---
layout: post
title: Cool bash hack(s)
date: 2015-09-11 11:22:33.000000000 +02:00
type: post
parent_id: '0'
published: true
password: ''
status: publish
categories:
- Linux/Ubuntu
tags: []
meta:
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login: mpho112
email<EMAIL_ADDRESS>display_name: Mpho
first_name: ''
last_name: ''
---
- This hack ensures that when you log on to a server/pc it will execute a screen session which minimizes the risks of you losing your work in case of network disruptions . Just copy command to your bashrc file and source it.
#Best used when you have to work on a different server via ssh.
[sourcecode language="bash"]if [ "$TERM" != "screen" ]; then
screen -xRR
fi[/sourcecode]
- This hack enables mouse scrolling and scrollbar history scrolling. Just copy command to your ~/.screenrc file and restart your shell.
[sourcecode language="bash"]
# Enable mouse scrolling and scroll bar history scrolling
defscrollback 5000
termcapinfo xterm* ti@:te@
[/sourcecode]
- Ever gotten to a point where, you can't remember a certain command you executed and when you try to grep it from history it suddenly disappeared well, this hack fixes that. Copy and paste following codes to ~/.bashrc and source it.
It creates a file named 'command_log' and logs all executed commands as well as timestamp. Nifty!! very Nifty
[sourcecode language="bash"]
# log every command typed and when
if [ -n "${BASH_VERSION}" ]; then
trap "caller >/dev/null || \
printf '%s\\n' \"\$(date '+%Y-%m-%dT%H:%M:%S%z')\
\$(tty) \${BASH_COMMAND}\" 2>/dev/null >>~/.command_log" DEBUG
fi
[/sourcecode]


- Lionel Robbins (1935)
- The Economy, CORE Project (2015)
individual agents and markets
entire economy
What is C?
What is $\pi?$
Why does the approximation hold?
The following slides have hints...
- Alfred A. Knopf
How does social structure fit into our decision-making model?
How does risk fit into our decision-making model?


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'''
Bland-Altman mean-difference plots
Author: Joses Ho
License: BSD-3
'''
import numpy as np
from . import utils
[docs]def mean_diff_plot(m1, m2, sd_limit=1.96, ax=None, scatter_kwds=None,
mean_line_kwds=None, limit_lines_kwds=None):
"""
Tukey's Mean Difference Plot.
Tukey's Mean Difference Plot (also known as a Bland-Altman plot) is a
graphical method to analyze the differences between two methods of
measurement. The mean of the measures is plotted against their difference.
For more information see
https://en.wikipedia.org/wiki/Bland-Altman_plot
Parameters
----------
m1, m2: pandas Series or array-like
sd_limit : float, default 1.96
The limit of agreements expressed in terms of the standard deviation of
the differences. If `md` is the mean of the differences, and `sd` is
the standard deviation of those differences, then the limits of
agreement that will be plotted will be
md - sd_limit * sd, md + sd_limit * sd
The default of 1.96 will produce 95% confidence intervals for the means
of the differences.
If sd_limit = 0, no limits will be plotted, and the ylimit of the plot
defaults to 3 standard deviatons on either side of the mean.
ax: matplotlib AxesSubplot instance, optional
If `ax` is None, then a figure is created. If an axis instance is
given, the mean difference plot is drawn on the axis.
scatter_kwargs: keywords
Options to to style the scatter plot. Accepts any keywords for the
matplotlib Axes.scatter plotting method
mean_line_kwds: keywords
Options to to style the scatter plot. Accepts any keywords for the
matplotlib Axes.axhline plotting method
limit_lines_kwds: keywords
Options to to style the scatter plot. Accepts any keywords for the
matplotlib Axes.axhline plotting method
Returns
-------
fig : matplotlib Figure
If `ax` is None, the created figure. Otherwise the figure to which
`ax` is connected.
References
----------
Bland JM, Altman DG (1986). "Statistical methods for assessing agreement
between two methods of clinical measurement"
Example
--------
Load relevant libraries.
>>> import statsmodels.api as sm
>>> import numpy as np
>>> import matplotlib.pyplot as plt
Making a mean difference plot.
>>> # Seed the random number generator.
>>> # This ensures that the results below are reproducible.
>>> np.random.seed(9999)
>>> m1 = np.random.random(20)
>>> m2 = np.random.random(20)
>>> f, ax = plt.subplots(1, figsize = (8,5))
>>> sm.graphics.mean_diff_plot(m1, m2, ax = ax)
>>> plt.show()
.. plot:: plots/graphics-mean_diff_plot.py
"""
fig, ax = utils.create_mpl_ax(ax)
if len(m1) != len(m2):
raise ValueError('m1 does not have the same length as m2.')
if sd_limit < 0:
raise ValueError('sd_limit ({}) is less than 0.'.format(sd_limit))
means = np.mean([m1, m2], axis=0)
diffs = m1 - m2
mean_diff = np.mean(diffs)
std_diff = np.std(diffs, axis=0)
scatter_kwds = scatter_kwds or {}
if 's' not in scatter_kwds:
scatter_kwds['s'] = 20
mean_line_kwds = mean_line_kwds or {}
limit_lines_kwds = limit_lines_kwds or {}
for kwds in [mean_line_kwds, limit_lines_kwds]:
if 'color' not in kwds:
kwds['color'] = 'gray'
if 'linewidth' not in kwds:
kwds['linewidth'] = 1
if 'linestyle' not in mean_line_kwds:
kwds['linestyle'] = '--'
if 'linestyle' not in limit_lines_kwds:
kwds['linestyle'] = ':'
ax.scatter(means, diffs, **scatter_kwds) # Plot the means against the diffs.
ax.axhline(mean_diff, **mean_line_kwds) # draw mean line.
# Annotate mean line with mean difference.
ax.annotate('mean diff:\n{}'.format(np.round(mean_diff, 2)),
xy=(0.99, 0.5),
horizontalalignment='right',
verticalalignment='center',
fontsize=14,
xycoords='axes fraction')
if sd_limit > 0:
half_ylim = (1.5 * sd_limit) * std_diff
ax.set_ylim(mean_diff - half_ylim,
mean_diff + half_ylim)
limit_of_agreement = sd_limit * std_diff
lower = mean_diff - limit_of_agreement
upper = mean_diff + limit_of_agreement
for j, lim in enumerate([lower, upper]):
ax.axhline(lim, **limit_lines_kwds)
ax.annotate('-SD{}: {}'.format(sd_limit, np.round(lower, 2)),
xy=(0.99, 0.07),
horizontalalignment='right',
verticalalignment='bottom',
fontsize=14,
xycoords='axes fraction')
ax.annotate('+SD{}: {}'.format(sd_limit, np.round(upper, 2)),
xy=(0.99, 0.92),
horizontalalignment='right',
fontsize=14,
xycoords='axes fraction')
elif sd_limit == 0:
half_ylim = 3 * std_diff
ax.set_ylim(mean_diff - half_ylim,
mean_diff + half_ylim)
ax.set_ylabel('Difference', fontsize=15)
ax.set_xlabel('Means', fontsize=15)
ax.tick_params(labelsize=13)
fig.tight_layout()
return fig


public class DiscoveryNodes extends AbstractDiffable<DiscoveryNodes> implements java.lang.Iterable<DiscoveryNode>
DiscoveryNode
in the cluster and provides convenience methods to
access, modify merge / diff discovery nodes.| 限定符和类型 | 类和说明 |
|---|---|
static class |
DiscoveryNodes.Builder |
static class |
DiscoveryNodes.Delta |
Writeable.Reader<V>, Writeable.Writer<V>
| 限定符和类型 | 字段和说明 |
|---|---|
static DiscoveryNodes |
EMPTY_NODES |
| 限定符和类型 | 方法和说明 |
|---|---|
static DiscoveryNodes.Builder |
builder() |
static DiscoveryNodes.Builder |
builder(DiscoveryNodes nodes) |
DiscoveryNodes.Delta |
delta(DiscoveryNodes other)
Returns the changes comparing this nodes to the provided nodes.
|
DiscoveryNode |
findByAddress(TransportAddress address)
Get a node by its address
|
DiscoveryNode |
get(java.lang.String nodeId)
Get a node by its id
|
ImmutableOpenMap<java.lang.String,DiscoveryNode> |
getDataNodes()
Get a
Map of the discovered data nodes arranged by their ids |
ImmutableOpenMap<java.lang.String,DiscoveryNode> |
getIngestNodes() |
Version |
getLargestNonClientNodeVersion()
Returns the version of the node with the youngest version in the cluster that is not a client node.
|
DiscoveryNode |
getLocalNode()
Get the local node
|
java.lang.String |
getLocalNodeId()
Get the id of the local node
|
ImmutableOpenMap<java.lang.String,DiscoveryNode> |
getMasterAndDataNodes()
Get a
Map of the discovered master and data nodes arranged by their ids |
DiscoveryNode |
getMasterNode()
Get the master node
|
java.lang.String |
getMasterNodeId()
Get the id of the master node
|
ImmutableOpenMap<java.lang.String,DiscoveryNode> |
getMasterNodes()
Get a
Map of the discovered master nodes arranged by their ids |
Version |
getMaxNodeVersion()
Returns the version of the node with the youngest version in the cluster
|
Version |
getMinNodeVersion()
Returns the version of the node with the oldest version in the cluster.
|
ImmutableOpenMap<java.lang.String,DiscoveryNode> |
getNodes()
Get a
Map of the discovered nodes arranged by their ids |
int |
getSize()
Get the number of known nodes
|
Version |
getSmallestNonClientNodeVersion()
Returns the version of the node with the oldest version in the cluster that is not a client node
If there are no non-client nodes, Version.CURRENT will be returned.
|
boolean |
isAllNodes(java.lang.String... nodesIds) |
boolean |
isLocalNodeElectedMaster()
Returns true if the local node is the elected master node.
|
java.util.Iterator<DiscoveryNode> |
iterator() |
DiscoveryNodes |
newNode(DiscoveryNode node) |
boolean |
nodeExists(DiscoveryNode node)
Determine if a given node exists
|
boolean |
nodeExists(java.lang.String nodeId)
Determine if a given node id exists
|
static Diff<DiscoveryNodes> |
readDiffFrom(StreamInput in,
DiscoveryNode localNode) |
static DiscoveryNodes |
readFrom(StreamInput in,
DiscoveryNode localNode) |
DiscoveryNode |
resolveNode(java.lang.String node)
Resolve a node with a given id
|
java.lang.String[] |
resolveNodes(java.lang.String... nodes)
resolves a set of node "descriptions" to concrete and existing node ids.
|
java.lang.String |
toString() |
void |
writeTo(StreamOutput out)
Write this into the StreamOutput.
|
diff, get, readDiffFrom
public static final DiscoveryNodes EMPTY_NODES
public java.util.Iterator<DiscoveryNode> iterator()
iterator
在接口中 java.lang.Iterable<DiscoveryNode>
public boolean isLocalNodeElectedMaster()
public int getSize()
public ImmutableOpenMap<java.lang.String,DiscoveryNode> getNodes()
Map
of the discovered nodes arranged by their idsMap
of the discovered nodes arranged by their idspublic ImmutableOpenMap<java.lang.String,DiscoveryNode> getDataNodes()
Map
of the discovered data nodes arranged by their idsMap
of the discovered data nodes arranged by their idspublic ImmutableOpenMap<java.lang.String,DiscoveryNode> getMasterNodes()
Map
of the discovered master nodes arranged by their idsMap
of the discovered master nodes arranged by their idspublic ImmutableOpenMap<java.lang.String,DiscoveryNode> getIngestNodes()
public ImmutableOpenMap<java.lang.String,DiscoveryNode> getMasterAndDataNodes()
Map
of the discovered master and data nodes arranged by their idsMap
of the discovered master and data nodes arranged by their idspublic DiscoveryNode get(java.lang.String nodeId)
nodeId
- id of the wanted nodenull
public boolean nodeExists(java.lang.String nodeId)
nodeId
- id of the node which existence should be verifiedtrue
if the node exists. Otherwise false
public boolean nodeExists(DiscoveryNode node)
node
- of the node which existence should be verifiedtrue
if the node exists. Otherwise false
public java.lang.String getMasterNodeId()
public java.lang.String getLocalNodeId()
public DiscoveryNode getLocalNode()
public DiscoveryNode getMasterNode()
public DiscoveryNode findByAddress(TransportAddress address)
address
- TransportAddress
of the wanted nodenull
if no such node existspublic boolean isAllNodes(java.lang.String... nodesIds)
public Version getSmallestNonClientNodeVersion()
public Version getLargestNonClientNodeVersion()
public Version getMinNodeVersion()
public Version getMaxNodeVersion()
public DiscoveryNode resolveNode(java.lang.String node)
node
- id of the node to discoverjava.lang.IllegalArgumentException
- if more than one node matches the request or no nodes have been resolvedpublic java.lang.String[] resolveNodes(java.lang.String... nodes)
public DiscoveryNodes newNode(DiscoveryNode node)
public DiscoveryNodes.Delta delta(DiscoveryNodes other)
public java.lang.String toString()
toString
在类中 java.lang.Object
public void writeTo(StreamOutput out) throws java.io.IOException
Writeable
public static DiscoveryNodes readFrom(StreamInput in, DiscoveryNode localNode) throws java.io.IOException
java.io.IOException
public static Diff<DiscoveryNodes> readDiffFrom(StreamInput in, DiscoveryNode localNode) throws java.io.IOException
java.io.IOException
public static DiscoveryNodes.Builder builder()
public static DiscoveryNodes.Builder builder(DiscoveryNodes nodes)


Diva
Diva is a visualization package used by some of the demos and by
vergil, the Ptolemy II user interface.
The Ptolemy II distribution includes $PTII/lib/diva.jar
.
As of Ptolemy II 4.0, a subset of the Diva sources and documentation
are included in the source release.
More information about Diva can be found at
http://embedded.eecs.berkeley.edu/diva/
.


MagicLang Module
Javascript multi-language switcher system.
This is module description.
Default (Indonesian)
Switch to Indonesian
Switch to English


We can’t “replace” the first character, because strings in JavaScript are immutable.
But we can make a new string based on the existing one, with the uppercased first character:
There’s a small problem though. If str
is empty, then str[0]
is undefined
, and as undefined
doesn’t have the toUpperCase()
method, we’ll get an error.
There are two variants here:
str.charAt(0)
, as it always returns a string (maybe empty).Here’s the 2nd variant:
```js run demo function ucFirst(str) { if (!str) return str;
return str[0].toUpperCase() + str.slice(1); }
alert( ucFirst(“john”) ); // John ```


Example: p {color: red;}
This whole block (selector/property/value) is a CSS rule.
selector{property: value;} or you can think of it as who{ what: how;}
The selector defines who is targeted, which HTML element(s)
The property defines what characteristic to alter
The value defines how to alter that characteristic
A CSS can have multiple selectors and multiple properties.
Because we do not want to style all our HTML elements at once, we need to be able to target a group of these HTML elements.
CSS selectors define which elements we want our styling to be applied to.
There are many different ways to select various HTML elements:Generic HTML Tags - Targeting generic HTML tags is easy: just use the tag name (like p, a, img, ul, li, etc)
In CSS:
a{ /* Links */ }
a{ /* Paragraphs */ }
a{ /* Unordered Lists */ }
a{ /* List Items */ }
HTML Classes - if you do not want to style all paragraphs or all titles identically, we need to differentiate them by using HTML Classes.
Of all HTML attributes, the class attribute is the most important for CSS. It allows us to define a group of HTML elements that we can target specifically. Just put a dot . in front of the class name you want to use:
In CSS:
.date{
color: red;
}
In HTML:

Saturday Feb 21

The event will be on Saturday

HTML IDs - You can also use the id attribute in your HTML, and target it with a hash # in your CSS:
In CSS: #tagline{ color: orange;}
In HTML:
 This heading will be orange.

Combined selectors: let us say you wanted to select in CSS all the paragraphs with a main class. To select these elements, you would use p.main
Parent-Child selectors - A space in a selector defines a ancestor/descendant relationship. Let us say we want the links in our header to be in red:
header a {
color: red;
}
This can be read from right to left as: Select all a elements that are within a header element. This will prevent all other links (that are not in the header) from being changed
You can use the CSS body selector to modify the style of your overall web page. This is helpful if you want all the text in the page to have the same font styles, or if you want to add a background color or image to your overall page.


Status and Status Markers/Tokens
KO'd
A hero is KO's as soon as their Wounds or Sanity Damage equal their max.
A hero may drag another hero at the cost of 2 MP per square (instead of the usual 1/square). Must in the same or adjacent square.
Recovery
At end of fight, roll for injury/madness. Then heal 2D6 Wounds/Sanity.
Mid-fight: A hero may revive another character on his turn if: * They are adjacent * There are no enemies on the same tile * Assisting hero gives up Attack.
Tip
Optional Rule Hard-Core Mode: If not recovered by start of next turn, hero is killed.
Status Markers/Tokens
-
Bleeding:
- -3 Health per token. (This lowers max health rather than adding damage.)
- Remove all tokens if KO'd.
- Remove 1 token for 1 grit at any time.
- Burning Marker: Remove 1 burning marker at start of activation. Inflicts 2 Wound, ignoring Defense.
- Death Mark: All enemies are +2 damage. Max 1 Death Mark token.
-
Ensnared: Max 1 Ensnared token.
- Cannot Move
- Defense rolls are -1
- Strength Test (5+) to remove token when activating (6+ on D6 if model has no Strength)
- Exertion: See Barriers
- Noise Marker: See below
-
Poison: Roll D6 for each token when activating:
- 1-2: Take 1 Wound, ignoring Defense
- 6: Poison wore off, remove token.
-
Stunned:
- Defense rolls are -1 (total, not per token)
- Remove all tokens if KO'd.
- Roll D6 when activating. 4+ removes one token.
-
Webbed:
- Move -1/Webbed token
- Strength Test (5+) when activating. Remove 1 token per success
- Adject heroes can use their Attack to make additional Strength Test
Noise Marker
- Remove D6 noise tokens when activating
- Remove D6 noise tokens if an explosion (e.g. dynamite/bomb) is denotated
-
Add 1 noise marker for each of
- Move 3+ squares (unless Stealth)
- Making an Attack
- Extra noise marker is using a Gun
- Casting a Spell
- Making a Free Attack
- Using a Side Bag token
- Making a Skill Check
- Making a Scavenge Roll


Understanding ThreadAbortException
Here. From Shawn Wildermuth: This is a link to Chris' new article on how he figure out why ThreadAbortException was being thrown by using the Rotor code...Like he's always said, the real documentation in in the source code...


LEAD: Elephant is on the biggest land animal found on earth. It is a wild animal but after giving training it, becomes a useful animal for man. It is a huge animal and its colour is grey.
TEXT-MUTED: uElephant is on the biggest land animal found on earth. It is a wild animal but after giving training it, becomes a useful animal for man. It is a huge animal and its colour is grey.
LEAD: Elephant is on the biggest land animal found on earth. It is a wild animal but after giving training it, becomes a useful animal for man. It is a huge animal and its colour is grey.
Elephant is on the biggest land animal found on earth. It is a wild animal but after giving training it, becomes a useful animal for man. It is a huge animal and its colour is grey.
<blockquote>
ctrl


1.Select a banker. If more than one player wants to start as the banker, have them roll dice. The highest roller is the banker for the first round.
2.Set the stake. The banker puts up a bet of any amount. If you like, your group can decide on a minimum stake.
3.Match the stake. Each other dice player gets a chance to match some or all of the stake. Move on once the full stake is matched, or when no one else wants to put up a bet.
4.Roll for an automatic win or loss. Once all bets are made, the banker rolls three dice. In most versions of this game, these results immediately end the round:[1]
5.Find the score for other results. If the banker didn't roll one of the combinations above, the round continues.
6.Try to beat the banker's score. If the banker rolled a score (not an automatic win or loss), each player gets a chance to roll three dice to try and beat it.
7.Start the next round. Once each player has rolled (or the banker rolled an automatic result), the round is over


CASUALTIES OF LOVE: THE 'LONG ISLAND LOLITA' STORY A film review by Ken Johnson Copyright 1993 Ken Johnson
1:40, not rated, Drama, 1993, Made-for-American-Television Director: John Herzfeld Cast: Jack Scalia, Alyssa Milano, Phyllis Lyons, Leo Rossi, J. E. Freeman, Peter Van Norden, Lawrence Tierney, Anne DeSalvo, Michael Bowden, John Perry, Nicky Corello
Alyssa Milano plays the notorious Amy Fisher in this adaption of the controversial story. Milano supposedly started an affair with Jack Scalia (Joe Buttafuoco) and ended up attempting to kill his wife Phyllis Lyons (Mary Jo Buttafuoco).
CASUALTIES OF LOVE: THE 'LONG ISLAND LOLITA' STORY, CBS's version of the Amy Fisher story, aired back on January 3. The film is currently set to be released on video cassette on April 7 (the same time as NBC's AMY FISHER: MY STORY, r.a.m.r.# 1664). Of the three Amy Fisher films, CASUALTIES OF LOVE is by far the best. On a scale of zero to five, I give CASUALTIES OF LOVE a five. The video cassette version will contain some scenes that were too racy for television.
The cast for this film is excellent and ends up being above the normal television film cast. Alyssa Milano (CRASH COURSE, LITTLE SISTER) is great as Amy Fisher. She even did better than Drew Barrymore (ABC's BEYOND CONTROL: THE AMY FISHER STORY), who tends to be excellent in this kind of role. Jack Scalia was very good as Joey Buttafuoco. I think that he did better in that role than the actors in the other two versions. I wasn't too impressed with Phyllis Lyons acting.
CASUALTIES OF LOVE is written from and biased towards Joe and Mary Jo Buttafuoco's point of view, so it is biased towards their point of view. If you are only going to watch one of the Amy Fisher films when they get released on video cassette, I suggest that you choose this one. I don't know how much of it is true, but it makes for good viewing.
-------- Ken Johnson<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Events
Do some networking, ear other people talk, see some new product or innovation launch
Filter
-
6
Feb
Humans before users
How do you think humans' needs, wants and behaviours can have an impact the on people use and buy? Laura will tell us all about it!
6 February 2020
-
6
Feb
Local Gov 4.0
The first of a series of events about the value and challenges of Digital in the Municipal Management transformation and the generation of local wealth.
6 February 2020
-
20
Feb
Ladies that UX
Ladies That UX is a collaborative community initially created in Manchester (2013) and actually active in more than 30 cities around the world. The idea here is the exchange of knowledge and experiences with the objective of defying womens who works in UX to achieve better results.
20 February 2020
-
29
Mar
Landing Festival
Landing Festival Lisbon 2020 is one of Europe’s biggest tech careers events, that brings together tech professionals and experts.
29 March 2020
-
27
Jul
-
7
Sep


The Mandalorian’s Dave Filoni Endorses “Baby Yoda” Name
by Robert WorkmanBy now, it’s clear that there’s a new breakout Star Wars character in Disney+’s The Mandalorian — “Baby Yoda.” It’s definitely not confirmed whether this small creature has any relation to the Jedi master. But he have a similar appearance, and that led to his instantly meme-worthy moniker.
Dave Filoni, who directed the debut episode of the series, recently spoke to Entertainment Tonight about The Mandalorian. He also believes it’s “very clear” why fans consider this character “Baby Yoda.”
“They don’t know yet, that’s fine,” said Filoni. He added that the baby’s backstory was “all part of the mystery.”
RELATED: J.J. Abrams Reacts To George Lucas’ Star Wars Regrets
At this point in the series (two episodes in), Baby Yoda doesn’t have a name. Or we simply haven’t learned his name yet. That may throw a few fans off considering the baby is 50 years old, as noted in the first episode.
The third episode of The Mandalorian will hit Disney+ after midnight tonight. Based on a previously posted synopsis, the lead character will collect a reward for his bounty. The first season will continue with new episodes premiering each Friday through December.
What do you think about the name “Baby Yoda” for the character? Let us know in the comments below!
Recommended Reading: Star Wars: Be More Yoda — Mindful Thinking From a Galaxy Far Far Away
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CME is a swiss army knife tool for all things Windows Testing (and some minor Linux support)
See the pages under Tools > Windows Auth / AD > Multi >
CrackMapExec


OT2KPX(1) Marc Penninga OT2KPX(1) NAME ot2kpx - extract kerning information from OpenType fonts SYNOPSIS ot2kpx fontfile DESCRIPTION ot2kpx extract the kerning data from OpenType fonts (both otf and ttf formats) and prints it (in afm format) to "stdout". OPTIONS AND ARGUMENTS fontfile The OpenType font (in either otf or ttf format). RESTRICTIONS - ot2kpx doesn't implement all of the OpenType specification. Things that are missing include: support for font files containing multiple fonts, LookupTables with LookupTypes other than 2, "kern" tables with format other than 0 and ValueRecords with other types of data than just XAdvance data. Most of these limitations don't really matter, since the missing features are rare (the only fonts I know of that use them are the non-western fonts that come with Adobe Reader). Furthermore, some of these features concern (according to the OpenType specification) "define subtle, device-dependent adjustments at specific font sizes or device resolutions". Since there's no way to express such adjustments in afm format, ignoring them seems to be the only option anyway. - ot2kpx collects kerning data first from the "kern" table, then from all LookupTables associated with the "kern" feature; if a kerning pair occurs multiple times, the first value seen is chosen. There are (or may be) several issues with this approach: - The OpenType specification says that fonts in otf format shouldn't use the "kern" table at all, just the lookups from the "GPOS" table. Many such fonts do, however, contain a "kern" table, but no "GPOS" table; so we use the "kern" table anyway. - Instead of reading all LookupTables, it might be better to let the user specify a script and language and process only the LookupTables for those values. However, at least in the fonts I checked, all script/language combinations eventually point to the same "kern" LookupTables, so this approach wouldn't make any difference (apart from further complicating the code). AUTHOR Marc Penninga<EMAIL_ADDRESS>COPYRIGHT Copyright (C) 2005-2012 Marc Penninga. LICENSE This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 2 of the License, or (at your option) any later version. A copy of the GNU General Public License is included with autoinst; see the file GPLv2.txt. DISCLAIMER This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. RECENT CHANGES (See the source code for the rest of the story.) 2012-02-01 Refactored the code, and fixed a number of bugs in the process. Updated the documentation. fontools 2012-03-06 OT2KPX(1)


End to End AI Discord Bot - Using NodeJS and Deploying to Azure App Services
27th March, 2021 | 11:00 a.m. IST(GMT +5:30)
Platform - Microsoft Teams
Joining Link - Click Here to Watch Now
About the Event 🏂
In this session, we will be developing an AI enabled discord bot that uses a Text Toxicity Detection model to warn and flag unwanted messages on any discord server. The bot will be built using the discord.js library and we will also deploy it on Azure App Services to make the bot available for all the discord servers.
Agenda
- Brief about Machine Learning and TensorFlow.js library
- Quick walkthrough of discord.js library
- Building a discord bot and running it locally
- Introduction to Microsoft Azure
- Apply for GitHub Student Developer Pack and enjoy benefits like GitHub Pro, Free domains, Canva Professional account, Free access to 25+ Microsoft Azure cloud services plus $100 in Azure credit, and many more
- Deploy the Discord bot to Azure App Services for free
- Learn more about the Microsoft Learn Student Ambassador program and how you can apply to become an MLSA


• My husband and I first got married in 1991 in Shanghai.
• At that time, my husband did not manage to divorce his first wife successfully but we still went ahead to move to Singapore.
• The bigamy charge was eventually dropped and my husband finally managed to divorce his first wife.
• Recently, my husband passed away
• I want to make an appeal to make sure I get back that money from my husband


4 writes to _consecutiveIdleSamples
System.Management.Automation (4)
engine\EventManager.cs (4)
580
_consecutiveIdleSamples
= 0;
586
_consecutiveIdleSamples
++;
590
_consecutiveIdleSamples
= 0;
595
_consecutiveIdleSamples
= 0;
1 reference to _consecutiveIdleSamples
System.Management.Automation (1)
engine\EventManager.cs (1)
593
if (
_consecutiveIdleSamples
== 4)


Associative Neural Networks Library
The library provides implementation of various Associative Neural Network models. Most of the work has been done as a part of my (still ongoing) PhD project and was supported by the INTAS Young Scientist Fellowship YSF 03-55-795.
Associative Neural Networks can be used for:
-
Associative
memories (Content-Addressable Memory);
-
Classification
problems;
-
Optimization
problems.
Key features:
-
Distributed
storage of information;
-
"Graceful
degradation", i.e., destruction of individual neurons or of small groups
of neurons reduces performance, but does not have the devastating effect;
-
Parallel
mode of operation (hardware friendly);
-
Different
learning rules, including fast noniterative one that allows to add/erase data.
The neural network model for associative memory was first proposed by J. Hopfield in 1982. It is a dynamical system of simple threshold units (neurons). The Hopfield model is fully connected, that is each neuron receives outputs from all other neurons (including itself).
Using the sparsely connected Hopfield network has a number of advantages:
- Biologically more feasible structure of the network;
- More suitable for hardware implementation;
- Faster and requires less memory in computer simulations;
- Reveals the influence of network connectivity pattern (architecture) on its behaviour.
Architecture of the sparse Hopfield network can be chosen in a number of ways:
- Random architecture – certain number of connections are chosen to connect random pairs of neurons.
- Adaptive architecture – location of connections is chosen so as to (sub-) maximize the associative performance of the network for a particular dataset [Dekhtyarenko2004, Dekhtyarenko2005].
- Cellular architecture – local connectivity pattern. Only neighboring neurons are connected. This architecture favors hardware implementation and is inspired by Cellular Neural Network (CNN) paradigm [Chua1988]. It provides the smallest value of total connection length what is crucial in some applications.
- Small-World architecture – it takes the best of two worlds – the associative performance of the network with random architecture and mostly local connectivity pattern of the cellular network.
|
|
Fully-connected |
Sparse with Adaptive Architecture |
Sparse with Random Architecture |
Small-World Architecture |
Cellular Architecture (regular grid) |
|
Implementation class |
FullProjectiveNet |
AdaptiveCellularNet |
PseudoInverseNet HebbianCellularNet DeltaCellularNet |
SmallWorldNet |
PseudoInverseNet HebbianCellularNet DeltaCellularNet |
|
Associative performance |
***** |
**** |
*** |
** |
* |
|
Total connection length (the smaller the
better) |
* |
** |
** |
*** |
**** |
|
Number of weights (the smaller the better) |
* |
***** |
***** |
***** |
***** |
Note:
Comparison of associative performance for Sparse Adaptive/Sparse
Random/Small-World/Cellular networks is given subject to the equal number of
weights.
There are a number of learning rules (LR)
that can be used to train the sparse Hopfield network. Here is a brief summary
of implemented LRs.
|
|
Projective |
Perceptron
(Hebbian) |
Delta Rule |
Pseudo-Inverse |
|
Associative performance |
* |
**** |
**** |
**** |
|
Iterative |
no |
yes |
yes |
no |
|
Possibility of incremental learning/deletion |
yes |
no |
no |
yes |
Note: In
Projective learning rule the weight matrix of fully connected network is
obtained by [Personnaz1986] and then all connections that do not satisfy
architectural constrains are simply cut.
The
given release is of August 15, 2005.
Library allows creating and testing various Associative Neural Networks (most of them are based on Hopfield model [Hopfield1982]). All networks function in discrete time with bipolar (+1/-1) states and synchronous convergence mode.
Testing functions allow tracking the evolution of network properties during the training or with the change of network parameters.
One of the most important network
characteristics is Attraction Radius, which quantifies the network
performance as associative memories. It is possible to find either absolute
value of network attraction radius (measured as Hamming distance
[test.cpp::getRAttraction] function), or it’s normalized value
[test.cpp::getNormalizedRAttraction].
class FullNet – abstract base class for
fully connected models.
class CellularNet - abstract base class
for sparsely connected models, provides efficient weights storage and
manipulation.
class FullProjectiveNet –
fully-connected network with Projective learning rule [Personnaz1986].
Implements desaturation technique [Gorodnichy1997] and various
retraining methods that allow network recovery after the failure of some
neurons (this implementation was used in [Reznik2003a]).
class PseudoInverseNet – sparse network
implementing Pseudo-Inverse learning rule (PI LR) [Brucoli1995], a
learning rule that allows guaranteed storage of memory patterns as stable
states for (almost) any network architecture.
class AdaptiveCellularNet – sparse
network with PI LR and architecture that is changing depending on a dataset.
That is for the specified number of connection network itself finds the
architecture that maximizes the associative performance on a given dataset
[Dekhtyarenko2004, Dekhtyarenko2005].
class SmallWorldNet – sparse network
with Small-World architecture [WattsStrogatz1998] and PI LR. Apart from
the originally proposed random rewiring SmallWorldNet implements new systematic
rewiring procedure, which further improves the associative properties of the
network using the same amount of shortcut connections [Dekhtyarenko2005a].
class HebbianCellularNet - sparse
network with Perceptron (Hebbian) learning rule [Diederich1987].
class DeltaCellularNet - sparse network
with Widrow-Hoff Delta learning rule [Widrow1960].
class ModularNet – growing modular
associative network with large memory capacity [Reznik2003b]. As its basic building blocks (modules) it can
use any of the network types mentioned above.
class BAMCellularNet – sparse
bi-directional associative memory (under construction).
Run compileBorland.bat or compileMS.bat files.
The library is being created in Win OS using
Borland C++ Builder 5.0 with “Language compliance” compiler option set to
“Borland”, therefore it requires a couple of tricks to compile it using
Microsoft CL v. 12 (from MS Visual Studio 6.0)
-
#
define for if (0) {} else for // scope of definition in “for” statements
-
FORCE:MULTIPLE
linker option
Run nets.exe with the name of ini-file as its
input. Provided file “_SmallWorld.ini” does the following:
1. SmallWorldNet network is created with
the following parameters:
-
dimension
= 256
-
neuron
connection radius = 10 (connectivity degree of about 8%)
-
no
diagonal weights
2. The network is trained with the data from the file “_256x256.dat” (bipolar patterns with random equiprobable and independent components) for patterns #1-14 (testNum = 1, numStored = 1:15:1+). After each additional stored vector the network is tested and test results (including attraction radius – “attrR” field) are stored in the “_report.txt” file.
In addition to the value of attraction radius
output file “_report.txt” contains a lot of other useful information, such as
network architecture properties (number of connections, total connection
length, ...), weight matrix properties (norm, trace, asymmetry degree, ...),
estimations of associative performance (kappa measure, min aligned local
field), actual testing results (average number of iterations, error portion,
...), etc.
The results in the output file are easy to
analyze using any table processor (MS Excel, ...).
If you
have any questions or comments I’d be glad to hear from you.
Oleksiy K. Dekhtyarenko, PhD student
Institute of
Mathematical Machines and Systems
Department of
Neurotechnologies
Glushkov Ave. 42,
Kiev 03187, Ukraine
Tel.: +380-44-5266221
Fax: +380-44-5266457
Mobile:
+380-67-2366157
E-mail: name@domain, name=olexii, domain=mail.ru
ICQ: 86473901
|
[Brucoli1995] |
M.
Brucoli; L. Carnimeo & G. Grassi - "Discrete-time cellular neural
networks for associative memories with learning and forgetting
capabilities", IEEE Transactions on Circuits and Systems, pp.
396–399, vol. 42, 1995 |
|
[Chua1988]
|
L.
Chua & L. Yang - "Cellular Neural Networks: Theory", IEEE
Transactions on Circuits and Systems, pp. 1257-1272, vol. 35(10), 1988 |
|
|
O.
Dekhtyarenko; A. Reznik & A. Sitchov - "Associative Cellular Neural
Networks with Adaptive Architecture", in proc. of The 8th IEEE
International Biannual Workshop on Cellular Neural Networks and their
Application (CNNA'04), pp. 219-224, Budapest, Hungary, July 22-24, 2004 |
|
|
O.
Dekhtyarenko; V. Tereshko & C. Fyfe - "Phase transition in sparse
associative neural networks", in proc. of European Symposium on
Artificial Neural Networks (ESANN'05), Bruges, Belgium, April 27-29, 2005 |
|
|
O.
Dekhtyarenko - "Systematic Rewiring in Associative Neural Networks with
Small-World Architecture", in proc. of International Joint Conference
on Neural Networks (IJCNN'05), pp. 1178-1181, Montreal, Quebec, Canada,
July 31 - August 4, 2005 (poster) |
|
[Diederich1987]
|
S.
Diederich & M. Opper - "Learning of correlated patterns in
spin-glass networks by local learning rules", Physical Review Letters,
pp. 949-952, vol. 58(9), 1987 |
|
[Gorodnichy1997] |
D.
Gorodnichy & A. Reznik - "Increasing Attraction of Pseudo-Inverse
Autoassociative Networks", Neural Processing Letters, pp.
123-127, vol. 5(2), 1997 |
|
[Hopfield1982] |
J.
Hopfield - "Neural networks and physical systems with emergent
collective computational abilities.", Proc Natl Acad Sci USA, pp.
2554-2558, vol. 79(8), 1982 |
|
[Personnaz1986] |
L.
Personnaz; I. Guyon & G. Dreyfus - "Collective computational
properties of neural networks: New learning mechanisms", Physical
Review A, pp. 4217–4228, vol. 34, 1986 |
|
|
A.
Reznik; A. Sitchov; O. Dekhtyarenko & D. Nowicki - "Associative
memories with "killed" neurons: the methods of recovery", in
proc. of International Joint Conference on Neural Networks (IJCNN'03),
Portland, Oregon, US, July 20-24, 2003 |
|
|
A.
Reznik & O. Dekhtyarenko - "Modular neural associative memory
capable of storage of large amounts of data", in proc. of International
Joint Conference on Neural Networks (IJCNN'03), Portland, Oregon, US,
July 20-24, 2003 |
|
[WattsStrogatz1998]
|
D.
Watts & S. Strogatz - "Collective dynamics of 'small-world' networks.",
Nature, pp. 440-442, vol. 393, 1998 |
|
[Widrow1960]
|
B.
Widrow & M.E. Hoff, J. - "Adaptive switching circuits", IRE
Western Electric Show and Convention Record, pp. 96-104, vol. 4, 1960 |


Hi, my name is Roberto Baldizon, welcome to my portfolio!
I am originally from Guatemala, where I went up to high school, while also traveling extensively though the world with my parents to exotic locations, repetitively to Miami (where we have a place) and extensively to Germany (to practice my German, which I learn since my grandmother is German). Thus, I feel comfortable pretty much anywhere and think fo the world as my home!
For college I went to Penn State where I graduated with degrees on: Energy Business and Finance Energy Systems Option, Energy Engineering, Petroleum and Natural Gas Engineering, and Mining Engineering
After college I continued taking numerous courses with Harvard Business School in Business, as well as with Georgetown and INCAE on Hospital and Health Administration, while also completing several computer science and coding courses.
I did also earn a post-graduate degree from Penn State in Solar Energy, so not all my education has been alternative...
While taking all those courses I also gained certification from LEED for BD+C due to my interest in Energy and Natural Resources, and how our civilization interacts with them
Work-wise I've worked with my familiy on their retail and distribution business in Guatemala all my life, while also helping my father recently in his medical businesses. I also worked in Petroleum Engineering and Financial Anlysis Projects in Belize (my grandfather was born there in British colonial times) with a local oul firm. After working for the oil firm, I went back to work with my family and started my own section of the company in the US, Luszlol LLC which has done several solar PV installations but has pivoted to a more comprehensive outlook on energy, targeting numerous sources of generation for a smarter, more reliable, and decentralized grid!
If you wanna know more about me, please follow up with the contact information! Can't wait to hear from you!


[gecode-users] FW: DOCTORAL AND POSTDOCTORAL RESEARCH POSITIONS IN CONSTRAINT PROGRAMMING
Christian Schulte
cschulte at kth.se
Thu Nov 5 11:48:45 CET 2009
Dear all,
for the following position knowledge of Gecode is considered a plus, that is
why I forward this announcement to the Gecode users list.
Christian
--
Christian Schulte, www.ict.kth.se/~cschulte/
-----Original Message-----
From: Tom Schrijvers [mailto:tom.schrijvers at cs.kuleuven.be]
Sent: Wednesday, November 04, 2009 4:37 PM
To: Tom Schrijvers
Subject: DOCTORAL AND POSTDOCTORAL RESEARCH POSITIONS IN CONSTRAINT
PROGRAMMING
Could you please forward this message to anyone who might be interested?
Thank you,
Tom
DOCTORAL AND POSTDOCTORAL RESEARCH POSITIONS IN CONSTRAINT PROGRAMMING
Declarative Languages and Artificial Intelligence Group
Department of Computer Science
Catholic University of Leuven, Belgium
The Declarative Languages and Artificial Intelligence Group at the
Catholic University of Leuven is urgently looking for doctoral and
postdoctoral research candidates in the area of Constraint Programming.
Both positions concern the application and development of Constraint
Programming techniques, and the Monadic Constraint Programming framework in
particular, for solving problems of industrial scale. Close collaboration
with
our industrial partners is expected, and involves aspects of mutual
interest,
modeling, search heuristics and parallelization among others. More
information on the Monadic Constraint Programming can be found at
http://www.cs.kuleuven.be/~toms/MCP/.
For the postdoctoral position, applicants should have a Ph.D. in Computer
Science and for the doctoral position, a masters degree in Computer Science
or
equivalent. A background and interest in Constraint Programming is
essential.
Experience in functional programming (Haskell), Gecode, software engineering
and implementation are important assets.
Appointment to the postdoctoral position will be for the period of up to two
years. Appointment to the doctoral position will be for the period of one
year
initially with possibility for extension to four years ending in a Ph.D.
The
salary is compatible with the departmental rates for postdoctoral and
doctoral
research fellows, and can take experience into consideration.
Please direct inquiries and applications, preferably by e-mail, to:
Professor Bart Demoen
Department of Computer Science
K.U.Leuven
Celestijnenlaan 200A
B-3001 Heverlee
Belgium
Email: bart.demoen at cs.kuleuven.be
Phone: +32 16 327547
Informal notice of interest should be received as soon as possible no later
than
November 25, 2009. Review of applications begins as of now. Positions start
as
soon as January 1, 2010.
Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm
More information about the gecode-users
mailing list


Basic Info
Name:
James SpittalNickname(s):
Jamie, Jimmy, Spits, Spitoon, ect...Date of Birth:
April 15, 1991Current Location:
Vancouver BC CanadaContact:
Objective
To find a career where I can express my love for solid, clean code while keeping to the pace of the ever-changing technological landscape.
Relevant Experience/Projects
Closefox - Modern Web App
Duration:
2014-2015Position:
Lead DeveloperResponsibilities:
Single handedly developed the front-end facing panel in AngularJS. Helped with architecture and development of a Laravel based web-api. Consulted with design, branding and marketing. All while working in a tight knit six person team.Technical Skills Exercised:
Growlerfill - Android App
Duration:
2015-PresentPosition:
Lead Android DeveloperResponsibilities:
Worked from scratch to develop a fully-featured material design inspired Android app that consumes a web-based API.Technical Skills Exercised:
The Stowe - Wordpress App
Duration:
2013-PresentPosition:
Lead DeveloperResponsibilities:
Functioned as part of a two man team to develop an entire wordpress theme for a small e-commerce business. Building upon the extremely popular WooCommerce plugin for wordpress we provided 'The Stowe' with a full-feature online marketplace.Technical Skills Exercised:
Education
Henry Wise Wood High School
Attended:
2005-2009Achievements:
Honours with Distinction
Multimedia Proficiency Award
Art Institute of Vancouver
Program:
Professional Recording ArtsAttended:
2009-2011Achievements:
GPA 4.0!
11 Merit Awards for Strong Performance
Skills
In Relation to One Another
100%
Effectively using Google and websites like Stack Overflow to find answers.
100%
Core Web Technologies
HTML5, CSS3, Javascript, ES6, Jquery, PHP
95%
Angular JS
Coding with Clean architecture, and using popular libraries like Ui-Router.
85%
Android Development
Writing efficient Java while leveraging the Android framework
65%
Polymer
Creating web components to build fast and modern web applications
70%
Laravel
Using this popular framework to create fast and maintainable web apis.
85%
Wordpress
Creating custom themes, or updating and maintaining legacy themes.


Object-Oriented Programming is an approach to coding that uses levels of classes with defined objects that work together. Classes define methods that work on data.
Functional Programming is an approach to coding that defines functions rather than on performing operations or defining objects.
You decide depending on how you foresee the evolution of your software. If you believe your code will have a set number of operations but you will add new things, then it would be good to use Object-Oriented Programming. Existing classes are left alone while new classes are added that use existing methods.
In contrast, Functional Languages are preferred if, as your code evolves, you have a set number of things but you add new operations to those existing things. This is done by adding new functions while leaving the existing functions alone.


CSPICE_DRDPGR computes the Jacobian matrix of the transformation
from planetographic to rectangular coordinates.
Given:
body name of the body with which the planetographic coordinate system
is associated.
[1,m] = size(body); char = class(body)
`body' is used by this routine to look up from the
kernel pool the prime meridian rate coefficient giving
the body's spin sense.
lon planetographic longitude of the input point. This is the angle
between the prime meridian and the meridian containing the input
point. For bodies having prograde (aka direct) rotation, the
direction of increasing longitude is positive west: from the +X
axis of the rectangular coordinate system toward the -Y axis. For
bodies having retrograde rotation, the direction of increasing
longitude is positive east: from the +X axis toward the +Y axis.
[1,n] = size(lon); double = class(lon)
The earth, moon, and sun are exceptions:
planetographic longitude is measured positive east for
these bodies.
The default interpretation of longitude by this
and the other planetographic coordinate conversion
routines can be overridden; see the discussion in
Particulars below for details.
Longitude is measured in radians. On input, the range
of longitude is unrestricted.
lat planetographic latitude of the input point. For a point P on the
reference spheroid, this is the angle between the XY plane and
the outward normal vector at P. For a point P not on the
reference spheroid, the planetographic latitude is that of the
closest point to P on the spheroid.
[1,n] = size(lat); double = class(lat)
Latitude is measured in radians. On input, the range of
latitude is unrestricted.
alt Altitude of point above the reference spheroid. Units of `alt'
must match those of `re'.
[1,n] = size(alt); double = class(alt)
re equatorial radius of a reference spheroid. This spheroid is a
volume of revolution: its horizontal cross sections are circular.
The shape of the spheroid is defined by an equatorial radius
`re' and a polar radius `rp'. Units of `re' must match those of
`alt'.
[1,1] = size(re); double = class(re)
f the flattening coefficient
[1,1] = size(f); double = class(f)
f = (re-rp) / re
where rp is the polar radius of the spheroid. (More importantly
rp = re*(1-f).) The units of `rp' match those of `re'.
the call:
jacobi = cspice_drdpgr( body, lon, lat, alt, re, f)
returns:
jacobi the matrix of partial derivatives of the conversion from
planetographic to rectangular coordinates evaluated at the
input coordinates. This matrix has the form
If [1,1] = size(lon) then [3,3] = size(jacobi)
If [1,n] = size(lon) then [3,3,n] = size(jacobi)
double = class(jacobi)
- -
| dx/dlon dx/dlat dx/dalt |
| |
| dy/dlon dy/dlat dy/dalt |
| |
| dz/dlon dz/dlat dz/dalt |
- -
evaluated at the input values of 'lon', 'lat' and 'alt'.
None.
It is often convenient to describe the motion of an object in the
planetographic coordinate system. However, when performing
vector computations it's hard to beat rectangular coordinates.
To transform states given with respect to planetographic
coordinates to states with respect to rectangular coordinates,
one makes use of the Jacobian of the transformation between the
two systems.
Given a state in planetographic coordinates
( lon, lat, alt, dlon, dlat, dalt )
the velocity in rectangular coordinates is given by the matrix
equation:
t | t
(dx, dy, dz) = jacobi| * (dlon, dlat, dalt)
|(lon,lat,alt)
This routine computes the matrix
|
jacobi|
|(lon,lat,alt)
In the planetographic coordinate system, longitude is defined
using the spin sense of the body. Longitude is positive to the
west if the spin is prograde and positive to the east if the spin
is retrograde. The spin sense is given by the sign of the first
degree term of the time-dependent polynomial for the body's prime
meridian Euler angle "W": the spin is retrograde if this term is
negative and prograde otherwise. For the sun, planets, most
natural satellites, and selected asteroids, the polynomial
expression for W may be found in a SPICE PCK kernel.
The earth, moon, and sun are exceptions: planetographic longitude
is measured positive east for these bodies.
If you wish to override the default sense of positive longitude
for a particular body, you can do so by defining the kernel
variable
BODY<body ID>_PGR_POSITIVE_LON
where <body ID> represents the NAIF ID code of the body. This
variable may be assigned either of the values
'WEST'
'EAST'
For example, you can have this routine treat the longitude
of the earth as increasing to the west using the kernel
variable assignment
BODY399_PGR_POSITIVE_LON = 'WEST'
Normally such assignments are made by placing them in a text
kernel and loading that kernel via cspice_furnsh.
The definition of this kernel variable controls the behavior of
the CSPICE planetographic routines
cspice_pgrrec
cspice_recpgr
cspice_dpgrdr
cspice_drdpgr
It does not affect the other SPICE coordinate conversion
routines.
For important details concerning this module's function, please refer to
the CSPICE routine drdpgr_c.
MICE.REQ
-Mice Version 1.0.0, 09-NOV-2012, EDW (JPL), SCK (JPL)
Jacobian of rectangular w.r.t. planetographic coordinates
|


Class GroupCollectionSpec
- PhpSpec\ObjectBehavior
- spec\IonAuth\IonAuth\Utilities\Collection\GroupCollectionSpec
Namespace: spec\IonAuth\IonAuth\Utilities\Collection
Located at spec/Utilities/Collection/GroupCollectionSpec.php
Located at spec/Utilities/Collection/GroupCollectionSpec.php
public
|
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public
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public
|
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public
|
|
public
|
|
public
|
|
public
|
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public
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public
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public
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public
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public
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public
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public
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{% extends "base.html" %} {% block title %}Amber. О приложении.{% endblock %} {% block main %}
The BSD 3-clause license ("New BSD license")
Copyright (c) 2012, Amber search system developers. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:


The film involves a group of misfit monsters hired by the United States Armed Forces to stop an alien invasion in exchange for freedom.
Barbie in a Mermaid Tale 2
×
Barbie in a Mermaid Tale 2
Merliah, the princess of Oceana, must perform tidal rituals to uphold her family's legacy. However, her evil aunt is plotting to ruin her efforts and take over the kingdom.
Step Dogs
×
Step dogs
Meatball, a farm dog, and Cassie, a Hollywood high-class canine, are two dogs with opposite characters who are forced to live under the same roof after their masters get married.
Max Rules
×
Max Rules
Max and his friends Jessica and Scott like to prank each other with the high-tech equipment owned by Max's uncle, a government scientist. When they discover the location of a stolen FBI microchip, they embark on an exciting mission.
The Games Maker
×
The Games Maker
Young Ivan Drago is thrilled when he wins a competition to invent the most amazing board game. However, after claiming the prize, Ivan is transported to the fantastical city of Zyl, where he must defeat the evil Morodian.
Ozzy
×
Ozzy
A lovable dog must rely on some new friends to help him break out of canine prison and get back home to his owners.


Issue 84
Welcome to another issue of Haskell Weekly!  This is a weekly summary of what’s going on in its community.
Featured
-
Stack, a cross-platform program for developing Haskell projects, released version 1.6.1.
-
Haskell has had a great year and 2017 was defined by vast quantities of new code, including 14,000 new Haskell projects on Github. The amount of writing this year was voluminous and my list of interesting work is eight times as large as last year.
-
I’m sure a number of readers have already seen something about the situation around Stack and Stackage Nightly/GHC 8.2. I tried to clarify how this happened on the relevant Github issue, plus the GHC Trac ticket, but thought I’d reshare as a blog post for others who are interested.
-
Pure functional validation in a nutshell
Why should you care about typed, functional programming? The data validation example sort of makes the point on its own: if you care about validating the data that users input into your forms, why shouldn’t you care just as much about the data that gets input into your functions?
-
This week marks the one year anniversary of Monday Morning Haskell! I’ve written an awful lot in the past year.
-
Making two garbage collectors be good neighbors (using linear types)
When two garbaged-collected languages share references to the same values, each garbage collector (GC) needs to be careful to not collect these values while the other language has references to them.
-
MOBA item optimization in Haskell
I love MOBAs, and I love Haskell. Since Paragon is my current go-to game, I wanted to determine the cards to buy to maximize my damage per second.
-
Req 1.0.0, HTTP client, and streaming
This post is about changes in
req-1.0.0
, some details of howhttp-client
works, in particular how it streams response bodies. Even if you do not want to usereq
for some reason, I hope the post still may be interesting. -
Several days of struggle didn’t yield much for capturing the video stream from a webcam. A few proof of concept libraries, not updated in years, are no match for the Python ones, which proves again that what matters is not really how “good” a programming language is, but its ecosystem!
-
Finding bugs in Haskell code by proving it
We have used
hs-to-coq
on various examples, as described in our CPP’18 paper, but it is high-time to use it for real.
Jobs
-
Confer Health seeking Software Engineer in Boston
We’re a small, world-class team combining Silicon Valley urgency with East Coast science. We just closed a large Series A led by one of Silicon Valley’s best investors and we’re looking to add a third person to our software team, so come work with us!
Are you looking to hire a Haskell developer? If so, advertise with us!
In brief
- Answering r/haskell: How to unit test code that uses polymorphic interfaces?
- Applicative functors and data validation
- Applicative functors in Haskell
- Book review: “Programming in Haskell” by Graham Hutton (2nd ed.)
- Constructing dependent vectors
- Debugging Church numerals in Haskell
- Introduction to golden testing
- Preprocessing
- Using Haskell on the frontend
- ZuriHac 2018: Registration now open
Package of the week
This week’s package of the week is Warp, a fast, light-weight web server for WAI applications.
Call for participation
- codeworld: Bookmarks in Guide
- duckling: Debugging tools for classifiers
- idris-vimscript: Choose optimisations to enable based on command-line arguments
- shakespeare: Many functions in shakespeare are undocumented
Events
- December 7: Raleigh Haskell Meetup: (hack . yack)
- December 11: Karlsruhe Haskell Lerngruppe: Typen und Typklassen: Kapitel 6
- December 12: FP AMS: Second category theory study group meetup
- December 13: Sydney Haskell User Group: Haskell Programming From First Principles Study Group
- December 14: Las Vegas Functional Programming User Group: Functional Programming Meetup


Holstee Manifesto
This is your
life.
Do what you love,
and do it often.
If you don’t like something, change it.
If you don’t like your job, quit.
If you don’t have enough time, stop watching TV. If you are looking for the love of your life, stop;
Stop over analyzing,
life is simple.
All emotions are beautiful.
When you eat, appreciate
every last bite.
When you eat, appreciate
every last bite.
Open your mind, arms, and heart to new things and people, we are united in our differences. Ask the next person you see what their passion is,
and share your inspiring dream with them.
Travel often;
getting lost will
help you find yourself.
Life is short.
Live your dream
and share
your passion.


About
News
7/29/21: Siobhan’s study on modeling plant genetic redundancy is published
Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Siobhan led a study aiming at understanding the factors contributing to genetic redundancy with machine learning approaches. In addition to Siobhan, Peipei, Serena, Beth, Fanrui, and Melissa have contributed significantly to the study. This work is now published in Molecular Biology & Evolution.
7/20/21: An essay on overcoming challenges to enhancing experimental biology with computational modeling is published
Computational modeling approaches are underutilized in plant biology. Lead by Renee Dale from Danforth Plant Science Center, Shinhan and seven other colleagues published an opinion piece in Frontiers in Plant Science identifying challenges one may face in adopting modeling approach.
4/17/21: Why are there no symptoms when a plant is infected with a fungal pathogen?
The broad host range of Fusarium virguliforme represents a unique comparative system to identify and define differentially induced responses between an asymptomatic monocot host, maize (Zea mays), and a symptomatic eudicot host, soybean (Glycine max). Led by Amy Baetsen-Young from Brad Day’s lab, Shinhan collaborated on a project examining how asymptomatic and symptomatic hosts respond to F. virguliforme transcriptionally. This work is now published in Plant Cell
12/7/20: Welcome the newest member of our lab - Kenia Sugura Abá
Kenia is a first year graduate student in the Genetics and Genomic Science Program. She rotated over the summer and has made the decision to join our lab today! She graduated from University of Texa-Austin with families in the San Antonio area. Welcome to our lab Kenia!
9/1/20: Collaborative work on maize ANT1 regulatory network published!
Arabidopsis AINTEGUMENTA (ANT), an AP2 transcription factor, is known to control plant growth and floral organogenesis. This study is led by Dr. Wen-Hsiung Li from Academia Sinica, Taiwan, which reveals biological roles of ANT1 in several developmental processes beyond its known roles in plant growth and floral organogenesis. It is published in the Sep. 1st issue of Proc. Natl. Acad. Sci., USA.
9/1/20: Christina’s story on predictions of plant stress response through multi-omics data integration is published!
Plants respond to their environment by dynamically modulating gene expression. Our findings demonstrate how in silico approaches can improve our understanding of the complex codes regulating response to combined stress and help us identify prime targets for future characterization. It is published in Nuc. Acid. Res. Genomics and Bioinformatics.
7/30/20: Beth’s story on using Arabidopsis annotation information to improve predictions of tomato metabolic genes is published!
Plant specialized metabolites mediate interactions between plants and the environment and have significant agronomical/pharmaceutical value. This study demonstrates that specialized and general metabolic genes can be better predicted by leveraging cross-species information. Additionally, our findings provide an example for transfer learning in genomics where knowledge can be transferred from an information-rich species to an information-poor one. The findings is now available form In Silico Plant.
6/25/20: Serena won!!
Serena is the winner of the SciComm Blog Contest! Here is her winning post KNOWLEDGE GRAPHS: A New Way To Reason. Congrats Serena!
6/21/20: Virtual lunch in its 3rd month… Now with Fanrui!!
Fanrui, our deep learning expert, Peipei’s beloved husband, and a great cook, is finally back after getting stuck in China for >6 months due to the COVID19, repeated flight canceldation, and long stay in the secondary screen through the immigration. Welcome back!
6/11/20: Kevin, Ally’s brother, says hi!
Kevin likes to draw/color while Ally does work. During our lab reading group meeting, Kevin decided to draw a picture for all of us. Here it is!
5/13/20: Virtual lab lunch at its second month
In our lab lunch today, Ronan is biting her mom’s face!
5/11/20: Christina’s review on interpretable machine learning is published
In collaboration with Jiliang Tang from Comp. Sci. & Engr, MSU, Christina’s review on Opening the Black Box: Interpretable Machine Learning for Geneticists is now published in Trends in Genetics. This work highlights current progress in making machine learning model interpretable and explanable using applications in genetics as examples. And her graphics was picked as the cover as shown on the right!
4/8/20: Virtual lab lunch for almost a month
With the social distancing in place, we continue to meet over Zoom. It seems that folks are doing ok!
3/31/20: Collaborative work with Dr. Yan Bao is published
Dr. Bao’s work on COST1 regulates autophagy to control plant drought tolerance is published where Peipei has contributed significantly using comparative genomics approaches.
3/19/20: Siobhan defended today
Siobahn defended her thesis titled Modeling and prediction of genetic redundancy in Arabidopsis thaliana and Saccharomyces cerevisiae today virtually via Zoom with 89 people in the audience! Both us showed up in the lab (while keeping >6 ft distance) to get the virtual defense setup. Originally due to move to England next week, Siobhan will stay in East Lansing to finish her work. Good job Siobhan! The picture on the right shows Siobhan right after the defense.
3/13/20: Our first virtual lab lunch
With all of us working remotely, we decide to host a 15-min lab lunch to check in with everyone. The photo on the right is all of us experimenting with the background feature in Zoom - the two “ghosts” are Siobhan and Serena!
3/11/20: Social distancing starts
Michigan State has indicated all classes will be online only today. Given the nature of most of our work is computational and considering the risks of COVID-19, from today on all lab personnel will be working remotely, except for those who need to keep our plant alive.
3/5/20: Welcome to the lab Ally
Ally has decided to join the Plant Biology Program and, instead of doing any rotation, is coming to our lab! She is currently finishing up her Bachelor’s degree in Manchester University, a liberal art institution. She is awarded an MSU College of Natural Science Fellowship as well as an Early-Start Fellowship to begin her doctoral training on July 1st! Welcome to our lab Ally!
2/21/20: Welcome to the lab Serena
Serena is a new graduate student in the, hold your breath, Plant Biology Program, the Computational Math., Sci., Math Program, AND the Moleculr Plant Science Program. She is a MSU Plant Science Fellow and an NSF Fellow of the IMPACTS training program. She graduated from the Cornell University in 2019 and has been rotating in two other labs. Here is the funny Slack exchange when I found out she is joining us!
1/1/20: Christina’s study on using genomic and transcriptomic information to predict trait is published
Can transcriptome data be used to predict traits? In the study titled Transcriptome-Based Prediction of Complex Traits in Maize, Christina, in collaboration with Jeremy, a rotating student at the time, and Bob VanBuren tested this and found out that not only we can predict traits with transcriptomes, but they may tell us more than genomic data can. The finding is featured in MSU Today!
10/25/19: Beth defended her PhD
Beth defended her PhD thesis today titled Specilized metabolism and stress response: studies in predicting gene function and regulation. The seminar was held in Plant Biology Lab 247 and was attended by both her colleagues, friends, and families. Beth is moving to University of Wisconsin-Madison to take a postdoctoral position applying molecular evolutionary and computaional approaches to plant biochemistry. We wish her all the best! The picture on the right shows Beth right before her defense.
10/20/19: Ronan is here!
Siobhan gave birth today to her baby boy Ronan! Congrats Siobhan and Geoff!
9/27/19: Christina defeneded her PhD
Christina today defended her PhD thesis titled Interpretable machine laerning in plant genomes: studies in the complex relationship between genotype and phenotype. The seminar was held in Plant Biology Lab 247 and was full! Christina’s parents also attended her seminar. In the talk, Christina laid out the overarching Christina is now moving to Australia working in algorithm development in single cell genomics studies. All the best Christina! The picture on the right showing Chrisitna leaving the lab for good on 10/3/2020…
9/24/19: Sahra and Christina’s work on the regulatory mechanisms of cell-type stress response is published
Our story titled Cis-regulatory code for predicting plant cell-type transcriptional response to high salinity is now published in Plant Physiology with Sahra and Christina as joint first authors. Using the root cell-type transcriptome data, Sahra and Christina identified cis-regulatory sequences likely specify cell-type response to high salinity stress. More importantly, machine learning models were built to ask how well the identified regulatory sequences can predict cell-type response. The findings not only advance our understanding of the regulatory mechanisms of the plant spatial transcriptional response, but also suggest broad applicability of the approach to any species, particularly those with little or no trans regulatory data.
9/18/19: Christina’s work on benchmarking genomic prediction algorithms is published
Chrisitina’s paper on Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits is published today in G3. This is a collaborative between our colleague Gustavo de los Compos, Chrisitna’s internship mentors Andrew McCarren and Mark Roantree, and Emily Bolger a highly productive summer research student. Using data of 18 traits across six plant species with different marker densities and training population sizes, Christina spearheaded a comparison of the performance of six linear and six non-linear algorithms. The findings highlight that simpler, linear algorithm can beat out more sophiscated machine learning approaches, and the importance of algorithm selection for the prediction of trait values.
8/20/19: John’s work on poaceae intergenic transcription is published
John’s paper on Evolutionary characteristics of intergenic transcribed regions indicate rare novel genes and widespread noisy transcription in the Poaceae is published in Scientific Report. Christina as well as Rosalie Sowers, a highly talented undergrad from Penn State also contribute to this work. Extensive transcriptional activity occurring in intergenic regions of genomes has raised the question whether intergenic transcription represents the activity of novel genes or noisy expression. To assess this, John et al. evaluated cross-species and post-duplication sequence and expression conservation of intergenic transcribed regions. This study provides a framework to identify novel genes using comparative transcriptomic data to improve genome annotation that is fundamental for connecting genotype to phenotype in crop and model systems.
8/19/19: Our newest addition to the lab, Thilanka
Thilanka is a new graduate student in the Plant Biology Graduate Program. He graduated from the University of Peradeniya in Sri Lanka in 2016 and worked as a teaching assistant in the past two years in Peradeniya. He is energetic, communicative, and excited about learning new topics. He is also easy to get alone with, and has a black belt in Taekwondo!! Welcome to the lab!
7/24/19: Siobhan got the Dissertation Completion Fellowship
Siobhan has been selected to receive a $7,500 College of Natural Science Dissertation Completion Fellowship during fall semester 2019. Congrats Siobhan!
7/13/19: Nick’s work on regulatory assymmetry of plant duplicate genes is published
Nick’s paper on Expression and regulatory asymmetry of retained Arabidopsis thaliana transcription factor genes derived from whole genome duplication is published in BMC Evoltionary Biology. Christina and Eamon Winship, an undergraduate reseracher at the time also contributed. Transcription factors (TFs) play a key role in gene regulation and tend to be retained after duplication. Nick’s work provide answers about about how TF duplicates have diverged in their expression and regulation that contribute to a better understanding of the elevated retention rate among TFs.
2/19/19: Our collaborative work on maize time-series transcriptome is published
Led by Dr. Wen-Hsiung Li’s group in Taiwan, Shinhan has contributed to the study: Comparative transcriptomics method to infer gene coexpression networks and its applications to maize and rice leaf transcriptomes published in PNAS. This study involves a comprehensive time series data and a novel approach to build time-resolved co-expression network to hypothesize regulatory relationships between genes.
2/5/19: Beth’s work on specialized metabolism is published and featured by NSF
Beth’s work on Robust predictions of specialized metabolism genes through machine learning is published in PNAS with Peipei, Johnny, Melissa, and multiple colleagues in Rob Last’s group as co-authors. This work features a machine learning approach to predict general and specialized metabolic gene that are notoriously hard to distinquish. The work is featured in MSU Today and in NSF’s Awesome Discoveries You Probably Didn’t Hear About This Week.


Before Change
BKS_query = predictions
// Use the BKS to filter the competence region
selected_idx = []
for sample_index in idx_neighbors:
T = (self.BKS_dsel[sample_index][:] == BKS_query)
S = sum(T) / self.n_classifiers
if S > self.similarity_threshold:
selected_idx.append(sample_index)
// Use the whole neighborhood if no sample is selected to form the region of competence
if len(selected_idx) == 0:
selected_idx = idx_neighbors
// Estimate the classifier competence for the filtered region of competence
for clf_index in range(self.n_classifiers):
// Check if the dynamic frienemy pruning (DFP) should be used used
if self.DFP_mask[clf_index]:
clf_competence = [self.processed_dsel[sample_idx][clf_index] for sample_idx in selected_idx]
competences[clf_index] = np.mean(np.array(clf_competence))
return competences
After Change
_, idx_neighbors = self._get_region_competence(query)
idx_neighbors = np.atleast_2d(idx_neighbors)
// Use the pre-compute decisions to transform the query to the BKS space
BKS_query = predictions


{::nomarkdown}
Download and install the latest version of a standards-compliant browser like Firefox, Chrome, or the latest version of Internet Explorer.
The name and product code identify the generic category of a device for FDA assigned to a device is based upon the medical device product classification designated under 21 CFR Parts 862-892.
{{ site.apiurl }}/device/recall.json?


Freddie Lochner
UNH COMP698 Project Website
These are a few of my favorite things:
Speed Racer
Archer


Apache C++ Standard Library User's Guide
Welcome to the Apache C++ Standard Library User's Guide.
Here are several ways you can access the information in this document:
- A Comprehensive Table of Contents showing all chapters, first- and second-level headings.
- Click on one of the chapter names below to go directly to that chapter. Each chapter begins with a chapter-level table of contents.
- A listing of Tables, Figures, and Examples.
- A topic index.
Main Sections


Robert Carlyle discusses struggles with being compared to ‘Trainspotting’ character Begbie: “That’s not all I am”
Carlyle shot to fame after playing the thug in the 1996 Danny Boyle classic
Robert Carlyle has opened up on the struggles he faced after shooting to fame as Begbie in 1996 classic Trainspotting.
The actor reprised his role as the notoriously chaotic character in the film’s 2017 sequel.
Speaking to the Daily Record, Carlyle spoke about the issues he faced in the aftermath of the film, with fans unable to understand differences between the actor himself and his character.
“I maybe struggled against it a wee bit, kicked against it a wee bit at first,” he comments, “because you’re like, ‘Ah, that’s not all I am'”.
Trainspotting
He does go on to show his thanks for the role, saying: “But thank God for it – it’s led to so many different things for me and people love it. It’s like, you’re in people’s psyche.
“I’m blessed to have been part of it. It seems like there’s not a week goes by that somebody doesn’t go, ‘Hey, Begbie’.”
In other Trainspotting news, author Irvine Welsh revealed last month that he’d be “massively interested” in completing the “holy trinity” with a third Trainspotting film.
Commenting on the prospect of Trainspotting 3, Carlyle jokes about the two decade wait for the sequel, saying: “It’d have to be set in a rest home, probably. Skagboys Rest Home. It might take that long but Danny might do it.”


This week has been all about Ruby in the Dev Bootcamp world. It's been a few months since I've flexed my Ruby muscles properly, and man, I have a lot of reading and studying to do!
This particular post will be on arrays and hashes. What are these things, you ask? Well, to put it quite simply, they're like containers that store things.
An array is an ordered collection of objects. It is integer-indexed, which means that each slot or object contained in the array is given an index number, beginning with 0. Each index corresponds to a slot in the array.
[] # an empty array
['kitten', 'puppy'] # an array with two strings
[5, 'elephant', [1,2]] # an array holding an integer, a string and an array
You can see how the numeric index works to point to a slot in an array below:
fruits = ['mango', 'banana', 'orange', 'papaya']
#an array called 'fruits' holding 4 strings
puts fruits[0] #=> 'mango'
puts fruits[1] #=> 'banana'
puts fruits[2] #=> 'orange'
puts fruits[3] #=> 'papaya'
print fruits #=> 'mango, banana, orange, papaya'
As a reminder, the command print
just tells the computer to print out something to the console without adding a newline, while puts
essentially tells the computer to print something out to the console and then adds a newline after executing the command (newline is really just like pressing enter on the keyboard to get to the next line). In the above example, we used puts fruits[0]
to puts out the first fruit in the array, puts fruits[1]
to puts out the second fruit in the array, and so on and so forth.
For our purposes, there are two basic ways to create an array (although there are really more ways to do so out in the world). The first, which I used in the above examples, is to use the literal constructor []. The second is to use Array.new as you can see below.
Array.new # an empty, unnamed array, similar to just []
Array.new(20) # an unnamed array with a size of 20 elements
fruits = Array.new # an empty array called 'fruits'
fruits = Array.new(4) # an array called 'fruits' with 4 elements
Hashes are a collection of unique keys and their values. They are similar to arrays, except instead of being integer-indexed, hashes allow you to use any kind of object to refer to a corresponding value. You can create a blank hash by using two curly braces {}
, or to make a hash that is populated, you may do something like this:
birthdays = {
'mom' => 'March 15',
'dad' => 'September 23',
'brother' => 'June 24',
'sister' => 'October 31',
'me' => 'September 8'
}
In the above example, the names are the 'keys' while the birthdays are the 'values.' We are able to retrieve values from a hash by using the [] operator and enclosing the key within it. For example, if we wanted to find when my mom's birthday was, we would have to type in birthdays['mom']
.
...Aaaaand that's all there really is to making arrays and hashes! Not too bad right? Or course, there are other method of creating them and working with them that I did not include here, but really, as an introduction, I would say that this pretty much covers it. Try it out for yourself and let me know how you find it! :D


{% get_permission 'can_vote_in_poll' poll request.user as user_can_vote_in_poll %}
{% with can_be_completed=user_can_vote_in_poll has_been_completed=poll|has_been_completed_by:request.user poll=poll %}
{{ poll.question }}
{% if can_be_completed and not view_results_action %}
{% if not has_been_completed or change_vote_action %}
{% if poll.max_options == 1 %}
{% blocktrans trimmed %}
You may select 1 option
{% endblocktrans %}
{% else %}
{% blocktrans trimmed with max_options=poll.max_options %}
You may select up to {{ max_options }} options
{% endblocktrans %}
{% endif %}
{% endif %}
{% endif %}
{% if can_be_completed and not has_been_completed and not view_results_action %}
{% include "forum_conversation/forum_polls/poll_vote_form.html" %}
{% elif can_be_completed and has_been_completed and change_vote_action %}
{% include "forum_conversation/forum_polls/poll_vote_form.html" %}
{% else %}
{% include "forum_conversation/forum_polls/poll_results.html" %}
{% endif %}


jj.github.io/curso-tdd/temas/servicios
use simple_logger::SimpleLogger;
#[derive(Debug)]
pub enum IssueState {
Open,
Closed,
}
#[derive(Debug)]
pub struct Issue {
state: IssueState,
project_name: String,
issue_id: u64,
}
pub fn issue_factory( project_name: String,
issue_id: u64) -> Issue {
log::debug!( "Creating closed issue for project {} with id {}", project_name, issue_id );
return Issue { state: IssueState::Closed,
project_name: project_name,
issue_id: issue_id }
}
fn main() {
SimpleLogger::new().init().unwrap();
let this_issue = issue_factory(String::from("CoolProject"), 1 );
let mut that_issue = issue_factory( String::from("CoolProject"), this_issue.issue_id + 1 );
that_issue.state = IssueState::Open; // Avoid warning
log::debug!("Changed state to Open");
}
nginx


These release notes cover new developer and user-facing incompatibilities, important issues, features, and major improvements.
Support for building C client lib on Illumos (and presumably OpenSolaris). Configure with "CPPFLAGS=-D_POSIX_PTHREAD_SEMANTICS LDFLAGS="-lnsl -lsocket" ./configure"
Allows spaces to be used for parameters in zkCli as long as they are in single or double quotes. ie: create /node1 "This will now work"
Fix a bug in zkfuse that causes an abort upon reading a node's content
Thanks, Jordan.
Branch 3.5: Committed revision 1740737. Trunk: Committed revision 1740738.
IPv6 addresses are now properly parsed in the config


Dear {{name}},
We are writing to let you know someone just tried to register or update a WWU Puzzle Hunt account at https://outinscience.com with your email.
The name used in the attempted account creation or update was "{{imposter}}".
(Note that anyone can input any name and this does not actually identify the user in question)
Your privacy is our primary concern. We take care not to leak any information that could indicate the existance of your account so that you may remain fully anonymous if you wish.
Cheers,
The {{appName}} Dev Team


And I'm a product manager
I'm a New York-based technical product manager with experience advising an agile lean startup.
Three years of management consulting drove me into a profound quarter-life crisis. When I started to be honest with myself about what I wanted to do, I thought back to when I was the most proud of my work: as an entreprenuer in high school.
In the summer of 2002 I was 14 years old, and I was mowing lawns. It wasn't the smell of grass-clippings I enjoyed - I'm actually allergic. Or the heat exhaustion I would suffer from, working in temperatures above 100 degrees. I was proud of building a crew from my group of friends that dominated our neighborhood's semi-professional landscaping market niche.
We were called The Neighborhood Lawn Guys. After distributing nearly 300 flyers on our bikes, Danny and I built our book of clients. By the second summer I was managing the scheduling, billing, and payroll of our 4 staff members. So, when we chilled at the mall with our friends, we were looking fresh as hell in our air force ones.
But at 16, I started a more lucrative business. I charged $30 an hour to teach adult students computer skills. I built my book of clients by referrals.
When I'm not building exciting software products, I love to snowboard, solve puzzles, go camping, and I'll school you at bridge.


Detailed Description
These IDs are found in various Device and Interface descriptors. Used when defining such descriptors in a USB device application, and when enumerating devices in a USB host application.
Define Documentation
| #define USB_CLASS_APP 0xfe |
Application-specific Class.
Definition at line 73 of file usb_ids.h.
| #define USB_CLASS_AUDIO 0x01 |
USB Audio Class.
Definition at line 67 of file usb_ids.h.
| #define USB_CLASS_CDC_COMM 0x02 |
CDC Communication Class Interface.
Definition at line 69 of file usb_ids.h.
| #define USB_CLASS_CDC_DATA 0x0a |
CDC Data Class Interface.
Definition at line 70 of file usb_ids.h.
| #define USB_CLASS_CDC_DEVICE 0x02 |
USB Communication Device Class.
Definition at line 68 of file usb_ids.h.
| #define USB_CLASS_HID 0x03 |
USB Human Interface Device Class.
Definition at line 71 of file usb_ids.h.
| #define USB_CLASS_MSC 0x08 |
USB Mass Storage Class.
Definition at line 72 of file usb_ids.h.
| #define USB_CLASS_NONE 0x00 |
| #define USB_CLASS_VENDOR 0xff |
Vendor-specific class.
Definition at line 74 of file usb_ids.h.
| #define USB_PID_ASF_TEST 0x21fe |
For internal testing only.
Definition at line 61 of file usb_ids.h.
| #define USB_PID_AVR32786 0x2112 |
AVR32786 High Speed USB Mass Storage Device SDHC.
Definition at line 60 of file usb_ids.h.
| #define USB_PROTOCOL_NONE 0x00 |
| #define USB_PROTOCOL_VENDOR 0xff |
Vendor-specific protocol.
Definition at line 85 of file usb_ids.h.
| #define USB_SUBCLASS_NONE 0x00 |
| #define USB_VID_ATMEL 0x03eb |
Atmel Corporation.
Definition at line 54 of file usb_ids.h.


My name is Stefani Krautstrunk, and am currently in a full stack developer program through University
of Arizona. Orginally from Chicago, IL but have been living in Tempe, AZ since 2017. I am an Accounting
Analyst with a Bachelor of Arts in English and a minor in Communications. I would describe myself as a
curious person who loves to learn and accepts challenges willingly. I guess that's why I'm in the Accounting
world with an English degree, and now earning a web development certificate.
I decided to do a coding boot camp because I use computers everyday, and I never knew how exactly they worked,
not even a website. What I didn't expect was to love the challenge is presents, and the joy of web design.
Please enjoy my porfolio, and be sure to take a look at some of the projects I've created on the portfolio page.
Any contact information can be found on the contact page.


Setup
Tutorial
References
License
Packaging
Testing
Documentation
Command-line Interfaces
Logging
Performance
On this page
Starting Point
Performance Profiling
Line Profiler
Memory Profiler
Use Existing Libraries
Just-in-Time Compilation
Parallel and Distributed Computing
Developing C-Extensions
Performance
¶
Starting Point
¶
…
Performance Profiling
¶
Line Profiler
¶
Memory Profiler
¶
…
Use Existing Libraries
¶
…
Just-in-Time Compilation
¶
…
Parallel and Distributed Computing
¶
…
Developing C-Extensions
¶
…
Logging
References


USA Educational Information
Percentage of adults age 25 and older with a bachelor's degree or higher (2010-2014)


Upon saving a template, the Validation tab shows recommendations and warnings based on the constraints defined in the template. The following rules are taken into consideration:
- Template context is not found within the schema. This only applies to templates that were designed in legacy versions of Trifolia.
- Custom Schematron Syntax. Trifolia compiles all custom Schematron when the template/profile is saved and determines if the Schematron is syntactically correct.
- Primitive constraint with no prose. Occurs when creating a primitive constraint but not specifying the constraint prose. Primitive constraints without any prose are ignored during export.
- Constraint’s context not found in schema. This can occur in templates that were designed using legacy versions of Trifolia, which allows template/profile authors to freely edit the context of the constraint. This can also occur when a template/profile has been moved from one type/context to another type/context (ex: section to entry) and the constraints within the template/profile have not been reconciled to the new type’s structure.
- Branch/slice without identifiers. At least one identifier should be present within every branch/slice to indicate how the one branch/slice is different from other branches.
- Invalid contained template. The context of a contained template/profile does not match the data-type of the constraint containing the template.
- Invalid constraint cardinality. The constraint loosens the cardinality requirements from the base standard/schema (ex: base schema requires 1..1 code and a constraint in the template/profile indicates 0..1 code).


{% extends "partials/tooltips/base.html" %}
{% block title %}
Proposed in the Order
{% endblock %}
{% block inner %}
When you file your completed divorce forms at the court registry they will include the "order"
you want made. Basically you're asking the court (via a judge) to approve what you are asking for
and / or have agreed upon (e.g. monthly child support amount).
{% endblock %}
{% block label %}
{% if text %}{{ text }}{% else %}proposed in the order{% endif %}
{% endblock %}


wikiwall¶
wikiwall is a CLI that downloads a random image from Wikiart’s Hi-Res page and sets it as your desktop background in macOS.
Features¶
- Easily customize your desktop with new hi-res artwork from the command line.
- Update your wallpaper periodically with your favorite scheduler.
Requirements¶
- Python 3.6 or higher
- macOS
Installation¶
$ pip3 install wikiwall
If you want, set your wallpaper to change every night with launchd:
$ git clone https://github.com/kylepw/wikiwall.git && cd wikiwall
$ sed -i.bak -e "s|WIKIWALL|$(which wikiwall)|g" wikiwall.plist
$ cp wikiwall.plist ~/Library/LaunchAgents
$ launchctl load ~/Library/LaunchAgents/wikiwall.plist
Usage¶
$ wikiwall --help
Usage: wikiwall [OPTIONS] COMMAND [ARGS]...
Set desktop background in macOS to random WikiArt image.
Options:
--dest TEXT Download images to specified destination.
--limit INTEGER Number of files to keep in download directory. Set to -1
for no limit. Default is 10.
--debug Show debugging messages.
--help Show this message and exit.
Commands:
show Show previous downloads in Finder.
Todo¶
- Set wallpaper on a desktop not currently being viewed.
- Add support for other operating systems.
- Provide preview of image before setting as background.


Pig dice is a simple dice game which in its basic form is playable with just a single dice. You win by being the first player to score 100 or more points


|
Border Surviellance Bot Group V
An attempt to model border surviellance using Firebird-V robot and Python
|
Public Member Functions | |
| def | __init__ |
| def | close |
| def | __del__ |
| def | writelines |
| def | flush |
| def | next |
| def | __iter__ |
| def | readline |
| def | readlines |
| def | xreadlines |
| def | seek |
| def | tell |
| def | truncate |
| def | isatty |
Public Attributes | |
| closed |
An abstract file like class. This class implements readline and readlines based on read and writelines based on write. This class is used to provide the above functions for to Serial port objects. Note that when the serial port was opened with _NO_ timeout that readline blocks until it sees a newline (or the specified size is reached) and that readlines would never return and therefore refuses to work (it raises an exception in this case)!
Definition at line 91 of file serialutil.py.
| def serial::serialutil::FileLike::__del__ | ( | self | ) |
Destructor. Calls close().
Reimplemented in serial::sermsdos::Serial.
Definition at line 112 of file serialutil.py.
| def serial::serialutil::FileLike::flush | ( | self | ) |
flush of file like objects
Definition at line 128 of file serialutil.py.
| def serial::serialutil::FileLike::readline | ( | self, | |
size = None , |
|||
eol = LF |
|||
| ) |
read a line which is terminated with end-of-line (eol) character ('\n' by default) or until timeout.
Definition at line 141 of file serialutil.py.
| def serial::serialutil::FileLike::readlines | ( | self, | |
sizehint = None , |
|||
eol = LF |
|||
| ) |
read a list of lines, until timeout. sizehint is ignored.
Definition at line 158 of file serialutil.py.
| def serial::serialutil::FileLike::xreadlines | ( | self, | |
sizehint = None |
|||
| ) |
Read lines, implemented as generator. It will raise StopIteration on timeout (empty read). sizehint is ignored.
Definition at line 174 of file serialutil.py.


DATE |
PR NO. |
PARTICULARS |
DATE REQUIRED/ NEEDED |
TOTAL |
|
2/27/2017 |
2017-12 |
Professional service of a facilitator for the Philippine- EAGA Trade and Investment Facilitation Cluster Plan- ning Workshop on March 27-28, 2017 |
2/27/2017 |
3,500 |
|
2/27/2017 |
2017-12 |
Professional service of a facilitator for the Philippine- EAGA Trade and Investment Facilitation Cluster Plan- ning Workshop on March 27-28, 2017 |
2/27/2017 |
3,500 |
|
2/27/2017 |
2017-12 |
Professional service of a facilitator for the Philippine- EAGA Trade and Investment Facilitation Cluster Plan- ning Workshop on March 27-28, 2017 |
2/27/2017 |
3,500 |
|
2/27/2017 |
2017-12 |
Professional service of a facilitator for the Philippine- EAGA Trade and Investment Facilitation Cluster Plan- ning Workshop on March 27-28, 2017 |
2/27/2017 |
3,500 |
|
2/27/2017 |
2017-12 |
Professional service of a facilitator for the Philippine- EAGA Trade and Investment Facilitation Cluster Plan- ning Workshop on March 27-28, 2017 |
2/27/2017 |
3,500 |
|
2/27/2017 |
2017-12 |
Professional service of a facilitator for the Philippine- EAGA Trade and Investment Facilitation Cluster Plan- ning Workshop on March 27-28, 2017 |
2/27/2017 |
3,500 |


Current temperature:
Thermostat
Current Temperature
degrees C
powerSavingMode On or Off
Current Energy Usage


Preparation time
over 2 hours
Cooking time
10 to 30 mins
Serves
Makes 8 biscuits
Dietary
These light puff pastry biscuits are flavoured with a touch of cinnamon - wonderful served with a coffee or ice cream.
Put the flours, salt, butter and 50ml/2fl oz cold water in a bowl and gently mix to form a dough.
Transfer the dough to a lightly floured work surface and knead for 5 minutes until smooth. Shape the dough into a square, wrap in cling film and chill for an hour (or freeze for 20-30 minutes).
For the butter layer, cream the butter and flours together using an electric mixer. Sandwich the mixture between two sheets of cling film and roll out to a rectangle the same width as the square of dough, but twice as long. Chill in the fridge for 25 minutes.
Unwrap the chilled dough and butter layer. Place the chilled butter layer, short end facing towards you on a lightly floured surface and place the square of dough in the centre of the butter sheet. Make sure it is positioned neatly and covers almost to the edges. Lift the exposed butter sheet at the top and fold it down over the dough, then fold the exposed butter sheet at the bottom up over the top, so the dough is completely enclosed in the butter sheet.
Place the dough on a lightly floured surface, short end towards you. Roll out to a rectangle, keeping the edges as even as possible. Fold the top quarter down and the bottom quarter up so they meet neatly in the centre. Then fold the dough in half along the centre line. This is called a book turn. Wrap the dough in cling film and chill for 25 minutes.
Remove the dough from the fridge and make another book turn. Wrap the dough in cling film and chill for 25 minutes.
For the filling, mix the granulated sugar and the cinnamon together in a bowl. On a lightly floured surface roll out the pastry to a rectangle as before and sprinkle over the sugar.
Make another book turn to incorporate the sugar, then roll out the pastry 1cm/½in thick, to a rectangle 12x20cm/4½x8in. Roll up the pastry from the short end like a Swiss roll. Wrap in cling film and chill for 25 minutes.
Preheat the oven to 200C/180C Fan/Gas 6. Line 2 baking trays lines with silicon sheets or non-stick baking paper.
Trim the ends of the roll and cut into 8 x 1cm/½in thick slices. Dust the work surface heavily with icing sugar and roll each piece of dough out very thinly, turning to coat in the sugar and to prevent sticking. Place the biscuits on the prepared trays and bake for 5 minutes. Remove from the oven, carefully turn the biscuits and cook for a further 3-4 minutes, or until golden-brown. Transfer to a wire rack to cool.