Upcomming Meetups
{% if meetups %}-
{% for meetup in meetups %}
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{{ meetup.title }}
Created on: {{ meetup.created_on.date }}Venue: {{ meetup.venue }}
When: {{ meetup.when }}
Description: {{meetup.description|slice:":400"}}...
{% endfor %}
No Meetups are available.
{% endif %}


WITNESS LIST Business & Industry Committee (Subcommittee on Healthcare Technology) May 31, 2000-8:00A Interim Charge On: Carr, Jr., William Plack (Self) McCurdy, James R. (Betagene, Inc) McKnight, Steve (Self) Schuppin, George (Self) Touchstone, Gifford (Self) Registering, but not testifying: On: Becker, Ph.D., Thomas C. (Betagene, Inc.)


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Assignment Number One
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Assignment Number Two
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Quiz 1
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Student Assessment First Semester
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Assignment Number Three
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Quiz 2
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Assignment Number Four
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First Test
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Assignment Number Five
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| John Smith | 100% | 90% | 94% | A | 77% | 85% | 92% | 89% | 100% | |
Curve Assignment
Weigh assignment
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Copy assignment
Download assignment


Ocala events
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national association of small businesses
Fable II - Legendary Weapons & Demon Doors Guide
Jun 7, 2010 . Fable II - Legendary Weapons & Demon Doors Guide . Legendary Rifle Demon realm name: Terry Cotter's Army Brightwood Demon door: .
http://www.ign.com/faqs/2010/fable-ii-legendary-weapons-demon-doors-guide-1057950fable 2 brightwood demon door - YouTube
May 7, 2011 . Walkthrough of the Brightwood Demon Door (Door 4 of 9) in Fable 2. sorry for the bad quality its the best i can do with a Diamond VC500. for .
http://www.youtube.com/watch?v=3yw5gMi-AjgDemon Door Requirements - Video Game Cheats, Strategy Guides ...
Aug 20, 2009 . Fable 2. for Xbox 360. .  ---- Brightwood Demon Door---- This annoying door will only open .
http://mycheats.1up.com/view/faq/3140434/64179/fable_2/xbox_360
who you really are lyrics Upcoming events in Ocala
- Sponsored event
housing forecast by metro area
Apr 6 - Dance It Studio
african american history syllabus free stain glass snowflake patterns
little kids nike hyperize shoes Hottest Tickets
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Apr 11size of nerve cell nucleus
Amway Center -
Apr 28wedding gown with mini jacket
Amway Center -
Jun 25nascar coca cola 600 2009
Bob Carr Performing Arts Center -
Mar 30carrabelle palms rv park store
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Aug 27theme from discotheque mp3
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Jun 21attic stairs insulator cover
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Sep 13how long for antibiotic to work
Francis Field -
Mar 17site map plat map
St Augustine Amphitheatre -
Oct 8top 10 songsd of the week
Amway Center -
May 4carrabelle palms rv park store
Bob Carr Performing Arts Center
Ocala's best venues
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How to Get in All the Demon Doors in Fable 3 | eHow.com
Demon Doors provide another way for you to earn money and better equipment, assuming . How Do I Open the Oldest Demon Door in the Game "Fable 2"?
http://www.ehow.com/how_12049915_demon-doors-fable-3.html -
The Sepulchre - The Fable Wiki - Fable, Fable 2, Fable 3, and more
Location Information Exits Brightwood The Sepulchre is a realm of Albion . There is a Demon Door in the Demon Door Retirement Home (Fable III) that .
http://fable.wikia.com/wiki/The_Sepulchre -
Demon Doors, Legendary Weapons & Expression Statues ...
Demon Doors, Legendary Weapons & Expression Statues Fable 2. . 2. Brightwood Wants cheese to start with (I ate some 4 star cheese in .
http://www.xbox360achievements.org/forum/showthread.php?t=98566is tool christian rock
98 people's favorite
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How to Find "Fable 2" Legendary Weapons | eHow.com
How to Revive Your Dog in "Fable 2" · How to Get the Legendary Katana in " Fable II" . IGN: "Fable II" Legendary Weapons and Demon Doors Guide .
http://www.ehow.com/how_4772027_fable-legendary-weapons.html -
Where are the demon doors in Fable 2 and how do you open them
There are a total of 9 demon doors in all of Albion and this is where you can find . To open the Bower Lake Demon door in Fable 2, you must respond to the .
http://wiki.answers.com/Q/Where_are_the_demon_doors_in_Fable_2_and_how_do_you_open_them


| MATLAB File Help: prtRegressRvmSequential |
prtRegressRvm Relevance vector machine regression object This code is based on: Michael E Tipping, Sparse bayesian learning and the relevance vector machine, The Journal of Machine Learning Research, Vol 1. Also see http://en.wikipedia.org/wiki/Relevance_vector_machine A prtRegressionRvm object inherits the PLOT method from the prtRegress object, and the TRAIN, RUN, CROSSVALIDATE and KFOLDS methods from the prtAction object. Example: dataSet = prtDataGenNoisySinc; % Load a prtDataRegress dataSet.plot; % Display data reg = prtRegressRvmSequential; % Create a prtRegressRvm object reg = reg.train(dataSet); % Train the prtRegressRvm object reg.plot(); % Plot the resulting curve dataSetOut = reg.run(dataSet); % Run the regressor on the data hold on; plot(dataSet.getX,dataSetOut.getX,'c.') % Plot, overlaying the % fitted points with the % curve and original data hold off; legend('Regression curve','Original Points','Selected Relevant Points','Fitted points',0)
| Superclasses | prtRegressRvm |
| Sealed | false |
| Construct on load | false |
| prtRegressRvmSequential | prtRegressRvm Relevance vector machine regression object |
| Sigma | Estimated in training |
| beta | Estimated in training |
| dataSet | The training prtDataSet, only stored if verboseStorage is true. |
| dataSetSummary | Structure that summarizes prtDataSet. |
| isCrossValidateValid | True |
| isSupervised | True |
| isTrained | Indicates if prtAction object has been trained. |
| kernels | |
| learningConverged | Whether or not the training converged |
| learningResults | Struct with information about the convergence |
| name | Relevance Vector Machine |
| nameAbbreviation | RVM |
| plotOptions | Plotting Options |
| showProgressBar | |
| sigma2 | Estimated in training |
| sparseBeta | Estimated in training |
| sparseKernels | Estimated in training |
| userData | User specified data |
| verbosePlot | Whether or not to plot during training |
| verboseStorage | Specifies whether or not to store the training prtDataset. |
| verboseText | Whether or not to plot during training |
| crossValidate | Cross validate prtAction using prtDataSet and cross validation keys. | |
| get | get the object properties | |
| kfolds | Perform K-folds cross validation of prtAction | |
| optimize | Optimize action parameter by exhaustive function maximization. | |
| plot | Plot the prtRegress object | |
| run | Run a prtAction object on a prtDataSet object. | |
| runRegressorOnGrid | ||
| set | set the object properties | |
| train | Train a prtAction object using training a prtDataSet object. |


Computing machines like calculators, computers or embedded controllers represent all the numbers in binary. Integers can be directly mapped to finite bit stream of binary digits. The common way to represent fractions is using floating points or fixed point. Floating point arithmetic supports wider range of values as it has "floating" decimal point. The number is represented using significant and exponent. [32] presents the various advantages and disadvantage of floating point numbers. Commonly embedded systems do not support floating point values since floating point arithmetic require large logic, needs more computation time and energy [39]. Common alternative is to use fixed point values. Fixed point use fixed number of digits after decimal point. Fixed point values are stored similar to integer values and the decimal position is known since its constant.


Make all players put one finger on the screen. After 2 seconds one player is chosen at random.
Update available. Reload to apply.


News Catchup
Catch up with the latest news
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{% block content %} {% endblock %}


| PostgreSQL 9.0.10 Documentation | ||||
|---|---|---|---|---|
| Prev | Up | Chapter 20. Database Roles and Privileges | Next |
It is frequently convenient to group users together to ease management of privileges: that way, privileges can be granted to, or revoked from, a group as a whole. In PostgreSQL this is done by creating a role that represents the group, and then granting membership in the group role to individual user roles.
To set up a group role, first create the role:
CREATE ROLE name;
Typically a role being used as a group would not have the LOGIN attribute, though you can set it if you wish.
Once the group role exists, you can add and remove members using the GRANT and REVOKE commands:
GRANT group_role TO role1, ... ; REVOKE group_role FROM role1, ... ;
You can grant membership to other group roles, too (since there isn't really any distinction between group roles and non-group roles). The database will not let you set up circular membership loops. Also, it is not permitted to grant membership in a role to PUBLIC.
The members of a group role can use the privileges of the role in two ways. First, every member of a group can explicitly do SET ROLE to temporarily "become" the group role. In this state, the database session has access to the privileges of the group role rather than the original login role, and any database objects created are considered owned by the group role not the login role. Second, member roles that have the INHERIT attribute automatically have use of the privileges of roles of which they are members, including any privileges inherited by those roles. As an example, suppose we have done:
CREATE ROLE joe LOGIN INHERIT; CREATE ROLE admin NOINHERIT; CREATE ROLE wheel NOINHERIT; GRANT admin TO joe; GRANT wheel TO admin;
Immediately after connecting as role joe, a database session will have use of privileges granted directly to joe plus any privileges granted to admin, because joe "inherits" admin's privileges. However, privileges granted to wheel are not available, because even though joe is indirectly a member of wheel, the membership is via admin which has the NOINHERIT attribute. After:
SET ROLE admin;
the session would have use of only those privileges granted to admin, and not those granted to joe. After:
SET ROLE wheel;
the session would have use of only those privileges granted to wheel, and not those granted to either joe or admin. The original privilege state can be restored with any of:
SET ROLE joe; SET ROLE NONE; RESET ROLE;
Note: The SET ROLE command always allows selecting any role that the original login role is directly or indirectly a member of. Thus, in the above example, it is not necessary to become admin before becoming wheel.
Note: In the SQL standard, there is a clear distinction between users and roles, and users do not automatically inherit privileges while roles do. This behavior can be obtained in PostgreSQL by giving roles being used as SQL roles the INHERIT attribute, while giving roles being used as SQL users the NOINHERIT attribute. However, PostgreSQL defaults to giving all roles the INHERIT attribute, for backwards compatibility with pre-8.1 releases in which users always had use of permissions granted to groups they were members of.
The role attributes LOGIN, SUPERUSER, CREATEDB, and CREATEROLE can be thought of as special privileges, but they are never inherited as ordinary privileges on database objects are. You must actually SET ROLE to a specific role having one of these attributes in order to make use of the attribute. Continuing the above example, we might choose to grant CREATEDB and CREATEROLE to the admin role. Then a session connecting as role joe would not have these privileges immediately, only after doing SET ROLE admin.
To destroy a group role, use DROP ROLE:
DROP ROLE name;
Any memberships in the group role are automatically revoked (but the member roles are not otherwise affected). Note however that any objects owned by the group role must first be dropped or reassigned to other owners; and any permissions granted to the group role must be revoked.


These are the various classes and parameters used within progress bars.
{% header "h3", "Classes" %}| Classes | Description |
|---|---|
.s-progress |
The parent class for all progress bars. |
.s-progress--bar |
A child element of .s-progress . It shows the user’s current progress to accomplishing a goal with a background color. |
.s-progress--label |
A child element of .s-progress . It’s used to describe what progress bar is tracking. |
.s-progress__brand |
A modifying class applied to .s-progress , it changes the bar color from green to orange. |
.s-progress__info |
A modifying class applied to .s-progress , it changes the bar color from green to blue. |
.s-progress__circular |
A modifying class applied to .s-progress , it changes the bar to be a percentage of a circle. |
.s-progress__privilege |
A modifying class applied to .s-progress , it changes the bar color from green to a lighter green (for legibility reasons). |
.s-progress__badge |
A modifying class applied to .s-progress , it is applied when a badge progress bar is desired. |
.s-progress__gold |
A modifying class applied to .s-progress__badge , it is used when a gold badge progress bar is desired. |
.s-progress__silver |
A modifying class applied to .s-progress__badge , it is used when a silver badge progress bar is desired. |
.s-progress__bronze |
A modifying class applied to .s-progress__badge , it is used when a bronze badge progress bar is desired. |
.s-progress__segmented |
A modifying class applied to .s-progress for a segmented progress bar |
.s-progress__stepped |
A modifying class applied to .s-progress for a stepped progress bar |
.s-progress--step |
A child of .s-progress__stepped that’s a wrapper for each individual step |
.is-active |
A modifier .s-progress__step that adds visual styling to the active step. |
.is-completed |
A modifier .s-progress__step that adds visual styling to a completed step. |
.s-progress--stop |
A child of .s-progress__step , the circle that might contain a checkmark if the step’s been completed. |
.s-progress--bar__left |
A modifier of .s-progress__bar that draws a line on the left side of a step. |
.s-progress--bar__right |
A modifier of .s-progress__bar that draws a line on the right side of a step. |
| Parameter | Description |
|---|---|
role="progressbar" |
Applied to .s-progress--bar , this parameter communicates that element is displaying progress status that takes a long time or consists of several steps. (Source) |
aria-valuemin |
Applied to .s-progress--bar , this parameter defines the minimum allowed value for the progress bar. (Source) |
aria-valuemax |
Applied to .s-progress--bar , this parameter defines the maximum allowed value for the progress bar. (Source) |
aria-valuenow |
Applied to .s-progress--bar , this parameter defines the current value for the progress bar. (Source) |
style="width: [value]%" |
Applied to .s-progress--bar , this is the aria-valuenow value displayed as a percentage. |
Since most progress bars are used to show successful completion of a task, the default fill color is green.
In addition to the standard progress bar, there is also a brand (orange) and info (blue) progress bar color variation.
In compact layouts, switching to a circular progress might be more appropriate. Our circular progress bar inherits the parent’s text color. You and your designer will need to choose an appropriate font color. You’ll also need to pass the --s-progress-value
CSS variable a value percentage value in decimal form.
By default, the circular progress bars are 32px. These dimensions can be modified by adding t-shirt sizing classes to .s-progress__circular
: .s-progress__sm
, .s-progress__md
, and .s-progress__lg
Taller progress bars used within Profiles to help users understand how close they are to achieving their next privilege.
Taller progress bars used within Profiles to help users understand how close they are to achieving their next gold, silver, or bonze badge.
Stacks provides dividers to create segmented progress bars. The progess bar can be either independent of the dividers, or locked to them.
{% tip, "warning", "mb24" %} Note: The segmented variant has been deprecated. A similar layout can be achieved with several.s-progress-bar
elements in a container.
{% endtip %}
If your experience has a small number of discrete steps, it might be appropriate to use a stepped progress bar.


Clonytopia
None selected
Guide
-
The player with the most cities at the end of 20 turns win.
-
To select something, click on it.
-
If what you are looking for is under something else (like a building under a unit), click it again, and so on.
-
If a unit is on a city that is not yours, you can capture it.
-
If you selected a unit, blue circles denoting the places that unit can move to will be shown. Red circles denote possible attacks.
-
Once a unit has moved, it cannot move for the rest of the turn.
-
You can create new units by selecting a city first. Each units cost 2 resources
-
At the start of each turn, each player receives the resources the same amount of cities they have.


{% extends "layout_task-list.html" %} {% block page_title %} Task list {% endblock %} {% block proposition_header %} {% set my_account="2" %} {% include "includes/propositional_navigation.html" %} {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} This service is temporarily unavailable We're doing work to improve the service. Please try again later. {% endblock %} {% block extra_js_1 %}{% endblock %} {% block extra_js_2 %}{% endblock %}
We're doing work to improve the service. Please try again later.


PLANET SICK-BOY: http://www.sick-boy.com "We Put the SIN in Cinema"
Based on a Rainer Werner Fassbinder play, Water Drops on Burning Rocks is the directorial debut of François Ozon, the reigning bad boy of French cinema. The film centers on the relationship between middle-aged Léopold (Bernard Giraudeau) and twenty-something Franz (Malik Zidi). Their affair begins torridly, but quickly turns dull and routine, with Franz basically becoming Léopold's houseboy.
Just a Franz appears to be ready to check out of the relationship, the situation gets complicated by the arrival of his curvy, sex-starved ex-fiancée (Ludivine Sagnier), who arrives just before Léopold's ex-lover – a man who became a woman to please her former boyfriend (Anna Levine). Hilarity, tragedy and a bizarre dance number ensue in this well-executed film debut.
1:30 – R for nudity, sexual content, adult language and violence
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


Changelog (NOT UPDATED)
9/14/18
Some mastery studies are rephrased thanks to Username5243.
Reduced performance of buying mastery studies while loading a preset.
Reworked EC studies in mastery studies that it work similar to EC studies in non-mastery studies.
Added sacrifice autobuyer for "Twice in the row" achievement.
Added replicants!
Added 8 new mastery studies.
Updated end-game-exclusive news ticker.
Added country codes notation. (inspired by jρʍ-ȼąяℓɨ$ţą's flag notation)
Reduced the minimum of tetration base to 1.6. (Suggested by Garnet420)
Fixed a bug that eternity challenge requirements always change that are in mastery studies.
Fixed a bug that bought EC-type mastery study would change all lines to purple that are linked into it.
Fixed a bug that buying EC-type mastery study would take you into eternity challenges tab while loading a preset.
Fixed a bug that quantuming wouldn't show quarks tab in quantum tab if you turned off 4.5 hours reward, got 13.3 minutes milestone, and the quantum tab shows electrons.
9/13/18
Added logarithm change option. (Suggested by CatIsFluffy!)
Added base options for logarithm & tetration notations. (Suggested by Username5243!)
9/10/2018 - 9/12/18
Just bugfixes...
9/9/2018
Added respec options, including respec only mastery studies button. (suggested by Meringer!)
Code refactoring.
Fixed a bug that passive TT generation breaks the game if you have below 1 tachyon particles.
9/8/2018
Added max QK mult!
Splitted options tab to 2 sub-tabs. One contains saving options and other one contains everything else.
Reworked switch theme option to make it non-toggleable but have its' menu instead.
Added small-size customizable Spazzy notation made by PlanetN9ne.
Added significant digits option for Scientific notation.
Changed "notation option" button text due to new notation option changes.
Code refactoring.
Fixed a bug that EP gain rate always appear before first eternity.
Fixed a bug that you cannot buy dimension cost multiplier increase upgrade after you upgraded it to 4x and then complete 5 completions of EC6.
Fixed a bug that in NG^, IP mult upgrade autobuyer always fail to buy.
9/7/2018
Updated Psi notation.
Added 2 customizable Psi notation options.
Fixed a bug that sacrifice gain multiplier always use pre-IC2 formula.
9/6/2018
Fixed a bug that if you gained over 1.79e307 DT in a game tick, the game breaks.
Fixed a bug that in NG^, the progress bar breaks when e315652 EP gain is reached.
Fixed a bug that in NG^, 2x IP mult upgrade autobuyer is broken.
9/5/2018
Notation menu UI is changed slightly.
Added more changelogs from 8/31/18 to today!
Added a hotkey for quantum. (coded by AnteSocial)
Added the support of NG^. (this made is created by Naruyoko)
Added NG^+- mode. (a mix of NG^ and NG+ with no changes)
Fixed a typo in how to play page.
Finished NG++ how to play sections and added placeholders for NG+++ how to play sections.
Added NG+++ guide link. (leads to Username5243's document)
Code refactoring.
Fixed a bug that time theorem shop is always hidden when passive TT generation rate is above 0.
Fixed a bug that in Eternity Respecced, the power is doubled instead of tripled when buying a time dimension.
Fixed a bug that in Firefox, you can't scroll notation menu.
9/4/2018
Added export & import time study specs.
Added time study spec presets!
More balancing for my ported Eternity Respecced.
Added more prehistoric stats. (up to 315,000 BC)
Updated 1 NG+++ end-game news ticker with Replicants sneak peak. (but there is a typo. I'm sorry.)
Added 1 new NG+++-exclusive news ticker that is impossible to get.
Fixed a bug that in Eternity Respecced, the power from bought time dimensions is squared.
Fixed a bug that importing with oldest Eternity Respecced saves may break the game.
Fixed a bug that buying ERS time study 2 does not change the tickspeed threshold, but ERS time study 6 can.
9/3/2018
1 B eternity milestone is now clarified thanks to Username5243.
"I already got rid of you..." achievement is now possible by reducing the requirement to 1e400,000 IP.
Added Psi notation. (coded by CatIsFluffy)
Added 2 other notations. (coded by me!)
Replaced 2 notation option buttons with a new notation option menu! (Suggested by 5 people)
Moved toggle progress bar option into a formerly-vacant spot.
Saved more performance for switching tabs with canvas.
Code refactoring.
Fixed a bug that you can buy TS302 without unlocking paired challenges.
Fixed a bug that sub-1ys replicate interval may be displayed with undefined.
9/2/2018
You can now show time study numbers in mastery studies by shifting! (thanks to zacki1)
Removed useless code.
Fixed a bug that you can break the game when starting a challenge after you bought TS231 and switched your number library to logarithmica_numerus.js.
Fixed a bug that in NG---, getting a galaxy would have different tickspeed multiplier.
Fixed a bug that you can probably break your save by loading a save with different extra replicated galaxies and different remote galaxy scaling start.
Fixed a bug that TS281 and TS282 displayed multiplier always does not change. (thanks to zacki1)
Fixed a bug that after quantuming without 4.5 hours reward enabled, quantum challenges tab wouldn't hide.
9/1/2018
Added toggle progress bar option.
Made visualized tachyon particles white if my theme is currently used.
QK gain rate peak value does not show anymore when it is greater than 1e30. (except you are using my theme)
Repharsed the color charge production.
Because people hates scrolling for buying gluon upgrades, the column of gluon upgrades is now spitted to 2 upgrades per row.
Nerfed remote galaxy scaling slowdown upgrades by a little.
Implemented "Pick all time studies before mastery studies" time study.
Added 1 new achievement row for current NG+++ end-game.
Saved performance for switching tabs with canvas.
Saved performance for displayed mastery study costs.
Saved performance for hidden always-updated animations.
Code refactoring.
Fixed a bug that you can reset to skip up to 4th dimension shift by buying only "You start with the 5th dimension unlocked" upgrade instead of buying reset skip upgrades.
Fixed a bug that loading a different save show a progress bar.
Fixed a bug that max buying meta dimensions would break your meta-antimatter amount.
Fixed a bug that loading a different save does not change selected quantum tab back to initial.
Fixed a bug that "Work Harder." achievement is impossible to get.
Fixed a bug that you can get "I already rid of you..." achievement with bought time studies in mastery studies.
Fixed a bug that switching notations (not on exponents) always change the hidden text of your best TP.
Prior changes
August 2018
Pre-August 2018
Base game


--- layout: post title: "No Title" ---
There’s a fantastic example on Tumblr of feature addition that’s invisible to anyone not using it. End your text, photo, link, audio or video post with a question mark and a new field with a check box appears asking to allow answers.
Sure you have to end in a question mark to know about the feature but unless you’re ending in a question mark, you have no need for the feature anyway.


Accurately representing words as vectors is a challenging, but necessary task in machine learning. Consider the following sentences:
- The garden is pretty
- The garden is beautiful
As humans we know that “pretty” and “beautiful” are similar, but how can we learn vector representations of these words so that they are “close” together? If this can be done, we can start to tackle bigger challenges, such as understanding customer reviews and summarizing content.
The goal of this post is to first explain the intuition behind these 3 methods for learning word embeddings (word2vec, GloVe, fasttext), then provide Python code to get started using them quickly.
Word2Vec
Word2vec really refers to two models for learning word vectors: the continuous bag-of-words (CBOW) and the skip-gram model. They are very similar - CBOW accepts context words as input and predicts a target word, whereas the skip-gram accepts a target word as input and predicts a context word.
Although we will primarily focus on the skip-gram, both models are single layer neural networks that accept one-hot encoded vectors as input. We learn the weights of the hidden layer, and each row of this weight matrix is a word vector. The algorithm forces word vectors closer to each other every time words appear in each other’s context, regardless of position in the context window. It does this by: (1) maximizing the probability that an observed word appears in the context of a target word and (2) minimizing the probability that a randomly selected word from the vocabulary appears in the context of the target word.
If your understanding of neural network weights and the weight matricies is still shakey, this chapter gives a good into.
Deep Dive
The main idea behind the skip-gram model is that we take a word in an input sequence as the target word, and predict its context words. The context of a word is the $m$ words surrounding it. In figure 1, the window size is 2, the target word (“into”) is in red and its context words (“problems”, “turning”, “banking”, “crises”) are in blue.
Figure 1: The skip gram prediction of target word “into” with window size 2. Taken from Stanford’s NLP course
Let’s formalize some notation. We have an input sequence of words, $w_1, w_2,.., w_T$, each of which has a context window, $-m \le j \le m$. We’ll call this input sequence the corpus, and all its unique words the vocabulary. Each word in the vocabulary will have 2 vector representations during training, $u_o$ when it’s a context word and $v_c$ when it’s a target word. In figure 1, $u_{turning}$ is the vector representation of “turning” as a context word, and $v_{banking}$ is the vector representation of “banking” as a target word.
We want to calculate the probability that each word in the window, $w_{t+j}$, appears in the context of the target word $w_t$. We’ll refer to this probability as $p(w_{t+j} \lvert w_t; \theta)$. This may seem weird, but the probability is based on the vector representations of each word. When we encounter a word in the context of another, we alter their vector representations to be “closer”. So the more we see words in each other’s context, the closer their vectors are. The function $J(\theta)$ below describes this; $\theta$ is a placeholder representing all the vector representations. We minimize $-J(\theta)$ in order to find the optimal parameters that will maximize $p(w_{t+j} \lvert w_t; \theta)$.
\[J(\theta) = -\frac{1}{T} \sum^{T}_{t = 1} \sum_{-m \le j \le m, j \ne 0} log(p(w_{t+j} \lvert w_t; \theta))\]The only problem here is we have no idea how to find $p(w_{t+j} \lvert w_t; \theta)$. We’ll start with using the softmax function. This essentially calculates how similar a context word, $u_o$, is to target word $v_c$, relative to all other context words in the vocabulary. The measure of similarity between two words is measured by the dot product $u_o^T v_c$, and a larger dot product means more similar words.
\[p(w_{t+j} \lvert w_t; \theta) = \frac{e^{u_o^T v_c}}{\sum_{w=1}^W e^{u_w^T v_c}}\]Where:
- $W$ is the size of the vocabulary
- $c$ and $o$ are indices of the words in the vocabulary at sequence positions $t$ and $j$ respectively
- $u_o = word2vec(w_{t+j})$
- $v_c = word2vec(w_t)$
Although we now have a way of quantifying the probability a word appears in the context of another, the $\sum_{w=1}^W e^{u_w^T v_c}$ term requires us to iterate over all words in the vocabulary, making it computationally inefficient. To deal with this, we must approximate the softmax probability. One way of doing this is called negative sampling.
Negative Sampling Loss
Negative sampling overcomes the need to iterate over all words in the vocabulary to compute the softmax by sub-sampling the vocabulary. We sample $k$ words and determine the probability that these words do not co-occur with the target word. The intuition behind this is that a good model should be able to differentiate between data and noise.
To incorporate negative sampling, the objective function needs to be altered by replacing $p(w_{t+j} \lvert w_t)$ with:
\[log(\sigma(u_o^T v_c)) + \sum_{i = 1}^{k}E_{j \sim P(w)} [log(\sigma(-u_j^T v_c))]\]Where $\sigma(.)$ is the sigmoid function.
The new objective function becomes:
\[J(\theta) = \frac{-1}{T} \sum_{t=1}^{T}J_t(\theta) \notag\] \[J_t(\theta) = log(\sigma(u_o^T v_c)) + \sum_{i = 1}^{k}E_{j \sim P(w)} [log(\sigma(-u_j^T v_c))] \notag\] \[P(w) = U(w)^{3/4}/Z \notag\]Let’s look at each component of and try to convince ourselves this makes sense.
The first part, $log(\sigma(u_o^Tv_c))$, can be interpreted as the log probability of the target and context words co-occurring. We want the model to find $u_o$ and $v_c$ to maximize this probability.
The second part, $\sum_{i = 1}^{k}E_{j \sim P(w)} [log(\sigma(-u_j^T v_c))]$, is where the negative sampling happens. Let’s break this up more to make it clearer. It’ll come in handy to note that $\sigma(-x) = 1 - \sigma(x)$.
We can first drop the $E_{j \sim P(w)}$ term, since we already know we will be sampling words from some distribution, $P(w)$. We end up with:
\[\sum_{i = 1}^{k} log(\sigma(-u_j^T v_c)) = \sum_{i = 1}^{k} log(1 - \sigma(u_j^T v_c)) \notag\]Now that this is easier to read, we’re taking the log of 1 minus the probability that the sampled word, $j$, appears in the context of the target word $c$. This is just log probability that $j$ does not appear in the context of the $c$. Since $j$ is randomly drawn out of ~$10^6$ words, there’s a very small chance it appears in the context of $c$, so this probability should be high. We do this for each of the $k$ sampled words.
Finally, we have to specify a distribution for negative sampling, $P(w) = U(w)^{3/4}/Z$. Here, $U(w)$ is the count of each word in the corpus (unigram distribution) and is raised to the $\frac{3}{4}$th power to sample rarer words in the vocabulary. $Z$ is just a normalization term to turn $P(w)$ into a probability distribution.
To summarize, this loss function is trying to maximize the probability that word $o$ appears in the context of word $c$, while minimizing the probability that a randomly selected word from the vocabulary does not appear in the context of word $c$. We use the gradient of this loss function to iteratively update the word vectors, $u_o$ and $v_c$, and eventually get our word embeddings.
Here is a great tutorial on the skip-gram model!
GloVe
GloVe (Global Vectors) is another architecture for learning word embeddings that improves on the skip-gram model by incorporating corpus statistics. Since the skip-gram model looks at each window independently, it loses corpus statistics. In contrast, GloVe uses word co-occurrence counts to capture global information about the corpus. GloVe learns word embeddings by minimizing the difference between word vector dot products and their log co-occurrence counts.
Deep Dive
The Co-Occurrence Matrix
The co-occurrence matrix, $X$, is generated from the corpus and vocabulary. The entry at $X_{ij}$ is then the number of times word $j$ occurs in the context of word $i$. Context is defined in the same way as the skip-gram model. Summing over all the values in row $i$, will give the number of words that occur in its context, $X_i = \sum_k X_{ik}$. Then the probability of word $j$ occurring in the context of word $i$ is $P(i \lvert j) = \frac{X_{ij}}{X_i}$.
Below is the co-occurrence matrix for the corpus containing:
- “I like deep learning.”
- “I like NLP.”
- “I enjoy flying.”
From Softmax to GloVe
We can find global loss using the softmax function, $Q_{ij}$, by summing over all target-context word pairs.
\[J = - \sum_{i \in corpus} \sum_{j \in context} log (Q_{ij}) \notag\]Since words $i$ and $j$ appear $X_{ij}$ times in the corpus, we don’t need to iterate over all windows in the corpus, but can iterate over the vocabulary and multiply by the co-occurrence count.
\[J = - \sum_{i = 1}^{W} \sum_{j = 1}^{W} X_{ij} log Q_{ij} \notag\]Re-arranging some terms, we can come up with this:
\[J = - \sum_{i = 1}^{W} X_{i} \sum_{j = 1}^{W} P_{ij}log(Q_{ij}) \notag\]
What’s going on here?
- Where did $P_{ij}$ come from? - Remember that $P_{ij} = \frac{X_{ij}}{X_i}$ and $X_i = \sum_k X_{ik}$, therefore we can substitute $X_{ij} = P_{ij}X_i$.
- What’s the relationship between $P_{ij}$ and $Q_{ij}$? - $P_{ij}$ is the probability that word $j$ appears in the context of word $i$, but $Q_{ij}$ is also the probability that word $j$ appears in the context of word $i$. The difference between the two lies in how they are calculated. $P_{ij}$ is calculated using the co-occurrence matrix and doesn’t change. $Q_{ij}$ is the naive softmax probability, that is calculated using the dot product of word vectors $u_j$ and $v_i$. We have the ability to change $Q_{ij}$ by changing these vectors.
- What’s the point of $P_{ij}log(Q_{ij})$? - Now that we’ve refreshed our memory of $P$ and $Q$, we can see that $P$ is the true probability distribution of context and target words, and $Q$ is some made up distribution based on the “goodness” of the word vectors. We really want these two distributions to be close to each other. Observing $H = P_{ij}log(Q_{ij})$, when $P$ and $Q$ are close to each other, $H$ is small, and when $P$ and $Q$ are far apart, $H$ is larger. Our end goal is the minimization of $J$, so the smaller $H$ is the better. This term is the cross-entropy between distributions $P$ and $Q$.
The problem here is that cross-entropy requires normalized versions of $Q_{ij}$ and $P_{ij}$ which we have to iterate over the entire vocabulary to calculate. This is the reason for using Negative Sampling in the skip-gram model. GloVe’s approach to this is dropping the normalization terms completely, so we end up with $\hat{P}$ and $\hat{Q}$, which are unnormalized distributions. The cross-entropy function is now useless, so we change our objective function to be a squared error function.
\[\hat{J} = \sum_{i = 1}^{W} X_{i} \sum_{j = 1}^{W} (\hat{P}_{ij} - \hat{Q}_{ij})^2 \notag\]Now we have the squared error, weighted by the number of co-occurrences of words $i$ and $j$. There’s one last problem with this, which is that some co-occurrence counts can be massive. This will affect both the weights, $X_i$, and $\hat{P_{ij}} = X_{ij}$. To deal with this explosion in the squared term, we take $log(hat{P})$ and $log(hat{Q})$) and to deal with the explosion of weights, we introduce a function, $f$ that caps the co-occurrence count weight. We’ll apply $f$ to each target-context pair, $X_{ij}$ as opposed to only $X_i$. The new loss function becomes:
\[\hat{J} = \sum_{w = 1}^{W} \sum_{w = 1}^{W} f(X_{ij}) (u_j^T v_i - log(X_{ij}))^2 \notag\]This is the loss function that the GloVe model minimizes.
Fasttext
Fasttext is a powerful library for learning word embeddings that was introduced by Facebook in 2016. Its roots come from the word2vec models.
Word2vec trains a unique vector for each word, ignoring important word sub-structure (morphological structure) and making out-of-vocabulary prediction impossible. Fasttext attempts to solve this by treating each word as a sum of its subwords. These subwords can be defined in any way, however the simplest form is a character n-gram. A vector representation is associated with each n-gram, then the vector for each word is simply the sum of each of its n-grams. Fasttext learns word embeddings for each subword, then treats each word as a sum of its subwords.
Deep Dive
Before starting, we’ll take a step back to quantifying how similar two word vectors are. Both GloVe and word2vec do this using dot products, however we can think of the similarity more generally as an arbitrary function, $s(u_j, v_c)$.
Fasttext redefines this similarity measure, and represents words as a sum of smaller words, each of length $n$, called n-grams. To help the model learn prefixes and suffixes, we append “<” to the front and “>” to the back of each word. Then for n=3, the n-grams of “where” are:
<where> = [<wh, whe, her, ere, re>]
We have no way of determining the difference between the subword “her” and the full-word “her” (there definitely should be a difference). Appending the special characters around each word helps with this, as the tri-gram “her” is now different from the sequence “<her>”.
More formally, suppose we have all n-grams in the vocabulary, $G$, each represented by a vector, $\boldsymbol{z}_g$. We can refer to all the n-grams of some word, $w$, by $G_w$. Then $w$ can be represented as a sum of all n-grams. The new similarity function becomes:
\[s(w, c) = \sum_{g \in G_w} \boldsymbol{z}_g^T v_c \notag\]We learn the embeddings of each character n-gram and then each word embedding is a sum of its n-gram vectors.
Word Embeddings in Python
Now let’s explore word embeddings using pre-trained models in the gensim
Python package. If you don’t have it installed, run pip install gensim
in your command line. Gensim offers pre-trained models from their gensim.downloader
method and each model used here embeds words in a 300-dimensional space. A full list of the models available can be found here, or by running python -m gensim.downloader --info
in your command line.
import gensim
import gensim.downloader as api
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
# clearer images if you're using a Jupyter notebook
%config InlineBackend.figure_format='retina'
Word2vec
For word2vec, we’ll use Google’s News dataset model, trained on news articles with a vocabulary of 3 million words and 300 dimensional embedding vectors. This repo has an in-depth analysis of the words in the model.
word2vec_model_path = "word2vec-google-news-300"
print(api.info(word2vec_model_path))
word2vec_model = api.load(word2vec_model_path)
w2v = word2vec_model.wv
# remove from env
del word2vec_model
GloVe
The glove-wiki-gigaword-300 model used here is trained on 6B tokens from Wikipedia 2014 and the Gigaword dataset, other pre-trained GloVe models can be downloaded from Stanford or from Gensim.
glove_model_path = "glove-wiki-gigaword-300"
print(api.info(glove_model_path))
glove_model = api.load(glove_model_path)
glove = glove_model.wv
del glove_model
Fasttext
Fasttext provides pre-trained models on for multiple languages, which can be used in different ways (through the command line, downloading the model, through gensim
, etc.). We’ll use the English model provided by Gensim
which is trained on Wikipedia 2017 and news data, but you can go through their Github to see more.
Word Comparison
Now we have vector representations for all words in the vocabulary in wv
and can compare the different models. We’ll add and subtract some word vectors, then see what the closest word to the resulting vector is. Papers and blog posts have exhausted the “king” - “man” + “woman” = “queen” example, so I’ll present some new ones.
Results generated by the find_most_similar
function are of the form (word, cosine similarity), where each word is the closest to the one parsed into the function. Cosine similarity values closer to 1 mean that the vectors (words) are more similar. The function definition can be found in the Appendix.
Start with: doctor - man + woman
print ("word2vec: Doctor - Man + Woman")
find_most_similar(w2v["doctor"] - w2v["man"] + w2v["woman"], w2v,
["man", "doctor", "woman"])
print ("GloVe: Doctor - Man + Woman")
find_most_similar(glove["doctor"] - glove["man"] + glove["woman"], glove,
["man", "doctor", "woman"])
print ("fasttext: Doctor - Man + Woman")
find_most_similar(fasttext["doctor"] - fasttext["man"] + fasttext["woman"], fasttext,
["man", "doctor", "woman"])
word2vec: Doctor - Man + Woman
[('gynecologist', 0.7276507616043091),
('nurse', 0.6698512434959412),
('physician', 0.6674120426177979)]
GloVe: Doctor - Man + Woman
[('physician', 0.6203880906105042),
('nurse', 0.6161285638809204),
('doctors', 0.6017279624938965)]
fasttext: Doctor - Man + Woman
[('gynecologist', 0.6874127388000488),
('nurse-midwife', 0.6773605346679688),
('physician', 0.6561880111694336)]
Interesting, what about if we make a subtle change to doctor - woman + man
?
print ("word2vec: Doctor - Woman + Man")
find_most_similar(w2v["doctor"] - w2v["woman"] + w2v["man"], w2v,
["man", "doctors", "doctor", "woman"])
print ("GloVe: Doctor - Woman + Man")
find_most_similar(glove["doctor"] - glove["woman"] + glove["man"], glove,
["man", "doctors", "doctor", "woman", "dr."])
print ("fasttext: Doctor - Woman + Man")
find_most_similar(fasttext["doctor"] - fasttext["woman"] + fasttext["man"], fasttext,
["man", "doctors", "doctor", "woman", "dr."])
word2vec: Doctor - Woman + Man
[('physician', 0.6823904514312744),
('surgeon', 0.5908077359199524),
('dentist', 0.570309042930603)]
GloVe: Doctor - Woman + Man
[('physician', 0.5128607153892517),
('he', 0.4661550223827362),
('brother', 0.46356332302093506)]
fasttext: Doctor - Woman + Man
[('physician', 0.6969557404518127),
('docter', 0.6826808452606201),
('non-doctor', 0.6698156595230103)]
This is a different result from the original results. Biases in the training data are expressed by the model. Also interestingly, there are some misspelled words in fasttext. This is because of the difference in learning methods.
Visualizing Embeddings
For the sake of completeness, I plotted words from different walks of life to see if the algorithms were able to unravel their semantic similarities/differences. The first 2 principal components of each word vector are plotted. Some expected similarities are seen here, however, we lose a lot of information from reducing the dimension from 300 to 2.
plot_embeds(["dog", "cat", "hamster", "pet"] + # animals
["boy", "girl", "man", "woman"] + # humans
["grown", "adult", "young", "baby"] + # age
["german", "english", "spanish", "french"] + # languages
["mathematics", "physics", "biology", "chemistry"], # natural sciences
w2v,
title = "word2vec Embedding")
# run this again, but changing w2v to glove and fasttext
Wrapping up, there are some key differences between word2vec (skip-gram), GloVe and fasttext. The skip-gram iterates over the corpus predicting context words given a target word. GloVe builds on this by incorporating global corpus statistics using word co-occurrences. The results are similar to word2vec. Fasttext also builds on word2vec by breaking each word into a sum of its sub-words. It learns vectors for each subword, then combines them for prediction. This allows out-of-vocabulary prediction, but introduces the risk of misspelled words.
Both word2vec and GloVe can be used as frameworks for learning general similarities in text without considering what each token is made of. This makes them useful for tasks like finding similar movies given a sequence of movies watched by users. Fasttext on the other hand is more robust for translation tasks, where the likelihood of encountering an out-of-vocabulary word is higher.
More Code
# find the 3 most similar words to the vector "vec"
def plot_embeds(word_list, wv, title = None, word_embeddings = None, figsize = (12,7)) :
# pca on the embedding
pca = PCA(n_components=2)
X = pca.fit_transform(wv[word_list])
ax = plt.figure(figsize=figsize)
ax.subplots()
_ = plt.scatter(X[:,0], X[:,1])
for label, point in list(zip(word_list, X)):
_ = plt.annotate(label, (point[0] - 0.075, point[1] + 0.075))
# Turn off tick labels
plt.title(title)
plt.xticks([])
plt.yticks([])
def find_most_similar (vec, wv, words = None) :
# vec: resulting vector from word Arithmetic
# words: list of words that comprise vec
s = wv.similar_by_vector(vec, topn = 10)
# filter out words like "king" and "man", or else they will be included in the similarity
if (words != None) :
word_sim = list(filter(lambda x: (x[0] not in words), s))[:3]
else :
return (s[:3])
return (word_sim)


TIFF CHANGE INFORMATION
This document describes the changes made to the software between the
previous and current versions (see above). If you don't
find something listed here, then it was not done in this timeframe, or
it was not considered important enough to be mentioned. The following
information is located here:
MAJOR CHANGES:
CHANGES IN THE SOFTWARE CONFIGURATION:
- test/Makefile.am: Add some tests for tiff2bw.
- * .appveyor.yml, .travis.yml, build/travis-ci: apply patches
0001-ci-Travis-script-improvements.patch and
0002-ci-Invoke-helper-script-via-shell.patch by Roger Leigh
(sent to mailing list)
- .travis.yml, build/travis-ci: new files from
0001-ci-Add-Travis-support-for-Linux-builds-with-Autoconf.patch by
Roger Leigh (sent to mailing list on 2017-06-08)
This patch adds support for the Travis-CI service.
- .appveyor.yml: new file from
0002-ci-Add-AppVeyor-support.patch by Roger Leigh (sent to mailing
list on 2017-06-08)
This patch adds a .appveyor.yml file to the top-level. This allows
one to opt in to having a branch built on Windows with Cygwin,
MinGW and MSVC automatically when a branch is pushed to GitHub,
GitLab, BitBucket or any other supported git hosting service.
- CMakeLists.txt, test/CMakeLists.txt, test/TiffTestCommon.cmake: apply
patch 0001-cmake-Improve-Cygwin-and-MingGW-test-support.patch from Roger
Leigh (sent to mailing list on 2017-06-08)
This patch makes the CMake build system support running the tests
with MinGW or Cygwin.
- test/tiffcp-lzw-compat.sh, test/images/quad-lzw-compat.tiff: new files
to test old-style LZW decompression
- test/common.sh, Makefile.am, CMakeList.txt: updated with above
- test/Makefile.am: add missing reference to images/quad-lzw-compat.tiff
to fix "make distcheck". Patch by Roger Leigh
- nmake.opt: support a DEBUG=1 option, so as to adjust OPTFLAGS and use
/MDd runtime in debug mode.
CHANGES IN LIBTIFF:
- libtiff/tif_color.c: TIFFYCbCrToRGBInit(): stricter clamping to avoid
int32 overflow in TIFFYCbCrtoRGB().
Fixes https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=1844
Credit to OSS Fuzz
- libtiff/tif_getimage.c: initYCbCrConversion(): stricter validation for
refBlackWhite coefficients values. To avoid invalid float->int32 conversion
(when refBlackWhite[0] ==<PHONE_NUMBER>.f)
Fixes https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=1907
Credit to OSS Fuzz
- libtiff/tif_dirinfo.c, tif_dirread.c: add _TIFFCheckFieldIsValidForCodec(),
and use it in TIFFReadDirectory() so as to ignore fields whose tag is a
codec-specified tag but this codec is not enabled. This avoids TIFFGetField()
to behave differently depending on whether the codec is enabled or not, and
thus can avoid stack based buffer overflows in a number of TIFF utilities
such as tiffsplit, tiffcmp, thumbnail, etc.
Patch derived from 0063-Handle-properly-CODEC-specific-tags.patch
(http://bugzilla.maptools.org/show_bug.cgi?id=2580) by Raphaël Hertzog.
Fixes:
http://bugzilla.maptools.org/show_bug.cgi?id=2580
http://bugzilla.maptools.org/show_bug.cgi?id=2693
http://bugzilla.maptools.org/show_bug.cgi?id=2625 (CVE-2016-10095)
http://bugzilla.maptools.org/show_bug.cgi?id=2564 (CVE-2015-7554)
http://bugzilla.maptools.org/show_bug.cgi?id=2561 (CVE-2016-5318)
http://bugzilla.maptools.org/show_bug.cgi?id=2499 (CVE-2014-8128)
http://bugzilla.maptools.org/show_bug.cgi?id=2441
http://bugzilla.maptools.org/show_bug.cgi?id=2433
- libtiff/tif_swab.c: if DISABLE_CHECK_TIFFSWABMACROS is defined, do not do
the #ifdef TIFFSwabXXX checks. Make it easier for GDAL to rename the symbols
of its internal libtiff copy.
- libtiff/tif_dirread.c: fix regression of libtiff 4.0.8 in
ChopUpSingleUncompressedStrip() regarding update of newly single-strip
uncompressed files whose bytecount is 0. Before the change of 2016-12-03,
the condition bytecount==0 used to trigger an early exit/disabling of
strip chop. Re-introduce that in update mode. Otherwise this cause
later incorrect setting for the value of StripByCounts/StripOffsets.
( https://trac.osgeo.org/gdal/ticket/6924 )
- libtiff/tif_dirread.c: TIFFFetchStripThing(): limit the number of items
read in StripOffsets/StripByteCounts tags to the number of strips to avoid
excessive memory allocation.
Fixes https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=2215
Credit to OSS Fuzz
- libtiff/tif_getimage.c: avoid many (harmless) unsigned int overflows.
- libtiff/tif_fax3.c: avoid unsigned int overflow in Fax3Encode2DRow(). Could
potentially be a bug with huge rows.
- libtiff/tif_jpeg.c: avoid (harmless) unsigned int overflow on tiled images.
- libtiff/tif_dirread.c: avoid unsigned int overflow in EstimateStripByteCounts()
and BYTECOUNTLOOKSBAD when file is too short.
- libtiff/tif_predict.c: decorate legitimate functions where unsigned int
overflow occur with TIFF_NOSANITIZE_UNSIGNED_INT_OVERFLOW
* libtiff/tif_dirread.c: avoid unsigned int overflow in EstimateStripByteCounts()
- libtiff/tiffiop.h: add TIFF_NOSANITIZE_UNSIGNED_INT_OVERFLOW macro to
disable CLang warnings raised by -fsanitize=undefined,unsigned-integer-overflow
- libtiff/tif_jpeg.c: add anti-denial of service measure to avoid excessive
CPU consumption on progressive JPEGs with a huge number of scans.
See http://www.libjpeg-turbo.org/pmwiki/uploads/About/TwoIssueswiththeJPEGStandard.pdf
Note: only affects libtiff since 2014-12-29 where support of non-baseline JPEG
was added.
- libtiff/tif_jpeg.c: error out at decoding time if anticipated libjpeg
memory allocation is above 100 MB. libjpeg in case of multiple scans,
which is allowed even in baseline JPEG, if components are spread over several
scans and not interleavedin a single one, needs to allocate memory (or
backing store) for the whole strip/tile.
See http://www.libjpeg-turbo.org/pmwiki/uploads/About/TwoIssueswiththeJPEGStandard.pdf
This limitation may be overridden by setting the
LIBTIFF_ALLOW_LARGE_LIBJPEG_MEM_ALLOC environment variable, or recompiling
libtiff with a custom value of TIFF_LIBJPEG_LARGEST_MEM_ALLOC macro.
- libtiff/tif_jbig.c: fix memory leak in error code path of JBIGDecode()
Fixes http://bugzilla.maptools.org/show_bug.cgi?id=2706
Reported by team OWL337
- libtiff/tif_dirread.c: in TIFFReadDirEntryFloat(), check that a
double value can fit in a float before casting. Patch by Nicolas RUFF
- libtiff/tiffiop.h, libtiff/tif_jpeg.c, libtiff/tif_jpeg_12.c,
libtiff/tif_read.c: make TIFFReadScanline() works in
CHUNKY_STRIP_READ_SUPPORT mode with JPEG stream with multiple scans.
Also make configurable through a LIBTIFF_JPEG_MAX_ALLOWED_SCAN_NUMBER
environment variable the maximum number of scans allowed. Defaults to
100.
- libtiff/tif_read.c: TIFFFillTile(): add limitation to the number
of bytes read in case td_stripbytecount[strip] is bigger than
reasonable, so as to avoid excessive memory allocation (similarly to
what was done for TIFFFileStrip() on 2017-05-10)
- libtiff/tif_getimage.c: use _TIFFReadEncodedStripAndAllocBuffer().
Fixes http://bugzilla.maptools.org/show_bug.cgi?id=2708 and
https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=2433 .
Credit to OSS Fuzz
- libtiff/tif_read.c, tiffiop.h: add a _TIFFReadEncodedStripAndAllocBuffer()
function, variant of TIFFReadEncodedStrip() that allocates the
decoded buffer only after a first successful TIFFFillStrip(). This avoids
excessive memory allocation on corrupted files.
- libtiff/tif_dirwrite.c: in TIFFWriteDirectoryTagCheckedXXXX()
functions associated with LONG8/SLONG8 data type, replace assertion that
the file is BigTIFF, by a non-fatal error.
Fixes http://bugzilla.maptools.org/show_bug.cgi?id=2712
Reported by team OWL337
- libtiff/tif_read.c: TIFFStartTile(): set tif_rawcc to
tif_rawdataloaded when it is set. Similarly to TIFFStartStrip().
This issue was revealed by the change of 2017-06-30 in TIFFFileTile(),
limiting the number of bytes read. But it could probably have been hit
too in CHUNKY_STRIP_READ_SUPPORT mode previously ?
Fixes https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=2454
Credit to OSS Fuzz
- libtiff/tif_error.c, tif_warning.c: correctly use va_list when both
an old-style and new-style warning/error handlers are installed.
Patch by Paavo Helde (sent on the mailing list)
- libtiff/tif_getimage.c: use _TIFFReadTileAndAllocBuffer().
Fixes https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=2470
Credit to OSS Fuzz.
- libtiff/tif_read.c, tiffiop.h: add a _TIFFReadEncodedTileAndAllocBuffer()
and _TIFFReadTileAndAllocBuffer() variants of TIFFReadEncodedTile() and
TIFFReadTile() that allocates the decoded buffer only after a first
successful TIFFFillTile(). This avoids excessive memory allocation
on corrupted files.
- libtiff/tif_pixarlog.c: avoid excessive memory allocation on decoding
when RowsPerStrip tag is not defined (and thus td_rowsperstrip == UINT_MAX)
Fixes https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=2554
Credit to OSS Fuzz
- libtiff/tif_lzw.c: fix 4.0.8 regression in the decoding of old-style LZW
compressed files.
- libtiff/tif_lzw.c: fix potential out-of-buffer read on 1-byte LZW
strips. Crashing issue only on memory mapped files, where the strip
offset is the last byte of the file, and the file size is a multiple
of one page size on the CPU architecture (typically 4096). Credit
to myself :-)
- libtiff/tif_dir.c: avoid potential null pointer dereference in
_TIFFVGetField() on corrupted TIFFTAG_NUMBEROFINKS tag instance.
Fixes http://bugzilla.maptools.org/show_bug.cgi?id=2713
- tools/tiff2pdf.c: prevent heap buffer overflow write in "Raw"
mode on PlanarConfig=Contig input images.
Fixes http://bugzilla.maptools.org/show_bug.cgi?id=2715
Reported by team OWL337
- libtiff/tif_read.c: TIFFFillStrip() / TIFFFillTile().
Complementary fix for http://bugzilla.maptools.org/show_bug.cgi?id=2708
in the isMapped() case, so as to avoid excessive memory allocation
when we need a temporary buffer but the file is truncated.
- libtiff/tif_read.c: TIFFFillStrip() / TIFFFillTile().
Complementary fix for http://bugzilla.maptools.org/show_bug.cgi?id=2708
in the isMapped() case, so as to avoid excessive memory allocation
when we need a temporary buffer but the file is truncated.
- libtiff/tif_read.c: in TIFFFetchStripThing(), only grow the
arrays that hold StripOffsets/StripByteCounts, when they are smaller
than the expected number of striles, up to 1 million striles, and
error out beyond. Can be tweaked by setting the environment variable
LIBTIFF_STRILE_ARRAY_MAX_RESIZE_COUNT.
This partially goes against a change added on 2002-12-17 to accept
those arrays of wrong sizes, but is needed to avoid denial of services.
Fixes https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=2350
Credit to OSS Fuzz
- libtiff/tif_read.c: in TIFFFetchStripThing(), only grow the
arrays that hold StripOffsets/StripByteCounts, when they are smaller
than the expected number of striles, up to 1 million striles, and
error out beyond. Can be tweaked by setting the environment variable
LIBTIFF_STRILE_ARRAY_MAX_RESIZE_COUNT.
This partially goes against a change added on 2002-12-17 to accept
those arrays of wrong sizes, but is needed to avoid denial of services.
Fixes https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=2350
Credit to OSS Fuzz
- libtiff/tif_read.c: add protection against excessive memory
allocation attempts in TIFFReadDirEntryArray() on short files.
Effective for mmap'ed case. And non-mmap'ed case, but restricted
to 64bit builds.
Fixes http://bugzilla.maptools.org/show_bug.cgi?id=2675
- libtiff/tif_read.c: add protection against excessive memory
allocation attempts in TIFFReadDirEntryArray() on short files.
Effective for mmap'ed case. And non-mmap'ed case, but restricted
to 64bit builds.
Fixes http://bugzilla.maptools.org/show_bug.cgi?id=2675
- libtiff/tif_luv.c: LogLuvInitState(): avoid excessive memory
allocation when RowsPerStrip tag is missing.
Fixes https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=2683
Credit to OSS-Fuzz
- libtiff/tif_getimage.c: gtTileContig() and gtTileSeparate():
properly break from loops on error when stoponerr is set, instead
of going on iterating on row based loop.
- libtiff/tif_getimage.c: fix fromskew computation when to-be-skipped
pixel number is not a multiple of the horizontal subsampling, and
also in some other cases. Impact putcontig8bitYCbCr44tile,
putcontig8bitYCbCr42tile, putcontig8bitYCbCr41tile,
putcontig8bitYCbCr21tile and putcontig8bitYCbCr12tile
Fixes http://bugzilla.maptools.org/show_bug.cgi?id=2637 (discovered
by Agostino Sarubbo)
and https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=2691 (credit
to OSS Fuzz)
- libtiff/tif_luv.c: further reduce memory requirements for temporary
buffer when RowsPerStrip >= image_length in LogLuvInitState() and
LogL16InitState().
Fixes https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=2700
Credit to OSS Fuzz
- libtiff/tif_dirwrite.c: replace assertion related to not finding the
SubIFD tag by runtime check (in TIFFWriteDirectorySec())
Fixes http://bugzilla.maptools.org/show_bug.cgi?id=2727
Reported by team OWL337
- libtiff/tif_dirwrite.c: replace assertion to tag value not fitting
on uint32 when selecting the value of SubIFD tag by runtime check
(in TIFFWriteDirectoryTagSubifd()).
Fixes http://bugzilla.maptools.org/show_bug.cgi?id=2728
Reported by team OWL337
- libtiff/tif_jpeg.c: accept reading the last strip of a JPEG compressed
file if the codestream height is larger than the truncated height of the
strip. Emit a warning in this situation since this is non compliant.
- libtiff/tiffiop.h, tif_aux.c: redirect SeekOK() macro to a _TIFFSeekoK()
function that checks if the offset is not bigger than INT64_MAX, so as
to avoid a -1 error return code of TIFFSeekFile() to match a required
seek to UINT64_MAX/-1.
Fixes http://bugzilla.maptools.org/show_bug.cgi?id=2726
Adapted from proposal by Nicolas Ruff.
- libtiff/tif_dirread.c: add NULL check to avoid likely false positive
null-pointer dereference warning by CLang Static Analyzer.
- libtiff/libtiff.def: add TIFFReadRGBAStripExt and TIFFReadRGBATileExt
Fixes http://bugzilla.maptools.org/show_bug.cgi?id=2735
- libtiff/tif_jpeg.c: add compatibility with libjpeg-turbo 1.5.2 that
honours max_memory_to_use > 0.
Cf https://github.com/libjpeg-turbo/libjpeg-turbo/issues/162
- libtiff/tif_getimage.c: avoid floating point division by zero in
initCIELabConversion()
Fixes https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=3733
Credit to OSS Fuzz
CHANGES IN THE TOOLS:
- tools/tiff2pdf.c: prevent heap buffer overflow write in "Raw"
mode on PlanarConfig=Contig input images.
Fixes http://bugzilla.maptools.org/show_bug.cgi?id=2715
Reported by team OWL337
- tools/tiffset.c: fix setting a single value for the ExtraSamples tag
(and other tags with variable number of values).
So 'tiffset -s ExtraSamples 1 X'. This only worked
when setting 2 or more values, but not just one.
- tools/fax2tiff.c (_FAX_Client_Data): Pass FAX_Client_Data as the
client data. This client data is not used at all at the moment,
but it makes the most sense. Issue that the value of
client_data.fd was passed where a pointer is expected was reported
via email by Gerald Schade on Sun, 29 Oct 2017.
- tools/tiff2pdf.c (t2p_sample_realize_palette): Fix possible
arithmetic overflow in bounds checking code and eliminate
comparison between signed and unsigned type.
- tools/tiff2bw.c (main): Free memory allocated in the tiff2bw
program. This is in response to the report associated with
CVE-2017-16232 but does not solve the extremely high memory usage
with the associated POC file.
CHANGES IN THE CONTRIB AREA:
Last updated $Date: 2017-11-18 19:38:06 $.


Projects - Land of Ideas - Space XR - Neural Implicits Students - Caio - Hallison - Thales - Thiago Other - BRICS - PROCAD
Last Update: by lvelho.


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NB! Metric fetching SQL-s should always have the "epoch_ns" column present (otherwise server time will be used). Additionally columns with "tag_" prefix will be
turned into indexed tags in InfluxDB or stored in the indexed "$metric_name.tag_data" column for Postgres for fast access. Also "dbname" (DB unique name) column will always be added by the framework
Available metric attributes:
- is_instance_level (default: false)
- Instance level metrics can be cached and shared between multiple DBs of a single instance to reduce query load on server. Default caching period is 30s,
use the --instance-level-cache-max-seconds (PW2_INSTANCE_LEVEL_CACHE_MAX_SECONDS env.) gatherer param to change or set to 0 to disable. Caching will only
be applied to 'continuous' DB types and only if metric interval is bigger than the --instance-level-cache-max-seconds value.
- statement_timeout_seconds
- Enables to override the default 'per monitored DB' statement timeouts on metric level
- metric_storage_name
- Enables dynamic "renaming" of metrics at storage level, i.e. declaring almost similar metrics
with different names but the data will be stored under one metric. Currently used (for out-of-the box metrics) only
for the 'stat_statements_no_query_text' metric, to not to store actual query texts from the "pg_stat_statements"
extension for more security sensitive instances.
- extension_version_based_overrides
- Enables to "switch out" the query text from some other metric based on some specific
extension version. See 'reco_add_index' for an example definition.
- disabled_days
- Enables to "pause" metric gathering on specified days. See metric_attrs.yaml for "wal" for an example.
- disabled_times
- Enables to "pause" metric gathering on specified time intervals. e.g. "09:00-17:00" for business hours.
NB! disabled_days / disabled_times can also be defined both on metric and host (host_attrs) level.
Available column attributes:
- prometheus_all_gauge_columns (default: false}
- Only relevant for Prometheus output to correctly set the (suggested, not validated) metric type. GAUGE = value changing with every scrape, COUNTER = value is normally incremented (like most postgres statistics)
- prometheus_gauge_columns (default: [])
- Only relevant for Prometheus output to correctly set the (suggested, not validated) metric type. List of GAUGE (value changing with every scrape) columns for the given metric. When not set all columns are considered to be of type COUNTER
- prometheus_ignored_columns (default: [])
- Only relevant for Prometheus output. For cases where we don't want some columns to be exposed


estimated_delivery is a trigger that calculates the estimated delivery date for the order shipment based on the date of the week and the price value of the order.


Editorial cartoons for Wednesday, Nov. 20 20 Nov 2019, 09:30 A sketchy look at the day in politics. Gallery


--- layout: post title: Website live tags: news author: Gertjan van Zwieten ---
Welcome at the new home of The Nutils Project, the open source finite element framework formerly know as "finity". Not much to see here at the moment, but over time this will be the go to location to read about releases, planned features, and other news surrounding the project. For now there is the about page, and of course the github repository.


Home
Dashboard
About Us
Contacts
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Project Landslide
Overview
Kevin Peredo
Dr. Cynthia Posadas
Promoter
Jess Leal
Leader
Kevin Von Erich Peredo
Assistant Leader
Genesis Vincent Dave Agbuya
Member
Donnyl Baysic
Member
Jan Ven Budod
Member
Nichol Kurt Fermin
Member
John Lawrence Puga
Member
Jenelle Loise Abrigo
Member
List of Emergency Contacts
POLICE AND LAW ENFORCEMENT AGENCIES
Baguio Police Station : 166
PNP-CMP Dangwa : 442-5645
NBI : 442-7203 or 442-7204
Highway Patrol : 442-4429
HOSPITAL AND HEALTH CENTER
Baguio General Hospital : 442-4216 or 169
BGHMC E.R. : 442-3765
BGH STOP DEATH program : 443-5678
SLU Sacred Heart Hospital : 442-5701 or 442-5702
Baguio Medical Center : 442-3338 or 442-2626
Sto. Nino Hospital : 442-7769 or 443-7770
Fil-Chinese Hospital : 442-3245
Baguio Health Department : 445-6671 or 442-6108
INFORMATION AND ASSISTANCE CENTERS
PAGASA : 442-3464 or 443-5515
BENECO : 442-2295
Baguio Water District : 442-6539
Department of Tourism : 442-8848


{{description}}
Players: {{player_count}}
Enroute to {{random_planet_name}}...
Logged in users:
Players in games:
Active rounds:


Ex-CIA agent jailed for 19 years for spying for China
WASHINGTON: The former CIA officer who may have devastated US intelligence collection in China by giving up its network of informants to Beijing agents was sentenced on Friday (Nov 22) to 19 years in prison.
Jerry Chun Shing Lee, a naturalised US citizen who worked for the Central Intelligence Agency from 1994 to 2007, was arrested in January 2018 for illegally possessing classified US defence information, which prosecutors alleged he turned over to Chinese intelligence agents.
His indictment said he moved to Hong Kong in 2010 and began accepting payments from Chinese agents who asked him for sensitive information on the CIA.
But in May, Lee, 55, pleaded guilty to one charge of conspiracy to deliver national defense information to aid a foreign government, while insisting the government never proved he gave the Chinese any information or accepted US$840,000 from the Chinese, as alleged.
"Lee betrayed his own country for greed and put his former colleagues at risk," said Timothy Slater, assistant director in the FBI's Washington field office.
"The seriousness of his betrayal and crime is demonstrated by today's sentencing."
CIA'S CHINA NETWORK EXPOSED
The case has driven much speculation about what really lies beneath, because it took the government six years from first investigating Lee to arresting him.
But media reports tied it to the catastrophic destruction of the CIA's web of informants in China between 2010 and 2012.
The New York Times reported in 2017 that the Chinese killed "at least a dozen" sources the CIA had inside China and imprisoned at least six others.
A hunt for a "mole" in the agency led to one person, a "former operative" who in 2017 was living elsewhere in Asia, the Times said.
Lee had been a case officer in China and other countries during his CIA career, which gave him knowledge of its covert collection methods and sources on the ground.
When FBI investigators first honed in on him in 2012, he was living in Hong Kong and had sought to be rehired by the CIA.
The FBI at the time searched his luggage and found data on the US spy agency's human sources and information they had provided, as well as meeting locations, phone numbers, and secret facilities.
"The information would have been of immense value" to Chinese intelligence, the indictment said.
It also said that between 2010 and 2012 he had received hundreds of thousands of dollars and "taskings" for information from two Chinese agents.
But prosecutors and the CIA never directly accused Lee of betraying the agency's China network.
They have also never acknowledged publicly that the network was exposed and brought down, though some intelligence experts have confirmed it.
Lee was the third American former intelligence official in a little more than a year convicted of offenses related to spying for China.


WITNESS LIST Human Services Committee May 4, 1999-2:00P SB 374 For: Russell, Paula (Self) On: Bost, Eric (DHS) Walker, Lesa (TDH) SB 1678 For: Hernandez, Leslie (NASW)


fast-uuid is a Java library for quickly and efficiently parsing and writing UUIDs. It yields the most dramatic performance gains when compared to Java 8 and older; in benchmarks, it's a little more than fourteen times faster at parsing UUIDs and six times faster at writing UUIDs than the stock JDK implementation. Compared to Java 9 and newer, it's about six times faster when it comes to parsing UUIDs and offers no benefits for writing UUIDs.
This library is intended for applications that work with large quantities of UUIDs or that work with UUIDs in performance-sensitive code, and probably won't be helpful in applications that work with UUIDs infrequently.
fast-uuid is available under the MIT License.


My Ph.D. research applies culture-independent molecular genetic and computational biology techniques to elucidate the diversity and drivers of the Canadian lake microbiome across large spatial and temporal scales in an era of accelerated environmental change.
We reconstructed over 1,000 bacterial genomes from 308 surface water metagenomes from lakes in 12 Canadian ecozones. Our article linking bacterial genomospecies diversity to lake resource availability and watershed land use is currently in press and is available as a preprint at doi:10.1101/2<IP_ADDRESS>3676.
We traced the distributions and environmental drivers of protists and their metabolic strategies in 366 freshwater lakes using 18S rRNA gene amplicon sequencing. Our article is available at doi:10.1128/msystems.00316-22.
Check out my Three Minute Thesis describing the power of paleogenetics to travel back in time:
Paleogenetics is a field of research under intense development, especially as applied to lake sediments. We pioneered a novel application of shotgun-metagenomics to detect preindustrial microbial diversity in lake sediments. Our article describing this method and our discovery of bacterioplankton and viruses preserved in sediment metagenomes is available at doi:10.1128/mSphere.00512-20. Watch my invited seminar to the Sedimentary Ancient DNA (sedaDNA) Scientific Society on this topic:
The IISD-Experimental Lakes Area is an iconic whole-lake experimentation facility in western Ontario. We are analyzing sediment cores from experimentally fertilized lakes to track the effects of eutrophication on microeukaryotic diversity across 50 years.
As a graduate trainee in the NSERC Canadian Lake Pulse Network, I had the fantastic experience of performing a full day of sampling for over 90 lakes in Nova Scotia and Newfoundland & Labrador in 2018, then in British Columbia in 2019. More information on LakePulse and its sampling campaign targeting 664 lakes across Canada can be found here.
In 2022, I participated in the Microbial Diversity summer course at the Marine Biological Laboratory in Woods Hole, Massachusetts. Six phenomenal weeks of microbiology learning were spent in lecture room and lab with a star line-up of faculty and TAs and 19 outstanding classmates. My individual Mini Project was a realization of a grad school dream: to explore microbial magnetotaxis.
Watch as the MMB are drawn to the south pole of a magnet positioned on the stage of a phase-contrast microscope:
MMB (in the cantina)! Watch as they perform "escape motility":
Video credit to Sarah Guest (Ph.D. candidate at UC Davis and MicDiv TA), who thankfully insisted on filming this last magnetotaxis assay and permitted me to false-colour the video and set it to cantina music (:
Huge thanks to Team MicroMagneto for their incredible help and enthusiasm!


---
tags:
- data
- environment
- overview
author:
display_name: Bert Granberg
email<EMAIL_ADDRESS>date: 2011-12-08 10:24:30 -0700
title: Environment Overview
categories: []
---
{% include table_overview.html name="Environmental" %}
| DEQ Land-Related
Contaminant and Cleanup Sites |
map data |
Utah Department of Environmental Quality (DEQ): DERR, Div. of Solid & Hazardous Waste |
The Utah Department of Environmental Quality (DEQ) land based datasets. This includes environmental response and
remediation, radiation control, and solid and hazardous waste. |
| DEQ Air-Related
Contaminant and Cleanup Sites |
map data |
Utah Department of Environmental Quality (DEQ): Division of Air Quality |
Utah Air Emissions Inventory |
| DEQ Water-Related
Monitoring |
map data |
Utah Department of Environmental Quality (DEQ): Division of Drinking Water |
The Utah Department of Environmental Quality (DEQ) water based datasets. Datasets include monitored lakes,
groundwater permits and National Pollution Discharge Elimination System (NPDES) data. |


EventEmitter
EventEmitter for dart. This can be used in any application to register for events and get notified once it is fired.
This library provide mechanism to register for event notifications with emitter or publisher and get notified in the event of an event.
One main advantage of the mehanism is that, it support context from where the subscriber is listening. The context could be any additional data the subscriber is interested in.
Another feature this has is the ability to receive sender information, which could be handy if the subscriber wish to know about the publisher.
Example
void performAddition(){
}
EventCallback cb = (ev, cont) {
if (null != ev) {
switch (ev.eventName) {
case "add":
performAddition();
break;
}
}
};
EventEmitter emitter = new EventEmitter();
Listener listener = emitter.on("add", this , cb);
emitter.emit("add","keyboard",10);
As shown in the above example, it is easy to register for events and get notified once it is triggered.
Libraries
- eventify
- Eventify library. Eventify provides an effective interface for subscribing and emitting events. This framework also emits context based events. Subscriptions can be cancelled using the default cancellation logic available with subscriber itself.


Main Concepts
Custom Components - What do we know (...)
-
Our Components/Directives
are parameterizables through the Property Binding
mechanism, which is a top to down communication
-
The properties bindings can be
declared through the inputs attribute
-
Or with the @Input() annotation
-
With the help of events, you can communicate from a child component
to a parent one, which is a down to top communication
-
the event to throw can be declare with the output attribute
-
Or with the @Output() annotation
-
Our Components/Directives could depend
on services, and we could want specific providers for them
- It's what the providers attribute is for
- Remember that it's available bot at the @Component level and at the @NgModule level
-
We have access to custom methods to make specific treatments at some points


I've just build my NanodeRF, and all basic tests seem to be ok. However, when using getDHCPandDNS or webClient sketches from the Ethercard library set of examples, DNS does not seem to work. I get an address via DHCP ok, but address lookup fails. Are there any known gotchas in this area?
Thanks
Lloyd
Re: DNS Not working?
I've managed to get this to work, albeit intermittently. Performance was improved by adding a short delay after each read. The same thing with ping tests. Is this normal behaviour?
Lloyd
Re: DNS Not working?
DNS normally works ok with us. Occasionally it can take a little while to obtain DNS. What length of delay have you added? If it's found the help we can add it into the NanodeRF github example sketches
Re: DNS Not working?
delay (5) seemed to do the trick, but I don't think this is the real answer. Yes it worked more often, but there were times when the ping test would never get (or appear to never get) a reply, and then a load would come through at once. Othertimes it would behave perfectly. I do get some compile warnings (arduino 1.0), so perhaps when I get a moment I should investigate those.
Anyhow I moved on and built my emonTx last night. Found and tweaked sketches for both base and tx, and had them reliably talking to my installation of emoncms. So I'm not sure if the test programmes are that useful. (I'm currently accessing my server via IP, so no DNS lookup required.)
I currently have emoncms running on an O2 joggler, with a 2GB USB pen drive as my storage. This also runs my weather station data gathering and website. So wview, emoncms, apache, mysql and sqlite, all for less than 8W. However, I don't expect the USB pen drive to last very long in this configuration (too many writes), so will have to move to something hosted elsewhere - but an interesting experiment.
Next the display....
Lloyd
Re: DNS Not working?
That sounds awesome! We have experimented with running emoncms on anything less than a full server. It's great to hear that it's working well. It would be great to see a photo.
Ah, I didn't realise that it was the EtherCard DNS example that you were having trouble with. We don't really use those examples much, although they should work.


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My personal thinking style would have to be a majority of half Abstract Sequential (AS), and the other 75 percent Abstract Random (AR) with a few characteristics of about 25 percent Concrete Sequential (CS).
DBC can help me with my learning style since, I learn best when I have access to experts or references and also when I am placed into a stimulating environment. I also enjoy my point to be heard and I feel that DBC would allow me to have my own voice, and I would apply my logic to solve or find solutions to the problems that are in front of me.
Since I am also a Abstract Random (AR) I do also feel that DBC will aid in an environment where it is very personalized and personable, but also allowing the instructions or guidelines to be broad so I can interpreted as I see fit. Having a smaller class size will also aid me in being able to participate in group activates, since I also am a personable I also like to establish healthy relationships with others by listen to them and bringing my own harmony to group situations.
Since beginning of Phase 0, my biggest struggle would have to be just learning everything that I am doing and retaining that information and making sure that I can use it for a later time. I feel as if it is related to the way I think, I learn best when I am in groups, or when I am surrounded in a stimulated learning environment with the aid of professionals or experts that can help me with my issues.
I feel that now knowing more about myself and my learning styles I can make some more changes to the way that I handle everything. Changing my environment and doing more peer pairing so that I can learn from others and use my group skills to really help me out.


{% extends 'base.html' %} {% block content %}
We are 4 Computer Science students in the Software Development Class at Harvey Mudd College and created this web app as our project for the class.
Perhaps the most tedious part of using LaTeX for homework assignments is representing matrices. Our web app does the work for the user by allowing users to convert images of handwritten matrices to LaTeX code that can be copied and pasted into their own LaTeX editors. This is achieved through the use of the YOLOv5 object recognition model to classify and locate the matrix elements and a simple algorithm taking advantage of a matrix’s grid-like structure to generate the LaTeX code.


What did you learn from your 'mindfulness conversation'? How did the other person find it?
Various sources propogate the theory that to be listening fully one must actively affirm the person speaking, and also ask questions to show one's interest. But I've always felt that active and full listening can be done without being too vigiorous about affirming and/or asking questions. This chapter confirmed my thinking. When done with a partner, they liked the full attention on them. I also tried to do the 'advanced version' of mindful-listening, where one practices full listening whilst being mindful of one's body.
Which of the three suggested techniques will you use to sustain your practice?
I like how they state that open awareness meditation is best done as a complement to focus awareness mediattion, and I also like how they compare focussed awareness to the 'higher gears' of a car. The open awareness technique being the lower gears. The car analogy hit home. In summary a combination of focussed awareness and open awareness is the way to go, for me.


Police in Lancaster close roads following police incident
by Colin AinscoughPolice in Lancaster have closed the A6 Caton Road One Way Street at Bulk Road following an incident in the early hours of this morning.
A section of Bulk Road leading from St Leonard's Gate to wards Caton Road is also closed.
The closure is still affecting traffic in the area while police carry out investigations.
A Lancashire Area Police spokesman said: "Following an incident there are road closures in place, Back Caton Rd and Bulk Rd as it goes towards Back Caton Rd where it meets the junction with St Leonards Rd."
"We will keep you informed as to when the roads are re opened."
READ MORE>>> Lancaster gridlocked as police investigate collision leaving man with serious injuries


This tutorial is contributed by Hatef Monajemi (@monajemi)
The following tutorial is intended to familiarize you with building docker images and using them.
Table of Contents
What is Docker
How to build a Docker image
Using Docker
- To see a list of your local docker images:
docker images
- To see a list of your containers:
docker ps -a
- To reattach your exited conatainer:
docker start <IMAGE_ID> docker attach <IMAGE_ID>
or simply,
docker start -a -i <IMAGE_ID>
This is a great command to remember, since you may exit your terminal, but this command allows you to start the exited containers and attach to the terminal again.


Find out if your email is likely to trigger spam filters. Run a free email spam and security test in less than one minute.


Welcome to cqparts
’ Documentation!¶
What is cqparts
?¶
cqparts
is CAD for Python programmers, short for “cadquery
parts”.
Using cqparts
you can wrap geometry made with cadquery
to build
complex and deeply parametric models.
What can I do with cqparts
?¶
You can build solid geometry in the same way you write an application; your geometry can be built on a headless system, automated, and revision controlled to your hearts content.
You can export your models ready to be processed for 3d printing, or milling, or to be displayed / imported to another 3d app.
What you should know¶
As this library is an extension of cadquery
, it’s highly recommended you
have an understanding of what cadquery
is, and how to use it first.


CMS_final, CMS_final_digest - finalise a CMS_ContentInfo structure
#include <openssl/cms.h>
int CMS_final(CMS_ContentInfo *cms, BIO *data, BIO *dcont, unsigned int flags);
int CMS_final_digest(CMS_ContentInfo *cms, const unsigned char *md,
unsigned int mdlen, BIO *dcont, unsigned int flags);
CMS_final() finalises the structure cms. Its purpose is to perform any operations necessary on cms (digest computation for example) and set the appropriate fields. The parameter data contains the content to be processed. The dcont parameter contains a BIO to write content to after processing: this is only used with detached data and will usually be set to NULL.
CMS_final_digest() finalises the structure cms using a pre-computed digest, rather than computing the digest from the original data.
These functions will normally be called when the CMS_PARTIAL flag is used. It should only be used when streaming is not performed because the streaming I/O functions perform finalisation operations internally.
To sign a pre-computed digest, CMS_sign(3) or CMS_sign_ex() is called with the data parameter set to NULL before the CMS structure is finalised with the digest provided to CMS_final_digest() in binary form. When signing a pre-computed digest, the security relies on the digest and its computation from the original message being trusted.
CMS_final() and CMS_final_digest() return 1 for success or 0 for failure.
ERR_get_error(3), CMS_sign(3), CMS_encrypt(3)
CMS_final_digest() was added in OpenSSL 3.2.
Copyright 2008-2022 The OpenSSL Project Authors. All Rights Reserved.
Licensed under the Apache License 2.0 (the "License"). You may not use this file except in compliance with the License. You can obtain a copy in the file LICENSE in the source distribution or at https://www.openssl.org/source/license.html.


Lionel Messi sends surprise birthday gift to 15-year-old Borussia Dortmund star Moukoko
- Lionel Messi sends a surprise birthday gift to Borrusia Dortmund youngster Moukoko
- The forward who turned 15 took to Instagram to express his happiness after receiving the gift
- Messi has been the teenager's idol but recently copied Ronaldo's celebration after scoring a goal
Barcelona superstar Lionel Messi surprised Borussia Dortmund youngster Youssoufa Moukoko after sending him a gift on for his 15th birthday.
The 32-year-old forward sent a couple of things to the teenage sensation which includes a postal of the five-time Ballon d'Or winner.
Moukoko was surprised after he received the gift from his idol and he took to social media to express his excitement.
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The wonder-kid said on Instagram: "Oh my god! Got this as a gift from my idol. Many thanks, Leo Messi. Thanks for all your birthday wishes,"
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Moukoko has been impressive for the German football league academy after showing the quality he was made of last season.
According to Sport Bible, the forward made his U17 debut at the age of 12, and currently averages at least a goal in every 54 minutes in this Bundesliga U19 season.
The teenage sensation has already recorded six goals in his debut for Dortmund's U-19 side back in August.
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Meanwhile, coach Michael Skibbe admits Moukoko is destined for greatness following the feat he achieved in the early stages of his time at the club.
"The boy will one day become a professional. That's as safe as the amen in the church. Only injuries could stop him." Skibbe told welt.de.
"Youssoufa is a totally relaxed guy, who handles his hype extremely cool. He has established himself very well so far and was an excellent fit in our team."
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Although Lionel Messi is Moukoko's idol, the forward has replicated Cristiano Ronaldo's celebration when he scores.
Legit.ng earlier reported that Chelsea legend John Obi Mikel wants Barcelona superstar Lionel Messi to win this year's Ballon d'Or award.
The Argentine already won the 'FIFA The Best Player' prestigious prize - beating Cristiano Ronaldo and Virgil van Dijk to the silverware.
Messi is among top contenders for the award, with the likes of Sadio Mane, Mohamed Salah, Eden Hazard and a few more listed in the shortlist released by France Football a few weeks back.
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Next: The Pathname Directory Component, Previous: The Pathname Host Component, Up: Pathname Components
Corresponds to the "device" or "file structure" concept in many host file systems: the name of a logical or physical device containing files.


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