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Growing up in Northern California, Tony was inspired by the first movie he had ever seen, "Mary Poppins", and as a boy began drawing and making Super 8 films. His drawings earned him a full scholarship from the Disney family to CalArts, the fabled art school that had bred so many Disney artists. Tony arrived at The Walt Disney Studios at age 20, hand-picked and trained by Walt Disney's "Nine Old Men", he focused solely on a career in animation. As a highly skilled animator, he worked on nearly every traditionally animated (i.e. hand-drawn) Disney feature film from 1980 to date, however, it was his apprenticeship with Clarence "Ducky" Nash, the original voice of Donald Duck, that would eventually lead Tony to his second career at Disney behind the microphone as Donald's voice. In 1990, when The Walt Disney Studios released The Prince and the Pauper, Tony became the first person to animate AND voice Donald Duck. He is a member of the Academy of Motion Picture Arts & Sciences, and serves on the Animation Executive Committee. In 2009, Tony became a recipient of The Walt Disney Company's Disney Legend Award.
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text:
Pete Freitag
Blog Consulting Products Contact Me
INFORMATION_SCHEMA Support in MySQL, PostgreSQL
I've known about the INFORMATION_SCHEMA views (or system tables) in SQL Server for a while, but I just leared recently that they are actually part of the SQL-92 standard and supported on other database platforms.
The INFORMATION_SCHEMA views provide meta data information about the tables, columns, and other parts of your database. Because the structure of these tables are standardized you can write SQL statements that work on various database platforms.
For example suppose you want to return a resultset with a list of all columns in a table called employees
SELECT table_name, column_name, is_nullable, data_type, character_maximum_length
FROM INFORMATION_SCHEMA.Columns
WHERE table_name = 'employees'
Quite a handy feature, but it's hard to find what versions the of various database platforms started supporting this feature, here's a quick list:
Microsoft SQL Server - Supported in Version 7 and up
MySQL - Supported in Version 5 and up
PostgreSQL - Supported in Version 7.4 and up
Oracle - Does not appear to be supported
Apache Derby - NOT Supported As of Version 10.3
I have been using the INFORMATION_SCHEMA views to build some automatic datatype validation. With the INFORMATION_SCHEMA you can get the datatype, max character length, and if null values are allowed, and perform validation before it hits the database. And if a column is made wider, you don't have to make any code changes.
Ofcourse if you are using ColdFusion 8, you can use the new cfdbinfo tag to get the same column information. The cfdbinfo actually uses the JDBC Driver's getMetaData() method (this is part of the JDBC Standard that Drivers implement this method). Apache Derby doesn't support the INFORMATION_SCHEMA views because they prefer to simply implement the JDBC Driver's getMetaData() method.
Here's a list of the information schema views:
INFORMATION_SCHEMA.SCHEMATA
INFORMATION_SCHEMA.TABLES
INFORMATION_SCHEMA.COLUMNS
INFORMATION_SCHEMA.STATISTICS
INFORMATION_SCHEMA.USER_PRIVILEGES
INFORMATION_SCHEMA.SCHEMA_PRIVILEGES
INFORMATION_SCHEMA.TABLE_PRIVILEGES
INFORMATION_SCHEMA.COLUMN_PRIVILEGES
INFORMATION_SCHEMA.CHARACTER_SETS
INFORMATION_SCHEMA.COLLATIONS
INFORMATION_SCHEMA.COLLATION_CHARACTER_SET_APPLICABILITY
INFORMATION_SCHEMA.TABLE_CONSTRAINTS
INFORMATION_SCHEMA.KEY_COLUMN_USAGE
INFORMATION_SCHEMA.ROUTINES
INFORMATION_SCHEMA.VIEWS
INFORMATION_SCHEMA.TRIGGERS
INFORMATION_SCHEMA.PROFILING
Roland Bouman has created a clickable ER diagram of the INFORMATION_SCHEMA for MySQL (click on the table to go to the MySQL documentation for the table)
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INFORMATION_SCHEMA Support in MySQL, PostgreSQL was first published on February 18, 2008.
If you like reading about sql, mysql, postgresql, sqlserver, derby, information_schema, standards, or cfdbinfo then you might also like:
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Nice post, keep it up.
Can you please provide the links for your list of Information Schema compatibility.
I found:
MySQL 5.0:
http://dev.mysql.com/doc/refman/5.0/en/information-schema.html
http://www.postgresql.org/docs/8.1/interactive/information-schema.html
However, I have been unable to locate this information for Oracle, and I was under the impression that Oracle does *not* provide an information_schema (although they do of course offer their own system views - the USER_%, DBA_% and ALL_% views)
Roland Bouman
by Roland Bouman on 02/18/2008 at 8:29:10 AM UTC
This is really nice. I have really liked the INFORMATION_SCHEMA views in SQL Server. It will be nice when those views have more ubiquitous support and it looks like vendors are catching on.
by Steve Bryant on 02/18/2008 at 8:40:14 AM UTC
Hi Roland,
http://www.postgresql.org/docs/7.4/static/release-7-4.html
I am trying to find something on Oracle's site, but it appears to be down? I found several web pages that mention 9i supporting it, but I will confirm and update if the information is incorrect.
Any easy way to find out is if anyone reading this has Oracle 9i installed?
by Pete Freitag on 02/18/2008 at 9:31:29 AM UTC
Yes, the INFORMATION_SCHEMA is part of the SQL-92 standard. But unfortunately it's not practical to rely on it as a means of cross-database standardized queries against metadata.
- MySQL's implementation was unusably slow prior to 5.1.23, but they appear to have improved it. http://bugs.mysql.com/19588
- PostgreSQL's implementation of INFORMATION_SCHEMA is buggy. I have read that most people INFORMATION_SCHEMA and instead use the old PostgreSQL-proprietary system tables.
http://www.postgresql.org/docs/8.2/static/catalogs.html
- Oracle has proprietary catalog tables, and does not support INFORMATION_SCHEMA.
http://download-west.oracle.com/docs/cd/B19306_01/server.102/b14237/statviews_1.htm
IBM DB2 also uses proprietary catalog tables, and does not support INFORMATION_SCHEMA.
http://publib.boulder.ibm.com/infocenter/db2luw/v8/index.jsp?topic=/com.ibm.db2.udb.doc/admin/r0001063.htm
- InterBase/Firebird also uses proprietary catalog tables and does not support INFORMATION_SCHEMA. The InterBase manual shows how to create views against its catalog tables to match the spec for INFORMATION_SCHEMA relations.
by Bill Karwin on 02/18/2008 at 11:11:15 AM UTC
Yes, too bad Firebird doesn't come with those views as standard.
But I have to disagree with Bill aobut PostgreSQL, it's information schema implementation is not buggy, it's just that people go with "true and tested" which means system tables (the same happens with mssql) and would happen with Oracle which right now doesn't support this standard.
by pp on 02/18/2008 at 1:41:48 PM UTC
Actually Oracle has had an information schema since at least version 7. Users are given views of the data in these schemas which depend upon their privileges.
Examples are user_tables for all tables owned by you, all_tables for all tables that you have access to and dba_tables for all tables.
These views are very comprehensive and would allow you to recreate any schema object.
by Paul on 02/18/2008 at 7:56:45 PM UTC
Pete - no.
The point is that Oracle has indeed its own set of system views for metadata. That is nice and all, and the information you get from these is very rich, no complaints there. But it is *not* an implementation of the standard SQL feature called "information_schema" and you cannot simply port queries from one platform to the other.
thank you all for this fruitful information. I am very new to DB.
I but i was also trying to figure out how to get the schema name, that table belongs to? Because the same table name can be used under different schema's. It would be good if we retrieve the schema name also.
I tried the below query
SELECT schema_name, table_name, column_name, is_nullable, data_type, character_maximum_length
WHERE table_name = 'Sales'
Error: column "schema_name" does not exist
by Deepak Murthy on 09/30/2008 at 2:24:07 PM UTC
Pete is a husband and father located in scenic Central New York area. He owns a ColdFusion Consulting & Products company, Foundeo Inc. Pete is a frequent speaker at national conferences including Adobe ColdFusion Summit, Into The Box and others. He holds a BS in Software Engineering from Clarkson University. Read more about pete here.

redpajama_set_name: RedPajamaCommonCrawl


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Hi sordum,please update this app,its not work in 1903 19H1 18362.53 build.
In Windows 10 1903, this app not working anymore. Not disabling.
I don't know why but the default Win10 v1607 Windows Defender app along with the default WD definitions (from 7/16/2016) is detecting and removing the Defender Control 1.4 (defendercontrol.exe) app. updating the defender av defitions to the latest ones seems to stop that behavior.
Once you've created the shortcut, right-click it and select the 'Run as administrator' option.
When you run the script and Windows Defender is disabled, you will see a pop-up telling you that it's been disabled. Rerun the script to enable windows defender again.
if possible to add an option to remove the defender from the winOS for ever not just disabling it.
al usarlo no me deja abrir el panel de configuracion en win10 1809. como revertir esto…..
Enables it if disabled, disables it if enabled.
Hi Frederick you can use a .bat file write the commands in it. Start with Windows options can be possible but this tool doesn't need such an function. One time run and thats all.
Can someone help me create a "toggle" script that enables and disables Windows Defender using Defender Control command line switches.
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It has been a long time, over three years in fact, since I last posted on the Pangea Blog site.
A fire on Memorial Day 2012 virtually destroyed our home and set in motion quite a journey as General Contractor on the rebuild. Many lessons from that experience that I will share in the right context.
Shortly before the fire, in December, 2011, I was at Dulles airport about to board a flight to Las Vegas for PowerGen 2011. I got a call from Donna with a request. Her business had started to take off and she needed help. Of course I had to do that. A growing family business requires all able bodies to engage and I soon found myself deeply absorbed in strategic planning, marketing, website development, business process design and eventually investment research and economic analysis, All things I love doing and doing them for the one I love. How good does it get! This is stuff I was trained to do in business school 40 years ago and I had never had the opportunity to apply until now. Amazing how relevant that training has remained. Some of that will come out in future blog posts as well.
By the middle of summer, 2012 the growth of Wood Smith Advisors, the details of an insurance claim, finding a builder and finding temporary housing absorbed all available band width. Building Pangea had taken a back seat. (Actually it was a box in the trunk.) It has remained that way until last summer when the house construction had largely been finished. I started thinking about what next. Oh yes, one other event. Following my presentation at PowerGen I was approached about contributing a chapter to a book the American Bar Association was planning on the Shale Energy Revolution as a guide for lawyers. I had to agree. My evening-weekend diversion became researching and writing the first chapter on the geology, engineering, and resource base of North American shale energy. The Shale Energy Revolution: A Lawyer's Guide was published by the ABA this March.
So, back to: "What next?" One option was to double down on my work with Wood Smith Advisors. During the fall it became imperative that I obtain a license as Registered Investment Advisor Representative. So I began study in October and scheduled to take the exam the day after Christmas. Not an exercise I would want to repeat, but the outcome is good. My duties though have really settled in as the business process work has gained traction and more time became available even with increased responsibility. I could pursue that full time and really have fun with it.
But, after a lot of thought and many conversations with friends and colleagues, I realized that my "Why?" is oil and gas exploration and production. Simon Sinek in his landmark book "Start with Why?" makes a crucial point: it is not What you do that is important, it is Why. Indeed one should have a why that will make you cry. Well, a little over the top, but you get the point.
My first job out of business school was as an economic analyst with Amoco International and from that first day I have loved the world of exploration and production. There was never a day that I didn't learn something. Mr. John E. Swearingen, at the time, Chairman and Chief Executive of Standard Oil (Indiana) parent company of Amoco, once commented that nobody would or could know all there was and true comprehensive expertise is an illusion. We can be experts in a niche area, but we always depend on our colleagues to put the full picture together. Many disciplines, skill sets, personalities, strengths and capabilities need to blend to achieve a successful outcome in the world of E&P. But in the end, it is a business and business is the glue that binds, the fluid that lubricates and the air that breathes life into a successful E&P venture. That is what I began to learn all those years ago and that is where I have my greatest contribution to make.
Now Pangea is reborn as a resource for those pursuing the Business of Oil & Gas. Leaders, managers, investors, lenders, suppliers, stakeholders at every level are who I serve. How I serve can be understood in fairly simple terms. Every business is supported by three pillars.
Strategy, the guide by which the business becomes a successful enterprise: Economically, socially, and environmentally.
Process, the procedures, policies, methods, tactics and means to implement the strategy.
People, the folks who make it all happen by pursuing the aims contained in the strategy, using the business processes to achieve sustained success.
When all three are working in concert good outcomes are inevitable. Whether gaining access to prospective geology, reducing costs, gaining the loyalty of stakeholders or delivering shareholder value, the principle of aligned strategy, process and people inevitably leads to a sustainable business.
I have been blessed to have acquired a pretty good box of tools over the years. I have also had some great teachers, made enough mistakes and achieved some success to learn how best to use them. Now it is back to work striving to help a few clients achieve sustained success.
Welcome back. Good luck with your efforts. Please call if I can assist.


text:
function editorDirective($analytics) {
  'ngInject';
  return {
    restrict: 'A',
    scope: {
    	editable: '@'
    },
    require: '^aframeScene',
		link(scope, elem, attrs, SceneCtrl) {
			const openEditor = event => {
				// If there's a right click active...
				console.log(scope.$parent)
				if (SceneCtrl._rightClick && SceneCtrl._editable) {
					event.preventDefault();
					// ...open the scene link editor:
					SceneCtrl.editItem(SceneCtrl._rightClick, scope.$parent[scope.editable], SceneCtrl[`${scope.editable}s`]);
					$analytics.eventTrack('click', {
						category: 'edit:scene',
						label: scope.$parent[scope.editable]._id
					});
				}
			};
			elem.on('click', openEditor);
    }
  };
}

export default editorDirective;

redpajama_set_name: RedPajamaGithub


text:
Kolumbien nahm 2008 zum 17. Mal an den Olympischen Sommerspielen teil. Das Land entsandte mit 67 Athleten in 15 Sportarten die bislang größte Delegation in der kolumbianischen Sportgeschichte.

Bei der Eröffnungsfeier trug die Radrennfahrerin María Luisa Calle die Flagge Kolumbiens.

Medaillengewinner

Silber

Bronze

Bogenschießen
 Anna Rendón
Frauen, Einzel
 Sigrid Romero
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 Natalia Sánchez
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Anna Rendón, Sigrid Romero, Natalia Sánchez
Frauen, Team

Boxen
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 Darleys Pérez
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 Eleider Álvarez
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Klasse über 91 kg

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 Ubaldina Valoyes
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 200 m
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redpajama_set_name: RedPajamaWikipedia


text:
\section{INTRODUCTION} \label{sec:intro}
\subsection{Infrared Dark Clouds}
Infrared dark clouds (IRDCs) are dense, cold clouds that appear as absorption features against the diffuse emission from the Galactic plane. Large numbers were first identified in observations from space-based instruments such as ISOCAM on {\it ISO} \citep{1996A&A...315L.165P,2001A&A...365..598H} and in the {\it Midcourse Space Experiment} ({\it MSX}) Galactic plane survey \citep{1998ApJ...508..721C,1999ESASP.427..671E} which had the sensitivity and spatial resolution to detect these compact, dark features in the mid-infrared. Studies of IRDCs showed them to be frequently associated with sites of massive star formation, and to contain cores and young stellar objects \citep[e.g.,][]{2005ApJ...630L.181R,2008ApJ...689.1141R,2006ApJ...653.1325S,2007ApJ...662.1082R,2006ApJ...641..389R,2006ApJ...653.1325S,2010ApJ...715..310R}. IRDCs have been identified as being the possible birthplaces of massive stars and stellar clusters \citep[see reviews by][and references therein]{2007ARA&A..45..339B,2018ARA&A..56...41M}, and so it is important to study the formation and evolution of these objects to understand this crucial phase in the star formation process.

A significant advance came with the {\it Spitzer\/}\ Space Telescope \citep{Werner2004}, which provided a factor of $\sim$10 better resolution and a factor of $\sim$1000 better sensitivity than previous surveys at 8~\micron. The GLIMPSE survey \citep{2003PASP..115..953B,2009PASP..121..213C}, one of the {\it Spitzer\/}\ Legacy surveys\footnote{https://irsa.ipac.caltech.edu/data/SPITZER/docs/\\ spitzermission/observingprograms/legacy/history/} conducted early in the mission, mapped the $|l|<65$\degr, $|b| \le 1$\degr\ region of the Galactic plane with the IRAC instrument \citep{Fazio2004} at 3.6, 4.5, 5.8 and 8~\micron. The MIPSGAL survey \citep{2009PASP..121...76C} mapped a similar region with the MIPS instrument \citep{2004ApJS..154...25R} at 24 and 70~\micron. Based on these surveys, \citet[][hereafter PF09]{peretto09} conducted a search for IRDCs in the {\it Spitzer\/}\ data using primarily 8~\micron\ opacity maps that they constructed from the available mosaics. They produced a catalog of over 11,000 clouds and up to 50,000 fragments within the clouds, indicating possible dense regions which could be forming cores. They also found many 24~\micron\ sources associated with the IRDCs, indicating active star formation. The improved resolution and sensitivity of {\it Spitzer\/}\ allowed them to identify many clouds that were missed in the earlier {\it MSX} survey.

Later in the {\it Spitzer\/}\ mission, further surveys by the GLIMPSE team and other groups filled in the full 360 degrees of the Galactic plane (see Figure \ref{GPplot} and \S \ref{obs}). We were interested in searching these parts of the plane for IRDCs. However, a large part of the survey was conducted in the GLIMPSE 360 program \citep{2008sptz.prop60020W,2009AAS...21421001W} during the {\it Spitzer\/}\ Warm Mission \citep{2010SPIE.7737E..1WM}, and so only IRAC 3.6 and 4.5~\micron\ data are available for those regions. Therefore our search technique would have to be different than those that used the 8~\micron\ IRAC band. We wanted to use an automated technique to search the large amount of data, for reasons of reproduceability and reliability. However, IRDCs can vary in size, shape, orientation, local background levels, and optical depth, and are often located near regions with complex emission from molecular clouds and star formation, which can make the IRDCs difficult to identify. False positives can occur in regions where emission is absent, rather than absorption from a foreground IRDC. We wanted a method that could maximize the identifications of true IRDCs and minimize false positives to produce a catalog of IRDCs for the entire Galactic plane.

\begin{figure}[ht]

\centering
	\includegraphics[height=0.4\textwidth]{fig1.jpg}
	\caption{From \cite{2014Meade}, a schematic of the Galactic Plane showing the areas covered by the GLIMPSE surveys, with the GLIMPSE 360 survey in blue and others in white. The GLIMPSE 3D surveys cover regions extending in and out of the plane of the Galaxy in the lower part of this figure. The approximate positions of Galactic spiral arms \citep{1993ApJ...411..674T} are indicated in red. The radius marking the expected truncation or break in the exponential Galactic stellar disk is also shown with a dashed line.
}\label{GPplot}
\end{figure}

\subsection{Contour Finding and Convolution Neural Networks}
Convolutional neural networks (CNNs) are specialized deep neural networks that are widely used in image recognition and classifications. See for example \citet[][chapter 9]{Goodfellow-et-al-2016} for a general description of CNNs and their use in image analysis. They are better at capturing the features in an image through the application of relevant kernels, which convolve over the entire image. See Figure \ref{CNN} for a diagram of the CNN process. The kernels are fixed in size and numbers, but part of training a CNN is to optimize the values within the kernels. After each convolutional layer, the resulting matrices are usually downsized to simplify and speed up the computation.  This process is repeated a few times to optimize the values. Finally the matrices are flattened, converted into a one dimensional array, and passed through a neural network to make the final judgement. Usually, and in our case, the output is a vector of probabilities, one value for a class. Because we want to use the CNN to decide whether the image contains an IRDC or not, we have the CNN output 2 values, one for the probability that the image is an IRDC and the other for not containing an IRDC. A CNN is trained in a supervised manner, meaning that it is fed a dataset of images with their corresponding labels, and the CNN is initialized with a random values for the kernels, weights and biases. The CNN then goes through the training set and calculates the loss at each step and adjusts all of the parameters and repeats the process until the error converges to a minimum value.

\begin{figure}[ht]
\includegraphics[width=\linewidth]{CNN.png}
\caption{From \citet{deshpande2016},
the diagram illustrates a convolutional neural network in its simplest form, where there are a predefined number of kernels with a fixed size that convolve over an image. The resulting image/matrix is then processed to reduce its size, in this illustration max pooling is used. The resulting matrix is then flattened and processed by a traditional neural network, which outputs the probability for each of the potential labels.}\label{CNN}
\end{figure}

We chose to use the MobileNet \citep{HowardZCKWWAA17} CNN model because it is computationally fast and can process our large images in a small amount of time.
 CNNs have been used before in astronomical applications; for example, they have analyzed images to automatically classify objects as stars or galaxies \citep{10.1093/mnras/stw2672}. Furthermore, they have been widely used outside of the astronomical community. For example, they have been employed to automatically classify animals in wildlife cameras \citep{NorouzzadehE5716}.
However, there is a downside to CNNs; they cannot determine how many of a certain object exists or their location within an image. This is why we also employ a contour finding algorithm to find all closed figures, which is useful when searching for IRDCs because they are closed and distinct from the background. The contour finding method we used is called Topological Structural Analysis of Digitized Binary Images by Border Following \citep{SuzukiA85}. It takes in a binary image, and by following the edges, it finds closed loops. It finds an edge and then checks its neighboring pixels to see if any of them are edges, and it keeps moving around the border in this manner. If it has reached the starting point, then it identifies it as a closed loop, or a contour. This is a popular method, which is why its incorporated into OpenCV, however it can also find closed loops within other closed loops. Unfortunately, this aspect of finding contours within contours was not useful for us, because almost all of the sub contours ended up being bright regions within the IRDCs caused by star clusters. This is shown in Figure \ref{contourExplain}. The contour method locates IRDCs and a sizeable amount of non-IRDC objects. This is why we use a series of filters and finally a pair of CNNs to decipher which of the contours are IRDCs.

\begin{figure}[ht]
\centering
	\includegraphics[width=0.8\linewidth]{figure2n.jpg}
	\caption{The IRDC G010.71-00.167 is outlined by the contouring finding method. However, within the blue oval there are contours found within the IRDC which in this and almost all cases contain clusters of bright stars. We are able to keep these contours from being identified as separate IRDCs through various types of filters as described in the text.}\label{contourExplain}
\end{figure}

\section{Observations}\label{obs}
The mosaics used here are based on images obtained with the IRAC instrument on {\it Spitzer\/}.
We used the mosaic images created by the GLIMPSE team for the GLIMPSE and other surveys conducted during the {\it Spitzer\/}\ mission. These include the original GLIMPSE survey \citep[GLIMPSE I - ][]{2003PASP..115..953B,2009PASP..121..213C}, GLIMPSE II  \citep{2005sptz.prop20201C}, GLIMPSE 360 \citep{2008sptz.prop60020W,2009AAS...21421001W}, GLIMPSE 3D \citep{2006sptz.prop30570B,2007AAS...211.1409B}, Deep GLIMPSE \citep{2011sptz.prop80074W}, Vela-Carina \citep{2007sptz.prop40791M,2009ApJ...707..510Z}, SMOG \citep{2008sptz.prop50398C}, and Cygnus-X \citep{2007sptz.prop40184H,2009AAS...21335601H}. The location of each of the surveys as a function of Galactic longitude is shown in Figure \ref{GPplot}. The 1\farcs{2}/pixel images in Flexible Image Transport System (FITS) format were used, and the background-matched and gradient-corrected or latest versions available were downloaded from the IRSA database\footnote{https://irsa.ipac.caltech.edu/data/SPITZER/GLIMPSE/\\ overview.html}.
The GLIMPSE I, GLIMPSE II, GLIMPSE 3D, Vela-Carina, SMOG, and Cygnus-X surveys were conducted in the four IRAC bands (3.6, 4.5, 5.8, and 8~\micron; Channels 1-4, respectively) during the cryogenic mission. The GLIMPSE 360 and Deep GLIMPSE surveys, which included most of the new area that we wished to survey for IRDCs, were conducted during the {\it Spitzer\/}\ Warm Mission and data were obtained only in the 3.6 and 4.5~\micron\ bands. We therefore conducted tests to determine whether to use Channel 1 or 2, or some combination, for our IRDC search. After some tests and examining the mosaics, we chose to use Channel 1 images only because the IRDCs appear more visually prominent compared to their appearance in Channel 2, possibly due to the higher dust extinction at 3.6~\micron\  \citep[e.g.,][]{2007ApJ...663.1069F} and the brighter stellar background. We looked at the same regions in both channels, with the images processed by histogram equalization and contour finding. The results were consistently better (fewer missed objects, more IRDC area identified) in Channel 1 than 2 because the IRDCs are more prominent in the Channel 1 images and the contour finding algorithm was able to find and more fully map the spatial extent of the IRDCs. A representative example is shown in Figure \ref{Channel}. The IRDC in the Channel 2 image is broken up into more contours than the same object in Channel 1. Furthermore, the contour finding procedure captured more of the size and area of the IRDC in the Channel 1 image.

\begin{figure}[ht]
  \includegraphics[width=\linewidth]{{figure4.jpg}}
  \caption{The IRDC G010.71-00.167 is shown in the 3.6 \micron\ image on the left and the 4.5 \micron\ image on the right. The images were processed with histogram equalization and contour finding using the same parameters. The green outline is the output of the contour finding method. It is clear that in the 3.6 \micron\ image the contours better represent the full size of the IRDC, and the IRDC is not broken up into as many separate contour areas as in the 4.5 \micron\ image.} \label{Channel}
\end{figure}

\section{Search Method}

\subsection{Overview of Procedure}
We implemented our procedure in Python3, and have uploaded the code to a GitHub repository\footnote{https://github.com/jyopari/IRDC}. Our code makes use of the \verb|Astropy(3.1.2)|\footnote{https://www.astropy.org/}, \verb|Pillow(5.2.0)|\footnote{https://pillow.readthedocs.io/en/5.2.x/index.html}, \verb|OpenCV(3.4.2.17)|\footnote{https://opencv.org/}, and \verb|Tensorflow(2.0.0a0)|\footnote{https://www.tensorflow.org/} libraries. The first steps of our procedure were to apply image filtering techniques and contouring finding in order to locate all the closed figures in the mosaic. We obtained the bounding boxes of the figures and evaluated which ones should be kept based on their characteristics. Finally, the remaining bounding boxes were then sent to two classifiers which provided the final classification and determined whether or not each figure is an IRDC. We describe these steps in more detail below.

\subsection{Processing Steps}\label{parmchoice}
For each mosaic in the dataset, we read the FITS-format file into an image array of intensity values using the \verb|Astropy| library. The values were then scaled by a multiplicative factor so that when the flux density values (in MJy sr$^{-1}$) are truncated and written to a file with values in the range 0 -- 255, there will be enough dynamic range in the lower flux levels to detect the IRDCs. We used a factor of 5 or 10, depending on the background level in the particular dataset being processed. The values used are given in Table \ref{params}. We then used the \verb|Pillow| functions \verb|fromarray()| and \verb|convert()| to convert the data into a grayscale image and saved the image in PNG format. The image was then processed using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique \citep{Zuiderveld:1994:CLA:180895.180940} through OpenCV's \verb|createCLAHE()| function. Regular histogram equalization looks at the distribution of all the pixel values in an image and stretches it over the entire domain of values. This will flatten any high density regions in the distribution, thus increasing the contrast in the image. However, one drawback of using histogram equalization is that it transforms the histogram of the whole image. This will not provide as much clarity in an image where different regions within the image are distinct. In our case, there is an especially strong background gradient across the Galactic plane in mosaics from GLIMPSE I and II, and many bright regions that can dominate the high end of the intensity histogram. Because of this, we employed the CLAHE technique\footnote{https://docs.opencv.org/master/d5/daf/tutorial\_py\_\\histogram\_equalization.html}. This method uses histogram equalization on small subsets of the image, so that parts of the image that are vastly different in character do not influence the local equalization process. We used a tile size of 8 by 8, which means that histogram equalization is performed on a square with a length of 8 pixels. A key component of the CLAHE process is the clip limit. If there is a region in an image where the values are very similar, there it will likely produce a peak as shown on the left side histogram in Figure \ref{clipLimit}. Histogram equalization tends to excessively spread the peak, to make it more uniform, which will create an unwanted high contrast in that region. To solve this, before histogram equalization is applied, bins in the histogram that are larger than a threshold, called the clip limit, will be reduced to stay at the threshold level. However, since reducing the size of some bins will cause the total sum of the bins to decrease, the amount that is taken away from the bins above the clip limit is then evenly distributed as shown in Figure \ref{clipLimit}. This step was performed in order to minimize the effects of the changing background levels in different parts of the image, as seen in the top image of Figure \ref{histogram}. The greater contrast that CLAHE produces compared to regular histogram equalization is illustrated in the lower image of Figure \ref{histogram}.

\begin{figure}[h]
  \includegraphics[width=\linewidth]{clipLimitfigure.png}
  \caption{The histogram on the left and right represent the color frequency  for a mosaic before and after the clip limit transformation. The dashed horizontal line that passes through one of the peaks in the histogram on the left represents the clip limit value. If there are values that exceed the clip limit value (shown in blue in the left panel), then they are evenly added to the all the bins (the blue region in the right figure), resulting in a constant increase to all the bins after the transformation.} \label{clipLimit}
\end{figure}

After CLAHE was applied, we used Gaussian blurring to smooth the image and reduce noise before a binary threshold was applied; both processes were conducted via \verb|OpenCV's| functions \verb|GaussianBlur()| and \verb|threshold()| respectively. The Gaussian kernel's dimensions were 5$\times$5. Using those dimensions, OpenCV calculated $\sigma$ using the following expression: $ \sigma = 0.3*(\frac{size-1}{2}-1) + 0.8 $. Our kernel size was 5 by 5 pixels, so $\sigma = 1.1$. Below is a rounded version of the kernel:
\par
\begin{center}
$
\begin{bmatrix}
$.01	.02	.03	.02	.01$\\
$.02	.06	.09	.06	.02$\\
$.03	.09	.14	.09 .03$\\
$.02	.06	.09	.06	.02$\\
$.01	.02	.03	.02	.01$\\
\end{bmatrix}
$
\end{center}

After the image is smoothed, it is then converted to a binary image before sending it to the contour finding algorithm. We experimented with various constant values for the threshold cutoff, and we found that values around 50 provided the most reliable results across the mosaics from the different surveys. The threshold can be related to the original image intensity through the scaling factor used for the region, given in Table \ref{params}.
We then employed Topological Structural Analysis of Digitized Binary Images by Border Following \citep{SuzukiA85} via OpenCV's \verb|findCountours()| function. This method uses border following in order to locate closed figures in a binary image.
\par
We then obtained the bounding box for each contour using the \verb|boundingRect()| function from OpenCV. The bounding boxes were then sorted based on their area, and we only kept the first 1000 largest bounding boxes in each mosaic. We found that values exceeding 1000 produced exceedingly more false positives than true positives, as illustrated in an example image in Figure \ref{threshold}. We then established a criterion to evaluate a potential IRDC based on the intensity difference between the inner part of the IRDC and its immediate surroundings. Because IRDCs are usually darkest near the center, we shrunk the original bounding box dimensions by a third while not moving its center and  calculated the average intensity within the bounding box. Then we defined another box which is 30 pixels larger in both dimensions with the same center, and calculated its average intensity and subtracted the average intensity of the smaller box from the larger one. The definition of inner and outer boxes is shown in Figure \ref{_difference}. We then created three subsets, each with their respective filter. The first subset consisted of bounding boxes whose contour areas were greater than 30000 pixels, the second subset received bounding boxes whose contour areas were between 30000 and 3000 pixels, inclusive, and the third subset was defined as having bounding boxes whose contour area was less than 3000 pixels. The contour area was calculated through OpenCV's \verb|contourArea|. We defined the parameters $T_{0}$, $T_{1}$, $T_{2}$ for the large, medium, and small subsets, respectively. The parameter value is the threshold for the intensity difference between the larger and smaller boxes as defined above, meaning for example if a bounding box has a contour area of 40000 pixels, then it would need an intensity difference greater than $T_{0}$ for it to not be eliminated from consideration.

These cutoffs were applied to the list of bounding boxes, and the remaining boxes were then processed by two \verb|MobileNets| \citep{HowardZCKWWAA17}. We used MobileNets, implemented in \verb|Tensorflow|, for their proven accuracy and smaller architecture, which increased the computational speed. The first MobileNet was trained on IRDCs that had bounding boxes smaller than 30000 pixels, while the other was trained on IRDCs that had bounding boxes larger than 30000 pixels. We used objects from the PF09 catalog as a guide for the GLIMPSE I and II regions for our training data. We visually confirmed that the PF09 objects selected appeared to be true IRDCs. Our training set for each classifier comprised of approximately 50 bounding boxes containing dark clouds and 50 bounding boxes that contained no IRDCs. We did not need a large training set because the MobileNets came pretrained on a general image dataset called ImageNet \citep{imagenet_cvpr09}. This means that they have already been trained from scratch on a large dataset, and they have ``learned" which features to observe in an image in order to classify it. We applied transfer learning, meaning that we trained the trained model for our dataset, i.e., the final layer of the network\footnote{https://www.tensorflow.org/hub/tutorials/image\_retraining}.

Since the first classifier was trained on the smaller IRDCs, we made it process the bounding boxes with an area smaller than 30000 pixels, while the other classifier viewed the remaining larger bounding boxes. We did not use contour area to determine which classifier viewed which bounding box, instead we estimated the area by multiplying the width and height of the box. Each classifier returned the probability of the image inside the bounding box containing an IRDC. We defined two more parameters $C_{0}$, $C_{1}$, which are the threshold that the returned probability needs to exceed in order for the bounding box to be recorded into the catalog. The parameters $C_{0}$, $C_{1}$ refer to the threshold for the small classifier and larger classifier, respectively. Once a bounding box passed all the conditions in the pipeline, its coordinates, width, and height were appended to a region file in Galactic coordinates. We used the \verb|all_pix2world()| function from Astropy to obtain the Galactic coordinates from the pixel coordinates of the image. We only needed two classifiers instead of three, one for each subset because the smaller classifier was able to accurately classify the IRDCs with a contour area of less than 30000 pixels.
\par
We also calculated an estimate of the maximum absorption depth of the IRDC. We created a rectangle 30 pixels wider and taller that shared the same center as the original bounding box. We then calculated the median intensity value of the ''border'' region (the 15 pixel wide region surrounding the original box), and subtracted from that the minimum non-negative intensity of a pixel within the original bounding box. We then divided the difference by the median border value as a measure of the maximum depth of the feature, with larger values indicating deeper absorption.

\begin{figure}[ht]
  \scalebox{1}[1]{\includegraphics[width=\linewidth]{img2.jpg}}
  \includegraphics[width=\linewidth]{clahe_img.jpg}
  \caption{The mosaic GLM\_01200+0000\_I1 shown on the top was processed using histogram equalization. The bottom image is the top image after the contrast limited adaptive histogram equalization technique was applied. The adaptive histogram technique helps reduce the effects of the large variations in background level across the image, and clarifies the structure of the image near the top and bottom edges where the background is lower than the center.} \label{histogram}
\end{figure}

\begin{figure}[ht]
  \includegraphics[width=\linewidth]{img3.jpg}
  \caption{The mosaic GLM\_01200+0000\_I1 showing the adverse effects when increasing the number of contours for processing from 1000 to 2000. The blue boxes represents the IRDCs found when 1000 contours are evaluated, whereas the green boxes represent the additional IRDCs found when 2000 contours are processed. The green areas are typically very small regions that do not correspond to real IRDCs in the image, or areas in or near larger IRDCs already identified.}
  \label{threshold}
\end{figure}

\begin{figure}[h]
  \includegraphics[width=\linewidth]{img4.png}
  \caption{A graphical representation of the process to determine the difference between the IRDC core and surrounding region. The red box represents the original bounding box for the contour, whereas the green box represents the expanded box used for determining the local level. The black box shows the region used for a measure of the IRDC depth. The difference value is calculated by subtracting the outbox's average from the inner box's average.} \label{_difference}
\end{figure}

\subsection{Parameter Optimization}
The algorithm parameters $T_{0}$, $T_{1}$, $T_{2}$, $C_{0}$, and $C_{1}$ needed to be optimized to reliably locate IRDCs in the mosaics. We found that one set of values would not work for the entire dataset, since the data were taken with different exposure times and covered regions with large differences in background levels and complexity. Thus, for each of the surveys listed in \S \ref{obs}, we systematically varied the parameters and evaluated the results for a small subset of the mosaics in the survey to optimize the parameter values. After observing the difference of the bounding boxes from a set of mosaics, we determined a base starting point of $T_{0}=6, T_{1}=15, T_{2}=40$. From there we then let $C_{0},C_{1} \in [0, 0.25, 0.5, 0.75]$. Since the classifiers' output impact results independently, when finding the optimum values for $C_{0}$ and $C_{1}$, we turned one off and ran the other classifier using all the values. Once we found the optimum value for one classifier, we then turned it off and repeated the process for the other classifier. We then repeated this process for each mosaic in the sample set and obtained a set of $C_{0}$ and $C_{1}$. We averaged them, and the result was the parameter for the whole dataset for that survey. However, in some regions the results were not improved when changing the values of $C_{0}$ and $C_{1}$. We then experimented with changing $T$ until we found optimum values for $T_{0}$, $T_{1}$, $T_{2}$. An example of the effect of changing these parameters is shown in Figure \ref{T_Change}. The final parameters we used for each survey are given in Table \ref{params}.

\begin{figure}[h]
  \includegraphics[width=\linewidth]{Figure5.jpg}
  \caption{A section of the mosaic from the 3D region, 00000-0300 is shown with objects identified using a value for $T_{2}$ of 40 in red and $T_{2}$ = 85 in green. A total of 457 and 16 objects were found in this mosaic for values of 40 and 85, respectively. The lower $T_{2}$ value resulted in many more small objects being identified which are not likely real IRDCs.}
  \label{T_Change}
\end{figure}

\begin{deluxetable}{ccccccc}
\caption{Parameters Used for Survey Areas}\label{params}
\tablehead{& & \multicolumn{2}{c}{Probability} & \multicolumn{3}{c}{Depth}\\
& \colhead{Scaling} & \multicolumn{2}{c}{Cutoff} & \multicolumn{3}{c}{Cutoff}\\
\colhead{Region} & \colhead{Factor} & \colhead{$C_{0}$} & \colhead{$C_{1}$} & \colhead{$T_{0}$} & \colhead{$T_{1}$} & \colhead{$T_{2}$}}
\startdata
 360     & 10 & 0.25 & 0.25 & 15 & 15 & 40  \\
 3D      & 10 & 0.5 & 0.0 & 6 & 6 & 85 \\
 CYGX    & 10 & 0.25 & 0.25 & 20 & 20 & 40  \\
 Deep    & 10 & 0.5 & 0.0 & 20 & 20 & 55  \\
 I       & 5 & 0.25 & 0.0 & 6 & 6 & 45\\
 II      & 5 & 0.5 & 0.0 & 6 & 6 & 40  \\
 SMOG    & 10 & 0.25 & 0.25 & 20 & 20 & 40  \\
 VelaCar & 10 & 0.25 & 0.25 & 25 & 25 & 55 \\
\enddata
\end{deluxetable}

\subsection{Further Processing}\label{method}
Once our initial catalog was generated, we applied several further processing steps to produce the final IRDC catalog. First, the contour algorithm had found contours within contours, which were not necessary for identifying the IRDCs, and led to duplicate identifications. In addition, because the mosaics in the survey overlap slightly, some IRDCs are identified in more than one image and produce duplicate identifications. As a result, we wrote another program which sifted through all the IRDCs in the catalog and removed any bounding boxes which had their centers within another bounding box, and their maximum extent did not exceed the larger bounding box by 10$''$ in any direction. In each case the smaller of the boxes were removed. We also applied a lower IRDC contour area cutoff of 300 pixels (432 arcsec$^2$) because it appeared that objects of this size or smaller were either false positives, or were identifying small fragments of IRDCs that were already in the catalog as larger IRDCs. This effect is illustrated in Figure \ref{smIRDCs}, which shows several small regions that were identified in the initial processing. The regions seem to be locating spaces between brighter stars, rather than true IRDCs. There are many similar regions in the image, and the identified areas do not stand out as distinctly different. This effect is a function of the IRAC instrument resolution and sensitivity, combined with the distribution of stars in the field.   By setting a lower limit to the size of the IRDCs in the catalog, we can minimize the inclusion of these regions that do not represent true IRDCs. Because of this effect, it is likely that a number of false positives remain in the catalog and the percentage is higher amongst the smallest IRDCs in the catalog.\\

\begin{figure}[h]
 \includegraphics[width=\linewidth]{smIRDCs.png}
  \caption{A sample section of the mosaic GLM\_04500+0000, showing several small regions initially identified in the processing but removed based on the small area cutoff level. The regions identified seem to be areas of low background between stars rather than due to absorption by an IRDC.}
  \label{smIRDCs}
\end{figure}

\section{Results and Discussion}\label{results}
Using our procedure as described above, we find a total of 18845 objects. We will refer here to the objects in our catalog as IRDCs, however they should be considered as candidate IRDCs -- not all of the objects are true IRDCs, and the catalog does not contain a complete list of all true IRDCs in the survey area (see \S \ref{limitations} for more discussion of the limitations of our method). Our catalog is shown in Table \ref{IRDCcat}, which gives the name, Galactic coordinates, size of the bounding box in arcsec of Galactic longitude and latitude, depth ratio of the IRDC, probability value returned by the classifier, and the area within the contour of each IRDC in arcsec$^2$. The depth ratio is the difference between the median 3.6~\micron\ flux density around the outside of the IRDC box and the non-negative minimum level of a pixel within the box divided by the median outside of the box. Therefore larger numbers represent deeper absorption values. The final column in the table lists the number of objects from the PF09 catalog that have their centers within the borders of the IRDC box defined in the table, for those objects that fall within the region surveyed by PF09. A histogram of the distribution of IRDC sizes is shown in Figure \ref{Histogram}.
\begin{figure}[h]
  \includegraphics[width=\linewidth]{20191010catalog_rd_hist.pdf}
  \caption{The distribution of IRDCs as a function of contour area. Over 84\% of the IRDCs have an area of 4000 arcsec$^2$ or less. The maximum area is 393870 arcsec$^2$, and there are 106 IRDCs with areas higher than 60000 arcsec$^2$.}
  \label{Histogram}
\end{figure}

\begin{figure*}[ht]
  \includegraphics[width=\linewidth]{Figure7.jpg}
  \caption{The mosaic GLM\_01200+0000 which was used as our test mosaic from the GLIMPSE I region. The blue boxes represents the true positives, where the red boxes represents the false negatives (IRDCs that were not identified by our process). The yellow boxes represents the false positives.}
  \label{Accuracy}
\end{figure*}

\begin{figure*}[ht]
  \includegraphics[width=\linewidth]{Figure8.jpg}
  \caption{The GLM\_01200+0000\_I1 mosaic with blue rectangles for our catalog. We also show objects from the PF09 catalog in red and the {\it MSX} catalog \citep{Simon_2006} in green. The objects are shown as ellipses here, with the major and minor axes and position angle based on the IRDC sizes and angles given in those catalogs. }
  \label{Overlay}
\end{figure*}

We list the probability values in the table, although these did not seem to correlate well with the quality of the IRDC identification. For example, some of the large IRDCs identified in the field shown in Figure \ref{Accuracy} had probabilities in the range 0 -- 0.3, yet all seem to be definite IRDCs that were also identified in the prior surveys. Many other objects have a probability of 1.0, even some of the small IRDCs that are probably not as reliable.

\begin{deluxetable*}{crrrrrrrr}
\tablecaption{IRDC Catalog\label{IRDCcat}}
\tablehead{& \multicolumn{2}{c}{Galactic Coordinates} &\multicolumn{2}{c}{IRDC Size}& \colhead{Depth}&\colhead{Proba-} &\colhead{Contour} & \colhead{PF09}\\
\colhead{Name} & \colhead{Longitude} & \colhead{Latitude} & \colhead{(longitude)} & \colhead{(latitude)} & \colhead{Ratio\tablenotemark{a}} &\colhead{bility}& \colhead{Area}& \colhead{Objects\tablenotemark{b}}\\
\colhead{} & \colhead{(degrees)} & \colhead{(degrees)} & \colhead{(arcsec)} & \colhead{(arcsec)} & & & \colhead{(arcsec$^2$)} &  }
\startdata
G000.01+00.275&	0.0095&	0.2752&	160.8&	138.0&	0.78&	1&	8428&	\nodata\\
G000.01$-$00.601&	0.0055&	-0.6012	&37.2	&62.4	&0.61	&1	&661&\nodata	\\
G000.02+00.359&	0.0152&	0.3592&	45.6&	44.4&	0.53&	1&	456&	\nodata\\
G000.02+02.797&	0.0249&	2.7972&	37.2&	42.0&	0.50&	1&	518&	\nodata\\
G000.02$-$00.433&	0.0195&	-0.4328&	142.8&	44.4&	0.92&	1&	2139&	\nodata\\
G000.03$-$00.349&	0.0342&	-0.3488&	48.0&	42.0&	0.71&	1&	596&	\nodata\\
G000.05+00.285&	0.0528&	0.2855&	73.2&	57.6&	0.59&	1&	1666&\nodata\\
G000.06+00.174&	0.0635&	0.1738&	52.8&	50.4&	0.77&	1&	1172&\nodata\\
G000.06$-$00.427&	0.0648&	-0.4272&	87.6&	45.6&	0.76&	1&	1607&\nodata\\
G000.07+00.215&	0.0735&	0.2152&	58.8&	40.8&	0.52&	1&	850&\nodata\\
\enddata
\tablenotetext{a}{The ratio of the difference between the median 3.6~\micron\ flux density around the outside of the IRDC box and the minimum level of a pixel within the box to the median outside of the box (see \S \ref{results}).}
\tablenotetext{b}{Number of objects from \cite[PF09;][]{peretto09} that have their centers within the IRDC box as defined in this catalog. An ellipsis is shown in this column for those objects that were not in the region included in the PF09 study.}
\tablecomments{This table is available in its entirety in machine-readable form.}
\end{deluxetable*}

\subsection{IRDC Catalog Characteristics and Verification}\label{verify}
To evaluate the accuracy of our technique, we took a sample mosaic from each survey and evaluated by eye the number of true positives, false positives, and false negatives (IRDCs that were missed). A true verification is difficult because there is no other dataset available that would allow us to unambiguously determine whether an IRDC is real or not. It is possible to compare to longer wavelength data such as \citet{Peretto2016} who found {\it Herschel} counterparts to the IRDCs in the PF09 catalog. However, that is not a completely reliable method to indicate which objects are true IRDCs, as is evident in the examples shown below. One reason is that many of the small and low-contrast objects found in our survey would not be detected with the factor of 10 or more poorer resolution and often complicated backgrounds in the long wavelength data. We therefore performed a visual test, inspecting by eye each bounding box in the test field to verify whether it contained a real IRDC or not. We used the SAOimageDS9\footnote{http://ds9.si.edu/site/Home.html} data visualization software to interactively adjust the scale and contrast as we scanned the image in order to best display features at various locations in the image to find IRDCs.  Furthermore, we scanned the image to find any IRDCs that were not identified by the surveys. For the regions where we had access to both the Channel 1 and 4 mosaics, we used both to conduct our inspection because some IRDCs are more visually apparent in the Channel 4 mosaics, and we wanted to evaluate how many objects were possibly being missed because of our use of Channel 1 only in our survey.

The results of our accuracy test for the mosaic GLM\_01200+0000 are shown visually in Figure \ref{Accuracy}, and Table \ref{accuracysummary} summarizes the results for all test regions. We found that our method performed the best in the inner Galaxy, and while the table conveys that some true IRDCs were missed in the test regions, a large fraction of the objects were found and the false positive rates were relatively low. Our method was not able to pick up some faint and small IRDCs, which could be caused by the Gaussian blurring which will tend to reduce our sensitivity to compact and low-contrast objects. This is a trade-off in the survey where the Gaussian blurring improves the IRDC detection and reliability for more distinct objects, but tends to remove some real low-contrast objects.

\par
Most of the IRDCs in our catalog are relatively small, which can be seen in the histogram of apparent IRDC sizes in Figure \ref{Histogram}. The sizes in Table \ref{IRDCcat} and shown in this figure are the projected area within the IRDC contour in units of arcsec$^2$. Without knowing the distances to each IRDC, we cannot determine the true physical sizes of the objects. The distribution of apparent sizes increases fairly smoothly as one moves towards smaller IRDCs, except for a feature in the histogram near an IRDC area of 4200 arcsec$^2$ where the distribution has a slight dip. This does not seem to be related to a change in parameters in the various size ranges discussed in \S \ref{parmchoice} since the feature is fully within the 3000 -- 30000 size range. The IRDCs below and above this feature do not seem to differ in character in terms of their distribution on the sky or the characteristics of the IRDCs. We therefore do not believe this is a real feature in the IRDC size distribution, but perhaps an artifact of the way that larger IRDCs are broken up into several smaller objects by the algorithm, or perhaps influenced by the way the data are binned for the histogram.

Because most of the objects we find are small IRDCs, most of the false positives tended to also be small, and typically related to small dark spaces between stars in the images. This is because an area with bright stars enclosing a dark area is interpreted as a contour, and its small size makes it hard for the CNN to classify it accurately. Many of the false positives were removed using a lower limit cutoff (see \S \ref{method}), but the cutoff was not set high enough to remove all of them since that would have removed many true IRDCs as well. In practice, any IRDC survey will have an inherent lower size cutoff due to the instrument resolution, sensitivity, and characteristics of the background mission. Here we have explicitly set that value slightly higher than the theoretical limit, at the cost of removing some true IRDCs that are smaller than the cutoff.

We noticed that the performance in the Cygnus-X region was poorest in terms of fraction of false positives and false negatives compared to the number of true IRDCs in this region. Many of the false positives in the Cygnus-X region tend to be low emission regions surrounded by brighter emission features. These structures mimic the appearance of IRDCs, but are often more diffuse and not as centrally concentrated as the true IRDCs. There were also a significant fraction of missed IRDCs in the Cygnus-X region.
These issues could be due to the fact that our classifier was trained on the inner parts of the Galaxy, where the IRDCs are more distant (typically 3-5~kpc compared to the ones in Cygnus-X which are 1.4~kpc or less) and there was insufficient data with low emission regions to train the classifier. This would be an issue that could possibly be addressed through transfer learning, where our model weights could be used as a starting point to train on a new dataset. However, we chose not to develop new methods for this region, and present the catalog that used uniform techniques over the entire dataset.\label{cygnusX}

\begin{figure*}[ht]
  \includegraphics[width=0.5\linewidth]{fig14a.jpg}
  \includegraphics[width=0.5\linewidth]{fig14b.jpg}
  \caption{A comparison of the PF09/\cite{Peretto2016} (red and green circles) and our catalog (yellow boxes) in an area covered by both surveys. The region shown is a 17$' \times 13.7'$ area centered at $l=29.323$\degr, $b=-0.708$\degr. The 3.6~\micron\ image is on the left, the 8~\micron\ image on the right. The IRDCs found to have a {\it Herschel} counterpart by \citet{Peretto2016} are plotted with the green circles with the radius given in that catalog, and the objects with no {\it Herschel} source are plotted with red circles. The IRDCs identified in both surveys are largely consistent, with the PF09 catalog tending to identify individual parts of an IRDC as separate objects, whereas our survey outlines larger areas.  Our survey has identified some of the lower density IRDCs that were not marked in the PF09 survey. There appears to be a faint extension of the IRDC just to the left of the center of the image that was missed by both methods.}
  \label{compreg1}
\end{figure*}

\subsection{Comparison to the PF09 and MSX Catalogs}
We compared our catalog to the IRDC catalogs from PF09 along with the {\it MSX} catalog by \cite{Simon_2006}. The PF09 catalog provides an estimate of the size of the IRDC in two dimensions and the orientation of the major axis, and Simon et al. provide the major and minor axes sizes and position angle of the major axis, whereas we provide rectangles to define the regions occupied by IRDCs. Therefore, for a long and thin IRDC that runs diagonally in Galactic coordinates, the PF09 and {\it MSX} catalogs will have a better representation of an IRDC's dimensions and orientation than our bounding boxes. However, from the example images shown here, one can see that many IRDCs are irregular and there is no clear orientation. Our method as well as PF09 tend to split large IRDCs into smaller segments, so the sizes we find do not vastly misrepresent the area of the IRDC. In addition, in Table \ref{IRDCcat} we give the contour area, which is a better estimate of the IRDC area than the bounding box or an estimate of the major and minor axes of the IRDC.

Both of the previous catalogs also used computational approaches for IRDC localization, and in Figure \ref{Overlay} we show a sample region where we overlay these catalogs along with ours. As one would expect from the lower spatial resolution of the {\it MSX} survey, the IRDCs in that catalog are larger, and tend to enclose regions that are broken up into individual IRDCs by the other surveys. There are some false positives that seem to be due to regions of lower emission surrounded by brighter regions, rather than absorption of emission by an IRDC. This is also an issue for the PF09 (see below) and our survey (see \S \ref{cygnusX}). The orientation and aspect ratio of the IRDCs seem to track the actual shapes reasonably well, although in many cases they seem to overestimate the IRDC size.

The PF09 catalog and ours tend to break up larger IRDCs and ones with complicated structure into smaller objects. This characteristic of the search technique is sensitive to the threshold level set, which can break up an object into different contours and then be identified as separate objects, whereas visually they appear to be parts of a larger spatially complex IRDC. The PF09 catalog seemed to further break apart IRDCs compared to ours. Some of the larger and more irregular IRDCs are better represented in terms of the cloud area and location in our survey whereas in the PF09 catalog only small parts of the clouds were identified.  It can be seen in Figure \ref{Overlay} that many of the blue boxes of our survey contain many small red PF09 objects. In this field, there were no PF09 IRDCs larger than the matching object in our catalog. In the full catalog, we found a total of 12331 IRDCs in the regions covered by the PF09 catalog. Of these, 2626 of our IRDCs had one or more PF09 objects with centers within the IRDC boxes listed in our catalog. A total of 4102 PF09 objects were within our IRDC regions. Regions in the two catalogs which did not overlap are a mix of PF09 objects that identify parts of IRDCs that we also found but their centers lie outside of our bounding boxes; IRDCs that are below our lower size cutoff (5769 objects) or otherwise rejected from our catalog; and possible false positives in the catalogs. For the PF09 objects below our size cutoff, many of these lie within the larger IRDC regions identified in our survey, as is apparent in Figure \ref{Overlay}, so they do not all correspond to IRDCs that are missing from our survey.
\par

\begin{deluxetable*}{ccccc}
\caption{Summary of Accuracy Tests}\label{accuracysummary}
\tablehead{\colhead{Mosaic} &\colhead{Survey}& \colhead{False Positives} & \colhead{True Positives} & \colhead{False Negatives}}
\startdata
GLM\_01200+0000 & I& 1 & 456 & 43 \\
GLM\_00000+0000 & II & 0 & 438 & 48 \\
GLM\_00000+0300 & 3D & 0 &5 & 3\\
corr\_CYGX\_07800+0250& CygX & 11 & 137 & 31\\
corr\_GLM\_02700+0150 & Deep& 4 & 209 & 9 \\
corr\_GLM\_06600+0105 & 360 & 32 & 166 & 0 \\
corr\_SMOG\_10500+0145 & SMOG & 7 & 0 & 1 \\
corr\_VELACAR\_25800+0050& VelaCar & 1 & 0 & 0 \\
\enddata
\end{deluxetable*}

\begin{figure*}[ht]
  \includegraphics[width=0.5\linewidth]{fig15a.jpg}
  \includegraphics[width=0.4975\linewidth]{fig15b.jpg}
  \caption{A comparison of the PF09 and our catalog, using the same symbols as in Figure \ref{compreg1}. The region shown is a 46$' \times 41'$ area centered at $l=48.905$\degr, $b=-0.239$\degr. The 3.6~\micron\ image is on the left, the 8~\micron\ image on the right. There is some correspondence between the surveys in the lower emission regions, but near the regions with bright 8~\micron\ emission the PF09 survey has identified many regions that appear to be lower intensity cavities near bright emission features. Many, but not all, of these regions were flagged by \citet{Peretto2016} as not having {\it Herschel} counterparts (plotted in red). Our survey seems to have avoided identifying objects in the bright areas, missing a few true IRDCs that appear in the lower left part of the region.}
  \label{compreg2}
\end{figure*}

\par
An example comparing the performance of the two surveys against the 3.6 and 8~\micron\ images of a field is shown in Figure \ref{compreg1}. The PF09 (green and red) and our catalog objects (yellow) are plotted on both images of the same region. The PF09 objects found to have a {\it Herschel} counterpart by \citet{Peretto2016} are plotted with green circles, the objects without are plotted in red. In general there is good correspondence between objects in the two catalogs in the region shown. The IRDCs identified in both surveys are largely consistent, with the PF09 catalog tending to identify individual parts of an IRDC as separate objects, whereas our survey outlines larger areas. This is by design since we have removed smaller regions identified within larger IRDCs. There are a few IRDCs that we found that were not listed in PF09, perhaps due to their low contrast with surrounding regions, and a couple IRDCs in the PF09 catalog that we did not list, due to their small size. The PF09 objects in this region without {\it Herschel} counterparts appear to be true IRDCs. There is a faint extension of the IRDC in the center of the image that was not identified in either survey.

A further comparison is shown in Figure \ref{compreg2}, which shows a larger region with some areas of bright emission in both channels. Again the 3.6 and 8~\micron\ images of the same region are shown and the PF09 objects overlaid in green and red, and our survey in yellow. Here there is lower correspondence between the two surveys, with the majority of PF09 sources being related to the bright emission features in the region. A large number of those catalog entries seem to be related to areas of lower emission surrounded by bright features, and are likely not true IRDCs. Many of these PF09 objects do not have {\it Herschel} counterparts (red circles). There are several objects that appear to be true IRDCs located in those bright regions that our survey failed to locate, probably due to the complicated backgrounds in the region. There is better agreement between the surveys in the lower background regions around the edges of the image.

\begin{figure}[ht]
  \includegraphics[width=\linewidth]{fig16.jpg}
  \caption{A comparison of the PF09 and our catalog, using the same symbols as in Figure \ref{compreg1}. The region shown is a $34' \times 29'$ area of the 8~\micron\ mosaic centered at $l=50.943$\degr, $b=0.091$\degr. The PF09 survey has identified several large regions near the bright-rimmed cloud as IRDCs where they appear to be regions of lower emission near the bright filamentary structure. These are plotted with the red circles, indicating that \citet{Peretto2016} found these to not have {\it Herschel} counterparts. Several smaller objects that appear to be true IRDCs were located by PF09 (green circles) and our survey (yellow) in the region as well.}
  \label{BRR1}
\end{figure}

Figure \ref{BRR1} shows another example of objects identified in the surveys near a bright-rimmed cloud. Many of the PF09 objects seem to be related to the emission voids near the bright emission regions. Fifteen objects are listed in the PF09 survey and three in our survey. One object is common to both surveys, and ten of the larger regions drawn in red were found by \citet[][]{Peretto2016} not to have {\it Herschel} counterparts. The five PF09 objects drawn in green locate what appear to be true small IRDCs, two of which our survey also found. The algorithm used in our survey largely ignored the areas near the bright rimmed cloud and found two low contrast regions near the right edge of the image.

Our survey located a number of objects with low density that perhaps did not satisfy the density cutoff used by PF09. Figure \ref{densityhist} shows histograms of the IRDC depth ratios as measured by our survey in the 3.6~\micron\ images. The blue histogram shows IRDCs that are in both surveys, the pink histogram shows objects in our survey that are in the PF09 area but not listed in that survey. The vertical plot ranges were normalized to the same fraction of the total number of objects in that histogram. The pink histogram is more sharply peaked and has a median value (0.57) lower than the histogram of objects in both surveys (0.71), indicating that objects common to both surveys have on average higher depth ratios than regions found only in our survey.

\begin{figure}[ht]
  \includegraphics[width=\linewidth]{DensityHist.PNG}
  \caption{Histograms of the IRDC depth ratios as measured by our survey in the 3.6~\micron\ images. The blue histogram shows IRDCs that are in both our new and the PF09 surveys, the pink histogram shows objects in our survey that are in the PF09 area but not found by that survey (where the histograms overlap is shown in purple). The vertical plot ranges were normalized to the same fraction of the total number of objects in that histogram. Objects common to both surveys have higher depth ratios than regions found only in our survey. }
  \label{densityhist}
\end{figure}

In summary, our survey is largely consistent with and in some ways complementary to the PF09 catalog. For low background and uncomplicated regions, the surveys often identify the same objects, with the PF09 highlighting the densest individual components and our survey grouping them into single larger IRDCs. The PF09 catalog may contain small dense IRDCs that ours will miss because of the lower size cutoff, and our survey appears to be more sensitive to low-contrast regions that might be below the minimum column density requirement of the PF09 catalog. In regions of bright emission, the PF09 catalog seems more prone to including emission voids near bright features, but also locates some true IRDCs in these regions that our survey missed because of the complicated background. The \citet{Peretto2016} comparison to the {\it Herschel} sources helps to indicate which PF09 IRDCs might be emission voids in the 8~\micron\ images rather than absorption due to an IRDC, but there are also apparently true IRDCs that are not verified in the {\it Herschel} data due to their size or the complex nature of some regions.

\subsection{Limitations of the IRDC Catalog}\label{limitations}
There are several limitations to the IRDC catalog presented here, some of which are described in \S \ref{verify} above. Figure \ref{ErrExamples} shows some examples of problem areas in the survey images. The region G295.58$-$01.011 was identified near a bright star because of an uncorrected ``muxbleed'' effect \citep[see][for a description of this and other IRAC array artifacts]{2004SPIE.5487...77H} and another bright source artifact where the background level of the array is artificially depressed in certain rows of the array near a bright star. This causes the algorithm to detect two IRDCs along the region near the bright star. Another region shown in Figure \ref{ErrExamples} is the large IRDC G172.86$-$00.455. There does appear to be a true IRDC in the center of this region, but it is also in a void of low emission surrounded by higher diffuse emission and a bright-rimmed cloud along the right side of the area. The algorithm was misled by this structure and overestimated the extent of the actual IRDC. In the lower left panel of Figure \ref{ErrExamples}, the catalog object G263.00$-$01.111 locates a column pulldown artifact near a bright star in the mosaic. Finally, in the lower right panel, G332.98$-$00.221 shows an example of two IRDC regions (in red) which were each identified as two separate IRDCs. Although they are mostly overlapping, there was enough of a difference between their centers and coverage areas that they were not removed as duplicates in our search for these objects described above.

Some IRDCs will be missed because they are too low contrast and/or too small to be identified, given the survey sensitivity and resolution of {\it Spitzer\/}, or because of the characteristics of the local background emission. An IRDC might have low contrast because it does not contain much absorbing mass, or it might not be in front of sufficiently bright background emission for it to stand out in the image. When the stellar density and background level is low, gaps between stars can be mistaken for regions of high absorption. This has been partially mitigated by using a lower limit on the IRDC size, but this will not remove all of these spurious IRDCs, and it removes the smallest true IRDCs from the list that fall below the cutoff size.

Bright emission and spatially complicated background regions can lead to missed IRDCs, or false positives when a lower emission region is surrounded by bright source structure. Large individual IRDCs or complexes are often broken up by this algorithm into separate objects, and separate IRDCs can be perceived as one object if they overlap spatially, even though they could be physically distant from each other. Another possibility is that an IRDC might have a unique morphology that is not recognized by the algorithm as an IRDC because it is not well-represented by the objects in the training set or the other members of the catalog. Most of these limitations exist to some degree for all search techniques, so any catalog can never be complete or accurately characterize all IRDCs. However, our method performs well when compared to the previous search methods used, and appears to find a large fraction of true IRDCs while minimizing false positives. It also avoids breaking up IRDCs into many individual objects, which would affect the perceived distribution of IRDC sizes.

\begin{figure}[ht]
\vskip 0.2in
  \includegraphics[width=\linewidth]{4fig.jpg}
  \caption{Examples of some issues with the images and IRDC identifications in our catalog. See \S \ref{limitations} for a description of these cases. Top Left: The region near G295.58-01.011 which a bright star causes artifacts in the image and the region is misidentified as an IRDC. Top Right: The large region G172.86-00.455. Lower Left: G263.00-01.111 which is caused by an uncorrected pulldown effect in the original IRAC images. Lower Right: The region including G332.98-00.221, which shows two IRDC regions (red rectangles) that are largely overlapping with other objects in the catalog.}
  \label{ErrExamples}
\end{figure}

\section{Conclusions}
Our goal was to use an objective, automated method for finding IRDCs in the large GLIMPSE dataset that could be applied to the regions where only {\it Spitzer\/}\ Warm Mission (3.6 and 4.5~\micron) data are available. We used open source astronomical, image processing, and neural network libraries to develop a method that sought to maximize the identification of true IRDCs and minimize the number of false positives and false negatives. We applied this method to the various GLIMPSE and other Galactic plane datasets to create an IRDC catalog over the full range of Galactic longitude covered in these surveys. The catalog identifies many of the previously-known IRDCs found in the earlier {\it MSX} and PF09 surveys, and finds new objects in survey regions that have recently become available late in the {\it Spitzer\/}\ mission.

\acknowledgements
This work is based on observations made with the {\it Spitzer\/}\ Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. Support for this work was provided by NASA through an award issued by JPL/Caltech. We also thank artist Mario Klingemann who was very helpful when dealing with the Tensorflow aspect of our procedure.
\facility{Spitzer (IRAC)}
\software{Astropy\citep{astropy}, Pillow, OpenCV, Tensorflow, SAOimageDS9}
\bibliographystyle{aasjournal}

redpajama_set_name: RedPajamaArXiv


text:
Laura Benanti (1979) – attrice e cantante statunitense
 Paolo Benanti (1973) –  presbitero, teologo e accademico italiano del Terzo ordine regolare di San Francesco


Blooming in rich autumn color and design, this wonderful tabletop piece fills your home with delightful style. The faux tree features red and orange fall leaves with berries throughout its metal branches wrapped in brown paper. Planted in a birch tree-like trunk for added detail, this beautiful floral accent is perfect for decoration on a mantel, fireplace hearth or table.


text:
How to watch Ravens vs. Bills: Playoff picture, TV, odds and what to read
Baltimore mayoral debate on Fox 45 becomes forum for just one candidate, Thiru Vignarajah, after others decline invitation
By Talia Richman and Emily Opilo
Baltimore Sun |
What was originally billed by Fox 45 as a debate among Baltimore's Democratic mayoral candidates instead became a roughly hourlong conversation with only one contender — former Deputy Attorney General Thiru Vignarajah — after three others declined the Sinclair Broadcast Group-owned station's invitation to Thursday night's event.
Former U.S. Treasury official Mary Miller's campaign manager wrote a Sinclair executive Wednesday to officially decline the invitation for WBFF-TV′s virtual debate. She said the campaign is concerned that the station's coverage of the election is biased in favor of Vignarajah, in part because Sinclair executives have made large contributions to his campaign.
Former Mayor Sheila Dixon's spokeswoman, Martha McKenna, said the former mayor declined to participate in Thursday's event due to short notice from organizers. City Council President Brandon Scott's campaign spokesman Marvin James said he decided not to participate after learning the original candidates he anticipated sharing the screen with were no longer attending.
The station hosted a more than hourlong online "conversation" with Vignarajah alone Thursday night, with anchor Mary Bubala noting at the beginning of the livestreamed program that they had hoped to be joined by Miller, Dixon and Scott. The station did not invite former Baltimore Police spokesman T.J. Smith and Mayor Bernard C. "Jack" Young because they require candidates to release five years of tax returns to qualify for these debates.
"I was looking forward to a debate," said Vignarajah, pitching himself as the candidate who will push for greater transparency in local government.
Progressive Maryland files complaint against super PAC backing Mary Miller »
In her letter to the station, Miller's campaign manager, Ann Beegle, noted that several people affiliated with Sinclair, a conservative-leaning Hunt Valley-based company, have made large financial contributions to Vignarajah's mayoral campaign.
Maryland Policy & Politics Newsletter
Keep up to date with Maryland politics, elections and important decisions made by federal, state and local government officials.
"We had hoped that Fox 45's news coverage would not be influenced by Sinclair with regards to this election," she wrote.
Scott Livingston, Sinclair's news vice president, denied any bias in an email to The Baltimore Sun, adding that Miller and other top candidates have been invited to appear on the station.
"I don't have any idea of what anyone associated with Sinclair does with their money," he wrote. "Our focus has been on what's in the best interest of the people of Baltimore."
Sinclair Chairman David D. Smith's children and their spouses donated a combined $30,000 to Vignarajah, each contributing the maximum $6,000 allowed under Maryland law, according to campaign finance reports. An additional $6,000 was donated by J. Duncan Smith, the vice president and secretary of Sinclair, as well as a board member. Others with ties to the station have donated as well.
Smith family members have made contributions to other candidates, too. Frederick Smith, a Sinclair vice president and board member, gave Mayor Young a $4,000 contribution. His sons, Eric Smith and Alexander Smith, gave Young $6,000 after a fundraiser at one of their restaurants. Eric Smith also gave $1,000 to Scott.
The Federal Communications Commission announced Wednesday that Sinclair faces a record $48 million civil penalty to resolve three investigations by the agency.
In Baltimore mayor's race final stretch, Vignarajah has most cash »


Voters are narrowly supportive of allowing businesses and religious organizations to opt out of government-mandated free birth control for their women employees if religious beliefs are at stake.
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( Want a free daily e-mail update? If it's in the news, it's in our polls). Rasmussen Reports updates are also available on Twitter or Facebook.


Plot: The documentary of Emperor Penguins in Antarctica.
March of the Penguins is the most relaxing and the most excellently shot documentary of 2005. Morgan Freeman's god like voice made this worth a watch.
A great documentary that I saw with my family in the theater. The story of family amidst the harshest realms on Earth drew fanciful parallels to that of mankind, nature imitating the greatest apers in history: humans. A beautiful soliloquy to the heart and soul of nature itself, detailing the exhausting exodus to create life and sustain an entire species, "March of the Penguins" is a hit.
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text:
Bananas (llamada en Venezuela La locura está de moda), de 1971, es el segundo filme que dirigió Woody Allen, y que además escribió y protagonizó. La película se desarrolla a modo de sátira política siguiendo una historia que hace bastante alusión a la Revolución Cubana (o alguna que otra sucedida en América Latina). El filme está estructurado mediante varias escenas que, por un lado responden a los hechos de la historia linealmente, y por el otro, introducen distintos sketchs cómicos que marcan el humor de la época yendo desde la influencia de los Hermanos Marx al futuro aporte de comedias como Airplane! (1980).

Argumento
La película empieza con una ridiculización de una cobertura periodística estadounidense del ascenso del nuevo dictador de San Marcos, pequeño país ficticio de Sudamérica.

Fielding Mellish es un ciudadano estrafalario de Manhattan que se dedica a probar nuevos productos en una clara analogía a Tiempos Modernos. Es un enano enclenque, inseguro, obsesivo y sin éxito con las mujeres. En ese momento, se desarrollará una escena que incluye al entonces muy joven Sylvester Stallone como un matón en el metro.

Por una casualidad, conoce a Nancy, una joven activista que se encontraba recogiendo firmas para que Estados Unidos apoyase en San Marcos a la democracia y no al régimen dictatorial. Los dos comienzan un romance, muy a pesar de Nancy, que al poco tiempo lo deja porque, según ella, le falta algo. Sucede que ella quiere a alguien más fuerte, más seguro de sí mismo: en definitiva, quiere a alguien con dotes de mando.

Luego de una situación en el quirófano de sus padres, Fielding decide ir a San Marcos para ver de cerca lo que está sucediendo en el país e impresionar a la que era su novia.

El general Vargas (nuevo dictador del país) lo recibe con honores. Pero solo para planear asesinarlo y hacer parecer que fue obra de Expósito y sus rebeldes. Según ellos, con eso se ganarían el total apoyo de Estados Unidos. De cualquier manera, Fielding escapa y va a parar con los rebeldes.

Como queda en deuda, Fielding se une a la guerrilla y sólo podrá volver a Manhattan una vez que la revolución haya triunfado. Es allí cuando aprende torpemente a ser un guerrillero. Son varias las escenas graciosas: el aprendizaje de la lucha, de la supervivencia, su romance con la guerrillera de la banda y el extremadamente absurdo saqueo a un almacén del pueblo.

Se desencadena la batalla y, con una parodia incluida de El Acorazado Potemkin, la Revolución triunfa. Vargas se exiliará en avión en una posible analogía respecto del derrocado Fulgencio Batista. Expósito, si bien es físicamente parecido al Che Guevara, se vuelve loco dando órdenes contradictorias y disparatadas al pueblo. Fielding gana popularidad entre los guerrilleros de una manera similar a la del personaje de Peter Sellers en la comedia Being there.<ref>La película Being There, de 1979, es adaptación de una novela de 1971, el mismo año del estreno de Bananas.</ref> Es elegido como nuevo presidente de San Marcos, y viaja a los Estados Unidos para pedir dinero disfrazado de Fidel Castro.

Al reunirse con la que era su novia, se descubrirá quién es él, y se lo someterá a juicio. Todos están en su contra, incluso el primer director del FBI, John Edgar Hoover, que está disfrazado de mujer negra. Fielding se defiende solo y al final es condenado, pero perdonado con la condición de que no viva en el barrio del juez.

La película terminará con el mismo reportaje extraño y absurdo del noticiero estadounidense, pero esta vez comparando la consumación del matrimonio con una pelea de boxeo.

 El nombre del filme

Responde a un chiste de doble sentido que mezcla los países bananeros con la frase to go bananas (volverse loco).

 Notas y referencias

 BJÖRKMAN, Stig: Woody por Allen''. Plot. 1995.
 Stig Björkman (n. 1938): escritor, director y crítico cinematográfico sueco.

Enlaces externos

 Ficha de la película; en español.

Películas cómicas
Woody Allen
Películas ambientadas en un país latinoamericano ficticio
Películas sobre dictaduras latinoamericanas en el siglo XX
Películas sobre juicios
Películas en inglés
Películas de Estados Unidos
Películas con temática LGBT de 1971


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Q: Revolving the region $y=6-x^2$, $y=2x+3$ about x=5 To find the volume of the resulting solid we can either use rings or shells (parallel or perpendicular slices)
For this particular example using shells (parallel slices) makes it simple:
We have $\int (5-x)(6-x^2-2x-3) dx $ going from $x=-1$ to $x=3$ and the rest is simple.
But when I attempt to setup the integrals of the inner and outer radii for the ring method :
1)the interval has to be divided to have one integral from $y=-3$ to $y=5$ and another one from $y=5$ to $y=6$ however I fail to setup the inner and outer radii and get the same result, I did a lot of tries involving random math to see if I would get the same result but no avail...
TL;DR
Can you find the volume using rings, explaining anything that has to be?

A: Firs of all, the bounds of integration for the shell method should be from $−3$ to $1$ because those are the roots of the equation $6-x^2=2x+3$:
$$
V=2\pi\int_{-3}^{1}\left[(5-x)(6-x^2-(2x+3))\right]\,dx=128\pi\ cubic\ units.
$$
Wolfram Alpha check.
And this is your integral for the washer method (I assume that's what you mean by "rings"):
$$
V=
\pi\int_{-3}^{5}\left[\left(5-\left(-\sqrt{6-y}\right)\right)^2-\left(5-\frac{y-3}{2}\right)^2\right]\,dy+\\
\pi\int_{5}^{6}\left[\left(5-\left(-\sqrt{6-y}\right)\right)^2-\left(5-\sqrt{6-y}\right)^2\right]\,dy=\\
\frac{344\pi}{3}+\frac{40\pi}{3}=
128\pi\ cubic\ units.
$$
As you can see, the integration should be done with respect to $y$. The first integral on Wolfram Alpha and the second one. That's just a check. If there is something you don't understand, don't hesitate to ask. But the main idea with the waster method is that you subtract the smaller volume from the bigger one.

And you do a similar thing for the other piece.

A: You are right we have to use parallel slices but the integrand should be $2\pi(5 - x)dxdy$.  You are also correct to split the volume into two integrals with different limits.  The integral should be
$$\int_{5}^{6}\int_{- \sqrt{6 - y}}^{\sqrt{6 - y}}2\pi (5 - x)dxdy + \int_{-3}^{5} \int_{-\sqrt{6 - y}}^{\frac{y - 3}{2}} 2\pi (5 - x)dxdy = 128\pi$$

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