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Feature Films
Short Films
Theatrical Shorts
Disney+
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Graphic Technologies
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Events
Extras
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Pixar in a Box
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Leadership
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Our Story
Company FAQ's
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Soul trailers
Inclusion and outreach


ALTER SERVER server_name [ VERSION 'new_version' ] [ OPTIONS ( [ ADD | SET | DROP ] option ['value'] [, ... ] ) ] ALTER SERVER server_name OWNER TO new_owner
ALTER SERVER changes the definition of a foreign server. The first form changes the server version string or the generic options of the server (at least one clause is required). The second form changes the owner of the server.
To alter the server you must be the owner of the server. Additionally to alter the owner, you must own the server and also be a direct or indirect member of the new owning role, and you must have USAGE privilege on the server's foreign-data wrapper. (Note that superusers satisfy all these criteria automatically.)
The name of an existing server.
New server version.
Change options for the server. ADD, SET, and DROP specify the action to be performed. ADD is assumed if no operation is explicitly specified. Option names must be unique; names and values are also validated using the server's foreign-data wrapper library.


Alex:
You have found the Map! Good job!
Ahhh, the World Map... Hurry to the place marked here and find the
lost gold
.
I have heard from my adjutant Leirynn that the delu lizardmen have sent assassins to retrieve the Map, so be very careful.


This tibble contains data of a few different classes, which makes it
well-suited for quick experimentation with the functions in this package. It
contains only eight rows with numeric, character, and factor columns. The
last 4 rows contain NA
values in the majority of this tibble's columns (1
missing value per column). The date
, time
, and datetime
columns are
character-based dates/times in the familiar ISO 8601 format. The row
and
group
columns provide for unique rownames and two groups (grp_a
and
grp_b
) for experimenting with the gt()
function's rowname_col
and
groupname_col
arguments.
exibble
A tibble with 8 rows and 9 variables:
a numeric column ordered with increasingly larger values
a character column composed of names of fruits from a
to
h
a factor column with numbers from 1 to 8, written out
character columns with dates, times, and datetimes
a numeric column that is useful for testing currency-based formatting
a character column in the format row_X
which can be
useful for testing with row captions in a table stub
a character column with four grp_a
values and four
grp_b
values which can be useful for testing tables that contain
row groups
11-6
Other Datasets:
countrypops
,
gtcars
,
pizzaplace
,
sp500
,
sza
#> Rows: 8 #> Columns: 9 #> $ num <dbl> 1.111e-01, 2.222e+00, 3.333e+01, 4.444e+02, 5.550e+03, NA, 7… #> $ char <chr> "apricot", "banana", "coconut", "durian", NA, "fig", "grapef… #> $ fctr <fct> one, two, three, four, five, six, seven, eight #> $ date <chr> "2015-01-15", "2015-02-15", "2015-03-15", "2015-04-15", "201… #> $ time <chr> "13:35", "14:40", "15:45", "16:50", "17:55", NA, "19:10", "2… #> $ datetime <chr> "2018-01-01 02:22", "2018-02-02 14:33", "2018-03-03 03:44", … #> $ currency <dbl> 49.950, 17.950, 1.390, 65100.000, 1325.810, 13.255, NA, 0.440 #> $ row <chr> "row_1", "row_2", "row_3", "row_4", "row_5", "row_6", "row_7… #> $ group <chr> "grp_a", "grp_a", "grp_a", "grp_a", "grp_b", "grp_b", "grp_b…


III. Personal Projects
Apr 2012 - Apr 2012
Personal
Project Description: HideWizard is a software which can hide windows, processes and files under Windows. The unregistered version will remind you to register every thirty minutes. So I analyzed its disassembled code and cracked it.
Responsibility: I am responsible for all the work of reverse engineering.
Introduction: HideWizard is a software which can hide windows, processes and files under Windows. The unregistered version will remind you to register every thirty minutes. So I analyzed its disassembled code and cracked it. The analysis is as follows, and the download link is provided here:
Languages used by HideWizard Crack are:
Disassembly
C/C++
Techniques used by HideWizard Crack are:
Ollydbg for dynamic debugging
IDA Pro for static disassembling
Useful links:
Download source from GitHub
https://github.com/hsluoyz/HideWizard
Ollydbg official website
http://www.ollydbg.de
IDA Pro official website
https://www.hex-rays.com/products/ida/index.shtml


This page will help you use Fiji to convert TIF stacks to PNG atlases, which can then be loaded and viewed in the Zebrafish Brain Browser (zbbrowser.com).
Fiji Setup
You can get Fiji here: https://imagej.net/Fiji. Follow the instructions on the Downloads page to download and install it.
Converting TIF Stacks to PNG Atlases
- Open Fiji
- Select File->Open... and select your TIF file
- After the TIF has loaded, select Image->Stacks->Make Montage...
- Enter the options values seen in the image below
- The higher you set the "Scale factor" value, the higher the image quality will be in the brain browser. We recommend you set this value to 0.25 for a low-resolution image, 0.5 for a medium-resolution image, 0.75 for a high-resolution image, or 1.0 for a very-high-resolution image. Note that higher-resolution images will require more high-end computer hardware to run in the brain browser.
- Select "OK"
- After the montage image appears, select File->Save As->PNG... and save the image with any name you'd like
- Open the image in the brain browser to view the line. You're done!


{% include "top.html" %} {% include "global_output_top.html" %}
| nse_symbol | comp_name | bhav price | bat | ftwhl low | Valuation | mos (%) | +52w low (%) | ca total | industry | cap type | plan oku | funda mental | funda reco cause |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| {{ advisor_row.nse_symbol }} | {{ advisor_row.comp_name }} | {{ advisor_row.bhav_price }} | {{ advisor_row.bat }} | {{ advisor_row.ftwhl_low }} | {{ advisor_row.valuation_reco }} | {{ advisor_row.safety_margin }} | {{ advisor_row.low_margin }} | {{ advisor_row.ca_total }} | {{ advisor_row.industry }} | {{ advisor_row.cap_type }} | {{ advisor_row.plan_oku}} | {{ advisor_row.funda_reco_type }} | {{ advisor_row.funda_reco_cause }} |


The Power to Lift is Missing from "The Wings of the Dove" by Homer Yen (c) 1997
Whenever I watch a film that boasts period costume pieces and whose central characters are primarily English aristocrats, rest assured that the core of the movie will be a heavy-minded drama. Frequently, the story focuses on elements of debauchery and deception. The burden of relaying the fears and joys and tension that inevitably exists lies not only on the shoulders of the characters and their ability to involve us in the story, but also on how the movie skillfully unfolds itself to reveal the characters' motivations and intentions. Here, the cast does a fine job in reaching out to our emotions, but the movie unfolds too quickly, which tells us too much too soon.
Set in the early 20th century, we meet up with Kate Croy (Helena Bonham Carter), who is secretly engaged to Merton Densher (Linus Roache), an underpaid writer and commonfolk. Kate is under the care of her wealthy socialite Aunt, who adamantly insists that she marry a man of higher standing. If not, the Aunt will not only cut her off from her generosity, but will also cease to support Kate's father, who has fallen onto hard times. Kate has what amounts to a tenuous foothold in society, but her heart belongs to Merton. She has a burning love, but no money.
Kate eventually befriends a very wealthy, American girl. Millie Theale (Alison Elliott) is a lonely American heiress who has money but no love. She is also dying from a rare ailment, which reinforces her drive to find love before her death. As Kate and Millie continue to bond, Kate sees that this doe-eyed girl may hold the key to breaking free from her Aunt's rule. With the help of Merton, they formulate a dastardly and unforgivable plan. But they are scoundrels of love, and it is this kind of delicious balance that gives it dramatic juice, allowing the characters to be simultaneously tender and cruel.
But the sin of the movie didn't rest with the characters; rather, it rested with the movie itself. Within the first 30 minutes, it told us everything that we needed to know. But as the film moved into its final 60 minutes, when the three main characters decide to take a vacation in Venice, it seems as if we go along with them as vacationers as well. Progress is slow, and the meaty scenes become heavily interspersed with scenic shots of the Basillica, street café's and the Venetian waterways. Also, the direction of the film looked and felt more like a stage play rather than a cinematic offering. And since we know where the story is heading, we are on cruise control for much of the film until the final few scenes where any remaining conflict is resolved.
The Wings of the Dove has a lush yet aching beauty that is both visual and dramatic. Also, Helena Bonham Carter brings to her role an incredible sense of maturity as a woman who is as earnest as she is conniving (Best Actress Oscar contender). Despite this bold effort by all, the award for 'greatest effort' will ultimately go to the audience member who must sit with their legs crossed as they patiently wait for this film to end.
Grade: C+
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


Ryan Rousseau
Continuous Delivery Architect @ Octopus Deploy
Why invest in automated deployments?
Fewer errors during deployments
More confidence in making changes
Ability to make changes quicker
The same process applied to test and production environments
A happier team (maybe)
What is Continuous Integration?
Merging code often to a shared repository
Automated builds and tests trigger on commit
What about Continuous Delivery and Continuous Deployment?
Continuous Delivery means that the software deployment process is automated
Every change can be deployed to production
Continuous Deployment is an extension of Continuous Delivery
Every change is automatically deployed to production
Simple CI/CD Process
Demo Setup
Use what's right for your team
Cake (C# Makefiles)
Enough slides, let's jump into the demo!
Thank you for hosting me!
Can I answer any questions?


To install React, follow the instructions on GitHub.
If you can see this, React is not working right. If you checked out the source from GitHub make sure to run grunt.
grunt
Measurement prediction and mesh generation API demo.


Email
{{ email_form.email }}
{{ email_form.email.errors }}
{% if email_confirmation %}
We've sent you an email to confirm your new email address: {{ email_confirmation.new_email }}.
Please check your inbox at that address for a message from us and visit the link given in it to confirm this change.
{% else %}
{% if email_updated %}
Your email address has been updated!
{% else %}
We never share your information with anyone and you can opt out of
receiving email below.
{% endif %}
{% endif %}
Change your password
{{ pw_form.old_password }}
{{ pw_form.old_password.errors }}
|
{{ pw_form.new_password1 }}
{{ pw_form.new_password1.errors }}
{{ pw_form.new_password2 }}
{{ pw_form.new_password2.errors }}
{% if password_updated %}
Your password has been updated!
{% endif %}


About
Ancient alien ancient religions ancient god contend extraterrestrial origin DNA manipulation, King Soloman electromagnetic sun disc targeted mutation sanskrit pre-colonial aerodynamics Mahabharata, foo fighter Nazca lines portal crystal skull megoliths aircraft inter-dimensional. I know it sounds crazy... Extraterrestrial origin, the vedas ancient alien theorists, ancient alien electromagnetic the vedas alien.
Find Campfire Coffee at
our various locations:
- Pike Place Market
- Capitol Hill
- Seattle Public Library
- South Lake Union
- SeaTac Airport


Before Change
data1 = np.cumsum(np.random.multivariate_normal(np.zeros(10), K, 250), axis=0)
data2 = copy(data1)
sample_inds = np.random.choice(data2.shape[0], data2.shape[0]*.1)
for sample in sample_inds:
data2[sample,:]=np.nan
print(data2[sample,0])
After Change
data2 = copy(data1)
missing = .1
inds = [(i,j) for i in range(data2.shape[0]) for j in range(data2.shape[1])]
missing_data = [inds[i] for i in np.random.choice(len(inds), len(inds)*missing)]
for i,j in missing_data:
data2[i,j]=np.nan
hyp.plot([data1,data2],linestyle=["-",":"])


[dscrdeql] <benohanlon> A Call to Arms! Cast your vote for Komodo Platform to be listed on KuCoin.
- Komodo will get a KuCoin exchange listing for free.
- You will get a share of 5 $BTC in $KMD
- Komodo will get a paid advertisement for the listing valued at 30,000 $USD
2 Min Read on Medium: https://blog.komodoplatform.com/kucoincompetition-c0e05fae6c05
Please share everywhere.(edited)


Over 5 years of experience in Software Development with Java and Python, focused on quality, clean code, and performance.
Communicative, passionate professional, self-taught, dedicated, and proactive.
Open-minded about programming languages, bringing knowledge of SOLID, WebServices


The M_time(3f) Fortran module and associated utility programs provide date and time-related procedures. Both a procedural and OOP (Object Oriented Programming) interface are provided. Each routine is accompanied by a man(1) page which includes a sample program for that procedure. This manual, the source and example utility programs are included in the download.
The M_time(3f) module
The M_TIME(3f) module complements the DATE_AND_TIME(3f) procedure, which is the standard intrinsic subroutine that returns the current date and time in the Gregorian calendar. That is, the primary way this module represents dates is as an integer array with the same meaning for elements as defined by the DATE_AND_TIME(3f) routine. In addition it can calculate or read many other date representations such as ...
Julian and Unix Epoch Dates are particularly useful for manipulating dates in simple numeric expressions.
The extensive formatting options include showing SYSTEM_CLOCK(3f) and CPU_USAGE(3f) information along with Gregorian date information, allowing for the easy incorporation of timing information into program messages. In addition to conventional Civilian Calendar dates, the module supports the ISO-8601 standard methods of displaying dates.
A Fortran-callable sleep(3c)/usleep(3c) procedure is also provided.
| UNIX EPOCH | ||
|---|---|---|
| date_to_unix(dat,UNIXTIME,IERR) | %epoch() | Convert date array to Unix Time |
| unix_to_date(unixtime,DAT,IERR) | Convert Unix Time to date array | |
| d2u(dat) result (UNIXTIME) | Convert date array to Unix Time | |
| u2d(unixtime) result (DAT) | Convert Unix Time to date array | |
| JULIAN | ||
| julian_to_date(julian,DAT,IERR) | Convert Julian Date to date array | |
| date_to_julian(dat,JULIAN,IERR) | %julian() | Converts date array to Julian Date |
| d2j(dat) result (JULIAN) | Convert date array to Julian Date | |
| j2d(julian) result (DAT) | Convert Julian Date to date array | |
| DAY OF WEEK | ||
| dow(dat,[WEEKDAY],[DAY],IERR) | %weekday() | Convert date array to day of the week as number(Mon=1) and name |
| WEEK OF YEAR | ||
| d2w(dat,ISO_YEAR,ISO_WEEK,ISO_WEEKDAY,ISO_NAME) | calculate iso-8601 Week-numbering year date yyyy-Www-d | |
| w2d(iso_year,iso_week,iso_weekday,DAT) | calculate date given iso-8601 Week date yyyy-Www-d | |
| ORDINAL DAY | ||
| d2o(dat) result(ORDINAL) | %ordinal() | given date array return ordinal day of year, Jan 1st=1 |
| o2d(ordinal,[year]) result(DAT) | given ordinal day of year return date array, Jan 1st=1 | |
| ordinal_to_date(ordinal,year,DAT) | given ordinal day of year return date array, Jan 1st=1 | |
| ordinal_seconds() | return seconds since beginning of year | |
| PRINTING DATES | ||
| fmtdate(dat,format) result (TIMESTR) | %format([STRING]) | Convert date array to string using format |
| fmtdate_usage(indent) | display macros recognized by fmtdate(3f) | |
| now(format) result (NOW) | return string representing current time given format | |
| box_month(dat,CALEN) | print specified month into character array | |
| MONTH NAME | ||
| mo2v(month_name) result (MONTH_NUMBER) | given month name return month number | |
| v2mo(month_number) result (MONTH_NAME) | given month number return month name | |
| mo2d(month_name) result (DAT) | return date array for first day of given month name in specified year | |
| ASTROLOGICAL | ||
| easter(year,dat) | calculate month and day Easter falls on for given year | |
| moon_fullness(DAT) result(FULLNESS) | percentage of moon phase from new to full | |
| phase_of_moon(DAT) result(PHASE) | return name for phase of moon for given date | |
| DURATION | ||
| sec2days(seconds) result(dhms) | converts seconds to string D-HH:MM:SS | |
| days2sec(string) result(seconds) | converts string D-HH:MM:SS to seconds | |
| READING DATES | ||
| guessdate(anot,dat) | Converts a date string to a date array, in various formats | |
| C INTERFACE | ||
| system_sleep(wait_seconds) | Call sleep(3c) or usleep(3c) |
FMTDATE
You can easily use Julian Ephemeris Dates and Unix Epoch Times to add and subtract times from dates or to calculate the interval between dates. But JEDs and UETs and even the Gregorian Calendar arrays in the DAT arrays are not the way we typically describe a date on the Civilian Calendar. So the fmtdate(3f) routine lets us print a DAT array in a variety of familiar styles.
The fmtdate(3f) and now(3f) procedures let you display a Gregorian date using either keywords for standard formats or using macros in a user-specified formatting string. A formatting string may contain the following macros:
Description Example Base time array: (1) %Y -- year, yyyy 2016 (2) %M -- month of year, 01 to 12 07 (3) %D -- day of month, 01 to 31 27 %d -- day of month, with suffix (1st, 2nd,...) 27th (4) %Z -- minutes from UTC -0240 %z -- -+hh:mm from UTC -04:00 %T -- -+hhmm from UTC -0400 (5) %h -- hours, 00 to 23 21 %H -- hour (1 to 12, or twelve-hour clock) 09 %N -- midnight< AM <=noon; noon<= PM <midnight PM (6) %m -- minutes, 00 to 59 24 (7) %s -- sec, 00 to 59 22 (8) %x -- milliseconds 000 to 999 512 Conversions: %E -- Unix Epoch time<PHONE_NUMBER>.5129952 %e -- integer value of Unix Epoch time<PHONE_NUMBER> %J -- Julian date 2457597.559 %j -- integer value of Julian Date(Julian Day) 2457597 %O -- Ordinal day (day of year) 209 %o -- whole days since Unix Epoch date 17009 %U -- day of week, 1..7 Sunday=1 4 %u -- day of week, 1..7 Monday=1 3 %i -- ISO week of year 1..53 30 %I -- iso-8601 week-numbering date(yyyy-Www-d) 2016-W30-3 Names: %l -- abbreviated month name Jul %L -- full month name July %w -- first three characters of weekday Wed %W -- weekday name Wednesday %p -- phase of moon New %P -- percent of way from new to full moon -1% Literals: %% -- a literal % % %t -- tab character %b -- blank character %B -- exclamation(bang) character %n -- new line (system dependent) %q -- single quote (apostrophe) %Q -- double quote Program timing: %c -- CPU_TIME(3f) output .78125000000000000E-001 %C -- number of times this routine is used 1 %S -- seconds since last use of this format .0000000000000000 %k -- time in seconds from SYSTEM_CLOCK(3f) 588272.750 %K -- time in clicks from SYSTEM_CLOCK(3f) 588272750 If no percent (%) is found in the format one of several alternate substitutions occurs. If the format is composed entirely of one of the following keywords the following substitution occurs: "iso-8601", "iso" ==> %Y-%M-%DT%h:%m:%s%z ! Example: 2017-08-26T18:56:33,510912700-04:00 "iso-8601W", "isoweek" ==> %I "sql" ==> "%Y-%M-%D %h:%m:%s.%x" "sqlday" ==> "%Y-%M-%D" "sqltime" ==> "%h:%m:%s.%x" "rfc-2822" ==> %w, %D %l %Y %h:%m:%s %T ! Example: Mon, 14 Aug 2006 02:34:56 -0600 "rfc-3339" ==> %Y-%M-%DT%h:%m:%s%z ! Example: 2006-08-14 02:34:56-06:00 "date" ==> %w %l %D %h:%m:%s UTC%z %Y "short" ==> %w, %l %d, %Y %H:%m:%s %N UTC%z "long"," " ==> %W, %L %d, %Y %H:%m:%s %N UTC%z "suffix" ==> %Y%D%M%h%m%s "formal" ==> The %d of %L %Y "lord" ==> the %d day of %L in the year of our Lord %Y "easter" ==> FOR THE YEAR OF THE CURRENT DATE: Easter day: the %d day of %L in the year of our Lord %Y "all" ==> A SAMPLE OF DATE FORMATS otherwise the following words are replaced with the most common macros: STRING MACRO EXAMPLE year %Y 2016 month %M 07 day %D 27 hour %h 21 minute %m 24 second %s 22 epoch %e<PHONE_NUMBER> julian %j 2457597 ordinal %O 209 weekday %u 3 if none of these keywords are found then every letter that is a macro is assumed to have an implied percent in front of it. For example: YMDhms ==> %Y%M%D%h%m%s ==><PHONE_NUMBER>2422
If you prefer an Object-oriented interface the M_time_oop module (included with the M_time module source) provides an OOP interface to the M_time module; as described in the subroutine OBJECT_ORIENTED() in the example section.
The following example program demonstrates the extensive options available for formatting a date as well as how to use the module to calculate dates such as "Yesterday" and "Tomorrow", as well as how to use the Object Oriented interface to the conventional procedures found in the M_time(3fm) module.
The example from the conventional calls looks like this ...
Today ISO 2015-12-22T08:07:34.025-0300 Friendly Tuesday, December 22nd, 2015 08:07:34 AM ISO week 2015-W52-2 Yesterday 2015-12-21T08:07:34.025-0300 Monday, December 21st, 2015 08:07:34 AM 2015-W52-1 Tomorrow 2015-12-23T08:07:34.025-0300 Wednesday, December 23rd, 2015 08:07:34 AM 2015-W52-3 Next Week 2015-12-29T08:07:34.025-0300 Tuesday, December 29th, 2015 08:07:34 AM 2015-W53-2
The example from the object-oriented calls looks like this ...
Various initialization styles DEFAULT CONSTRUCTOR %FORMAT() 1970-01-01T00:00:00.000+0000 DEFAULT CONSTRUCTOR %FORMAT("") 1970-01-01T00:00:00.000+0000 DEFAULT CONSTRUCTOR %FORMAT(user-specified) 1970-01-01T00:00:00.000+0000 DEFAULT CONSTRUCTOR %FORMAT("USA") Thursday, January 1st, 1970 12:00:00 AM DEFAULT CONSTRUCTOR SETTING VALUES 1492-10-12T00:00:00.000+0000 DEFAULT CONSTRUCTOR WITH PARTIAL VALUES 2016-06-11T00:00:00.000+0000 DEFAULT CONSTRUCTOR WITH VALUES BY NAME 2016-06-11T21:09:11.500-0240 CONSTRUCTOR WITH A DAT ARRAY 1776-07-04T00:00:00.000+0000 CONSTRUCTOR WITH A PARTIAL DAT ARRAY 1776-07-04T20:00:00.000-0240 Print members of type(DATE_TIME) EVENT=2016,6,14,-240,22,22,31,253 Year................... 2016 Month.................. 6 Day.................... 14 Timezone............... -240 Hour................... 22 Minute................. 22 Second................. 31 Millisecond............ 253 Print methods of type(DATE_TIME) Ordinal day of year.... 166 Weekday................ 3 Unix epoch time........<PHONE_NUMBER>.2529941 Julian date............ 2457554.5989728356 Formatted Strings Short month............ "Jun" Month.................. "June" Short week............. "Tue" Week .................. "Tuesday" Longer format.......... "Tuesday, June 14th, 2016 10:22:31 PM" DAT=2016,6,14,-240,22,22,31,253 TOMORROW="Wednesday, June 15th, 2016 10:22:31 PM" YESTERDAY=="Wednesday, June 13th, 2016 10:22:31 PM" DIFFERENCE (subtracting one date_time from another)= 86400.000000000000 T F F T T F F T F T F T F F T F T T %DELTA tests starting with date 2016,6,14,-240,22,22,31,253 Tuesday, June 14th, 2016 10:22:31 PM Remember years and months are not constant units FOR DELTA YEAR=+1 Wednesday, June 14th, 2017 10:22:31 PM FOR DELTA YEAR=-1 Sunday, June 14th, 2015 10:22:31 PM FOR DELTA MONTH=+24 Saturday, June 16th, 2018 10:22:31 PM FOR DELTA MONTH=-24 Saturday, June 14th, 2014 10:22:31 PM FOR DELTA WEEK=+1 Tuesday, June 21st, 2016 10:22:31 PM FOR DELTA WEEK=-1 Tuesday, June 7th, 2016 10:22:31 PM FOR DELTA DAY=+1 Wednesday, June 15th, 2016 10:22:31 PM FOR DELTA DAY=+1 Monday, June 13th, 2016 10:22:31 PM FOR DELTA HOUR=+4 Wednesday, June 15th, 2016 02:22:31 AM FOR DELTA HOUR=-4 Tuesday, June 14th, 2016 06:22:31 PM FOR DELTA MINUTE=+180 Wednesday, June 15th, 2016 01:22:31 AM FOR DELTA MINUTE=-180 Tuesday, June 14th, 2016 07:22:31 PM FOR DELTA SECOND=+1800 Tuesday, June 14th, 2016 10:52:31 PM FOR DELTA SECOND=-1800 Tuesday, June 14th, 2016 09:52:31 PM FOR DELTA MILLISECOND=+10000 Tuesday, June 14th, 2016 10:22:41 PM FOR DELTA MILLISECOND=-10000 Tuesday, June 14th, 2016 10:22:21 PM FOR DELTA ONE-OF-EACH Sunday, November 24th, 2019 11:39:01 AM
A "date_and_time" array "DAT" has the same format as the array of values generated by the Fortran intrinsic DATE_AND_TIME(3f). That is, it is an 8-element integer array containing year, month, day, Time zone difference from UTC in minutes, hour, minutes, seconds, and milliseconds of the second. This array represents a date on the Proleptic Gregorian Calendar.
The Proleptic Gregorian Calendar assumes the Gregorian Calendar existed back to the beginning of the Julian Day calendar (4713 BC). This means historic dates will often be confused, as the Julian Calendar was used in the USA until 1752-09-03, for example. The Gregorian Calendar was formally decreed on 1582-10-15 but was not adapted in many countries. The Julian Calendar was first used around 45 BC. Note that the Proleptic Gregorian Calendar includes a year zero (0). It is frequently used in computer software to simplify the handling of older dates. For example, it is the calendar used by MySQL, SQLite, PHP, CIM, Delphi, Python and COBOL. The Proleptic Gregorian Calendar is explicitly required for all dates before 1582 by ISO 8601:2004 (clause <IP_ADDRESS> The Gregorian calendar) if the partners to information exchange agree.Unix Epoch Time (UET) is defined as the number of seconds since 00:00:00 on January 1st. 1970, UTC.
A JED is defined as a Julian Ephemeris Date. JED days start at noon (not at midnight). 4713-01-01 BC at noon is defined as JED 0.0.
If you are not familiar with them, in this context Julian Dates and Unix Epoch Times are scalar numbers that allow for easy computations using dates (to go back one day just subtract one from a Julian Date, for example). Since these values are generally not considered intelligible, routines are included to convert between these scalar values and the date array so human-readable results can be obtained.
Coordinated Universal Time (French: Temps universel coordonn'e), abbreviated as UTC, is the primary time standard by which the world regulates clocks and time. It is within about 1 second of mean solar time at 0o longitude;[1] it does not observe daylight saving time. It is one of several closely related successors to Greenwich Mean Time (GMT). For most purposes, UTC is considered interchangeable with GMT, but GMT is no longer precisely defined by the scientific community.
Like most collections of date and time procedures M_time is not a high-precision library that accounts internally for leap seconds and relativistic effects.
M_time(3f) is intended for use in the recent era and is not appropriate for use with historical dates that used some other calendar scheme such as the Julian Calendar. That is, you have to remember to account for conversions to other calendar systems when using historical dates.
When Daylight Savings is in effect calculations will generally be correct, as the date model includes a timezone value; but you are responsible for ensuring dates you create use the correct timezone value or otherwise account for Daylight Savings Time as needed.
Currently, dates are manipulated using the current system timezone, which can typically be set using the environment variable TZ. So if you desire to set the default timezone you generally set the environment variable before executing your program. This is compatible with current observed behavior for the intrinsic procedure DATE_AND_TIME(3f) with compilers I have tested with, but does not seem to be a specified behavior as far as the standard is concerned. That is, DATE_AND_TIME(3f) returns a vector that contains a current time zone, but does not specify how a current time zone can be explicitly set. Since this library is intentionally designed to complement DATE_AND_TIME(3f) it adopts the same behavior. A routine to let you set a default time zone could be added in the future.
Note the environment variable can be set using put_environment_variable(3f) from the libGPF library:
use M_system, only : put_environment_variable call put_environment_variable('TZ','America/New_York',ierr)
There is no warranty on this code, and it is certain to change.
The ISO-8601 standard is often used for business-related transactions.
There are (of course) the C/C++ intrinsics which provide much of the same functionality that should be bindable to Fortran via the ISO_C_BINDING module.
If you are looking for a high-precision Fortran library that is well tested for manipulating dates I would suggest looking at the NASA SPICElib library. If you care about Leap Seconds, Orbital Mechanics, GPS/Satellite communications, and Astronomy it is worth a look.
The Fortran Wiki fortranwiki.org contains information on other libraries and modules that provide date-time procedures.


Dual Degree, Electronics & Electrical Communication Engineering Indian Institute of Technology Kharagpur, India2012 to 2017


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India’s external affairs minister recently gave a speech at the United Nations General Assembly (UNGA) lambasting Pakistan. She termed the state ‘Terroristan’ and also extolled how India had devevloped institutes of excellence and become an IT superpower.
A lot of people I know or have observed on social media hailed the speech. I understand that these points were supposed to be rhetorical, meant to highlight the different paths the two countries have taken. What I do not get is why are people congratulating themselves for the achievements listed.
Sometimes a comparison can be useful as in the case of what are the approaches being taken to reduce malnutrition and how can we do better.
However, comparisons do not help when we have to measure progress.  Saying we have got metro rails while you still use buses is useless.
Pakistan’s economy is one-sixth ours. It does not clamour for membership to the UNSC or to become a superpower. We do. However, getting to our goal is tough and we don’t often progress at the rate we wish to. In such cases, it becomes tempting to compare ouselves to people who are worse off and feel better about ourselves. This is a dangerous tendency and indulging in it belies a lack of confidence in our ability to fulfill our ambitions.
If anything in India has to compared to Pakistan, it is the state of UP. The two entities have almost identical population. Has UP really created more institutions of excellence than Pakistan in the last 70 years ? How about multi-national companies ? And how about infant mortality, per capita income and other such standard human development measures ?
This comparison won’t go down as well as the one Sushma ji presented.
So should UP aspire to become Pakistan ?
Not all. It should rather compare itself in the short term to a benchmark like Indonesia, which has a per capita GDP that is 3-4 times that of UP.
Yet another tendency I have noticed is residents of Indian states comparing themselves to each other and demeaning others. I find this a pointless exercise. Even with the relative disparity among states, all of us are far far away from where we want to be.
People from the rest of this part of the world are us, however poorly they may be doing at this point of time. We should aspire to be higher.
So Andhra Pradesh should compare itself to South Korea, Maharasthra to Germany, Delhi to the Netherlands and so on. The nation as a whole should get away from the Pakistan fixation and compare itself to China or the US.
Only when you set your sights higher would you work hard or smart enough to reach there.


dataset stores JSON objects and can store also data frames similar to that used in Python, R and Julia. This document outlines the ideas behind dataset's implementation of data frames.
Collections are at the core of the dataset tool. A collection is either a pairtree directory structure storing JSON objects in plain text or a SQL table with a JSON column for the object. Both support optional attachments. The root folder should contain two files collection.json and codemeta.json. collection.json file contains operational metadata, metadata that dataset needs to work with the collection of JSON documents. The codemeta.json file is general metadata describing the collection. See codemeta for details on the codemeta.json file structure.
For the pairtree based collections one of the guiding ideas behind has been to keep everything in plain text (i.e. UTF-8) whenever reasonable. The affords the opportunity to easily interact with the data via standard Unix tools or easily from any language that supports working with JSON documents. A pairtree to a large degree is future proof as it is very likely the ability to read the file system and text files will persist long after dataset development stops.
Over the course of the last several years the limitations of storing JSON documents directly on the file system have been more compelling. Additionally common, mature SQL database systems have pickup the ability to store JSON as a column type (e.g. current systems include SQLite3, MySQL 8, Postgres 14). With version 2 of dataset this ability has been embraced more directly. Collections of the JSON documents can be stored in a table in a supported database. While this is less future proof than plain text it does increase the flexibility of working with large collections, likewise a dataset collection running with a SQL storage engine can be cloned to a pairtree storage collection for long term preservation.
The dataset project provides Go package for working with dataset collections. This means you can use dataset as a storage engine directly in your own Go based projects. As of version 2 this is documented along with command line interactions.
Dataset isn't a database (there are plenty of JSON oriented databases out there, e.g. CouchDB, MongoDB and No SQL storage systems for MySQL and Postgresql). dataset's focus is on providing a mechanism to manage JSON documents (objects). It supports the ability to attach non-JSON documents to a JSON document record as well as for working with JSON collections as data frames. Dataset collections are like a mini repository system avoiding the complexity of more mature repostiory back ends like Fedora Repository.
By working with JSON documents dataset can be used to feed full text search engines like Solr, OpenSearch, and small engines like LunrJS. Likewise when combined with Go’s struct types can be used to support building structured data repositories customized to specific needs.
Working with subsets of data in a collection is useful, particularly ordered subsets. Implementing this started me thinking about the similarity to data frames in Python, Julia and Octave. A frame is an ordered list of objects. Frames can be retrieved as a list of objects or as a list of keys. Frames contain a additional metadata to help them persist. Frames include enough metadata to efficiently refresh framed objects or even replace all objects in the list based on a new set of keys.
dataset stores frames with the collection so they are is available for later processing. The objects in a frame reflect the objects as they existed when the frame was generated. Frames can be refreshed to match the current state of the collection.
Frames become handy when moving data from JSON documents (tree like) to other formats like spreadsheets (table like). This is because the data frame’s structure is defined based on paths into objects in the collections. These pathes are mapped to “labels” structure the framed objects. Frames can be used to simplify a complex record into a simpler model for indexing in your favorite search engine.
Frames are stored in the collection’s _frames
sub
directory. One JSON document per frame combining both the definition and
frame content.
Example creating a frame named "dois-and-titles"
dataset keys Pubs.ds >pubs.keys
dataset frame-create -i pubs.keys Pubs.ds dois-and-titles \
".doi=DOI" \
".title=Title"
An example of getting the frame's object list only.
dataset frame-objects Pubs.ds dois-and-titles
Regenerating "dois-and-titles".
dataset refresh Pubs.ds dois-and-titles
In this example we want to “reframe” our “titles-and-dois” data frame. We get the current list of keys in the collection and regenerate the objects in the data frame using the new list of keys.
dataset Pubs.ds keys >updated.keys
dataset reframe -i updated.keys Pubs.ds titles-and-dios
dataset delete-frame Pubs.ds titles-and-dios
dataset frames Pubs.ds


Event Location:
The River Club
Liesbeek Parkway Observatory Road, Observatory, Cape Town, South Africa
Cape Town, Western Cape, South Africa
Abstract:
SQL Server is a high frequently used piece of software which need to serve single requests and/or hundreds of thousands of requests in a minute. Within these different kinds of workloads Microsoft SQL Server has to handle the concurrency of tasks in a fashion manner. This demo driven session shows different scenarios where Microsoft SQL Server has to wait and manage hundreds of tasks. See, analyze and solve different wait stats due to their performance impact: - CXPACKET: when a query goes parallel - ASYNC_IO_COMPLETION: speed up IO operations (Growth / Backup / Restore) - ASYNC_NETWORK_IO: What happens if your application refuses data? - THREADPOOL starvation: crush of requests for Microsoft SQL Server - PAGELATCH_xx: How does Microsoft SQL Server protects data?
Speaker(s): Uwe Ricken,
Abstract:
Reporting requests that are required ‘yesterday or sooner’, oft times necessitate working more efficiently and effectively. We have all been through this at one time or another. In this hands on presentation we shall be looking some of the more challenging techniques of extracting our data from our Multidimensional and Tabular Models, in addition to our Data Mining Models. We shall be utilizing concepts of ‘Openquery()’ and ‘linked servers’ as a means to extract data, in addition to looking at the way that these two concepts can help us extract our data utilizing MDX, DMX and DAX expressions; AND YET maintain the flexibility and the ability to utilize all those wonderful techniques that may be done with T-SQL.
Speaker(s): Steve Simon,
Abstract:
Have you ever been in the situation that an application performed well the last day and - suddenly - you run into problems? Although you have Index Maintenance jobs and statistics up to date you will monitor a big degrade of performance! This session will demonstrate in a over 80 percent demo based presentation the following scenarios which happend this way in real life! - Why performance degradation in spite of maintenance jobs? - just one more database in the result set and the query runs really bad - why? - usage of literals is completely different from the usage of variables - why? - identical data types but the query runs in part perfect and in part bad... - your execution plan shows an INDEX SEEK - but the table will be scanned! - you get a query from the business which performs really bad but when you test it it runs pretty cool - why?
Speaker(s): Uwe Ricken,
Abstract:
In 2009 John Allspaw and Paul Hammond delivered the session "10 deploys per day - Dev ops cooperation at Flickr." In forty six minutes they changed the way millions of people would think about the software delivery process for years to come. It didn't have a name yet, but DevOps was born. DevOps folk preached about the cloud, automation, rapid delivery and any database technology that wasn't relational. In 2013 Kenny Gorman declared "The DBA is Dead". For the record, I don't believe that, but a lot of people do. What is certain is that the world of IT is changing and traditional roles are changing with it. We all need to adapt. In this interactive talk I'll explain what DevOps is, where it came from, and its implications for SQL Server. I'll demo example solutions to some of the foundational technical challenges (source control and test/deployment automation) and will leave plenty of time at the end to answer your questions.
Speaker(s): Alex Yates,
Abstract:
Large volumes of data originate at high velocity from various sources including IoT devices and sensors, web sites and social media feeds. A common approach is to simply store these data for offline processing. Processing that in many instances never happens... Wouldn't it be more efficient and effective to process the data stream as it arrives? Azure Stream Analytics is an event-processing engine which enables processing of streaming data in real time. It can handle up to 1 Gb of data per second, which should suffice for most applications. In this tutorial I'll take you through the processing of setting up a job on Azure Stream Analytics. I'll look specifically at the following: - connecting to a data source - extracting information - identifying patterns and relationships - using machine learning - triggering actions - reporting tools and - offline processing. To illustrate these concepts we'll build a real time sentiment analysis system for streaming data from Twitter.
Speaker(s): Andrew Collier,
Abstract:
It’s an age-old problem: developers want prod data for dev and test purposes. It helps them to write better code and to test it effectively. Self-service access to usable test-data aligns well with DevOps principles that encourage teams to adopt a shift-left mentality to testing. Unfortunately, in the age of data breaches and tighter regulation it's unwise to give developers access to some types of sensitive production data. So what do you do? In this session I’ll talk about the GDPR, anonymisation, pseudonymisation and 5 techniques you can use to provide appropriate data that is as “production-like” as possible (within the legal and technical constraints). I’ll demo these techniques both in raw T-SQL and using some of the Microsoft and third party tools that are available to make the task easier. After this session you’ll be equipped to discuss the problem with your colleagues in an informed manner and you’ll be able to suggest several solutions and their relative pros and cons.
Speaker(s): Alex Yates,
Abstract:
Data preparation is often the most labour-intensive component of an analytics project and getting this right is vital if the results are to be accurate. While the growth of self-service BI has empowered users to answer important questions, ensuring well managed data is available to all employees remains one of businesses biggest challenges. Dataflows is an extension of the Power BI ecosystem, providing a low code approach using Power Query to create curated datasets that can be easily consumed by business analysts, thereby improving the speed at which business value can be delivered. Dataflows provides a single-version-of-the-truth which was traditionally attained by building a data warehouse. You will learn -How Dataflows can be used to create rich, vetted datasets -How Dataflows can be created with the click of a button, ingesting data from many well knows SAAS solution such as Dynamics and Salesforce -How to manage Dataflows using advanced data preparation tools such as Databricks
Speaker(s): Michael Johnson,
Abstract:
A significant issue for enterprises moving to private or public clouds is how to trust the infrastructure and providers with their sensitive workloads. Customers need the ability to assess security standards, trust security implementations, and prove infrastructure compliance to auditors. This session will highlight the issues with cloud architectures and discuss ways to achieve visibility, compliance, and security. We will examine solution stacks that enable trusted computing and illustrate several usages that demonstrate policy enforcement, compliance, and end-to-end trust in the cloud. The session will also examine how organizations can monitor and enforce geolocation restrictions, ensuring that their workloads in the cloud are deployed on trusted hardware in known locations to meet security policy compliance. Trusted geolocation allows organizations to establish security and physical boundaries that limit which systems process and store sensitive information and applications in th
Speaker(s): Adnan Hendricks,
Abstract:
The August release of Power BI Desktop features a preview of Python integration. Python script is now available in the Query Editor, and a new Py visual supports Python script. Come and learn how to get started with Python in Power BI. Lots of demos in this session that cover some basic, yet useful patterns. Between demos we will chat a bit about the importance of Python integration and why it is a significant step for Power BI. Python is widely used and runs across platforms and applications. Any investment you have made in learning Python (or are planning to make), means that you are on the road to build skills considered to be the universal data science and utility language ... often referred to as the Swiss army knife of programming. When you wrap your Python script in the best business analytics tool on the market (no prizes for guessing its Power BI ... and yes I am biased), you are well on your way to become a "Power Py" guru!
Speaker(s): Toufiq Abrahams,
Abstract:
The Intelligent Edge: Make hybrid cloud and Internet of Things (IoT) solutions a reality with Azure IoT Edge, a fully managed service that delivers cloud intelligence locally. Seamlessly deploy and run artificial intelligence, Azure services, and custom logic directly on cross-platform IoT devices—from a small Raspberry Pi to large industrial machines. And manage it all centrally in the cloud with the security of Microsoft. Use the same programming models you use in the cloud to create and test your IoT applications and then push them to your edge devices. This session is an introduction to Azure Iot Edge and what it can do for you.
Speaker(s): Allan Pead,
Abstract:
One of the new features in SQL Server 2017 was Graph tables. These represent a different way of storing data to what we’re use to in the relational model. In this session, I’ll take you through the design process for graph tables, and then implement and query some graphs in both SQL Server 2017 and Cosmos DB using its graph model.
Speaker(s): Gail Shaw,
Abstract:
Xamarin enables C# devs to become native iOS, Android, and Windows mobile app dev overnight. Learn how to leverage your existing .NET and C# skills to create iOS and Android mobile apps in Visual Studio. In addition to allowing you to write your iOS and Android apps in C#, Xamarin lets you reuse existing .NET libraries and share your business logic across iOS, Android, and Windows apps. During this session we cover the Xamarin platform and how to create native iOS, Android, and Windows apps in C#. See what's new and next for Xamarin development inside of Visual Studio.
Speaker(s): Allan Pead,
Abstract:
In this talk we'll discuss how to do Machine Learning with R inside Power BI with a brief explanation on different algorithms. We'll cover: * How to do machine learning inside power query using R * How to write R scripts to create functions inside Power Query for data transformation * A brief description of some algorithms with some R sample code
Speaker(s): Luis de Sousa,
Abstract:
Master Data Services can look complicated. In this session I will take you through how to install MDS to having a complete solution that will work for you. This session will include installing MDS, setting up a model with entities and attributes, integrating with Excel and setting up security.
Speaker(s): Jeanne Combrinck,
Abstract:
The Azure SQL Datawarehouse is intended for large-scale data processing in the cloud, but it’s not just a really big managed SQL Server instance. In this session, I’ll take you through the architecture of Azure SQL Datawarehouse, how it differs from a regular SQL Server and what those differences mean for database design, query design and usage patterns.
Speaker(s): Gail Shaw,
Abstract:
This is an introduction to machine learning in Azure, and how to use Azure ML Studio - the IDE for machine learning in the cloud.
 They always tend to happen at the worst possible time, are extremely difficult to recreate and the deadlock graph looks like some sort of abstract art. So when your DBA tells you that you need to fix a deadlock: what is it and how should you go about resolving it?
Speaker(s): Jodi Craig,
Abstract:
Have you ever watched a demo of DAX functionality, and marvelled at how simple the language is, but yet, when you work on your own data model, it just doesn't work as expected? In this session, we'll look at 4 common problems which occur when you're using more complicated data modelling techniques, and design and implement a simple solution to overcome these complications. From role-playing dimensions and bi-directional filters to advanced filter context and overriding the default blank behaviour, we'll learn to solve a variety of problems and add value to our data models.
Speaker(s): James McGillivray,
Abstract:
PowerBI is a powerful business analytics platform created to support both self-service and enterprise-class reporting. In this session, you will see how easily great looking reports can be created using multiple data sources and easily shared within your organisation. We will also discuss licensing options available ranging from free through to Premium licencing.
Speaker(s): Michael Johnson,
Abstract:
A quickstart guide for SQL developers in the dark to start making sense of their new environment
Speaker(s): Warren Rocchi,
Abstract:
Change Tracking over the years has been handled in many different ways with People often building their own bespoke auditing systems. This session will introduce Temporal Table's pros and cons allowing someone to make a much more informed decision as to whether or not this could add value to their systems.
Speaker(s): Sean Cremer,
Abstract:
This session will focusing on providing a SQL/BI developer with the basics to rapidly getting started working with data generated from Internet of Things(IoT) devices using Windows 10 IoT core running on a Raspberry Pi as an IoT source collect data and send data to Azure IoT hub. Some of the aspects that will be looked at is getting the development environment setup, getting Windows 10 IoT core running on a Raspberry Pi , connecting IoT device to Azure and the challenges faced, then we will move on to getting an app running that sends data to IoT hub and visualizing this data in Power BI
Speaker(s): Arohan Naidoo,
Abstract:
sp_execute_external_script (SPEES) is the "entry point" into SQL Server Machine Learning Services. In this session, we look at the various parameters it takes and what they are used for. The session is code and demo-heavy!
Speaker(s): Niels Berglund,
Abstract:
With the rapid advance in analytic features and the increased capabilities to produce more informative reports, SQL Server 2017 opens a plethora of new and exciting opportunities. In the hands-on presentation we shall be constructing a few simple analytic reports (from background financial data). More importantly we shall see how our conclusions help us target new prospective business opportunities.
Speaker(s): Steve Simon,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
LinkedIn: https://nl.linkedin.com/in/ahendricks
Adnan is co-founder www.AzurePlatformExperts.com and one of the world’s leading infrastructure experts a Microsoft MVP ,MCT (Regional Lead in NL 2014-2017)Member Microsoft Onedrive Experts Program (MODE), Microsoft Windows Technical Experts Program Follow him on twitter @Microspecialist
LinkedIn: http://www.linkedin.com/in/stephenrsimon/
Contact: http://www.sqlservercentral.com/blogs/simon_says/
Steve Simon is a Database Solutions Architect with Business Knowledge Services Inc. He has been involved with database design and analysis for over 35 years. Steve has presented papers at numerous PASS Summits and PASS SQL Rallies. He is also regular presenter on the SQL Saturday circuit and he is a contributor on SQLShack.com
Contact: http://sqlinthewild.co.za
Gail Shaw is a Database Specialist focusing on database performance tuning and database recovery, with a particular interest in topics such as indexing strategies, execution plans, and writing T-SQL code that performs well and scales gracefully. Gail holds a Microsoft Certified Master certification for SQL Server 2008 and is a Data Platform MVP. She's a frequent poster on the SQLServerCentral forums, a Pluralsight author, writes articles for both SQLServerCentral.com and Simple-Talk.com, and has spoken at SQLSaturdays, SQL Bits and at the PASS Community Summit. Gail has been responsible for extending the lifespan of many an application, by performance-tuning their databases and providing technical guidance on all things SQL Server related.
LinkedIn: http://www.linkedin.com/profile/view?id=270775013amp;trk=tab_pro
Contact: http://www.sqlmaster.de
Uwe Ricken is working with IT systems since the 90’s. The primary passion for developments with Microsoft SQL Server expanded in 2007 with his engagement as a DBA for Deutsche Bank AG in Frankfurt am Main. After 6 years of operational experiences as a DBA and over 14 years as a developer of complex database models he achieved the “Microsoft Certified Master – SQL Server 2008” certification which “was” the highest technical certification. In the same year he earned his first award as a Data Platform MVP for his support to the Microsoft SQL Server community in Germany and Europe. Uwe Ricken is a speaker on many international conferences and events and preferred topics are “Database Internals”, “Indexing” and “Development”.
LinkedIn: https://za.linkedin.com/in/niels-berglund-0122593
Contact: https://nielsberglund.com
Niels works as Software Architect at Derivco, where he develops software as well as "dreaming" up new and innovative ways to use existing and new technology. Before joining Derivco, Niels worked as researcher/instructor at Developmentor where he was responsible for the database curriculum. He was a co-author of the first SQL Server 2005 book for developers: quot; A First Look at SQL 2005 for Developersquot;. He has presented at all leading technical conferences; Tech-ED, SQL PASS, DevWeek, Ignite the Tour, etc., and is also a Microsoft Data Platform MVP.
LinkedIn: http://www.linkedin.com/profile/view?id=270775013amp;trk=tab_pro
Contact: http://www.sqlmaster.de
Uwe Ricken is working with IT systems since the 90’s. The primary passion for developments with Microsoft SQL Server expanded in 2007 with his engagement as a DBA for Deutsche Bank AG in Frankfurt am Main. After 6 years of operational experiences as a DBA and over 14 years as a developer of complex database models he achieved the “Microsoft Certified Master – SQL Server 2008” certification which “was” the highest technical certification. In the same year he earned his first award as a Data Platform MVP for his support to the Microsoft SQL Server community in Germany and Europe. Uwe Ricken is a speaker on many international conferences and events and preferred topics are “Database Internals”, “Indexing” and “Development”.
LinkedIn: https://za.linkedin.com/in/niels-berglund-0122593
Contact: https://nielsberglund.com
Niels works as Software Architect at Derivco, where he develops software as well as "dreaming" up new and innovative ways to use existing and new technology. Before joining Derivco, Niels worked as researcher/instructor at Developmentor where he was responsible for the database curriculum. He was a co-author of the first SQL Server 2005 book for developers: quot; A First Look at SQL 2005 for Developersquot;. He has presented at all leading technical conferences; Tech-ED, SQL PASS, DevWeek, Ignite the Tour, etc., and is also a Microsoft Data Platform MVP.
LinkedIn: http://www.linkedin.com/in/stephenrsimon/
Contact: http://www.sqlservercentral.com/blogs/simon_says/
Steve Simon is a Database Solutions Architect with Business Knowledge Services Inc. He has been involved with database design and analysis for over 35 years. Steve has presented papers at numerous PASS Summits and PASS SQL Rallies. He is also regular presenter on the SQL Saturday circuit and he is a contributor on SQLShack.com
LinkedIn: http://za.linkedin.com/pub/michael-johnson/22/73/735
Contact: http://michaeladrianjohnson.com/
Michael Johnson is a Microsoft data platform MVP and leader of the Johannesburg SQL User Group and Business Analytics Virtual Group. He works as an Independent Business Intelligence Consultant and regularly presents at local user groups, SQL Saturdays and PASS Virtual Chapters.
LinkedIn: https://www.linkedin.com/in/warren-rocchi-06035138/
Contact: http://www.ebsphere.com
Commonly described as the boy next door (although next door to whom has never been quite defined), Warren fell in love with SQL when he was accidentally thrust into it after going to see a girl at a tech company. He liked it so he put a ring on it. That was the girl, and as for SQL the affair continues. Warren consults as a SQL dev for EBSphere (Pty) Ltd and lectures on all aspects of SQL in his spare time. Amongst other things he dabbles in MySQL and is passionate about IT Security and recycling.
LinkedIn: http://uk.linkedin.com/in/alexanderyates
Contact: http://www.workingwithdevs.com
Alex Yates has been helping organisations to apply DevOps principles to relational database development and deployment since 2010. He’s most proud of helping Skyscanner develop the ability to deploy 95 times a day. Originally for Redgate, later for DLM Consultants, Alex has worked with clients on every continent except Antarctica – so he’s keen to meet anyone who researches penguins. A keen community member, he co-organises SQL Relay, is the founder of www.SpeakingMentors.com and has been recognised as a Microsoft Data Platform MVP since 2017.
LinkedIn: https://www.linkedin.com/in/datawookie/
Contact: https://datawookie.netlify.com/blog/
Itinerant Data Scientist; Geek; Runner.
LinkedIn: https://www.linkedin.com/in/adpead/
Contact: https://explorationspace.co.za/
Allan Pead is a Xamarin MVP and a Visual Studio and Development Technologies MVP based in Cape Town South Africa. He is the founder and CTO of development and consulting company AD Soft Systems. He is also organizer of the Xamarin User groups in South Africa. He has more than 20 years’ experience in the industry. While mobile development is not the only type of development he is involved in, the love for technology (and gadgets of all descriptions) instills a particular passion in him for the mobile space. He is very happy to be in a position where his passion and hobby is also his day job.
Arohan is a Business Intelligence developer working at Entelect primarily on the Microsoft BI stack who has an interest in using Big Data and IoT to find insights into ways to improve daily life as well as focused research on using technology to enhance learning.
Contact: http://sqlinthewild.co.za
Gail Shaw is a Database Specialist focusing on database performance tuning and database recovery, with a particular interest in topics such as indexing strategies, execution plans, and writing T-SQL code that performs well and scales gracefully. Gail holds a Microsoft Certified Master certification for SQL Server 2008 and is a Data Platform MVP. She's a frequent poster on the SQLServerCentral forums, a Pluralsight author, writes articles for both SQLServerCentral.com and Simple-Talk.com, and has spoken at SQLSaturdays, SQL Bits and at the PASS Community Summit. Gail has been responsible for extending the lifespan of many an application, by performance-tuning their databases and providing technical guidance on all things SQL Server related.
LinkedIn: https://za.linkedin.com/in/thejimmyrsa
Contact: https://jimbabwe.co.za/blog-posts/
James McGillivray, Data Management and Analytics MCSE, has been working in Business Intelligence for over a decade. He spends his working hours building solutions that allow people to make informed decisions quickly and accurately. James is a director at Cobalt Analytics. Before his career in data, James was an avid musical theatre student and musician. This love for performance has developed into a passion for developing and delivering presentations that are informative and enjoyable. James speaks frequently at the Johannesburg SQL User Group, and has delivered sessions at numerous SQL Saturdays, PASS Summit, Power BI World Tour and SQL Bits.
LinkedIn: http://www.linkedin.com/profile/preview?locale=en_UStrk=prof-0-sb-preview-primary-button
Contact: https://seancremer.github.io/
Sean Cremer is a Senior Developer at Derivco. He has worked with SQL Server for nearly two decades on Large OLTP and Data Warehousing Systems and has a wealth of accumulated Database Design and Implementation knowledge.
LinkedIn: https://www.linkedin.com/in/adpead/
Contact: https://explorationspace.co.za/
Allan Pead is a Xamarin MVP and a Visual Studio and Development Technologies MVP based in Cape Town South Africa. He is the founder and CTO of development and consulting company AD Soft Systems. He is also organizer of the Xamarin User groups in South Africa. He has more than 20 years’ experience in the industry. While mobile development is not the only type of development he is involved in, the love for technology (and gadgets of all descriptions) instills a particular passion in him for the mobile space. He is very happy to be in a position where his passion and hobby is also his day job.
I am passionate about striving for Technical Excellence and designing the right solution for the business need. I have been in the IT industry for 20+ years and spent majority of that time in a technical / management role. I particularly enjoy working with data anything related to SQL Databases.
LinkedIn: http://uk.linkedin.com/in/alexanderyates
Contact: http://www.workingwithdevs.com
Alex Yates has been helping organisations to apply DevOps principles to relational database development and deployment since 2010. He’s most proud of helping Skyscanner develop the ability to deploy 95 times a day. Originally for Redgate, later for DLM Consultants, Alex has worked with clients on every continent except Antarctica – so he’s keen to meet anyone who researches penguins. A keen community member, he co-organises SQL Relay, is the founder of www.SpeakingMentors.com and has been recognised as a Microsoft Data Platform MVP since 2017.
LinkedIn: http://za.linkedin.com/pub/michael-johnson/22/73/735
Contact: http://michaeladrianjohnson.com/
Michael Johnson is a Microsoft data platform MVP and leader of the Johannesburg SQL User Group and Business Analytics Virtual Group. He works as an Independent Business Intelligence Consultant and regularly presents at local user groups, SQL Saturdays and PASS Virtual Chapters.
LinkedIn: https://za.linkedin.com/in/jeannecombrinck
Contact: http://thesqlgirl.com
Senior BI Developer at Britehouse. In her spare time she can be found spending time with her animals, doing SQL puzzles and writing on her blog http://thesqlgirl.com/.
LinkedIn: http://za.linkedin.com/in/luisdza
Contact: http://www.luisdesousa.co.za
Consult to large financial institutions in South Africa providing Business Intelligence and Analysis insight.
Contact: http://www.osmosisconsulting.co.za
Toufiq Abrahams is the Principal Consultant at Osmosis Consulting and Technology Director at Plain Sight Analytics. He has a passion for turning data into business value with one foot in the cloud and the other on-prem.
The following is a list of sponsors that helped fund the event.


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NYC Jewelry Week-The 2nd Edition
by Beth BernsteinNow it’s second year, NYCJW (New York City Jewelry Week) is a jewelry enthusiast’s dream. The only question is –how to fit in all of the events you may want to attend during November 18-24th? And, wait, there are more events taking place this week that are ahead of the regularly scheduled programming that you won’t want to miss. 2019’s edition guides us from the small progress studios and shops in Brooklyn to a behind the scenes look at New York’s iconic jewelry mecca-47th street to the renowned houses on Madison Avenue through the artistic scene in Soho and Tribeca. From lectures on jewelry’s past to up & coming designer studio visits to curated museum tours, interviews with the legends that keep the rich history of this city alive and panel discussions, there is something for everyone interested in this wearable art. Here are five events that offer just a small glimpse of the different programs that the next two weeks will have in store:
The Protagonist , Presented by Vogue Italia
Salon Art & Design, Park Avenue Armory
November 14-18, 2019
643 Park Avenue
Check Venue for Hours
Although not officially on the NYCJW schedule, it is launching few days earlier and then running concurrently with the other programs. The Protagonist is an important showcase of fine jewelry, now celebrating it’s fifth anniversary which features 10 designers whose aesethetic is based on mindful practices, sustainable materials and responsible sourcing. Jewelry designers and artistic director of the program Alexandra Mor is joined by Amedeo by Farone Menella, Lydia Courteille, Munnu the Gem Palace, Neha Dani, and Yair Shimansy, Feng J and K. Brunini and comprise just a sampling of the designer involved who will feature natural and ethically-minded materials such as recycled gemstones and precious metals, reclaimed wood and botanical alternatives to materials such as leather and ivory.Since 2017, Mor has challenged leading fine jewelry houses to design pieces using alternative materials.
Just a few quotes about sustainability from two of the partipating designers:
Katey Brunini; “Sustainability is a symbiotic relationship between man and nature. Mining is a touchy subject in the jewelry industry, and it can be argued that artisanal production by indigenous people is the answer to third world development and global sustainability. Being related to small production, alluvial mining, and collection rather than mass conglomerates. There is no pure answer but If we take, we must give back.”
Feng J: “Sustainability is a choice, an attitude, and responsibility for the world. To me, it’s a sense of mindfulness in my creations. jewelry piece with Aesthetic sense, superior craftsmanship and a good meaning to the environment is a collectable for the future.”
Rings: 1957 – 2019, Legends and Tales from Athens to New York
Lalaounis
31 East 64th Street
Monday, November 18th
10:00 am - 6:00 pm
This exhibit features rings created by artists across the globe as a platform to explore range of aesthetic and conceptual ideas between studio and fine jewelry. The exhibition includes studio finger rings from the private collection of Helen Drutt alongside rings from the permanent collection of the Ilias Lalaounis Jewelry Museum; and a selection of new creations designed by Maria Lalaouni.
The exhibition runs November 18th – 23rd, 10am – 6pm. In Collaboration with The Ilias Lalaounis Jewelry Museum
How To Date Period Pieces: A Guide To Vintage and Estate Jewelry
Macklowe Gallery
445 Park Avenue
Wednesday, November 20, 2019
2:00-3:00 PM
In business for over the owners and specialists of Macklowe Gallery will share tips on how to examine and date pieces of antique and vintage jewelry from the Victorian era onward. You will participate in activities that will allow you to see and touch the jewelry and learning the clues of the history of different pieces and how to tell if they are the real thinkg.
Macklowe’s team of experts will provide infographics, interactive activities and a how-t0 lecture on how to place a piece of jewelry in history, how to examine pieces from past to present and how to tell if it’s the real thing!
A Passion For Jewelry: Talking About The Leading Collectors of The 20th Century And The Collectors of Today
Christie’s
1230 Avenue of The Americas, 2oth Floor
Wednesday, November 20th
6:00 pm - 8:00 pm
An informal conversation with Christie’s specialists, jewelry historians and jewelry authors about some of the major women collectionf of the 20th century including but not limited to Doris Duke, Hélène Beaumount, Barbara Hutton, Elizabeth Taylor, Carroll Petrie, the Duchess of Windsor, Jayne Wrightsman and Lily Safra. What drew these woman to the jewelry they collected? How they recognized the rare and everyday jewelry that would fit with their individual tastes and how to define style. This expert panel will also discuss what modern independent woman are looking for as collectors today.
Brooches Go Modern
Fred Leighton
Thursday, November 21st
3:00 PM
773 Madison Avenue
For two days, starting on November 20th legendary antique and vintage NYC shops present Vintage Goes Modern on Madison Avenue, invite you to take an insider’s look at different aspects of jewelry throughout history and the relevance it holds today
Rebecca Selva’s Chief Executive officer at Fred Leighton is offer her visionary styling technique in Brooches Go Modern. Join her for a show-and-tell on the timeless allure of brooches and how to wear them wearing brooches with contemporary fashion.
For a full schedule, please go to NYCJW


torchfunc.performance.layers¶
Check any performance caveats related to PyTorch and it’s layers.
Using functionalities below you can check whether your architecture follows
current good practices related to performance of torch.nn.Module
concrete layers.
-
class
torchfunc.performance.layers.
Depthwise
(checkers: Tuple[Callable[[torch.nn.modules.module.Module], bool]] = None)[source]¶ Check whether any convolution layer is a so-called depthwise convolution.
Depthwise convolution is faster for images with input data in format (batch, height, width, channel) as specialized kernels are available.
Currently PyTorch does not support this functionality, so using those may actually slow down your neural network.
Depthwise convolution might still be useful in order to save memory, not so performance-wise.
For easy to follow guidelines, use
tips
method of this class.Example:
model = torch.nn.Sequential( torch.nn.Conv1d(64, 64, kernel_size=3, groups=64), torch.nn.Conv2d(3, 32, kernel_size=3, groups=1), torch.nn.Conv2d(32, 32, kernel_size=3, groups=32), ) for index in torchfunc.performance.layers.Depthwise().children(model): print(index) # Should print 0 and 2
-
checkers
¶ Functions checking whether given module is depthwise convolution. Should return True in such case, False otherwise. Default:
Depthwise.default_checker
; if module’s groups count is equal to module’sin_channels
True is returned. Works for PyTorch’sConvNd
layers.- Type
Tuple[Callable], optional
-
children
(module: torch.nn.modules.module.Module)[source]¶ Look for Depthwise convolution using module’s
children()
method (shallow scanning).- Parameters
module (torch.nn.Module) – Module to be scanned
- Yields
int – Indices where module is considered depthwise convolution.
-
classmethod
default_checker
(module)[source]¶ Default checking method suitable for PyTorch’s built-in convolution layers.
Checks whether count of groups is equal to count of in_channels.
Important:
If you want to provide custom checker, you should return
True
(module being depthwise convolution) orFalse
for any module that is passed to this function.- Parameters
module (torch.nn.Module) – Module (or submodule) for which True means it’s depthwise.
- Returns
Submodule’s indices where depthwise convolution was located.
- Return type
List[int]
-
modules
(module: torch.nn.modules.module.Module)[source]¶ Look for Depthwise convolution using
modules()
method (recursive scanning).- Parameters
module (torch.nn.Module) – Module to be scanned
- Yields
int – Indices where module is considered depthwise convolution.
-
tips
(module: torch.nn.modules.module.Module) → str[source]¶ Return
str
representation ofmodules()
method.It is advised to use this function to get tips in order to easily fix performance issues related to depthwise convolution.
- Parameters
module (torch.nn.Module) – Module to be scanned
- Returns
String representing tips related to depthwise convolution.
- Return type
str
-
-
class
torchfunc.performance.layers.
Inplace
(inplace: Tuple[str] = ('inplace', ))[source]¶ Check whether any submodule/child of module is set to inplace mode.
Inplace operations may interfere with traced module (kernel fusion) and cause slowdowns. See this issue for more information.
Example:
model = torch.nn.Sequential( torch.nn.Conv2d(3, 64, kernel_size=3, groups=64), torch.nn.ReLU(inplace=True), torch.nn.Conv2d(64, 64, kernel_size=3), torch.nn.ReLU6(inplace=True), torch.nn.Conv2d(64, 128, kernel_size=3, groups=32), ) for index in torchfunc.performance.layers.Inplace().children(model): print(index) # Should print 1 and 3
For easy to follow guidelines, use
tips
method of this class.-
attribute
¶ Attributes names indicating whether current op is inplace. Do not specify if you are not using custom modules not following pytorch’s conventions. Default:
("inplace",)
. Existence of all those attributes will be checked in module. If any of them exists and isTrue
, it will be considered as inplace operation.- Type
Tuple[str], optional
-
children
(module: torch.nn.modules.module.Module)[source]¶ Look for inplace operation using
children()
method (shallow scanning).- Yields
int – Indices where module is probably
inplace
.
-
inplace
= ('inplace',)¶
-
modules
(module: torch.nn.modules.module.Module)[source]¶ Look for inplace operation using
modules()
method (recursive scanning).- Yields
int – Indices where module is probably
inplace
.
-
tips
(module: torch.nn.modules.module.Module) → str[source]¶ Return
str
representation ofmodules()
method.It is advised to use this function to get tips in order to easily fix performance issues related to inplace operations.
- Parameters
module (torch.nn.Module) – Module to be scanned
- Returns
String representing tips related to inplace operations.
- Return type
str
-


Police believe Harley Quinn may help identify a suspect
Police are looking for Harley Quinn. Not Gotham Police — the Regina Police Service.
by Austin M. DavisPolice are looking for Harley Quinn.
Not Gotham Police — the Regina Police Service.
Police did not provide any information about the incident in question.
However, police say the possible witness was dressed as DC Comics character Harley Quinn, but have no more specifics about the costume. Police believe she may have information to identify a suspect.
There is no indication police are looking for the Joker.
The character first appeared in Batman: The Animated Series and was most recently portrayed by Margot Robbie in the 2016 Suicide Squad live-action film.


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java.lang.Object Maths.AbstractMatrix
public abstract class AbstractMatrix
Abstract implementation of a Matrix.
This class offers the basic functionalities for a matrix.
| Constructor Summary | |
|---|---|
AbstractMatrix()
|
| Method Summary | |
|---|---|
AbstractMatrix |
add(AbstractMatrix m)
Adds an other matrix to this matrix. |
AbstractMatrix |
clone()
Clones a matrix. |
abstract int |
cols()
Returns number of cols |
abstract java.lang.String |
debugString()
Returns a string representation of this matrix containing relevant information for debugging processes. |
AbstractMatrix[] |
decomposePenta()
Decomposes a matrix if it is pentadiagonale. |
boolean |
equals(java.lang.Object o)
Checks if two matrices are the same. |
abstract double |
get(int row,
int col)
returns a value in the matrix. if the value has not been set yet it returns 0. |
boolean |
isPentadiagonale()
Checks if a matrix is pentadiagonale (only the diagonale and the two elements next to it are set, so special form of a band matrix) |
boolean |
isPositiveSemiDefinit()
Checks if a matrix is positiv semi definite |
boolean |
isQuadratic()
Checks if a matrix is quadratic |
boolean |
isSymmetric()
checks if a matrix is symmetric |
AbstractMatrix |
mult(AbstractMatrix m)
Multiplies a matrix with another matrix. |
abstract AbstractMatrix |
mult(double a)
multiplication of this matrix with a real value. |
Vector |
mult(double[] x)
Multiplies a matrix with a vector (vector is represented by the array of real numbers). |
Vector |
mult(Vector x)
Multiplies a matrix with a vector. |
static AbstractMatrix |
parse(java.lang.String s)
Parses a string representation of a matrix to a matrix. |
abstract int |
rows()
Returns number of rows |
abstract void |
set(int row,
int col,
double value)
Sets a entry of the Matrix. |
double[][] |
toArray()
Array representation of the Matrix. |
void |
toFile(java.lang.String filename)
Saves a matrix to a file in text form. |
boolean |
toFileSync(java.lang.String filename)
Stores a matrix in a file. |
java.lang.String |
toString()
Method to print a matrix. |
AbstractMatrix |
transpose()
Transposed matrix of this matrix. |
| Methods inherited from class java.lang.Object |
|---|
getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
|---|
public AbstractMatrix()
| Method Detail |
|---|
public AbstractMatrix add(AbstractMatrix m)
m
- other matrix
java.lang.IllegalArgumentException
- if the matrizes do not match.public AbstractMatrix clone()
clone
in class java.lang.Object
public abstract int cols()
public abstract java.lang.String debugString()
public AbstractMatrix[] decomposePenta() throws java.lang.IllegalArgumentException
java.lang.IllegalArgumentException
- if the matrix is not pentadiagonale this error is thrown.public boolean equals(java.lang.Object o)
equals
in class java.lang.Object
o
- other matrix
public abstract double get(int row, int col)
row
- row of the matrix element.col
- col of the matrix element
java.lang.IndexOutOfBoundsException
- if the row or col are not in the matrix range.public boolean isPentadiagonale()
public boolean isPositiveSemiDefinit()
public boolean isQuadratic()
public boolean isSymmetric()
public AbstractMatrix mult(AbstractMatrix m)
m
- other matrix
java.lang.IllegalArgumentException
- if the matrizes do not match.public abstract AbstractMatrix mult(double a)
a
- value to multiply this matrix
public Vector mult(double[] x)
x
- vector to multiply this matrix with
java.lang.IllegalArgumentException
- if the Vector doesn't match the size of the matrix (x.length() != this.cols())public Vector mult(Vector x)
x
- vector to multiply this matrix with
java.lang.IllegalArgumentException
- if the Vector doesn't match the size of the matrix (x.length() != this.cols())public static AbstractMatrix parse(java.lang.String s)
s
- string representation (see above)
public abstract int rows()
public abstract void set(int row, int col, double value)
row
- Row of the matrix elementcol
- Col of the matrix elementvalue
- value for the matrix element
java.lang.IndexOutOfBoundsException
- if the row or col are not in the matrixs range.public double[][] toArray()
public void toFile(java.lang.String filename)
filename
- the filename where to save it.public boolean toFileSync(java.lang.String filename)
filename
- the file where to save it.
public java.lang.String toString()
toString
in class java.lang.Object
public AbstractMatrix transpose()
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TSMC: It is not feasible to build a fab in the US - Gizchina.com
by Efe UdinTSMC: It is not feasible to build a fab in the US
3.3 (66.67%) 3 votes
For manufacturers, the location of their factories is very important because it determines the price of the end product. This does not only entail the cost of labour but it also looks at the country’s policies and taxation. For some time now, the US has not been favourable to pitch a factory, especially for non-US companies. TSMC is a global leading chip manufacturer. In addition to its American commercial customers such as Apple and AMD, the Taiwanese chipmaker also manufactures chips for DARPA (American Defense Advanced Research Projects Agency).
Last month, there were reports that high-ranking US officials contacted TSMC. According to speculation, the meeting was to lobby the transfer of production of sensitive chips to the US. In this regard, TSMC Chairman and Co-CEO, Liu Deyin, said recently that the US has not directly contacted TSMC. Instead, TSMC’s US customers have received pressure to move production facilities back to the US.
Liu Deyin believes that it is not feasible to transfer TSMC factories to the United States due to cost constraints. He pointed out that the company will open a new research institute in Hsinchu to explore advanced materials after silicon. According to the company’s plan, silicon will end in the 2nm era and may be replaced with new substrates such as carbon nanotubes.


| Events |
|
Sydney
.NET User Group
Wednesday 18th September
|
|
This month
Adam Cogan will be reviewing some .NET Code Generation utilities.
Code generators aren't for everybody, but they do serve a valuable purpose. If
used wisely they can save you heaps of time.
Wednesday September 18 2002, 5:45 pm at Microsoft, 1 Epping Road.
See
You There! |
Sorry I can't make it
|
| |
| Managing
J2EE, .NET & Enterprise Application Infrastructure -
18 - 20th September 2002 |
| This conference will be
looking at how J2EE, .NET and integrated enterprise applications will impact
your business activities. |
| |
|
SSW Tech Breakfast - The Best 3rd Party Tools for SQL Server -
Friday 18th October |
|
In this session we are
going to introduce 15 of the best SQL Server tools from Lumigent, Leccotech,
Red-Gate, Embarcadero, FMS and others. Adam Cogan will be showing you live
demonstrations on how each tool works and where it can add value for you.
Book
NOW
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| MS RD - The Cutting
Edge |
|
At SSW we email the
tasks we plan to achieve that day (only!) to our client (CCing our manager)
within 10 minutes of sitting at the desk, before we make any phone calls or
write any emails. This ensures our clients are fully aware of the work taking
place and gives them a chance to change the priorities. It also allows us to
get better at estimating what we will achieve in a day. More
Cheers until next time,
Adam
SSW Database Services Manager and Microsoft Regional Director, Australia
Got a comment for Adam? Email Adam to AdamCogan
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| SSW Win Best Use of XML |
| SSW entered a "Spent a
Day with .NET" competition. This meant staying up very late and seeing what
kind of code we could churn out in 24 hours. Despite the bleary eyes, SSW won
"Best Use of XML" category with SSW Diagnostics.
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Testimonials |
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redefining its products. We get tonnes of downloads a month. We'd love to get
your opinion on the things you like, and things you don't like. Please 696e666f72407373772e636f6d2e6175
and let us know. |
| |
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Manage SQL Server
Replication simply with SSW SQL Total Compare
SQL Server replication
is a fantastic piece of functionality but can lead to a database administration
nightmare. Data conflicts are a common occurrence and require constant
attention. SSW SQL Total Compare reports the differences between the Publisher
and Subscribers databases and makes the changes necessary to fully synchronize
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Download the free trial
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How do I find out free disk space on a local or remote drive in .NET? Do I need
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"Your mom kind of freaked me out." Almost Famous
By Ross Anthony
"Almost Famous" is almost fantastic. Superb dialogue, outstanding direction, acting and casting; unfortunately this near perfect script is marred by a single pivotal scene.
The film opens in the year 1969 with William's eighteen-year-old sister dropping the needle on a Simon and Garfunkel record as she steps out the door, "This song explains why I'm leaving home to become a stewardess!"
William, after recently discovering that he's only eleven and not thirteen, explores the mother-forbidden albums left behind by his sister: The Who, Led Zeppelin, Cream.
Fast-forward four years, the nerdy adolescent William finds himself assigned to write a story on the up-and-coming rock band "Stillwater."
Patrick Fugit (William) does a major chord job of staging that awkwardly wonderful adventure of maturing in the wild circus around him. Kate Hudson (the woman in the poster) plays groupie (sorry, that should be "band-aid") Penny Lane warmly and whimsically. "One day, you'll be cool," She says with her hands on William's shoulders. Shot with the camera point blank between her eyes ... we feel like she's resting on our shoulders and speaking directly to us.
Then there's Billy Crudup (guitar player Russell Hammond). Just before the film a fellow reviewer friend mentioned that he was supposed to be the next best thing ... for the past five years. "Almost Famous," I thought. Actually, enjoying his performances in several less than great films, I expected fame from him as well. He's "incendiary" ... in a low-key, gentle, kind of way.
Frances McDormand as William's "Don't do drugs" mom is also smashing. You'll remember her from her other awesome performance in "Fargo."
The film progresses behind the scenes of a rock concert tour that had me just waiting for that final rock chord crescendo so that I could stand up, cheer and light my lighter (if I had one). But I didn't have a lighter, and then that one flawed scene just seemed to fly out of the clear beautiful blue sky like a stalled jet plane striking the screen like nails on a chalkboard; ultimately deflating my ovation to grin.
 Frampton chuckles, "I remember sitting with Cameron in the studio - just like William with his notepad in the film - we were fast friends from then on. And now I'm working for him."
Almost one of my top ten titles for the year, this imperfect diamond still has much to enjoy.
Almost Famous. Copyright © 2000. Rated R. Starring Billy Crudup, Patrick Fugit, Frances McDormand, Kate Hudson, Jason Lee, Zooey Deschanel, Michael Angarano. Written and Directed by Cameron Crowe. Produced by Cameron Crowe, Ian Bryce at Dreamworks(C)2000.
Grade..........................A-
-- Copyright © 2000. Ross Anthony, currently based in Los Angeles, has scripted and shot documentaries, music videos, and shorts in 35 countries across North America, Europe, Africa and Asia. For more reviews visit: http://RossAnthony.com
The review above was posted to the
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