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---
layout: post
title: Upgrading to Lenny
tags:
- ! '*nix'
- apache
- debian
- linode
- MySQL
status: publish
type: post
published: true
meta:
aktt_notify_twitter: 'no'
_edit_last: '2'
---
It's been while since I attended to my VPS, I decided to spend some time last night upgrading my distribution from Etch to Lenny. This is normally a moment where your heart sinks as quite a few things do tend to go belly up, but I am happy to report that I only came across some minor issues and these were resolved in minutes as opposed to hours.
- MySQL : failed to start, complaining about:[code]/etc/init.d/mysql: ERROR: Using expire_logs_days without log_bin crashes
the server. See README.Debian.gz[/code]
Commenting out the expire_logs_days in the my.cnf file allowed me to restart MySQL.
- After the upgrade of Apache, my virtual hosts weren't working. A quick search via Google pointed me to this post - a quick edit of all of my host files and it was all working again.
- the php-mysql connector somehow hadn't been upgraded/installed so a quick[code]apt-get install php5-mysql[/code]fixed that problem.
- OpenBD : the only thing that remains broken was my tomcat5.5 Open BD install. Tomcat was working fine but Open BD refused to start up complaining about :[code]javax.servlet.ServletException: Open BlueDragon Engine Failed to initialise tags: java.awt.Color[/code]Since I am not really using it, it's not that important, but at some stage I'd like to get it working again. If you have any suggestions, please leave a comment.


I love crafts, learning, and my family. Some of my most enjoyable memories are all due to my enthusiasm to learn, even in moments when it is unexpected.
I became interested in coding and embarked on teaching myself the basics of JavaScript. It was through that process that I found a real passion for coding and enrolled into DevMountain. Writing code has encouraged me to continue learning as well as express my creativity.
Skills


okama.assets.AssetList¶
-
class
okama.assets.
AssetList
(symbols=None, *, first_date=None, last_date=None, ccy='USD', inflation=True)¶ The list of financial assets implementation.
-
__init__
(symbols=None, *, first_date=None, last_date=None, ccy='USD', inflation=True)¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([symbols, first_date, last_date, …])Initialize self.
describe
([years, tickers])Generate descriptive statistics for a list of assets.
get_cagr
([period])Calculate assets Compound Annual Growth Rate (CAGR) for a given trailing period.
get_cumulative_return
([period])Calculate cumulative return over a given period for each asset.
get_cvar_historic
([time_frame, level])Calculate historic Conditional Value at Risk (CVAR, expected shortfall) for the assets.
get_dividend_mean_growth_rate
([period])Calculate geometric mean of dividends growth rate time series for a given trailing period.
get_rolling_cagr
([window])Calculate rolling CAGR (Compound Annual Growth Rate) for each asset.
get_rolling_cumulative_return
([window])Calculate rolling cumulative return for each asset.
get_var_historic
([time_frame, level])Calculate historic Value at Risk (VaR) for the assets.
index_rolling_corr
([window])Compute rolling correlation with the index (or benchmark) time series for the assets.
kstest
([distr])Perform Kolmogorov-Smirnov test for goodness of fit the asset returns to a given distribution.
kurtosis_rolling
([window])Calculate rolling Fisher (normalized) kurtosis of the return time series for each asset.
skewness_rolling
([window])Compute rolling skewness of the return time series for each asset.
Attributes
Calculate annual rate of return time series for each asset.
Return the base currency of the Asset List.
Return the number of years when the annual dividend was growing for each asset.
Return the number of years of consecutive dividend payments for each asset.
Calculate last twelve months (LTM) dividend yield time series (monthly) for each asset.
Return calendar year dividends sum time series for each asset.
Calculate drawdowns time series for the assets.
Compute beta coefficient time series for the assets.
Compute expanding correlation with the index (or benchmark) time series for the assets.
Perform Jarque-Bera test for normality of assets returns historical data.
Calculate expanding Fisher (normalized) kurtosis of the return time series for each asset.
Calculate annualized mean return (arithmetic mean) for the assets.
Calculates annualized real mean return (arithmetic mean) for the assets.
Calculate annualized risks (standard deviation) for each asset.
Calculate monthly risks (standard deviation) for each asset.
Return semideviation annualized values for each asset.
Calculate semideviation monthly values for each asset.
Compute expanding skewness of the return time series for each asset returns.
Return a list of financial symbols used to set the AssetList.
Return a list of tickers (symbols without a namespace) used to set the AssetList.
Return tracking difference for the rate of return of assets.
Calculate annualized tracking difference time series for the rate of return of assets.
Calculate tracking error time series for the rate of return of assets.
Calculate wealth index time series for the assets and accumulated inflation.
-


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--- layout: null section-type: about title: About --- ## About Water from thin air, open source style. This project lives on GitHub


Welcome To Online Complain Site
Passenger
Cleaner
Station-Head
Menu Layout
Passenger
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Input :
Submit


FTPs, DASTCOM5 and dtypes (SOCIS 2017)
In the previous entry, I said that we had found a new database, and we would have to take a closer look to it.
Actually, what I found was a JPL public FTP, with lots of data in it (I have barely taken a glance to all the folders that contains, so have fun looking into it :P).
In that FTP, inside ssd
folder (Solar System Dynamics, not Solid State Drive), there were several different files, some of them with .DB extension, some of them with .dat extension, and a README.
The README explained some of the files (a few of them are still a mystery), and I quote what it said about a file named dastcom5.zip
.
dastcom5.zip
Link to a a portable/programmable version of the JPL/Horizons database of asteroids and comets ("DASTCOM5"), updated as often as hourly
Interesting, isn't it?
Well, after a week, I can now assure that this database has been a great discovery, so this week entry will address mainly about it.
DASTCOM5
As README says, DASTCOM5 is the name of a JPL-maintained database of asteroids and comets (I think D
stands for Database, AST
for asteroid, COM
for comets, and 5
is the current version, but I'm not sure haha).
After downloading and unzipping the file that JPL provides, you can see the following folder structure:
Fortunately, dastcom5.zip
provided a doc folder.
Inside this folder there was a long README (more than 1200 lines) with a lot of information, which I quote below:
DASTCOM5 is a direct-access binary database. It contains heliocentric ecliptic osculating elements for the known asteroids and comets, determined by a least-squares orbit solution fit to optical and radar astrometric measurements.
[...]
A total of 142 parameters per object are defined.
So DASTCOM5 format was binary (more difficult than JSON), but it contained 142 parameters. Not bad! Let's continue:
The DASTCOM5 distribution .zip file archive contains the following:
two binary database files (one holding asteroid data, the second holding comet data),
a plain-text index (linking all objects to their DASTCOM5 record, permitting look-up based on name, designation, SPK ID, packed MPC designation, and historical aliases),
documentation,
latest database reader source code (FORTRAN). The software has been tested using GFORTRAN (gfortran), Lahey (lf95), Intel (ifort), and SunStudio (f95) compilers in both 32 and 64-bit builds, under RedHat Linux 4/5/6.
Apparently, DASTCOM5 provided all of the necessary files for building a module poliastro.neos.dastcom5
. We decided to read the two binary databases with Python instead of creating a FORTRAN wrapper, which didn't sound appealing at all.
Therefore, the first task was to read a comet or asteroid binary record, and the chosen tool was numpy.fromfile(), which, as stated on its docs, needs a known data-type. Fortunately, the extensive DASTCOM5 README had a section named DASTCOM5 BYTE MAP
which was really useful.
Knowing the number of bytes and type of each variable, the only difficulty was writing it for each of the 142 variables manually.
Once that was done, we were able store the full asteroid or comet database as a numpy array:
with open(AST_DB_PATH, "rb") as f:
data = np.fromfile(f, dtype=AST_DTYPE)
or obtain a single record given a physical record number using:
with open(AST_DB_PATH, "rb") as f:
f.seek(PHYSICAL_RECORD, os.SEEK_SET)
body_data = np.fromfile(f, dtype=COM_DTYPE, count=1)
However, the problem was that DASTCOM5 physical record didn't correspond to a logical record, such as 433 for Eros, or 900001 for 1P/Halley. So, the next task consisted of reading database headers in order to get information about bias between logical and physical record. The resultant function can be found, as usual, on Github.
After dealing with records, it was time to start with the lookup function. As stated above, DASTCOM5 package come with a plain text index file containing names, designations, SPK-IDs, packed MPC designations, and historical aliases.
The file format was sort of CSV, but really ugly to parse, given that alternative designations, alternative SPK-IDs, etc. were all mixed without any order.
Therefore, as the file was plain text, we decided to read line by line the index, comparing with a string passed as argument. Code is on Github and anyone is welcome to improve the lookup function as well as any other part of the code :)
Downloading DASTCOM5
DASTCOM5 isn't an online API, but an online available file, which you need to download in order to use. Therefore, poliastro needed to provide a good way to download the database without having to stop Python console.
Unlike previous entry, where we used Request
library in order to communicate with the internet, in this module urllib.request was chosen, since urllib.request.urlretrieve()
can be called using a function as an argument, which is called after each block read. That gave us the opportunity of showing download progress, with the following code:
def _show_download_progress(transferred, block, totalsize):
trans_mb = transferred * block / (1024 * 1024)
total_mb = totalsize / (1024 * 1024)
print('%.2f MB / %.2f MB' % (trans_mb, total_mb), end='\r', flush=True)
Also, once the download function was finished, we decided to provide CLI support, adding poliastro --download-dastcom5
command.
So, from now, you can download DASTCOM5 database automatically to ~/.poliastro folder either from a Python interpreter or from CLI.
NeoWs and DASTCOM5
Both NeoWs and DASTCOM5 are currently part of poliastro.neos
, and their functionalities can somehow intersect. But they are completely different in the background, with mainly two differences:
-
DASTCOM5 database has data about every asteroid and comet in the solar system, but NeoWs is only available for NEAs (Near Earth Asteroids).
-
NeoWs is an online API, but DASTCOM5 has to be downloaded in order to be used (~230 MB).
Therefore, depending on your needs, you will have to decide which one fits you better, considering that both of them can create an Orbit
given a name, SPK-ID, etc.
However, the resultant orbits using the two functions are exactly equal, as you can check in the following example using Eros:
In [1]: from poliastro.neos import neows
WARNING: Constant 'Earth equatorial radius' already has a definition in the None system [astropy.constants.constant]
WARNING: Constant 'Jupiter equatorial radius' already has a definition in the None system [astropy.constants.constant]
In [2]: from poliastro.neos import dastcom5
In [3]: from poliastro.plotting import OrbitPlotter
In [4]: eros_neows = neows.orbit_from_name('eros')
In [5]: eros_dastcom5 = dastcom5.orbit_from_name('eros')[0]
In [6]: frame = OrbitPlotter()
In [7]: frame.plot(eros_neows, label='Eros from NeoWs')
Out[7]:
[<matplotlib.lines.Line2D at 0x1e0c0d68940>,
<matplotlib.lines.Line2D at 0x1e0b9a71438>]
In [8]: frame.plot(eros_dastcom5, label='Eros from DASTCOM5')
Out[8]:
[<matplotlib.lines.Line2D at 0x1e0c0e38780>,
<matplotlib.lines.Line2D at 0x1e0c0e3a8d0>]
That was all for this week (two weeks actually, sorry for that). Now poliastro has two ways of dealing with NEOs, each one with its advantages and disadvantages, and you can try any of them. Probably, the next week entry will be about a completely different topic, not exactly related to NEOS, but I can't disclose anything right now :P See you!


{% from "custom_description.html" import get_description_body %}
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| G.A.R. 2 |
| (See 11 (4) and 80 ) |
| (Obverse) |
| LAST PAY CERTIFICATE |
| Last Pay Certificate of Shri/Shrimati |
........................................................................................................ |
|
| of the office |
| of |
................................................................................................................................................................... |
|
| proceeding |
| on |
............................................................................................................................................................................. |
|
| to |
........................................................................................................ |
|
| 2. He/She has been paid uptor |
...................................................................................................................... |
at |
|
| |
Particulars |
| Substantive Pay |
|
| Officiating Pay |
|
| Special Pay |
|
| Personal Pay |
|
| Leave Salary |
|
| Allowances: |
|
| (a) D.A./A.D.A. |
|
| (b) C.C.A. |
|
| (c) H.R.A. |
|
| (d) ............... |
|
|
| (Rate of Deductions) |
| G.P.F |
.................................................................. |
-TAX |
............................................................... |
C.G.E.I.S/ |
|
| C.G. Employees Group Insurance Scheme, |
| 1980 |
............................................................................................................................. |
|
| (a) Composite rate. |
| or (b) Insurance rate only |
| Licence fee for Govt. accommodation @ |
..................................................................... |
P.M. |
|
| 3. His General Provident Fund Account/GPF Account No. |
.......................................... |
is maintained by the Pay |
|
| and Accounts Office/DDO/AG |
.......................................... |
{Refer Correction Slip 19} |
|
| 4. He made over charge of the office of |
................................................................ |
|
| ............................... |
on the |
................................. |
noon |
|
| of |
................................................................................................................................................................... |
|
| 6. He is entitled to draw the following:— |
| 7. He has been sanctioned leave proceeding joining time for |
................................. |
days. |
|
| 8. He finances the insurance policies detailed below from the Provident Fund:— |
| Name of Insurance Company |
No. of Policy |
Amount of Premium Rs. |
Due date for the payment of premium |
|
| 9. Details of P.L.I. recovery through pay bill:— |
| 10. The details of income-tax recovered from him upto the date from the beginning of the
current financial year are noted on the reverse. |
| Signature |
................................... |
|
| Designation |
................................... |
|
| Dated |
............................ |
19 |
.......................... |
|
| (Reverse) |
| LAST PAY CERTIFICATE |
| Details of recoveries |
| Name of advance involving recovery/ adjustment |
Total amount of advance sanctioned with month of drawal |
Outstanding amount recoverable |
Rate of Instalment |
| |
Rs. |
Rs. |
Rs. |
| Pay |
|
|
|
| Advance |
............................. |
|
| T.A. |
|
|
|
| Advance |
.............................. |
|
| H.B. |
|
|
|
| Advance |
.............................. |
|
| ........................................ |
| ........................................ |
| ........................................ |
| Licence fee arrears Rs. |
.............................................. |
for the period from |
.................................................... |
|
| to |
.............................................. |
|
|
| Name of months |
Pay/Leave Salary & allowances |
Fee, Honorarium etc. |
Funds & other deductions |
Amount of I-Tax recovered |
Remarks |
| Rs. |
Rs. |
Rs. |
Rs. |
| April 19... |
|
|
|
|
|
| May 19... |
|
|
|
|
|
| June, 19... |
|
|
|
|
|
|
| Signature |
................................... |
|
| Designation |
................................... |
|
| (Rupees |
.................................................................................................... |
which tallies with the closing |
|
| balance as worked out in the cash book". |
| (2) Self cheques drawn under cheque drawing powers, for payments to staff
etc., in cash, should also be entered on the receipts side of the cash
book. |
| (3) Closing balance should be analysed in the following manner:— |
| Rs. P. |
|
|
| (ii) |
Self Cheques Not transferable category of cheques and
bank drafts. |
|
|
|
|
|
|
|
| |
(a) |
.......................................... |
|
| |
(b) |
.......................................... |
|
| (vi) |
.......................................... |
|
| |
Total cash balance [Items (i) to (vi)]. |
|
|


|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |
| Classes in org.openjena.atlas.lib used by com.hp.hpl.jena.sparql.core | |
|---|---|
| Closeable
|
|
| Sync
|
| Classes in org.openjena.atlas.lib used by com.hp.hpl.jena.sparql.engine | |
|---|---|
| Closeable
|
| Classes in org.openjena.atlas.lib used by com.hp.hpl.jena.sparql.engine.iterator | |
|---|---|
| Closeable
|
| Classes in org.openjena.atlas.lib used by com.hp.hpl.jena.sparql.engine.main | |
|---|---|
| Closeable
|
| Classes in org.openjena.atlas.lib used by com.hp.hpl.jena.sparql.engine.main.iterator | |
|---|---|
| Closeable
|
| Classes in org.openjena.atlas.lib used by com.hp.hpl.jena.sparql.engine.ref | |
|---|---|
| Closeable
|
| Classes in org.openjena.atlas.lib used by com.hp.hpl.jena.sparql.modify | |
|---|---|
| Closeable
|
|
| MultiMap
|
| Classes in org.openjena.atlas.lib used by com.hp.hpl.jena.update | |
|---|---|
| Closeable
|
| Classes in org.openjena.atlas.lib used by org.openjena.atlas.io | |
|---|---|
| Closeable
|
|
| Sink
Interface for the destination of things |
| Classes in org.openjena.atlas.lib used by org.openjena.atlas.iterator | |
|---|---|
| ActionKeyValue
|
|
| Closeable
|
|
| Sink
Interface for the destination of things |
| Classes in org.openjena.atlas.lib used by org.openjena.atlas.json.io.parser | |
|---|---|
| Closeable
|
| Classes in org.openjena.atlas.lib used by org.openjena.atlas.lib | |
|---|---|
| ActionKeyValue
|
|
| AlarmClock
An AlarmClock is an object that will make a call back at a preset time. |
|
| Cache
A cache |
|
| CacheSet
|
|
| CacheStats
A cache with some readable statistics |
|
| Callback
|
|
| Closeable
|
|
| ColumnMap
General descriptor of a reordering (mapping) of columns in tuples to columns in indexes, for example, from triples to triple index order. |
|
| InternalErrorException
|
|
| Map2
A map with parent sharing. |
|
| MultiMap
|
|
| Pingback
|
|
| Pool
|
|
| Sink
Interface for the destination of things |
|
| SinkNull
|
|
| SinkWrapper
Wrap one sink in another - to pass on behaviour, the derived Sink must call super.operation See also: SinkSplit |
|
| Slot
Key-value slot, with chaining for lookup. |
|
| Tuple
Tuple class - tuples are immutable and must be created initialized |
| Classes in org.openjena.atlas.lib used by org.openjena.atlas.lib.cache | |
|---|---|
| ActionKeyValue
|
|
| Cache
A cache |
|
| CacheSet
|
|
| CacheStats
A cache with some readable statistics |
| Classes in org.openjena.atlas.lib used by org.openjena.riot | |
|---|---|
| Sink
Interface for the destination of things |
| Classes in org.openjena.atlas.lib used by org.openjena.riot.lang | |
|---|---|
| Closeable
|
|
| Sink
Interface for the destination of things |
| Classes in org.openjena.atlas.lib used by org.openjena.riot.out | |
|---|---|
| Closeable
|
|
| Sink
Interface for the destination of things |
| Classes in org.openjena.atlas.lib used by org.openjena.riot.pipeline | |
|---|---|
| Closeable
|
|
| Sink
Interface for the destination of things |
|
| SinkWrapper
Wrap one sink in another - to pass on behaviour, the derived Sink must call super.operation See also: SinkSplit |
| Classes in org.openjena.atlas.lib used by org.openjena.riot.pipeline.inf | |
|---|---|
| Sink
Interface for the destination of things |
| Classes in org.openjena.atlas.lib used by org.openjena.riot.pipeline.normalize | |
|---|---|
| Closeable
|
|
| Sink
Interface for the destination of things |
|
| SinkWrapper
Wrap one sink in another - to pass on behaviour, the derived Sink must call super.operation See also: SinkSplit |
| Classes in org.openjena.atlas.lib used by org.openjena.riot.system | |
|---|---|
| Closeable
|
|
| Pair
|
|
| Sink
Interface for the destination of things |
| Classes in org.openjena.atlas.lib used by org.openjena.riot.tokens | |
|---|---|
| Closeable
|
|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |


---
layout: post
title: 1/11
date: '2013-01-12T23:55:00.001-07:00'
author: Jacob .
tags:
modified_time: '2013-01-12T23:56:09.403-07:00'
blogger_id: tag:blogger.com,1999:blog-6400085873105072120.post-8313639427532560344
blogger_orig_url: http://www.svlark.com/2013/01/110_12.html
---
I'm sick today.
Audrey worked on lesson 50. She was doing great and then just got frustrated with herself. I'm not sure what's going on exactly. Will have to consider how to proceed.
Boat show tomorrow.
-25, 246.2


The meal plan that hinders the gut’s recovery from antibiotics
Experiments in mice show that a regimen of low-fibre foods keeps the gut microbiome from bouncing back.
A low-fibre diet could hamper the recovery of a healthy gut microbiome after antibiotic therapy.
Kerwyn Casey Huang at Stanford University in California and his colleagues studied intestinal bacteria in mice whose guts had been populated with microbes from a human donor. For five days, the team treated the ‘humanized’ mice with common antibiotics, such as streptomycin and ciprofloxacin.
Gut-microbe density dropped up to 100,000-fold within half a day of the rodents beginning antibiotics. Certain microbial species began to recover by about the third day of antibiotics, but recovery was delayed in mice fed a fibre-poor diet.
The researchers also gave streptomycin to mice with rodent microbiomes and found that animals housed alone were slower to recover than those housed in groups. Streptomycin can eliminate different bacterial strains in different animals, so mice living communally might have reconstituted their gut microbiomes more quickly by taking up microbes from their roommates.
The authors suggest that the fibre-poor diets and highly sanitized environments common in Western society could hinder the recovery of the microbiome in humans who’ve taken antibiotics.


---
layout: default
title: Publications
---
Publications
Publications
Journal Article
-
Wei-Ning Chen, and I-Hsiang Wang,
"Anonymous Heterogeneous Distributed Detection: Optimal Decision Rules, Error Exponents, and the Price of Anonymity,"
to appear at IEEE Transaction on Information Theory
[arXiv]
Conference Proceedings
-
Wei-Ning Chen, and I-Hsiang Wang,
"On the Price of Source Anonymity in Heterogeneous Parametric Point Estimation,"
IEEE International Symposium on Information Theory (ISIT), Paris, July 2019
[Full Version]
[Slides]
-
Wei-Ning Chen, Ho-Chun Chen, and I-Hsiang Wang,
“On the Fundamental Limits of Heterogeneous Distributed Detection: Price of Anonymity," IEEE International Symposium on Information Theory (ISIT), Vail, June 2018
[Full Version]
[Slides]
-
Wei-Ning Chen, and I-Hsiang Wang,
"Partial Data Extraction via Noisy Histogram Queries: Information Theoretic Bounds,"
IEEE International Symposium on Information Theory (ISIT), Aachen, June 2017
[Full Version]
[Slides]
Master Thesis
-
Wei-Ning Chen,
"Fundamental Limits of Anonymous Statistical Inference: Privacy Preserving Crowd-sourcing and Heterogeneous Sensor Networks," NTU Master Thesis, July 2018
[pdf]
[Slides]


Soon to be an honor graduate with 2 degrees: MS in Information systems from NJIT (2016-2018) BA in Information systems from UTM (2009-2013)
My area of knowledge and expertise include but are not limited to: Relational databases Software development Project management Web design Quality Assurance SDLC Agile Scrum watefall and other software engineering methodologies
A mobile software development group started by a number of University of Tennessee students and developed a number of apps for children with learning disabilities in addition to cross platform indie games.
SABIC (Mar. 2014 - Feb. 2016) QA TesterOne of the largest Saudi Arabia Corporations and is a diversified manufacturing company that is active in multiple fields.
Al Ghadeer Foundation (Sep. 2016 - Present) Teaching AidA nonprofit cultural foundation that provide a number of services for the Arabic speaking community in greater New York Area
Feel free to email me with any questions or opportunities you might have!


Cell Biology The ribosomal RNA m5C methyltransferase NSUN-1 modulates healthspan and oogenesis in Caenorhabditis elegans Clemens Heissenberger Teresa L. Krammer Jarod A. Rollins Fabian Nagelreiter Isabella Stocker Ludivine Wacheul Anton Shpylovyi Santina Snow Johannes Grillari Aric N. Rogers Denis L.J. Lafontaine Markus Schosserer 3 reviews Reviewed Apr 8, 2020 by eLife
Cell Biology KAT2-mediated acetylation switches the mode of PALB2 chromatin association to safeguard genome integrity Marjorie Fournier Jean-Yves Bleuyard Anthony M. Couturier Jessica Ellins Svenja Hester Stephen J. Smerdon László Tora Fumiko Esashi 4 reviews Reviewed Apr 9, 2020 by EMBOpress


The Stone Series
A Great Addition
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---
layout: post
title: ''
date: '2012-04-05T09:38:00-05:00'
tags:
- heroku
- rails
- web
- app
- deploy
permalink: /post/20525742163/mama-schneems-deploys-a-web-app-when-my-mother
---
When my Mother asked me what I do at my new job at Heroku, I decided to grab a camera and show her. With a little help she deployed a Rails app that I made in around 30 minutes. How fast can your mom deploy your web app? Hi Def Video


|
STOR
Server-less, Embeddable NoSQL Document Store
|
collection is a logical partition of a store, where documents representing similar or correlated entities can be added. More...
#include <collection.h>
Public Member Functions | |
| collection (const collection &o)=delete | |
| Copy Constructor. Not available. | |
| collection & | operator= (const collection &o)=delete |
| Copy Assignment. Not available. | |
| ~collection () | |
| Destructor. | |
| bool | clear_indices () |
| Remove all stored indices for this collection. More... | |
| const std::unordered_set< index_path > & | indices () const |
| Returns a set of all the indices currently in use. More... | |
| void | add_index (const index_path &path) |
| Add index to 'path' to allow lookup during query operations. More... | |
| template<typename indices_container > | |
| void | add_indices (indices_container &&ic) |
Add all the index_paths provided by ic . More... | |
| template<typename index_path_iter > | |
| void | add_indices (index_path_iter beg, index_path_iter end) |
| Add all the index_paths in the range [beg, end) More... | |
| const std::string & | name () const |
| Return name of collection. More... | |
| bool | put (const document &doc) |
| Insert/Update document 'doc' in the collection. More... | |
| bool | remove (const document &doc) |
| Remove document 'doc' from the collection. More... | |
| bool | remove (const document::identifier &id) |
| Remove document from the collection that has id = 'id'. More... | |
| document | operator[] (const stor::document::identifier &id) const |
| Retrieve document with id = 'id' from the collection. More... | |
| bool | has (const stor::document::identifier &id) const |
| Checks whether document with id = 'id' exists in the collection. More... | |
| std::unordered_set< document > | find (const query &q) |
| Execute query against the collection and return all matching documents. More... | |
| void | deflate (std::ostream &o, bool keep_indices=false) const |
| Write data and indices of store to std::ostream. More... | |
| void | inflate (std::istream &i) |
| Read data and indices from std::istream and build collection from them. More... | |
Protected Member Functions | |
| collection (store *s, document &info) | |
| Constructor. More... | |
| collection (collection &&o) | |
| Move Constructor. | |
| collection & | operator= (collection &&o) |
| Move Assignment. | |
| void | persist () |
| Stores any structural changes made to this collection in the store it belongs to. | |
| void | put (leveldb::WriteBatch &batch, const document &d) const |
| Register insertion of document key:value id:json in batch. More... | |
| void | put (leveldb::WriteBatch &batch, const index_path &index, const document &d) const |
| Register insertion of document key:value index_path:value_of_path in batch. More... | |
| void | remove (leveldb::WriteBatch &batch, const index_path &index, const document &d) const |
| Register removal of value at index_path 'index' of document 'd' in batch. More... | |
| void | remove (leveldb::WriteBatch &batch, const document &d) const |
| Register removal of of document 'd' in batch. More... | |
| std::unordered_set< std::string > | process (const eq_instruction &i, const leveldb::ReadOptions &ro) const |
| Execute equality instruction 'i' on this collection using ReadOptions 'ro'. More... | |
| std::unordered_set< std::string > | process (const gt_instruction &i, const leveldb::ReadOptions &ro) const |
| Execute greater-than instruction 'i' on this collection using ReadOptions 'ro'. More... | |
| std::unordered_set< std::string > | process (const gte_instruction &i, const leveldb::ReadOptions &ro) const |
| Execute greater-equal-than instruction 'i' on this collection using ReadOptions 'ro'. More... | |
| std::unordered_set< std::string > | process (const neq_instruction &i, const leveldb::ReadOptions &ro) const |
| Execute non-equality instruction 'i' on this collection using ReadOptions 'ro'. More... | |
| std::unordered_set< std::string > | process (const lt_instruction &i, const leveldb::ReadOptions &ro) const |
| Execute less-than instruction 'i' on this collection using ReadOptions 'ro'. More... | |
| std::unordered_set< std::string > | process (const lte_instruction &i, const leveldb::ReadOptions &ro) const |
| Execute less-equal-than instruction 'i' on this collection using ReadOptions 'ro'. More... | |
Friends | |
| class | store |
| class | eq_instruction |
| class | gt_instruction |
| class | gte_instruction |
| class | neq_instruction |
| class | lt_instruction |
| class | lte_instruction |
collection is a logical partition of a store, where documents representing similar or correlated entities can be added.
Constructor.
| s | pointer to parent store |
| info | document containing initializing values for collection |
| void esft::stor::collection::add_index | ( | const index_path & | path | ) |
Add index to 'path' to allow lookup during query operations.
| path | path to a datapoint to store index for |
|
inline |
Add all the index_paths provided by ic
.
| ic | iterable container of index_paths |
|
inline |
Add all the index_paths in the range [beg, end)
| beg | beginning of the range |
| end | one after last element to add in the range |
| bool esft::stor::collection::clear_indices | ( | ) |
Remove all stored indices for this collection.
| void esft::stor::collection::deflate | ( | std::ostream & | o, |
| bool | keep_indices = false |
||
| ) | const |
Write data and indices of store to std::ostream.
| o | output stream to write collection to |
Execute query against the collection and return all matching documents.
| q | query that provides instructions to retrieve a set of documents from collection |
| bool esft::stor::collection::has | ( | const stor::document::identifier & | id | ) | const |
Checks whether document with id = 'id' exists in the collection.
| id | identifier of document to check for existence |
| const std::unordered_set<index_path>& esft::stor::collection::indices | ( | ) | const |
Returns a set of all the indices currently in use.
| void esft::stor::collection::inflate | ( | std::istream & | i | ) |
Read data and indices from std::istream and build collection from them.
| i | input stream to read collection from |
| const std::string& esft::stor::collection::name | ( | ) | const |
Return name of collection.
| document esft::stor::collection::operator[] | ( | const stor::document::identifier & | id | ) | const |
Retrieve document with id = 'id' from the collection.
|
protected |
Execute equality instruction 'i' on this collection using ReadOptions 'ro'.
| i | instruction to execute against collection |
| ro | ReadOptions to be used for any needed reads to collection |
|
protected |
Execute greater-than instruction 'i' on this collection using ReadOptions 'ro'.
| i | instruction to execute against collection |
| ro | ReadOptions to be used for any needed reads to collection |
|
protected |
Execute greater-equal-than instruction 'i' on this collection using ReadOptions 'ro'.
| i | instruction to execute against collection |
| ro | ReadOptions to be used for any needed reads to collection |
|
protected |
Execute non-equality instruction 'i' on this collection using ReadOptions 'ro'.
| i | instruction to execute against collection |
| ro | ReadOptions to be used for any needed reads to collection |
|
protected |
Execute less-than instruction 'i' on this collection using ReadOptions 'ro'.
| i | instruction to execute against collection |
| ro | ReadOptions to be used for any needed reads to collection |
|
protected |
Execute less-equal-than instruction 'i' on this collection using ReadOptions 'ro'.
| i | instruction to execute against collection |
| ro | ReadOptions to be used for any needed reads to collection |
| bool esft::stor::collection::put | ( | const document & | doc | ) |
Insert/Update document 'doc' in the collection.
| doc | document to insert into collection |
|
protected |
Register insertion of document key:value id:json in batch.
| batch | operation holder used for atomic writes |
| d | document to persist as json string |
|
protected |
Register insertion of document key:value index_path:value_of_path in batch.
| batch | operation holder used for atomic writes |
| index_path | to value of document to store and key to such value |
| d | document whose value at index_path 'index' is to be stored |
| bool esft::stor::collection::remove | ( | const document & | doc | ) |
Remove document 'doc' from the collection.
| doc | document to remove from collection |
| bool esft::stor::collection::remove | ( | const document::identifier & | id | ) |
Remove document from the collection that has id = 'id'.
| id | identifier for document to remove from collection |
|
protected |
Register removal of value at index_path 'index' of document 'd' in batch.
| batch | operation holder used for atomic writes |
| index | index_path to value of document to remove |
| d | document whose value at index_path 'index' is to be removed |
|
protected |
Register removal of of document 'd' in batch.
| batch | operation holder used for atomic writes d document to remove |


---
layout:post
categories:toeza
tags:toeza
title:How to Play Fort of Solitude
date:2016-12-07 14:32:45 +0800
---
Edit Article
How to Play Fort of Solitude
Community Q&A
Fort of solitude is a warfare based card game similar to chess. Its quite complicated but is fun and takes a while to play. Here's how to play it. The object of the game is to capture the 'fort' by killing the kings.
Steps
1
Divide the pack into reds and blacks and make sure each color has just one joker.  From the two shorter ends of the two cards count out four card in a line. At the end of the line do the same with the black kings as with other kings. Make sure they line up. Then place the red pile of cards next to the red king and the black cards next to the black king.
3
That's the setup, now the game. We'll start with basic infantry(number cards). They come in three tiers, 2,3,4...5,6,7...and 8,9,10. Cards 4,7 and 10 are archer(more about them later). At the start you only have access to the first tier. If you pick up cards from other tiers you have to put them in the discard pile. To access the next tier you have to pick up the joker which allows you the next tier. When the cards run out and you use the discard pile you have another chance to get the next tier. Higher cards defeat lower cards but cards of the same value can't attack each other(apart from archers). Melee cards(everything apart from archer) can only attack cards next to them: not diagonally.
4
Now to archers. Archers can attack from two squares(see movement) away. They can kill lower ranking units but if the unit is a higher number or a face card they can stop them moving on the next go. They move and can be attacked by melee units exactly the same though. Having a unit between the archer and his target does not matter. Archers can shoot a unit if it is next to them but can't use melee.
5
Movement. Basically one 'square' is the size of whatever cards you're playing with
6
Movement Every unit can only go one square(apart from kings who can't move). Units can move however far they want to sideways but they can only move forwards and backwards in the space between the two kings.
7
They can go next to the king though.
8
Face cards. This includes aces. Apart from the queen these cards can't be killed by number cards. They can be stopped by archers though. Kings can kill any cards in the deck.  None of the face cards are archers. Queens are the only none combat unit. Any card that has a queen next to it has to be killed twice to be put on the discard pile.
9
Playing. Each turn you can do either: place a card. You pick a card each turn from your pile. If you can't use it or there's no space you put on the discard pile. You can move a unit(see moving). You place cards next to the kings but not behind the kings or diagonal to them. If there is nowhere to place a card you must discard it.  If the king attacks the jack/ace though you must use a different jack/ace because the card will go down to 0ne life(queens give you two lives).


Using Objects for Lookups
Using Objects for Lookups
Objects instead of Switch
Objects can be thought of as a key/value storage, like a dictionary. If you have tabular data, you can use an object to "lookup" values rather than a switch statement or an if/else chain. This is most useful when you know that your input data is limited to a certain range.
Here is an example of a simple reverse alphabet lookup:
var alpha = {
1:"Z",
2:"Y",
3:"X",
4:"W",
...
24:"C",
25:"B",
26:"A"
};
alpha[2]; // "Y"
alpha[24]; // "C"
var value = 2;
alpha[value]; // "Y"
if(alpha[24] === "?"){
//do somthing
}


data class RpcNamedQueryRequest
Request containing information to execute named queries with optional post processing.
The standard naming format for named queries is "{EntityName}.{MethodName}".
The standard naming format for post-processors is "{CordappName}.{PostProcessorFunction}".
Corda defines a set of built-in post-processors for converting PersistentStates
into ContractStates
or StateAndRefs
. These have the
names "Corda.IdentityContractStatePostProcessor", "Corda.IdentityStateAndRefPostProcessor".
The following example executes the named query "VaultState.findByStateStatus" for UNCONSUMED
states and uses
the "Corda.IdentityContractStatePostProcessor" to return ContractStates.
RpcNamedQueryRequest(
queryName = "VaultState.findByStateStatus",
namedParameters = mapOf("stateStatus" to RpcNamedQueryParameterJson("\"UNCONSUMED\"")),
postProcessorName = "Corda.IdentityContractStatePostProcessor"
)
The following example executes the named query "LinearState.findByUuidAndStateStatus" for UNCONSUMED states with the given UUID.
RpcNamedQueryRequest(
queryName = "LinearState.findByUuidAndStateStatus",
namedParameters = mapOf(
"stateStatus" to RpcNamedQueryParameterJson("\"UNCONSUMED\""),
"uuid" to RpcNamedQueryParameterJson("\"531a6660-7cdb-4fee-b8c9-c9fea484651d\"")
)
)
queryName
- the name of a named query registered in the persistence context.
namedParameters
- the named parameters to be set in the named query.
postProcessorName
- optional name of a post-processor that will process named query results.
namedParameters |
The named parameters to be set in the named query. The keys are the names of parameters, the RpcNamedQueryParameterJson contains JSON marshalled representation of the named parameter value. val namedParameters: Map<String, RpcNamedQueryParameterJson> |
postProcessorName |
Optional name of a post-processor that will process named query results. val postProcessorName: String? |
queryName |
The name of a named query registered in the persistence context. val queryName: String |


Class Mobile
Mobile agents have some methods to interact with the environment.
Methods
- Mobile:_init (Table)
-
Mobil agents are are the most common agent to work with.
Usage:
new_instance = Mobil()
- Mobile:xcor ()
-
Getter function to get the x coordinate of the agent
Usage:
an_agent:xcor()
- Mobile:ycor ()
-
Getter function to get the y coordinate of the agent
Usage:
an_agent:ycor()
- Mobile:zcor ()
-
Getter function to get the z coordinate of the agent
Usage:
an_agent:zcor()
- Mobile:same_pos (ag_or_pos)
-
Checks if the agent has the (exactly) same position as a vector or agent.
Usage:
agent:same_pos({1,1})
- Mobile:update_cell ()
-
Checks for FamiliCells and if is anyone, updates the parameter current_cell of the agent (if this is needed).
Usage:
agent:fd(4):update_cell()
- Mobile:rt (num)
-
It produces a right turn in the agent
Usage:
an_agent:rt(90)
- Mobile:lt (num)
-
It produces a left turn in the agent
Usage:
an_agent:lt(30)
- Mobile:face (ag)
-
It makes the agent points to another agent by modifiing the heading parameter of the first agent.
Usage:
an_agent:face(another_agent)
- Mobile:fd (num)
-
Advance in the faced direction. The distance is specified with num
Usage:
an_agent:fd(3)
- Mobile:move_to (another_agent)
-
Moves to an agent to the position of other agent
Usage:
an_agent:move_to(another_ag) -- As the agent is returned, this allows us to do things like this: an_ag:move_to(ag2):rt(90):fd(1) -- This will position the agent near of another agent but not in the same position
- Mobile:dist_euc_to (ag_or_point)
-
This function give us the euclidean distance from the agent to another agent or point.
Usage:
ag:dist_euc_to( {23, 50.1, 7} ) -- or: dist_euc_to(ag, {23, 50.1, 7})
- Mobile:dist_manh (point)
-
This function give us the manhattan distance from the agent to another point.
Usage:
ag:dist_manh( {23, 50, 7} )


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Name
Phone Number
Table
Number of Guests


disease
Contents
7.1. disease#
ECT - shock therapy given to brain when patient is struggling with severe mental illness (~100k patients in the US / year)
7.1.1. alzheimer’s#
overview
age-associated - tons of people get it
doesn’t kill you, secondary complications like pneumonia will kill you
rate is going up
very expensive to treat
declarative memories are affected by Alzheimer’s
these are memories that you know
first 2 areas to go in Alzheimer’s
hippocampus
patient HM had no hippocampus
no anterograde memory - learning new things
hippocampus stores 1 day of info
offloading occurs during sleep (REM sleep) to prefrontal cortex, temporal lobe, V4
dreaming - might see images as you are offloading
basal forebrain - spread synapses all over cortex
uses Ach
ignition key for entire cortex
alzheimer’s characteristics only found in autopsy
amyloid plaques
maybe A-beta causes it
A-beta comes from APP
A-beta42 binds to itself
prion (starts making more of itself)
this cycle could be exacerbated by injury
clumps and attracts immune system which kills local important cells
this could cause Alzheimer’s
rare genetic mutations in A-beta increase probability you get Alzheimer’s
anti-inflammation may be too late
can take drugs that increase Ach functions - ex. cholinergic agonists, cholinesterase inhibitors
tangles
tangles made of protein called Tau
most people think these are just dead cells resulting from Alzheimer’s but some think they cause it
7.1.2. parkinson’s#
loss of substantia nigra pars compacta dopaminergic neurons
when you get down to 20% what you were born with
dopaminergic neurons form melanin = dark color
hits to head can give inflammation
know what they need to do - don’t have enough dopamine to act
treat with L Dopa -> something like dopamine -> take out globus pallidus
Lewy bodies are clumps of alpha synuclein - appear at dopaminergic synapses
clumps like A-beta42
associated with early-onset Parkinson’s (rare) associated with genetic mutations
bradykinesia - slowness of movement
age can give parksinson’s
no evidence that toxins can induce parkinsons
PTP/ pesticides can induce Parkinson’s in test animals
1/500 people
7.1.3. pathology#
<IP_ADDRESS>. basics#
pathologists work with tissue samples either visually or chemically
anatomic pathology relies on the microscope whereas clinical pathology does not
pathologists convert from tissue image into written report
when case is challenging, may require a second opinion (v rare)
steps (process takes 9-12 hrs):
tissue is surgically removed
more tissue collected is generally better (gives more context)
this procedure is called a biopsy
much is written down at this step (e.g. race, gender, locations in organ, different tumors in an organ) that can’t be seen in slide alone
fixation: keeps the tissue stable (preserves dna also) - basicallly just soak in formalin
dissection: remove the relevant part of the tissue
tissue processor - removes water in tissue and substitute with wax (parafin) - hardens it and makes it easy to cut into thin strips
microtone - cuts very thin slices of the tissue (2-3 microns)
staining
H & E - hematoxylin and eosin stain - most popular (~80%) - colors the cells in a specific way, bc cells are usually pretty transparent
hematoxylin stains nucleic acids blue
eosin stains proteins / cytoplasm pink/red
immunohistochemistry (IHC) - tries to identify cell lineage: 10-15%
identifies targets
use antibodies tagged with chromophores to tag tissues
gram stain - highlights bacteria
giemsa - microorganisms
others…for muscle, fungi
viewing
usually analog - put slide on something that can move / rotate
whole-slide image (WSI) - resulting entire slide
tissue microarray (TMA) - smaller, fits many samples onto the same slide
with paige: put slide through digital scanner (only 5% or so of slides are currently digital)
later on, board meets to decide on treatment (based on pathology report)
usually some discussion betweeon original imaging (pre-biopsy) and pathologist’s interpretation
resection - after initial diagnosis, often entire tumor is removed (resection)
how can ai help?
can help identify small things in large images
can help with conflict resolution
after (successful) neoadjuvant chemotherapy, problem becomes more difficult
very few remaining cancer cells
cancer/non-cancer cells become harder to distinguish (esp. for prostate)
tumor bed is patchily filled with cancer cells - need to better clarify presence of cancer
<IP_ADDRESS>. papers#
Deep Learning Models for Digital Pathology (BenTaieb & Hamarneh, 2019)
note: alternative to histopathology are more expensive / slower (e.g. molecular profiling)
to promote consistency and objective inter-observer agreement, most pathologists are trained to follow simple algorithmic decision rules that sufficiently stratify patients into reproducible groups based on tumor type and aggressiveness
magnification usually given in microns per pixel
WSI files are much larger than other digital images (e.g. for radiology)
DNNs can be used for many tasks: beyond just classification, there are subtasks (e.g. count histological primitives, like nuclei) and preprocessing tasks (e.g. stain normalization)
challenge: multi-magnification + high dimensions (i.e. millions of pixels)
people usually extract smaller patches and train on these
this loses larger context
one soln: pyramid representation: extract patches at different magnification levels
one soln: stacked CNN - train fully-conv net, then remove linear layer, freeze, and train another fully-conv net on the activations (so it now has larger receptive field)
one soln: use 2D LSTM to aggregate patch reprs.
challenge: annotations only at the entire-slide level, but must figure out how to train individual patches
e.g. use aggregation techniques on patches - extract patch-wise features then do smth simple, like random forest
e.g. treat as weak labels or do multiple-instance learning
could just give slide-level label to all patches then vote
can use transfer learning from related domains with more labels
challenge: class imbalance
can use boosting approach to increase the likelihood of sampling patches that were originally incorrectly classified by the model
challenge: need to integrate in other info, such as genomics
when predicting histological primitives, often predict pixel-wise probability maps, then look for local maxima
can also integrated domain-knowledge features
can also have 2 paths, one making bounding-box proposals and another predicting the probability of a class
alternatively, can formulate as a regression task, where pixelwise prediction tells distance to nearest centroid of object
could also just directly predict the count
can also predict survival analysis
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images (campanella et al. 2019)
use slide-level diagnosis as “weak supervision” for all contained patches
1st step: train patch-level CNNs using MIL
if label is 0, then all patches should be 0
if label is 1, then only pass gradients to the top-k predicted patches
2nd step: use RNN (or another net) to combine info across S most suspicious tiles
Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes (diao et al. 21)
An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study (pantanowitz et al. 2020 - ibex)
549 train, 2501 internal test slides, 1627 external validation
predict cancer prob., gleason score 7-10, gleason pattern 5, perneural invasion, cancer percentage
algorithm
GB classifies background / non-background / blurry using hand-extracted features for each tile
each tile gets predicted probability for 18 pre-defined classes (e.g. GP 3)
ensemble of 3 CNNs that operate at different magnifications
aggregation: 18-probability heatmaps are combined to calculate slide-level scores
ex (for predicting cancer): sum the cancer-related channels in the heatmap , apply 2x2 local averaging, then take max
<IP_ADDRESS>. datasets#
ARCH - multiple instance captioning dataset to facilitate dense supervision of CP tasks
7.1.4. cancer#
<IP_ADDRESS>. overview#
tumor = neoplasm - a mass formation from an uncontrolled growth of cells
benign tumor - typically stays confined to the organ where it is present and does not cause functional damage
malignant tumor = cancer - comprises organ function and can spread to other organs (metastasis)
relation network based aggregator on patches
lymphatic system drains fluids (non-blood) from organs into lymph nodes
cancer often mestastasize through these
staging - describes where cancer is located and where it has spread
clinical staging - based on non-tissue things
pathological staging - elements of staging pTNM
size / depth of tumor “T”
number of lymph nodes / how many had cancer “N”
number of metastatic foci in non-lymph node organ “M”
these are combined to determine the cancer stage (0-4)
prognosis - chance of recovery
<IP_ADDRESS>.1. treatments#
chemo
traditional chemotherapy disrupts cell replication
hair loss and gastrointestinal symptoms occur bc these cells also rapidly replicate
adjuvant chemotherapy - after cancer is removed, most common
neoadjuvant chemo - after biopsy, but before resection (when very hard to remove)
targeted therapies
ex. address genetic aberration found in cancer cells
immunotherapy - enhance body’s immune response to cancer cells (so body will attack these cells on its own)
want the antigens on the tumor to be as different as possible (so they will be characterized as foreign)
to measure this, can conduct total mutational burden (TMB) or miscrosatellite instability (MSI) test
genetic tests - hard to do by looking at glass slide
some tumors express receptors (e.g. CTLA4, PD1) that shut off immune cells - some drugs try to block these receptors
<IP_ADDRESS>. prostate cancer#
tests
feel with finger
antigen test - blood test
ultrasound - probe inserted
biopsy - needle inserted to take out tissue
grading
stages (they have subdivisions, e.g. IIA, IIB, IIC)
I - early, slow-growing
II - small, but risky
III - likely to spread
IV - has spread beyond the prostate
recurrent - has come back after treatment
in addition to stages 0-4, prostate cancer is also given Gleason score
look at 2 biggest cancer regions and identifies them as a Gleason pattern from 3 (best) to 5 (worst)
this results in a sum (e.g. 5+4, 3+4) - note 3+4 is not same as 4+3
treatments
prostatectomy - remove the prostate
radiation therapy - kills specifically cancer cells
radiative seed implants - implated into prostate to kill cancer cells
cryotherapy - kill prostate cancer cells by freezing them
hormone therapy - block hormone which grows prostate cancer cells
chemotherapy
human benchmarks
-
71 patients, 213 scored observations, 3 pathologists
weighted pairwise kappas: 0.16, 0.29, 0.23
(unweighted): 0.15, 0.29, 0.24
Interobserver reproducibility of Gleason grading of prostatic carcinoma: General pathologists
38 biopsies, 41 pathologists
consensus grade groups: [2-4, 5-6, 7, 8-10]
overall kappa: 0.435
Interobserver variability in Gleason histological grading of prostate cancer
407 slides, 2 pathologists
primary gleason: k=0.34
secondary gleason: k=0.37
sum: k=0.43
-
ai papers
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs (anklin et al. 2021)
SegGini, a weakly supervised segmentation method using graphs
constructs a tissue-graph for WSI (node is tissue region)
weakly-supervised segmentation via node classification
data
UZH dataset - 5 five TMAs with 886 spots (each 3100×3100 pixels) with complete pixel-level annotations and inexact image-level gradess
SICAPv2 dataset - 155 WSIs and 18,783 tiles of size 512×512 with complete pixel annotations
<IP_ADDRESS>. bladder cancer#
tests
urinalysis - look for things like blood in urine
urine cytology - use microscope to look for cancer cells in urine
urine tests for specific tumor parkers
cystoscopy - invasive lens takes image of bladder
tests lead to a biopsy
grading
invasiveness: can be non-invasive, invasive (grows into deeper layers of bladder)
superficial = non-muscle invasive - hasn’t grown into main muscle layer of bladder
grade: again asigned stages 0 - IV based on TNM
low-grade = well-differentiated
high-grade (worse) = poorly differentiated, undifferentiated
human benchmark
The reliability of staging and grading of bladder tumours. Impact of misinformation on the pathologist’s diagnosis (olsen et al. 1993)
4 consultant pathologists
40 biopsy specimens of bladder tumours staging invasion
grading using Bergkvist classification
kappa < 0.50
ai papers
Bladder cancer in the time of machine learning: Intelligent tools for diagnosis and management (2021)
bladder cancel ranks tenth in worldwide absolute cancer incidence
non-pathology
Integrating Diagnosis Rules into Deep Neural Networks for Bladder Cancer Staging - bladder cancer staging from MR images
Deep Learning Approach for Assessment of Bladder Cancer Treatment Response - bladder cancer treatment assessment from CT scans
cystoscopy - few DNN papers here
pathology
Urinary Bladder Tumor Grade Diagnosis Using Online Trained Neural Networks (2003)
92 patients with BC
90%, 94.9%, and 97.3%, for Grade I, II, and III respectively
builds on Neural network-based segmentation and classification system for automated grading of histologic sections of bladder carcinoma (2002)
Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides (woerl et al. 2020) - predict molecular subtype using histopathology images in Cancer Genome Atlas Urothelial Bladder Carcinoma dataset
bladder basics
muscles in bladder contract and force urine out
urethelium - inner layer that is able to stretch (has many layers) - this is where cancer originates
in situ - cancer only here
invasive - goes into the muscle
if it goes into the urine, can easily test (also usually triggers blood in the urine)
biopsy usually looks mostly at urethelium and vessels right next to it (will not go all the way to the muscle, as this could puncture the bladder)
very targeted (unlike prostate biopsy), slide will come with some tag like “in area with redness” from scopy
4 possibilities
big mass - should see cancer
inflammation - could be cancer or many other things (e.g. atypia vs carcinoma)
get many parts / sites of biopsies
H & E slide
shape:
papillary |
flat |
can also have a combo |
|---|---|---|
grade:
low |
high |
|---|---|
when shape is flat, grade often can’t be determined reliably
lots of names for uncertain (e.g. upump - uncertain malignant potential, or atypia)
much easier to decide shape than grade
once you find high grade, look for invasiveness (and deeper layers are worse)


Class Image
Represents an Image which can be drawn using a Graphics surface.
Implements
Inherited Members
Namespace: GameOverlay.Drawing
Assembly: GameOverlay.dll
Syntax
public class Image : IDisposable
Constructors
| Improve this Doc View SourceImage(Graphics, Byte[])
Initializes a new Image for the given device by using a byte[].
Declaration
public Image(Graphics device, byte[] bytes)
Parameters
| Type | Name | Description |
|---|---|---|
| Graphics | device | The Graphics device. |
| Byte[] | bytes | A byte[] containing image data. |
Image(Graphics, String)
Initializes a new Image for the given device by using a file on disk.
Declaration
public Image(Graphics device, string path)
Parameters
| Type | Name | Description |
|---|---|---|
| Graphics | device | The Graphics device. |
| String | path | The path to an image file on disk. |
Image(RenderTarget, Byte[])
Initializes a new Image for the given device by using a byte[].
Declaration
public Image(RenderTarget device, byte[] bytes)
Parameters
| Type | Name | Description |
|---|---|---|
| SharpDX.Direct2D1.RenderTarget | device | The Graphics device. |
| Byte[] | bytes | A byte[] containing image data. |
Image(RenderTarget, String)
Initializes a new Image for the given device by using a file on disk.
Declaration
public Image(RenderTarget device, string path)
Parameters
| Type | Name | Description |
|---|---|---|
| SharpDX.Direct2D1.RenderTarget | device | The Graphics device. |
| String | path | The path to an image file on disk. |
Fields
| Improve this Doc View SourceBitmap
The SharpDX Bitmap
Declaration
public Bitmap Bitmap
Field Value
| Type | Description |
|---|---|
| SharpDX.Direct2D1.Bitmap |
Properties
| Improve this Doc View SourceHeight
Gets the height of this Image
Declaration
public float Height { get; }
Property Value
| Type | Description |
|---|---|
| Single |
Width
Gets the width of this Image
Declaration
public float Width { get; }
Property Value
| Type | Description |
|---|---|
| Single |
Methods
| Improve this Doc View SourceDispose()
Releases all resources used by this Image.
Declaration
public void Dispose()
Dispose(Boolean)
Releases all resources used by this Image.
Declaration
protected virtual void Dispose(bool disposing)
Parameters
| Type | Name | Description |
|---|---|---|
| Boolean | disposing | A Boolean value indicating whether this is called from the destructor. |
Equals(Image)
Returns a value indicating whether two specified instances of Image represent the same value.
Declaration
public bool Equals(Image value)
Parameters
| Type | Name | Description |
|---|---|---|
| Image | value | An object to compare to this instance. |
Returns
| Type | Description |
|---|---|
| Boolean | true if |
Equals(Image, Image)
Returns a value indicating whether two specified instances of Image represent the same value.
Declaration
public static bool Equals(Image left, Image right)
Parameters
| Type | Name | Description |
|---|---|---|
| Image | left | The first object to compare. |
| Image | right | The second object to compare. |
Returns
| Type | Description |
|---|---|
| Boolean | true if |
Equals(Object)
Returns a value indicating whether this instance and a specified Object represent the same type and value.
Declaration
public override bool Equals(object obj)
Parameters
| Type | Name | Description |
|---|---|---|
| Object | obj | The object to compare with this instance. |
Returns
| Type | Description |
|---|---|
| Boolean | true if |
Overrides
| Improve this Doc View SourceFinalize()
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
Declaration
protected void Finalize()
GetHashCode()
Returns the hash code for this instance.
Declaration
public override int GetHashCode()
Returns
| Type | Description |
|---|---|
| Int32 | A 32-bit signed integer hash code. |
Overrides
| Improve this Doc View SourceToString()
Converts this Image instance to a human-readable string.
Declaration
public override string ToString()
Returns
| Type | Description |
|---|---|
| String | A string representation of this Image. |
Overrides
Operators
| Improve this Doc View SourceImplicit(Image to Bitmap)
Converts an Image to a SharpDX Bitmap.
Declaration
public static implicit operator Bitmap(Image image)
Parameters
| Type | Name | Description |
|---|---|---|
| Image | image | The Image object. |
Returns
| Type | Description |
|---|---|
| SharpDX.Direct2D1.Bitmap |


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