Idea
During one of the poster sessions of ACL 2022 in Dublin a few days ago, I came up with the idea to look back at more recent MT papers and the impact they had. A “test-of-time”-like review, if you will, but with an MT-focus and on more recent papers that could still be interesting from a current deployment or reproducibility perspective. This is a first attempt at finding out what the community thinks is useful closer to now. As a proxy, I choose citation counts since publication and rank based on that.
For now, I chose the two years of 2018 and 2019 as a compromise between recency and time passed to accumulate citations. I also separate the years so that younger papers do not get crowded out by earlier papers that had more time to collect citations. The number of papers (20) is a compromise between wanting to go a bit deeper and the amount of time I can spend on this. I also believe that the very top (say top-10) might be a too limited view on the field. So is top-20, probably. The search queries are listed below and it’s worth going a few pages deeper if you are interested. Another reason I chose 2018 as the starting point is that Transformers were already firmly established and hence many things should carry over to today.
In the future, I may pick a couple of those papers that I find particularly interesting and discuss them in more detail, see how they hold up 3-4 years later and - if I am particularly interested in them - check if I am able to meaningfully reproduce or generalize the results. This list is a start. I might do 2020 and 2021, too, but the results there will probably be less informative due to shorter time for accumulating citation counts. On the other hand that will be even more cutting edge.
In general, there is a lot of papers in these lists that I will have to (re-)read soon. Most of them I have at least seen before, but a whole bunch is new to me.
Method and Caveats
I am simply searching on Semantic Scholar for all papers from the date range I am interested in and contain the term “machine translation” (in the title?). That is of course very crude. I am not quite sure, but it seems that Semantic Scholar is limiting the results to the paper title and there is no option to change that (hence the asterisk in the title of the post). This means that a paper on machine translation that does not contain the term in the title might be omitted. That’s probably the biggest problem with these lists. If anyone has a better idea how to achieve a less limiting search, let me know.
I have chosen Semantic Scholar over Google Scholar since it seems that Google does not allow to sort search results by citation count which makes the task somewhat impossible there. Also, it should be noted that Semantic Scholar still seems to drastically undercount citations compared to Google; I am not doing anything about that here.
I am omitting papers from the search result if the listed date is not from the exact year in the query. Seems that happens when papers appear on Arxiv first and are later published in “proper” venues. I am using the first date, but that might be the wrong thing to do. This results for instance in dropping the first two entries from the search for 2018 since they appeared on Arxive in 2017. Other than that I am not doing any selection.
That may result in papers being listed that strictly speaking are not “on machine translation” but just happen to use it for other things. Entry 19 in 2019 might be an example for that, but it is still interesting if viewed as an application of MT in a different field.
Here you can see the exact search query for as long as the query format for Semantic scholar does not change after publication of this post. The results are not going to be stable though due to changing citation counts:
If available, I use a mash-up of the ACL plain text citation format and the markdown citation to produce the entries. Citations counts are added based on the today’s number from Semantic Scholar. For other sources I used the Chicago-style citation from Semantic Scholar and adjust it to resemble the ACL format a bit closer. Instead of the PDFs that Semantic Scholar links to, this lists contains links to the correct summary pages.
I guess it would be good to automate this somehow so that the lists can change over time, but that would have been too much work at the moment. Maybe someone wants to pick this up as a project.
If you disagree with any of my choices here, let me know in the comments. I am open to workshopping this.
And here we go.
The 20 top-cited Machine Translation papers of 2018
- Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato. 2018. Phrase-Based & Neural Unsupervised Machine Translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 5039–5049, Brussels, Belgium. Association for Computational Linguistics. Citations: 536
- Myle Ott, Sergey Edunov, David Grangier, and Michael Auli. 2018. Scaling Neural Machine Translation. In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 1–9, Brussels, Belgium. Association for Computational Linguistics. Citations: 464
- Ashish Vaswani, Samy Bengio, Eugene Brevdo, Francois Chollet, Aidan Gomez, Stephan Gouws, Llion Jones, Łukasz Kaiser, Nal Kalchbrenner, Niki Parmar, Ryan Sepassi, Noam Shazeer, and Jakob Uszkoreit. 2018. Tensor2Tensor for Neural Machine Translation. In Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), pages 193–199, Boston, MA. Association for Machine Translation in the Americas. Citations: 372
- Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, André F. T. Martins, and Alexandra Birch. 2018. Marian: Fast Neural Machine Translation in C++. In Proceedings of ACL 2018, System Demonstrations, pages 116–121, Melbourne, Australia. Association for Computational Linguistics. Citations: 342
- Mia Xu Chen, Orhan Firat, Ankur Bapna, Melvin Johnson, Wolfgang Macherey, George Foster, Llion Jones, Mike Schuster, Noam Shazeer, Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Zhifeng Chen, Yonghui Wu, and Macduff Hughes. 2018. The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 76–86, Melbourne, Australia. Association for Computational Linguistics. Citations: 323
- Ondřej Bojar, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Philipp Koehn, and Christof Monz. 2018. Findings of the 2018 Conference on Machine Translation (WMT18). In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 272–303, Belgium, Brussels. Association for Computational Linguistics. Citations: 263
- Ye Qi, Devendra Sachan, Matthieu Felix, Sarguna Padmanabhan, and Graham Neubig. 2018. When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 529–535, New Orleans, Louisiana. Association for Computational Linguistics.
- Elena Voita, Pavel Serdyukov, Rico Sennrich, and Ivan Titov. 2018. Context-Aware Neural Machine Translation Learns Anaphora Resolution. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1264–1274, Melbourne, Australia. Association for Computational Linguistics. Citations: 195
- Jiatao Gu, Hany Hassan, Jacob Devlin, and Victor O.K. Li. 2018. Universal Neural Machine Translation for Extremely Low Resource Languages. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 344–354, New Orleans, Louisiana. Association for Computational Linguistics. Citations: 194
- Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2018. Unsupervised Statistical Machine Translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3632–3642, Brussels, Belgium. Association for Computational Linguistics. Citations: 185
- Alessandro Raganato and Jörg Tiedemann. 2018. An Analysis of Encoder Representations in Transformer-Based Machine Translation. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 287–297, Brussels, Belgium. Association for Computational Linguistics. Citations: 177
- Chenhui Chu and Rui Wang. 2018. A Survey of Domain Adaptation for Neural Machine Translation. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1304–1319, Santa Fe, New Mexico, USA. Association for Computational Linguistics. Citations: 174
- Lesly Miculicich, Dhananjay Ram, Nikolaos Pappas, and James Henderson. 2018. Document-Level Neural Machine Translation with Hierarchical Attention Networks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2947–2954, Brussels, Belgium. Association for Computational Linguistics. Citations: 173
- Gongbo Tang, Mathias Müller, Annette Rios, and Rico Sennrich. 2018. Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4263–4272, Brussels, Belgium. Association for Computational Linguistics. Citations: 169
- Rachel Bawden, Rico Sennrich, Alexandra Birch, and Barry Haddow. 2018. Evaluating Discourse Phenomena in Neural Machine Translation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1304–1313, New Orleans, Louisiana. Association for Computational Linguistics.
- Myle Ott, Michael Auli, David Grangier, Marc’Aurelio Ranzato. 2018. Analyzing Uncertainty in Neural Machine Translation. Proceedings of the 35th International Conference on Machine Learning, PMLR 80:3956-3965. Citations: 160
- Samuel Läubli, Rico Sennrich, and Martin Volk. 2018. Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4791–4796, Brussels, Belgium. Association for Computational Linguistics. Citations: 157
- Matt Post and David Vilar. 2018. Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1314–1324, New Orleans, Louisiana. Association for Computational Linguistics. Citations: 156
- Jiatao Gu, Yong Wang, Yun Chen, Victor O. K. Li, and Kyunghyun Cho. 2018. Meta-Learning for Low-Resource Neural Machine Translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3622–3631, Brussels, Belgium. Association for Computational Linguistics. Citations: 151
- Zhen Yang, Wei Chen, Feng Wang, and Bo Xu. 2018. Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1346–1355, New Orleans, Louisiana. Association for Computational Linguistics. Citations: 147
The 20 top-cited Machine Translation papers of 2019
- Loïc Barrault, Ondřej Bojar, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Philipp Koehn, Shervin Malmasi, Christof Monz, Mathias Müller, Santanu Pal, Matt Post, and Marcos Zampieri. 2019. Findings of the 2019 Conference on Machine Translation (WMT19). In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 1–61, Florence, Italy. Association for Computational Linguistics. Citations: 314
- Roee Aharoni, Melvin Johnson, and Orhan Firat. 2019. Massively Multilingual Neural Machine Translation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3874–3884, Minneapolis, Minnesota. Association for Computational Linguistics. Citations: 255
- Qiang Wang, Bei Li, Tong Xiao, Jingbo Zhu, Changliang Li, Derek F. Wong, and Lidia S. Chao. 2019. Learning Deep Transformer Models for Machine Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1810–1822, Florence, Italy. Association for Computational Linguistics. Citations: 249
- Naveen Arivazhagan, Ankur Bapna, Orhan Firat, Dmitry Lepikhin, Melvin Johnson, Maxim Krikun, Mia Xu Chen, Yuan Cao, George F. Foster, Colin Cherry, Wolfgang Macherey, Z. Chen, and Yonghui Wu. 2019. Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges. ArXiv abs/1907.05019. Citations: 213
- Francisco Guzmán, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, and Marc’Aurelio Ranzato. 2019. The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6098–6111, Hong Kong, China. Association for Computational Linguistics. Citations: 166
- Gabriel Stanovsky, Noah A. Smith, and Luke Zettlemoyer. 2019. Evaluating Gender Bias in Machine Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1679–1684, Florence, Italy. Association for Computational Linguistics. Citations: 155
- Xu Tan, Yi Ren, Di He, Tao Qin, Zhou Zhao and Tie-Yan Liu. Multilingual Neural Machine Translation with Knowledge Distillation. ArXiv abs/1902.10461. Citations: 151
- Yong Cheng, Lu Jiang, and Wolfgang Macherey. 2019. Robust Neural Machine Translation with Doubly Adversarial Inputs. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4324–4333, Florence, Italy. Association for Computational Linguistics. Citations: 141
- Ankur Bapna and Orhan Firat. 2019. Simple, Scalable Adaptation for Neural Machine Translation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1538–1548, Hong Kong, China. Association for Computational Linguistics. Citations: 141
- Wen Zhang, Yang Feng, Fandong Meng, Di You, and Qun Liu. 2019. Bridging the Gap between Training and Inference for Neural Machine Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4334–4343, Florence, Italy. Association for Computational Linguistics. Citations: 141
- Rico Sennrich and Biao Zhang. 2019. Revisiting Low-Resource Neural Machine Translation: A Case Study. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 211–221, Florence, Italy. Association for Computational Linguistics. Citations: 133
- Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey, Chung-Cheng Chiu, Semih Yavuz, Ruoming Pang, Wei Li, and Colin Raffel. 2019. Monotonic Infinite Lookback Attention for Simultaneous Machine Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1313–1323, Florence, Italy. Association for Computational Linguistics. Citations: 118
- Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabas Poczos, and Tom Mitchell. 2019. Competence-based Curriculum Learning for Neural Machine Translation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1162–1172, Minneapolis, Minnesota. Association for Computational Linguistics. Citations: 112
- Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2019. An Effective Approach to Unsupervised Machine Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 194–203, Florence, Italy. Association for Computational Linguistics. Citations: 102
- Yiren Wang, Fei Tian, Di He, Tao Qin, ChengXiang Zhai and Tie-Yan Liu. 2019. Non-Autoregressive Machine Translation with Auxiliary Regularization. ArXiv abs/1902.10245. Citations: 102
- Elena Voita, Rico Sennrich, and Ivan Titov. 2019. When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1198–1212, Florence, Italy. Association for Computational Linguistics. Citations: 100
- Elena Voita, Rico Sennrich, and Ivan Titov. 2019. The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4396–4406, Hong Kong, China. Association for Computational Linguistics. Citations: 89
- Joel Escudé Font and Marta R. Costa-jussà. 2019. Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques. In Proceedings of the First Workshop on Gender Bias in Natural Language Processing, pages 147–154, Florence, Italy. Association for Computational Linguistics. Citations: 89
- Michele Tufano, Jevgenija Pantiuchina, Cody Watson, Gabriele Bavota and Denys Poshyvanyk. 2019. On Learning Meaningful Code Changes Via Neural Machine Translation. 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE). Citations: 88
- Marcin Junczys-Dowmunt. 2019. Microsoft Translator at WMT 2019: Towards Large-Scale Document-Level Neural Machine Translation. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 225–233, Florence, Italy. Association for Computational Linguistics. Citations: 87
Comments
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Hello YouTube
Hellow YouTube
This idea started while playing Astroneer online with my best friend. This avenue will give me the change of tapping/creating the creative side of my mind as I’m usually always in the analytical mindset.
Inspiration
For years my friends and I have watched Philip DeFranco and he has always been inspirational.
One of his videos covers the idea of why someone wants to run a YouTube channel.
So to answer the main two questions that come up during that conversation of why someone is running their channel:
Why
Why do I want to create content for YouTube?
- I’m very analytical and this gives me the change to be in a creative mindset.
- In my friend circle I tend to be the more quiet one and this will hopefully force me to have a more outside dialog.
So it basically breaks down to me using this as an avenue to force myself into a more foreign and uncomfortable realm.
How
How do I plan on creating content for YouTube?
It can be very daunting thinking about the process as a whole and the feedback that will be given to each video. Breaking things down into small managable tasks can ease this whole process.
To start out we plan on trying to maintain once a week postings to get familiar with the over all process. Once certain effecienies are met upping the content to two to three per week.
Gears of War: Ultimate


Apple releases Smart Battery Case for the iPhone 11 series
by PrasadApple has released its Smart Battery Case for the latest generation of iPhones. This includes the iPhone 11, the iPhone 11 Pro and the iPhone 11 Pro Max.
The Smart Battery Case, as the name suggests, is a case with an integrated battery that perpetually charges the phone until the battery in the case drains. Once that happens, the phone switches to using its own battery. Apple claims you get 50% additional battery life with the case on top of the already excellent battery life on these new iPhones. You also see the battery level for the case in the iOS battery widget.
This is the third generation of Smart Battery Cases from Apple. The first model was available for the iPhone 6s and later the iPhone 7. Then last year, Apple released the second generation version for the iPhone XS series. Now, we have the third generation.
What separates this generation from last year's, despite the similar appearance, is that this year's case also features a dedicated camera button on the right edge, where a shutter button normally would be on a camera. Pressing the button launches the camera, even if the phone is locked. A tap takes a picture and longer press captures QuickTake video.
As with last year's models, the new Smart Battery Case is also Qi-certified and so you can charge both the case and the phone within using a wireless charger. It also supports USB-PD fast chargers using the Lightning connector.
The new Smart Battery Case is available in White, Black and Pink Sand for the iPhone 11 Pro and the iPhone 11 Pro Max and just White and Black for the iPhone 11. All versions are priced at $129.


| Package | com.yahoo.astra.utils |
| Class | public class InstanceFactory |
| Property | Defined by | ||
|---|---|---|---|
| methods : Object
A set of methods to call once the object has been created and
properties have been initialized.
| InstanceFactory | ||
| properties : Object
The initial values to pass to the properties of the
newly-instantiated object.
| InstanceFactory | ||
| targetClass : Class
The class that will be instantiated.
| InstanceFactory |
| Method | Defined by | ||
|---|---|---|---|
|
InstanceFactory(targetClass:Class, properties:Object = null, methods:Object = null)
Constructor.
| InstanceFactory | ||
|
createInstance():Object
Creates a new instance of the target class and initializes it.
| InstanceFactory | ||
|
restoreInstance(instance:Object):void
Initializes an object with the properties and methods.
| InstanceFactory |
| methods | property |
methods:Object
[read-write]A set of methods to call once the object has been created and properties have been initialized. Format is a set of key-value pairs where the key is the name of the method and the value is an Array of parameter values.
Example: { load: [ "image.gif" ] }
public function get methods():Object
public function set methods(value:Object):void
| properties | property |
properties:Object
[read-write]The initial values to pass to the properties of the newly-instantiated object.
Implementation public function get properties():Object
public function set properties(value:Object):void
| targetClass | property |
targetClass:Class
[read-write]The class that will be instantiated.
Implementation public function get targetClass():Class
public function set targetClass(value:Class):void
| InstanceFactory | () | constructor |
public function InstanceFactory(targetClass:Class, properties:Object = null, methods:Object = null)
Constructor.
ParameterstargetClass:Class |
|
properties:Object (default = null )
|
|
methods:Object (default = null )
|
| createInstance | () | method |
public function createInstance():Object
Creates a new instance of the target class and initializes it.
ReturnsObject |
| restoreInstance | () | method |
public function restoreInstance(instance:Object):void
Initializes an object with the properties and methods. The object
must be an instance of the targetClass
property, or
this method will throw an ArgumentError
.
instance:Object |


When I Am Sad, I Simply Watch The ‘Parks And Recreation’ Bloopers
by Patrick LentonHere’s the thing — Parks and Recreation is a television show that is already a ray of sunshine, a warm hug, a hot cup of soup being thrown into the face of a soft baby.
Related
It’s the only thing I can watch when I have the flu, for example. It’s funny, wholesome, and lovely.
But you don’t need wholesome, exactly. Sometimes you just need to laugh. Sometimes… you NEED to laugh. And that’s where the Parks and Rec bloopers come into it. It’s one of the only things on the internet that I KNOW will successfully cheer me up if I need it.
The other is videos of brides falling over in their dresses.
There’s a lot of the same wholesome energy as the show in the bloopers– obviously. But there’s also a deep mania, a current of vibrant cursed energy, the friction that comes from a bunch of funny people who genuinely love each other being forced to act together in a TV comedy.
I LIVE off that energy.
A good blooper reel is never particularly about the bloops — it’s about the reactions. It’s about the willingness to take the moment of comedy and treasure it, expand where needed, play with it, continue the joke. Or simply to leave it there on the floor, perfect and sparkling, and just laugh at it.
I believe that because so many of the Parks and Rec cast come from an improv background — Amy Poehler most notably — they’re trained to react to spontaneous comedy, which is what makes this blooper reel so perfect. It’s better than literally any other gag reel on the internet. It is very funny.
I will note that it has soured a little bit for me — both Aziz Ansari and Chris Pratt aren’t my favourite people anymore, and unlike with the show, this is them being THEM. Pratt is particularly hard to watch, as he’s a bit of a jokester, and features heavily. Ah, more innocent times.
But, it’s Amy Poehler who makes this evergreen, who keeps this one of the most hilarious corners of the internet. Her laugh alone would make it worthwhile — that cackle is genuinely perfect. She also exemplifies in these bloopers the kind of comedy I love to watch — a funny person being genuinely tickled by something else funny. She is so supportive and generous with her laughter, and it’s why these work.
There’s some truly iconic moments over the many seasons of Parks and Recreation bloopers. We’ll give you some highlights, to help you watch more efficiently.
‘Parks And Recreation’ Season 2 Bloopers
Season 2 of Parks and Rec is still gearing up, and the bloopers are of a similar energy. There’s not a LOT of iconic moments, but the energy is THERE. My favourite part is when Leslie and Ann are trying to force pills down Ron’s mouth, and they’re just rolling around and giggling. Seems fun.
‘Parks And Recreation’ Season 3 Bloopers
See, things are kickings off! This is so wholesome! This is so lovely! They’re all laughing and I think they are friends! I think they are my friends.
‘Parks And Recreation’ Season 4 Bloopers
Now THIS is the business. We start with Amy absolutely losing it when Christ Pratt improvises a perfect little Andy moment — losing concentration during an involved set of instructions.
This also features one of my favourite jokes in the entire show “dude, is she going to powder her vagina?” Watching the cast react to this perfect moment helps you re-appreciate just how good it is. Or what about Kathryn Hahn saying “he’s actually in me”? Perfect.
I will say these bloopers have the most Chris Pratt toilet humour, which I am not a fan of.
‘Parks And Recreation’ Season 5 Bloopers
Another good gag reel! Isn’t this hilarious!
One of my fave bits is when Amy can’t say “Colonel Plump’s Slop Trough”. That’s funny. I also really like it when Adam Scott doesn’t want to get slapped again, because he is a precious angel who must be saved.
The back and forth where Adam Scott answers “my butthole?” to the imperative of “keep it tight” is part of my regular lexicon now. The bit where Aubrey answers Andy’s question of “what’s that other thing you’re always telling me?” with “don’t take it out until I scream” shows us that she’s 7000 times better at dirty humour than Chris Pratt.
‘Parks And Recreation’ Season 6 Bloopers
Ron’s in Blush!
She drove me here!
Season 6 is getting to a lot of manic energy, and it’s good high energy fun.
One of my favourite bits is when Jim, the guy who plays Jerry, absolutely does a Jerry and speaks while rolling. Such a Jerry!
And that’s all the bloopers you can find reliably on YouTube. There’s more around, but you gotta go hunting for them! I hope you feel better.
Patrick Lenton is the Editor of Junkee. He tweets @patricklenton.


Welcome to Goclone Documentation!
Goclone is a utility that allows you to download a website from the Internet to a local directory. Get html, css, js, images, and other files from the server to your computer. goclone arranges the original site's relative link-structure. Simply open a page of the mirrored website in your browser, and you can browse the site from link to link, as if you were viewing it online.
Here’s what you’ll need before you start cloning:
Install Homebrew
To install Goclone with Homebrew enter these two commands!
$ brew tap imthaghost/goclone
$ brew install goclone
To build the binary manually we need to have Go installed on our machine.
$ go get github.com/imthaghost/goclone
$ cd $GOPATH/src/github.com/imthaghost/goclone


--- layout: post status: publish published: true title: Vista SP1 Installed author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 757 wordpress_url: http://winterdom.com/2008/02/vistasp1installed date: '2008-02-15 05:11:15 +0000' date_gmt: '2008-02-15 05:11:15 +0000' categories: - Vista tags: [] comments: [] ---
Service Pack 1 for Vista is now available for download from MSDN (I guess the Windows Update rollout will still wait to march or something).
I downloaded it last night and installed it this morning without too much fuss, except for Windows Live Messenger now keeps triggering Windows Installer on startup and failing (but it works after that, funky).
Haven't really noticed many improvements yet, though moving/copying stuff around in explorer seems quite bit faster (that alone might be worth it).


This theme will look great on any device, no matter the size!
Featuring the latest build of the new Bootstrap 4 framework!
Ready to use with your own content, or customize the source files!
"This is fantastic! Thanks so much guys!"
"Bootstrap is amazing. I've been using it to create lots of super nice landing pages."
"Thanks so much for making these free resources available to us!"


Feb 10th
Database
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Feb 24th
Build API
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Mar 16th
UX
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Mar 30th
UI
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Apr 6th
Interactive app
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Apr 20th
Feature complete
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Ready to release
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Coverage Summary for Class: GameController (it.polimi.ingsw.controller)
| Class | Method, % | Line, % |
|---|---|---|
| GameController | 92,3% (12/13) | 98,3% (58/59) |
| GameController$1 | 50% (1/2) | 33,3% (1/3) |
| Total | 86,7% (13/15) | 95,2% (59/62) |
1 package it.polimi.ingsw.controller; 2 3 import it.polimi.ingsw.commons.ResourceType; 4 import it.polimi.ingsw.commons.User; 5 import it.polimi.ingsw.model.Game; 6 import it.polimi.ingsw.model.GameState; 7 import it.polimi.ingsw.model.Player; 8 import it.polimi.ingsw.network.NetworkRemoteView; 9 import it.polimi.ingsw.network.RemoteView; 10 import it.polimi.ingsw.utils.messages.clientMessages.*; 11 import it.polimi.ingsw.utils.messages.serverMessages.Errors.ActionError; 12 import it.polimi.ingsw.utils.messages.serverMessages.Errors.InvalidMessageError; 13 import it.polimi.ingsw.utils.messages.serverMessages.Updates.NewTurnUpdate; 14 import it.polimi.ingsw.utils.Pair; 15 16 import java.util.List; 17 import java.util.Timer; 18 import java.util.TimerTask; 19 import java.util.concurrent.atomic.AtomicInteger; 20 import java.util.logging.Level; 21 import java.util.logging.Logger; 22 import java.util.stream.Collectors; 23 24 /** 25 * This class represents the Controller in MVC design pattern. The whole 26 * application, in fact, is based on MVC pattern, and in particular 27 * Game Controller is called whenever a {@link GameControllerHandleable} message is received. 28 * It handles initial choices done by players before starting the first turn. 29 * Messages related to an action performed in a {@link it.polimi.ingsw.model.Turn} are forwarded to {@link TurnController}. 30 */ 31 public class GameController { 32 private final Logger LOGGER = Logger.getLogger(GameController.class.getName()); 33 private final Game model; 34 private final TurnController turnController; 35 private final AtomicInteger receivedLeaderChoices = new AtomicInteger(0); 36 private final AtomicInteger receivedResourceChoices = new AtomicInteger(0); 37 private Timer gameCleaner; 38 39 /** 40 * Initializes a game controller with a game and a turn controller. 41 */ 42 public GameController(Game game) { 43 this.model = game; 44 this.turnController = new TurnController(this.model, model.getCurrPlayer()); 45 } 46 47 public TurnController getTurnController() { 48 return turnController; 49 } 50 51 public Game getModel() { 52 return model; 53 } 54 55 /** 56 * Handles a resource choice message and updates number of processed messages. 57 * Notifies remote view if any error has been detected before performing action. 58 * @param message message to be handled. 59 * @param sender remote view that has forwarded the message. 60 */ 61 public synchronized void handleResourceChoiceMessage(ResourceChoiceMessage message, RemoteView sender) { 62 if(message.isValidMessage()) { 63 if(checkPlayerResourceChoice(message, sender.getPlayer())){ 64 if(sender.getPlayer().getPosition()==4) { 65 if (isValidFourthPlayerPositioning(message.getChosenResources())) { 66 sender.getPlayer().performInitialResourcesChoice(model, message.getChosenResources()); 67 receivedResourceChoices.getAndIncrement(); 68 startFirstTurn(); 69 } else { 70 sender.sendError(new ActionError(sender.getPlayer().getUser(), ActionError.Trigger.INITIALRESOURCEPOSITIONINGERROR)); 71 } 72 } else { 73 sender.getPlayer().performInitialResourcesChoice(model, message.getChosenResources()); 74 receivedResourceChoices.getAndIncrement(); 75 startFirstTurn(); 76 } 77 } else { 78 sender.sendError(new ActionError(sender.getPlayer().getUser(),ActionError.Trigger.RESOURCECHOICEMISMATCH)); 79 } 80 }else { 81 sender.sendError(new InvalidMessageError(sender.getPlayer().getUser())); 82 } 83 } 84 85 /** 86 * Handles an initial leader cards choice. It takes into account of how many messages has been 87 * correctly processed because messages are received asynchronously in the sense that a player can choose leaders while 88 * another one is choosing his resources, in this way it's possible to detect if all choices have been made before starting the 89 * first turn. 90 * @param message message to be handled. 91 * @param sender remote view that has forwarded the message. 92 */ 93 public synchronized void handleLeaderChoiceMessage(LeaderChoiceMessage message, RemoteView sender) { 94 if (message.isValidMessage()) { 95 sender.getPlayer().performInitialLeaderChoice(model, message.getLeader1ToDiscard(), message.getLeader2ToDiscard()); 96 receivedLeaderChoices.getAndIncrement(); 97 startFirstTurn(); 98 } else { 99 sender.sendError(new InvalidMessageError(sender.getPlayer().getUser())); 100 } 101 } 102 103 /** 104 * Handles an user reconnection. 105 * @param reconnectingUser user that is reconnecting. 106 */ 107 public synchronized void handlePlayerReconnection(User reconnectingUser){ 108 if(model.getGameState() == GameState.PAUSED) gameCleaner.cancel(); 109 turnController.handlePlayerReconnection(reconnectingUser); 110 } 111 112 /** 113 * Handles an user disconnection. If this event happens before that leaders choice is done by disconnected user, 114 * game controller makes an autonomous decision about which leader cards have to be discarded. 115 * In case of disconnection during a game turn further decisions are handled by turn controller. 116 * If who has just disconnected was the last playing user, it starts a timer that after its deadline will delete 117 * the game. 118 * @param disconnectingUser user that lost connection. 119 * @param remoteView remote view of user that lost connection. 120 */ 121 public synchronized void handlePlayerDisconnection(User disconnectingUser, NetworkRemoteView remoteView) { 122 if(model.getGameState() == GameState.INITIALCHOICES) { 123 if(model.getPlayer(disconnectingUser).getLeaderCards().size() > 2) { 124 model.getPlayer(disconnectingUser).performInitialLeaderChoice(model,4,3); 125 receivedLeaderChoices.getAndIncrement(); 126 } 127 if(model.getPlayer(disconnectingUser).getPosition() > 1 && hasNotDoneResourceChoice(disconnectingUser)) { 128 receivedResourceChoices.getAndIncrement(); 129 } 130 turnController.handlePlayerDisconnection(disconnectingUser); 131 startFirstTurn(); 132 } else if(model.getGameState() == GameState.GAMEFLOW) 133 turnController.handlePlayerDisconnection(disconnectingUser); 134 if(model.getGameState() == GameState.PAUSED) { 135 List<User> usersToBeRemoved = model.getPlayerList().stream().map(Player::getUser).collect(Collectors.toList()); 136 gameCleaner = new Timer(); 137 gameCleaner.schedule(new TimerTask() { 138 @Override 139 public void run() { 140 LOGGER.log(Level.INFO, "Match is being deleted"); 141 remoteView.deleteMatch(usersToBeRemoved); 142 } 143 }, 20000); 144 } 145 } 146 147 private boolean checkAllLeaderChoicesDone(AtomicInteger leaderChoicesDone) { 148 return leaderChoicesDone.get() == model.getNumOfPlayers(); 149 } 150 151 private boolean checkAllChoicesDone(AtomicInteger resourceChoiceDone) { 152 return (resourceChoiceDone.get() == model.getNumOfPlayers()-1)&&checkAllLeaderChoicesDone(receivedLeaderChoices); 153 } 154 155 /** 156 * Method that handles decisions about starting the first turn, that can be done 157 * only if all the players have chosen leader cards and for those who are able to, resources. 158 */ 159 private void startFirstTurn() { 160 if (checkAllChoicesDone(receivedResourceChoices)) { 161 model.nextState(GameState.GAMEFLOW); 162 if(model.getNumOfPlayers()>1) { 163 model.notifyTurn(new NewTurnUpdate(model.getCurrPlayer().getUser())); 164 } 165 } 166 } 167 168 /** 169 * Boolean method that checks if a resource choice is legal given player position. 170 * @param message message containing chosen resources. 171 * @param sender player that made resource choice. 172 */ 173 private boolean checkPlayerResourceChoice(ResourceChoiceMessage message, Player sender){ 174 if(sender.getPosition()==2 || sender.getPosition()==3) return message.getChosenResources().size() == 1; 175 if(sender.getPosition()==4) { 176 return message.getChosenResources().size()==2; 177 } 178 return true; 179 } 180 181 /** 182 * Method to check if the fourth player in a game has correctly chosen destination for his resources. 183 * Since only the fourth player has the possibility to choose two resources, he is also the only one that can make errors 184 * choosing them, so this control is necessary to avoid any errors. 185 */ 186 public boolean isValidFourthPlayerPositioning(List<Pair<ResourceType,Integer>> resourceDestination) { 187 if(resourceDestination.get(0).getKey() == resourceDestination.get(1).getKey()) { 188 if(resourceDestination.stream().map(Pair::getValue).anyMatch(x -> x.equals(1))) return false; 189 return resourceDestination.get(0).getValue().equals(resourceDestination.get(1).getValue()); 190 } else { 191 if(resourceDestination.get(0).getValue().equals(resourceDestination.get(1).getValue())) return false; 192 if(resourceDestination.stream().map(Pair::getValue).anyMatch(x -> x > 3||x < 1)) return false; 193 } 194 return true; 195 } 196 197 /** 198 * Boolean that performs a player warehouse inspection and checks if a player has done his resources choice 199 * before losing the connection. 200 * @param disconnectingUser user that lost connection during initial choices phase of the game. 201 */ 202 private boolean hasNotDoneResourceChoice(User disconnectingUser) { 203 return model.getPlayer(disconnectingUser).getPersonalBoard().getWarehouse().getAvailableResources().entrySet().stream().allMatch(x -> x.getValue() == 0); 204 } 205 206 } 207


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Provide a brief introduction to the course, the Python programming language and practice with the VeroSkills interface.
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The original MPC, the MPC-60, was a collaboration between the Japanese company Akai and the American engineer Roger Linn. Linn had designed the successful LM-1 and LinnDrum, two of the earliest drum machines to use samples (prerecorded sounds).[2] His company Linn Electronics had closed following the failure of the Linn 9000, a drum machine and sampler. According to Linn, "[The collaboration] was a good fit because Akai needed a creative designer with ideas and I didn't want to do sales, marketing, finance or manufacturing, all of which Akai was very good at.
E-mu SP-1200 is a sampler, released in August 1987 by E-mu Systems, Inc.. Like the product it was meant to replace, the SP-12, the SP-1200's intended use was as a drum machine and sequencer for dance music producers. However, its use as a phrase sampler produces a "gritty" sound due to the machine's 26.04 kHz sampling rate (roughly half the fidelity of a compact disc), its SSM2044 filter chips and its 12-bit sampling resolution. This distinctive sound, often said to capture the "warmth" of vinyl recordings,[1] has sustained demand for the SP-1200 more than thirty years after its discontinuation, despite the introduction of digital audio workstations and samplers/sequencers with far superior technical specifications, such as the Akai MPC.
The Roland TR-808 Rhythm Composer, commonly known as the 808, is a drum machine manufactured by the Roland Corporation between 1980 and 1983. It was one of the first drum machines to allow users to program rhythms instead of using preset patterns. Unlike its nearest competitor at the time, the more expensive Linn LM-1, the 808 generates sounds using analog synthesis rather than playing samples (prerecorded sounds).
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The sample CRUD application to hotel reservation
Author: Pavel Ponec<EMAIL_ADDRESS>2013-2014
Document license: Creative Commons
BY-ND
The java library Ujorm was introduced in 2008 under the old name UJO Framework. Since then, five years have passed and the source code has been modified many times but the basic idea - accessing to the properties by constants called keys - remains. It is true that the HashMap object uses a similar approach, but the keys in Ujorm are a little different:
Features common to Ujo and the HashMap class:
It is obvious that accessing data via the Ujo keys is less comfortable than accessing through the getters and setters. The question is, what is the practical use then? The simple answer is that the Ujo objects are useful wherever we need a reference to attributes of a domain object (not its value). Because such claims are insufficiently illustrative, I decided to present a small web project for hotel booking. Graphical interface is rendered by the popular Apache Wicket framework. The core of the application consist of the dialogs and preview tables, all data are provided from a relational database using an ORM module from the Ujorm library. During the development, several classes have been optimized for general use so I moved them into a separate module called ujo-wicket. All the Java libraries have an open license and they are available at the central Maven repository.
The following figure describes the use of class model of the persistent classes:
Entity Booking is a relation of type M:N between the Hotel and the Customer. The customer can therefore book an accommodation in more hotels, and one hotel reservation holds more customers. To simplify, we do not care about the capacity of the hotel. To make the data model more interesting, we should add next entity called City, which is an attribute of the Hotel, however the project have no administration for the City entity.
Table with a list of hotels in a picture below:
Table probably won’t surprise the standard users of internet applications, it handles paging, sorting columns and booking the selected hotel. Data are retrieved from a relational database using the SQL statements. The source code is more interesting, the individual table columns (components) provides a factory method - according to the Ujo keys, wow :-). Some CSS styles were also assigned by the key’s attribute type. The table component is built using the source code below:
And that's it. The selection of database rows are restricted by an object Criterion, which is passed to the constructor of the UjoDataProvider class, where the class provides an instance of the table component, too. An ORM configuration with a database connection provider is obtained from the HotelsApplication class. We do not have to care about sorting column tables now, because each column is mapped to a database column and then a click event generates the necessary SQL query automatically.
Nearly same as the table columns, this dialog form lines are also created using a factory method according the Ujo keys keys, while the keys provide an additional input, a data validator. All the texts are localized using the tools of the Wicket. Sample source code follows:
It remains only to add some Java code to save the modified hotel on the Save button. In our project we are going to use some methods/services managed by the Spring framework.
Getters and setters are not required in any Ujo class, but putting them in will make more transparent source code in service layer implementations. The NetBeans IDE has an available plugin for generating the required methods by keys, similar plugin for Eclipse is missing yet, perhaps it will be a challenge for new fans :-).
Database requests are modelled using the Key object, which provides special methods to create a new object called Criterion used for describing the logical conditions. Two Criterions can be joined to a binary tree. See a simple code to get a logged user:
For more information see the document Ujorm User Guide.
On some user action, a related component sends an event to another component using the method Component.send(..) with a payload argument type of UjoEvent. Recipients can detect such an event inside a method called Component.onEvent (...). Request to view a modal dialogue can be distinguished by setting a flag UjoEvent.showDialog with a value true. Sample of event hotel management are as follows:
To be clear, I’m enclosing a source code of persistent hotel entity:
Application Demo-Hotels uses the H2 database running under in-memory in the default configuration. A database structure create ORM based on the object model and configurations. To change the database connection can be modified:
where each solution with higher number overlaps the previous solution. I have put two files into the project to connect to the MySQL and PostgreSQL databases type:
which can serve as a template to connect to an own database. I remind you that the XML configuration can be activated in the DatabaseConfig class.
Application Demo-Hotels was created in a hope that some parts of the source code would clarify the using of Ujo objects to our readers, but the application itself is not intended for a production use. If you have the impression that some parts of the code can be handled better or that the project can still continue working, you may be right. All graphic components are descendants of the Wicket framework so you can extend it for your specific needs. Some of the ideas were inspired by the WebFort project from the end of 2008, but I cannot exclude the possibility that a similar code did not appear ever before. Thank you for your constructive feedback.


Schonell Score Calculator
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This is a tool to easily calculate your spelling age using the score of a Schonell spelling test.


FuncJS is a small library which enables functional programming in JavaScript.
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Complete Agreement.
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NAB to pay compensation to customers sold junk insurance
by ABCThe National Australia Bank (NAB) has agreed to pay $49.5 million in compensation to tens of thousands of customers who were sold junk credit card and personal loan insurance.
The bank, and its subsidiary MLC Limited, have settled a class action taken against them in the Federal Court.
The amount to be paid to each customer is still to be worked out, but the court hopes they will be notified by Christmas if they will receive a payout.
The action, taken by law firm Slater and Gordon, alleged NAB and MLC engaged in unconscionable conduct in selling consumer credit insurance (CCI) to customers.
They included pensioners, casual workers, and unemployed and critically ill people who were ineligible to claim or unlikely to benefit from the policies.
It was also alleged NAB engaged in misleading and deceptive conduct when selling the insurance.
Earlier this year, the corporate regulator gave a scathing assessment of CCI policies offered in Australia, finding they were “extremely poor value for money”.
On average, customers only received 11 cents for every dollar spent on CCI premiums linked to their credit cards, the Australian Securities and Investments Commission (ASIC) revealed in its July report.
CCI policies offer to provide payouts to help cover loan or credit card repayments if someone is sick, injured or loses their job and cannot meet the minimum repayments.
The regulator scrutinised all CCI products – including mortgages and personal loans – offered by Australia’s 11 biggest banks and lenders, including Westpac, ANZ, NAB and the Commonwealth Bank/Bankwest.
Last November, the selling of junk CCI came under sharp criticism at the banking royal commission — in particular, cold call selling of the products.


Welcome to pdsimage’s documentation!¶
Contents:
Cookbook¶
Import the modules¶
from pdsimage.Structure import *
from pdsimage.PDS_Extractor import *
Loading part of an image¶
Lola PDS Images can be found here and WAC images can be found here.
Let’s say we want to load a window defined at the bottom left by (lon0,lat0) and at the upper right by (lon1,lat1) on the 128 ppd (pixel/degree) LOLA image.
lon0,lon1,lat0,lat1 = 0,20,-10,10
path ='/Users/thorey/Documents/These/Projet/FFC/'
img = BinaryTable('LDEM_16',path)
X, Y, Z = img.Extract_Grid(lon0,lon1,lat0,lat1)
PDS FILES used are in: /Users/thorey/Documents/These/Projet/FFC/
which returns an array of longitudes (X), an array of latitudes (Y) and the grid of altitude are contained in Z. No more. For a window 10° by 10°, it runs in less than 2s on my mac book pro.
Looking at impact crater ? ————————==
Let’s say, we want to get some detail about the crater Copernicus.
path ='/Users/thorey/Documents/These/Projet/FFC/'
Copernicus = Crater('n','Copernicus',path)
Copernicus.ppdlola = 64
Copernicus.ppdwac = 64
Copernicus.Overlay(True,name='Corpernicus.png')
No overlap - Processing should be quick
PDS FILES used are in: /Users/thorey/Documents/These/Projet/FFC/
The size is ?: 1061.8 Mo
Do you really want to download WAC_GLOBAL_E000N1800_064P ?
[y/n]
y
The file WAC_GLOBAL_E000N1800_064P.IMG need to be download - Wait
100.00%
The download of the file WAC_GLOBAL_E000N1800_064P.IMG has succeded
No overlap - Processing should be quick
PDS FILES used are in: /Users/thorey/Documents/These/Projet/FFC/
let you with this nice beautiful plot which overlay a WAC image and a LOLA image. Pixel/degree are pretty high by default.
For a specific location, the program is able to automatically detect the corresponding patch images at the lunar surface and proposed to download it for you. Be careful with large resolution though, downloads can be very long.
The default window in centred on the crater with a radius equal to the 80% of the crater diameter. However this can easily be changed and for instance, zooming in resume to
Copernicus.Change_window(0.4*Copernicus.Diameter)
Copernicus.Overlay(True,name = 'CopernicusZoom.png')
No overlap - Processing should be quick
PDS FILES used are in: /Users/thorey/Documents/These/Projet/FFC/
No overlap - Processing should be quick
PDS FILES used are in: /Users/thorey/Documents/These/Projet/FFC/
If you prefer working with the array directly, use the method Get_Arrays...
Xl , Yl , Zl = Copernicus.Get_Arrays('Lola')
Xw , Yw , Zw = Copernicus.Get_Arrays('Wac')
No overlap - Processing should be quick
PDS FILES used are in: /Users/thorey/Documents/These/Projet/FFC/
No overlap - Processing should be quick
PDS FILES used are in: /Users/thorey/Documents/These/Projet/FFC/
They can then be used for further analysis, histograms of the topography...
Topographic profiles¶
The Structure class also contained a method which let your draw topographic profiles (or WAC profile if you want) without effort. For instance, if we look at an intrusive dome called ‘M13’ within the lunar maria and we want to plot three topographic profile - one vertical passing through the centre - one horizontal passing through the centre - one oblique
path_pdsfiles ='/Users/thorey/Documents/These/Projet/FFC/'
M13 = Dome('n','M13',path_pdsfiles)
M13.Change_window(.9*M13.Diameter)
M13.ppdlola = 64
midlon = (M13.window[0]+M13.window[1])/2.0
midlat = (M13.window[2]+M13.window[3])/2.0
profile1 = (midlon,midlon,11.1,12.5)
profile2 = (M13.window[0]+0.2,M13.window[1]-0.2,midlat,midlat)
profile3 = (360-32.1,360-31.3,11.1,12.5)
save_figure = os.path.join('/Users/thorey/Documents/These/Projet/FFC/pdsimage/Image','BaseProfile.png')
M13.Draw_Profile((profile1,profile2,profile3), save = True ,name = save_figure)
No overlap - Processing should be quick
PDS FILES used are in: /Users/thorey/Documents/These/Projet/FFC/
(328.47000000000003,<PHONE_NUMBER>0000003, 11.1, 12.5)
No overlap - Processing should be quick
PDS FILES used are in: /Users/thorey/Documents/These/Projet/FFC/
No overlap - Processing should be quick
PDS FILES used are in: /Users/thorey/Documents/These/Projet/FFC/
(327.55882087492716,<PHONE_NUMBER>2507289, 11.68213679250616, 11.68213679250616)
No overlap - Processing should be quick
PDS FILES used are in: /Users/thorey/Documents/These/Projet/FFC/
No overlap - Processing should be quick
PDS FILES used are in: /Users/thorey/Documents/These/Projet/FFC/
(327.9, 328.7, 11.1, 12.5)
No overlap - Processing should be quick
PDS FILES used are in: /Users/thorey/Documents/These/Projet/FFC/


My Goals
Career Goals
- Begin my career this July at Alight Solutions.
- Move up in my field.
- Eventually take on a leadership role within the company.
Academic Goals
- Potentially go back to NIU in the future to recieve a Master's degree in Business Administration.
Personal Goals
- Live a healthy lifestyle.
- Eventually get married and raise a family.


Experienced Data Scientist with a demonstrated history of working in multiple industries. Skilled in Statistical Modeling, Cloud Computing, Machine Learning, Data Visualization, Management, and Technical Leadership. Strong engineering professional with a Bachelor’s Degree in Mathematics and Statistics from Rutgers University.
Churn Prediction, Family Mix Recommendation Systems, Wrapped 2019, and Stations
Within Consumer Banking, I worked on Merchant Tagging and Bot Detection. In the Commercial Bank, I did risk analysis.
 During the warmer months here in New York, I enjoy mountain biking, hiking, and kayaking.
When forced indoors, I follow a number of sci-fi and fantasy genre movies and television shows, I am an aspiring chef, and I spend a large amount of my free time exploring the latest technology advancements and contributing to the data science community.
NLP Research
Time Series of Reddit Mentions


| # | File | Line | Problem |
|---|---|---|---|
| 1 | HelloWorld.java | 1 | All classes and interfaces must belong to a named package |
| 2 | HelloWorld.java | 1 | All methods are static. Consider using a utility class instead. Alternatively, you could add a private constructor or make the class abstract to silence this warning. |
| 3 | HelloWorld.java | 2 | Parameter 'args' is not assigned and could be declared final |
| 4 | HelloWorld.java | 4 | Avoid unused local variables such as 'msg'. |
| 5 | HelloWorld.java | 4 | Local variable 'msg' could be declared final |


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Gecode::Int::Linear::NqBoolInt< VX > Class Template Reference
[Integer propagators]
#include <linear.hh>
Inherits BinaryPropagator< VX, PC_INT_VAL >.
Detailed Description
template<class VX>
class Gecode::Int::Linear::NqBoolInt< VX >
Propagator for integer disequal to Boolean sum (cardinality)
Requires
#include "gecode/int/linear.hh"
Definition at line 1101 of file linear.hh.
Public Member Functions | |
| virtual Actor * | copy (Space *home, bool share) |
| Copy propagator during cloning. | |
| virtual PropCost | cost (ModEventDelta med) const |
| Cost function (defined as PC_LINEAR_LO). | |
| virtual ExecStatus | propagate (Space *home, ModEventDelta med) |
| Perform propagation. | |
| virtual Reflection::ActorSpec | spec (const Space *home, Reflection::VarMap &m) const |
| Return specification for this propagator given a variable map m. | |
Static Public Member Functions | |
| static void | post (Space *home, Reflection::VarMap &vars, const Reflection::ActorSpec &spec) |
| Post propagator according to specification. | |
| static Support::Symbol | ati (void) |
| Mangled propagator name. | |
| static ExecStatus | post (Space *home, ViewArray< VX > &b, int c) |
| Post propagator for . | |
Protected Member Functions | |
| bool | resubscribe (Space *home, VX &y) |
| Update subscription. | |
| NqBoolInt (Space *home, ViewArray< VX > &b, int c) | |
| Constructor for posting. | |
| NqBoolInt (Space *home, bool share, NqBoolInt< VX > &p) | |
| Constructor for cloning p. | |
Protected Attributes | |
| ViewArray< VX > | x |
| Views not yet subscribed to. | |
| int | c |
| Righthandside. |
Constructor & Destructor Documentation
template<class VX>
| Gecode::Int::Linear::NqBoolInt< VX >::NqBoolInt | ( | Space * | home, | |
| ViewArray< VX > & | b, | |||
| int | c | |||
| ) | [inline, protected] |
template<class VX>
| Gecode::Int::Linear::NqBoolInt< VX >::NqBoolInt | ( | Space * | home, | |
| bool | share, | |||
| NqBoolInt< VX > & | p | |||
| ) | [inline, protected] |
Member Function Documentation
template<class VX>
| bool Gecode::Int::Linear::NqBoolInt< VX >::resubscribe | ( | Space * | home, | |
| VX & | y | |||
| ) | [inline, protected] |
template<class VX>
| Actor * Gecode::Int::Linear::NqBoolInt< VX >::copy | ( | Space * | home, | |
| bool | share | |||
| ) | [inline, virtual] |
Copy propagator during cloning.
Implements Gecode::Actor.
Definition at line 680 of file bool-int.icc.
template<class VX>
| PropCost Gecode::Int::Linear::NqBoolInt< VX >::cost | ( | ModEventDelta | med | ) | const [inline, virtual] |
Cost function (defined as PC_LINEAR_LO).
Reimplemented from Gecode::BinaryPropagator< VX, PC_INT_VAL >.
Definition at line 686 of file bool-int.icc.
template<class VX>
| ExecStatus Gecode::Int::Linear::NqBoolInt< VX >::propagate | ( | Space * | home, | |
| ModEventDelta | med | |||
| ) | [inline, virtual] |
template<class VX>
| Reflection::ActorSpec Gecode::Int::Linear::NqBoolInt< VX >::spec | ( | const Space * | home, | |
| Reflection::VarMap & | m | |||
| ) | const [inline, virtual] |
Return specification for this propagator given a variable map m.
Reimplemented from Gecode::Actor.
Definition at line 698 of file bool-int.icc.
template<class VX>
| void Gecode::Int::Linear::NqBoolInt< VX >::post | ( | Space * | home, | |
| Reflection::VarMap & | vars, | |||
| const Reflection::ActorSpec & | spec | |||
| ) | [inline, static] |
template<class VX>
| Support::Symbol Gecode::Int::Linear::NqBoolInt< VX >::ati | ( | void | ) | [inline, static] |
template<class VX>
| ExecStatus Gecode::Int::Linear::NqBoolInt< VX >::post | ( | Space * | home, | |
| ViewArray< VX > & | b, | |||
| int | c | |||
| ) | [inline, static] |
Member Data Documentation
template<class VX>
ViewArray<VX> Gecode::Int::Linear::NqBoolInt< VX >::x [protected] |
template<class VX>
int Gecode::Int::Linear::NqBoolInt< VX >::c [protected] |
The documentation for this class was generated from the following files:
- gecode/int/linear.hh (Revision: 7285)
- gecode/int/linear/bool-int.icc (Revision: 6017)


|
gtsam
4.0.0
gtsam
|
Parameters for pre-integration: Usage: Create just a single Params and pass a shared pointer to the constructor.
Public Member Functions | |
| PreintegrationParams (const Vector3 &n_gravity) | |
| The Params constructor insists on getting the navigation frame gravity vector For convenience, two commonly used conventions are provided by named constructors below. | |
| void | print (const std::string &s) const |
| bool | equals (const PreintegratedRotation::Params &other, double tol) const |
| void | setAccelerometerCovariance (const Matrix3 &cov) |
| void | setIntegrationCovariance (const Matrix3 &cov) |
| void | setUse2ndOrderCoriolis (bool flag) |
| const Matrix3 & | getAccelerometerCovariance () const |
| const Matrix3 & | getIntegrationCovariance () const |
| const Vector3 & | getGravity () const |
| bool | getUse2ndOrderCoriolis () const |
| Public Member Functions inherited from gtsam::PreintegratedRotationParams | |
| void | setGyroscopeCovariance (const Matrix3 &cov) |
| void | setOmegaCoriolis (const Vector3 &omega) |
| void | setBodyPSensor (const Pose3 &pose) |
| const Matrix3 & | getGyroscopeCovariance () const |
| boost::optional< Vector3 > | getOmegaCoriolis () const |
| boost::optional< Pose3 > | getBodyPSensor () const |
Static Public Member Functions | |
| static boost::shared_ptr< PreintegrationParams > | MakeSharedD (double g=9.81) |
| static boost::shared_ptr< PreintegrationParams > | MakeSharedU (double g=9.81) |
Public Attributes | |
| Matrix3 | accelerometerCovariance |
| continuous-time "Covariance" of accelerometer | |
| Matrix3 | integrationCovariance |
| continuous-time "Covariance" describing integration uncertainty | |
| bool | use2ndOrderCoriolis |
| Whether to use second order Coriolis integration. | |
| Vector3 | n_gravity |
| Gravity vector in nav frame. | |
| Public Attributes inherited from gtsam::PreintegratedRotationParams | |
| Matrix3 | gyroscopeCovariance |
| continuous-time "Covariance" of gyroscope measurements | |
| boost::optional< Vector3 > | omegaCoriolis |
| Coriolis constant. | |
| boost::optional< Pose3 > | body_P_sensor |
| The pose of the sensor in the body frame. | |
Protected Member Functions | |
| PreintegrationParams () | |
| Default constructor for serialization only: uninitialized! | |
| template<class ARCHIVE > | |
| void | serialize (ARCHIVE &ar, const unsigned int) |
Friends | |
| class | boost::serialization::access |
| Serialization function. | |


Want to have: Software
VM: Windows Domain Controller (windows server), 4GB RAM, 40GB HD
VM: Windows Host (random apps / sandbox), 4GB RAM, 50GB HD
VM: WSUS, 4GB RAM, 50GB HD
VM: OSX server manager?
Windows Gaming PC (gaming)
Kali main box
Pi-Hole (DNS): 4GB RAM, 20GB HD
PfSense (Firewall): 8GB RAM, 50GB HD
OSSIM (SIEM / vuln management / compliance): 16GB RAM 100GB HD
RetroPie (more than just pi for higher end consoles wii/ps2)
Kodi and Parsec
Estimated minimum:
40GB RAM
300GB HD


$timeout
ngMock
This service is just a simple decorator for $timeout
service
that adds a "flush" and "verifyNoPendingTasks" methods.
$timeout();
Flushes the queue of pending tasks.
| Param | Type | Details |
|---|---|---|
| delay (optional) | number | maximum timeout amount to flush up until |
Verifies that there are no pending tasks that need to be flushed.


SimpleAuth Acl
As said in the introduction of the Auth package, an authentication system comes with
three different drivers, each dealing with a part of the system.
The SimpleAuth acl driver stores is role driven, and stores it's acl definitions in the simpleauth configuration file.
It provides the logic for checking if a user has access to a named ACL.
Configuration
The SimpleAuth acl driver stores it's group definitions in the simpleauth configuration file. The acls are defined as a set of
access criteria linked to a specific role. It is defined as a multi-dimensional array, which must have the following structure:
array(
<role> => array( // where <role> is the name of the role you're defining the ACL for
<location> => array( // where <location> is what you're specifying the ACL for
'right', 'otherright' // the specific rights for this location
),
),
You can specify as many locations as you need, and every location can have as many rights as needed. The simpleauth config
file supplied with the Auth package contains some examples that you can use.
You can use the system defined role "#" to specify default rights that will be set for every user, and which is commonly used
to define the public rights to your application. For example, if everyone is allowed to read blog posts and their comments,
you could define:
// default role for all users
'#' => array(
'blog' => array('read'), // read access to 'blog'
'comments' => array('read'), // read access to 'comments'
),
There are also two specific location definitions available for you to use. One that will simply deny all access, and one that
will allow all access. You the last one with care!
// special role definitions
'banned' => false, // deny all access to users having the role 'banned'
'administators' => true // allow all access to users having the 'administrator' role
Class methods
has_access($condition)
The has_access method allows you to check if the current logged-in user has access to a specific location with specific rights.
| Static |
No |
| Parameters |
| Param |
Default |
Description |
| $condition |
required |
The access condition you want to check |
|
| Returns |
boolean. true if the user has access, or false if not. |
| Example |
// check if the user has access to read blog posts
if (Auth::has_access('blog.read'))
{
// yes, the user has access
}
// if you have multiple instances, use the instance to call this method.
// you can also check for multiple rights in one go
if (Auth::instance('simpleauth')->has_access('blog.[read,write,delete]'))
{
// yes, the user has access and may read, write and delete
}
// you can also specify the rights to check as an array
if (Auth::has_access(array('blog' => array('read'), 'comments' => array('read')))
{
// yes, the user has access to read blogs and comments
}
|