executing teleportation of
// qubit state to another.
const std::string src("__qpu__ teleport (qbit qreg) {\n"
" cbit creg[3];\n"
" // Init qubit 0 to 1\n"
" X(qreg[0]);\n"
" // Now teleport...\n"
" H(qreg[1]);\n"
" CNOT(qreg[1],qreg[2]);\n"
" CNOT(qreg[0],qreg[1]);\n"
" H(qreg[0]);\n"
" creg[0] = MeasZ(qreg[0]);\n"
" creg[1] = MeasZ(qreg[1]);\n"
" if (creg[0] == 1) Z(qreg[2]);\n"
" if (creg[1] == 1) X(qreg[2]);\n"
" // Check that 3rd qubit is a 1\n"
" creg[2] = MeasZ(qreg[2]);\n"
"}\n");
int main (int argc, char** argv) {
// Initialize the XACC Framework
xacc::Initialize(argc, argv);
// Create a reference to the Rigetti
// QPU at api.rigetti.com/qvm
auto qpu = xacc::getAccelerator("rigetti");
// Allocate a register of 3 qubits
auto qubitReg = qpu->createBuffer("qreg", 3);
// Create a Program
xacc::Program program(qpu, src);
// Request the quantum kernel representing
// the above source code
auto teleport = program.getKernel("teleport");
// Execute!
teleport(qubitReg);
// Finalize the XACC Framework
xacc::Finalize();
return 0;
}
Now, to build simple run: .. code:
$ cd test-xacc-rigetti/build
$ make
To execute this code on the Rigetti QVM, you must provide your API key. You can do this the same way you do with PyQuil (in your $HOME/.pyquil_config file, or in the $PYQUIL_CONFIG environment variable). You can also pass your API key to the XACC executable through the –rigetti-api-key command line argument:
$ ./test-xacc-rigetti --rigetti-api-key KEY
[2017-06-20 17:43:38.744] [xacc-console] [info] [xacc] Initializing XACC Framework
[2017-06-20 17:43:38.760] [xacc-console] [info] [xacc::compiler] XACC has 3 Compilers available.
[2017-06-20 17:43:38.760] [xacc-console] [info] [xacc::accelerator] XACC has 2 Accelerators available.
[2017-06-20 17:43:38.766] [xacc-console] [info] Executing Scaffold compiler.
[2017-06-20 17:43:38.770] [xacc-console] [info] Rigetti Json Payload = { "type" : "multishot", "addresses" : [0, 1, 2], "quil-instructions" : "X 0\nH 1\nCNOT 1 2\nCNOT 0 1\nH 0\nMEASURE 0 [0]\nMEASURE 1 [1]\nJUMP-UNLESS @conditional_0 [0]\nZ 2\nLABEL @conditional_0\nJUMP-UNLESS @conditional_1 [1]\nX 2\nLABEL @conditional_1\nMEASURE 2 [2]\n", "trials" : 10 }
[2017-06-20 17:43:40.439] [xacc-console] [info] Successful HTTP Post to Rigetti.
[2017-06-20 17:43:40.439] [xacc-console] [info] Rigetti QVM Response:
[[0,1,1],[1,1,1],[1,1,1],[0,1,1],[1,0,1],[1,1,1],[0,1,1],[0,1,1],[0,0,1],[0,0,1]]
[2017-06-20 17:43:40.439] [xacc-console] [info]
[xacc] XACC Finalizing
[xacc::compiler] Cleaning up Compiler Registry.
[xacc::accelerator] Cleaning up Accelerator Registry.
You should see the console text printed above.
You can also tailor the number of executions that occur for the multishot execution type:
$ ./test-xacc-rigetti --rigetti-trials 1000
[2017-06-20 17:50:57.285] [xacc-console] [info] [xacc] Initializing XACC Framework
[2017-06-20 17:50:57.301] [xacc-console] [info] [xacc::compiler] XACC has 3 Compilers available.
[2017-06-20 17:50:57.301] [xacc-console] [info] [xacc::accelerator] XACC has 2 Accelerators available.
[2017-06-20 17:50:57.307] [xacc-console] [info] Executing Scaffold compiler.
[2017-06-20 17:50:57.310] [xacc-console] [info] Rigetti Json Payload = { "type" : "multishot", "addresses" : [0, 1, 2], "quil-instructions" : "X 0\nH 1\nCNOT 1 2\nCNOT 0 1\nH 0\nMEASURE 0 [0]\nMEASURE 1 [1]\nJUMP-UNLESS @conditional_0 [0]\nZ 2\nLABEL @conditional_0\nJUMP-UNLESS @conditional_1 [1]\nX 2\nLABEL @conditional_1\nMEASURE 2 [2]\n", "trials" : 100 }
[2017-06-20 17:50:57.909] [xacc-console] [info] Successful HTTP Post to Rigetti.
[2017-06-20 17:50:57.909] [xacc-console] [info] Rigetti QVM Response:
[[1,0,1],[0,0,1],[1,1,1],[0,1,1],[1,0,1],[0,1,1],[0,0,1],[1,1,1],[1,0,1],[1,0,1],[0,0,1],[1,0,1],[1,1,1],[0,1,1],[0,0,1],[1,1,1],[1,0,1],[1,1,1],[0,0,1],[1,1,1],[1,0,1],[0,0,1],[0,0,1],[1,0,1],[0,1,1],[0,0,1],[1,1,1],[0,0,1],[0,1,1],[1,1,1],[1,0,1],[1,0,1],[0,1,1],[0,1,1],[1,1,1],[1,1,1],[1,1,1],[0,1,1],[1,1,1],[1,0,1],[1,0,1],[1,1,1],[1,1,1],[0,0,1],[1,1,1],[0,0,1],[1,0,1],[1,1,1],[1,0,1],[1,1,1],[0,1,1],[0,1,1],[1,0,1],[0,0,1],[1,1,1],[0,1,1],[0,1,1],[1,1,1],[1,0,1],[1,1,1],[0,0,1],[0,0,1],[1,0,1],[0,1,1],[0,0,1],[0,1,1],[1,0,1],[0,1,1],[1,0,1],[0,0,1],[1,0,1],[1,1,1],[1,0,1],[1,1,1],[0,0,1],[0,1,1],[1,0,1],[1,1,1],[1,1,1],[0,1,1],[1,0,1],[1,1,1],[0,1,1],[1,0,1],[1,0,1],[0,0,1],[1,0,1],[0,0,1],[0,0,1],[1,0,1],[1,1,1],[0,1,1],[0,1,1],[0,1,1],[1,0,1],[1,1,1],[1,1,1],[0,1,1],[0,1,1],[0,1,1]]
[2017-06-20 17:50:57.910] [xacc-console] [info]
[xacc] XACC Finalizing
[xacc::compiler] Cleaning up Compiler Registry.
[xacc::accelerator] Cleaning up Accelerator Registry.
Note above we let XACC find the API Key in the standard .pyquil_config file.


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Dear Dry,
We’re still streaming the general assemblies [ http://www.ustream.tv/channel/national-young-feminist-leadership-conference-2013 ] of this year’s 2013 National Young Feminist Leadership Conference in DC!
Go to our Ustream channel to watch live [ http://www.ustream.tv/channel/national-young-feminist-leadership-conference-2013 ]. Follow @feministcampus on twitter or follow the hashtag - we’ll tweet when a session is starting so you know when to watch!
Here’s the schedule of the sessions we are streaming today [ http://www.ustream.tv/channel/national-young-feminist-leadership-conference-2013 ]:
Ustream Schedule [ http://www.ustream.tv/channel/national-young-feminist-leadership-conference-2013 ]
You can also check out the detailed schedule online [ http://www.feministcampus.org/leadership/2013NYFLC/agenda.asp ] to see who is speaking in each session. Enjoy!
For equality,
*FMF Campus Team*
duVergne, Brooke, Adriana, Francesca, Miranda and Kari
You have received this e-mail because of your interest in women’s issues. To unsubscribe, please click here. [ http://salsa.democracyinaction.org/o/1400/p/salsa/supporter/unsubscribe/public/index.sjs?unsubscribe_page_KEY=494 ]