Tutorial example on how to use AND/OR/XOR constraints. More...
Go to the source code of this file.
Functions | |
| def | and_constraint (v=1, sense="minimize") |
| def | or_constraint (v=0, sense="maximize") |
| def | xors_constraint (v=1) |
| def | xorc_constraint (v=0, sense="maximize") |
Tutorial example on how to use AND/OR/XOR constraints.
Definition in file logical.py.
| def logical.and_constraint | ( | v = 1 , |
|
sense = "minimize" |
|||
| ) |
AND constraint
Definition at line 39 of file logical.py.
| def logical.or_constraint | ( | v = 0 , |
|
sense = "maximize" |
|||
| ) |
OR constraint
Definition at line 50 of file logical.py.
| def logical.xors_constraint | ( | v = 1 | ) |
XOR (r as boolean) standard constraint
Definition at line 60 of file logical.py.
| def logical.xorc_constraint | ( | v = 0 , |
|
sense = "maximize" |
|||
| ) |
XOR (r as variable) custom constraint
Definition at line 69 of file logical.py.


Better yet, let's use the HTML5 <audio> element to do so!
Your browser does not support the audio element.


gopen¶
-
dlinputs.gopen.
find_shards
(urls, size=17, complete=False)[source]¶ Given a list of shard URLs, find the first one that exists.
-
dlinputs.gopen.
gopen
(url, mode='rb')[source]¶ Open the given URL. Supports unusual schemes and uses subprocesses.
-
dlinputs.gopen.
sharditerator
(url, epochs=1000000, shuffle=True, **kw)[source]¶ Iterate over sharded tar records.
-
dlinputs.gopen.
sharditerator_multi
(url, epochs=1000000, shuffle=True, multi=1, **kw)[source]¶ Iterate over sharded tar records, opening multiple shards in parallel.
-
dlinputs.gopen.
sharditerator_once
(url, **kw)[source]¶ Iterate over sharded tar records (no shuffling, one epoch only).
-
dlinputs.gopen.
test_curl_write
(self, location)[source]¶ Test whether we can write to a location using curl.


Toggle navigation
dashQC_fmri
Summary
Registration
Motion
Load QC
Save QC
Individual vs template T1 scan for subject
Click or press left/right arrows for prev/next subject. Hover over image for template scan. Move mouse away to revert to original image.
BOLD vs T1 scan for subject
Click or press left/right arrows for prev/next subject. Hover over image for individual T1 scan. Move mouse away to revert to original image.


PublicApi
ui.grid.saveState
Public Api for saveState feature
Restores the provided state into the grid
a scope that we can broadcast on
the state that should be restored into the grid
Packages the current state of the grid into an object, and provides it to the user for saving
the state as a javascript object that can be saved


Staff
login
Not registered?
Create an account
Forgot Password?
Password
Student
login
Not registered?
Create an account
Forgot Password?
Password


--- layout: post esbma_id: 1687 title: 3: RenderTargetBitmap date: 2007-07-15 19:01:11 ---
This entry is part 3 of a 12-part series on WPF 3D.
WPF has some really handy classes in it. Have you seen RenderTargetBitmap? Basically, it's a simple way to capture a bitmap of any WPF Visual. It works with 3D too:
RenderTargetBitmap bmp = new RenderTargetBitmap(
width, height, 96, 96, PixelFormats.Pbgra32);
bmp.Render(myViewport3D);
Wanna copy that image to the clipboard so you can paste it into some other application?
Clipboard.SetImage(bmp);
Wanna save that image to a PNG file?
PngBitmapEncoder png = new PngBitmapEncoder();
png.Frames.Add(BitmapFrame.Create(bmp));
using (Stream stm = File.Create(filepath))
{
png.Save(stm);
}
Nifty. The pictures in this series of blog entries were rendered in just this fashion. But there are a couple of details worth mentioning.
When using RenderTargetBitmap, you should probably erase it before you draw anything into it:
Rectangle vRect = new Rectangle();
vRect.Width = width;
vRect.Height = height;
vRect.Fill = Brushes.White;
vRect.Arrange(new Rect(0, 0, vRect.Width, vRect.Height));
bmp.Render(visual);
If your Viewport3D was created offscreen, it's not ready to draw. You need to give it a size (the same as the bitmap you're rendering into) and call Measure() and Arrange() to get it ready:
myViewport3D.Width = width;
myViewport3D.Height = height;
myViewport3D.Measure(new Size(width, height));
myViewport3D.Arrange(new Rect(0, 0, width, height));
Note that none of the above is specific to the 3D features of WPF. RenderTargetBitmap just works with a Viewport3D like any other visual. This is one of the best things about WPF: The 3D features are not special or weird. They're seamlessly integrated into the framework.
If you have experience programming with some other 3D API such as OpenGL or Direct3D (upon which WPF is built, by the way), you are probably accustomed to thinking of 3D stuff as very distinct from other stuff. Simple things like getting a 3D graphic to appear in the same window next to a listbox can require all kinds of gymnastics. WPF doesn't have those sorts of boundaries. If you want to put an animated 3D scene as the graphic for a toolbar button, you can.
That level of integration is deeply neato.
Just one more detail about RenderTargetBitmap, and this one is sort-of 3D-specific:
If your Viewport3D was created offscreen and you're using our friend ScreenSpaceLines3D, you'll need to make sure your lines get scaled at least once. Since I hacked my copy of ScreenSpaceLines3D.cs to remove the use of CompositionTarget.Rendering, I simply call my Rescale() method on every instance just after I call Measure() and Arrange() on the offscreen Viewport3D.


Christian Critic's Movie Parables - http://www.christiancritic.com
SHILOH 2: SHILOH SEASON *** out of **** stars ======================= DIRECTED BY: Sandy Tung STARRING: Zachary Browne Michael Moriarty Scott Wilson Rod Steiger WRITTEN BY: Dale Rosenbloom RATED: PG for mild thematic elements SCRIPTURE Colossians 3:9-10 REFERENCES: Ephesians 4:22-24 Romans 12:2
Getting lost amid all the gross-out humor, sexual innuendo, and potty-mouthed characters which fill our theaters and pass for modern day entertainment, are the wholesome, family-oriented films the likes of SHILOH 2. This is a pity. Families need and deserve films such as these to help establish the qualities of morality, integrity, honesty, kindness, and forgiveness that we'd like to think parents are teaching their children.
Based on the award winning series of books by Phyllis Reynolds Naylor, SHILOH 2 continues the telling of a simple story about a boy and his dog, and the mean-spirited neighbor who threatens to come between them.
Whereas the first movie dealt with Marty's attempts to save Shiloh, a precious, floppy-eared beagle, from his abusive owner, its sequel deals with Marty's attempts to save Shiloh's former owner from himself. Judd Travers is an ill-tempered, antisocial man angered further by losing his best hunting dog to Marty. He is drinking heavily, driving erratically, and generally terrorizing the surrounding community which gives him a wide berth.
Marty begins to ask the question which becomes the dominant theme for the film: "Can a person change? If he is mean, is he mean forever?"
Writer Dale Rosenblum, who wrote the screenplay for the first SHILOH movie, and director Sandy Tung (CONFESSIONS OF A SEXIST PIG) have brought back most of the cast of the 1997 original film, including Michael Moriarty as Ray Preston, Rod Steiger as kindly Doc Wallace, and Scott Wilson as the ornery Judd Travers.
The younger cast members playing the roles of the Preston children have been replaced. Zachary Browne (TV's ER) takes over the role of Marty Preston, the 12 year old owner of the most familiar beagle since Snoopy.
Most of the acting is adequate for the story, the standout exception being Mr. Wilson's performance. His realistic portrayal of the backwoods curmudgeon gives the movie's message a depth of meaning that many other moralistic films do not achieve. Judd is not a stereotypical bad man. He is a bitter, unhappy, lonely soul who has never had a friend and has little prospect of ever making one.
Some of the dialogue admittedly contains a few "groaners." Marty, after seeing Judd shoot a squirrel, indicating his lack of respect for the hunting season, worriedly asks, "What if he makes it Shiloh season?" The movie also takes a while to get its legs under itself. But once Marty makes it his task to find out if Judd could ever stop being mean, the story finds its pace and our interest.
Shiloh is no Lassie or Rin Tin Tin. Those heroic dogs are often featured as the most intelligent character appearing on their respective screens. The dog in this film is less of a super-powered canine that saves the day and more of a catalyst for the actions of the humans with whom he lives.
Thankfully, the filmmakers also refrained from churning out an unrealistic fable where a bad man is miraculously made good overnight. Judd initially rebukes Marty's intentions, and later ignores his further attempts of friendship. It is going to take a great deal of perseverance and a gargantuan effort on Marty's part to break through Judd's crusty shell and inspire a behavioral change.
It will be hard... But it is not impossible. All of us are asked to change on a daily basis. We are to continually "put off" our "old man nature" and renew ourselves according to the standard of righteousness.
"You were taught, with regard to your former way of life, to put off your old self, which is being corrupted by its deceitful desires; be made new in the attitude of your minds; to put on the new self." Ephesians 4:22-24a [NIV]
As Marty learns, this is not something that happens overnight. It takes a great deal of commitment, perseverance, and an unwillingness to settle for less. It requires a daily decision to change bad or spiritually unhealthy habits by adopting new and godly habits to take their place. We could all use some of Marty's driving determination at times in order to be successful. Or perhaps we just need to have a love for someone other than ourselves to motivate us to be better.
Michael Elliott August 1999 http://www.christiancritic.com
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


|
STEP 9: Other Control Panel Layouts
|
By using different values for the WIDTH, HEIGHT, DISPLAY, MODE, and HIDDEN embedding properties, several different control panel layouts can be achieved. In order to learn more about the specific settings used to achieve these different control panel layouts, consult the appendix Beatnik Player Control Panel Layouts and the reference section Beatnik Player's Embedding Properties.
To make your life as a Beatnik content developer a little easier, Beatnik Inc. has also gone to the trouble of designing the free Beatnik EMBEDifier tool - a convenient way of creating your own custom EMBED / OBJECT tag code. Using the EMBEDifier is a breeze. You can use the EMBEDifier as an alternative to handcoding your EMBED / OBJECT tags.


| Manufacturer ID | State of Charge | State | Station (Slot) | Customer | Charging State | Last Charging Update | |
|---|---|---|---|---|---|---|---|
| {{pedelec.manufacturerId}} |
|
{{pedelec.state}} {{pedelec.state}} {{pedelec.state}} | {{pedelec.station.stationName}} Slot {{pedelec.station.stationSlotPosition}} | {{pedelec.transaction.firstname + " " + pedelec.transaction.lastname}} {{pedelec.transaction.majorCustomerName}} | {{pedelec.chargingState}} {{pedelec.chargingState}} {{pedelec.chargingState}} | {{pedelec.lastChargingUpdate | date:'short'}} |


---
layout: page
title: Publications
description: This is what I wrote.
background: '/img/bg-pencils.jpg'
---
2023
-
Daphne Miedema, Michael Liut, George Fletcher, Efthimia Aivaloglou. MSMI1: Towards a Validated SQL Misconceptions Instrument. In Proceedings of the 2023 ACM Conference on International Computing Education Research (ICER '23).
Paper, poster and MSMI questions.
-
Daphne Miedema. Toward a Fundamental Understanding of SQL Education. In Proceedings of the 2023 ACM Conference on International Computing Education Research (ICER '23).
Paper and poster.
-
Toni Taipalus, Daphne Miedema, Efthimia Aivaloglou. Engaging Databases for Data Systems Education. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education (ITiCSE '23). Paper.
Best paper candidate
-
Daphne Miedema, Toni Taipalus, Efthimia Aivaloglou. Students' Perceptions on Engaging Database Domains and Structures. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education (SIGCSE '23). Paper.
-
Efthimia Aivaloglou, George Fletcher, Michael Liut, Daphne Miedema. Report on the First International Workshop on Data Systems Education (DataEd'22). In ACM SIGMOD Record. Paper.
2022
-
Dennis Collaris, Hilde J.P. Weerts, Daphne Miedema, Jarke J. van Wijk, Mykola Pechenizkiy. Characterizing Data Scientists’ Mental Models of Local Feature Importance. In NordiCHI '22: Nordic Human-Computer Interaction Conference.
Paper and layman explanation.
-
Leonardo Mathon, Daphne Miedema. Increasing Awareness of SQL Anti-Patterns for Novices: A Study Design. In Proceedings of the 2022 ACM Conference on International Computing Education Research (ICER '22).
Paper, poster and tool link.
-
Daphne Miedema, George Fletcher, Efthimia Aivaloglou. Expert Perspectives on Student Errors in SQL. In ACM Transactions on Computing Education (TOCE).
Paper.
-
Daphne Miedema, George Fletcher, Efthimia Aivaloglou. So many brackets! An analysis of how SQL learners (mis)manage complexity during query formulation. In Proceedings of the 30th International Conference of Program Comprehension (ICPC '22).
Paper and replication package.
-
Reprint from ICER 2021: Daphne Miedema, Efthimia Aivaloglou, George Fletcher. Identifying SQL Misconceptions of Novices: Findings from a Think-Aloud Study. In ACM Inroads, Volume 13, Issue 1, pages 52-65. Article.
2021
2020
-
Daphne Miedema. Visual Query Representations for Supporting SQL Learners. Presentation for ICT.OPEN 2020.
Cancelled
-
Daphne Miedema, George Fletcher. Visual Query Representations for Supporting SQL Learners. Poster (pdf) for ICT.OPEN 2020.
Cancelled
-
Daphne Miedema. What are common mistakes that novices make when learning query languages? Poster (pdf) for Alice&Eve 2020.
2nd prize
2019
-
Daphne Miedema. Towards successful interaction between Humans and Databases. MSc Thesis.


absolute_file_name/3
. This is to simulate the case
insensitivity used by Windows file systems. This means that files
created by SICStus may have names on disk that differs in case from
what was specified when the file was created.
comint-interrupt-subprocess
) will not interrupt a
blocking read from standard input. The interrupt will be noted as soon
as some character is sent to SICStus. The characters typed will not be
discarded but will instead be used as debugger commands, sometimes
leading to undesirable results.
comint-send-eof
),
closes the connection to the SICStus process. This will cause SICStus to
exit. This problem cannot be fixed in SICStus; it is a limitation of
current versions of FSF Emacs and XEmacs (at least up to FSF Emacs 20.7
and XEmacs 21.5).
Instead of sending and end of file, you can enter the symbol
end_of_file
followed by a period. Alternatively, a C-z can
be generated by typing C-q C-z.
statistics(runtime, ...)
measures
user time of the thread running SICStus (the main thread) instead of
process user time. This makes statistics(runtime, ...)
meaningful also in a multi-threaded program.


Spectra¶
Download all the Jupyter notebooks from: https://github.com/HeloiseS/hoki/tree/master/tutorials
Initial Imports¶
[1]:
from hoki import load
from hoki.spec import dopcor
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.preprocessing import MinMaxScaler
%matplotlib inline
plt.style.use('tuto.mplstyle')
In this tutorial you will:¶
Load BPASS synthetic spectra through
hoki
Manipulated an plot these synthetic spectra
Compare them to observational data and find best matches.
Get more familiar with
pandas
if you are not already.
Loading Spectra in hoki
¶
The synthetic spectra returned by BPASS are given in ASCII files, grouped by metallicity (zXXX
) and whether the population is made up of just single stars (-sin-
) or also contains binary stars (-bin
). Each file contains the wavelenght bin in its first column and the following 51 columns are the synthetic spectra of the population at each BPASS time bins. The wavelength ranges from 1 to 100,000 Angstrom.
We can load each file into a pandas.DataFrame
using the hoki.load.model_output()
function. This may take a few seconds. For the purposes of this tutorial we are only going to load two sets of synthetic spectra: solar metallicity and a tenth solar - both including binary stars.
[2]:
ss_z002 = load.model_output('./data/spectra/spectra-bin-imf135_300.z002.dat')
ss_z020 = load.model_output('./data/spectra/spectra-bin-imf135_300.z020.dat')
[3]:
ss_z002.head()
[3]:
| WL | 6.0 | 6.1 | 6.2 | 6.3 | 6.4 | 6.5 | 6.6 | 6.7 | 6.8 | ... | 10.1 | 10.2 | 10.3 | 10.4 | 10.5 | 10.6 | 10.7 | 10.8 | 10.9 | 11.0 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.0 | 2.745775e-40 | 2.702434e-40 | 3.047202e-40 | 4.195247e-38 | 7.912627e-38 | 2.884525e-38 | 9.237552e-39 | 1.976199e-38 | 5.478071e-38 | ... | 3.143051e-40 | 4.732241e-40 | 2.407628e-40 | 1.854254e-40 | 1.770071e-40 | 3.505892e-40 | 1.656219e-40 | 1.040945e-40 | 2.193011e-40 | 3.402488e-41 |
| 1 | 2.0 | 2.745775e-40 | 2.702434e-40 | 3.047202e-40 | 4.195247e-38 | 7.912627e-38 | 2.884525e-38 | 9.237552e-39 | 1.976199e-38 | 5.478071e-38 | ... | 3.143051e-40 | 4.732241e-40 | 2.407628e-40 | 1.854254e-40 | 1.770071e-40 | 3.505892e-40 | 1.656219e-40 | 1.040945e-40 | 2.193011e-40 | 3.402488e-41 |
| 2 | 3.0 | 2.745775e-40 | 2.702434e-40 | 3.047202e-40 | 4.195247e-38 | 7.912627e-38 | 2.884525e-38 | 9.237552e-39 | 1.976199e-38 | 5.478071e-38 | ... | 3.143051e-40 | 4.732241e-40 | 2.407628e-40 | 1.854254e-40 | 1.770071e-40 | 3.505892e-40 | 1.656219e-40 | 1.040945e-40 | 2.193011e-40 | 3.402488e-41 |
| 3 | 4.0 | 2.745775e-40 | 2.702434e-40 | 3.047202e-40 | 4.195247e-38 | 7.912627e-38 | 2.884525e-38 | 9.237552e-39 | 1.976199e-38 | 5.478071e-38 | ... | 3.143051e-40 | 4.732241e-40 | 2.407628e-40 | 1.854254e-40 | 1.770071e-40 | 3.505892e-40 | 1.656219e-40 | 1.040945e-40 | 2.193011e-40 | 3.402488e-41 |
| 4 | 5.0 | 2.745775e-40 | 2.702434e-40 | 3.047202e-40 | 4.195247e-38 | 7.912627e-38 | 2.884525e-38 | 9.237552e-39 | 1.976199e-38 | 5.478071e-38 | ... | 3.143051e-40 | 4.732241e-40 | 2.407628e-40 | 1.854254e-40 | 1.770071e-40 | 3.505892e-40 | 1.656219e-40 | 1.040945e-40 | 2.193011e-40 | 3.402488e-41 |
5 rows × 52 columns
Additionally, we’re going to be working with optical data, so let’s crop our spectra.
Because we are using pandas
, we don’t have to individually crope each spectrum, we can easily take care of the whole DataFrames.
[4]:
ss_z002 = ss_z002[(ss_z002.WL>4000) & (ss_z002.WL<9000)]
ss_z020 = ss_z020[(ss_z020.WL>4000) & (ss_z020.WL<9000)]
Plotting a spectrum¶
Now that we have loaded our spectra we can plot them!
At different metallicities
[5]:
plt.figure(figsize=(10,5))
plt.plot(ss_z002.WL, ss_z002['9.0'], label='Z=0.1 Z_sun', lw=0.5)
plt.plot(ss_z002.WL, ss_z020['9.0'], label='Z=Z_sun', lw=0.5)
plt.title('Different Metallicities - log(Age)=9.0')
plt.xlabel(r'Wavelength ($\AA$)')
plt.ylabel(r'Flux')
plt.legend()
[5]:
<matplotlib.legend.Legend at 0x7ff5920e3278>
At different ages
[6]:
plt.figure(figsize=(10,5))
plt.plot(ss_z002.WL, ss_z020['6.0'], label='1 Myr', lw=1)
plt.plot(ss_z002.WL, ss_z020['8.0'], label='100 Myr', lw=1)
plt.plot(ss_z002.WL, ss_z020['9.0'], label='1 Gr', lw=1)
plt.plot(ss_z002.WL, ss_z020['10.0'], label='10 Gyr', lw=1)
plt.title('Different Ages - Z=Z_sun')
plt.xlabel(r'Wavelength ($\AA$)')
plt.ylabel(r'Flux')
plt.legend()
[6]:
<matplotlib.legend.Legend at 0x7ff5a99af390>
The vast difference in scales makes it tough to see the older spectra (and their forests of lines) so let’s normalise everything. We could write a little function but why re-invent the wheel? sklearn
has some great statistical tools including a variety of normalising methods. Let’s use MinMaxScaler()
, which simply makes sure the top values == 1 and lowest value == 0.
[7]:
WL = ss_z020.WL.values # Since all the wavelength ranges are the same let's give it a single name
# The 'fit_transform()' function might sound like a weird name but it makes sense when
# you are familiar with the whole sklearn framework: it is consistent with all their other tools.
# Also note that sklearn takes in numpy arrays and returns numpy arrays. That means we have to
# input our dataframe with .values, and place the output back into a DataFrame (if that's the format we want).
ss_z020_scaled = pd.DataFrame(MinMaxScaler().fit_transform(ss_z020.values)[:,1:], # Not doing the wavelength column
columns = ss_z020.columns[1:]) # We also have to provide column names.
# And now we do the same for a tenth solar!
ss_z002_scaled = pd.DataFrame(MinMaxScaler().fit_transform(ss_z002.values)[:,1:],
columns = ss_z002.columns[1:])
[8]:
plt.figure(figsize=(10,5))
plt.plot(WL, ss_z020_scaled['6.0'], label='1 Myr', lw=2, zorder=5)
plt.plot(WL, ss_z020_scaled['8.0'], label='100 Myr', lw=0.5, zorder=4)
plt.plot(WL, ss_z020_scaled['9.0'], label='1 Gr', lw=0.5, zorder=3)
plt.plot(WL, ss_z020_scaled['10.0'], label='10 Gyr', lw=0.5, zorder=2)
plt.title('Different Ages (Normalised)')
plt.xlabel(r'Wavelength ($\AA$)')
plt.ylabel(r'Flux')
plt.legend()
[8]:
<matplotlib.legend.Legend at 0x7ff5a9999320>
[9]:
# Now you can try to make a similar plot for a tenth solar and a different range of ages!
Comparing to some real data¶
Now let’s play with some real data! We’re going to be looking at NGC 4993 (the host galaxy of GW1708017) and then at some SDSS templates.
NGC 4993¶
First, let’s put it in a pandas.DataFrame
and see what we’ve got.
[10]:
ngc4993 = pd.read_csv('./data/spectra/ngc4993.dat', sep=r"\s+", engine='python')
[11]:
# Just to see what the Data Frame looks like
ngc4993.head()
[11]:
| WL | Counts | |
|---|---|---|
| 0 | 4036.82 | 5437.012 |
| 1 | 4038.36 | 5506.620 |
| 2 | 4039.90 | 5747.366 |
| 3 | 4041.44 | 5748.782 |
| 4 | 4042.99 | 5761.701 |
[12]:
# and now we plot it
plt.figure(figsize=(10,5))
plt.plot(ngc4993.WL, ngc4993.Counts)
plt.xlabel(r'Wavelength ($\AA$)')
plt.ylabel(r'Flux (Counts)')
plt.title('NGC 4993')
[12]:
Text(0.5, 1.0, 'NGC 4993')
This spectrum is in counts and obviously the amplitude is not going to match that of our synthetic spectra. So let’s normalise this too!
[13]:
ngc4993_scaled = pd.DataFrame(MinMaxScaler().fit_transform(ngc4993.values), columns = ['WL', 'Counts'])
ngc4993_scaled['WL'] = ngc4993['WL'] # Because with the operation above we also scaled the WL -- makes no sense.
Another thing to remember is that a real spectrum will be redshifted. There is a basic doppler correction within ``hoki`` which you can use to take care of that. Note it will change in place.
[14]:
dopcor(ngc4993_scaled, z=0.009727)
Finding the best match¶
The goal here is to find the best synthetic spectrum corresponding to our observed spectrum. In this tutorial we will do this visually. Let’s plot a range of ages and see what we get.
[15]:
plt.figure(figsize=(11,5))
WL = ss_z002.WL
# The BPASS spectra
plt.plot( WL, ss_z020_scaled['7.0'], alpha=0.95, lw=0.5, label='log(age)=7.0')
plt.plot( WL, ss_z020_scaled['8.0'], alpha=0.95, lw=0.5, label='log(age)=8.0')
plt.plot( WL, ss_z020_scaled['8.7'], alpha=0.95, lw=0.5, label='log(age)=8.7')
plt.plot( WL, ss_z020_scaled['9.0'], alpha=0.95, lw=0.5, label='log(age)=9.0')
plt.plot( WL, ss_z020_scaled['10.0'], alpha=0.95, color='grey', lw=0.5, label='log(age)=10.0')
# The observational data
plt.plot(ngc4993_scaled.WL, ngc4993_scaled.Counts, alpha=0.95, c='k', label='NGC4993')
# Polishing the plot
plt.xlabel(r'Wavelength ($\AA$)')
plt.ylabel(r'Normalised Flux')
plt.xlim([4000,10500])
plt.title('Synthetic Spectra and NGC 4993')
plt.legend(loc='right', fontsize='large')
[15]:
<matplotlib.legend.Legend at 0x7ff5a547eb38>
There is an obvious drop in the flux in the red part of our NGC 4993 spectrum. That’s not the galaxy - it could be something to do with the filter used or another observational reason, so let’s focus on the short wavelengths.
It looks like older ages are a better match. Here is the one I thought fitted best, but feel free to make your own plots and see if you can get a better result!
[16]:
plt.figure(figsize=(11,5))
plt.plot( WL, ss_z020_scaled['10.4'],
alpha=0.95, lw=0.5, label='log(age)=10.4')
plt.plot(ngc4993_scaled.WL, ngc4993_scaled.Counts-0.1, alpha=0.95, c='k', label='NGC4993')
plt.xlabel(r'Wavelength ($\AA$)')
plt.ylabel(r'Normalised Flux')
plt.xlim([4000,10500])
plt.title('Best (?) Synthetic Spectra and NGC 4993')
plt.legend(loc='right', fontsize='large')
[16]:
<matplotlib.legend.Legend at 0x7ff5a53d6cf8>
SDSS data¶
Now let’s take a look at some SDSS templates. In the folder ./data/spectra
you should have two fits files corresponding to an early-type galaxy and an indescriminate galaxy template (spDR2-023.fit
and spDR2-024.fit
, respectively).
Note that these spectra are given as fits files and it’s beyond the scope of this tutorial to walk you through turning them into a pythonic data structure, so I’ve made the extract_sdss_spectrum()
function which you can simply run to turn these spectra into a simple and convenient DataFrame.
[17]:
from astropy.io import fits
def extract_sdss_spectrum(dataloc):
"""
Some Python magic to turn sdss fits files into dataframes ;)
You don't have to pay too much attention.
If you're curious read the comments or feel free to send me an email<EMAIL_ADDRESS>or DM me @sydonahi
"""
sdss_data = fits.open(dataloc)
# Normalising with sklearn
scaled_flux = MinMaxScaler().fit_transform(sdss_data[0].data[0].reshape(-1,1))
# A list comprehension incantation to make the wavelength appear **** wizard ****
wl = [10**(sdss_data[0].header['COEFF0']+sdss_data[0].header['COEFF1']*i)
for i in range(sdss_data[0].data.shape[1])]
# Making the dataframe
df = pd.DataFrame(np.array([wl,scaled_flux.reshape(sdss_data[0].data[0].shape[0])]).T,
columns=['WL', 'Flux'])
return df
Let’s get our SDSS spectra and take a look at them.
[18]:
early_type_gal = extract_sdss_spectrum('./data/spectra/spDR2-023.fit')
gal = extract_sdss_spectrum('./data/spectra/spDR2-024.fit')
[19]:
plt.figure(figsize=(11,5))
plt.plot(early_type_gal.WL, early_type_gal.Flux, label='Early Type Galaxy template')
plt.plot(gal.WL, gal.Flux, label='Random Galaxy template')
plt.xlabel(r'Wavelength ($\AA$)')
plt.ylabel(r'Normalised Flux')
plt.xlim([4000,9000])
plt.title('SDSS templates')
plt.legend()
[19]:
<matplotlib.legend.Legend at 0x7ff5e2a37908>
Note a very interesting effect here in our Random Galaxy template: the large nebular line is messing with our scaler and when we want to fit it we’re probably going to have to go and add a scale factor by hand so that the other features are of the same kind of amplitude as those in our synthetic spectra!
Anyway, for now we’re going to focus on the Early Type Galaxy and see if we can find a template that matches. Again, let’s just do it by eye.
[20]:
f, ax = plt.subplots(3, figsize=(10,15))
ax[0].plot( WL, ss_z020_scaled['9.0'],
alpha=0.95, lw=0.5, label='log(age)=9.0', color='grey')
ax[0].text(6000, 0.2, 'Too Young', fontsize=20)
ax[1].plot( WL, ss_z020_scaled['11.0'],
alpha=0.95, lw=0.5, label='log(age)=11.0', color='grey')
ax[1].text(6000, 0.2, 'Too Old', fontsize=20)
ax[2].plot( WL, ss_z020_scaled['10.4'],
alpha=0.95, lw=0.5, label='log(age)=10.4', color='grey')
ax[2].text(6000, 0.2, 'Not Bad ;)', fontsize=20)
for axis in ax:
axis.plot(early_type_gal.WL, early_type_gal.Flux-0.05, label='Early type galaxy')
axis.set_xlabel(r'Wavelength ($\AA$)')
axis.set_ylabel(r'Norm. Flux')
axis.set_xlim([4000,9000])
axis.legend()
Feel free to play around with other ages (or even metalicities!) but a log(age) = 10.4 is not unsurprising for an Early Type Galaxy which we expect to have a relatively old stellar population (and log(age)=11.0 is much older than the age of the Universe so that was never going to work haha).
Doing operations on Spectra¶
Within the pandas
framework it is trivial to add spectra and perform mathematical operations on them.
As an example, let’s add together an old and a very young stellar population. Arbitrarily I’ve picked a log(age) of 6.2 and 10.5, but feel free to play around with it yourself!
[21]:
old_and_young = ss_z020_scaled['6.2'] + ss_z020_scaled['10.5']
[22]:
f, ax = plt.subplots(3, figsize=(10,15))
ax[0].plot( WL, ss_z020_scaled['6.2'],
alpha=0.95, color='grey')
ax[0].text(6000, 0.6, 'log(Age)=6.2', fontsize=20)
ax[1].plot( WL, ss_z020_scaled['10.5'],
alpha=0.95, lw=0.5, color='grey')
ax[1].text(6000, 0.2, 'log(Age)=10.5', fontsize=20)
ax[2].plot( WL, old_and_young,
alpha=0.95, lw=0.5, color='grey')
ax[2].text(6000, 0.4, 'log(Age)= 6.2 + 10.5', fontsize=20)
for axis in ax:
axis.set_xlabel(r'Wavelength ($\AA$)')
axis.set_ylabel(r'Norm. Flux')
axis.set_xlim([4000,9000])
You can also add spectra in different proportions by simply adding scale factors. Multiplications, divisions and most daily mathematical operations are easily done:
[23]:
plt.figure(figsize=(10,7))
plt.plot(WL, (ss_z020_scaled['6.2'] + ss_z020_scaled['10.5']*0.5)+1,
lw=0.5, alpha=0.8, label='Old*0.5 + Young')
plt.plot(WL, (ss_z020_scaled['6.2'] + ss_z020_scaled['10.5'])+0.3,
lw=0.5, alpha=0.8, label='Old + Young')
plt.plot(WL, (ss_z020_scaled['6.2']*0.5 + ss_z020_scaled['10.5']),
lw=0.5, alpha=0.8, label='Old + Young * 0.5')
plt.xlabel(r'Wavelength ($\AA$)')
plt.ylabel(r'Normalised Flux + Offset')
plt.xlim([4000,9000])
plt.legend()
[23]:
<matplotlib.legend.Legend at 0x7ff5e27cf828>
It’s your turn now!¶
Remember the random galaxy with the naughty nebular line that messed with the effectivnes of our normalisation?
Let’s go back to it!
You now know how to select different synthetic spectra and how to scale them, now go ahead and find the best synthetic spectrum and scale factor combination!
(You ca just assume a stellar population with a single age, but feel free to have fun and combine stuff if you feel like it)
[24]:
### Your code here
YOU’RE ALL SET!
I hope you found this tutorial useful. If you encountered any problems, or would like to make a suggestion, feel free to open an issue on hoki
GitHub page here or on the hoki_tutorials
GitHub there.


HP’s Black Friday 2019 Sale Is Live! Ad Scan, Doorbuster Flash Sales And Discount Codes Inside
by Abdullah SaadEthics disclaimer: This is a paid-for, sponsored post. We do not collect any commission on sales. Discounts are only valid while stock lasts.
HP's Black Friday 2019 sale is now live and features some great deals which you can make even sweeter using the following discount codes. For PCs over $999 use the discount code HOLIDAYPC to get a 5% discount and for PCs over $1399 use HOLIDAYPC10 to get a 10% discount! This is the company's biggest sale of the year and has a ton of deals for the holiday shopper. If you want to take a look at the complete list, be sure to head over to the site using the link below otherwise we have some curated deals for you just over yonder!
HP Black Friday Sale is now live, view the complete list of deals here, use discount codes HOLIDAYPC and HOLIDAYPC10
HP's deals are divided into multiple categories, included Top Deals, Laptops, Desktops, Business Desktops, Printers, Monitors, etc. so be sure to click on the category that you want the most. Also, keep in mind that the HOLIDAYPC and HOLIDAYPC10 coupon codes are only valid for PCs that have a deal value of over $999 and $1399 respectively! Check out all categories over here.
Deal 1: Omen Laptop 15t with GTX 1650 - $699 (was $999)
This is a great gaming laptop at a steep discount. HP is offering a flat $300 off the MSRP here for gamers that want to utilize the power of the OMEN's entry-level gaming GPU the GTX 1650. This beauty can handle just about any AAA game at medium settings and is even equipped with a 356GB SSD so load times will be blazing fast. Coupled with a 1080p screen, this is the perfect package. We do recommend upgrading to 16GB of RAM, however.
Deal 2: HP Laptop 15z at a dirt-cheap $249 (was $519)
Equipped with an AMD A9 dual-core, AMD R5 graphics, and a 128GB SSD, this is the cheapest "real" laptop you are going to find anywhere and even comes preloaded with genuine Windows 10!
Deal 3: HP Pavilion Laptop 15z with AMD Ryzen 5 and Vega 8 graphics for $399 (was $1299)
If you want something with a little more power and still don't want to break the bank then this laptop is for you. In just 400 bucks you are going to get a very powerful processor, vega 8 graphics (which is enough for casual gaming) and a 256GB SSD! It does not get better than this.
Deal 4: OMEN Desktop PC with a GTX 1660 TI for $949 (was $1499)
GPU prices are skyrocketing but you can take advantage of OEM pricing by getting this OMEN tower that packs a GTX 1660 Ti 6GB along with a super capable Intel Core i7 processor for a total damage of just $949.
Deal 5: HP 27er 27 inch 1080p monitor for just $139 (was $249)
Not looking for a laptop or desktop? Maybe you want a monitor? Well this deal for a 27inch 1080p monitor is looking great.
HP Black Friday Sale 2019 Complete Ad Scan
The full ad-scan is given below:


Oops, this page doesn't exist...
This may be because this page has moved, or because it never existed. Spooky stuff.
To get back on your way:
- Please report the problem to us.
- Go back to the home page.
--- layout: page title: These are not the droids you are looking for... permalink: /404.html ---
This may be because this page has moved, or because it never existed. Spooky stuff.
To get back on your way:


- Added: Tab-Indentation Mode to the Visual Editor to align content with the
tab
key
- Added: Kotlin Language Support
- Added: GFM Markdown Inline language identifier syntax
- Added: EnlighterJS v2.11.1
- Added: Visual Editor Plugin (EnlighterJS.TinyMCE) v3.3.1
- Added: Tutorials and Developer docs
- Bugfix: Underscore not allowed in xml tags - thanks to higginbg on GitHub
- Bugfix: Fixed invalid external links of the help section


Requirements¶
General¶
# |
Description |
Source |
Priority |
Version |
WBS |
|---|---|---|---|---|---|
P.1 |
Existing Assessment Methods |
||||
P.1.1 |
Implement the high-resolution loss assessment methodologies |
GC |
|||
P.1.1.1 |
Implement the scenario-based assessment from FEMA-P58 |
SP |
M |
Implemented |
|
P.1.1.2 |
Implement the time-based assessment from FEMA-P58 |
SP |
D |
InProgress |
<IP_ADDRESS> |
P.1.1.3 |
Implement high-resolution assessment of buildings under wind hazards |
SP |
M |
InProgress |
<IP_ADDRESS> |
P.1.1.4 |
Implement high-resolution assessment of buildings under water hazards |
SP |
M |
InProgress |
<IP_ADDRESS> |
P.1.1.5 |
Implement high-resolution assessment of transportation networks |
SP |
M |
InProgress |
<IP_ADDRESS> |
P.1.1.6 |
Implement high-resolution assessment of buried pipelines |
SP |
M |
InProgress |
<IP_ADDRESS> |
P.1.2 |
Implement the efficient loss assessment methodologies from HAZUS |
GC |
InProgress |
<IP_ADDRESS> |
|
P.1.2.1 |
Implement the assessment of buildings under earthquake hazard from HAZUS |
SP |
M |
Implemented |
|
P.1.2.2 |
Implement the assessment of buildings under hurricane wind hazard from HAZUS |
SP |
M |
Implemented |
|
P.1.2.3 |
Implement the assessment of buildings under storm surge hazard from HAZUS |
SP |
M |
||
P.1.2.4 |
Implement the assessment of buried pipelines under earthquake hazard from HAZUS |
SP |
M |
InProgress |
<IP_ADDRESS>.3 |
P.1.2.5 |
Implement the assessment of transportation networks under earthquake hazard from HAZUS |
SP |
M |
InProgress |
|
P.1.2.6 |
Implement the assessment of power networks under earthquake hazard from HAZUS |
SP |
M |
InProgress |
|
P.2 |
Control |
||||
P.2.1 |
Analysis & Data |
||||
P.2.1.1 |
Allow users to set the number of realizations |
SP |
M |
Implemented |
|
P.2.1.2 |
Allow users to customize fragility and consequence function parameters |
SP |
D |
Implemented |
|
P.2.1.3 |
Allow users to specify dependencies between logically similar parts of the stochastic models |
SP |
D |
Implemented |
|
P.2.2 |
Response Model |
||||
P.2.2.1 |
Allow users to specify the added uncertainty to EDPs (increase in log-standard dev.) |
SP |
M |
Implemented |
|
P.2.2.2 |
Allow users to specify the EDP ranges that correspond to reliable simulation results |
SP |
D |
Implemented |
|
P.2.2.3 |
Allow users to specify the type of distribution they want to fit to the empirical EDP data |
UF |
D |
Implemented |
|
P.2.2.4 |
Allow users to choose if they want to fit a distribution only to the non-collapsed EDPs |
UF |
M |
Implemented |
|
P.2.3 |
Performance Model |
||||
P.2.3.1 |
Allow users to prescribe a different number of inhabitants on each floor |
SP |
D |
Implemented |
|
P.2.3.2 |
Allow users to customize the temporal distribution of inhabitants |
SP |
D |
Implemented |
|
P.2.3.3 |
Allow users to prescribe different component quantities for each floor in each direction |
SP |
D |
Implemented |
|
P.2.3.4 |
Allow users to specify the number of component groups and their quantities in each performance group |
UF |
D |
Implemented |
|
P.2.4 |
Damage Model |
||||
P.2.4.1 |
Allow users to specify the residual drift limits that determine irrepairability |
SP |
D |
Implemented |
|
P.2.4.2 |
Allow users to specify the yield drift value that is used to estimate residual drifts from peak drifts |
SP |
D |
Implemented |
|
P.2.4.3 |
Allow users to specify the EDP limits that are used to determine collapse probability |
SP |
D |
Implemented |
|
P.2.4.4 |
Allow users to specify arbitrary collapse modes and their likelihood |
SP |
D |
Implemented |
|
P.2.4.5 |
Allow users to prescribe the collapse probability of the structure |
UF |
M |
Implemented |
|
P.2.5 |
Loss Model |
||||
P.2.5.1 |
Allow users to decide which DVs to calculate |
SP |
D |
Implemented |
|
P.2.5.2 |
Allow users to specify the likelihood of various injuries in each collapse mode |
SP |
D |
Implemented |
|
P.3 |
Hazard Model |
InProgress |
<IP_ADDRESS> |
||
P.3.1 |
Hazard Occurrence Rate |
||||
P.3.1.1 |
Enable estimation of the likelihood of earthquake events |
SP |
M |
||
P.3.1.2 |
Enable estimation of the likelihood of wind events |
SP |
M |
||
P.3.1.3 |
Enable estimation of the likelihood of storm surge events |
SP |
M |
||
P.3.1.4 |
Enable estimation of the likelihood of tsunami events |
SP |
M |
||
P.4 |
Response Model |
InProgress |
<IP_ADDRESS> |
||
P.4.1 |
EDP (re-)sampling |
||||
P.4.1.1 |
Enable coupled assessment by using raw EDP values as-is |
UF |
M |
Implemented |
|
P.4.1.2 |
Enable non-Gaussian EDP distributions |
UF |
D |
||
P.4.2 |
EDP Identification |
||||
P.4.2.1 |
Implement automatic identification of required EDP types based on the performance model |
SP |
M |
||
P.5 |
Performance Model |
||||
P.5.1 |
Auto-population of performance models |
||||
P.5.1.1 |
Implement framework to enable user-defined auto-population scripts |
UF |
D |
Implemented |
|
P.5.1.2 |
Prepare script to perform auto-population based on normative quantities in FEMA P58 |
UF |
D |
||
P.6 |
Damage Model |
InProgress |
<IP_ADDRESS> |
||
P.6.1 |
Collapse estimation |
||||
P.6.1.1 |
Estimate collapse probability of the structure using EDP limits and the joint distribution of EDPs |
SP |
D |
Implemented |
|
P.6.1.2 |
Estimate the collapse probability of the structure using empirical (raw) EDP data |
UF |
M |
Implemented |
|
P.6.1.3 |
Enable user-defined collapse probability |
UF |
M |
Implemented |
|
P.6.2 |
Building Damage |
||||
P.6.2.1 |
Implement earthquake fragility functions for building components from FEMA P58 |
SP |
M |
Implemented |
|
P.6.2.2 |
Implement earthquake fragility functions for buildings from HAZUS |
SP |
M |
Implemented |
|
P.6.2.3 |
Implement wind fragility functions for buildings from HAZUS |
SP |
M |
Implemented |
|
P.6.2.4 |
Implement inundation fragility functions for buildings from HAZUS |
SP |
M |
Implemented |
|
P.6.2.5 |
Implement high-resolution wind fragility functions for building components |
SP |
M |
InProgress |
<IP_ADDRESS>.1 |
P.6.2.6 |
Implement high-resolution inundation fragility functions for building components |
SP |
M |
InProgress |
<IP_ADDRESS>.2 |
P.6.3 |
Lifeline Damage |
||||
P.6.3.1 |
Implement earthquake fragility functions for buried pipelines from HAZUS |
SP |
M |
InProgress |
<IP_ADDRESS>.4 |
P.6.3.2 |
Implement earthquake fragility functions for bridges from HAZUS |
SP |
M |
InProgress |
|
P.6.3.3 |
Implement earthquake fragility functions for power networks from HAZUS |
SP |
M |
||
P.6.3.4 |
Implement high-resolution fragility functions for buried pipelines |
SP |
M |
InProgress |
<IP_ADDRESS>.4 |
P.6.3.5 |
Implement high-resolution fragility functions for transportation networks |
SP |
M |
InProgress |
<IP_ADDRESS>.3 |
P.6.4 |
Cascading Damages |
||||
P.6.4.1 |
Implement fault tree-based cascading damage model |
SP |
M |
InProgress |
<IP_ADDRESS> |
P.7 |
Loss Model |
||||
P.7.1 |
Consequence functions for buildings |
||||
P.7.1.1 |
Implement functions for repair cost and time as per FEMA P58 |
SP |
M |
Implemented |
|
P.7.1.2 |
Implement functions for red tag triggering as per FEMA P58 |
SP |
M |
Implemented |
|
P.7.1.3 |
Implement functions for injuries and fatalities as per FEMA P58 |
SP |
M |
Implemented |
|
P.7.1.4 |
Implement functions for repair cost and time as per HAZUS earthquake |
SP |
M |
Implemented |
|
P.7.1.5 |
Implement functions for debris as per HAZUS earthquake |
SP |
D |
||
P.7.1.6 |
Implement functions for business interruption as per HAZUS earthquake |
SP |
D |
||
P.7.1.7 |
Implement functions for repair cost and time as per HAZUS wind |
SP |
M |
Implemented |
|
P.7.1.8 |
Implement functions for repair cost and time as per HAZUS inundation |
SP |
M |
||
P.7.1.9 |
Implement functions for environmental impact estimation as per FEMA P58 2nd edition |
SP |
M |
Implemented |
<IP_ADDRESS> |
P.7.1.10 |
Implement functions for high-resolution repair cost and time assessment for wind hazards |
SP |
M |
InProgress |
<IP_ADDRESS>.1 |
P.7.1.11 |
Implement functions for high-resolution repair cost and time assessment for water hazards |
SP |
M |
InProgress |
<IP_ADDRESS>.2 |
P.7.2 |
Consequence functions for other assets |
||||
P.7.2.1 |
Implement functions for repair cost and time for buried pipelines as per HAZUS earthquake |
SP |
M |
Implemented |
|
P.7.2.2 |
Implement functions for repair cost and time for bridges as per HAZUS earthquake |
SP |
M |
InProgress |
<IP_ADDRESS>.3 |
P.7.2.3 |
Implement functions for repair cost and time for power networks as per HAZUS earthquake |
SP |
M |
||
P.7.2.4 |
Implement high-resolution functions for repair cost and time for transportation networks |
SP |
M |
InProgress |
<IP_ADDRESS>.3 |
P.7.2.5 |
Implement high-resolution functions for repair cost and time for buried pipelines |
SP |
M |
InProgress |
<IP_ADDRESS>.4 |
Databases & Files¶
# |
Description |
Source |
Priority |
Version |
WBS |
|---|---|---|---|---|---|
DLD |
Database for Damage and Loss Fragilities and Consequence Functions: Loss computations use fragility and consequence functions for modern and archaic structural and nonstructural components and assem- blies in structures. The database of such functions for components and assemblies is small and must be expanded through coordinated numerical and experimental simulations |
GC |
M |
||
DLD.1 |
Data Sources |
||||
DLD.1.1 |
Make the component fragility and consequence functions from FEMA P58 available |
SP |
M |
||
DLD.1.1.1 |
FEMA P58 First Edition |
SP |
M |
Implemented |
|
DLD.1.1.2 |
FEMA P58 Second Edition |
UF |
M |
Implemented |
|
DLD.1.1.3 |
Extend FEMA P58 Second Edition consequence functions with environmental impact parameters |
SP |
M |
||
DLD.1.2 |
Make the building fragility and consequence functions from HAZUS available |
SP |
M |
||
DLD.1.2.1 |
HAZUS earthquake damage and reconstruction cost and time |
SP |
M |
Implemented |
|
DLD.1.2.2 |
HAZUS hurricane wind damage and reconstruction cost and time |
SP |
M |
Implemented |
|
DLD.1.2.3 |
HAZUS storm surge damage and reconstruction cost and time |
SP |
M |
||
DLD.1.3 |
Make the lifeline fragility and consequence functions from HAZUS available |
SP |
M |
||
DLD.1.3.1 |
HAZUS bridge damage and reconstruction cost and time |
SP |
M |
||
DLD.1.3.2 |
HAZUS buried pipeline damage and reconstruction cost and time |
SP |
M |
||
DLD.1.3.3 |
HAZUS power network damage and reconstruction cost and time |
SP |
M |
||
DLD.1.4 |
Extend available high-resolution building damage and loss model parameters |
SP |
M |
||
DLD.1.4.1 |
Building damage and loss model parameters under wind hazards |
SP |
M |
||
DLD.1.4.2 |
Building damage and loss model parameters under water hazards |
SP |
M |
||
DLD.1.5 |
Make high-resolution damage and loss model parameters available for lifelines |
SP |
M |
||
DLD.1.5.1 |
Transportation network damage and loss model parameters |
SP |
M |
||
DLD.1.5.2 |
Buried pipeline network damage and loss model parameters |
SP |
M |
||
DLD.2 |
Data Storage |
||||
DLD.2.1 |
Generic JSON format |
SP |
M |
||
DLD.2.1.1 |
Develop a generic JSON data format for component fragility and consequence functions |
SP |
D |
Implemented |
|
DLD.2.1.2 |
Store FEMA P58 and HAZUS component data in the new JSON format and make them available |
SP |
D |
Implemented |
|
DLD.2.2 |
HDF5 Data Storage |
SP |
M |
||
DLD.2.2.1 |
Store the JSON files in an HDF5 data structure for each data source |
SP |
M |
Implemented |
|
DLD.2.3 |
Online Database |
SP |
M |
||
DLD.2.3.1 |
Create an online database for storing parameters of damage and loss models for buildings |
SP |
M |
||
DLD.2.3.2 |
Extend online database to store parameters of damage and loss models for transportation networks |
SP |
M |
||
DLD.2.3.3 |
Extend online database to store parameters of damage and loss models for buried pipeline networks |
SP |
M |
||
DLD.2.3.4 |
Populate building database with high-resolution model parameters from researchers |
SP |
M |
||
DLD.2.3.5 |
Populate lifeline database with high-resolution model parameters from researchers |
SP |
M |


LOGIN
CART (0)
HOME
ABOUT
CATALOGUE
SHIPPING
CONTACT
Login to your account
First name:
Last name:
Search


---
layout: post
title: ClinEpiDB 2 Released
category: [ClinEpiDB, release]
---
We are pleased to announce the release of ClinEpiDB 2
New Features in this Release
- A new tutorial explains the options ClinEpiDB provides to explore results after performing a search. These include the dynamic tabular outputs, column visualization tools, and data download options.
- In the Distributions app we have added automatically generated summary statistics: mean, median, range, SD (standard deviation) & interquartile range (IQR)
- Improvements to our axis labels in our analysis apps
- Implementation of indicators of available data. Filters are now annotated with eg. 1 of 44,508 Observations have no data provided for this filter.
- Improvements to Active Filters summary popup describing more clearly filter selections, especially in the Related Observations step.
No new Data in this Release


Xerox threatens to take its acquisition bid directly to shareholders if HP doesn't rethink its offer, Business Insider - Business Insider Singapore
by Ben Winck, Business Insider US- Xerox threatened to approach HP shareholders with a $33.5 billion buyout bid if the company doesn’t rethink its rejection of the offer.
- The printer company was “very surprised” to hear of the rejection and is “determined to expeditiously pursue” the buyout, Xerox CEO John Visentin wrote in a letter to HP’s directors.
- Visentin laid out a November 25 deadline for HP to rethink its choice, and warned that Xerox will approach HP shareholders with the “compelling case” if an agreement can’t be reached.
- Watch Xerox trade live here.
Xerox may take its buyout offer directly to HP’s shareholders if the company doesn’t rethink its decision to reject a recent $33.5 billion bid, Xerox CEO John Visentin wrote in a letter to HP’s directors.
The printer company was “very surprised” to hear HP’s board rejected the offer, and said HP’s reply “lacks a clear path forward.” Visentin noted that the takeover bid represents a 29% premium over HP’s 30-day average trading price, and that fears of the purchase harming Xerox’s financial stability are unfounded.
“Our offer is neither ‘highly conditional’ nor ‘uncertain’ as you state,” Visentin wrote. “There will be NO financing condition to the completion of our acquisition of HP.”
Xerox rose as much as 1.3% on the news. HP traded as much as 1% higher before paring gains.
The printer manufacturer said it’s still willing to complete the due diligence necessary for a deal, and is “determined to expeditiously pursue” the purchase, the letter said. Xerox’s CEO added that, unless HP agrees to reconsider the offer by November 25 at 5:00 p.m. ET, it will take the offer to shareholders for them to decide on an acquisition.
“The overwhelming support our offer will receive from HP shareholders should resolve any further doubts you have regarding the wisdom of swiftly moving forward to complete the transaction,” Visentin wrote.
The Thursday letter is the latest development in the back-and-forth between the two legacy tech companies. HP’s board of directors rejected the offer in a Monday note, saying the bid “significantly undervalues HP” and isn’t in its shareholders’ best interests. The computer company noted it remains open to future collaboration and the possibility of a smaller transaction.
“We recognize the potential benefits of consolidation, and we are open to exploring whether there is value to be created for HP shareholders through a potential combination with Xerox,” HP CEO Enrique Lores and Board Chairman Chip Bergh said in the Monday letter.
Xerox first extended the bid on November 5, offering a mix of stock and cash for the acquisition. Visentin claimed the cost synergies created from a combined company would reach $2 billion within two years, and that the new firm could take leading roles in new sectors like 3D printing and workflow software.
Both companies have faded from their past popularity as other tech giants stole the spotlight. HP and Xerox in the process of cutting expenses and focusing on core businesses to regain appeal among investors.
Now read more markets coverage from Markets Insider and Business Insider:
The world’s best-performing stock of 2019 just lost 98% of its value in a single morning
Charles Schwab will reportedly acquire TD Ameritrade for $26 billion
Spotify lays off about 30 in ad sales as the company restructures


protected fun FileSpec.rawWriteTo(destination: Path): Unit
Writes this FileSpec to the provided file Path


Privacy Policy
Rentally is distributed through the App Store and Google Play Store, use of Rentally is also subject to the Apple Privacy Policy. This policy does not address any data that Apple may collect, which may include but not be limited to, usage statistics and crash reports.
- Data in the Rentally app is stored on the cloud (Firebase). All data stored is compliant with GDPR. If you provide Rentally with personal information about someone else, you must ensure that you are authorised to disclose that information to Rentally in accordance with applicable data protection or privacy laws. (Data in Rentally might also be automatically saved in Apple's iCloud as part of iCloud backup of your device if you have enabled iCloud backup and there is enough free space for backup in your iCloud account).
- Certain features of the Rentally app allow you to interact and share information with Third Parties and other applications (e.g. backup/export app data to Dropbox or share rent invoice with tenant using WhatsApp). If you decide to share your information with any Third Party from your device, privacy policies will not apply - any information those websites or applications collect may be stored, processed or sent to other Third parties. Third Parties have their own privacy policies that you should review and agree with.


Teenage pregnancy
MORE and more people believe that a concrete plan of action is required to address the worsening problem of teenage pregnancy in the Philippines, particularly in the rural areas.
Aware of the situation, two senators now want a Senate inquiry, in aid of legislation, into how to strengthen the comprehensive sexuality education (CSE) and curb teenage pregnancies.
They are Senators Sherwin “Win” Gatchalian, chairman of the Senate committee on basic education, arts and culture, and Juan Edgardo “Sonny” Angara, head of the Senate committee on finance.
“Despite the existence of CSE and the Reproductive Health (RH) Law, teenage pregnancy rates in the country are still alarming,” according to Senator “Win” Gatchalian of Valenzuela City.
Gatchalian said “this prompts us to revisit and strengthen our existing policies so we can save teenagers from getting pregnant and falling into the trap of intergenerational poverty.”
Earlier, the Commission on Population and Development (PopCom) called for the declaration of a national emergency on teenage pregnancy in this Asian nation of more than 100 million people.
PopCom reported that between 2011 and 2017, pregnancies in the 10-14 age group increased by 50 percent from around 1,000 to 2,000 cases, 30-50 of which are among 10-year olds.
A Philippine Statistics Authority survey also showed that family matters or marriage is the top reason why female out-of-school children and youth aged 6-24 are not attending school.
Of course, we share the view of various quarters, including members of Congress, that teaching CSE in schools plays a stellar role in curbing teenage pregnancies across the country.


Open Data Button Server ~ v0.1
Dokumentation
API Services
Post an item:
URL:
Title:
Context:
Personal
Academic
Government
Commercial
Reason:
Legal
Technical
Location:
lon:
lat:


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @this() @(phaseId: String, isAgent: Boolean = false) @feedbackUrl = @{ if(isAgent) controllers.feedback.routes.FeedbackController.showAgent else controllers.feedback.routes.FeedbackController.show }


This page contains information on {{ records['object'] }} that was manually entered, as well as 3rd party supplemental information that can aide an analyst in research. 3rd party sources can include VirusTotal, PassiveTotal, Whois, geolocational data,
and more. You can enable these 3rd party integrations in the settings menu.
On this page, you can also edit the entry to add new fields or change any attributes previously entered. You can also delete the entry if it's no longer needed.
| Attribute | Value |
|---|---|
| Object Type | {{ records['type'] }} |
| Campaign | - |
| Campaign | {{ records['campaign'] }} |
| Confidence | {{ records['confidence'] }} |
| Confidence | {{ records['confidence'] }} |
| Confidence | {{ records['confidence'] }} |
| Diamond Model | {{ records['diamondmodel'] }} |
| First Seen | - |
| First Seen | {{ records['firstseen'] }} |
| Last Seen | - |
| Last Seen | {{ records['lastseen'] }} |
| Comments | - |
| Comments | {{ records['comments'] }} |
| Tags | - |
| Tags | {% for tag in records['tags'].split(",") %} {% endfor %} |
| {{ key }} | {{ records[key] }} |
Relationships
This table shows the indicators related to {{ records['object'] }}
| Attribute | Value | {% for rel in temprel %}
|---|---|
| {{ rel }} | {{ temprel[rel] }} |
Whois Information
More information can be found at who.is
| Attribute | Value | {% for line in whoisdata %} {% if line == "raw" or line == "raw_referral" or line == "referral" %} {% elif line == "nets" %} {% for i in whoisdata['nets'][0] %}||
|---|---|---|---|
| {{ i }} | - | {% else %}{{ i }} | {{ whoisdata['nets'][0][i] }} | {% endif %}
| {{ line }} | {{ whoisdata[line] }} | ||
| {{ line }} | {{ whoisdata[line] }} |
Whois Information
More information can be found at who.is
| Attribute | Value | {% for line in whoisdata %}
|---|---|
| {{ line }} | {{ whoisdata[line] }} |
VirusTotal Information
More information can be found at VirusTotal
| Last Resolved | Hostname | {% for record in jsonvt['resolutions'] %}
|---|---|
| {{ record['last_resolved'] }} | {{ record['hostname'] }} |
VirusTotal Information
More information can be found at VirusTotal
| Last Resolved | IP Address | {% for record in jsonvt['resolutions'] %}
|---|---|
| {{ record['last_resolved'] }} | {{ record['ip_address'] }} |
OpenDNS Investigate Information
More information can be found at OpenDNS
| Last Malicious | {% for record in odnsdata %}
|---|
| {{ record }} |
OpenDNS Investigate Information
More information can be found at OpenDNS
| Key | Value | {% for k,v in odnsdata.iteritems()%}
|---|---|
| {{ k }} | {{ v }} |
ThreatCrowd Visualization
More information can be found at ThreatCrowd
ThreatCrowd Visualization
More information can be found at ThreatCrowd
CIRCL Passive DNS
More information can be found at CIRCL
| Record Type | Record Data | First Seen | Last Seen | {% for i in circldata %}
|---|---|---|---|
| {{ i['rrtype'] }} | {{ i['rdata'] }} | {{ i['time_first'] }} | {{ i['time_last'] }} |
PassiveTotal Passive DNS
More information can be found at PassiveTotal
| Resolved | First Seen | Last Seen | {% for result in ptdata['results']['records'] %}
|---|---|---|
| {{ result['resolve'] }} | {{ result['firstSeen'] }} | {{ result['lastSeen'] }} |
Farsight Passive DNS
More information can be found at Farsight
| Count | Record Type | Record Name | First Seen | Last Seen | {% for result in farsightdata %}
|---|---|---|---|---|
| {{ result['count'] }} | {{ result['rrtype'] }} | {{ result['rrname'] }} | {{ result['time_first'] or result['zone_time_first'] }} | {{ result['time_last'] or result['zone_time_last'] }} |
Farsight Passive DNS
More information can be found at Farsight
| Count | Record Type | Record Name | First Seen | Last Seen | {% for result in farsightdata %}
|---|---|---|---|---|
| {{ result['count'] }} | {{ result['rrtype'] }} | {% for i in result['rdata'] %} {{ i }}, {% endfor %} | {{ result['time_first'] or result['zone_time_first'] }} | {{ result['time_last'] or result['zone_time_last'] }} |


This documentation explains how to install and update
the BulkUsb driver. The BulkUsb
sample is in the DDK’s wdm\usb\bulkusb\sys directory.
1.
Make
an OEM installation diskette or equivalent installation source directory for Bulkusb.sys by copying Bulkusb.sys and
Bulkusb.inf into it. The sample BulkUsb.Inf is in the DDK BulkUsb\Sys source
directory.
2.
Make
sure your device has been programmed with the device VID/PID in the Bulkusb.inf
file. If not, edit the device VID, PID, and description text to match your test
board/device.
3.
Depending
on the operating system you are using:
On Windows NT 5.0:
When
you plug in the device, the "Found New Hardware Wizard" dialog box
will appear with the subheading "Install Hardware Device Drivers".
Hit the radio button labeled "Search for a suitable driver for your device
(Recommended)" and then hit the "Next" button. The following
screen has you specify your installation source floppy or directory. Do so, and
then hit the "Next" button. The next screen should indicate that
Windows NT is ready to install the driver. Near the middle of the box, you
should see the full source path to BulkUsb.Inf. Hit the "Next"
button. You'll briefly see a "copying Files" message box, then once
again the "Found New Hardware Wizard" box, now displaying the
subheading, "Hardware Install: The hardware installation is
complete". Hit the "Finish" button. You should now have a copy
of BulkUsb.Sys in your \System32\Drivers directory, a BulkUsb.Inf in your
\Winnt\Inf directory, and a newly-created BulkUsb.Pnf file, which is a
precompiled setup info file that NT creates. If the final "Add New
Hardware Wizard" box indicates any error, or if the OS says you must
reboot to finish installation of this device, something has gone wrong. Check
your Inf file, Install directory, or driver code,
follow the instructions in the section below on simulating a 'first-time'
install, and start over.
On Win98:
Plug
in the device. The "Add New Hardware Wizard" dialog box will appear,
indicating that "This wizard searches for new drivers for: [your
deviceName as programmed into your board's hardware]. Hit the "Next"
button. On the following screen, hit the radio button labeled "Search for
the best driver for your device (Recommended)”. Hit the "Next"
button. The next screen has you specify your installation source floppy or
directory. Do so, and then hit the "Next" button. The next screen
should indicate that Windows is ready to install the driver. You should see the
full source path to BulkUsb.Inf under the label: "Location of
Driver". Hit the "Next" button. You will see the "Building
Driver Information Database" message box, then, if the installation and
loading of the driver have succeeded, you will see the final wizard box saying
“Windows has finished installing the software that your new hardware device
requires." Hit the "Finish" button. If the final "Add New
Hardware Wizard" box indicates any error, or if the OS says you must
reboot to finish installation of this device, something has gone wrong. Check
your Inf file, Install directory, or driver code, follow the below instructions
on simulating a 'first-time' install, and start over.
To
install a new version of your driver after a successful initial installation,
simply replace the binary in \System32\Drivers. If the initial or last installation
fails for any reason, follow the instructions below to simulate a 'first-time'
install.
If
you want to test modifications to an Inf
file or installation program, or if your first install failed and you need to do
a new 'first-time' driver installation without reinstalling Win98 or NT5, do
the following:
1.
Delete
BulkUsb.inf from the \Windows\Inf or \Winnt\Inf
directory. On Winnt, also delete BulkUsb.PNF from the \Winnt\Inf directory.
2.
Delete
BulkUsb.Sys from the \System32\Drivers directory.
3.
Using
RegEdit on Win98, or RegEdt32 on NT5, purge the registry of the following
driver information:
On Windows NT 5.0:
Delete
the following registry key:
\LocalMachine\System\CurrentControlSet\Enum\USB\[the
key with your deviceId/PID]
On
NT5, you must restart the system before reinstalling the driver; this is not
necessary on Win98.
On
Win98:
Delete
the following registry key:
\LocalMachine\System\Enum\USB\[the
key with your device ID/PID],
(
in the case of our sample as published, this would be:
\LocalMachine\System\Enum\USB\VID_045E&PID_930A
)
BulkUsb.Inf is in the Sys
subdirectory. It allows you to set registry overrides for:
1.
"DebugLevel"
debug verbosity level, where 0 == no debug output, 1 == default, higher == more
verbose.
2.
"MaximumTransferSize"
The 'chunk size' (default 4k) large IO requests are broken up into by the
driver.
|
|
© 2005 Microsoft Corporation


Toggle navigation
Vishwas Navada
About
Blog
Tutorials
Projects
Vishwas Navada
Electronics and tech blog
About
Blog
Tutorials
Projects
Tutorials


The data you provided:
ClientID = {{@userId}}
PrivateKey = {{@privateKey}}
Form = {{@formGuid}}
Callback Url = {{@callbackUrl}}
{{@error}}
Enter data for request and press "Make request" button
Results:


I've generally been using GCC (= The GNU Compiler Collection) exclusively for compiling Freecell Solver under Linux for its constant development. For a time, I used to compile the Windows binaries using Microsoft Visual C ("cl.exe") and a really hideous batch file I wrote for the purpose (so it would work on the Technion's computers). Lately, however, I've switched to compiling using MinGW on Windows, which is gcc based.
A few weeks ago, I decided to try out other compilers and see what they had to offer. I downloaded the proprietary icc (Intel's C Compiler), which is gcc-compatible and has a Linux trial version. It was a huge download and ended up occupying a lot of space on the hard-disk. At first, I could not get it to emit working code, but after messing a little with my makefiles, I was successful. It is about the same speed as gcc for compiling my ANSI C program (maybe a bit slower), and generates somewhat faster code.
.icc kept emitting many of the so-called "remarks" which I eventually took upon myself to eliminate. This improved the quality of my code. But it worked properly with these many remarks being emitted. After installing icc, msec (the Mandriva security mechanism) complained about world-writable files in the icc installation, so I reported it on the Intel forum.
The next compiler I tried was tcc, the Tiny/Turbo C Compiler. The version in the Mandriva repositories was old and did not yield a working binary, and so I tried upgrading to version 0.9.24 and then to the git version, but none of them worked either. I reported this attempt to the tcc mailing list.
I also tried icc's -fast flag, which is supposed to make the output faster, but it actually made the binaries slower (much slower than even gcc).
However, later on, after cleaning up my makefile, tcc produced perfectly working binaries. I don't know what the problem was, but it was solved. (I reported that in a reply). Compiling with tcc is much faster than with gcc, but the resulting code is much slower than gcc's, and occupies more space on the disk.
The next compiler was TenDRA. Now the homepage a 403 (and still does) and so I went on Freenode and asked. I eventually did:
$ svn co http://svn.tendra.org/trunk/
Only to discover that http://svn.tendra.org/trunk/tendra/INSTALL is empty except for a reference to the Trac web-site (which was also off-line). So I needed to use the guidance of the people from Freenode's #tendra to build it.
In the process, I had to install bmake, because that's the only make that would compile TenDRA. The ./configure in the standard distribution failed to work here, but I was able to find an .src.rpm which compiled and installed fine. The TenDRA boot process also complained about one of my system's header which had a list or enum with a trailing comma, and I had to manually fix the code.
But I was able to build TenDRA and install it, and it was able to compile a working program. Before it did, though, I had to fix many compilation errors it reported, as TenDRA is stricter than gcc. It also didn't recognise my use of strdup, and I had to specify -Ysystem for it to work (the previous -Y's I tried did not work properly). But then it worked.
TenDRA generated larger and slower executables than gcc but it worked.
I failed to get lcc (which isn't strictly open source ) and pcc (which is open source ) to either compile or produce working code. Seems like neither of them are ready for prime time - at least not here on Mandriva Linux Cooker.
So after a lot of frustration, my experiments with different compilers were mostly successful. As a result, my code is more strict, and better tested. So I guess it was worth it.


Select all the necessary tasks for the workflow, in any order, to create the workflow level tasks pool.
Re-order the selected tasks, by drag-drop to finalize the order in which they will be executed.
Define and wire the input and output parameters for all the selected and order fixated tasks.
Provide the workflow metadata like name, version, description to create the workflow with the defined tasks.
{{newInputValidationMessage}}
| Input Param |
|
|---|
| # | Task Type | Worker | Task Name | Task Description | Task Custom Name | I/O | Optional? | Action | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| {{workflow_task_counter+1}} |
|
|
{{workflow_task.taskDefinition.description}} |
Input Parameter(s)
Total of {{workflow_task.taskDefinition.inputKeys.length}} Params Found
Output Parameter(s)
Total of {{workflow_task.taskDefinition.outputKeys.length}} Params Found
|
|


{% set title = "Alessio Laiso Type" %} {% set description = "Alessio Laiso Type is an independent type foundry established in 2013 and based in Lisbon, Portugal." %} {% set nav_title = "Alessio Laiso Type" %} {% extends "_layout.html" %} {% block content %}
Typefaces that make your brand shine, from an independent type foundry
High-quality, original typefaces available in complete families.
Shop now on:
We offer a variety of services: from the development of new fonts from scratch, to customisations and consultancy.
Discover more


health Health DRR climate change CCA risk disaster Disaster Climate Public adaptation public Risk collab: Change risks synergies reduction vulnerability important links disasters technical resilience systems Environ. However, building potential role World development impacts operational Int. Organization linking literature professionals {border-style Adaptation approach climate-sensitive health. New health, px;} #ffffff;} Reduction Framework action addressing conceptual policy approaches build including long-term preparedness research ./s--- Sci. USA address particularly strategies understanding Ebi Resilience Sendai impact knowledge review K.L. Nations United complex determinants framework global hazards implementation joint need reducing challenges human key work Development Geneva, Glob. Switzerland areas concepts increase management opportunities pathways response suggested sustainable used Disasters Linking Murray Pacific UNFCCC UNISDR essential greater increasing integration international significant ./.. Action Hazards IPCC Lancet Policy Res. York common consideration context extreme policies px; strengthening {font-family ./ijerph Assessment Kelman Med. NY, Nat. Report Therefore, articles countries current draw emergency expertise font-size future identifying link needed needs recognition resilience, resilience: underlying Aitsi-Selmi Am. American City Emergency Furthermore, Intergovernmental Manag. Pan Preparedness Thomalla actors article broader concept dealing developed double; events example, growing little national natural necessary process reduce result sector social strengthen synergies. vulnerabilities working Banwell Building Chang. DC, DRR, Ebola Germany Hospitals Integrated J.J. Panel Rev. Washington, York, adaptation: administrators border-color border-width change, collaboration data direct early engagement existing experience experienced financial goals health: highlighted identified improve indirect integrated known opportunity order planning practice primary processes provide resources resulting similar solid; tools use {font-style {font-weight ./AJPH.. ./S-()- ./SX Additionally, Ann. Approach Bonn, CCA. Clim. Commission Community Dev. EWS Gaillard Hess Integrating International Lessons M.E. Management Mercer National Paris Research Resilient Routledge Safe Similarly, Sustainable Technical Thus, Vulnerability Working aims analysis assessments bold bridging capacity change: changing coherence communication communities contexts courier; cross-cutting currently day definition developing discussed efficiency em; empirical exist facilities foundation guidance identify include includes infrastructure level links. maximize mitigation multiple occur overlaps play populations present presented progress pt;} recognized relevant responses risks, risks. sectors state useful utility warning weather


class: center, middle, inverse, title-slide # Application of Registry Data
to Cancer Risk Prediction ## ⚔
made with xaringan ### Martin Skarzynski ### 2019-02-15 ---  # Cancer Research - So many questions - So little time (and money) - Prevention research can have tremendous impact - Catch cancer early - Before it becomes entrenched - More 💥 for our 💰 --- # Data Collection methods - Active - e.g. Observational Studies - Expensive - Passive - e.g. Cancer registry - More 💥 for our 💰 Common challenges: - Data access - Combining data from various sources - Integrating data into risk predition models --- # Cancer risk is not distributed uniformly .center[] --- # Risk depends on cancer type (liver) .center[] --- # Risk depends on gender (males) .center[] --- # Main question .large[How can we leverage cancer registry data to improve cancer risk prediction methods?] ![](https://20tqtx36s1la18rvn82wcmpn-wpengine.netdna-ssl.com/wp-content/uploads/2018/04/vpr-2.png) --- # Cancer-focused Data Sources ![](https://20tqtx36s1la18rvn82wcmpn-wpengine.netdna-ssl.com/wp-content/uploads/2016/10/naaccr-logo-1.png) - Cancer in North American (CiNA) dataset - Surveillance, Epidemiology, and End Results (SEER) - United States Cancer Statistics (CDC) .pull-left[] .pull-left[] --- # Socioeconomic and Environmental Data Sources - US Census Bureau American Community Survey (ACS) - USDA Economic Research Service - Environmental Quality Index (EQI)* *Jagai et al. (2017) Cancer, 123: 2901-2908. --- # Statistical Models 1. Bach Model - .small[Bach et al., (2003) Variations in lung cancer risk among smokers.JNCI 95: 470-478] 1. Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012 (PLCOM2012) - .small[Tammemägi et al., (2013) Selection criteria for lung-cancer screening. NEJM 368:728-736] 1. Lung Cancer Risk Assessment Tool (LCRAT) - .small[Katki et al., (2016) Development and validation of risk models to select ever-smokers for CT lung cancer screening. JAMA 315:2300-2311] --- # Statistical Models Lung Cancer Risk Assessment Tool plus Computed Tomography (LCRAT+CT) $$ \Huge r(x)={r_0(x)}^{β_0+CTemp*β_1} $$ - `\(r(x)\)` : future risk of lung cancer - `\(r_0(x)\)` : pre-screening risk - `\(x\)` : risk factors in LCRAT - `\(CTemp=1\)` if emphysema was detected - `\(CTemp=0\)` if not --- # Future Directions - Determine how socioeconomic factors interact with cancer risk - Create combined lung risk calculator - that image-detected features and air quality data - to calculate lung cancer risk - for individuals that have undergone chest CT screening - Calculate how the impact of screening (life years gained) varies geographically --- # Acknowledgements - Hormuzd Katki, PhD (preceptor) - Christine Berg, MD - Anil Chaturvedi, PhD - Paul Pinsky, PhD - Ronald M. Summers, MD, PhD - Li Cheung, PhD --- class: center, middle # Thanks for listening! [**remark.js**](https://remarkjs.com) slides written in [**R Markdown**](https://rmarkdown.rstudio.com) and built via the [**xaringan**](https://github.com/yihui/xaringan) and [**knitr**](http://yihui.name/knitr) R packages.


If this option is selected, the connection credentials (port, client, user name, and charset) are specified
manually.
- Port:
In this text box, type the server and the port which your Perforce client will listen to. For the default
Perforce server configuration, it looks like perforce:1666.
- Client:
In this text box, type the name of your Perforce workspace name.
- User:
In this text box, type your user name to authenticate to the server.
- Charset:
From this drop-down list, select the character set to be used.


To be held along with Third Conference on Machine Learning and Systems (MLSys)
Due to the complexity in putting ML into production, the actual machine learning capability is a small part of a complex system and its lifecycle. This new evolving field is known as MLOps. Informally MLOps typically refers to the collaboration between data scientists and operations engineers (e.g. SRE) to manage the lifecycle of ML within an organization. This space is new and has yet to be explored from a research perspective.
In this workshop we aim to cover research problems in MLOps, including the systems and ML challenges involved in this process. We will also cover the software engineering questions including specification, testing and verification of ML software systems. We will bring together a wide variety of experts from both industry and academia, covering persona ranging from data scientists to machine learning engineers.
We solicit short/position papers (2 pages + references) that will be presented as posters. The page limit includes figures, tables, and appendices, but excludes references. Please use standard LaTeX or Word ACM templates. All submissions will need to be made via EasyChair: submit your paper here.
Topics that are relevant to this workshop include:
ML model specification
ML model management
ML model or Concept drift/change detection
ML training pipelines specification, verification
ML model monitoring
ML model serving techniques and systems
ML model specification
ML Experiment tracking and management
System design for metadata management systems
Audits, assurance, security and compliance for MLOps.
ML CI/CD
Scheduling and Cost Optimization of ML workflows
Debugging ML workflows and pipelines
Hyperparameter tuning systems
MLops for Federated, Split and Distributed Learning
Paper submission deadline: January 15, 2020 (submit here)
Author Notification: January 27, 2020
Camera-ready papers due: February 21, 2020
Workshop Date: (tentative) March 4, 2020
All deadlines are at midnight anywhere on earth (AoE), and are firm.
| Time Slot | Title | Authors | 9:00-9:05 | Introductions | Ce, Debo, Matei | 9:05-9:35 | Invited Talk: Automating data quality ops | Theodoros (Theo) Rekatsinas, U. Wisconsin (Madison) | 9:35-10:05 | Invited Talk: Overton | Chris Re, Stanford/Apple | 10:05-10:30 | Break and Poster Setup | 10:30-11:00 | Invited Talk: Elements of Learning Systems | Tianqi Chen, U. Washington. | 11:00-11:30 | Invited Talk: Building a Trusted Process: How MLOps can Enable Responsible AI Development | Sarah Bird, Microsoft | 11:30-12:30 | Lightning Talks (poster introductions) | Various | 12:30-14:00 | Lunch break and Posters | 14:00-14:30 | Invited Talk: ML Prediction Serving | Jospeh Gonzalez, UC Berkeley | 14:30-15:00 | Invited Talk: ML Engineering: Emergence, Evolution, and Lessons Learned from Google's Production ML Pipelines | Alkis (Neoklis) Polyzotis, Google | 15:00-15:30 | Coffee break and Posters | 15:30-16:00 | Invited Talk: TFX, Kubeflow, and MLflow: An incomplete history of ML infrastructure and the emergence of MLOps | Clemens Mewald, Databricks | 16:00-16:30 | Invited Talk: Model Versioning: A keystone for Robust MLOps | Manasi Vartak, Verta.ai | 16:30-17:00 | Wrap up | Ce, Debo, Matei |
|---|
Organizing Committee
Debo Dutta, Cisco Systems
Ce Zhang, ETH Zurich
Matei Zaharia, Stanford University & Databricks
Program Committee
Debo Dutta, Cisco Systems
Ce Zhang, ETH Zurich
Matei Zaharia, Stanford University & Databricks


"Experience Transformation at Relevance Lab - Enjoy Work like never before!! Relevance Lab offers a fast paced career with an opportunity to be at the forefront of leading edge technologies in Cloud, DevOps, Mobility, User Experience Designs, Big Data etc. If “Passion to Achieve” is what drives you then Relevance Lab is the place to be."
| Position : | Ruby on Rails Developer |
| Work Location : | Bangalore |
| Experience : | 6 to 8yrs |
Job Description :
Interested candidates can mail in their CVs to<EMAIL_ADDRESS>| Position : | Performance Engineer |
| Work Location : | Bangalore |
| Experience : | 5 to 7yrs |
Job Description :
Interested candidates can mail in their CVs to<EMAIL_ADDRESS>| Position : | Drupal Developer |
| No of Positions : | 2 |
| Work Location : | Bangalore |
| Experience : | 3 to 5yrs |
Job Description:
Interested candidates can mail in their CVs to<EMAIL_ADDRESS>| Position : | QA Engineer |
| Work Location : | Bangalore |
| Experience : | 3 to 5yrs |
| Technical Key Skills : | Web application testing, Test Management Tools, Function automation tools |
Job Description
Interested candidates can mail in their CVs to<EMAIL_ADDRESS>| Position : | Cloud DevOps Engineer |
| Work Location : | Bangalore |
| Experience : | 3 to 5yrs |
| Technical Key Skills : | Ruby, Chef/ Puppet, Unix/Linux, Amazon, EC2/S3, Open Stack, Cruise Control, Bamboo/Jenkins |
Job Description
Interested candidates can mail in their CVs to<EMAIL_ADDRESS>| Position : | Cloud Linux System Administrator |
| Work Location : | Bangalore |
| Experience : | 3 to 5yrs |
| Technical Key Skills : | Linux, Unix Scripting (Bash, Perl,Shell), VMware |
Job Description
Interested candidates can mail in their CVs to<EMAIL_ADDRESS>| Position : | Java Lead |
| Work Location : | Bangalore |
| Experience : | 5 to 7yrs |
| Technical Key Skills : | Core Java, Spring, Hibernate, Javascript, Ext.JS, JQuery |
Job Description
Interested candidates can mail in their CVs to<EMAIL_ADDRESS>| Position : | Build and Release Manager |
| Work Location : | Bangalore |
| Experience : | 10 to 13yrs |
Job Description
Interested candidates can mail in their CVs to<EMAIL_ADDRESS>| Position : | DevOps Architect |
| Work Location : | Bangalore |
| Experience : | 11 to 13yrs |
Job Description
Interested candidates can mail in their CVs to<EMAIL_ADDRESS>| Position : | Junior Build and Release Engineer |
| Work Location : | Bangalore |
| Experience : | 3 to 5yrs |
Job Description
Interested candidates can mail in their CVs to<EMAIL_ADDRESS>| Position : | Junior DevOps Engineer |
| Work Location : | Bangalore |
| Experience : | 3 to 5yrs |
Job Description
Interested candidates can mail in their CVs to<EMAIL_ADDRESS>| Position : | Senior Backend PHP Developer |
| Duration : | 3 - 6 mths (C2H Position) |
| Work Location : | Lexington, MA |
Description :
Marketing and Advertising Solutions Company is looking for a Senior Backend PHP Developer who will be responsible for development, implementation, and maintenance activities for firm's loyalty shopping platform. The ideal candidate is someone with XML, JavaScript, and MySQL.
Job Description :
Interested candidates can mail in their CVs to<EMAIL_ADDRESS>| Position : | Chef resource |
| Work Location : | NYC or Boston |
| Duration : | 6+ mths |
Description :
This will be a strong Chef resource, with deep scripting hopefully in Ruby, Good exposure to Linux system, Some flavour of Release Management tools and Basic monitoring capabilities – Splunk and Nagios
Job Description :
Interested candidates can mail in their CVs to<EMAIL_ADDRESS>| Position : | Drupal Developer |
| Work Location : | Bellevue, WA |
| Duration : | 6+ mths |
As a Senior Mobile Software Engineer, you will:
Requirements :
Bonus points for :
Interested candidates can mail in their CVs to<EMAIL_ADDRESS>| Position : | DevOps CMS System Administrator |
| Work Location : | Herndon, VA |
Description :
This candidate will be one of several team members on the DevOps team supporting operations tasks for multiple CMS sites. This role combines the skills of multiple roles including traditional System Admin, Build Manager, and Development Manager. These skills are used to create and run a continuous integration environment. Must have prior experience with managing continuous integration environments.
Job Description :
Required Skills:
Interested candidates can mail in their CVs to<EMAIL_ADDRESS>| Position : | Junior DevOps Engineer |
| Work Location : | San Jose, CA |
| Experience : | 3 to 5yrs |
| Skill Set : | Amazon EC2/Open Stack, Chef, Ruby and Shell Script |
Job Description
Interested candidates can mail in their CVs to<EMAIL_ADDRESS>| Position : | DevOps Architect |
| Work Location : | San Jose, CA |
| Experience : | 11 to 13yrs |
| Skill Set : | Amazon EC2/Open Stack, Chef, Ruby and Shell Script |
Job Description
Interested candidates can mail in their CVs to hr@relevancelab.com


@main("About") {
Software engineer currently living in Manchester. Will update soon with anything which interests me.
Work in progress, this is a very rough draft. Source available at github.


{% extends 'base.html' %} {% from 'daily_matchup.html' import matchup_view as daily_matchup_macro %} {% from 'team_last_x_matches_table.html' import team_matches_table as team_table_macro %} {% from 'match_player_stats.html' import match_player_table as player_table_macro %} {% block head %} {% endblock%} {% block body %}


preferences
What kind of things do you want in your night?
Food?
Beer?
Liquor?
Date Night?
Go To Map


You need to pass your
Management Console password
as an authentication token to every Management Console API endpoint except
/setup/api/start
.
Use the api_key
parameter to send this token with each request.
For example:
curl -L 'https://hostname:admin_port/setup/api?api_key=your-amazing-password'
You can also use standard HTTP authentication to send this token. For example:
curl -L 'https://api_key:your-amazing-password@hostname:admin_port/setup/api'


Redirecting (10 seconds) to online documentation at http://openwateranalytics.github.com/epanet-rtx/
You can also build the documentation locally using Doxygen.