On 1 September, the Heikamp boarding school in Maasmechelen, Limburg, will launch a new programme to develop students’ entrepreneurial skills.
“Today’s education system insufficiently prepares students for the business world in general, and for entrepreneurship in particular,” Denis Vonckers, general director of the community education network’s Maasland school group, told Het Nieuwsblad. “With our new approach, we target parents who don’t want their children to be the followers, but the leaders of the future.”
The boarding school will work with monthly themes, which will be introduced by an expert. “To make sure that our programme is balanced, we asked the professional associations of entrepreneurs for advice,” explained school principal Manette Bambust. The team of educators will adapt the course materials to the level of class groups, which span the ages of six to 18.
The themes addressed will include public relations and marketing, communication and presentation techniques, sales methods, financial management, leadership skills, teamwork and organisational abilities.
The project is being supported by Hasselt University’s faculty of economics and by the Erasmus University College Brussels.
...click for more
WHY ENTREPRENEURSHIP EDUCATION?
Young preneur rigorous, standards-based entrepreneurship and science, technology, engineering, arts, and math (STEAM) K-12 programs that prepare students for the 21st century. We empower teachers with fun, innovative training and easy-to-use materials. These teachers bring the entrepreneurial mindset to their classroom, where students become active, engaged learnings tackling real-world problems. As they research, brainstorm, and prototype, students learn to develop creative solutions and confidently share their ideas.
No matter a student’s age, background, or experiences, we believe they have the power to change the world around them. Let us help you show them how.
...Read more
Why Young preneur?
“Young preneur helps students think creatively and leads to good problem solving skills. I’m so excited to finally bring something real to my students.” – Rita, Teacher
“Students became more confident, now saying, ‘I can do this’. Students were more enthusiastic about learning, and about being in school. I’m excited that we collaborated with Young preneur.” – Blanca, Vice Principal
“This is exactly what we need happening in our schools. We’re very fortunate to have Young preneur to provide this enriching experience to our children.” – Mike, Parent


pronouns: he/him
is a software-ish kind of person living and working in Seattle, WA, USA.
By day, he works at Substantial, with a variety of web and mobile technologies, most recently focusing on React and React Native. (Lists of buzzwords and acronyms available on request.)
By night, he tinkers with Notch, TouchDesigner, Processing, and other things of that ilk. When said tinkerings look good, something usually ends up on his Instagram account.
Other interests and activities include: volunteering with Ada Developers Academy, cooking, baking, bread, acting, learning people languages (currently working on Japanese), ultimate (though less so now that his back is made of old man parts).
Aspirational interests include: travel, sailing, photography, AR/VR/MR/XR, video games (playing and development).


@inproceedings{zhang2021alignment, title={Alignment Attention by Matching Key and Query Distributions}, author={Shujian Zhang and Xinjie Fan and Huangjie Zheng and Korawat Tanwisuth and Mingyuan Zhou }, booktitle = {NeurIPS 2021: Neural Information Processing Systems}, month={Dec.}, Note = {(the first two authors contributed equally)}, year = {2021} }


Questions:
Posted: Thu Dec 21, 2006 9:16 pm
1) will this game be called volucris?
2) this looks like a project that can potentially put tha current xenimus out of business.
3) looking at tha screen shots bring back memories.
mmmm, so ... are yous tryn to use tha old models from way back and use em for this project or are yas making completely new models.
good lucks guys.
2) this looks like a project that can potentially put tha current xenimus out of business.
3) looking at tha screen shots bring back memories.
mmmm, so ... are yous tryn to use tha old models from way back and use em for this project or are yas making completely new models.
good lucks guys.


TACC specifics and extendability to other installations¶
The pylauncher source has a number of classes and routines
that are tailored to the use at the Texas Advanced Computing Center.
For starters, there are two classes derived from HostList
,
that parse the hostlists for the SGE and SLURM scheduler.
If you use Load Leveler or PBS, you can write your own
using these as an example.
-
class
pylauncher3.
SGEHostList
(**kwargs)[source]¶ Bases:
pylauncher3.HostList
-
class
pylauncher3.
SLURMHostList
(**kwargs)[source]¶ Bases:
pylauncher3.HostList
-
pylauncher3.
HostListByName
(**kwargs)[source]¶ Give a proper hostlist. Currently this work for the following hosts:
ls5
: Lonestar5 at TACC, using SLURMmaverick
: Maverick at TACC, using SLURMstampede
: Stampede at TACC, using SLURMfrontera
: Frontera at TACC, using SLURMlonghorn
: Longhorn at TACC, using SLURM``frontera*’’ : Frontera at TACC, using SLRUM
pace
: PACE at Georgia Tech, using PBSmic
: Intel Xeon PHI co-processor attached to a compute node
We return a trivial hostlist otherwise.
-
class
pylauncher3.
DefaultHostPool
(**kwargs)[source]¶ Bases:
pylauncher3.HostPool
A HostPool object based on the hosts obtained from the
HostListByName
function, and using theSSHExecutor
function.
Two utility functions may help you in writing customizations.
TACC launchers¶
-
pylauncher3.
ClassicLauncher
(commandfile, *args, **kwargs)[source]¶ A LauncherJob for a file of single or multi-threaded commands.
The following values are specified for your convenience:
hostpool : based on HostListByName
commandexecutor : SSHExecutor
taskgenerator : based on the
commandfile
argumentcompletion : based on a directory
pylauncher_tmp
with jobid environment variables attached
- Parameters
commandfile – name of file with commandlines (required)
resume – if 1,yes interpret the commandfile as a queuestate file
cores – number of cores (keyword, optional, default=1)
workdir – (keyword, optional, default=pylauncher_tmp_jobid) directory for output and temporary files; the launcher refuses to reuse an already existing directory
debug – debug types string (optional, keyword)
-
pylauncher3.
IbrunLauncher
(commandfile, **kwargs)[source]¶ A LauncherJob for a file of small MPI jobs.
The following values are specified for your convenience:
hostpool : based on HostListByName
commandexecutor : IbrunExecutor
taskgenerator : based on the
commandfile
argumentcompletion : based on a directory
pylauncher_tmp
with jobid environment variables attached
- Parameters
commandfile – name of file with commandlines (required)
cores – number of cores (keyword, optional, default=4, see
FileCommandlineGenerator
for more explanation)workdir – directory for output and temporary files (optional, keyword, default uses the job number); the launcher refuses to reuse an already existing directory
debug – debug types string (optional, keyword)


We know how hard it can be to give up your pet. We want you to do everything possible to keep your pet, but if that becomes impossible, we will do everything in our power to give your pet a second chance to find his forever home.
We will always accept pets adopted from us back into care, no matter how long ago the adoption occurred. It may take us a few days to accommodate your request for us to accept your animal, however, depending on shelter space availability.
Get in contact with us here
If you have found a Stray dogs and would like to bring it to us, we can only accept it if we have available space and it was found in the municipality of Lagos, If you have found a stray animal in another area, please contact the animal control in that jurisdiction.
Contact number is here
Even though we may not have room to accept new animals, we will make room for only Stray dogs not other animals, who are desperately in need of our immediate assistance because of injury, illness or because they are too young to care for themselves.
Let us know exactly the case.here
We do not accept wild animals. If you have found a wild animal and are certain that it needs help, please contact Fox Algarve Wildlife Center in Faro at<PHONE_NUMBER>.


Mourinho rules out Zlatan signing for Spurs
by Pulse News Agency International by AFPJose Mourinho says there is "no chance" of Tottenham signing Zlatan Ibrahimovic after the veteran forward was linked with a move to the club following his exit from LA Galaxy.
Mourinho worked with Ibrahimovic at Inter Milan and Manchester United but said signing the 38-year-old would not make sense with Harry Kane at his disposal.
"You are right about the connection, we have more than a connection," Mourinho said when asked on Monday whether he would be trying to sign the Swede.
"I would say also passion and understanding, an amazing player and an amazing guy, but I would say 'no'. No chance.
"We have the best striker in England, we have one of the top two or three strikers in the world, it doesn't make any sense for a striker of Zlatan's dimension, obviously in his late 30s, but still a striker that can play in any club in the world."
It has been a hectic first week for Mourinho at the Spurs helm following his appointment last week.
After his unveiling to the media on Thursday, he had his first game 48 hours later at West Ham, which Spurs won 3-2, and is now preparing for a Champions League clash with Olympiakos on Tuesday.
Tottenham, who reached the Champions League final last season, would qualify for the knockout stages with a win or if they drew and Red Star failed to beat Bayern.
Mourinho said he had not yet contacted his friend Mauricio Pochettino, who was sacked by Spurs last week.
"I needed that little bit of space to process things when I left previous clubs," said the former Chelsea and Manchester United boss. "It does not take just five or six days to be open again.
"You take a little bit of time to process things and understand and to think and rethink. You speak with people and in a couple of weeks he will be fine and I will call him."
Part of Mourinho's remit at Spurs is to do one thing that Pochettino could not do -- win silverware.
"I love the competition (Champions League) as much as everyone in football," said Mourinho, who won the trophy with Porto and Inter Milan.
"It's something that everybody dreams about, to win it. Not everyone has the privilege to be a Champions League winner, which I was happy enough to do twice.
"Do I want to win it a third time? Of course, I would love it but I know the difficulties of it.
"But with these boys, I will never be afraid of any Champions League match that comes into our faces. I repeat, we need to qualify, that is the focus. I always say that."
jw/pb


| 1188 | (A) | For sałł no wardis one the walles vs the wallis lette |
| 1191 | (C) | There shałł no warde on þe wałł . vs þe weye lette |
| 1174 | (D) | For shal no warde of þe wałł vs þe way lette |
| 1151 | (E) | for schal no waarde of þis wal . vs þe wey lette |
| (Ex) | ---- no text ---- | |
| 1212 | (L) | For schal no ward on ȝour wal vs þe way lette |
| (P) | ---- no text ---- | |
| 1159 | (U) | For shal no warde on this wal / the wayes vs let |
| 252 | (V) | Shall no warde on þe walle . vs þe wey setten |


This is a highly subjective question, but if you are new to web development, you will be asking these kinds of questions. Given that you're considering python, logically it makes sense to pick a python-based web framework. If you're going to do AJAX, you should really use JSONRPC. If you're using JSONRPC, you should pick a framework that is known to have mature JSONRPC server-side support, and that means Django, Web2Py, Flask, and many more. See the Pyjamas Wiki for links to various JSONRPC integration HOWTOs.
That having been said: if you're starting entirely from scratch, Django has to come out overall on top, even though you will not be using all of its features. The SQL ORM in Django is excellent; the tutorials to get started are excellent; the developers and users on the (busy) IRC channel are very helpful and responsive.


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Why Downs Star Wants To Change Positions
by Mazola MolefeRivaldo Coetzee has officially made up his mind about his best position, revealing that he has no real intentions to return to playing as a centre back.
Even his Mamelodi Sundowns coach Pitso Mosimane has publicly argued that the only way the 23-year-old can attract interest abroad is if he switches to defensive midfield.
Read: Mnqgithi: It Won't Be Enough Until He Wins
Mosimane began experimenting with Coetzee late last season and the Bafana Bafana international, whose dream of moving to Scottish Premier League outfit Celtic two years ago collapsed after he failed a medical, collected a few Man of the Match awards along the way and received rave reviews from fans, media and pundits.
“If it’s up to me, then yes I would like to play there,” said Coetzee.
“I think I can contribute a lot to the team in that position, especially now that I am training in that position almost every day and that helps me to improve on it. There’s obviously a few things I need to work on myself, which the coaching staff is helping me with.”
‘Riva’ said he doesn’t mind the competition – alongside captain Hlompho Kekana, Tiyani Mabunda, Andile Jali and Sammy Seabi.
“I am really enjoying playing there. The competition is always good for the team because that’s how I can improve. I don’t really want to go back to playing centre back. I want to be further forward,” he added.
Read: Ntseki Spells Out Bafana Identity
Post Selfie’s On Soccer Laduma’s Social Media App & Earn Airtime and more! Download Now


--- layout: default title: About ---
Hi! My name is Maciej ("mah-chay" or just call me Matt).
Born and raised in Poland, I moved to the US for college when I was 19. I spent the first three years at Harvard pursuing a mix of Statistics, Computer Science and Finance. I also focused heavily on East Asia, having participated in a summer school in Japan, having interned in China twice, and having studied Mandarin for 3 years.
After junior year I realized I wasn't sure what I want to do after graduation, and spontaneously decided to take a gap year. I spent the next 14 months exploring different career paths I could pursue. I spent the first few months at home in Warsaw, interning as a strategy analyst at White Star Real Estate and later as an associate consultant at Bain & Company. I then moved to London, where I explored the world of credit risk at Mercuria Energy Trading. Finally, I spent my summer in Singapore working at J.P. Morgan's investment banking division.
The year off taught me that I want to focus on social impact and entrepreneurship. That's why after returning to Harvard I co-founded newspark and became an advisor at Our Future Foundation.
Check out my collection of articles and thoughs here


CISC 360: Computer Architecture - Project 2
It is time to start adding features to the Gemini Simulator. In this project you will add a L1 Cache to the design and compare some different cache designs. You will be required to design two different kinds of caches and compare the performance of each. After this, you will write a small paper to explain your findings.
Project Implementation
Part 1 - Cache!
Your first goal will be to make sure you understand the differences between the possible types of caches. Refer to Gurpur Prabhu's tutorials on the subject to see some applets that will demonstrate what you need to implement for the Gemini cache controller. You will focus on: - Direct Mapped (1-way Set Associative) - 2-way Set Associative From the tutorial site, you will see that you need to concern yourself with code for: - Block placement This will be two cases you need to test between 1-way / 2-way set associative - Block identification You will need to implement a tag system to identify your blocks. - Block replacement You will implement the Random replacement strategy (for ease of implementation). - Interactions with Main Memory Read Policy: Hit: Simply reads the data from cache. Miss: Use No Read Through to store data into the cache every time there is a miss. Write Policy: Hit: Implement Write Back so that the cache will be marked as dirty and only updated when needed. Miss: Use No Write Allocate to keep the implementation simple.
GUI Modifications
You will need to add a field somewhere in the simulator GUI to show you if the operation resulted in a cache hit or miss. You will also need a counter for the total number of cache hits and misses over the course of the programs life. For debugging, you might want to consider using the built-in debugger to see how your algorithm is going. Another option is to add a log file to your program to see your progress. The choice is yours, but the log file might be more useful in future projects with this... Consider making a general purpose log class to make this happen.
Class Modifications
The way you implement this cache algorithm is entirely up to you. I will give one suggestion to give you some ideas. You should (of course) modify the Memory class to include the cache. If you recall from the original project, we used an integer array to represent main memory (int Stack[256]). You can adjust this to be a struct or class or whatever you feel is best. Your cache will need to be a struct or class array or some size much smaller than main memory. (NOTE: Your cache WILL NOT need to handle the Instruction Vector (IV)). Some suggestions to implement the cache- try and override the offset [] operator for your main memory to make use of the cache directly. This would mean that Stack[x] would still work (from the main program in Project 1). Or create a Stack.at(x) function that will do all the cache work behind the scenes. Your Memory class will need to include counters (or pointers into the Stack class's counters) so that your GUI can access the counters for display.
Testing
For the cache portion of testing, I am providing two sample .s files. You will run these .s files through your simulator and you should see some vast differences in how they run. As part of the turn in, you are to write a paragraph or so describing your findings.Test 1
Test 2
Part 2 - Instruction Formatting!
Our Gemini Simulator currently has a serious issue in that it runs pure Gemini ASM code. This makes it almost like a lisp machine, and we simply can't have that... In this project you will have to make an architectural decision about *your* Gemini simulator! At this point, each person in the class will determine thier own implementation for the simulator. For this project, we will create a separate program to assemble the Gemini ASM into binary instructions for us. Then we will modify the Gemini simulator to accept the binary files generated by the assembler and run the programs defined in them.
The project data flow is listed here (NOTE: these values are simply examples): Gemini ASM file (something like test1.s): main: lda #$1 add #$1 -- Processed by a new program you write called gasm:<PHONE_NUMBER>000001 = lda #$1<PHONE_NUMBER>000001 = add #$1 -- Binary stored into g.out ./Gemini g.out -- New Gemini will read binary files and parse them.
Using your knowledge of the different types of ISA's available, you will determine what type of machine you wish to create. In general, you may create a RISC, CISC or a more modern combination of the two out of the Gemini architecture. This means that the first thing you need to do is seriously evaluate the pros and cons for each ISA. At this point, you may find it nessecary to change the original Gemini Instruction Set from Project 1 into something that will work for you. This is certainly allowed, as long as your machine can handle the following operations: (Most of this is as expected. NEW Instructions you will need to add have a * next to them) - Addition - Subtraction - Logical And - Logical Or - Logical Not - Load/Store (You may chose to stay with the Accumulator design, but you are allowed to add more registers (up to 4 General Purpose, and any number of extra registers)) - Branching instructions (note that RISC machines will not require a delay slot yet- as we have not implemented pipelining!) - BA, BE,BL, BG - *BGE, *BLE, *BNE - Multiplication (*MUL) - Division (*DIV) - Subroutines (*JMP, *RET) Your binary instructions can be formatted any way you choose. No matter what implementation you choose, you may find the need to add more instructions than the ones listed above.
Implementation Choice
The goal here is to have the whole class come up with a few different implementation decisions. This way, the final project and presentation will be very interesting and most beneficial for everyone. I will tell you right now that the final project has to do with adding a pipeline or dual core to this machine. I will wait until the project is assigned to tell you what exactly I would like to see, but you will be adding to this project one last time. What this means is that your decision here will impact your next project as well. Here is the general view of what that means based on your decision: - RISC: Will be harder to implement on this project as the assembler will be more complex and you may find the need for additional instructions. However, as we saw in the SPARC machine, pipelining will be easier than with a CISC machine (but not much). - CISC: Will be eaiser to implement on this project since our instruction format can be anything we want. However, pipeling this will be far from simple making the next project more difficult. - Combo: Will be slightly more difficult to implement on this project, but slightly eaiser than pure CISC on the next project. As you can see, none of them are going to get you out of any implementation difficulty, so its really just a matter of picking one and sticking with it!
GUI Modifications
We will only need to add one element to the GUI for this program- just show the instruction in hex, as well as what the simulator thinks the binary means. This means that the IR will now be set to the actual binary value, not the address of that value anymore! The Instruction: [] should still hold the 'Gamini ASM' text of what this instruction is trying to do.Class Modifications
You will need to pull code out of your Project 1 (namely the Instruction Parser Engine (IPE)) to allow your assembler to read Gemini ASM. The assembler simply then writes a new file (consider command line options and defaults similar to what gcc does) with the binary format you decide on.  The assembler is a stand-alone application that you will run once- think of it as the gas program that can take pure assembly and convert it into binary. If you choose RISC, it will be easy to read the file since you just need to read one word (short int) at a time. CISC will need an instruction length header in front of each instruction. Mix will just need to be like CISC with a very defined Instruction Word format (to be able to split it up into manageable chunks). You then need to modify the Gemini Simulator to read the binary file you just created instead of pure strings. You can still use the Instruction Vector (IV) to store each instruction if you would like- you should change the data type from strings to something more suitable for your implementation (a short int for RISC, keep a string for CISC?). As the program runs, you should decode each instruction into what it really means. Show both the instruction and the decode decision in the GUI. The next step will be adding the new instructions into your implmentation. Here are some guidelines: MUL / DIV: You will need to add another register (AccD) to hold the upper bits result of a MUL or a DIV. JMP / RET: You will need to add another register to hold the return address (label/line number really) so when a subroutine finishes, it will go back to where it came from. So, they can be treated as a BA with a special register for RET. RISC: - Branching/Jumping does not require a delay slot! (We have not pipelined yet) - Load/Store does not require any delay stalls either. - Retain the ability to move larger values into your Acc! lda #$1048576 No way that will fit in your instruction word, yet it will fit into a register! Do what SPARC did here and add a 'set' instruction. - MUL and DIV will finish in one cycle in this project, but not project 4. CISC: - Think about the fact that the instruction can vary in length. If you can implement this successfully, the rest of your code will be much simpler. Everyone should see page 402 and on to the end of the chapter for some nice ideas on getting started with instruction formatting!Testing
In addition to the previous .s files, you will need to run the following code sample through the Gemini simulator. (You need to manually convert the C code to your Gemini ASM first). Show the assembler running through the assembly, then show a hexdump of the binary file the assembler created (run with hexdump). Finally, show a screen shot of the GUI after it has run the binary code. Code Samples: Test 1 - Test basic operation int main(void){ int x = 0; int g = 0x0, h = 0x37, j = 0xad, k = 0xff, l = 0xffff; int y = 10; for(int i = 0; i < y; i++){ x += y * i; g = h & j & i | k; g = !g; } if(x & 0x02ab00 > y){ x = 0; } } Turn In
You will need to turn in the following: - Entire Solution (Cleaned of bin/obj) - Baseline Gemini ASM code that you might have created. - Screen shots of the simulator running both types of cache. - Small (less than one page) paper describing your results for Part 1 - Cache.


title = "Giới Thiệu" url = "/gioi-thieu" layout = "default" is_hidden = 0 robot_index = "index" robot_follow = "follow" [page_detail] pages = "welcome-irelandos" == {% partial "title-page" title_page='About us'|_ %} {% set page_detail=page_detail.pageDetail %}
{{'IrelandOS is a Vietnamese registered corporation (an education referral agency) that provides educational advice to Vietnamese students who plan to study overseas.'|_}}


| Posted 8/26/2019 | Click here to verify Key Dates | Download page to print | |||
| August 19 | University Convocation, Fall semester begins | ||||
| August 20 | Fall classes begin | ||||
| ELECTRICAL AND COMPUTER ENGINEERING | September 2 | Labor Day, University closed | |||
| November 11 | Veterans Day, University closed | ||||
| November 25-27 | Fall Recess | ||||
| Fall SEMESTER 2019 COURSE LIST | November 28-30 | Thanksgiving Holiday, University closed | |||
| December 10-16 | Final Examinations | ||||
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| COURSE | TITLE | CR | FACULTY | DAYS/TIMES | ROOM |
| EE2040 | Circuit Analysis I | 3 | Tabrizi | TR 925-1040AM | SH240 |
| EE2049-01 | Electrical Measurements and Circuits Laboratory | 1 | Linker | M 800-1030AM | ETC249 |
| EE2049-02 | Electrical Measurements and Circuits Laboratory | 1 | Linker | T 600-830PM | ETC249 |
| EE2440-01 | Digital Engineering | 2 | Ghaforyfard | W 8-950AM | ETC256 |
| EE2440-02 | Digital Engineering | 1 | Ghaforyfard | F 150-420PM | ETC256 |
| EE2440-03 | Digital Engineering | 2 | Ghaforyfard | F 800-950AM | ETC256 |
| EE2440-04 | Digital Engineering | 1 | Ghaforyfard | W 150-420PM | ETC256 |
| EE2440-05 | Digital Engineering | 2 | Wang | T 1010AM-12PM | ETC256 |
| EE2440-06 | Digital Engineering | 1 | Wang | R 1055AM-125PM | ETC256 |
| EE2449-01 | Digital Logic Laboratory | 1 | Lin | F 800-1030AM | ETC248 |
| EE2449-02 | Digital Logic Laboratory | 1 | Kim | T 600-830PM | ETC248 |
| EE2449-03 | Digital Logic Laboratory | 1 | Kim | R 600-830PM | ETC248 |
| EE2450-01 | Embedded System Programming I | 2 | Ghaforyfard | W 1000-1150AM | ETC256 |
| EE2450-02 | Embedded System Programming I | 1 | Ghaforyfard | F 1055AM-125PM | ETC256 |
| EE2450-05 | Embedded System Programming I | 2 | Wang | T 1210-200PM | ETC256 |
| EE2450-06 | Embedded System Programming I | 1 | Wang | R 150-420PM | ETC256 |
| EE3000 | Econ for Engineers | 3 | Gharibian | F 400-645PM | ETA126 |
| EE3001-01 | Numerical Analysis and Modeling Using MATLAB | 1 | Lin | T 800-1030AM | ETC255D |
| EE3001-02 | Numerical Analysis and Modeling Using MATLAB | 1 | Zhang | F 1055AM-125PM | ETC255D |
| EE3020 | Signals and Systems | 3 | Tabrizi | TR 1050AM-1205PM | SH240 |
| EE3040 | Probability, Random Variable, and Random Processes | 3 | Tabrizi | TR 1215-130PM | SH358B |
| EE3050 | Electric and Magnetic Fields | 3 | Tabrizi | MW 1215-130PM | SH358B |
| EE3209 | Communications Laboratory | 1 | Alvarado | M 600-830PM | ETB105 |
| EE3300-01 | Electric Machines | 3 | Rad | TR 925-1040AM | ETA129 |
| EE3300-02 | Electric Machines | 3 | Rad | TR 725-840PM | ETA129 |
| EE3309 | Electromagnetic Energy Conversion Laboratory | 1 | Fragoso | T 600-830PM | ETA209 |
| EE3445-01 | Computer Organization (for CS students only) | 3 | Lim | MW 140-255PM | FA223 |
| EE3445-02 | Computer Organization (for CS students only) | 3 | Lim | MW 1215-130PM | KHB2015 |
| EE3450-01 | Embedded System Programming II | 2 | Lin | M 600-740PM | ETC256 |
| EE3450-02 | Embedded System Programming II | 1 | Lin | W 600-830PM | ETC256 |
| EE3450-03 | Embedded System Programming II | 2 | Zhang | T 210-400PM | ETC256 |
| EE3450-04 | Embedded System Programming II | 1 | Zhang | R 600-830PM | ETC256 |
| EE3709 | Electronics Laboratory | 1 | Zhang | F 800-1030AM | ETC251 |
| EE3710 | Electronics II | 3 | Rad | MW 140-255PM | ETA129 |
| EE3810-01 | Sensors, Data Acquisition, and Instrumentation with application to Biomedical Engineering | 2 | Won | TR 210-300PM | KHC4075 |
| EE3810-02 | Sensors, Data Acquisition, and Instrumentation with application to Biomedical Engineering | 1 | Karuhaka | F 1055AM-125PM | ETC251 |
| EE3810-05 | Sensors, Data Acquisition, and Instrumentation with application to Biomedical Engineering | 1 | Karuhaka | F 800-1030AM | ETC252 |
| EE3810-06 | Sensors, Data Acquisition, and Instrumentation with application to Biomedical Engineering | 1 | Lee | F 600-830PM | ETC251 |
| EE4130 | Systems Engineering | 3 | Harris | MW 430-545PM | ETA129 |
| EE4200 | Digital Communication Systems | 3 | Mondin | TR 430-545PM | ETA227 |
| EE4300 | Introduction to Power Systems Engineering | 3 | Shahverdi | TR 140-255PM | ETA209 |
| EE4310 | Power Systems Analysis | 3 | Samaan | TR 600-715PM | ETA126 |
| EE4320 | Electric Power Distribution | 3 | Rad | TR 1215-130PM | ETA209 |
| EE4330 | Power Electronics | 3 | Shahverdi | TR 430-545PM | ETA209 |
| EE4400 | Data Communications and Networking | 3 | Zhao | MW 430-545PM | ETC256 |
| EE4440 | Computer Organization | 3 | Ghaforyfard | F 650-935PM | ETC256 |
| EE4450 | Embedded Architectures | 3 | Lin | TR 140-255PM | ETC254 |
| EE4600 | Control Systems II | 3 | Rad | TR 850-1005PM | ETA126 |
| EE4620 | Modern Control Systems | 3 | Emrani | TR 600-715PM | KHB4017 |
| EE4689 | Control Systems Lab | 1 | Fragoso | F 600-830PM | ETC156 |
| EE4810 | Biomedical Devices | 3 | Won | MW 140-255PM | ETA332 |
| EE4961 | Senior Design I | 3 | Thorburn | F 200-340PM | KHLH1 |
| EE5140 | Systems Risk Analysis | 3 | Karimlou | MW 600-715PM | ETA209 |
| EE5200 | Advanced Digital Communications I | 3 | Mondin | TR 600-715PM | ETC255G |
| EE5230 | Wireless Communications | 3 | Mondin | TR 725-840PM | ETC255G |
| EE5340 | Power System Stability | 3 | Abed | MW 430-545PM | ETA209 |
| EE5350 | Power System Protection | 3 | Abu-Jaradeh | F 400-645PM | ETA209 |
| EE5400 | Advanced Computer Networks | 3 | Zhao | TR 430-545PM | ETA210 |
| EE5610 | Stochastic Systems and Estimation | 3 | Karimlou | MW 725-840PM | ETA126 |


--- layout: page title: "All Terasology tutorials, linked from one place" ---
Terasology is both a game and a game engine, written in Java. Because of this, while it is possible to create simple assets, like new blocks, without knowledge of Java, for anything more complicated at least a bit of knowledge about it is required. Don't worry though, if you don't know Java, as there exists a great deal of good Java tutorials out there on the internet, and Terasology codebase was made with simplicity for module developers —that is, YOU — in mind. So, go google for one of those tutorials and then come back here!
Back already? Then continue to our tutorials following, which will guide you through setting up the developer workspace.
TutorialDone that already? Then pop on our server at https://discord.gg/Terasology/ (or, if you prefer IRC, the same chat is bridged both ways to #terasology on freenode, so feel free to pop in there).
From this point onwards, you can start trying to figure things out on your own, or continue following our handy tutorials. If you opt for the first option, though, be wary of several things:
If you want to create only simple assets, that is blocks/shapes/whatever, that don't have any functionality and just are, you don't need to have much coding experience. In fact, you don't need to have any.


epifitter
provides functions for the fit of (non-flexible) two-parameter population dynamics models to disease progress curve (DPC) data: exponential, monomolecular, logistic and Gompertz.
The goal of fitting these models to DPCs is to estimate numerically meaningful parameters: y0
that represents primary inoculum and r
, representing the apparent infection rate. Hence, the importance of choosing the model that best describe the epidemics to better understand its proterties and compare epidemics.
Two approaches can be used to obtain the parameters:
Both approaches are available in epifitter. The simplest way to fit these models to single epidemic data is using the fit_lin()
and fit_nlin()
functions. For the latter, the alternative fit_nlin2()
allows estimating a third parameter, the upper asymptote, when maximum disease intensity is not close to 100%. The fit_multi()
function is the most flexible and allows to fit all models to multiple epidemic datasets.
First, we need to load the packages we’ll need for this tutorial.
To use epifitter data at least two variables are needed, one representing the time of each assessment of disease intensity during the course of the epidemics and the other representing a disease intensity variable as proportion (e.g. incidence, severity, prevalence). For the case of designed experiments with replicates, a third variable is needed.
Let’s simulate a DPC dataset for one epidemics measured at replicated plots. The simulated data resemble a polyciclic epidemics of sigmoid shape. We can do that using the epifitter sim_logistic()
function of epifitter (more about ?sim_logistic
here).
dpcL <- sim_logistic( N = 100, # duration of the epidemics in days y0 = 0.01, # disease intensity at time zero dt = 10, # interval between assessments r = 0.1, # apparent infection rate alpha = 0.2, # level of noise n = 7 # number of replicates )
Let’s give a look at the simulated dataset.
head(dpcL)
## replicates time y random_y
## 1 1 0 0.01000000 0.01000000
## 2 1 10 0.02672668 0.01723613
## 3 1 20 0.06946279 0.06154274
## 4 1 30 0.16868385 0.13693906
## 5 1 40 0.35549349 0.39359715
## 6 1 50 0.59989486 0.56812740
The dpc_L
object generated using sim_logistic()
is a dataframe with four columns. The y
variable is a vector for the disease intensity as proportion (0 < y < 1). To facilitate visualization, let’s make a plot using the ggplot
function of the ggplot2 package.
ggplot( dpcL, aes(time, y, group = replicates ) ) + geom_point(aes(time, random_y), shape = 1) + # plot the replicate values geom_point(color = "steelblue", size = 2) + geom_line(color = "steelblue") + labs( title = "Simulated 'complete' epidemics of sigmoid shape", subtitle = "Produced using sim_logistic()" )+ theme_minimal_hgrid()
fit_lin()
The fit_lin()
requires at least the time
and y
arguments. In the example, we will call the random_y
which represents the replicates. A quick way to call these variables attached to the dataframe is shown below.
f_lin <- fit_lin( time = dpcL$time, y = dpcL$random_y )
fit_lin()
outputs a list object which contains several elements. Three elements of the list are shown by default: stats of model fit, Infection rate and Initial Inoculum
f_lin
## Results of fitting population models
##
## Stats:
## CCC r_squared RSE
## Logistic 0.9986 0.9971 0.1736
## Gompertz 0.9776 0.9562 0.4939
## Monomolecular 0.9350 0.8779 0.6661
## Exponential 0.9111 0.8368 0.6384
##
## Infection rate:
## Estimate Std.error Lower Upper
## Logistic 0.10086264 0.0006256741 0.09961623 0.09961623
## Gompertz 0.07198494 0.0017800392 0.06843892 0.06843892
## Monomolecular 0.05575383 0.0024005851 0.05097162 0.05097162
## Exponential 0.04510881 0.0023005411 0.04052590 0.04052590
##
## Initial inoculum:
## Estimate Linearized lin.SE Lower Upper
## Logistic 9.715847e-03 -4.624234 0.03701538 9.031430e-03 0.0104515832
## Gompertz 2.589362e-05 -2.357217 0.10530854 2.200752e-06 0.0001910769
## Monomolecular -1.825121e+00 -1.038551 0.14202053 -2.748931e+00 -1.1289562738
## Exponential 2.771775e-02 -3.585682 0.13610184 2.113529e-02 0.0363502712
The Stats
element of the list shows how each of the four models predicted the observations based on three measures:
CCC
(Lin 2000), a measure of agreement that takes both bias and precision into accountr_squared
(R2), a measure of precisionRSE
for each model.The four models are sorted from the high to the low CCC
. As expected because the sim_logistic
function was used to create the synthetic epidemic data, the the Logistic model was superior to the others.
The estimates, and respective standard error and upper and lower 95% confidence interval, for the two coefficients of interest are shown in the Infection rate
and Initial inoculum
elements. For the latter, both the back-transformed (estimate) and the linearized estimate are shown.
The element f_lin$stats_all
provides a wide format dataframe with all the stats for each model.
f_lin$stats_all
## # A tibble: 4 x 14
## best_model model r r_se r_ci_lwr r_ci_upr v0 v0_se r_squared RSE
## <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 Logi~ 0.101 6.26e-4 0.0996 0.102 -4.62 0.0370 0.997 0.174
## 2 2 Gomp~ 0.0720 1.78e-3 0.0684 0.0755 -2.36 0.105 0.956 0.494
## 3 3 Mono~ 0.0558 2.40e-3 0.0510 0.0605 -1.04 0.142 0.878 0.666
## 4 4 Expo~ 0.0451 2.30e-3 0.0405 0.0497 -3.59 0.136 0.837 0.638
## # ... with 4 more variables: CCC <dbl>, y0 <dbl>, y0_ci_lwr <dbl>,
## # y0_ci_upr <dbl>
The predicted values are stored as a dataframe in the data
element called using the same $
operator as above. Both the observed and (y
) and the back-transformed predictions (predicted
) are shown for each model. The linearized value and the residual are also shown.
head(f_lin$data)
## time y model linearized predicted residual
## 1 0 0.01000000 Exponential -4.60517019 2.771775e-02 -0.0177177469
## 2 0 0.01000000 Monomolecular 0.01005034 -1.825121e+00 1.8351211709
## 3 0 0.01000000 Logistic -4.59511985 9.715847e-03 0.0002841529
## 4 0 0.01000000 Gompertz -1.52717963 2.589362e-05 0.0099741064
## 5 10 0.01723613 Exponential -4.06074730 4.351741e-02 -0.0262812725
## 6 10 0.01723613 Monomolecular 0.01738641 -6.177123e-01 0.6349484227
The plot_fit()
produces, by default, a panel of plots depicting the observed and predicted values by all fitted models. The arguments pont_size
and line_size
that control for the size of the dots for the observation and the size of the fitted line, respectively.
plot_lin <- plot_fit(f_lin, point_size = 2, line_size = 1 ) # Default plots plot_lin
The plots are ggplot2 objects which can be easily customized by adding new layers that override plot paramaters as shown below. The argument models
allows to select the models(s) to be shown on the plot. The next plot was customized for the logistic model.
# Customized plots plot_fit(f_lin, point_size = 2, line_size = 1, models = "Logistic")+ theme_minimal_hgrid(font_size =18) + scale_x_continuous(limits = c(0,100))+ scale_color_grey()+ theme(legend.position = "none")+ labs( x = "Time", y = "Proportion disease " )
The fit_nlin()
function uses the Levenberg-Marquardt algorithm for least-squares estimation of nonlinear parameters. In addition to time and disease intensity, starting values for y0
and r
should be given in the starting_par
argument. The output format and interpretation is analogous to the fit_lin()
.
NOTE: If you encounter error messages saying “matrix at initial parameter estimates”, try to modify the starting values for the parameters to solve the problem.
f_nlin <- fit_nlin( time = dpcL$time, y = dpcL$random_y, starting_par = list(y0 = 0.01, r = 0.03) ) f_nlin
## Results of fitting population models
##
## Stats:
## CCC r_squared RSE
## Logistic 0.9989 0.9979 0.0187
## Gompertz 0.9973 0.9957 0.0299
## Monomolecular 0.9144 0.8665 0.1547
## Exponential 0.8871 0.8249 0.1753
##
## Infection rate:
## Estimate Std.error Lower Upper
## Logistic 0.10100422 0.001546405 0.09792362 0.09792362
## Gompertz 0.07119455 0.001694747 0.06781844 0.06781844
## Monomolecular 0.02276982 0.001502074 0.01977754 0.01977754
## Exponential 0.01901896 0.001393512 0.01624294 0.01624294
##
## Initial inoculum:
## Estimate Std.error Lower Upper
## Logistic 9.564961e-03 6.928700e-04 8.184693e-03 1.094523e-02
## Gompertz 6.327240e-08 7.938704e-08 -9.487469e-08 2.214195e-07
## Monomolecular -1.929188e-01 4.629696e-02 -2.851470e-01 -1.006905e-01
## Exponential 1.812723e-01 2.099534e-02 1.394474e-01 2.230972e-01
We can check the results using plot_fit
.
plot_fit(f_nlin) + theme_minimal_hgrid()#changing plot theme
K
(maximum disease)In many epidemics the last measure (final time) of a DPC does not reach the maximum intensity and, for this reason, estimation of maximum asymptote (carrying capacity K
) may be necessary. The fin_lin2()
provides an estimation of K
in addition to the estimates provided by fit_lin()
.
Before demonstrating the function, we can transform our simulated data by creating another variable with y_random2
with maximum about 0.8 (80%). Simplest way is to multiply the y_random
by 0.8.
dpcL2 = dpcL %>% mutate(random_y = random_y * 0.8)
Then we run the fit_nlin2()
for the new dataset.
f_nlin2 <- fit_nlin2( time = dpcL2$time, y = dpcL2$random_y, starting_par = list(y0 = 0.01, r = 0.2, K = 0.6) ) f_nlin2
## Results of fitting population models
##
## Stats:
## CCC r_squared RSE
## Logistic 0.9989 0.9979 0.0150
## Gompertz 0.9576 0.9612 0.0894
## Monomolecular 0.9469 0.9058 0.1014
##
## Infection rate:
## Estimate Std.error Lower Upper
## Logistic 0.10130088 0.001884182 0.09754657 0.09754657
## Gompertz 0.10521025 0.016617407 0.07209934 0.07209934
## Monomolecular 0.01625929 0.003083416 0.01011545 0.01011545
##
## Initial inoculum:
## Estimate Std.error Lower Upper
## Logistic 7.568782e-03 6.270115e-04 6.319435e-03 8.818130e-03
## Gompertz 3.127783e-18 7.532715e-17 -1.469648e-16 1.532204e-16
## Monomolecular -1.462800e-01 3.231788e-02 -2.106748e-01 -8.188519e-02
##
## Maximum disease intensity:
## Estimate Std.error Lower Upper
## Logistic 0.7989610 0.003674896 0.7916386 0.8062834
## Gompertz 0.6480742 0.018342803 0.6115254 0.6846231
## Monomolecular 1.0000000 0.107222594 0.7863543 1.2136457
plot_fit(f_nlin2)
NOTE: The exponential model is not included because it doesn’t have a maximum asymptote. The estimated value of
K
is the expected 0.8.
Most commonly, there are more than one epidemics to analyse either from observational or experimental studies. When the goal is to fit a common model to all curves, the fit_multi()
function is in hand. Each DPC needs an unique identified to further combined in a single data frame.
Let’s use the sim_
family of functions to create three epidemics and store the data in a single data.frame
. The Gompertz model was used to simulate these data. Note that we allowed to the y0
and r
parameter to differ the DPCs. We should combine the three DPCs using the bind_rows()
function and name the identifier (.id
), automatically created as a character vector, for each epidemics as ‘DPC’.
epi1 <- sim_gompertz(N = 60, y0 = 0.001, dt = 5, r = 0.1, alpha = 0.4, n = 4) epi2 <- sim_gompertz(N = 60, y0 = 0.001, dt = 5, r = 0.12, alpha = 0.4, n = 4) epi3 <- sim_gompertz(N = 60, y0 = 0.003, dt = 5, r = 0.14, alpha = 0.4, n = 4) multi_epidemic <- bind_rows(epi1, epi2, epi3, .id = "DPC" ) head(multi_epidemic)
## DPC replicates time y random_y
## 1 1 1 0 0.00100000 0.00100000
## 2 1 1 5 0.01515505 0.01007585
## 3 1 1 10 0.07878459 0.06413633
## 4 1 1 15 0.21411521 0.25593106
## 5 1 1 20 0.39266393 0.33195906
## 6 1 1 25 0.56723412 0.61641071
We can visualize the three DPCs in a same plot
p_multi <- ggplot(multi_epidemic, aes(time, y, shape = DPC, group = DPC))+ geom_point(size =2)+ geom_line()+ theme_minimal_grid(font_size =18) + labs( x = "Time", y = "Proportion disease " ) p_multi
Or use facet_wrap()
for ploting them separately.
p_multi + facet_wrap(~ DPC, ncol = 1)
fit_multi()
fit_multi()
requires at least four arguments: time, disease intensity (as proportion), data and the curve identifier (strata_cols
). The latter argument accepts one or more strata include as c("strata1",strata2")
. In the example below, the stratum name is DPC
, the name of the variable.
By default, the linear regression is fitted to data but adding another argument nlin = T
, the non linear regressions is fitted instead.
multi_fit <- fit_multi( time_col = "time", intensity_col = "random_y", data = multi_epidemic, strata_cols = "DPC" )
All parameters of the list can be returned using the $ operator as below.
head(multi_fit$Parameters)
## DPC best_model model r r_se r_ci_lwr r_ci_upr
## 1 1 1 Gompertz 0.10102759 0.002329434 0.09634879 0.10570640
## 2 1 2 Monomolecular 0.07299717 0.002971135 0.06702947 0.07896487
## 3 1 3 Logistic 0.15916441 0.007231719 0.14463908 0.17368975
## 4 1 4 Exponential 0.08616725 0.008836459 0.06841869 0.10391580
## 5 2 1 Gompertz 0.12064521 0.002045528 0.11653664 0.12475377
## 6 2 2 Monomolecular 0.09420589 0.003221497 0.08773532 0.10067646
## v0 v0_se r_squared RSE CCC y0
## 1 -1.9311512 0.08235792 0.9741061 0.3142578 0.9868832 0.0010103624
## 2 -0.6119105 0.10504548 0.9235035 0.4008280 0.9602307 -0.8439508205
## 3 -4.5503386 0.25567988 0.9064380 0.9756122 0.9509232 0.0104532036
## 4 -3.9384281 0.31241602 0.6553827 1.1921035 0.7918201 0.0194788093
## 5 -1.9689969 0.07232035 0.9858302 0.2759569 0.9928646 0.0007743496
## 6 -0.7582087 0.11389713 0.9447603 0.4346037 0.9715956 -1.1344493369
## y0_ci_lwr y0_ci_upr
## 1 0.0002921556 0.002891901
## 2 -1.2770950279 -0.493198389
## 3 0.0062812407 0.017347785
## 4 0.0104001594 0.036482519
## 5 0.0002526702 0.002039670
## 6 -1.6831133263 -0.697980449
Similarly, all data can be returned.
head(multi_fit$Data)
## DPC time y model linearized predicted residual
## 1 1 0 0.00100000 Exponential -6.90775528 0.019478809 -1.847881e-02
## 2 1 0 0.00100000 Monomolecular 0.00100050 -0.843950820 8.449508e-01
## 3 1 0 0.00100000 Logistic -6.90675478 0.010453204 -9.453204e-03
## 4 1 0 0.00100000 Gompertz -1.93264473 0.001010362 -1.036237e-05
## 5 1 5 0.01007585 Exponential -4.59761341 0.029968997 -1.989314e-02
## 6 1 5 0.01007585 Monomolecular 0.01012696 -0.280082621 2.901585e-01
If nonlinear regression is preferred, the nlim
argument should be set to TRUE
multi_fit2 <- fit_multi( time_col = "time", intensity_col = "random_y", data = multi_epidemic, strata_cols = "DPC", nlin = TRUE)
## Warning in log(y0/1): NaNs produzidos
## Warning in log(y0/1): NaNs produzidos
## Warning in log(y0/1): NaNs produzidos
## Warning in log(y0/1): NaNs produzidos
## Warning in log(y0/1): NaNs produzidos
head(multi_fit2$Parameters)
## DPC model y0 y0_se r r_se df
## 1 1 Gompertz 0.001285655 0.0009068164 0.09790763 0.004497741 50
## 2 1 Logistic 0.033066900 0.0061158199 0.13988035 0.007508601 50
## 3 1 Monomolecular -0.179984958 0.0455413612 0.04258735 0.002709457 50
## 4 1 Exponential 0.227470772 0.0289006034 0.02766148 0.002631392 50
## 5 2 Gompertz 0.001594229 0.0008054165 0.11381729 0.003917603 50
## 6 2 Logistic 0.034074174 0.0050456688 0.16353669 0.007093510 50
## CCC r_squared RSE y0_ci_lwr y0_ci_upr r_ci_lwr
## 1 0.9905505 0.9813924 0.05238858 -5.357394e-04 0.003107049 0.08887365
## 2 0.9879959 0.9776336 0.05880710 2.078291e-02 0.045350886 0.12479888
## 3 0.9468662 0.9128863 0.11821003 -2.714575e-01 -0.088512442 0.03714525
## 4 0.8662004 0.7923232 0.17951461 1.694222e-01 0.285519342 0.02237617
## 5 0.9954249 0.9909629 0.03659580 -2.349813e-05 0.003211955 0.10594856
## 6 0.9933308 0.9878864 0.04401047 2.393965e-02 0.044208698 0.14928896
## r_ci_upr best_model
## 1 0.10694161 1
## 2 0.15496182 2
## 3 0.04802946 3
## 4 0.03294678 4
## 5 0.12168603 1
## 6 0.17778443 2
If you want to estimate K
, set nlin = TRUE
and estimate_K = TRUE
.
NOTE: If you do not set both arguments
TRUE
,K
will not be estimated, becausenlin
defaut isFALSE
. Also remember that when estimating K, we don’t fit the Exponential model.
multi_fit_K <- fit_multi( time_col = "time", intensity_col = "random_y", data = multi_epidemic, strata_cols = "DPC", nlin = T, estimate_K = T )
## Warning in log(y0/K): NaNs produzidos
## Warning in log(y0/K): NaNs produzidos
## Warning in log(y0/K): NaNs produzidos
## Warning in log(y0/K): NaNs produzidos
## Warning in log(y0/K): NaNs produzidos
## Warning in log(y0/K): NaNs produzidos
## Warning in log(y0/K): NaNs produzidos
head(multi_fit_K$Parameters)
## DPC model y0 y0_se r r_se K
## 1 1 Gompertz 0.001285666 0.0011436084 0.09790758 0.006922048 1
## 2 1 Logistic 0.033067145 0.0069921277 0.13988010 0.009488550 1
## 3 1 Monomolecular -0.179990399 0.0504098328 0.04258786 0.007096232 1
## 4 2 Gompertz 0.001594224 0.0009504817 0.11381732 0.005358546 1
## 5 2 Logistic 0.034073808 0.0055301536 0.16353715 0.008331526 1
## 6 2 Monomolecular -0.169866500 0.0464104274 0.05047215 0.006672790 1
## K_se df CCC r_squared RSE y0_ci_lwr y0_ci_upr
## 1 0.01975755 49 0.9905505 0.9813924 0.05292046 -0.0010125014 0.003583833
## 2 0.01824093 49 0.9879959 0.9776336 0.05940414 0.0190159387 0.047118352
## 3 0.07151026 49 0.9468665 0.9128849 0.11941016 -0.2812927506 -0.078688047
## 4 0.01088041 49 0.9954249 0.9909629 0.03696735 -0.0003158408 0.003504288
## 5 0.01116260 49 0.9933308 0.9878863 0.04445729 0.0229605478 0.045187067
## 6 0.04965422 49 0.9575543 0.9311096 0.10758524 -0.2631317461 -0.076601255
## r_ci_lwr r_ci_upr K_ci_lwr K_ci_upr best_model
## 1 0.08399720 0.11181795 0.9602957 1.039704 1
## 2 0.12081214 0.15894805 0.9633435 1.036657 2
## 3 0.02832745 0.05684827 0.8562948 1.143705 3
## 4 0.10304892 0.12458572 0.9781350 1.021865 1
## 5 0.14679432 0.18027997 0.9775679 1.022432 2
## 6 0.03706267 0.06388162 0.9002161 1.099784 3
Use ggplot2
to produce elegant data visualizations of models curves and the estimated parameters.
The original data and the predicted values by each model are stored in multi_fit$Data
. A nice plot can be produced as follows:
multi_fit$Data %>% ggplot(aes(time, predicted, color = DPC)) + geom_point(aes(time, y), color = "gray") + geom_line(size = 1) + facet_grid(DPC ~ model, scales = "free_y") + theme_minimal_hgrid()+ coord_cartesian(ylim = c(0, 1))
Using the dplyr function filter
only the model of interest can be chosen for plotting.
multi_fit$Data %>% filter(model == "Gompertz") %>% ggplot(aes(time, predicted, color = DPC)) + geom_point(aes(time, y), color = "gray", size = 2 ) + geom_line(size = 1.2) + theme_minimal_hgrid() + labs( x = "Time", y = "Disease Intensity" )
The multi_fit$Parameters
element is where all stats and parameters as stored. Let’s plot the estimates of the apparent infection rate.
multi_fit$Parameters %>% filter(model == "Gompertz") %>% ggplot(aes(DPC, r)) + geom_point(size = 3) + geom_errorbar(aes(ymin = r_ci_lwr, ymax = r_ci_upr), width = 0, size = 1 ) + labs( x = "Time", y = "Apparent infection rate" ) + theme_minimal_hgrid()


In this example, class Test
has noexplicit constructor. So a default constructor
(that is never visible in the code) is automatically
provided by the compiler, when the new keyword,
at line 10, prompts the creation of Test object reference,
anInstance.
Since class Test extends class A (at line 7), constructor
chaining occurs between the (invisible) default
constructor of class Test and the default constructor of class A – causing the flow of
code to transition from the subclass to the super class, and then back to the subclass (just prior to the new
Test object being instantiated at line 10).
Note that in this example however, nothing is output
by either default constructor during these transitions.
Finally, since the object created at line 10 explicitly
references class A’s default
constructor (i.e. the reference is, of type A), the value
of the local variable n, located inside class A’s default
constructor, is output via the println statement at line
11.
Note that the instance variable n, at line 8, is ordinarily associated with any class Test object
instance that is created — as long as that object is not created within a
static context (which is not the case for the object reference anInstance,
in this example).
In other words, since the reference, anInstance, is executed within static method main,
at line 10,
(which is the only method available in class Test),
the variable n (at line 8) currently has no
code with which it could possibly be associated.
Note: If n were preceded by the word static, it would be
a class variable, and able to be referenced (at line 11) with - or without being preceded by
the name of the class.


| < Day Day Up > |
Hack 52 Distributed Command Execution
Use tentakel for parallel, distributed command execution. Often you want to execute a command not only on one computer, but on several at once. For example, you might want to report the current statistics on a group of managed servers or update all of your web servers at once. 5.12.1 The Obvious ApproachYou could simply do this on the command line with a shell script like the following: # for host in hostA hostB hostC > do ssh $host do_something > done However, this has several disadvantages:
5.12.2 How tentakel Can HelpWhile you could write a shell script to address some of these disadvantages, you might want to consider tentakel, which is available in the ports collection. Its execution starts multiple threads that run independently of each other. The maximum waiting time depends on the longest running remote connection, not on the sum of all of them. After the last remote command has returned, tentakel displays the results of all remote command executions. You can also configure how the output should look, combining or differentiating the results from individual hosts. tentakel operates on groups of hosts. A group can have two types of members: hosts or references to other groups. A group can also have parameters to control various aspects of the connection, including username and access method (rsh or ssh, for example). 5.12.3 Installing and Configuring tentakelInstall tentakel from the ports collection: # cd /usr/ports/sysutils/tentakel # make install clean You can instead install tentakel by hand; consult the INSTALL file in the distribution. A make install should work in most cases, provided that you have a working Python environment installed. After the installation, create the configuration file tentakel.conf in the directory $HOME/.tentakel/. See the example file in /usr/local/share/doc/tentakel/tentakel.conf.example for a quick overview of the format. Alternatively, copy the file into /usr/local/etc/ or /etc/, depending on your system's policy, in order to have a site-wide tentakel.conf that will be used when there is no user-specific configuration. As an administrator, you may predefine groups for your users this way. Assuming that you have a farm of three servers, mosel, aare, and spree, of which the first two are web servers, your configuration might resemble this: set format="%d\n%o\n" group webservers(user="webmaster") +mosel +aare group servers(user="root") @webservers +spree With this definition, you can use the group name servers to execute a command on all your servers as root and the group name webservers to execute it only on your web servers as user webmaster. The first line defines the output format, as explained in Figure 5-1.
This commands tentakel to print the hostname, followed by the lines of the remote output for each server sequentially. You can enrich the format string with additional directives, such as %s for the exit status from commands. See the manpage for more information. As you can see from the servers definition, there is no need to list all servers in each group; include servers from other groups using the @groupname notation. On the remote machines, the only required configuration is to ensure that you can log into them from the tentakel machine without entering a password. Usually that will mean using ssh and public keys, which is also tentakel's default. tentakel provides the parameter method for using different mechanisms, so refer to the manpage for details. 5.12.4 Using tentakelTo update the web pages on all web servers from a CVS repository: % tentakel -g webservers "cd /var/www/htdocs && cvs update" ### mosel(0): cvs update: Updating . U index.html U main.css ### aare(1): C main.css cvs update: Updating . % Note the use of quotes around the command to be executed. This prevents the local shell from interpreting special characters such as & or ;. If no command is specified, tentakel invokes interactive mode: % tentakel interactive mode tentakel(default)> use webservers tentakel(webservers)> exec du -sh /var/www/htdocs ### mosel(0): 364k /var/www/htdocs ### aare(0): 364k /var/www/htdocs tentakel(webservers)> quit % While in interactive mode, the command help prints further information. 5.12.5 See Also
|
| < Day Day Up > |


{{ now }}
0">{{ day.badgeTotal }}
{{ day.date | calendarDate:'monthViewDayNumber':locale }}
You did the {{ this.results[1] }} thing! So cool!

You have logged your {{ this.results[1] }} habit for today!


| Struts 2 > Simple validators |
The following validators are included in the default validators.xml:
| Name | JavaScript aware | Description |
|---|---|---|
| #required | Field value must have a value (non-null) | |
| #requiredstring | x | Field value is non-null and has a length > 0 |
| #regex | If not empty, field value must match a regular expression | |
| #int | x | Field value must be an integer and within a range |
| #date | Field value must be a date (the format is based on locale) and within a range | |
| #expression | A given OGNL expression is evaluated against the value stack and must return true. This is mostly usefully for cross-field validation. Errors are added as action errors | |
| #fieldexpression | A given OGNL expression is evaluated against the value stack and must return true. This is similar to expression but errors are added as field errors | |
| x | Field value must be a valid e-mail address | |
| #url | x | Field value must be a valid url |
| [visitor] | Allows you to forward validation to object properties of your action using the objects own validation files | |
| conversion | Add conversion errors from ActionContext to field errors of the action. This does the same thing as WebWorkConversionErrorInterceptor |
Note: the above name can be changed if you supply your own validators.xml.
In SimpleAction-validation.xml:
<validators> <field name="bar"> <field-validator type="required"> <message>You must enter a value for bar.</message> </field-validator> </field> </validators>
In LoginAction-validation.xml:
<validators> <field name="userName"> <field-validator type="requiredstring"> <message>You must enter an username.</message> </field-validator> </field> </validators>
The error is shown if request parameter userName is missing or an empty string
<validators> <field name="phone"> <field-validator type="regex"> <param name="regex">\([\d][\d][\d]\) [\d][\d][\d]-[\d][\d][\d][\d]</param> <message>Phone number must be in the format (XXX) XXX-XXXX</message> </field-validator> </field> </validators>
<validators> <field name="foo"> <field-validator type="int"> <param name="min">0</param> <param name="max">100</param> <message key="foo.range">Could not find foo.range!</message> </field-validator> </field> </validators>
<validators> <field name="startDate"> <field-validator type="date"> <param name="min">12/22/2002</param> <param name="max">12/25/2002</param> <message>The date must be between 12-22-2002 and 12-25-2002.</message> </field-validator> </field> </validators>
<validators> <validator type="expression"> <param name="expression">foo > bar</param> <message>Foo must be greater than Bar. Foo = ${foo}, Bar = ${bar}.</message> </validator> </validators>
The validator is not associated with a single field. You may need to place your expression within a CDATA if it contains bad xml characters.
<validators> <field name="productCode"> <field-validator type="fieldexpression"> <param name="expression">name.length() == 5</param> <message>Product code must be 5 characters, it is currently '${productCode}'</message> </field-validator> </field> </validators>
<validators> <field name="email"> <field-validator type="email"> <message>You must enter a valid email address.</message> </field-validator> </field> </validators>
The address must be in the format<EMAIL_ADDRESS><validators> <field name="companyUrl"> <field-validator type="url"> <message>You must enter a valid URL.</message> </field-validator> </field> </validators>


Consolidated Hallmark profit rises to 46% - The Nation Newspaper
CONSOLIDATED Hallmark Insurance Plc has posted a Profit-After-Tax (PAT) of N519.6 million in its third quarter 2019 results, as against the N355.9 million recorded in the corresponding period of 2018, representing a 46 per cent rise.
This was contained in its Group Unaudited Financial Results for the period ended September 30, 2019 presented to shareholders at the Nigerian Stock Exchange.
The company’s Gross Premium Written for the period grew by 23.7 per cent to N6.687 billion from N5.404 billion reported in September 2018. Also, Total Assets of the Group rose to N11.159 billion from N10.821 billion during the corresponding period.
Commenting on the financial result, the Managing Director of Consolidated Hallmark Insurance Plc, Mr. Eddie Efekoha, expressed delight with the company’s consistent impressive performance.
He said the unwavering support of the Board and Management of the company was pivotal to the success being recorded.
He said: “The strong performance reflects the Board and Management’s commitment towards rewarding our shareholders with good returns. There is no gainsaying that the consistent improvement is also being driven by the increased confidence by our customers.”


Trie
A comprehensive Trie implementation in Dart, for Dart developers. Optimized for autocomplete. Made by Christopher Gong and Ankush Vangari.
Created from templates made available by Stagehand under a BSD-style license.
Usage
A simple usage example:
import 'package:trie/trie.dart';
main() {
List<String> names = []; //your list goes here
Trie trie = new Trie.list(names);
trie.addWord("TURING");
print("All names are: " + trie.getAllWords().toString());
print("All names that begin with T are: " + trie.getAllWordsWithPrefix("T").toString());
}
Features and bugs
Please file feature requests and bugs at the issue tracker.


Reveal
Basics
A modal should open in the center of the page when either of these links are clicked.
Clicking any of these triggers should close the modal.
Clicking on the overlay should close the modal.
Nested
Clicking this link should open the initial modal.
Clicking this link should replace this modal with a new one.
Clicking this link should close this modal, but not go back to the first one.
With Offsets
Clicking this link should open a reveal with offset 50, 50 from top/left
Am I in the top left?
Slower fade in
Clicking this link should open a reveal fading in slow
Did I open slower? Did the overlay also open slow?
Faster fade in
Clicking this link should open a reveal fading in fast
Did I open faster? Did the overlay also open fast?
Multiple opened
Each link should open a separate modal.
Modal 1 - Now click the other one.
Modal 2 - Now click the other one.
Eventlistener for anchor
When triggering the modal with the keyboard (space or enter) it should change content.
Something went wrong


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sales page
salmon cookies
locations for shopes
seattle
Address:85 Pike St, Seattle, WA 98101
Phone:+1<PHONE_NUMBER>
Website :http://pikeplacemarket.org/
open:12am-12pm
Dubai
Address: Dubai United Arab Emirates
Phone:+ 206-896-698
Website:http://TOUR DUBAI/
open: 24huors
Paris
Address: Fos-sur-Mer
Phone: + 531-856-987
Website:http://Spicy Gringo's/
open:10am-11pm
lima
Address:
Phone:+256-598-741
Website:http://Puerto Norte/
open:7am-10pm
pictures


SHOP
Buy our peripheral products to maintain our operation
Selected:
0
Total Price:
$0
reset
PAY
🔥HOT ITEMS
Jacket (BLACK)
Casual Gaming Chair
Pro-Gamer Chair
OTHERS ITEMS
G2 Bracelet
G2 T-Shirt
G2 Key Chain
G2 Pin
Snapback
Jersey (2017)
Hoodie (2017)


Home
Home
Shark Tank App
Explore deals and companies from the Tank
Get Started
Sharks
{{shark.name}}
$X million in investment
Update
Delete
New Shark
Edit
Name
Deal
Equity
Amount
{{company.name}}
{{company.tvDealReached}}
{{company.tvDealEquity}}
{{company.tvDealAmount}}
{{company.name}}
Company Name
TV Deal Rached
TV Deail Equity
TV Deal Amount
Update
Delete
Done
Company Name
TV Deal Rached
TV Deail Equity
TV Deal Amount
Update
Delete


"Imagination is the beginning of creation.  George Bernard Shaw


None.
Orbits, paranasal sinuses, and skull base:
Nasopharynx:
Suprahyoid neck:
Infrahyoid neck:
Thyroid:
Thoracic inlet:
Lymph nodes:
Vascular structures:
Other findings:
Normal examination.


Birdfish
At the core of my dissertation, I developed a way to scale Twitter text into ideological positions. Applying canonical correspondence analysis,
I estimate and visualize ideological dimensions and the positions individual politicians take on it. This allows answering diverse research questions
from representation, agenda setting and factionalism. In contrast to its inspiration,
wordfish
, by Jonathan Slapin and Sven-Oliver Proksch,
it allows extracting multiple dimensions and therefore makes application to noisy social media text more feasible. I deployed a shiny app to visualize these results.
Oolong
Oolong is an R package providing functions for semantic validation of topic modeling and
dictionary-based methods, two main tools for doing automated content analysis (Boumans &
Trilling, 2016; Günther & Quandt, 2016).
While the validation of statistical properties of topics models is well established, the substantive
meaning of categories uncovered is often less clear and their interpretation reliant on
“intuition” or “eyeballing”. As Chang, Gerrish, Wang, Boyd-Graber, & Blei (2009, p. 1) put
it: “qualitative evaluation of the latent space” or figuratively, reading tea leaves.
The story for dictionary-based methods is not better. Researchers usually assume these dictionaries
have built-in validity and use them directly in their research. However, multiple validation
studies (Boukes, Velde, Araujo, & Vliegenthart, 2020; González-Bailón & Paltoglou,
2015; Ribeiro, Araújo, Gonçalves, Gonçalves, & Benevenuto, 2016) demonstrate these dictionaries
have very limited criterion validity.
Oolong provides a set of tools to objectively judge substantive interpretability to applied users
in disciplines such as political science and communication science. It allows standardized
content based testing of topic models as well as dictionary-based methods with clear numeric
indicators of semantic validity. Oolong makes it easy to generate standard validation tests
suggested by Chang et al. (2009) and Song et al. (2020).
2020 U.S. Election Database
Developed as a course project, students collected Twitterdata, encyclopedic information and campaign finance
data for the upcoming election in the United States. Using web scraping, APIs and hand coding, more than 2200 candidates from the primary process onwards
are linked to their Twitter profiles and campaign records. Image source: https://twitter.com/aoc
InterpellaR
As part of the DFG funded project "Populist Challenge in Parliaments" we scrape, download, OCR and parse more than
300,000 parliamentary questions in 15 German federal states and link them to a relational database. Image source: Bayrischer Landtag.
StateLegislatoR
As part of the DFG funded project "Populist Challenge in Parliaments" we create a relational database of state legislators after the role model
of Simon Munzert's and Sascha Göbel's legislatoR. Like legislatoR, it is based on wikipedia. Image source: www.wikipedia.de
POPPA Shiny App
This shiny app I developed for
the POPPA
expert survey for populist parties by
Maurits Meijers
and
Andrej Zaslove


By making it possible to have Polipo listen on a non-routable address
(for example the loopback address ‘<IP_ADDRESS>’), the variable
proxyAddress
provides a very crude form of access
control: the ability to decide which hosts are allowed to connect.
A finer form of access control can be implemented by specifying
explicitly a number of client addresses or ranges of addresses
(networks) that a client is allowed to connect from. This is done
by setting the variable allowedClients
.
Every entry in allowedClients
can be an IP address, for example
‘<IP_ADDRESS>’ or ‘::1’. It can also be a network
address, i.e. an IP address and the number of bits in the network
prefix, for example ‘<IP_ADDRESS>/24’ or
‘2001:660:116::/48’. Typical uses of ‘allowedClients’
variable include
allowedClients = <IP_ADDRESS>, ::1, <IP_ADDRESS>/24, 2001:660:116::/48
or, for an IPv4-only version of Polipo,
allowedClients = <IP_ADDRESS>, <IP_ADDRESS>/24
A different form of access control can be implemented by requiring
each client to authenticate, i.e. to prove its identity before
connecting. Polipo currently only implements the most insecure form
of authentication, HTTP basic authentication, which sends
usernames and passwords in clear over the network. HTTP basic
authentication is required when the variable authCredentials
is
not null; its value should be of the form ‘username:password’.
Note that both IP-based authentication and HTTP basic authentication are insecure: the former is vulnerable to IP address spoofing, the latter to replay attacks. If you need to access Polipo over the public Internet, the only secure option is to have it listen over the loopback interface only and use an ssh tunnel (see Parent proxies)1.
[1] It is not quite clear to me whether HTTP digest authentication is worth implementing. On the one hand, if implemented correctly, it appears to provide secure authentication; on the other hand, and unlike ssh or SSL, it doesn't make any attempt at ensuring privacy, and its optional integrity guarantees are impossible to implement without significantly impairing latency.


Implements variants of the strategy frequency estimation method by Dal Bo & Frechette (2011) <doi:10.1257/aer.101.1.411>, including its adaptation to behavioral memory-one Markov strategies by Breitmoser (2015) <doi:10.1257/aer.20130675>, and the extension in the spirit of latent-class regression by Dvorak & Fehrler (2018) <doi:10.2139/ssrn.2986445>.
| Version: | 0.1.1 |
| Depends: | R (≥ 2.10) |
| Imports: | Rcpp (≥ 0.12.18) |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | testthat |
| Published: | 2018-12-17 |
| Author: | Fabian Dvorak |
| Maintainer: | Fabian Dvorak <fabian.dvorak at uni.kn> |
| BugReports: | http://github.com/fdvorak/stratEst/issues |
| License: | GPL-3 |
| Copyright: | See the file COPYRIGHTS for copyright, authorship and license details |
| URL: | http://github.com/fdvorak/stratEst |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | stratEst results |
| Reference manual: | stratEst.pdf |
| Package source: | stratEst_0.1.1.tar.gz |
| Windows binaries: | r-devel: stratEst_0.1.0.zip, r-release: stratEst_0.1.0.zip, r-oldrel: stratEst_0.1.0.zip |
| OS X binaries: | r-release: stratEst_0.1.0.tgz, r-oldrel: stratEst_0.1.0.tgz |
| Old sources: | stratEst archive |
Please use the canonical form https://CRAN.R-project.org/package=stratEst to link to this page.


Home
Trees
Indices
Help
RIDE
[
Identifiers
|
To Do
]
Identifier Index
[
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y Z
_
]
R
read()
(in
Process
)
remove_datafile()
(in
ChiefController
)
RideImportSettingAdded
(in
robotide.publish.messages
)
readboth()
(in
Process
)
remove_empty_columns_from_beginning()
(in
StepController
)
RideImportSettingChanged
(in
robotide.publish.messages
)
readerr()
(in
Process
)
remove_empty_columns_from_end()
(in
StepController
)
RideImportSettingRemoved
(in
robotide.publish.messages
)
RecalcSizes()
(in
HorizontalFlowSizer
)
remove_hook()
(in
PopupCreator
)
RideInitFileRemoved
(in
robotide.publish.messages
)
RecentFileEntry
(in
robotide.recentfiles.recentfiles
)
remove_resource()
(in
ChiefController
)
RideItem
(in
robotide.publish.messages
)
RecentFileEntry
(in
robotide.ui.recentfiles
)
remove_toolbar_button()
(in
ToolBar
)
RideItemNameChanged
(in
robotide.publish.messages
)
recentfiles
(in
robotide
)
remove_var()
(in
VariableTableController
)
RideItemSettingsChanged
(in
robotide.publish.messages
)
recentfiles
(in
robotide.recentfiles
)
RemoveMacro
(in
robotide.controller.commands
)
RideItemStepsChanged
(in
robotide.publish.messages
)
recentfiles
(in
robotide.ui
)
RemoveVariable
(in
robotide.controller.commands
)
RideListenerHandler
(in
robotide.contrib.testrunner.testrunnerplugin
)
RecentFilesPlugin
(in
robotide.recentfiles.recentfiles
)
rename()
(in
ItemNameController
)
RideListenerServer
(in
robotide.contrib.testrunner.testrunnerplugin
)
RecentFilesPlugin
(in
robotide.ui.recentfiles
)
rename()
(in
ResourceRootHandler
)
RideLog
(in
robotide.publish.messages
)
RecreateMacro
(in
robotide.controller.commands
)
rename()
(in
TestDataHandler
)
RideLogException
(in
robotide.publish.messages
)
RedBullet
(in
robotide.contrib.testrunner.TestSuiteTreeCtrl
)
rename()
(in
VariableHandler
)
RideLogMessage
(in
robotide.publish.messages
)
Redo
(in
robotide.controller.commands
)
rename_tab()
(in
NoteBook
)
RideMessage
(in
robotide.publish.messages
)
redo()
(in
TestCaseEditor
)
RenameKeywordOccurrences
(in
robotide.controller.commands
)
RideNewProject
(in
robotide.publish.messages
)
Redraw()
(in
TestSuiteTreeCtrl
)
RenameProgressObserver
(in
robotide.ui.progress
)
RideNotebookTabChanged
(in
robotide.publish.messages
)
refresh()
(in
SettingEditor
)
RenameTest
(in
robotide.controller.commands
)
RideNotebookTabChanging
(in
robotide.publish.messages
)
refresh()
(in
VirtualList
)
RepeatSectionError
(in
robotide.context.configobj
)
RideOpenResource
(in
robotide.publish.messages
)
refresh_datafile()
(in
SettingEditor
)
replace_keyword()
(in
Occurrence
)
RideOpenSuite
(in
robotide.publish.messages
)
refresh_datafile()
(in
KeywordEditor
)
replace_keyword()
(in
ItemNameController
)
RidePopupWindow
(in
robotide.editor.popupwindow
)
refresh_datafile()
(in
RideFrame
)
replace_keyword()
(in
FixtureController
)
RideSaveAll
(in
robotide.publish.messages
)
refresh_datafile()
(in
Tree
)
replace_keyword()
(in
TemplateController
)
RideSaved
(in
robotide.publish.messages
)
register()
(in
MenuBar
)
replace_keyword()
(in
StepController
)
RideSaving
(in
robotide.publish.messages
)
register()
(in
ShortcutRegistry
)
report_file()
(in
SocketListener
)
RideSelectResource
(in
robotide.publish.messages
)
register()
(in
ToolBar
)
report_parsing_errors()
(in
Logger
)
RideSettings
(in
robotide.context
)
register()
(in
ToolBarButton
)
report_regex
(in
TestRunnerPlugin
)
RideSettingsChanged
(in
robotide.publish.messages
)
register_action()
(in
Plugin
)
ReportIcon
(in
robotide.contrib.testrunner.testrunnerplugin
)
RideSuiteAdded
(in
robotide.publish.messages
)
register_action()
(in
ActionRegisterer
)
requires_focus()
(in
robotide.editor.kweditor
)
RideTestCaseAdded
(in
robotide.publish.messages
)
register_actions()
(in
Plugin
)
reset()
(in
ConfigObj
)
RideTestCaseRemoved
(in
robotide.publish.messages
)
register_actions()
(in
ActionRegisterer
)
Reset()
(in
TestSuiteTreeCtrl
)
RideTreeSelection
(in
robotide.publish.messages
)
register_content_assist_hook()
(in
Namespace
)
reset()
(in
ContentAssistPopup
)
RideUserKeyword
(in
robotide.publish.messages
)
register_content_assist_hook()
(in
Plugin
)
Reset()
(in
ContentAssistCellEditor
)
RideUserKeywordAdded
(in
robotide.publish.messages
)
register_context_menu_hook()
(in
GridEditor
)
reset_resource_and_library_cache()
(in
Namespace
)
RideUserKeywordRemoved
(in
robotide.publish.messages
)
register_context_menu_hook()
(in
Tree
)
resolved_path
(in
ImportController
)
RideVariableAdded
(in
robotide.publish.messages
)
register_context_menu_hook_to_grid()
(in
EditorPlugin
)
resource_import_modified()
(in
ChiefController
)
RideVariableMovedDown
(in
robotide.publish.messages
)
register_editor()
(in
RIDE
)
resource_import_modified()
(in
ImportSettingsController
)
RideVariableMovedUp
(in
robotide.publish.messages
)
register_editor()
(in
EditorProvider
)
ResourceCache
(in
robotide.namespace.namespace
)
RideVariableRemoved
(in
robotide.publish.messages
)
register_editor()
(in
Plugin
)
ResourceDialog
(in
robotide.editor.editordialogs
)
RideVariableUpdated
(in
robotide.publish.messages
)
register_editors()
(in
EditorCreator
)
ResourceFileController
(in
robotide.controller.filecontrollers
)
RIGHT_CHAR
(in
robotide.action.shortcut
)
register_for_namespace_updates()
(in
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Home
Trees
Indices
Help
RIDE
Generated by Epydoc 3.0.1 on Wed Apr 13 15:09:36 2011
http://epydoc.sourceforge.net


--- layout: post status: publish published: true title: Episode 3.15 - 40% of Rotation to the DL author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 4831 wordpress_url: http://ivyenvy.com/?p=4831 date: '2011-04-09 12:20:44 +0000' date_gmt: '2011-04-09 17:20:44 +0000' categories: - Podcast tags: - Podcast comments: [] ---
Andy, Corey and Kurt talk about Randy Wells and Andrew Cashner going to the DL and tendency of Cubs fans to throw around the names of Prior and Wood when injuries strike pitching. We look at a few early season stats and discuss some players that are off to slow starts. We also touch on the incident at Dodger Stadium because we will take any opportunity to bash Dodgers fans. At the end of the show, we look at the photo caption contest and pick a winner.


Hi, I am Richard Kalinec, a student of Faculty of Informatics of Masaryk University. I have already worked with GitHub several times, but I am not an experienced developer.
My GitHub profile: https://github.com/RichardKalinec/


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Going
Not Going
Alternatively, you can click on this link to respond in a browser.


SEE THE
UNSEEN
TAKE ACTION
SEE THE
356
The number of anti-asian cases reported to
FBI
in 2020.
UNSEEN
20
The number of anti-asian case from microaggressions to physical attacks reported to 'See the Unseen' in just
one
week.


BLACK PINK
love music
Black Pink (Hangul:블랙핑크 ), stylized as BLACKPINK or BLΛƆKPIИK, is a South Korean girl group formed by YG Entertainment, consisting of members Jisoo, Jennie, Rosé, and Lisa.
The group officially debuted on August 8, 2016, with their single Square One, which spawned their first number-one hit, "Whistle." The single also included "Boombayah," their first number-one hit on Billboard World Digital Songs chart, which set the record as the most-viewed debut music video by a Korean act. With the group's commercial success in their first five months, they were hailed as the New Artist of the Year at the 31st Golden Disc Awards and the 26th Seoul Music Awards.
Black Pink is the highest-charting female K-pop act on Billboard Social 50, peaking at no. 15. They are also the first K-pop act to be on YouTube's Global Top 25 Summer Songs, with their 2017 hit "As If It's Your Last." They are also the first female K-pop act to have three number-one hits on Billboard's World Digital Song Sales chart.


How It's Made
Planting
Harvesting the Cherries
Processing the Cherries
Drying the Beans
Milling the Beans
Exporting the Beans
Tasting the Coffee
Roasting the Coffee
Grinding Coffee
Brewing Coffee
Facts
The drink dates back to 800 A.D
Coffee beans are technically seeds.
Brazil grows the most coffee in the world
Only two U.S. states produce coffee
Tea VS Coffee
The difference is that coffee is a strongly brewed beverage while tea is an infusion that is generally weaker. Coffee is typically brewed at higher temperatures, which allows more of the caffeine molecules to release from the beans into your cup of Joe.


Let's Get
Through This
Together
Connecting with those around you is the first step in changing the world
We aspire to connect the neighbourhoods of the world in order to create more friendly, connected communities. The ability to easily interact with people living around you can open up new opportunities, so we designed our website to help you do just that. Neighbour Network can also be used for people in need of help as it provides the option to make grocery and medicine purchase requests. Ultimately, we belive in making change through small steps, such as buying food for your neighbors, setting up food drives, and even discussing mental health issues to make sure your community is doing all right.


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Character Monitoring And Building
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Have you ever changed a person’s life in a single day? The Texas Ramp Project does it hundreds of times a year! We build wheelchair ramps for disabled or elderly people who can’t afford to buy one. In a few hours on a Saturday morning, we give these folks the freedom to leave their home again—the home where they want to remain but whose steps have imprisoned them.
Austin Animal Center runs the largest No Kill municipal animal shelter in the United States, providing shelter to more than 18,000 animals each year and animal protection services to all of Austin and Travis County.
Inside Books Project is an Austin-based community service volunteer organization that sends free books and educational materials to prisoners in Texas. Inside Books is the only books-to-prisoners program in Texas, where over 140,000 people are incarcerated. Inside Books Project works to promote reading, literacy, and education among incarcerated individuals and to educate the general public on issues of incarceration.
The mission of Love-A-Bull is to promote responsible guardianship and improve the image and lives of pit bull type dogs through community support, education, advocacy, and rescue.
The CEC Microfarm is the University of Texas at Austin’s first student-run, on-campus farm. We seek to provide an open space for establishing a community that grows together, explores environmental inquiries, and connects to the natural world through food.
Posada Esperanza is the family shelter of Casa Marianella. We are a full-service transitional housing program for immigrant mothers and their children, escaping domestic or cultural violence. Occupying four homes in East Austin, we currently house 14 mothers and nearly 30 children!


Essntialism by Greg McKeown
Essentialism is a disciplined, systematic approach for determining where our highest point of contribution lies, then making execution of those things almost effortless.
The way of the Essentialist rejects the idea that we can fit it all in. Instead it requires us to grapple with real trade-offs and make tough decisions. In many cases we can learn to make one-time decisions that make a thousand future decisions so we don’t exhaust ourselves asking the same questions again and again.
The way of the Essentialist means living by design, not by default. Instead of making choices reactively, the Essentialist deliberately distinguishes the vital few from the trivial many, eliminates the non-essentials, and then removes obstacles so the essential things have clear, smooth passage. In other words, Essentialism is a disciplined, systematic approach for determining where our highest point of contribution lies, then making execution of those things almost effortless.
If you don’t prioritise your life, someone else will.
When we don’t purposefully and deliberately choose where to focus our energies and time, other people - our bosses, our colleagues, our clients, and even our families - will choose for us, and before long we’ll have lost sight of everything that is meaningful and important. We can either make our choices deliberately or allow other people’s agendas to control our lives.
After all, there is still a feeling of sunk-cost bias: studies have found that we tend to value things we already own more highly than they are worth and thus that we find them more difficult to get rid of. If you’re not quite there, ask the killer question: “If I didn’t already own this, how much would I spend to buy it?” This usually does the trick.
Essentialists spend as much time as possible exploring, listening, debating, questioning, and thinking. But their exploration is not an end in itself. The purpose of the exploration is to discern the vital few from the trivial many.
To embrace the essence of Essentialism requires we replaces these false assumptions with three core truths: “I choose to,” “Only a few things really matter,” and “I can do anything but not everything.”
Up to that point I had always known logically that I could choose not to study law. But emotionally it had never been an option. That’s when I realised that in sacrificing my power to choose I had made a choice - a bad one. By refusing to choose “not law school,” I had chosen law school — not because I actually or actively wanted to be there, but by default. I think that’s when I first realised that when we surrender our ability to choose, something or someone else will step in to choose for us.
When we forget our ability to choose, we learn to be helpless. Drip by drip we allow our power to be taken away until we end up becoming a function of other people’s choices - or even a function of our own past choices.
The reality is, saying yes to any opportunity by definition requires saying no to several others.
The fear of missing out goes into full effect. How can we say no; the offer is right here for the taking. We might never have gone after it, but now it is so easy to get it we consider it. But if we just say yes because it is an easy reward, we run the risk of having later to say no to a more meaningful one.
Non-Essentialists say yes because of feelings of social awkwardness and pressure. They say yes automatically, without thinking, often in pursuit of the rush one gets from having pleased someone.But Essentialists know that after the rush comes the pang of regret. They know they will soon feel bullied and resentful — both at the other person and at themselves. Eventually they will wake up to the unpleasant reality that something more important must now be sacrificed to accommodate this new commitment.
The Essentialist designs a routine that makes achieving what you have identified as essential to the default position.
As you continue to clear out the wardrobe of your life, you will experience a reordering of what really matters. Life will become less about efficiently crossing off what was on your to-do list or rushing through everything on your schedule and more about changing what you put on there in the first place.


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