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Albany is the capital city of the U.S. state of New York, the seat of Albany County, and the central city of New York's Capital District.
New York is the most populous city in the United States and the center of the New York Metropolitan Area, one of the most populous urban agglomerations in the world.
Niagara Falls is the collective name for three waterfalls that straddle the international border between the Canadian province of Ontario and the U.S. state of New York.


Below I'll describe the options that you can use when configuring vlibTemplate to work to your needs.
All of the following options detailed below can be edited in your vlibIni.php file (called vlibIni.php-dist in the archive).
Each option can be set in the vlibIni.php file which will mean that this value is taken as a default for each template file you use, or they can be set in an array as the second parameter to vlibTemplate() the main class constructor.
-
TEMPLATE_DIR
- the default include path to you where you store your templates.
This option can make it easier for you to access your templates when scripting.
-
MAX_INCLUDES
- to avoid recursive includes using <tmpl_include>,
this options sets the include depth that vlibTemplate will drill down when including files. Defaults to 10.
-
GLOBAL_VARS
- When you use a <tmpl_var> tag inside a loop and the loop variable is null vlibTemplate will
check the global scope as well, if this option is set to 1.
-
GLOBAL_CONTEXT_VARS
- when set to 1, the following variables are made available in the global scope.
NB: more variables may be added in the future. All variable start and end with 2 underscores.
-
__SELF__ - this variable value is the value of $PHP_SELF.
-
__REQUEST_URI__ - same as __SELF__ but appends any GET method parameters.
-
__PARSE_TIME__ - time taken to parse template. Only used when TIME_PARSE option is set to 1.
-
GLOBAL_CONTEXT_VARS
- when set to 1, the following variables are made available within the scope of each <tmpl_loop>.
NB: more variables may be added in the future.
-
__FIRST__ - is true when you are currently on the first row of the loop.
-
__LAST__ - is true when you are on the last row of the loop.
-
__INNER__ - is true when you are on neither the first or last row of the loop.
-
__EVEN__ - is true when you are on an even row of the loop.
-
__ODD__ - is true when you are on an odd row of the loop.
-
__ROWNUM__ - an integer of the current row number starting at 1.
-
SET_LOOP_VAR
- because global variables and loop variables are handled seperately in vlibTemplate, to use a
<tmpl_if/unless/elseif> on a loop you must set this option to 1. This will in turn set a variable to
true in the global scope with the same name as the loop only if the loop is not empty.
-
DEFAULT_ESCAPE
- when using the template in an html-based application you should escape most of the string passed into
the template using htmlspecialchars(). This option will allow you to specify a default escape type for all
of your <tmpl_var>'s. To overwrite this option, you can either use a different setting when instantiating
vlibTemplate using the options array, or on per tag basis i.e. <tmpl_var name="varname" escape="url">.
The following values are all possible escape values.
NB: more escape attributes may be added in the future.
-
HTML - html escapes a string.
-
URL - url escapes a string.
-
RAWURL - rawurl escapes a string.
-
SQ - escapes single quotes in a string.
-
DQ - escapes double quotes in a string.
-
NONE - turns off escaping.
-
1 - alias for html escaping.
-
0 - alias for NONE. (1 and 0 are for compatibility with Perl's HTML::Template).
-
HEX - hex encodes a string.
-
HEXENTITY - encodes a string to hex entities.
-
STRICT
- when set to 1, vlibTemplate will die() upon finding an incorrect <tmpl>'esque tag. i.e.
<tmpl_vat name="varname">
-
CASELESS
- when set to 1, vlibTemplate will become case-INSENSITIVE. setVar('myvar1') will be called even using
<tmpl_var name="mYvAr1">.
NB: All <tmpl_*> tags are case INSENSITIVE, this option refers to the variable and loop names.
-
UNKNOWNS
- will define how variables like <tmpl_var name="myvar"> are handled if they have not been set using setVar().
-
IGNORE - (default) completely ignores the unfound variable.
-
REMOVE - removes the <tmpl_var> tag, but notes the tag for use with the unknown functions.
-
LEAVE - will leave the tag as it is, and note it.
-
PRINT - escapes the actual unknown tag using html encoding.
-
COMMENT - comments out the tag using html style comments, and note it.
-
TIME_PARSE
- when set to 1, vlibTemplate will time how long it takes to parse the template. see getParseTime().
If TIME_PARSE and GLOBAL_CONTEXT_VARS are both set to 1, then a Global var
called __PARSE_TIME__ is set with the time it took to parse the template.
-
ENABLE_PHPINCLUDE
- Will allow template to include a php file using <TMPL_PHPINCLUDE>
-
CACHE_LIFETIME
- lifetime of the cached file in seconds. Defaults to 604800 seconds (1 week).
-
CACHE_EXTENSION
- extension used when caching files.


In Photos: Pop sensation Katy Perry returns to the States after her Mumbai concert
by Kajol MathurKaty Perry last night took over Mumbai and raised the mercury at her first-ever concert here. She performed with pop star Dua Lipa and made the night a memorable one for everyone present. Katy touched down in Mumbai earlier this week and did several interesting activities. During her visit, she was seen bonding with Karan Johar, Alia Bhatt, Gauri Khan, Ananya Panday and several other Bollywood celebs.
After staying here for almost a week, Katy kissed India goodbye last night. She was snapped at the Mumbai airport wearing a grey tracksuit, a baseball cap and sneakers. Before entering inside, she was also seen giving a few autographs to fans. At the One Plus Music Festival, Perry performed on her chartbusters like Last Friday Night, Bon Appetit, Supernatural, California Gurls and I Kissed A Girl, amongst others.
Below, you check out all her pictures from the airport.


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|
| | OgreXmlSerializer (XmlParser *xmlParser) |
| |
| void | ReadAnimationKeyFrames (XmlNode &node, Animation *anim, VertexAnimationTrack *dest) |
| |
| void | ReadAnimations (XmlNode &node, Skeleton *skeleton) |
| |
| void | ReadAnimationTracks (XmlNode &node, Animation *dest) |
| |
| template<> |
| int32_t | ReadAttribute (XmlNode &xmlNode, const char *name) const |
| |
| template<> |
| uint32_t | ReadAttribute (XmlNode &xmlNode, const char *name) const |
| |
| template<> |
| uint16_t | ReadAttribute (XmlNode &xmlNode, const char *name) const |
| |
| template<> |
| float | ReadAttribute (XmlNode &xmlNode, const char *name) const |
| |
| template<> |
| bool | ReadAttribute (XmlNode &xmlNode, const char *name) const |
| |
| template<typename T > |
| T | ReadAttribute (XmlNode &xmlNode, const char *name) const |
| |
| void | ReadBoneAssignments (XmlNode &node, VertexDataXml *dest) |
| |
| void | ReadBoneHierarchy (XmlNode &node, Skeleton *skeleton) |
| |
| void | ReadBones (XmlNode &node, Skeleton *skeleton) |
| |
| void | ReadGeometry (XmlNode &node, VertexDataXml *dest) |
| |
| void | ReadGeometryVertexBuffer (XmlNode &node, VertexDataXml *dest) |
| |
| void | ReadMesh (MeshXml *mesh) |
| |
| void | ReadSkeleton (XmlNode &node, Skeleton *skeleton) |
| |
| void | ReadSubMesh (XmlNode &node, MeshXml *mesh) |
| |
◆ OgreXmlSerializer()
| Assimp::Ogre::OgreXmlSerializer::OgreXmlSerializer |
( |
XmlParser * |
xmlParser | ) |
|
|
explicitprivate |
◆ ImportMesh()
Imports mesh and returns the result.
- Note
- Fatal unrecoverable errors will throw a DeadlyImportError.
◆ ImportSkeleton() [1/2]
◆ ImportSkeleton() [2/2]
Imports skeleton to mesh
.
If mesh does not have a skeleton reference or the skeleton file cannot be found it is not a fatal DeadlyImportError.
- Returns
- If skeleton import was successful.
Last fallback if .skeleton failed to be read. Try reading from .skeleton.xml even if the XML file referenced a binary skeleton.
- Note
- This logic was in the previous version and I don't want to break old code that might depends on it.
◆ OpenXmlParser()
◆ ReadAnimationKeyFrames()
◆ ReadAnimations()
| void Assimp::Ogre::OgreXmlSerializer::ReadAnimations |
( |
XmlNode & |
node, |
|
|
Skeleton * |
skeleton |
|
) |
| |
|
private |
◆ ReadAnimationTracks()
| void Assimp::Ogre::OgreXmlSerializer::ReadAnimationTracks |
( |
XmlNode & |
node, |
|
|
Animation * |
dest |
|
) |
| |
|
private |
◆ ReadAttribute() [1/6]
template<>
| int32_t Assimp::Ogre::OgreXmlSerializer::ReadAttribute |
( |
XmlNode & |
xmlNode, |
|
|
const char * |
name |
|
) |
| const |
|
private |
◆ ReadAttribute() [2/6]
template<>
| uint32_t Assimp::Ogre::OgreXmlSerializer::ReadAttribute |
( |
XmlNode & |
xmlNode, |
|
|
const char * |
name |
|
) |
| const |
|
private |
◆ ReadAttribute() [3/6]
template<>
| uint16_t Assimp::Ogre::OgreXmlSerializer::ReadAttribute |
( |
XmlNode & |
xmlNode, |
|
|
const char * |
name |
|
) |
| const |
|
private |
◆ ReadAttribute() [4/6]
template<>
| float Assimp::Ogre::OgreXmlSerializer::ReadAttribute |
( |
XmlNode & |
xmlNode, |
|
|
const char * |
name |
|
) |
| const |
|
private |
◆ ReadAttribute() [5/6]
template<>
| bool Assimp::Ogre::OgreXmlSerializer::ReadAttribute |
( |
XmlNode & |
xmlNode, |
|
|
const char * |
name |
|
) |
| const |
|
private |
◆ ReadAttribute() [6/6]
template<typename T >
| T Assimp::Ogre::OgreXmlSerializer::ReadAttribute |
( |
XmlNode & |
xmlNode, |
|
|
const char * |
name |
|
) |
| const |
|
private |
◆ ReadBoneAssignments()
Normalize bone weights. Some exporters won't care if the sum of all bone weights for a single vertex equals 1 or not, so validate here.
◆ ReadBoneHierarchy()
| void Assimp::Ogre::OgreXmlSerializer::ReadBoneHierarchy |
( |
XmlNode & |
node, |
|
|
Skeleton * |
skeleton |
|
) |
| |
|
private |
◆ ReadBones()
| void Assimp::Ogre::OgreXmlSerializer::ReadBones |
( |
XmlNode & |
node, |
|
|
Skeleton * |
skeleton |
|
) |
| |
|
private |
- Note
- Left this from original authors code, but not sure if this is strictly necessary as per the Ogre skeleton spec. It might be more that other (later) code in this imported does not break.
◆ ReadGeometry()
◆ ReadGeometryVertexBuffer()
| void Assimp::Ogre::OgreXmlSerializer::ReadGeometryVertexBuffer |
( |
XmlNode & |
node, |
|
|
VertexDataXml * |
dest |
|
) |
| |
|
private |
◆ ReadMesh()
| void Assimp::Ogre::OgreXmlSerializer::ReadMesh |
( |
MeshXml * |
mesh | ) |
|
|
private |
◆ ReadSkeleton()
| void Assimp::Ogre::OgreXmlSerializer::ReadSkeleton |
( |
XmlNode & |
node, |
|
|
Skeleton * |
skeleton |
|
) |
| |
|
private |
◆ ReadSubMesh()
| void Assimp::Ogre::OgreXmlSerializer::ReadSubMesh |
( |
XmlNode & |
node, |
|
|
MeshXml * |
mesh |
|
) |
| |
|
private |
- Todo:
- Fix above comment with better read logic below
- Todo:
- Support quads if Ogre even supports them in XML (I'm not sure but I doubt it)
◆ mParser
| XmlParser* Assimp::Ogre::OgreXmlSerializer::mParser |
|
private |
The documentation for this class was generated from the following files:


Preparation time
overnight
Cooking time
30 mins to 1 hour
Serves
Serves 4-6
A yoghurt marinade keeps this bird juicy and tender. Opening out the chicken reduces the cooking time for a quick roast.
For the chicken, mix all the marinade ingredients together in a bowl.
With a sharp knife, carefully slash the chicken legs two or three times down to the bone. Rub the marinade into the chicken, massaging into the meat.
Place on a plate in the fridge for at least 30 minutes, but you can leave it for 4-6 hours.
Preheat the oven to 200C/400F/Gas 6.
Heat a griddle pan until very hot. Remove the chicken from the marinade, letting the excess drip off, but do not wipe clean. Pour a little olive oil onto the griddle pan then add the chicken, skin side down. Cook for 2-3 minutes on each side, until golden and scored with brown griddle marks.
Transfer to a roasting tin and place in the oven for 25-30 minutes, or until completely cooked through. Test the chicken is cooked by pushing a skewer into the thigh meat - if the juices run clear the chicken is cooked.
Remove from the oven and leave to rest for 5-10 minutes.
For the cucumber salad, cut the cucumbers into halves lengthways and scoop out the watery seeds.
Cut the cucumber into wedges and place in a bowl. Add the remaining ingredients and toss together to coat. Season to taste with more lemon juice or salt and freshly ground black pepper.
To serve, carve the chicken into eight pieces (legs, thighs and breasts). Place the cucumber salad into a pile on a plate and a bit of chicken alongside. Serve with crusty bread or a cous cous salad.
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Testing whether turkey, chicken and poultry are cooked


Game Prediction
问题描述 :
Suppose there are M people, including you, playing a special card game. At the beginning, each player receives N cards. The pip of a card is a positive integer which is at most N*M. And there are no two cards with the same pip. During a round, each player chooses one card to compare with others. The player whose card with the biggest pip wins the round, and then the next round begins. After N rounds, when all the cards of each player have been chosen, the player who has won the most rounds is the winner of the game.
Given your cards received at the beginning, write a program to tell the maximal number of rounds that you may at least win during the whole game.
Given your cards received at the beginning, write a program to tell the maximal number of rounds that you may at least win during the whole game.
输入:
The input consists of several test cases. The first line of each case contains two integers m (2 <= m <= 20) and n (1 <= n <= 50), representing the number of players and the number of cards each player receives at the beginning of the game, respectively. This followed by a line with n positive integers, representing the pips of cards you received at the beginning. Then a blank line follows to separate the cases.
The input is terminated by a line with two zeros.
输出:
For each test case, output a line consisting of the test case number followed by the number of rounds you will at least win during the game.
样例输入:
2 5 1 7 2 10 9 6 11 62 63 54 66 65 61 57 56 50 53 48 0 0
样例输出:
Case 1: 2 Case 2: 4
题目链接：
http://acm.hdu.edu.cn/showproblem.php?pid=1338
题目意思：
有m个人，每个人有n张牌，牌点为在1~n*m中的不同的数。每回合每个人出一张牌，点数最大的那个人赢，给出A人初始时的n张牌的牌点，问A至少赢的次数。
解题思路：
其他人要想赢得最多，肯定赢A中牌点小的容易，而只要有一个人的牌点大于A牌点就行，此时可以把小牌都出掉。
所以先按A的牌点排序，依次找到大于A的牌点的最小的没出的牌点，然后在出掉较小的m-2张牌。如果连A中较小的牌点都不能赢，后面的肯定不能赢。
这样就达到了，赢得最实算。这就是本题的贪心思想。
代码：
#include<iostream> #include<cmath> #include<cstdio> #include<cstdlib> #include<string> #include<cstring> #include<algorithm> #include<vector> #include<map> #include<set> #include<stack> #include<list> #include<queue> #define eps 1e-6 #define INF 0x1f1f1f1f #define PI acos(-1.0) #define ll __int64 #define lson l,m,(rt<<1) #define rson m+1,r,(rt<<1)|1 //#pragma comment(linker, "/STACK:1024000000,1024000000") using namespace std; /* freopen("data.in","r",stdin); freopen("data.out","w",stdout); */ int my[60]; bool vis[1100]; int main() { int n,m,ca=0; while(scanf("%d%d",&m,&n)&&m+n) { memset(vis,false,sizeof(vis)); for(int i=1;i<=n;i++) { scanf("%d",&my[i]); vis[my[i]]=true; } sort(my+1,my+n+1); //按自己手上的牌，从大到小排序 //如果其他人能赢，则只要有一张牌超过自己手上牌就行了，这时可以丢掉其他的小牌 //当没有比自己大的牌时，后面的一定自己赢了 int pos=1,ans=0; for(int i=1;i<=n;i++) { int tt=my[i],j; for(j=tt+1;j<=n*m;j++) //找到一张没出的比自己最靠近的大牌 if(!vis[j]) { vis[j]=true; break; } if(j>n*m) //找不到后面肯定自己赢 { ans=n-i+1; break; } if(m==2) //其他人已经出完了 continue; for(int k=0;;) //其他人出较小的牌 { if(!vis[pos]) { vis[pos]=true; k++; } pos++; if(k>=m-2) break; } } printf("Case %d: %d\n",++ca,ans); } return 0; }
解题报告转自：http://blog.csdn.net/cc_again/article/details/9492605
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Thanks i enjoy your write-up about Why We Keep track of | J Squared Consulting
网站不错，支持一个
约瑟夫也用说这么长……很成熟的一个问题了，分治的方法解起来o(n)就可以了，有兴趣可以看看具体数学的第一章，关于约瑟夫问题推导出了一系列的结论，很漂亮
good !
第一题是不是可以这样想，生了n孩子的家庭等价于n个家庭各生了一个1个孩子，这样最后男女的比例还是1:1
想问下computeLSPArray那里，if(len!=0) len=lps[len-1]是什么意思
值得分享！
楼主放题时候没注意下？xxx定律见：
hdoj3782：http://acm.hdu.edu.cn/showproblem.php?pid=3782
比较基础，找些自信
great!
处凡愚而不减，在圣贤而不增；
住烦恼而不乱，居禅定而不寂。
代码可真够长的！
不是有公式吗？何不直接计算
红绯鱼：
这模板神了


A listening logging server for MiStaMover. This logs to a file (only).
The main object instantiated in the calling code is LoggerServer, which listens for messages on a port and logs them. The classes SingleFileLogger and MultiFileLogger are helpers that actually log these to the relevant files.
Bases: object
A top level logger server, which will receive messages on a port and pass them to either SingleFileLogger or MultiFileLogger
Main loop.
Bases: object
A class whose ‘handle’ method will return the handle method of a different file logger class for each tag, so that different tags get logged to different files.
The SingleFileLogger objects are created on the fly in handle() when new tags appear that are have not been seen yet, so there is no need to specify the set of possible files on instantiation
e.g. if a message appears with tag “foo” it will start logging to foo.log
Handles a record by calling the necessary SingleFileLogger, creating it if not already cached.
Bases: object
A logger class which logs all messages it receives to one file
Get full path for log file with given tag
Handle a record (delegates it to the underlying logger object)
Takes a tag (part of filename), and returns a logger object from the logging module


Listener
I agree to hear you until you are satisfied that I have considered your trouble
and known your soul in adversity.
I agree to…
-
Listen from your perspective
-
Show respect by staying in the conversation
-
Not complain, explain, or blame
-
Restate the meaning of the message to your satisfaction
-
Gain wisdom and develop understanding to establish a relationship of trust,
and to use the knowledge to resolve our issues for good, not for evil
I agree to seek God’s will for our lives and our relationship.


Is your newspaper or milk business in need of a billing, accounting and online money collection?
Download the pamtap vendor app today for all your needs!
+918788395685
darshanpamtap@gmail.com


Cinema Publications
If using Cinema in your analysis, please cite the original publication introducing Cinema:
- James Ahrens, Sébastien Jourdain, Patrick O'Leary, John Patchett, David H. Rogers, and Mark Petersen. An image-based approach to extreme scale in situ visualization and analysis. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '14). IEEE Press, Piscataway, NJ, USA, 424-434, 2014. PDF Download / DOI: https://doi.org/10.1109/SC.2014.40 / Bibtex
If using Cinema:Debye-Scherrer in your analysis, please cite the following publication introducing Cinema:Debye-Scherrer:
- Sven C. Vogel, Chris M. Biwer, David H. Rogers, James P. Ahrens, Robert E. Hackenberg, Drew Onken and Jianzhong Zhang. Interactive visualization of multi-dataset Rietveld analyses using Cinema:Debye-Scherrer. J. Appl. Crystallogr. Vol. 51, 2018. PDF Download / DOI 10.1107/S1600576718003989 / Bibtex
Other publications leveraging Cinema databases or using Cinema-based analysis and visualization approaches:
- Pascal Grosset, Christopher Biwer, Jesus Pulido, Arvind Mohan, Ayan Biswas, John Patchett, Terece Turton, David Rogers, Daniel Livescu, James Ahrens. Foresight: Analysis That Matters for Data Reduction 2020 IEEE 10th Symposium on Large Data Analysis and Visualization (LDAV) (2020) PDF Download / DOI: 10.1109/SC41405.2020.00087 / Bibtex
- Petar Hristov, Gunther Weber, Hamish Carr, Oliver Rübel, James Ahrens. Data Parallel Hypersweeps for In Situ Topological Analysis 2020 IEEE 10th Symposium on Large Data Analysis and Visualization (LDAV) (2020) PDF Download /
- Jonas Lukasczyk, Christoph Garth, Matthew Larsen, Wito Engelke, Ingrid Hotz, David Rogers, James Ahrens, Ross Maciejewski. Cinema Darkroom: A Deferred Rendering Framework for Large-Scale Datasets 2020 IEEE 10th Symposium on Large Data Analysis and Visualization (LDAV) (2020) PDF Download /
- Daniel Orban, Divya Banesh, Cameron Tauxe, Christopher M. Biwer, Ayan Biswas, Ramon Saavedra, Christine Sweeney, Richard L. Sandberg, C. A. Bolme, James Ahrens and David Rogers. Cinema:Bandit: a visualization application for beamline science demonstrated on XFEL shock physics experiments. Journal of Synchrotron Radiation (2020) 27, 1-10. PDF Download / DOI: 10.1107/S1600577519014322 / Bibtex
- Terece L. Turton, Divya Banesh, Trinity Overmyer, Benjamin H. Sims, David H. Rogers. Enabling Domain Expertise in Scientific Visualization With CinemaScience. IEEE Computer Graphics and Applications (2020) 40:1 90-98. PDF Download / DOI: 10.1109/MCG.2019.2954171 / Bibtex
- Daniel Orban, Ayan Biswas, James Ahrens and David Rogers. Drag and Track: A Direct Manipulation Interface for Contextualizing Data Instances within a Continuous Parameter Space. IEEE Trans Vis Comput Graph. (2019) 25:1, 256-266. PDF Download / DOI: 10.1109/TVCG.2018.2865051 / Bibtex
- Robin G. C. Maack, David H. Rogers, Hans Hagen, Christina Gillmann. Exploring Cinema Databases using multi-dimensional Image Measures. ILeipzig Symposium on Visualization in Application. (2019) PDF Download / Bibtex
- Divya Banesh, Mark Petersen, Joanne Wendelberger, James Ahrens, and Bernd Hamann. Comparison of piecewise linear change point detection with traditional analytical methods for ocean and climate data. Environ Earth Sci (2019) 78: 623. PDF Download / DOI: 10.1007/s12665-019-8636-y / Bibtex
- Jonas Lukasczyk, Eric Kinner, James Ahrens, Heike Leitte, and Christoph Garth. VOIDGA: A View-Approximation Oriented Image Database Generation Approach. Leipzig Symposium on Visualization In Applications (LEVIA). Leipzig, Germany, 2018. PDF Download / DOI: 10.1109/LDAV.2018.8739204 / Bibtex
- Divya Banesh, Joanne Wendelberger, Mark Petersen, James P. Ahrens, and Bernd Hamann. Change Point Detection for Ocean Eddy Analysis. Workshop on Visualisation in Environmental Sciences (EnvirVis), K. Rink, D. Zeckzer, R. Bujack and S. Janicke, eds., The Eurographics Association, 2018. PDF Download / DOI: 10.2312/envirvis.20181134 / Bibtex
- Roxana Bujack, David H. Rogers, and James Paul Ahrens. Reducing Occlusion in Cinema Databases through Feature-Centric Visualizations. 2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV) Berlin, Germany, 2018. PDF Download / Semantic Scholar Link / Bibtex
- Vignesh Adhinarayanan, Wu-Chun Feng, David H. Rogers, James P. Ahrens, and Scott Pakin. Characterizing and Modeling Power and Energy for Extreme-Scale In-Situ Visualization. 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Orlando, FL, 2017, pp. 978-987. PDF Download / DOI: 10.1109/IPDPS.2017.113 / Bibtex
- Divya Banesh, Joseph A. Schoonover, James P. Ahrens, Bernd Hamann. Extracting, Visualizing and Tracking Mesoscale Ocean Eddies in Two-dimensional Image Sequences Using Contours and Moments. In Workshop on Visualisation in Environmental Sciences (EnvirVis), K. Rink, A. Middel, D. Zeckzer, and R. Bujack, eds., The Eurographics Association, 2017. PDF Download / DOI: 10.2312/envirvis.20171103 / Bibtex
- Jonathan Woodring, James P. Ahrens, John Patchett, Cameron Tauxe, and David H. Rogers. High-dimensional Scientific Data Exploration via Cinema. 2017 IEEE Workshop on Data Systems for Interactive Analysis (DSIA). pp. 1-5, 2017. PDF Download / DOI: 10.1109/DSIA.2017.8339086 / Bibtex
- Anne S. Berres, Terece L. Turton, Mark Petersen, David H. Rogers, James P. Ahrens. Video Compression for Ocean Simulation Image Databases. In Workshop on Visualisation in Environmental Sciences (EnvirVis), K. Rink, A. Middel, D. Zeckzer, and R. Bujack, eds., The Eurographics Association, 2017. PDF Download / DOI:10.2312/envirvis.20171104 / Bibtex
- Patrick O'Leary, James Ahrens, Sébastien Jourdain, Scott Wittenburg, David H. Rogers, and Mark Petersen. Cinema image-based in situ analysis and visualization of MPAS-ocean simulations. Parallel Comput. ,55, C, p 43-48, 2016. PDF Download / DOI: 10.1016/j.parco.2015.10.005 / Bibtex
- Vignesh Adhinarayanan, Wu-Chun Feng, Jonathan Woodring, David H. Rogers and James P. Ahrens. On the Greenness of In-Situ and Post-Processing Visualization Pipelines. 2015 IEEE International Parallel and Distributed Processing Symposium Workshop Hyderabad, 2015, pp. 880-887. PDF Download / DOI: 10.1109/IPDPSW.2015.132 / Bibtex


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rails update_all이란?
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rails before_action이란
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rails6.0에서 refinements 활용법
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Rails6.0 present? any? exits?차이
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rails pluck이란?pluck을 써야하는 이유
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Rails presence vs present?의 차이
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rails select_tag form을 알아보자
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rails select form을 알아보자
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rails6 EachValidator을 이용해서 validators 커스텀 하는법
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Rails6.0 Action Text를 사용해보자
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rails undefined method `xx' for nil:NilClass에러
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The marker weight is composed of two factors, namely the physical distribution function f and the marker distribution function g. The time evolution of the weight w is thus determined by the time evolution of f and g, which are discussed in Sec. 4.1 and 4.2, respectively.)


Do you really want to delete this Domain with all its Urls?
{% extends 'base.html' %} {% block content %}
Do you really want to delete this Domain with all its Urls?
| # | Url | Status Code | Hash | Scanned |
|---|---|---|---|---|
| {{- url.id }} | {{- url.url }} | {{- url.status_code }} | {{- url.sha256 }} | {{- url.last_check }} |


27
1 Do not boast about tomorrow, for you do not know what a day may bring.
2 Let another praise you, and not your own mouth;
a stranger, and not your own lips.
3 A stone is heavy, and sand is weighty,
but a fool's provocation is heavier than both.
4 Wrath is cruel, anger is overwhelming,
but who can stand before jealousy?
5 Better is open rebuke
than hidden love.
6 Faithful are the wounds of a friend;
profuse are the kisses of an enemy.
7 One who is full loathes honey,
but to one who is hungry everything bitter is sweet.
8 Like a bird that strays from its nest
is a man who strays from his home.
9 Oil and perfume make the heart glad,
and the sweetness of a friend comes from his earnest counsel.
10 Do not forsake your friend and your father's friend,
and do not go to your brother's house in the day of your calamity.
Better is a neighbor who is near
than a brother who is far away.
11 Be wise, my son, and make my heart glad,
that I may answer him who reproaches me.
12 The prudent sees danger and hides himself,
but the simple go on and suffer for it.
13 Take a man's garment when he has put up security for a stranger,
and hold it in pledge when he puts up security for an adulteress.
14 Whoever blesses his neighbor with a loud voice,
rising early in the morning,
will be counted as cursing.
15 A continual dripping on a rainy day
and a quarrelsome wife are alike;
16 to restrain her is to restrain the wind
or to grasp oil in one's right hand.
17 Iron sharpens iron,
and one man sharpens another.
18 Whoever tends a fig tree will eat its fruit,
and he who guards his master will be honored.
19 As in water face reflects face,
so the heart of man reflects the man.
20 Sheol and Abaddon are never satisfied,
and never satisfied are the eyes of man.
21 The crucible is for silver, and the furnace is for gold,
and a man is tested by his praise.
22 Crush a fool in a mortar with a pestle
along with crushed grain,
yet his folly will not depart from him.
23 Know well the condition of your flocks,
and give attention to your herds,
24 for riches do not last forever;
and does a crown endure to all generations?
25 When the grass is gone and the new growth appears
and the vegetation of the mountains is gathered,
26 the lambs will provide your clothing,
and the goats the price of a field.
27 There will be enough goats' milk for your food,
for the food of your household
and maintenance for your girls.


Perform Queen's hit with a group of players. Everyone can choose a part of the song.
Hold the device sidewards like this:
Drums: Strike with the device down for the kick and left for the snare.
Voice Solo: Strike in any direction for each beat of the verse.
Choirs: Strike left-right-left-right-up-down to perform the we-will-we-will-rock-you choirs.
‘Sing it’: Strike in any direction to fill in some voice samples.
Power Chord: Hold the device vertically up, turn it slowly into the horizontal, and suddenly turn it up side down to perform the power chord introducing the guitar solo on the last "rock you".
Guitar Riff: Strike up and down like playing a guitar to perform the guitar riff at the end of the song.
“We Will Rock You” is a song written by Brian May. It was recorded and performed by Queen for the album “News of the World”, published by EMI in 1977.


Applying a function to the webcam data
Here the graph of both f and g is shown in addition to the effect of the function on the image.


A Programming Language
Two Variables
x
,y
Three Operations
x++
x--
x = 0 ? L1 : L2
Example Program
(What does it do?)
L1: x++
y--
y = 0 ? L2 : L1
L2: ...
Fact The above language is equivalent to every PL!
- But good luck writing QuickSort …
- … or Plants v. Zombies
- … or Spotify!
So Why Study Programming Languages?
A different language is a different vision of life.
So Why Study Programming Languages?
The principle of linguistic relativity holds that the structure of a language affects its speakers world view or cognition.
Or more simply:
Programming Language shapes Programming Thought.
Language affects how:
- Ideas are expressed
- Computation is expressed
Course Goals
Free Your Mind.
Goal: Learn New Languages / Constructs
New ways to describe and organize computation,
To create programs that are:
- Readable
- Correct
- Extendable
- Modifiable
- Reusable
New languages come (and go …)
There was no
- Java 20 years ago,
- C# 15 years ago,
- Rust 10 years ago,
- Go 5 years ago…
Goal: Learn to Learn
In 130 you will learn the anatomy of a PL
- Fundamental building blocks
- Different guises in different PLs
Help to better understand the PLs you already “know”.
Goal: How to Design new Languages
Buried in every extensible system is a PL
- Emacs, Android: LISP
- Word, Powerpoint: Macros, VBScript
- Firefox, Chrome, Safari: JavaScript
- SQL, Renderman, Latex, XML …
If you work on a large system, you will design a new PL!
Goal: Enable You To Choose Right PL
But isn’t that decided by
- Libraries
- Standards
- Hiring
- Your Boss?!
Yes.
My goal: Educate tomorrow’s leaders so you’ll make informed choices.
Speaking of Right and Wrong
Imperative Programming is Wrong (default)
Tim Sweeney (Epic, Creator of the UNREAL Gaming Engine)
In a concurrent world, imperative is the wrong default.
Imperative Programming is Wrong (default)
John Carmack, creator of FPS (Doom, Quake, …)
Functional Programming
- No assignment
- No mutation
- No loops
OMG! Who uses FP?!
- Google (MapReduce, …)
- Microsoft (LinQ, F#, …)
- Facebook (Erlang, Reason,…)
- Twitter (Scala, …)
- Wall St. (Haskell, Ocaml, …)
and, most importantly ….
- CSE 130
Course Mechanics: Web Page
All information lives online:
Course Mechanics: Clickers
Make class interactive
– Help you and me understand whats tricky
Clickers Are Not Optional
– Cheap ones are fine – Respond to 75% questions – Worth 5% of your grade
Laptops are welcome in lecture (but optional)
Course Mechanics: In Class Exercises
Solo Vote: Think for yourself, select answer
- Discuss: Analyze Problem in Groups
- Reach consensus
- Have questions, raise your hand!
- Group Vote: Everyone in group votes
- Must have same vote to get points
- Class Discuss: What was easy or tricky?
Course Mechanics: Grading
(30%) Assignments
- 6-7 assignments
- All programming
(30%) Midterm
- In-class (TBA)
- 2-sided printed “cheat sheet”
(35%) Final
- 2-sided printed cheat sheet
(05%) Clickers
- Attempting to answer > 75% questions
(05%) Piazza Extra Credit
- To top 20 best participants
Course Mechanics: Grading
CSE 130 is graded on a curve
- Lots of work
- Don’t worry (too much) about grade
Course Mechanics: No Recommended Text
Online lecture notes and links
Pay attention to lecture
Do Assignments Yourself
Weekly (ish) Programming Assignments
Accessed and submitted via
github
Schedule up on webpage (due Fridays 5 PM)
Weekly (ish) Programming Assignments
Unfamiliar Languages and Environments
Forget
- Java, C, C++ …
- … and other 20th century languages
Don’t Complain
- That X-language is hard
- That X-language is @!%$#$
Do
- Start early
- Ask lots of questions!
Weekly (ish) Programming Assignments
No Deadline Extension
- Four late days, used as whole unit
- 5 mins late = 1 late day
- Plan ahead no other extensions
A Word from Our Sponsor
Programming Assignments done alone
- We use plagiarism detection software
- I am an expert
- Have code from all previous classes
MOSS is fantastic, plagiarize at your own risk
- Zero Tolerance
Offenders punished ruthlessly
Please see academic integrity statement
Plan
FP
- Haskell
- 5 weeks
OO
- Scala
- 3 weeks
Logic
- Prolog
- 2 weeks
Hello Haskell
Here is QuickSort in C
void sort(int arr[], int beg, int end){
if (end > beg + 1){
int piv = arr[beg];
int l = beg + 1;
int r = end;
while (l != r-1){
if(arr[l] <= piv)
l++;
else
swap(&arr[l], &arr[r--]);
}
if(arr[l]<=piv && arr[r]<=piv)
l=r+1;
else if(arr[l]<=piv && arr[r]>piv)
{l++; r--;}
else if (arr[l]>piv && arr[r]<=piv)
swap(&arr[l++], &arr[r--]);
else
r=l-1;
swap(&arr[r--], &arr[beg]);
sort(arr, beg, r);
sort(arr, l, end);
}
}
and here it is in Haskell
sort [] = []
sort (x:xs) = sort ls ++ [x] ++ sort rs
where
ls = [ l | l <- xs, l <= x ]
rs = [ r | r <- xs, x < r ]
Why Readability Matters
QuickSort in J
sort =: (($:@(<#[),(=#[),$:@(>#[))({~ ?@#))^: (1:<#)
Plan for next 5 weeks
Tiny core language (Lambda Calculus)
Fast forward intro to Haskell
Rewind and go over anatomy individually


What is SVG Typewriter?
A library for manipulating text on SVG.
Here is some example of usage
Simple text measurment
Text measurment with applied class
Simple text wrapping
Different write alignments
Different text rotations
First text animation
Second text animation


{% extends 'dashboard/base.html' %} {% block dashboard_body %} {{ breadcrumb(network.name, url('network_dashboard', network_id=network.id)) }} {% trans name=member.email, net=network.name %}Remove {{ name }} from {{ net }}?{% endtrans %}
{% trans %}Are you sure you want to remove this member from the network? You will be able to re-add them again in the future, however all of the podcasts in the network that the user owns will be removed.{% endtrans %}
{{ _('The following podcasts will be removed from the network if you remove this user:') }}
{% trans %}Are you sure you want to remove this member from the network? You will be able to re-add them again in the future.{% endtrans %}


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Medication Name: Furosemide
Medication Dosage: 40 mg PO QDAY
Dosage period and trend:
8m
8 months
Decrease
Refill:
4m
in 4 months
This table represents the history of the patient's prescription and intake behavior categories by date range and behavior
| Date Range |
Behavior |
| 2013/05 - 2015/03 |
Prescribed and taken |
| 2011/05 - 2013/03 |
Not used |
Medication Name:
Furosemide - Second Order
This medication order originated at KODAK
Medication Dosage: 40 mg PO QDAY
Dosage Period and trend: -
No Change
Refill:
-24d Exp
This medication refill expired 24 days ago
This table represents the history of the patient's prescription and intake behavior categories by date range and behavior
| Date Range |
Behavior |
| 2013/05 - 2015/03 |
Prescribed and taken |
| 2011/05 - 2013/03 |
Not used |
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LESSOPEN(1) LESSOPEN(1) NAME lessfile, lesspipe - "input preprocessor" for less. SYNOPSIS lessfile, lesspipe DESCRIPTION This manual page documents briefly the lessfile, and lesspipe commands. This manual page was written for the Debian GNU/Linux distribution because the input preprocessor scripts are provided by Debian GNU/Linux and are not part of the original program. lessfile and lesspipe are programs that can be used to modify the way the contents of a file are displayed in less. What this means is that less can automatically open up tar files, uncompress gzipped files, and even display something reasonable for graphics files. lesspipe will toss the contents/info on STDOUT and less will read them as they come across. This means that you do not have to wait for the decod‐ ing to finish before less shows you the file. This also means that you will get a 'byte N' instead of an N% as your file position. You can seek to the end and back to get the N% but that means you have to wait for the pipe to finish. lessfile will toss the contents/info on a file which less will then read. After you are done, lessfile will then delete the file. This means that the process has to finish before you see it, but you get nice percentages (N%) up front. USAGE Just put one of the following two commands in your login script (e.g. ~/.bash_profile): eval "$(lessfile)" or eval "$(lesspipe)" FILE TYPE RECOGNITION File types are recognized by their extensions. This is a list of cur‐ rently supported extensions (grouped by the programs that handle them): *.a *.arj *.tar.bz2 *.bz *.bz2 *.deb, *.udeb *.doc *.gif, *.jpeg, *.jpg, *.pcd, *.png, *.tga, *.tiff, *.tif *.iso, *.raw, *.bin *.lha, *.lzh *.tar.lz, *.tlz *.lz *.7z *.pdf *.rar, *.r[0-9][0-9] *.rpm *.tar.gz, *.tgz, *.tar.z, *.tar.dz *.gz, *.z, *.dz *.tar *.tar.xz, *.xz *.jar, *.war, *.xpi, *.zip *.zoo USER DEFINED FILTERS It is possible to extend and overwrite the default lesspipe and lessfile input processor if you have specialized requirements. Create an exe‐ cutable program with the name .lessfilter and put it into your home directory. This can be a shell script or a binary program. It is important that this program returns the correct exit code: return 0 if your filter handles the input, return 1 if the standard lesspipe/less‐ file filter should handle the input. Here is an example script: #!/bin/sh case "$1" in *.extension) extension-handler "$1" ;; *) # We don't handle this format. exit 1 esac # No further processing by lesspipe necessary exit 0 FILES ~/.lessfilter Executable file that can do user defined processing. See section USER DEFINED FILTERS for more information. BUGS When trying to open compressed 0 byte files, less displays the actual binary file contents. This is not a bug. less is designed to do that (see manual page less(1), section INPUT PREPROCESSOR). This is the answer of Mark Nudelman<EMAIL_ADDRESS>"I recognized when I designed it that a lesspipe filter cannot output an empty file and have less display nothing in that case; it's a side effect of using the "no output" case to mean "the fil‐ ter has nothing to do". It could have been designed to have some other mechanism to indicate "nothing to do", but "no output" seemed the simplest and most intuitive for lesspipe writers." Sometimes, less does not display the contents file you want to view but output that is produced by your login scripts (~/.bashrc or ~/.bash_pro‐ file). This happens because less uses your current shell to run the lesspipe filter. Bash first looks for the variable $BASH_ENV in the envi‐ ronment expands its value and uses the expanded value as the name of a file to read and execute. If this file produces any output less will dis‐ play this. A way to solve this problem is to put the following lines on the top of your login script that produces output: if [ -z "$PS1" ]; then exit fi This tests whether the prompt variable $PS1 is set and if it isn't (which is the case for non-interactive shells) it will exit the script. SEE ALSO less(1) AUTHOR This manual page was written by Thomas Schoepf<EMAIL_ADDRESS>for the Debian GNU/Linux system (but may be used by others). Most of the text was copied from a description written by Darren Stalder<EMAIL_ADDRESS>LESSOPEN(1)


The Effect of Catholic School on Student Achievement
Example inspired by Simon Ejdemyr: https://sejdemyr.github.io/r-tutorials/statistics/tutorial8.html
Reference links:
- MatchIt: https://cran.r-project.org/web/packages/MatchIt/vignettes/matchit.pdf
- Cobalt (optional library for matching plots and extra features): https://cran.r-project.org/web/packages/cobalt/vignettes/cobalt_A0_basic_use.html
- kableExtra (for formatting data frames): https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html
- Stargazer (for formatting model outputs): https://www.jakeruss.com/cheatsheets/stargazer/
- Video overview of matching concepts: https://fr.coursera.org/lecture/crash-course-in-causality/overview-of-matching-JQfPC
library(dplyr) # for data wrangling
library(readr) # for loading the .csv data
library(ggplot2) # for creating plots
library(stargazer) # for formatting model output
library(kableExtra) # for formatting data frames
library(MatchIt) # for matching
set.seed(123) # for consistent results
Data Prep and EDA
First, let's read in the data. We will remove a few variables that won't be useful for our analysis. Then, we will compare the students pre-matching on their standardized math scores (c5r2mtsc_std).
Exploration of the data
ecls <- readr::read_csv("https://raw.githubusercontent.com/seramirezruiz/stats-ii-lab/master/Session%204/data/ecls.csv")
ecls <- ecls %>%
dplyr::select(-childid, -race, -w3daded,
-w3momed, -w3inccat) #drop these columns (-)
ecls %>%
dplyr::group_by(catholic) %>%
dplyr::summarize(n_students = n(),
mean_math = mean(c5r2mtsc_std),
std_error = sd(c5r2mtsc_std) / sqrt(n_students)) %>%
round(3) %>% # round the results
kable() %>% # create kable table
kable_styling() # view kable table
| catholic | n_students | mean_math | std_error |
|---|---|---|---|
| 0 | 9568 | -0.031 | 0.010 |
| 1 | 1510 | 0.194 | 0.022 |
We can see that we have many more students that did not attend Catholic school than those who did, and the Catholic school students have a mean math score that is higher.
NATE
First, let's calculate the NATE of Catholic school on student achievement (without adjusting for any covariates) to compare the means of the two groups.
summary(lm(c5r2mtsc_std ~ catholic, data = ecls))
##
## Call:
## lm(formula = c5r2mtsc_std ~ catholic, data = ecls)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5299 -0.6634 0.0609 0.6770 3.4643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03060 0.01019 -3.002 0.00269 **
## catholic 0.22446 0.02761 8.130 4.75e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9971 on 11076 degrees of freedom
## Multiple R-squared: 0.005932, Adjusted R-squared: 0.005842
## F-statistic: 66.1 on 1 and 11076 DF, p-value: 4.752e-16
Balance Table
Now, let's create a balance table to identify the relevant co-variates in the unmatched dataset.
ecls %>%
dplyr::group_by(catholic) %>%
dplyr::summarize_all(funs(mean(., na.rm = T))) %>% # summarize the mean of every variable
t() %>% # transpose data
as.data.frame() %>% # after transposing, convert from matrix to df
add_rownames("variable") %>% # rownames to explicit column
dplyr::rename(no_catholic = "1", catholic = "2") %>% # rename columns that are created by as.data.frame
dplyr::mutate(difference = catholic - no_catholic,
differencePerc = difference / (catholic + no_catholic)) %>% # create new columns for difference between groups
dplyr::mutate_if(is.numeric, round, 3) %>% # round numeric columns
arrange(desc(differencePerc)) %>%
knitr::kable() %>% # create kable table
kableExtra::kable_styling() # view kable table
| variable | no_catholic | catholic | difference | differencePerc |
|---|---|---|---|---|
| c5r2mtsc_std | -0.031 | 0.194 | 0.224 | 1.375 |
| catholic | 0.000 | 1.000 | 1.000 | 1.000 |
| w3income | 54889.159 | 82074.301 | 27185.141 | 0.198 |
| race_white | 0.556 | 0.725 | 0.169 | 0.132 |
| w3momscr | 43.114 | 47.492 | 4.379 | 0.048 |
| w3dadscr | 42.713 | 46.356 | 3.643 | 0.041 |
| p5hmage | 37.561 | 39.575 | 2.014 | 0.026 |
| c5r2mtsc | 50.209 | 52.389 | 2.180 | 0.021 |
| p5hdage | 40.392 | 41.988 | 1.596 | 0.019 |
| p5numpla | 1.133 | 1.093 | -0.040 | -0.018 |
| race_asian | 0.066 | 0.052 | -0.014 | -0.118 |
| race_hispanic | 0.181 | 0.131 | -0.050 | -0.161 |
| w3daded_hsb | 0.488 | 0.259 | -0.229 | -0.306 |
| w3momed_hsb | 0.464 | 0.227 | -0.237 | -0.343 |
| race_black | 0.136 | 0.054 | -0.083 | -0.436 |
| w3povrty | 0.219 | 0.038 | -0.181 | -0.705 |
| p5fstamp | 0.129 | 0.015 | -0.114 | -0.788 |
We can see that the largest magnitude differences are for:
- variables on race
- w3momed_hsb: dummy variable with 1 = mother's education level is high school or below, 0 = some college or above
- w3income: family income
- w3povrty: poverty score dummy
- p5fstamp: food stamp dummy
Matching with MatchIt
MatchIt
is designed for causal inference with a dichotomous treatment variable and a set of pretreatment control variables. Any number or type of dependent variables can be used.
The basic syntax is as follows:
matched <- matchit(treat ~ x1 + x2, data = mydata)
where treat is the dichotomous treatment variable, and x1 and x2 are pre-treatment co-variates, all of which are contained in the data framemydata. As you can see, the outcome variable is not included in the matching procedure.
MatchIt is capable of using several matching methods:
Exact (method = "exact"): The simplest version of matching is exact. This technique matches each treated unit to all possible control units with exactly the same values on all the covariates, forming subclasses such that within each subclass all units (treatment and control) have the same covariate values.
Subclassification (method = "subclass"): When there are many covariates (or some covariates can take a large number of values), finding sufficient exact matches will often be impossible. The goal of subclassification is to form subclasses, such that in each of them the distribution (rather than the exact values) of covariates for the treated and control groups are as similar as possible.
- Nearest Neighbor (method = "nearest"): Nearest neighbor matching selects the best control matches for each individual in the treatment group. Matching is done using a distance measure (propensity score) specified by the distance option (default = logit).
As well as optimal matching, full matching, genetic matching, and coarsened exact matching, all of which are detailed in the documentation.
A few additional arguments are important to know about:
distance: this refers to propensity scores. There are many options for how to calculate these within MatchIt.
discard: specifies whether to discard units that fall outside some measure of support of the distance measure (default is "none", discard no units). For example, if some treated units have extremely high propensity scores that are higher than any control units, we could drop those.
replace: a logical value indicating whether each control unit can be matched to more than one treated unit (default is replace = FALSE, each control unit is used at most once).
ratio: the number of control units to match to each treated unit (default is ratio = 1).
There are also some optional arguments for most of the matching methods, which you can read about in the documentation if you are interested.
Exact Matching
We can use a combination of the results from our balance table and theory to identify which variables to use for matching. Let's perform an exact match with:
- race_white: Is the student white (1) or not (0)?
- p5hmage: Mother’s age
- w3income: Family income
- p5numpla: Number of places the student has lived for at least 4 months
- w3momed_hsb: Is the mother’s education level high-school or below (1) or some college or more (0)?
# first we must omit missing values (MatchIt does not allow missings)
match_data <- ecls %>%
dplyr::select(catholic, c5r2mtsc_std, race_white, p5hmage,
w3income, p5numpla, w3momed_hsb) %>%
na.omit()
str(match_data) #Let's see how many observations do we have
## tibble [9,267 × 7] (S3: tbl_df/tbl/data.frame)
## $ catholic : num [1:9267] 0 0 0 1 0 0 0 0 0 0 ...
## $ c5r2mtsc_std: num [1:9267] 0.982 0.594 0.491 1.451 2.596 ...
## $ race_white : num [1:9267] 1 1 1 1 1 1 1 0 1 1 ...
## $ p5hmage : num [1:9267] 47 41 43 38 47 30 41 38 28 31 ...
## $ w3income : num [1:9267] 62500 45000 62500 87500 150000 ...
## $ p5numpla : num [1:9267] 1 1 1 1 1 1 1 1 1 1 ...
## $ w3momed_hsb : num [1:9267] 0 0 0 0 0 0 0 1 1 1 ...
## - attr(*, "na.action")= 'omit' Named int [1:1811] 3 20 22 27 33 36 45 48 52 55 ...
## ..- attr(*, "names")= chr [1:1811] "3" "20" "22" "27" ...
# perform exact match
exact_match <- MatchIt::matchit(catholic ~ race_white + p5hmage + w3income +
p5numpla + w3momed_hsb,
method = "exact",
data = match_data)
# Try seeing the output in the console with summary(exact_match)
# grab the matched data
data_exact_match <- MatchIt::match.data(exact_match)
str(data_exact_match)
## 'data.frame': 5405 obs. of 9 variables:
## $ catholic : num 0 0 0 1 0 0 0 0 0 0 ...
## $ c5r2mtsc_std: num 0.982 0.594 0.491 1.451 2.596 ...
## $ race_white : num 1 1 1 1 1 1 1 1 1 1 ...
## $ p5hmage : num 47 41 43 38 47 41 38 38 27 40 ...
## $ w3income : num 62500 45000 62500 87500 150000 ...
## $ p5numpla : num 1 1 1 1 1 1 1 1 1 1 ...
## $ w3momed_hsb : num 0 0 0 0 0 0 0 0 1 1 ...
## $ weights : num 0.147 1.744 1.031 1 0.191 ...
## $ subclass : int 381 153 122 1 365 27 221 15 8 249 ...
# estimate effect again
summary(lm(c5r2mtsc_std ~ catholic, data = data_exact_match))
##
## Call:
## lm(formula = c5r2mtsc_std ~ catholic, data = data_exact_match)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5618 -0.5838 0.0506 0.6410 3.1143
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.31932 0.01453 21.981 < 2e-16 ***
## catholic -0.10072 0.02991 -3.368 0.000764 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9336 on 5403 degrees of freedom
## Multiple R-squared: 0.002094, Adjusted R-squared: 0.00191
## F-statistic: 11.34 on 1 and 5403 DF, p-value: 0.0007638
Now we can see that the mean in the group that did not attend Catholic school is actually about 0.10 higher than the mean for those who did. The results are statistically significant given that the confidence interval does not contain zero, and we have a fairly small p-value.
Propensity Scores
If we want to perform non-exact matching, we can use propensity scores. We can generate these manually using a probit model on the unmatched data set.
# create a new column with income by the thousands for more interpretable output
ecls <- ecls %>%
dplyr::mutate(w3income_1k = w3income / 1000)
# estimate logit model
m_ps <- glm(catholic ~ race_white + w3income_1k +
p5hmage + p5numpla + w3momed_hsb,
family = binomial(link = "probit"), # you can also use a logit link here
data = ecls)
# extract predicted probabilities
# type = "response" option tells R to output probabilities of the form P(Y = 1|X)
prs_df <- dplyr::tibble(pr_score = predict(m_ps, type = "response"),
catholic = m_ps$model$catholic) # the actual values
Let's plot the propensity scores by treatment group to explore common support:
ggplot(prs_df, aes(x = pr_score, fill = factor(catholic))) +
geom_histogram(alpha = 0.5) +
theme_minimal() +
labs(x = "Propensity Score Distribution: Treatment and Control Groups",
fill = "Catholic School Attendance")
ggplot(prs_df, aes(x = pr_score, fill = factor(catholic))) +
geom_density(alpha = 0.5) +
theme_minimal() +
labs(x = "Propensity Score Distribution: Treatment and Control Groups",
fill = "Catholic School Attendance")
Non-Exact Matching
MatchIt
can generate propensity scores itself, so we don't need to manually go through the process above. Let's try putting together a non-exact matching formula yourself! Try:
- nearest neighbor matching
- with replacement
- with a one-to-one ratio
- on the match_data dataset
one_match <- MatchIt::matchit(catholic ~ race_white + w3income + p5hmage +
p5numpla + w3momed_hsb,
method = "nearest",
ratio = 1,
replace = TRUE,
data = match_data)
summary(one_match)
##
## Call:
## MatchIt::matchit(formula = catholic ~ race_white + w3income +
## p5hmage + p5numpla + w3momed_hsb, data = match_data, method = "nearest",
## ratio = 1, replace = TRUE)
##
## Summary of balance for all data:
## Means Treated Means Control SD Control Mean Diff eQQ Med
## distance 0.1927 0.1379 0.0845 0.0549 5.67e-02
## race_white 0.7411 0.5914 0.4916 0.1497 0.00e+00
## w3income 82568.9357 55485.0210 43961.0872 27083.9146 2.50e+04
## p5hmage 39.5932 37.5658 6.5506 2.0274 2.00e+00
## p5numpla 1.0917 1.1298 0.3910 -0.0380 0.00e+00
## w3momed_hsb 0.2234 0.4609 0.4985 -0.2375 0.00e+00
## eQQ Mean eQQ Max
## distance 0.0548 7.60e-02
## race_white 0.1501 1.00e+00
## w3income 27069.1775 6.25e+04
## p5hmage 2.2544 7.00e+00
## p5numpla 0.0399 2.00e+00
## w3momed_hsb 0.2374 1.00e+00
##
##
## Summary of balance for matched data:
## Means Treated Means Control SD Control Mean Diff eQQ Med eQQ Mean
## distance 0.1927 0.1927 0.0846 0.0000 0.0037 0.0036
## race_white 0.7411 0.7493 0.4336 -0.0081 0.0000 0.0000
## w3income 82568.9357 81775.6653 46655.3555 793.2703 0.0000 2221.9818
## p5hmage 39.5932 39.6169 5.1757 -0.0237 0.0000 0.1434
## p5numpla 1.0917 1.0777 0.2839 0.0141 0.0000 0.0139
## w3momed_hsb 0.2234 0.2226 0.4162 0.0007 0.0000 0.0130
## eQQ Max
## distance 1.39e-02
## race_white 0.00e+00
## w3income 6.25e+04
## p5hmage 9.00e+00
## p5numpla 1.00e+00
## w3momed_hsb 1.00e+00
##
## Percent Balance Improvement:
## Mean Diff. eQQ Med eQQ Mean eQQ Max
## distance 99.9987 93.426 93.4405 81.6882
## race_white 94.5656 0.000 100.0000 100.0000
## w3income 97.0711 100.000 91.7915 0.0000
## p5hmage 98.8326 100.000 93.6413 -28.5714
## p5numpla 63.0544 0.000 65.1961 50.0000
## w3momed_hsb 99.6886 0.000 94.5111 0.0000
##
## Sample sizes:
## Control Treated
## All 7915 1352
## Matched 1151 1352
## Unmatched 6764 0
## Discarded 0 0
We can interpret the resulting output as follows:
- Summary of balance for all data: Comparison of the means for all the data without matching
- Summary of balance for matched data: Comparison of means for matched data. Looking for them to become similar.
- Percent balance improvement: Higher is better, close to 100 is ideal.
- Sample sizes: How many units were matched in the control/treatment groups.
Now, let's plot the propensity scores for the treated and untreated units.
# simple plot - check out the cobalt package for fancier options, or use ggplot2 to create your own!
plot(one_match, type = "hist")
Try extracting the data from one_match and creating a balance table like the one we did before, just this time using the new data. Scroll down for the answer when you're ready.
# grab data set
data_prop_match <- MatchIt::match.data(one_match)
# check balance
data_prop_match %>%
dplyr::group_by(catholic) %>%
dplyr::summarise_all(funs(mean)) %>%
t() %>%
as.data.frame() %>%
add_rownames("variable") %>%
dplyr::rename(no_catholic = "1", catholic = "2") %>%
dplyr::mutate(difference = catholic - no_catholic,
differencePerc = difference / (catholic + no_catholic)) %>%
dplyr::mutate_if(is.numeric, round, 3) %>%
knitr::kable() %>%
kableExtra::kable_styling()
| variable | no_catholic | catholic | difference | differencePerc |
|---|---|---|---|---|
| catholic | 0.000 | 1.000 | 1.000 | 1.000 |
| c5r2mtsc_std | 0.376 | 0.210 | -0.166 | -0.283 |
| race_white | 0.741 | 0.741 | 0.000 | 0.000 |
| p5hmage | 39.492 | 39.593 | 0.101 | 0.001 |
| w3income | 80248.128 | 82568.936 | 2320.808 | 0.014 |
| p5numpla | 1.079 | 1.092 | 0.013 | 0.006 |
| w3momed_hsb | 0.236 | 0.223 | -0.013 | -0.028 |
| distance | 0.189 | 0.193 | 0.003 | 0.009 |
| weights | 1.000 | 1.000 | 0.000 | 0.000 |
Those means look very close. Hooray.
Finally, estimate the treatment effect on the matched data set:summary(lm(c5r2mtsc_std ~ catholic, data = data_prop_match))
##
## Call:
## lm(formula = c5r2mtsc_std ~ catholic, data = data_prop_match)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5271 -0.5706 0.0549 0.6095 3.0581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.37552 0.02680 14.013 < 2e-16 ***
## catholic -0.16585 0.03646 -4.548 5.66e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9092 on 2501 degrees of freedom
## Multiple R-squared: 0.008204, Adjusted R-squared: 0.007808
## F-statistic: 20.69 on 1 and 2501 DF, p-value: 5.661e-06
As with the exact matching, we can see that those that did not attend Catholic school performed better on the test than those who did, and the results are statistically significant.
We can see the direction and magnitude of the covariate effects using a simple linear model:
lm_matched <- lm(c5r2mtsc_std ~ catholic + race_white + p5hmage +
I(w3income / 1000) + p5numpla + w3momed_hsb,
data = data_prop_match)
# use the stargazer package to view the output
# NOTE: you MUST include results = "asis" in the chunk header for this to be visible once knitted
stargazer(lm_matched, type = "text")
=============================================== Dependent variable:
--------------------------- c5r2mtsc_std
----------------------------------------------- catholic -0.178***
(0.035)
race_white 0.315***
(0.040)
p5hmage 0.012***
(0.003)
I(w3income/1000) 0.003***
(0.0004)
p5numpla -0.051
(0.058)
w3momed_hsb -0.287***
(0.042)
Constant -0.477***
(0.154)
Observations 2,503
R2 0.114
Adjusted R2 0.112
Residual Std. Error 0.860 (df = 2496)
F Statistic 53.716*** (df = 6; 2496)
=============================================== Note: p<0.1; p<0.05; p<0.01


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My research interests are in Statistics and Machine Learning, including convolutional neural networks, interpretability and statistical inference. I was born and raised in Taiwan, graduated from National Cheng Kung University and was very fortunate to pursue my PhD studies in the department of civil engineering and operations research at Princeton University. Now work in fiber optics manufacturing field with research interests in Light Detection and Ranging(LiDAR). At my previous research position at Honeywell, I worked on MicroOptics Devices and Optical Sensors. I received Chairman's Technical Achievement Award as a member of the Backlight Team. I live in Las Vegas.


There are several interfaces currently available to extend Sentry. These are a work in progress and the API is not frozen.
Note
If you write a plugin be prepared to maintain it until we’re content with the API.
Sentry includes several plugins by default. To enable a plugin, it’s as simple as adding it to your INSTALLED_APPS:
INSTALLED_APPS = [
...
'sentry.plugins.sentry_servers',
'sentry.plugins.sentry_sites',
'sentry.plugins.sentry_urls',
]
Enables a list of most seen servers in the message details sidebar, as well as a dedicated panel to view all servers a message has been seen on.
INSTALLED_APPS = [
'sentry.plugins.sentry_servers',
]
Enables a list of most seen urls in the message details sidebar, as well as a dedicated panel to view all urls a message has been seen on.
INSTALLED_APPS = [
'sentry.plugins.sentry_urls',
]
New in version 1.3.13.
Enables a list of most seen sites in the message details sidebar, as well as a dedicated panel to view all sites a message has been seen on.
INSTALLED_APPS = [
'sentry.plugins.sentry_sites',
]
The plugin interface is a work in progress and the API is not frozen.*
More and better docs coming soon.
For the technical, here’s some further docs:
If you wish to access these within your own views and models, you may do so via the standard model API:
from sentry.models import Message, GroupedMessage
# Pull the last 10 unresolved errors.
GroupedMessage.objects.filter(status=0).order_by('-last_seen')[0:10]
You can also record errors outside of handler if you want:
from sentry.client.base import SentryClient
try:
...
except Exception, exc:
SentryClient().create_from_exception([exc_info=None, url=None, view=None])
If you wish to log normal messages (useful for non-logging integration):
from sentry.client.base import SentryClient
import logging
SentryClient().create_from_text('Message Message'[, level=logging.WARNING, url=None])
Both the url and level parameters are optional. level should be one of the following:
If you have a custom exception class, similar to Http404, or something else you don’t want to log, you can also add skip_sentry = True to your exception class or instance, and sentry will simply ignore the error.


from setuptools import setup
with open("README.md") as f:
readme = f.read()
with open("LICENSE") as f:
from setuptools import setup
import os
with open(os.path.join(os.path.dirname(__file__), "Readme.md")) as f:
readme = f.read()
with open(os.path.join(os.path.dirname(__file__), "LICENSE")) as f:


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H.B. Woodrose
Hawaian Baby Wood Rose afforded one of the more compelling experiences I, and those with whom I indulged in this brain food, ever had.
The dosage we took was based in part by information found on the internet, in part by instinct. We broke 24 seeds into dust with a hammer, then simply ate the seeds with water. The taste was not awful, but I certainly see why people don't use Rose for garnish.
The experience was, for lack of a better word, immensely communal. The set was a group of four people, but expanded eventually to six with three viewers. We were all comfortable with one another before the experience, but I certainly feel the experience enhanced the social bonds between the Group. Three dosed, initially, and one remained Seedless. The setting was inside a house where we feel comfortable and at one point we took a walk outside, but this did not last long.
Communication between people was, at times, difficult, as it was often difficult to find the proper words to express the sensations discovered. However, in other moments we seemed to be thinking with a single group mind. Thoughts flowed like water between cups, for lack of anything less prosaic. There was no differentiation between the self and the other. Facial features took on an incredibly important meaning, as did body language, tone, and gesture. The individual words dissolved and became less important.
The chemical enhanced the pleasure received from music, and one musician in our group reacted very favorably and felt the Seed enhanced his ability to play his instrument. A major joy for me was to watch the group dynamics unfold, to watch the way the wind changed slowly when we sat outdoors, and to simply sit and be.
These experiences are inevitably innefable. That is, they will not be described, refuse to be described in any accurate wording. If you can describe the experience scientifically, it is likely, I believe, that the description will always and inevitably fall short of the event.
The experience lasted many hours -- full on, no longer than 8; peaked at about 3 or 4 hours; made it difficult to sleep; mind was very alive. It even lingered into the next day. It was a gentle, commmunal experience, with a great sense of unity between those in -- and even, if I have judged correctly -- without of the Seed's Grip. This, to me, was one of the best qualities of the experience. Some chemicals tend to isolate those within the experience from those outside the experience. Wood Rose seems to create a larger sphere of influence, so even those who have not enjoyed the Seed directly still receive some of its Shamanic -- brainfoodmindexpanding -- qualities.
As I understand it, the active chemical in HBWR is similar to the active chemical in Lysergic Acid. This would not surprise me, as the hallucinogenic quality of HBWR, when ingested, was enormous, to say the least. Momentarily, we were convinced that we had 'rediscovered' the catalyst for the expansive, highly charged, unitive experience of the previous generation. A link opened between ourselves -- already united by the common experience -- and a larger world, and the inevitable call -- it is, I believe, quite a timely one -- became 'peace' and 'love.' The words are old, but they're valuable nevertheless.
Language became pressing. Words immediately flowed from intention and became action, expressed what they needed to. Strange words, words forced upon us, into our vocabulary, by the outside, became either malevolent or benevolent, depending on the assisting vibrations of past experience, love and hate, fear and trust.
The group, through the experience, swung between extremes of benevolent loathing to communal and of course Platonic love. The social atmosphere was intoxicating, even while inside the experience. It is perhaps unfortunate that we, or at least I, am not able, or have not in the past been able, to approach life and those around me with the joy and wonder that this Seed helped engender in me last night. However, I believe that the Seed itself is not the whole reason for this communal experience. There is, rather, a larger element to take into consideration.
A warning to anybody interested: the first hour or so, myself and another in our group reacted to the chemical by throwing up. This, of course, is not uncommon in the use of such things, but it need be warned. Before I use Rose Seeds again I intend to eat less meat during the day. I'd had some pork and some chicken the day of the experience, and I believe my body reacted to the mix. I would also drink only water, no coffee or alcohol, before ingesting.
The experience did not provide full on hallucinations. The world outside remained, physically, mostly as is. However, visions could be transposed onto the outside world, and the visions had with eyes closed were enormous and reveletory.
I am surprised that the lid has been kept on Wood Rose for as long as it has. I feel that these little seeds might be the best kept secret since Free Will.
kassiel
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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| Title | Author(s) | Edition | ISBN | Condition | Course | Price | Buyer |
|---|---|---|---|---|---|---|---|
| {{ book.title }} | {{ book.authors }} | {{ book.edition }} | {{ book.isbn }} | {{ book.get_condition_display }} | {{ book.course.code }} | ${{ book.price }} | {{ book.buyer.get_full_name }} |
This course doesn't have any books for sale yet. Post a new one?
{% endif %} {% endblock %}


{% extends "base.html" %} {% block head %} {{ super() }}
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{% if incorrect_token %}Ack! That token doesn't appear to be right. Care to try again?
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--- layout: post title: The time complexity tagline: " of an algorithm" permalink: /algorithms/time-complexity.html categories: [algorithms, literate programming] tags: [computation, complexity, analysis] ---
Figure 1: Graphs of number of operations, N vs input size, n for common complexities, assuming a coefficient of 1
In computer science, the time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the length of the string representing the input. The time complexity of an algorithm is commonly expressed using big O notation, which excludes coefficients and lower order terms. When expressed this way, the time complexity is said to be described asymptotically, i.e., as the input size goes to infinity. For example, if the time required by an algorithm on all inputs of size n is at most 5n3 + 3n for any n (bigger than some n0), the asymptotic time complexity is O(n3).
The expression O is also called
Landau's symbol
.Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, where an elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm differ by at most a constant factor.
In other words:
The number of (machine) instructions which a program executes during its running time is called its time complexity in computer science.
| Name | Running time | Example |
|---|---|---|
| constant | O(1) | statement |
| linear | O(n) | single_loop(statement) |
| quadratic | O(n2) | double_loop(statement) |
| logarithmic | O(log2n) | binary search α |
| linearithmic | n*O(log2n) | β |
α The running time of the algorithm is proportional to the number of times N can be divided by 2. This is because the algorithm divides the working area in half with each iteration.
β The running time consists of N loops (iterative or recursive) that are logarithmic, thus the algorithm is a combination of linear and logarithmic.
The full list of common time complexities
import timeit def merge_dicts(*dict_args): """ Given any number of dicts, shallow copy and merge into a new dict, precedence goes to key value pairs in latter dicts. """ result = {} for dictionary in dict_args: result.update(dictionary) return result x = {'a': 1, 'b': 2} y = {'b': 3, 'c': 4} print(min(timeit.repeat(lambda: merge_dicts(x, y), number=100))) print(min(timeit.repeat(lambda: merge_dicts(x, y)))) # min(timeit.repeat(lambda: {**x, **y})) ==> for Python 3.5 # dictionary item overwrite caveat illustration z = {'c': 1} assert(merge_dicts(x, y, z)['c'] == 1)
0.00024804499980746186 2.233158576999813


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attribute
Image 91:
Group 10: Image with missing alt
attribute should not be included in results
Image 101:


Stylist shares simple trick to create perfect winged eyeliner in seconds
Perfect, winged eyeliner is the holy grail of sleek, sophisticated make-up looks. But it’s damn-near impossible to get right.
We normally end up with one perfect eye, and one panda eye – and then spend the next 45 minutes trying to even them out. And we do not have time for this nonsense.
Luckily, a stylist has revealed her simple trick for drawing professional-looking winged eyeliner in seconds.
Susie Hasler, 36, from East Grinstead, West Sussex, posted a video to her Instagram followers revealing how she mastered the look by using eyeshadow pads from Amazon – which only cost about £1.
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The mum-of-one, who specialises in styling women whose body shapes have changed, shared a tutorial showing how she applied her eyeliner, and the finished look.
The post, entitled ‘cheat your way to winged eyeliner’, has already attracted more than 6,000 views in just a few days on Susie’s account.
Susie said in the footage that a makeup stylist friend had recommended the eyeshadow pads, adding: ‘I have tried so many tutorials and just can’t do it.’
Susie demonstrates in the video where to apply the pads on her face, and then continues to add eyeshadow to the edges of her eyelid, working her way in.
‘It’s basically like a stencil,’ Susie explains. ‘And it stops those dark colours going under your eyes.’
She also reveals how using brighter colours towards the centre of the eyelids helps to ‘open up your eyes’.
Susie uses a pen eyeliner to draw along the curve of the eyeshadow pad, away from her eye. She then reveals the finished look.
Susie added: ‘Winged eyeliner is a great way to at a bit of edginess to a look, but it’s also really tricky to do it right.
‘I loved hearing about this simple trick and wanted to share it with my followers, as it really does work!’
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The post attracted loads of comments from fans and people desperate to try the technique for themselves. Hagardashery wrote: ‘They look great what a good idea!’
Daughterofpearl added: ‘Ooh I love that! I’m always 50/50 when it comes to liquid eyeliner. It either looks badass or terrible.’
‘You can buy those Make up pads in primark too 50 for £1.50 :)’ said Itsaboss_life.


Uploaded on October 27, 2016
In the international team version of kabaddi, two teams of seven members each occupy opposite halves of a field of 10 by 13 metres (33 ft × 43 ft) in case of men and 8 by 12 metres (26 ft × 39 ft) in case of women.[23] Each has three supplementary players held in reserve. The game is played with 20-minute halves and a five-minute halftime break during which the teams exchange sides.
The teams take turns sending a "raider" into the other half. To win a point, the raider must take a breath, run into the opposing half, tag one or more members of the opposite team, then return to his/her home half before inhaling again.The raider will chant "kabaddi, kabaddi" with his/her exhaling breath to show the referee s/he has not inhaled. The raider will be declared "out" and will not gain the point if s/he inhales before returning to his/her side, or returns without touching an opponent. The tagged defender(s) will be "out" if they do not succeed in catching the raider who tagged them. Wrestling the raider to the ground can prevent him/her escaping before s/he needs to inhale.
Defenders may not cross the centre line (the "lobby") of the field and the raider may not cross the boundary lines. However, there is one bonus line which can grant extra points for the raider if he manages to touch it and return successfully. Players who are out are temporarily sent off the field. Each time a player is out, the opposing team earns a point. A team scores a bonus of two points (called a "lona"), if the entire opposing team is declared out. At the end of the game, the team with the most points wins. Matches are categorised based on age and weight. Six officials supervise a match: one referee, two umpires, a scorer and two assistant scorers.


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